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CLAUDE CODE MARKETING FULL COURSE (6 HOURS)

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CLAUDE CODE MARKETING FULL COURSE (6 HOURS)

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11250 segments

0:00

Hello and welcome to the 0ero to1 course

0:01

all on cloud code for marketing. By the

0:03

end of this video, you guys are going to

0:04

know how to use modern AI tools like

0:06

Cloud Code to automate virtually the

0:08

entirety of the marketing function. Uh

0:10

whether you guys run your own company or

0:12

working inside of somebody else's,

0:13

that's all good. My name is Nick. I run

0:15

a marketing business that just did over

0:16

$500,000 in revenue last month using an

0:19

overwhelming amount of cloud code. I've

0:21

also been working with AI for over 7

0:23

years now, way back when the smartest

0:25

model was called GPT2. Uh and I've

0:27

trained some big teams on AI now, like

0:29

the Mr. beast team and a couple of

0:30

others. So, I know all about how to take

0:32

somebody from never having used or

0:33

applied an AI tool all the way up to,

0:35

you know, generating large returns in a

0:37

real functioning business. So, all that

0:39

to say, you don't need any programming

0:41

experience. You don't need any prior AI

0:42

experience to understand all this. I

0:44

don't have a degree in coding or

0:46

anything like that. I learned everything

0:47

I'm about to teach you from free YouTube

0:49

videos just like this one. We're going

0:50

to start by looking at a really brief

0:52

outline and then I'm going to walk you

0:53

through it. And you don't need to jump

0:55

around. Literally just follow the course

0:56

as I laid it out and by the end of it

0:58

you'll know everything you need in order

0:59

to automate your marketing. Let's get

1:01

into it. So the course is going to be

1:02

focused on actionable real

1:05

implementations. In order to get there,

1:07

first I'll show you how to download,

1:09

install, and set up Claude Code. I've

1:12

uninstalled it from my computer and I'll

1:13

actually walk you guys through every

1:15

step including signing up, creating an

1:16

account, and then what every button in

1:18

the interface means. So it'll be very

1:20

simple. Then I'll cover the five

1:22

marketing functions that we'll be

1:24

automating today using Claude code. I'll

1:26

also show you guys a framework I'm going

1:28

to use for that. Then I'll run you guys

1:30

through the difference between prompts,

1:32

skills, loops, and cloud routines. And

1:35

the way we're going to approach the rest

1:36

of the course is I'm going to be showing

1:38

you guys how to automate things using

1:40

Claude uh at each of these four levels.

1:42

And you can think of prompting as the

1:43

simplest level, Claude routines as the

1:46

most advanced level. Prompting requires

1:48

you working on your computer whereas

1:50

cloud routines effect effectively is a

1:52

loop that is scheduled that runs in the

1:54

cloud without you. Then we'll actually

1:56

go and build stuff. So I'm first going

1:58

to show you guys top of funnel how to

2:00

automate the generation of creative. You

2:01

guys will be able to automate ads,

2:03

creative for organic. We'll be doing

2:05

this via images, video, even some audio.

2:08

Then I'll show you guys the next

2:09

function which is the personalization of

2:11

copy. And so we'll be doing newsletters,

2:14

we'll be doing pulled emails, we'll

2:16

basically be taking a template, and then

2:17

I'll show you guys how to use Claude to

2:19

automatically um automate the process of

2:21

filling really fuzzy variables that it

2:23

goes out and finds on the internet and

2:25

so on. In the middle of funnel sense,

2:27

I'll then show you guys how to make

2:29

speed tole systems for appointment

2:31

booking. These are systems that allow

2:32

you to squeeze way more juice out of ad

2:34

spend and then organic growth. Then I'll

2:37

show you guys how to set up data

2:39

collection, tracking, dashboards, and so

2:41

on and so forth. Actually building your

2:43

own proprietary analytics platform, uh,

2:46

interpreting the data through cloud, and

2:47

then ultimately scanning that up

2:49

somewhere on a dashboard so that you or

2:51

your clients can see what that looks

2:53

like. And then finally, at the end, I'll

2:55

show you guys bottom of funnel, which is

2:56

how to automate highquality follow-ups.

2:58

Now, this is basically nurturing. It's

3:00

starting to verge onto sales, and I

3:01

don't want this to be a sales course.

3:03

Um, but I'll show you guys how to

3:04

automate the process of sending emails,

3:06

SMS, notifications, and so on and so

3:08

forth, and doing so in a way that seems

3:10

very human despite being entirely

3:12

automated. Finally, I'll show you guys

3:14

how to maintain and then upgrade these

3:15

systems over time before giving you guys

3:17

some advanced tips that I have hard

3:19

learned, I sort of learned the hard way

3:21

after working with a lot of large

3:22

businesses, multi-billion dollar

3:24

enterprises, and so on. Okay? So, you

3:25

don't need to worry too much about any

3:27

of this stuff right now. We're going to

3:28

go step by step. We're going to start

3:30

slow and by the end of the course as

3:31

mentioned you'll be able to do

3:32

everything that I just talked about. The

3:34

first step we need to do is we need to

3:35

create an account and the way to do that

3:37

is to head over to claude.com.

3:40

So I'm going to do that first with you

3:42

guys. claude.com right here. Now in

3:45

order to create an account all you need

3:47

to do is click continue with Google or

3:49

continue with email. The interface may

3:51

look a little bit different by the time

3:52

that you guys are looking at it. But

3:53

what I'll do is I'll just go continue

3:55

with email and I'm just going to pump in

3:57

my own little email address over here.

4:00

So this is the name of my startup. I'm

4:03

just going to go continue to click the

4:04

link sent to this. So I'm now I'm going

4:06

to go to my email. Okay. And I have it

4:08

right over here. I'm now going to click

4:09

sign in to claude.ai.

4:11

It's going to take me back to this page

4:13

right here. Now this will quickly

4:16

basically ask you a couple of questions.

4:17

I agree. I consent. And I think I'm not

4:20

going to subscribe to the promotional

4:21

emails and notifications. Sorry,

4:23

Anthropic. Now, we're going to have to

4:25

do some onboarding questions. So, just

4:26

for the sake of not doxing myself, I'm

4:28

just going to put in a bunch of random

4:30

dates here. We'll go 010190.

4:34

Makes me squarely millennial. Uh, I'm

4:36

going to be using this for personal use,

4:38

and I'd recommend that you do as well.

4:40

They're trying a team/enterprise

4:42

feature, but if it's just you for now,

4:44

just click personal use. Now, you will

4:46

have to pay in order to use claude code.

4:49

Hopefully that's clear. This is a

4:51

financial transaction. The idea being

4:53

you invest, in my case, $24 Canadian per

4:56

month into this. Then you make far more

4:58

than $24 Canadian a month back in ROI. I

5:01

can say as somebody that operates a

5:03

business that does half a million per

5:04

month. Um, this is by far my number one

5:06

investment. I probably receive an ROI uh

5:09

something to the tune of an absurd

5:11

10,000% or something like that just off

5:13

my Cloud Code subscription. Um, you can

5:16

use Cloud for free. It's just you can't

5:18

use the code aspect. And as you'll see,

5:19

the code aspect is very foundational to

5:21

us being able to use Cloud to do really,

5:23

really high level automation super

5:25

quickly. So, I'd recommend that you guys

5:27

do this for sure. But if you don't have

5:29

the funds, know that you can also just

5:31

watch me build things in the course.

5:34

Maybe wait until you have the 28

5:36

Canadians a month, let's say, to sign up

5:38

and then circle back after you watched

5:39

it end to end. So, don't worry too much

5:41

about that. Uh, but if you want to do

5:42

things with this, you will most

5:44

definitely have to eventually pay. Okay.

5:46

Okay. So, monthly right up here. I'm

5:47

going to go down to payment method to

5:48

actually put in some info. Okay. And I

5:50

just wrapped that up. Cool. So, now once

5:53

we're done, it'll automatically take us

5:55

to the next step. As mentioned, if your

5:57

interface looks a little bit different,

5:59

that's okay. What we do after this is

6:02

we're going to download it according to

6:04

our operating system. Now, in my case,

6:06

my operating system is MacOSS. If you

6:09

guys couldn't already tell, just based

6:10

off the way the Windows look and stuff

6:12

like that. It'll be a little bit

6:13

different uh if you guys are using

6:15

Windows, but it'll automatically pop up

6:17

right over here based off of like a

6:19

little browser cookie thing. So, I'm

6:20

going to give this button a quick click.

6:22

And as you can see, a lot of the time on

6:24

Chrome, it will automatically block the

6:25

download. So, what you can do is you can

6:27

just go to download unverified file in

6:30

the top right hand corner. Okay? And

6:32

then you'll actually be able to see it.

6:33

So, claude 6.d. Okay. And then once

6:35

you're done with that, all you have to

6:37

do is just drag this little Claude app

6:39

over to the applications folder. So, I'm

6:41

doing that. And I just heard a little

6:43

chime in the back end. That's because it

6:45

has gone through and actually copied

6:47

Claude to my applications. That's just

6:48

how you do things on a Mac. On a

6:50

Windows, you'll have to go through that

6:51

whole prompt uh that you guys are

6:53

probably pretty familiar with. And then

6:54

once that's done, all you need to do is

6:56

actually launch Claude. And then we will

6:58

have opened, you know, the Claude

6:59

desktop app. So on Mac, all I do that uh

7:02

in order to do that, all I do is I take

7:04

this little window. This is called

7:05

Raycast. Then I just type Claude. You

7:07

can see I'm actually right over here.

7:08

Don't pay attention to the rest of the

7:10

stuff. Once I give that a quick click,

7:12

it will then open Claude. And if I go

7:15

down to the bottom here, it'll say it's

7:16

an app downloaded from the internet. Are

7:18

you sure you want to open it? After

7:19

you're done, it'll then look something

7:20

like this. And all we have to do is

7:22

click get started. I'm now going to

7:24

relog in. So, continuing with my email

7:27

address here. I'm then going to have to

7:29

go to my email address and then enter

7:31

the verification code, which I will get

7:32

right over here. When you click sign in,

7:34

it'll take you back to the app and we

7:37

should be good to go. Cool. So, I've

7:40

already created the account to be clear.

7:41

Um, what it's doing here is it's just

7:43

double-checking a lot of the stuff. So,

7:45

I'm just going to click continue. We'll

7:47

start with my name, Nick. What kind of

7:49

role we do, I'm just going to say

7:51

product management. I'm then going to

7:53

say I have my own topic. And now we are

7:56

in. So, first of all, there's a lot

7:58

going on on this interface and you'll

8:00

see so many little widgets and buttons

8:02

and tools and things you can push and

8:04

pull. Uh, I'm not going to cover every

8:06

single one because I don't think that

8:07

would be a very good use of time. And

8:08

what you'll quickly find is Anthropic,

8:10

the parent company of Cloud, is testing

8:12

a lot of different user interface

8:14

features and they're trying to collect a

8:15

lot of data on what people actually

8:16

like. So they'll change these up pretty

8:18

often and they're pretty short-lived.

8:21

But what I will do is show you a couple

8:23

of them. Um top lefthand corner. Okay,

8:25

we have obviously the three little close

8:27

the window buttons. If you're on a

8:28

Windows, it'll probably be in the top

8:30

right hand corner. Then you have a tab

8:33

open or close button, which is how you

8:34

open or close the sidebar. Then you have

8:36

a search chats and projects button which

8:39

allows you to basically um search

8:40

through all conversations that you've

8:42

had to find more or less any info.

8:44

That's actually pretty useful. Next up

8:46

you have the home versus code tab. And

8:48

so basically there's almost like two

8:49

products here. There's like a clawed

8:51

product and then there's like a clawed

8:52

code product. And so basically what we

8:54

want to do is we don't want to use the

8:56

home product, okay, which is simpler to

8:58

be clear. We want to use the code

8:59

product. When you jump over to the code

9:01

product, a lot will change. you know, if

9:03

it's the first time you've run it, it'll

9:04

look something like this. When you don't

9:06

have any conversations in the queue or

9:07

sessions going on, what it'll do is

9:09

it'll give you a quick overview of all

9:11

of the conversations that you have

9:12

started and uh basically had over the

9:14

course of I think it's like the last 6

9:17

months or something like that, 3 months

9:19

anyway, a lifetime basically from when

9:20

you created your account. Now, in my

9:22

case, you know, I've created the account

9:23

quite a while ago and I've had a lot of

9:25

conversations with Claude. So, as you

9:26

can see here, it's saying that you sent

9:28

12,28 messages, 1,123,

9:31

you know, 3,289. I've I've had a lot of

9:34

back and forth with them. As a result,

9:36

I've used more 241 times more tokens

9:38

than Pride and Prejudice supposedly,

9:40

which is pretty cool. You can also break

9:42

that down on a 7-day basis, 30-day

9:44

basis, or all. And, um, you can also see

9:47

which models you use the most. In my

9:49

case, I use the smartest model as of the

9:50

time of this recording, Fable, pretty

9:52

often. Okay. And you can see that my

9:53

usage started right around July. So

9:55

that's enough of everything over here.

9:57

Let's go back to the left hand side.

9:58

Underneath new is artifacts. In case you

10:01

guys didn't know, an artifact is

10:03

basically a visual that Claude can

10:05

create for you. And so if you wanted to

10:07

create a brief visual, what you can do

10:09

is build a code artifact. Um well, in

10:12

this case, I just clicked new and it

10:13

automatically populated this. I want to

10:15

build a code artifact, a self-contained

10:16

web page published with the artifact

10:18

tool. Um make this page about Nick Sar.

10:23

Okay. Okay. And then I'm going to press

10:24

enter. And what it will do now is it'll

10:27

actually go through presumably ask me a

10:28

couple of questions. And then it's going

10:30

to design a visual interface for me, an

10:33

artifact inside of Claude that I can

10:34

then access anytime I want. So now it's

10:37

confirming the angle, gathering a couple

10:38

key facts before designing. And now it's

10:41

going to go through and get me some

10:42

questions. So what's this page's job? A

10:45

personal bio portfolio site. I'm just

10:46

going to say it's a personal bio

10:49

portfolio page emphasizing the YouTube

10:50

channel and automation agency. And what

10:53

you'll notice just happened there is

10:54

Claude actually suggested an

10:56

autocomplete down at the bottom. And so

10:58

all I had to do when that autocomplete

11:00

popped up is I just pressed tab and then

11:02

it automatically went all the way to the

11:03

end. This is basically now letting

11:05

Claude drive your conversation. And it's

11:07

not always the best thing to do to be

11:09

clear. Um but it is pretty neat and I

11:10

would say it knows what I want to do

11:12

next maybe 30 40% of the time which

11:15

helps. And you can see it's now coming

11:17

up with a bunch of uh information based

11:19

off of publicly available work like

11:21

leftclick maker school my subscribers

11:23

where I live and so on and so forth. Not

11:26

all this is up to date because it's been

11:28

a while since you know the web pages

11:29

that hosted all this information um have

11:32

been created but it's pretty dang solid.

11:34

It did that in just a few seconds. Okay.

11:36

Now here we have what's called a

11:39

request. This is asking to see if I

11:41

should allow Claude to run this command

11:43

because this command requires my

11:45

approval. And so you can see there are

11:47

three things that have popped up here.

11:49

There's a deny button, a one, an always

11:51

allow button, which is two, or an allow

11:53

once button, which is three. Generally,

11:55

it thinks that allow once is the right

11:57

answer, which is why it's popping up as

11:59

that little highlighted thing. In my

12:00

case, I'm just going to go always allow

12:02

because this is just building a simple

12:03

web page. It's probably not going to try

12:05

and delete my computer by accident. Now,

12:07

once we've done this, you can see the

12:08

interface change more. There's this

12:10

little tab that says creating. If you

12:12

click on this tab, it'll actually show

12:13

you guys the commands that it's running

12:15

under the hood. If you guys feel like

12:16

supercomputer programming, you want to

12:18

see what's going on. And you can

12:19

actually see as well the amount of total

12:21

time that it's spent working as well as

12:23

the total number of tokens that it is

12:25

spent as well. Now, tokens are important

12:28

to understand because tokens are a big

12:30

part of how you are build at the end of

12:31

the month. They're also a big part of

12:33

how much uh usage you basically have

12:35

with Claude. The more usage you have

12:38

with Claude, the more tokens you can,

12:39

you know, run through. And typically,

12:41

the bigger and sexier the project, the

12:43

more tokens you will require. So, it's

12:45

important to understand and sort of have

12:46

visibility into your token budgets,

12:47

which is why Anthropic does it right

12:49

here. Now, I'm running this in real time

12:51

because I want you to actually be able

12:52

to see everything that is running. Like,

12:54

I want you to know how long it'll take

12:55

to run one of these things. Um, I find a

12:58

lot of the time on YouTube people will

12:59

just cut to the end. And while I will be

13:01

doing some cutting later on in the

13:02

course when we do larger and bigger

13:04

builds, let's say ones that are, I don't

13:05

know, 15 or 20 minutes, I'm not going to

13:07

force you guys to sit down and and watch

13:09

all of that, um, it's important to at

13:11

least see what a real flow looks like

13:13

start to finish. Okay, once all of this

13:16

is done, if I just move my big fat head

13:18

out of the way, you can see that we now

13:19

have um, you know, we actually have a

13:21

web page that we built. Okay, and this

13:23

web page is the artifact. Now, this

13:26

isn't on the internet to be clear, and

13:27

it's not going to be perfect all the

13:28

time, but I'd say it's pretty good. You

13:30

can see it basically built me this

13:31

little portfolio site saying Calgary,

13:33

Canada, building in public automation

13:35

systems that run without him in the

13:36

room. Oo, super fancy. So, it gives a

13:39

bunch of information right over here

13:40

about my YouTube subscribers, views on

13:42

the channel, the number of maker school

13:43

graduates. You can then see that uh, you

13:46

know, I went to behavioral neuroscience

13:47

in school. Uh, this was actually wrong

13:50

because I think somebody made a big post

13:52

about how I left college or something

13:54

that ended up getting really popular,

13:56

but I didn't actually leave college. I

13:57

graduated with my degree. Um, you can

13:59

then see this sort of like timeline here

14:02

with maker school and YouTube and stuff.

14:03

All all very cool. Anyway, the point

14:05

that I'm making is not, you know, you

14:06

can make a website. Hopefully you guys

14:07

know that by now. If I move my head back

14:09

here in the top right hand corner, you

14:11

can um click this little button here to

14:13

expand it or open it in a default

14:16

browser. So now this is actually open in

14:18

my Chrome and the really cool part about

14:20

opening it in your Chrome is once you

14:22

have the artifact online you can

14:23

actually share it with people. So what

14:25

you can do is you can actually share it

14:26

with people that are in your uh company

14:29

using a teams or enterprise plan or you

14:31

can share it with people um that are

14:33

just on the internet like basic any

14:34

basically anybody. And so I'm now gone

14:37

to share anyone with the link. If I go

14:39

copy here, then I open this up in let's

14:41

say an incognito tab and then I paste

14:43

this, you'll see that I'm now accessing

14:45

that artifact despite the fact that I'm

14:46

not even signed in. And so in this way,

14:48

you can basically host web pages now

14:50

with anthropic and and cloud. And it's

14:52

really useful particularly for like

14:54

dashboards and for managing projects for

14:56

clients. You know, I do a lot of client

14:58

work now and a big chunk of my client

15:00

work these days is actually building

15:02

like web interfaces and internal tools

15:03

for people. Well, Artifacts solve that

15:05

for me completely. I no longer need to

15:07

worry about actually hosting a website.

15:09

I can just whip up a quick artifact and

15:10

then give somebody the link. So that's

15:12

that artifacts tab is right here. Okay.

15:15

Next up, you have the ability to

15:17

customize. I know the second you click

15:19

this, it'll look really intimidating and

15:21

frankly quite terrifying. Uh but this

15:23

customize button is is quite valuable.

15:25

It'll basically take you all the way

15:26

down to the skills tab under customize,

15:29

which is where you can add uh what is

15:30

probably going to be one of the most

15:31

important parts of this course. skills

15:34

which are almost like programs that you

15:35

can run on knowledge work. So, Enthropic

15:38

will have created a couple for you

15:39

assuming you're watching this at least

15:40

sometime in the next month or two uh

15:42

like morning and uh they'll also have

15:45

done I don't know setup writing style

15:46

and skill creator. These are just meta

15:49

skills to show you how they work but I'm

15:50

just going to run you through this one

15:51

here morning to start. Now the idea is

15:54

this is a skill. This is something you

15:55

actually have access to. Render the

15:58

user's morning brief as a styled HTML

16:00

artifact or set it up as a recurring

16:02

weekday task. Use only when the user

16:05

explicitly asks to run, see, or set up

16:07

their morning brief or if they invoke

16:09

/morning by name. You may be wondering

16:12

exactly what that means. I'll show you

16:13

guys in a second. A question about their

16:16

day, schedule, or calendar is not by

16:17

itself a request for the brief. So,

16:19

answer it directly instead. And so what

16:22

this is going to do basically is this is

16:24

going to automate the process of coming

16:25

up with a prompt and telling a model

16:27

what to do. Um you know before I said

16:29

hey make me a website. The website is a

16:31

one page we're about nixar. Well now

16:32

what we have is we have a skill which

16:34

all we need to do in order to actually

16:35

generate a page is invoke it by going

16:38

/wmarning. When you do it'll pump this

16:40

prompt in to claude which is quite long

16:42

as you guys can see here. You can

16:43

actually add a lot of detailed

16:44

information. Then all the way down at

16:46

the bottom it'll basically generate a

16:48

page for you. Okay. So why is this under

16:51

the customize button? This is under the

16:53

customize button because this is how you

16:55

customize your cloud instance. This is

16:57

how you optim uh customize cloud code.

16:59

You add skills which are basically

17:01

domainspecific programs that you can run

17:04

to do things for you. And so for

17:06

instance when we do ad creative uh later

17:08

on in the course when I actually

17:09

automate that for you using cloud code

17:11

we will be using like an ad creative

17:14

skill. I will create one alongside you

17:16

and that ad creative skill will automate

17:18

the process of like producing a bunch of

17:19

ad variants that we can test for

17:21

marketing. You know, if you create a uh

17:24

uh I'm not sure a dashboard skill or

17:26

something. What you can do is I don't

17:27

know once a day you can quickly set that

17:30

on a loop to automatically pull all of

17:32

the different data sources. Let's say

17:33

Google Analytics API, a big Google sheet

17:35

that contains data, a bunch of I don't

17:37

know s you know agency analytics

17:39

software and stuff like that and then

17:41

just pull all that in and use it to

17:42

populate a dashboard. You can do all

17:43

that stuff just using skills. So, it's

17:45

very important to understand um sort of

17:47

what they are and how they work and

17:48

we're going to go a lot into detail

17:49

about that later. But that's not the

17:52

only way you can customize your cloud

17:53

code instance in the app. You can also

17:55

do so with connectors. Now, connectors,

17:57

those you guys that don't know, are

17:58

basically just uh ways to hook up apps

18:01

to cla. And so what we have here is we

18:04

have the ability to connect our Gmail.

18:06

We have the ability to connect Google

18:07

Drive. We have the ability to connect

18:09

Slack. And so as I create these

18:13

automations with you guys, we'll be

18:14

using connectors to integrate a variety

18:17

of different data sources into cloud

18:19

code like emails, like you know, Slack

18:21

channels and so on and so forth. And you

18:23

can automate virtually whatever the heck

18:25

you want. I mean, as you guys see, we're

18:26

even doing this with Spotify, right?

18:29

Spotify is connected right now and has

18:31

access to get the current track, get the

18:32

player state, get the volume, get the

18:34

position, get the repeat, get the

18:35

shuffle. These are all functions inside

18:36

of this connector. And so, you know, you

18:38

could actually ask uh Claude to do

18:40

something for you, like, hey, you know,

18:41

I want you to turn up the volume of

18:43

Spotify and it and it can do so via

18:45

connections. So, you can connect things,

18:47

disconnect things, and so on and so

18:48

forth. A lot of really cool stuff to

18:50

play around with there. Finally, you

18:52

have plugins. Now, plugins sort of

18:54

depend on exactly what you want to do

18:56

with Claude. And as you guys can see, I

18:58

have a couple set up right here. One's

19:00

called Claude Mem, another Superbase,

19:02

another's clanged LSP. You don't need to

19:04

know what any of these three mean, but I

19:05

just wanted to show you guys what this

19:06

looks like in an interface. So, when you

19:08

click on it, okay, you'll see that there

19:10

is a source version, and an author. And

19:13

this source is pretty interesting

19:14

because if you go on, if you click that

19:16

button, you'll see there's actually a

19:17

variety of plugins that have been

19:19

created by Anthropic and Anthropic's

19:21

partners to allow Claude to do things

19:23

easier. So for instance there is a

19:26

finance plugin which automates the

19:29

process of creating journal entries,

19:30

financial statement reconciliation,

19:32

streamlining your accounting workflows

19:34

and so on. Legal which speeds up

19:36

contract review, NDA triage, compliance

19:39

workflows for in-house legal teams and

19:40

so on. There is bio research which

19:44

connects to pre-clinical research tools

19:45

and databases. There's code

19:48

modernization which is a plugin used to

19:50

modernize really old code. There's a

19:52

variety of different plugins here and

19:53

they do everything from changing how

19:55

Claude looks. So actually changing I

19:57

don't know in this case the output style

19:59

or I don't know doing structured actions

20:02

for you that are similar to skills but

20:03

not exactly skills like code review.

20:06

Okay. So we're going to show you guys

20:07

how to do all of that in this course

20:09

which I think is going to be quite

20:10

valuable. Then finally at the very

20:12

bottom you have memory. Memory is just

20:14

sort of what Claude knows about you. And

20:17

so I don't know, remember I'm, you know,

20:20

we talked about height, you know, a

20:23

couple days ago with Claude and I

20:24

remember asking it to do some BMI stuff

20:27

for me and it was like, I don't know how

20:29

tall you are, so how tall are you? So

20:30

what I said now was, hey, you know, I'm

20:32

6'2. So what it's done is it's created a

20:35

memory profile with basic identity facts

20:38

about the user details is 6'2 tall. You

20:40

know, you could put in weight

20:41

information. My dog's name is beans. you

20:44

know, you could put in uh information

20:45

about your career, about how much money

20:47

you make, about, you know, your

20:48

marketing channel, about uh the

20:51

priorities of your business and so on

20:52

and so forth. And so that is what the

20:55

customiz tab is for. Next, you also have

20:57

this more tab, which exposes routines,

21:00

which is going to be quite an important

21:01

part of our work um as we get on a

21:04

little bit later. What this allows you

21:06

to do is basically create templated

21:08

skills. They call them routines, but

21:09

they're templated skills that are kicked

21:12

off on schedule by API or web hook. Now,

21:15

you don't need to know what API or web

21:16

hooks mean, but obviously a schedule is

21:18

just when you want it to. So, what you

21:20

can do, for instance, is you can build a

21:22

skill, which is a program like I talked

21:24

about earlier. And then you can just run

21:26

that at, I don't know, 5:30 a.m. every

21:28

morning. And so, this is the last step

21:30

of automation. This is like the max

21:32

automation where we're not actually

21:34

talking to Claude anymore, nor are we

21:36

even running skills, nor are we looping

21:39

those skills at a regular interval.

21:40

We're actually scheduling these to run

21:41

on the cloud outside of our computer.

21:43

And this is where you really unlock like

21:45

fully automated business stuff, which is

21:47

typically what people work with me for.

21:49

Um, the full sort of endto-end pipeline

21:51

automation of a aspect of their

21:53

business. So, it's actually quite easy

21:55

to do and I'll show you guys how to do

21:56

that soon. Finally, you have the ability

21:59

to customize the sidebar here. So, I'm

22:00

actually going to open that routines tab

22:02

so we can also see that underneath you

22:05

have the various conversations and

22:07

folders that you're working in. So, I'm

22:08

actually working in a folder called for

22:10

YouTube right now, which is why it says

22:12

for YouTube here. If I just zoom in on

22:14

this just a tad, you also have the

22:15

ability to create new conversations by

22:18

clicking this plus button. Then you can

22:20

change a bunch of information about

22:22

these the status of your conversation,

22:24

the environment that you're working on.

22:26

um you know all of the stuff is a little

22:28

bit more technical and not going to be

22:29

super relevant to us immediately. So I'm

22:31

just going to leave that for now. Okay.

22:33

And then that takes us to the actual

22:35

chat window itself. Now as you guys can

22:37

see here when you don't have any

22:38

sessions in play you have this big um

22:41

sort of like dashboard with all the

22:42

information about how many tokens you

22:44

consumed and stuff like that. So the

22:46

number of sessions, the number of

22:46

messages, the number of total tokens and

22:48

stuff like that. You also have the

22:50

ability to check model specific usage

22:51

and break that down. Well, I think they

22:53

just included this information so that

22:54

you wouldn't have to look at a blank

22:55

screen and because this looks way sexier

22:57

than a blank screen. The parts that are

22:59

important for us are first of all this

23:01

button which is how you switch between

23:03

running stuff locally on your own

23:04

computer running stuff in the cloud K

23:07

which is Anthropic computer using remote

23:10

control which is running it on another

23:12

computer that you own and SSH which

23:15

stands for secure shell which is uh just

23:18

a protocol used back in the good old

23:20

days to communicate with computers in a

23:22

secure way using passwords and stuff

23:23

like that. So we're probably not going

23:25

to touch on the SSH stuff. I'm going to

23:27

show you guys remote control just sort

23:29

of so we all know how to run marketing

23:30

workflows um on another computer. But

23:33

really the two we're going to be using

23:34

the most are local and cloud. And local,

23:37

as I'm sure you guys can guess, is

23:38

actually quite easy. Um basically what

23:40

it is is it's my local computer. And

23:42

when you click on that little settings

23:43

gear icon, you can actually update that

23:44

local environment with a bunch of

23:45

usernames and passwords. Now the

23:47

usernames and passwords are written in

23:49

kind of intimidating ways. Nodeenv

23:51

equals production. But what these are,

23:52

these are just usernames and passwords.

23:54

You know, you can imagine it be like

23:55

user nick atclairvo.io,

23:58

I don't know, password, hey bros, I love

24:02

claude code for marketing, whatever.

24:04

Well, now we've actually stored like a

24:06

credential called user, which equals

24:07

nicer.io, and a credential called

24:10

password, which equals that. So, we can

24:11

save that. Now, my my local environment

24:13

actually has that information. You can

24:15

see it's even sort of hidden some of

24:16

that from me. Um, so I'm just going to

24:18

save the changes back here so that we

24:20

don't have anything that confuses

24:21

Claude, but you can use that to log into

24:23

apps and various APIs like the meta ads

24:25

API and whatnot will typically require

24:27

you to have like env keys and stuff like

24:29

that. Okay, then we also have the cloud

24:32

button here. I can click add cloud

24:34

environment that'll take me to a panel

24:35

here which basically allows me to do the

24:37

same thing. Cloud is speaking loosely

24:39

here. What this is is this is on

24:41

Anthropic's own computer. So, Anthropic

24:43

is running this for you and you just pay

24:45

Anthropic instead of paying, you know, a

24:47

computer company or a server company to

24:49

host. Um, I won't get super deep into

24:52

the pricing here because we're probably

24:53

not going to use cloud environments for

24:54

most stuff. We may use them a little bit

24:56

later, but this is valuable in larger

24:58

teams. And the reason why is because if

24:59

you think about it, right now I'm

25:01

running stuff on my computer

25:02

specifically, but my computer works a

25:04

little bit differently than other

25:04

people's computers, right? And so

25:06

because of that, sometimes you'll create

25:08

something that works on my computer uh

25:10

but it won't work on somebody else's

25:11

computer. And so the benefit to using

25:14

cloud environments a lot of the time is

25:15

you're standardizing it. Everybody uses

25:17

the same cloud environment. It's the

25:19

same server with the same environment

25:20

variables, same usernames and passwords,

25:22

same setup scripts, same everything. And

25:24

then as a result, you just know that

25:26

like the app that you're creating for

25:27

your cloud code for marketing course is

25:29

going to work the same whether it's on

25:30

my computer or yours. Down over here,

25:33

you can actually select the folder that

25:34

you want to play in. And so I'm just

25:36

going to do um for YouTube. I'll keep

25:38

that. You can also just add a new folder

25:40

here anytime that you want. So I have a

25:41

couple more. Clarbo backend, Fable 25,

25:43

and so on and so forth. U over here on

25:46

the right, you actually have this little

25:47

claude widget. And if you just kind of

25:48

move your mouse around, it'll do some

25:49

cute fun stuff. Uh kind of funny cuz

25:51

this thing is actually galaxy sized

25:53

brain, but the company made it look

25:55

super cute. It's breaking out of your

25:57

shell. Uh and then on the bottom

25:59

lefthand corner, you'll see the mode,

26:00

which is very important. My

26:02

recommendation is to run most things in

26:04

auto mode. Basically what modes are is

26:07

you guys remember when um I just

26:09

launched Nixrive webpage before and

26:11

asked me hey do you want to allow the

26:13

ability to run XYZ command? Well a

26:17

manual will always ask you before it

26:19

does anything that is even remotely

26:21

considered like you know autonomy in

26:22

your computer. And that's good if you're

26:25

working on like something really secure,

26:26

but obviously eventually it becomes a

26:28

pain in the butt, right? Because I don't

26:29

want to have to like wait, you know,

26:31

around to see what it needs me for. So,

26:33

what I typically do is I'll typically

26:35

use um accept edits, plan, auto, or

26:38

bypass permissions. Now, what accept

26:40

edits is is it won't be able to create

26:43

new files, but it will be able to edit

26:45

pre-existing old files. And so, that's

26:47

pretty useful, right? You can run it a

26:48

mock in like a codebase or I don't know,

26:50

some marketing folder or something like

26:51

that and have it upgrade everything. um

26:53

but it won't be able to create or delete

26:54

any files, which is sort of a a certain

26:57

level of control that you could imagine

26:58

would be useful. You can also use plan

27:01

mode, which is where instead of making

27:03

any changes, you create a really

27:04

in-depth plan about how you're going to

27:06

do it instead. You can then do auto

27:08

mode, which is where Cloud will handle

27:10

most permission decisions for you. It'll

27:12

only ask for your help if there's

27:13

something that it considers to be kind

27:15

of scary. You know, if you're playing

27:16

around in your file systems, and I don't

27:18

know, the file systems on your Windows

27:19

will delete a certain system 32 folder.

27:22

um you know, Claude will cla will tread

27:24

very lightly and it'll ask you uh as you

27:26

work on things that are close to that

27:27

folder, but if you're just working on

27:29

like random ad creative or something,

27:30

for the most part, you'll never actually

27:31

have to worry about a little um dialogue

27:33

box popping up. Then finally, you have

27:35

bypass permissions mode. Uh I love

27:37

bypass permissions mode. It's basically

27:38

just like full you only live once, let's

27:41

go, Claude, 1v one me. Uh you know, it's

27:45

not going to ask you a single thing.

27:46

Claude's just going to do whatever the

27:47

heck it wants all the time. Um, I am

27:50

typically in the loop in some way,

27:51

shape, or form. And I typically do not

27:53

work on things that are super sensitive.

27:55

You know, cyber security projects, super

27:57

sensitive like passwords, credentials. I

27:59

don't do any of that stuff. So, you

28:00

know, I'm just working on little

28:01

marketing tasks. I'm usually going to be

28:02

auto or bypass permissions. So, as you

28:04

can see, right now, I'm actually on

28:06

bypass permissions. When you click on

28:08

that button, you can then scroll all the

28:09

way down to the page and it'll actually

28:11

ask you, hey, do you want to allow

28:13

bypass permissions mode? And you have to

28:15

do that check because obviously there is

28:17

the possibility of some sort of

28:19

long-term impact. So what I'll do is

28:22

I'll just go back here. And now once

28:23

I've accepted or allowed this, I can now

28:25

just go five and bypass permissions.

28:28

When you do so, it'll kind of make your

28:29

screen seem kind of scary. So, you know,

28:30

don't do it if you're not like actually

28:32

okay with it. Um, auto mode, plan mode,

28:36

accept edits mode, everything is fine.

28:38

Uh, I just wouldn't really recommend

28:39

using manual because you'll have to

28:40

check in like every 5 seconds as it does

28:42

anything. Okay. Okay. Then a little bit

28:44

to the right, we are now exposing the

28:47

capability to add files or photos. So I

28:48

can actually add any file or photo to my

28:51

claude code conversation. So I have some

28:53

videos here that you could see I did

28:55

with, you know, Jack Roberts. I have a

28:56

big list of community members. I can

28:58

actually export all of these and

28:59

actually upload them directly into the

29:01

chat. So this is a picture of uh me,

29:03

Alexi, and Sam Ovens at uh you know the

29:06

school games where they were

29:07

interviewing me about stuff. For

29:08

instance, maybe I want to ask it

29:09

questions about how to make this better

29:11

or have it generate a new image using

29:12

another model or something like that. I

29:13

could do so really easily. You can then

29:16

add a whole folder as well, which is

29:19

basically the entire folder's worth of

29:20

context. It'll just go through

29:22

everything in that folder, consume it,

29:24

and then, you know, sort of understand

29:25

what is going on. And then you have the

29:27

ability to expose slash commands, which

29:30

if you guys remember earlier were those

29:32

slash warnings, the skills and so on and

29:34

so forth. So slash commands are kind of

29:36

cool. Um, there are a lot, so I'm not

29:38

going to go through every single one,

29:39

but I am just going to show you guys

29:40

what the interface looks like. If you

29:42

type slash, you'll see there is a

29:44

massive list of all of the different

29:45

possible things that you could do here.

29:46

There's slash schedule/btwreview

29:51

slrefork/mcp/feedback/model/reame.

29:56

You know, this is sort of like um you

29:58

reading through the help docs of a a

30:00

very complicated piece of software.

30:02

There are a lot of these and we're

30:04

adding more and more and more all the

30:05

time. But what is really cool is this

30:08

will also include um skills. So just

30:11

like you know we had um you know all of

30:14

those different functions here we also

30:15

have anthropic-skills colon morning. And

30:17

so for instance that would actually run

30:19

the morning flow the same thing that um

30:22

you know we were reading through a

30:24

moment ago. So as you can see I'm saying

30:26

anthropic-skills morning. This will take

30:28

a few minutes. Let me check what's

30:29

connected before gathering. And the

30:30

reason why it's doing that is because

30:31

it's looking to see, hey, do you have

30:33

email? Do you have calendar? Do you have

30:34

chat, Slack, Gmail, or Outlook? Because

30:36

those were the five platforms or six

30:38

platforms that were used in the skill in

30:40

order to, I don't know, give you your

30:42

morning brief. The value to the morning

30:44

brief is composed of data you would find

30:46

on places like this. So, the fact that I

30:48

don't have any of those platforms

30:50

connected means that it's probably not

30:51

going to be all that valuable of a skill

30:53

run, right? But as you guys see, these

30:54

things are quite organic and uh Claude

30:56

will let you know as you proceed through

30:59

building skills and stuff like that,

31:00

whether you have the appropriate

31:01

permissions to make that skill valuable

31:03

in the first place. Okay, there are a

31:06

couple of these um slash commands that I

31:08

would recommend understanding. Um one is

31:10

called BTW. This allows you to ask a

31:12

quick side conversation before you

31:14

actually stop the prompt. Um so for

31:17

instance, I could say, "Hey, what's my

31:19

name?" When I do this, what it'll do is

31:21

actually open up this little side chat

31:22

right next to your main conversation.

31:25

And you can ask it a question without

31:27

actually interrupting the conversation

31:29

that you guys are having down here. And

31:30

so that's what's going on up here. Okay,

31:32

I'm actually just having a conversation

31:34

with it. And it's still technically in

31:36

the thread. It has all the context of

31:37

the thread, but um we're talking without

31:40

actually interfering with anything down

31:41

here. So then I'm just going to close

31:43

out the /btw. And you can see here it's

31:44

now asking me for, hey, can claude open

31:47

this folder? I'm now going to click

31:48

allow once. And you can see that it's

31:50

actually gone through and created the

31:51

page for me. But the page is pretty

31:53

boring. It says nothing's connected yet.

31:55

So there is no day to show you. Nick,

31:56

you should get your stuff together,

31:58

right? And so that's what that works.

32:00

It's going to ask me multiple times. I'm

32:02

just going to keep on closing this out.

32:03

I don't really need to worry about that.

32:05

Okay. Now, I don't like the fact that

32:07

this has been talking for a while. So

32:08

what I want to do is I actually want to

32:09

pause it. And how do you do so? You can

32:11

click this button right here. You can

32:12

also press escape. The reason why that's

32:15

valuable is because sometimes cloud gets

32:16

caught in loops where it just reopens

32:18

the thing over and over and over again

32:19

because you keep exiting it or it keeps

32:21

on editing a file for you or you change

32:23

tac and you don't want to use /b2w or

32:25

something like that. Now moving down

32:27

slash commands. Another one that I

32:29

really like is slash usage. This will

32:31

show your usage plan as well as the um

32:33

rate limits and how far you are through

32:34

them. So you know I've done a couple of

32:36

things with you here on my $20 plan. And

32:38

so uh you can see my 5-hour limit resets

32:42

in 4 hours and 21 minutes is at 9%. So

32:44

I've used 9% of my 5h hour limit. To

32:47

make a long story short, Anthropic

32:48

currently gives you five whole hours

32:52

um as a window in which you can use

32:54

Claude and then they give you a certain

32:56

amount of cloud usage within those 5

32:58

hours. After you hit 100% you can no

33:01

longer proceed in that 5 hours.

33:04

Then when it resets, you go back down to

33:06

zero and then you can run it up all the

33:07

way up to 100% again. In addition to the

33:10

five hour limit, they will also give you

33:12

a weekly limit. And so this allows you

33:14

to access all models. The reason why

33:16

they do this is because sometimes they

33:17

give you limits to specific models,

33:18

usually new ones, which are very in

33:20

demand. Uh, and so this one here resets

33:22

Friday, 9:00 a.m. And you can see I'm 1%

33:24

of the way through my weekly limit, 9%

33:26

of my way through the 5h hour limit.

33:28

[gasps] It'll also give me some

33:30

information about the session cost. So

33:32

this cost an additional 88 cents. I

33:34

think cuz I was using uh I don't know

33:35

some smarter model or some API or

33:37

something. Um looks like I was using an

33:39

API for 1 minute and 2 seconds and I was

33:43

using mostly sonnet for this task which

33:45

is one of the many claude models. Then

33:47

we also have the token breakdown which

33:49

is quite valuable as well as what is

33:51

actually using my limits. 67% of all of

33:53

my limits were used by that one skill.

33:55

Okay. And you can see that consumed like

33:57

I don't know I guess like 6% or so of my

33:59

total 5 hour limit usage which means I

34:01

could probably run warning maybe every I

34:04

don't know I could run it like 20 times

34:06

in a row. So that's pretty cool.

34:09

Okay. And then finally uh the last skill

34:11

that I wanted to show you guys was uh if

34:13

we scroll down here a little bit

34:16

there's loop right here. Now loop is

34:19

valuable because it allows you to define

34:21

an interval. So I could say, I don't

34:23

know, let's just say 5 seconds. And then

34:27

I'll just say just say hey Nick.

34:31

And so what this will do is every 5

34:33

seconds it will start a loop that every

34:36

5 seconds responds to me as hey Nick.

34:40

[gasps] Now there's a rounding rule

34:42

currently in place. I think that's

34:44

because there's a one minute floor

34:46

currently put on by um Claude. But

34:49

anyway, just to show you guys, let's do

34:52

the one minute loop. Um, what this will

34:56

do now is this will set a loop where

34:57

basically once a minute, every minute,

35:00

this will just say the words, "Hey,

35:01

Nick," to me. And so, as you can see,

35:03

this is a pretty straightforward thing

35:04

to do. Uh, but as I'm sure you guys can

35:07

imagine, we can go much more complicated

35:08

than this. And in fact, we can actually

35:09

run skills like this /morning skill, and

35:12

we can do that on a daily or that on a

35:15

one minute loop or that every, you know,

35:17

45 minutes or whatever. We could also

35:19

very trivially set skills that'll do

35:22

things like check pages for us. And so,

35:24

you know, for a lot of SEO or newsletter

35:26

purposes, that's pretty valuable because

35:27

what you do is you'll like add

35:28

connectors and then you will check,

35:30

let's say you're running a big campaign,

35:32

a flash sale. Well, you actually set a

35:34

loop where cloud will check to see every

35:36

5 minutes how many new customers have

35:38

signed up. Let's say you can take all

35:39

those customers that have signed up and

35:41

then provision them access to some asset

35:43

or whatever. Um, and it'll just continue

35:45

doing this in the background over and

35:46

over and over and over again. You can

35:47

see we've now done that three times.

35:50

Okay. So, if you want to stop the loop,

35:51

just say something like stop the loop.

35:53

Cloud will kind of hear what you have to

35:55

say, find the tool, and actually like

35:57

cancel the tool call that it just

35:58

generated. And then, uh, yeah, you're

36:00

good to go. All right. So, hopefully

36:02

that was a reasonable walkthrough of all

36:04

of the slash commands. Last thing I want

36:06

to do here, um, is, you know, obviously

36:08

we have those connectors, which I've

36:10

talked about before. We have the ability

36:11

to add plugins. You'll see there's a lot

36:13

of um redundancy built into this because

36:16

they have the same buttons three or four

36:17

different places just because they're

36:18

testing to see which ones people like

36:20

the most. Next, we have the microphone

36:22

feature, which is basically how you um

36:25

basically talk to Claude via your

36:26

microphone.

36:28

So, if I hold command D or just press

36:30

this button, you'll hear those little

36:32

two and then you can talk to Claude

36:34

however long you want. And so, that's

36:36

what I'm doing right now. I'm actually

36:37

having a conversation with Claude via

36:39

voice transcription tools. This is built

36:42

into Cloud. Now, I also can use my own

36:44

voice transcription tools to pump uh

36:46

words into this little field if I wanted

36:48

to. But you'll find that this tends to

36:49

work pretty well. And you can actually

36:50

see real time the text that you're

36:52

generating being transcribed. So, that's

36:55

kind of neat, right? In practice, I will

36:57

actually usually end up prompting just

36:59

through my voice these days. The reason

37:00

why I tend to prompt through my voice is

37:02

not because I think I am a better

37:03

speaker than I am a better writer. Most

37:05

people tend to be far better writers,

37:06

but because when you uh use dictation,

37:08

you can typically just generate a lot

37:10

more in a short period of time than if

37:12

you write, the average human being

37:14

speaks at around three to four times

37:15

faster than they write with their

37:17

fingers. So, um you know, this is just a

37:19

quick and easy way to like brain dump

37:20

whatever you need into cloud. And I

37:22

think you'll find that there are a lot

37:23

of situations in which you'll probably

37:24

want to brain dump stuff into cloud.

37:25

I'll show you guys some as we build.

37:27

Anyway, you can change your microphones

37:29

and stuff like that right over here if

37:30

you want. Then we have the actual model.

37:33

So, there's Fable 5, which requires

37:35

usage credits right now. There's Opus 5,

37:37

which is not Sonnet 5, Haiku 4.5.

37:40

Variety of different ones here, too,

37:41

that are kind of old last generation

37:43

ones. Uh, I'm just going to use Sonnet 5

37:45

right now because it is pretty smart.

37:46

Does most of what I want it to do, and

37:48

I'm not super worried about, I don't

37:50

know, solving the mathematical formulas

37:53

of the galaxy. Finally, we have the

37:55

effort tab, which is basically a slider

37:58

that allows you to um define how hard

38:00

you want Claude to work on one of your

38:02

tasks.

38:03

This allows you to basically say like,

38:04

"Hey, like is this work that I'm doing

38:06

today super important? If not, I don't

38:07

want you to think too hard. I just kind

38:09

of want you to go with your best guess.

38:10

Whereas, you know, if you go all the way

38:12

to the right, it's like, hey, I don't

38:13

want you to try guessing at anything. I

38:15

actually want you to triple, quadruple,

38:17

sex tuple check everything that you tell

38:18

me." And in doing so, um, cloud will

38:20

consume a lot more tokens, but the

38:22

probability of a high quality answer

38:23

goes up way more. It's also note also

38:26

worth noting that the speed changes

38:27

quite a bit here. Um, when something is

38:29

at effort low, it usually works much

38:31

much faster than when something is at

38:32

effort smarter. So, keep that in mind.

38:34

If you're going ultra code, it'll take,

38:36

I don't know, like way way longer than

38:38

if something is effort low. Effort low

38:39

might take like 2 seconds. Uh, effort

38:41

ultra code might take like 5 minutes.

38:43

So, worth usually operating somewhere in

38:45

the middle. I like defaulting to medium

38:47

or high for the most part. Um, sonnet

38:49

high tends to work pretty quick while

38:50

also delivering pretty good results.

38:52

Now, you can also see your context

38:54

window here in the bottom right hand

38:56

corner. What that is is the total number

38:57

of things that are consuming your

38:59

tokens. So, I just move my again fat

39:02

head out of the way so you guys could

39:03

see this a little better. Um, as you can

39:05

see, we have a certain number of

39:06

messages up here at the top that are

39:08

consuming a certain section of our

39:10

context window. Certain number of system

39:12

tools, system prompts, MCP tools,

39:13

skills, memory files. You don't need to

39:14

know what all these things mean. What

39:16

you do need to know though is that

39:17

Claude can only hold a certain amount of

39:20

information in its head. And that

39:21

information is currently bounded by

39:23

tokens. And so a token is very similar

39:25

to a word. And pretty soon, I'm sure

39:27

they will just be called words. But for

39:29

now, they're just a little bit

39:30

different. Uh right now, you know,

39:32

sonnet 5, which is the model that I'm

39:33

using, can host 967,000 of those tokens.

39:37

We've consumed 112,000, which puts us as

39:39

12% of the way to the entirety of Sonnet

39:42

5's context window. When you hit the

39:45

context window, usually what'll happen

39:47

is Claude will compact your context,

39:49

meaning it will just like try rewriting

39:51

it so that it's way shorter and then

39:52

stick all that information up at the

39:54

very top. The reason why that's valuable

39:55

is now you can continue the

39:57

conversation, but a lot of the BS words

39:59

are sort of taken out of your context

40:00

and you'll usually get build less for

40:02

it, too. And that does take me to the

40:04

sort of billing and usage aspect. The

40:06

longer your context window is while you

40:08

talk with Claude, the faster you will

40:09

burn through your usage limit. So, my

40:11

recommendation is try not to keep a

40:12

thread running all the 967,000

40:15

tokens uh that are available. I'd like

40:17

working somewhere between 0 to maybe

40:19

500k or so. I find that's also my

40:22

performance sweet spot. If you add more

40:23

information than that, cloud starts to

40:25

perform a little poorer. That's just

40:27

because it gets confused. It doesn't

40:28

fully know what it is it's doing. You've

40:30

given it a lot of instructions at that

40:31

point that it needs to juggle around in

40:32

its memory and obviously that can be

40:33

pretty tough. Okay, so that context

40:36

window can be consumed of messages. As

40:38

mentioned, system tools are just

40:39

different things that it internally has

40:41

access to. System prompt is uh basically

40:44

a little snippet of text that you can

40:45

define that will be prepunded at the

40:47

beginning of every conversation. MCP

40:49

tools is another sort of thing like a

40:52

skill. It's just skills that other

40:53

people host for you. So what you can do

40:54

is you can load basically external

40:56

skills via MCP tools that other people

40:58

have created very easily. And then

41:00

memory files are the things that I

41:02

showed you guys earlier that basically

41:04

define I don't know like in my my case

41:05

my height earlier 6'2 and stuff, right?

41:08

The rest of the stuff not super

41:10

valuable. Obviously that's free space.

41:11

We're just going to skip out of that and

41:13

you have your plan usage limits. Okay,

41:15

so I told you this would be

41:16

comprehensive, didn't I? Yeah, we've

41:18

covered uh probably 90% of the entire

41:20

interface. There's just one more thing.

41:22

At the top right hand corner, you have

41:24

the ability to spawn a terminal. You're

41:26

probably not going to do this, but for

41:27

those of you guys that don't know, a

41:28

terminal is just um kind of the way that

41:31

we used to interact with computers back

41:32

in the day. We had a variety of

41:34

different commands that you could, you

41:36

know, access cdls, print working

41:39

directory and stuff like that. You could

41:40

see all the files that are in this

41:41

directory and so on. Um, but anyway,

41:44

sometimes cloud actually needs to use

41:45

this terminal in order to do things. We

41:48

also have the ability to check your

41:49

diff, which is just the changes to the

41:51

code that you have made. If you're

41:52

developing an application, that can be

41:53

quite valuable. You also then have a

41:56

browser. So we now have a browser built

41:58

into cloud that can do things like

41:59

access web pages for you. That's pretty

42:01

valuable as I'm sure you can imagine

42:02

because you can use that to automate

42:04

browser activities. And then finally at

42:06

the top right hand corner you also have

42:08

a couple of things that are pretty cool.

42:10

One is an iOS simulator. So you can

42:12

actually set up like apps in cloud code.

42:14

Now um these are iPhone apps and they're

42:16

fairly cool. Um you can like download

42:19

you know Xcode which is how you do it.

42:21

Open up the Mac app store and so on and

42:23

so forth. I won't do that just for uh my

42:25

own sanity. I don't like looking at the

42:27

app store any more than I have to. Uh

42:29

but anyway, you can you can create like

42:31

iPhone apps now, which is pretty badass.

42:33

Uh and then you can also open in

42:34

whatever you want. You can open in a new

42:36

window, VS Code Finder. You can rename

42:39

your transcript. You see this Nyx Drive

42:40

web page right over here. Well, if I go

42:42

to the top rightand corner and I click

42:43

rename, I'll say Nyx demo.

42:47

You can see we've actually fundamentally

42:48

and foundationally changed that. Uh you

42:50

can also see the transcripts. you can

42:52

actually like see all of the thinking

42:54

that Claude has done if you just expand

42:56

this transcript view. So, in my case,

42:58

I'm just going to do summary. And what

43:00

it'll do is it'll just summarize

43:01

everything that we have done, our whole

43:03

conversation back and forth. I'm now

43:04

going to get a summary, which is pretty

43:06

cool. Uh, as well as a full transcript

43:08

of the conversation at any time. So,

43:10

transcripts over here, summaries over

43:11

here. This is going to take a fair

43:12

amount of time to spawn, unfortunately,

43:14

so probably won't happen entirely real

43:16

time. Okay. And yeah, now you can see

43:18

it's giving me a summary of this

43:19

conversation. First, I wanted to build a

43:21

self-contained publish artifact page

43:22

profiling Nixx as a personal bio

43:24

portfolio page. And then uh I don't know

43:27

there's a bunch of things like the

43:28

outcome, you know, what had what ended

43:30

up happening in the session. Well, the

43:31

next drive artifact is now done and

43:33

sharable, right? Then you can fork a

43:36

conversation where you just start

43:37

another conversation at the same place.

43:39

So that's me forking the conversation

43:40

right now. And then you can also archive

43:42

a conversation if you don't want to look

43:44

at it anymore. So that's what I just did

43:45

to that Nyx demo fork. And then uh you

43:48

can also delete a conversation if you

43:49

want to. That's what I just did to

43:50

Nick's demo.

43:52

Okay, we are now done. Virtually every

43:55

button in this godforsaken interface.

43:57

Just kidding. Uh there's a lot and

43:58

that's because the Anthropic team has

44:00

added a tremendous amount of value to

44:01

their app in just uh I don't know maybe

44:03

3 or 4 months. So the app was not always

44:05

like this. The app is very high quality

44:07

now. The app will probably look a little

44:08

bit different from what you guys see

44:10

just because they are consistently and

44:12

constantly updating it. But now that you

44:13

guys know how to use the app, you have

44:15

no excuses. we know every little literal

44:18

button on this entire interface. Um

44:20

it'll be very very easy for you to

44:21

navigate and do virtually everything

44:23

that you need in order to make something

44:24

cool happen. Which takes us to learning

44:26

the interface right here which is now

44:28

check. We have an optional task here

44:30

called set up the CLI. CLI in case you

44:33

didn't know stands for command

44:37

line

44:38

interface

44:40

which is just a different way of

44:41

communicating with cloud code. As

44:43

mentioned, you know, they've given us

44:45

many different ways to talk to Claude

44:46

and and work with Claude. Um, so what

44:49

I'm going to do here is I'm going to I'm

44:50

going to set up another one just as a

44:51

demonstration. And I may use the CLI. I

44:53

may end up using the cloud desktop app.

44:55

Um, it's really just about what is

44:56

simpler and easier for you. Before I

44:58

show you guys that, it's worth a brief

45:00

uh kind of piece of education here. Uh,

45:02

just like we have different websites on

45:04

the internet, okay, that are accessible

45:05

through different browsers. Like in this

45:07

case, this website Google can be

45:09

accessed through Chrome, Safari, Firefox

45:11

or something else. We also have the same

45:13

concept of clawed code which is

45:15

accessible through I don't know your

45:17

desktop app, your terminal VS code or

45:19

something else. And so there are variety

45:20

of different ways basically to get to

45:22

the same resource. And so what I just

45:24

showed you here was I actually just

45:25

showed you the desktop app, right? And

45:27

so that's what we just looked at, but

45:28

you can also use a terminal to do the

45:30

same thing. You can also use VS Code.

45:32

And so what I'm going to do now is I'm

45:33

just going to show you guys what it

45:34

looks like in terminal version. And it's

45:36

very very straightforward and easy. All

45:38

I'm going to do is I'm going to go to

45:39

Ghost TTY. When I open up ghost tty,

45:42

you'll see I have a terminal. Same thing

45:43

that we had before. It's inside of a

45:45

folder called business. That's just sort

45:46

of the layout. And in order to access

45:48

Claude, all I need to do is type

45:49

cla-verbose. I like verbose because it

45:52

allows me to see what it's thinking. And

45:54

if I scroll and zoom way in, you'll see

45:56

that, you know, our interface looks a

45:58

little bit different. You know, our

45:58

cloud is up here. And I don't know what

46:00

what is it doing? It's looking up. It's

46:02

going down a couple times, but it says

46:04

Claude Code Fable 5 with medium effort

46:06

in business. So, what's going on? So

46:08

maybe I'll say, "Hey,

46:11

um, what you'll notice is this is the

46:12

exact same product. It's just I'm

46:15

accessing the product now in a different

46:16

way. Instead of me using that app that

46:18

we saw earlier, I'm using my own app and

46:20

I'm talking to it through a channel. If

46:22

I type slash, you'll see that there are

46:24

a variety of different skills that I

46:26

could use as well. And so in my case,

46:28

this is actually my preferred way of

46:29

communicating with cloud code. And the

46:31

reason why is because I just, you know,

46:33

uh, uh, I I've done a lot more

46:34

development stuff over the last 6 or 12

46:36

months. I'm just more used to

46:38

communicating with it in like a terminal

46:40

style interface. That said, it's not the

46:42

only way to do so. And hopefully you

46:44

guys see now that uh the cloud desktop

46:45

app is a very very strong and viable

46:47

candidate. And just for simplicity sake,

46:49

I'll prefer that wherever possible. But

46:51

there are some situations in which I I

46:52

do like just diving into the terminal.

46:54

Again, it's almost like a different car,

46:57

right? Different browser, different car.

46:58

Everybody has slightly different

46:59

preferences about how they drive it. you

47:01

could have the exact same horsepower,

47:03

but obviously if you're accessing cloud

47:04

code or if you're accessing the the same

47:06

horsepower of the engine through a

47:08

different model, um it'll react

47:09

differently, handle differently and

47:10

stuff like that. And that now takes us

47:12

to the end of the first step, which was

47:14

to download, install, and set up cloud

47:16

code. Next, I want to cover the five

47:18

marketing functions that we'll be

47:19

automating today and the framework I'm

47:21

going to use to show you guys how. So I

47:23

want to talk a little bit about business

47:24

because obviously you're doing this not

47:26

for the sake of you know building up

47:29

just a cool personal portfolio but

47:32

ultimately we want to we want to build

47:33

our marketing skills here with cloud

47:35

code to do something some sort of

47:36

economically valuable outcome and so the

47:39

way that I see most businesses and it's

47:41

a service business up here but uh really

47:44

the more I think about it I think it's

47:46

all businesses is as the following

47:49

pipeline. All businesses will start with

47:51

some form of marketing. Okay, that is

47:54

just to get strangers to notice you and

47:56

that is basically to get eyeballs on the

47:58

business itself or the brand. Then you

48:00

will have some sort of sales

48:02

transformation event which is what turns

48:04

these eyeballs and this interest into

48:06

some form of money. Afterwards you have

48:09

fulfillment which is where you deliver

48:11

what you promised and then finally at

48:13

the end you have some sort of

48:14

administrative wheel that is what keeps

48:16

the machine running. Now the whole idea

48:18

here is basically you know during this

48:21

process as you go from top to bottom you

48:23

convert somebody who is a total stranger

48:26

into some form of profit and you can

48:28

imagine the same thing applies across a

48:30

variety of different business stacks.

48:32

You can do this with ecom. You know in

48:34

ecom you have strangers that notice a

48:37

product. You sell that product. they

48:39

receive delivery of the product and you

48:41

have some sort of admin to keep uh I

48:43

don't know the purchasing and the

48:45

inventory in line with like uh

48:47

expectations and forecasts. You have

48:49

hiring, you have vendor relationships,

48:51

logistics managed with your your

48:53

manufacturers and stuff like that. Uh

48:55

and then you know ultimately these four

48:57

working in perfect concert is what makes

48:59

you profit. So you know this isn't like

49:01

an MBA or anything like that. Certainly

49:03

I don't think I could hope to do uh you

49:06

know an MBA in this this this time

49:08

period that I've allotted for this

49:09

course. But you know I want to talk

49:11

mostly about this marketing and then

49:14

this sales aspect. Um and so this is

49:16

really going to be like our focus. This

49:18

is what we're going to spend most of our

49:19

time working through in this course. Um

49:21

but I think it's also important to

49:22

understand that marketing and sales they

49:24

work in tandem, right? It's not just one

49:25

or the other. Realistically there's

49:27

there's there's back and forth here. So

49:28

a lot of the stuff that we are going to

49:30

build for marketing today with cloud

49:31

code um we will also sort of build with

49:35

uh or build for our sales department at

49:37

least to like understand and and know

49:39

what's going on. And so this is kind of

49:41

secondary and I'm going to have future

49:42

courses that are focused specifically on

49:44

sales. So you don't need to worry too

49:45

much about that. Now the framework we're

49:47

going to be using to really crush these

49:49

cloud code automations is called race.

49:53

race stands for reach, acquire, close

49:58

and expand. And the differentiator or

50:01

sort of the delineator between sales and

50:03

marketing in my view occurs right here.

50:06

So everything to the left of this is

50:09

technically marketing and everything to

50:12

the right of this is technically sales.

50:15

Which means our course today is going to

50:18

be focused on these two which is how do

50:21

I get seen which you know if you think

50:24

about the key performance indicators of

50:25

getting seen how do I get impressions

50:28

how do I get views so how do I get my

50:30

brand in front of more eyeballs and then

50:32

how do I turn these impressions and

50:34

views into some form of intent so how do

50:36

I acquire you know so how do I I don't

50:39

know book meetings or get form fills get

50:42

people to sign up to my newsletter get

50:45

people to opt built in to some offer and

50:47

so on. And so all of the automations

50:49

we're going to be developing are really

50:51

going to focus on uh these levers. Okay,

50:54

it's all going to be about reach and

50:56

acquire. And if you think about business

50:58

in this framework and if you you know

51:01

race to implement automations that help

51:03

you with reach, acquisition, close, and

51:06

expansion, typically uh you'll allocate

51:08

your time a lot more effectively than

51:10

otherwise. So, I'm going to come back to

51:12

race pretty often. Uh, when, you know,

51:14

somebody wants to work with Leftclick,

51:16

my AI automation agency. Um, you know,

51:19

typically they're like, "Oh, like I want

51:20

to do this crazy admin automation. I

51:22

want to do this backend thing." And I'm

51:23

like, "Well, why don't we start with

51:24

race? Why don't we start with reach,

51:26

acquire, close, and expand, and then we

51:27

can worry about that later." I find

51:28

results are are through the roof. Uh,

51:30

people focus on revenue and growth uh

51:34

way less than they realistically should.

51:36

Race is how I tie them back to, you

51:38

know, revenue and growth levers

51:39

constantly. And on the right hand side

51:41

here we have close and expand. So you

51:43

and if you think about it, this is like

51:44

a sales transformation threshold. Um,

51:46

you know, once we've booked the meeting

51:48

or gotten some sort of intent or

51:49

something like that for our product,

51:50

it's not really a marketer's job to

51:52

close them and expand them. Obviously,

51:53

we can tee them up for, you know, the

51:56

greatest spike of all time. Um, but

51:58

that's sort of out of our hands at this

51:59

point. So, we're going to work on

52:01

optimizing reach and acquisition. And

52:03

then later on I'll have courses on sales

52:04

that'll also allow you guys to implement

52:06

systems that improve your ability to

52:08

close and your ability to grow the

52:09

lifetime value of your deals. Okay. So

52:11

if we think about this now a little more

52:14

simply what does that mean for us? Well

52:17

under reach there really three major

52:20

places that I'm going to uh help you

52:23

guys build cloud automations with today.

52:25

The first is creative. Now this is the

52:28

generation using artificial intelligence

52:31

and orchestrated by claude of images,

52:35

videos, audio and so on and so forth

52:38

typically for the purpose of advertising

52:40

although also for organic uh kind of

52:42

brand reach and brand growth. So the

52:45

cool thing is with claude if you think

52:46

about this as like one of these analog

52:48

dials on like a control panel we can

52:50

pump this puppy all the way up to the

52:52

top super easy. you can significantly

52:54

reduce any bottlenecks at the creative

52:56

stage just by throwing, you know, tons

52:59

of uh a couple of different creative

53:01

models to Claude and then giving Claude

53:03

the ability to to press buttons and

53:04

arrange prompts um you know, in its own

53:07

way. I'll show you guys how to do a

53:09

define system for this. This is the same

53:11

system I've used at uh over a company

53:14

that does over a billion dollars a year

53:15

in revenue to massively increase their

53:17

top of funnel and then ultimately the

53:18

total number of pieces of content and

53:20

ads that they can get out. After that,

53:22

you also have copy which is sort of in

53:25

tandem with creative for advertising.

53:27

Um, but we can also use, you know, cloud

53:29

code to customize a lot of the copy in

53:31

our marketing mix. So, we could do the

53:33

customization of newsletter copy for

53:35

instance, which I think is a big lever

53:36

not a lot of people are pulling

53:38

realistically. You know, your

53:39

newsletters don't have to say, "Hey, my

53:42

offer does X, Y, and Z." You know what

53:43

you can do is you can actually customize

53:45

it to say, "Hey, Nick, just wanted to

53:47

send you this because I know you talked

53:50

about X, Y, and Z last month in your

53:52

LinkedIn. You know, my offer does X, Y,

53:54

and Z and I think it can help you." That

53:55

sort of stuff does really, really well.

53:57

And it also starts blurring the line

53:59

between direct outreach, too. A lot of

54:01

the time people think marketing and then

54:02

they go, "Okay, this is all inbound. So,

54:04

I make a brand, I make some sort of ad

54:07

campaign and then people come to me."

54:08

Um, well, you know, marketing can also

54:10

be done outbound, which is where, you

54:12

know, I have a product or service and

54:13

I'm I'm actually actively looking for

54:15

customers for it. And so, this is where

54:16

a lot of my own experience comes in

54:18

because I used to do door-to-d dooror

54:19

marketing. So, I would literally acquire

54:20

my leads by walking up to a business,

54:23

opening the door, shaking the business

54:24

owner's hand, and just giving them a big

54:26

pitch. So, I'm sure you can imagine it

54:28

didn't work well super often, but uh

54:29

when it did, it did. And so, I have a

54:31

lot of experience around applying AI,

54:33

cloud code, specifically to the outreach

54:35

stack. And I think it would be remiss of

54:37

me if I did not show you guys how to do

54:38

that as well. It's a very high

54:39

opportunity lever for outbound growth.

54:42

Okay, so all of these improve your

54:43

reach. Okay, so that's sort of the R in

54:45

race. Now on the right hand side here,

54:48

um we also have a couple of, you know,

54:50

ways to improve the total fraction of

54:51

people that opt in or book an offer. And

54:54

so the first one is speed to lead. Now

54:56

what is this? This is basically just

54:58

capitalizing on interest. you know,

55:00

presumably between the reach and the

55:02

acquisition stage, if you think about it

55:03

here, there's some sort of like um

55:05

opt-in, which is where somebody will

55:07

fill out a form or something like that

55:08

to to demonstrate interest. You would be

55:11

surprised at how many hundreds of

55:12

millions of dollars are lost between

55:14

these two stages. Um if you just, you

55:17

know, respond to leads really quickly,

55:18

which is what speed to lead means. If

55:20

you build systems that allow Claude,

55:21

let's say, to orchestrate a conversation

55:23

within 30 seconds of somebody filling

55:25

out a form, you can make companies 300

55:27

to 400%. And um that's what the hat that

55:30

I'm wearing uh right now represents. I

55:32

run a company that actually just does

55:34

speed to lead all day. And we've

55:37

produced crazy outcomes like taking a

55:39

home services company that was

55:40

previously doing around $3 million a

55:41

month in sales. Um literally just by

55:43

implementing the greatest speed to lead

55:45

system ever. We took them to $9 million

55:47

a month. That's 300% increase in total

55:50

revenue simply by optimizing one of

55:52

these, you know, six levers here. Okay.

55:55

Another is booking percentage. So we can

55:57

actually improve booking percentage

55:59

through speed to lead, but also through

56:01

economizing um you know a lot of the

56:03

form fills and a lot of the systems that

56:05

people use right now to to gain

56:07

interest. So we can have Claude, for

56:08

instance, do a lot of research on the

56:09

prospect to eliminate the total number

56:11

of fields we need in an opt-in form.

56:13

Typically, if you have, let's say, 10

56:14

questions in an opt-in form, you break

56:16

that down to three or four questions,

56:18

you'll see a significant improvement in

56:19

booking percentage. You do so at the

56:21

cost of qualification, but Claude Code

56:23

can now do research on the prospect for

56:24

you sort of autonomously. um which means

56:26

you don't actually need all that much

56:27

information. Then also following up. So

56:30

this is like nurturing. Um what we can

56:31

do is we can use cloud code to

56:33

orchestrate autonomous follow-ups and

56:34

then do so in like really customized

56:36

ways that people seem and feel like

56:38

they're are being treated like as people

56:40

not as dollar signs which is kind of

56:42

funny if you think about it because like

56:43

you know we're using AI for this stuff

56:45

but the end result is our prospects that

56:48

people are marketing to are a lot more

56:50

likely to feel like we're talking to

56:51

them directly. If you think about it,

56:53

one of the reasons why many companies

56:55

have been forced to treat people like

56:57

dollar signs, you know, not personalize

56:59

their outreach, not even personalize

57:01

their their billing or their

57:02

communications or the templates of their

57:04

invoices and stuff like that is simply

57:05

because we haven't had the manpower to

57:07

do that and provide a good experience.

57:10

But what I really like about cloud code

57:11

and AI is that it allows us the ability

57:13

to produce the manpower to give every

57:15

single person that works with our

57:17

business a customized experience.

57:19

They'll have customized creative,

57:20

customized copy, customized outreach.

57:22

Their speed to lead will look really

57:24

customized. We'll we'll customize, you

57:26

know, information by doing research

57:28

automatically on people to enrich, you

57:30

know, fields in our newsletter databases

57:31

will u customize follow-ups. Everything

57:33

will be super super unique and and also

57:35

very personalized. Before we actually

57:37

build things, you need to understand

57:39

bottleneck thinking, just sort of

57:41

constraintbased thinking. And this is

57:44

quite important in marketing because

57:47

what you'll find is a lot of the

57:48

companies that you work with or maybe

57:49

your own company um they have a lot of

57:52

one of the things that we talked about.

57:54

Okay? Maybe they have a lot of creative,

57:57

maybe they have a lot of capacity around

57:58

the follow-up end, but you'll find that

58:01

there are certain parts of their funnel

58:03

that just really suck. And the way that

58:06

bottlenecks and pipelines work are the

58:09

entire operation is always constrained

58:12

by the narrowest step of that chain uh

58:16

or like the weakest link in the chain is

58:18

another way of thinking about it as

58:19

well. The classic example is a factory.

58:24

And for those of you guys that have

58:25

played those like I don't know factorial

58:28

games or whatever um you'll intuitively

58:30

understand this but let's say I have a

58:32

factory and the way it works is every

58:35

day I get 100 units of some raw

58:40

material. I don't know let's say it's a

58:41

rock or something like that. And now

58:43

this enters my big factory. Okay. And

58:45

this is what my factory looks like.

58:48

[sighs and gasps] And basically what

58:49

happens is you know what we have to do

58:51

every day is we have to put this raw

58:54

good through two steps. We have step A

58:58

and step A has a capacity of 100 units

59:03

per day

59:05

and step B

59:08

has a capacity of 10 units per day.

59:16

How many units in one day can we get

59:19

through this pipeline? How many units

59:22

can we get through this factory? Well,

59:24

if we start with 100 units on the left

59:26

side, okay, and then we pass it through

59:28

A, A has a capacity of 100 units per

59:30

day. So, what that means is the

59:32

intermediate between, you know, A and B,

59:34

like we'll we'll generate 100 units.

59:36

That's fine. You know, we've made it

59:37

past the first step. You'll see B only

59:39

has 10 units per day, which means, you

59:41

know, despite the fact that we're

59:42

feeding in 100 units, we can't get all

59:45

of them. Um what we can do is we can

59:47

transform 10 units per day. What that

59:49

means is what is the sum total daily

59:51

output of the factory? It's 10 units per

59:53

day.

59:55

You know basically the entire factory

59:57

can only work as quickly as the weakest

60:00

step in their process.

60:02

Now if I give you an mission to improve

60:05

the total capacity of this product you

60:07

know in total number of units generated

60:09

per day um you know and then I say hey

60:12

like which step would you improve in

60:15

order to increase the total output of

60:17

the factory would you improve step A or

60:19

would you improve step B you know let's

60:21

say I allow you to double any of these

60:24

well if you doubled A you'd be at 200

60:26

units per day what would the total

60:27

capacity of the factory now be well you

60:30

wouldn't actually have changed anything

60:31

because the bottle neck the thing that

60:33

all other steps are sub subservient to

60:35

is still 10 units per day. However, if

60:38

you changed B from 10 to 20 units per

60:41

day, then the total output of the

60:43

factory would go up. Okay, so anyway,

60:45

this is just a very simple and

60:47

straightforward um analogy hopefully

60:49

that allows you to think about how to

60:51

optimize a marketing process. The

60:53

reality is, you know, we have a lot of

60:55

processes in our business that are very

60:57

similar to what I just talked about. And

60:58

in this hypothetical example, what we

61:00

have is we have um you know a lot of

61:02

capacity at the creative step. We have a

61:05

lot of capacity at the lead step, but we

61:07

don't have a lot of capacity the booking

61:09

step. So this is sort of our you know uh

61:11

example that is analogous to 10 units

61:14

per day. It doesn't really matter if we

61:17

improve our creative in this example.

61:20

Doesn't really matter if we improve our

61:22

follow-ups or the total number of leads

61:23

we have. you know, none of that's going

61:25

to matter because the pipeline, the

61:27

narrowest part of our whole business is

61:28

this booking stuff. So, what that means,

61:30

too, is it's not bad. It's actually

61:32

quite freeing. Um, we can spend zero

61:34

work on creative leads or following up

61:37

at this point. If we just improve our

61:39

booking by a tiny little bit, our entire

61:41

business will improve by that same

61:42

multiple. For instance, if we can move

61:44

it from 10 units a day of capacity to 20

61:46

units a day of capacity, what we've done

61:49

is we've effectively doubled our

61:50

business. Okay? Then we do it 30. you

61:53

know, maybe we'll triple our business

61:54

and so on. And so I I just bring you

61:57

guys this analogy because I find a lot

61:59

of the time people want to rush to

62:01

automate literally everything that they

62:03

can in a business. And despite the fact

62:05

that that sounds nice and it's sexy,

62:07

you'll make way more money for yourself

62:09

or for the companies that you work with,

62:11

if you spend a little bit of time trying

62:12

to figure out what the bottleneck is and

62:14

then building systems that alleviate

62:15

that bottleneck versus just trying to do

62:17

everything everywhere all at once. So

62:19

all the systems that I'm going to show

62:20

you from now on, I would like you to

62:22

think of as solutions to bottlenecks.

62:25

But don't just throw these systems at

62:26

the companies that you work with because

62:28

that isn't necessarily going to do

62:29

anything unless that is actually the

62:31

current constraining step. Like if

62:33

creative for instance was a very narrow

62:35

bottleneck in this instance, you know,

62:37

if it looked maybe something more like

62:39

this, right? Um and it was a lot more

62:41

narrow, then widening it with the

62:43

creative system that I'll show you guys

62:45

in a minute would make sense. You know,

62:46

if booking was really the bottleneck as

62:49

mentioned here, then widening it,

62:50

widening it with a booking system and

62:51

doing more speed to lead, that would

62:53

make sense. But if you guys are already

62:56

really really good across the stack and

62:58

everything that you guys are attempting

63:00

to process automate is already sort of

63:02

not the bottleneck in a process, you're

63:03

not actually going to see uh much

63:04

monetary return. Okay, so that takes us

63:07

back to the five marketing functions

63:09

we'll be automating today and the

63:10

framework to do it. Why don't we now

63:12

talk about how to prompt versus build

63:14

skills versus schedule loops versus

63:18

create routines? And once we're done

63:19

with this, we can actually start the

63:21

building of our cloud code systems. So,

63:23

a simple and easy way for you to think

63:25

about how we are going to progress

63:27

through this course is for all of the

63:30

systems that I'm going to be building

63:31

with you guys, we're going to start them

63:33

very simply as prompts. Now, prompts are

63:36

sort of the first step in the automation

63:38

pipeline. What they are is it's kind of

63:41

like uh an old school Flintstones car

63:44

where you know you would pick the car up

63:45

and then in Flintstones they would sort

63:47

of run with their feet because they

63:48

supposedly hadn't invented wheels yet.

63:51

It is manual every single time that you

63:54

do the process you need to be in the

63:56

driver's seat actually steering it and

63:58

asking the AI to do the next step and

64:00

getting the AI to you know do things in

64:02

sort of a self-directed manner. And as a

64:04

result, it's also quite slow. But

64:07

obviously, I mean, if it's the first

64:09

time you've done a process, how else are

64:11

you going to do it? And so when we build

64:14

all of our automations in our course,

64:16

we're going to do them primarily through

64:18

prompting to start. But after a while,

64:20

obviously, business processes tend to be

64:23

quite similar. We tend to just do the

64:24

same thing over and over and over again,

64:26

right? That's kind of how business

64:27

works. We find a hack in the market that

64:29

pays us more money than the work and

64:31

money that went into it. And then we

64:33

just repeat it over and over and over

64:34

again. So we'll transform our prompts to

64:38

the next stage which are skills. Now you

64:40

can basically think about skills as just

64:42

a saved and much more efficient

64:45

instruction set that you can reuse. And

64:47

so we showed you guys how to do that

64:49

earlier through slash commands, right?

64:50

There was that morning skill. Well, you

64:52

can get really really granular with

64:54

skills. And once you have a big set of

64:56

skills, you can also just run those

64:58

skills as a meta skill. uh and in that

65:00

way chain together you know a process

65:02

that might have previously taken many

65:03

many man-h hours into one. So for

65:05

example I have a bunch of skills that

65:07

manage my YouTube channel and a lot of

65:08

people wonder how I'm capable of

65:10

self-managing such a large output. Well

65:13

the reason why I can is because I have

65:14

skills that manage everything for me now

65:16

from the generation of title candidates

65:18

to the generation of thumbnails to the

65:20

generation of uh you know descriptions

65:24

to the actual publishing of the content

65:26

the selection of the keywords. It

65:28

automatically adds things like uh end

65:30

screen cards and so on and so forth. You

65:32

know, this is stuff that previously

65:33

every time I wanted to publish or record

65:35

a video, I would have had to, you know,

65:36

sit down and probably spend an extra

65:38

hour or two uh doing. Well, now I I

65:40

don't need to do any of that at all. And

65:42

once I built all of those granular

65:44

indivi individual skills, I then created

65:46

master skills that just ran all of them

65:47

in sequence, sort of one skill after the

65:49

other, skill after the other skill, and

65:51

in that way chained together small

65:52

tasks, turned them into big ones, and

65:53

eventually replaced entire roles. Okay.

65:56

Now after the skills which are

65:58

themselves quite powerful, we are still

66:00

technically constrained by you running

66:02

the skill. And so that takes us to the

66:05

next step which is the loop. Now what a

66:07

loop is is it's basically a skill except

66:10

it's a skill that runs on its own

66:12

without your involvement. And so in the

66:14

example of, you know, that big like meta

66:16

um skill that I talked about that like

66:18

let's say manages my YouTube channel,

66:20

you can imagine how me having to run the

66:23

skill at the end of every big publishing

66:25

session or whatever is kind of a pain in

66:27

the butt, right? What if instead I just

66:29

set a loop that automatically ran the

66:30

skill on my computer once every day? And

66:33

maybe, you know, my deadline was 12:00,

66:34

so I just had it run every day at 12:00.

66:37

Well, in that way, I'm no longer really

66:40

in the driver's seat. What I've done is

66:42

I've prompted built up a saved

66:44

instruction set and now I have a

66:45

scheduleuler that's just running that

66:47

instruction set maybe on a set of

66:48

resources. You could see that with like

66:50

the morning brief idea, right? You could

66:53

just set the morning brief to run

66:54

completely autonomously without you

66:55

maybe at like 5:00 a.m. every morning or

66:58

maybe you know you're doing some sort of

66:59

marketing role and you have to scrape a

67:01

bunch of um competitor ads. Well, you

67:03

can actually set a loop to scrape the

67:04

competitor ads every morning at like

67:05

5:00 a.m. before you get into the office

67:07

or before you start working on your own

67:08

business. And that way, the second that

67:10

you show up, you will have a list of all

67:11

of the competitor ads. All you need to

67:13

do logically is just figure out, okay,

67:15

which ones am I going to try and

67:16

duplicate and swoop. You could build

67:18

loops like this across virtually every

67:20

part of your marketing stack. And I will

67:22

show you guys how to do so. But

67:23

obviously, the first step here is you

67:24

need you need the skill. And in order to

67:26

build the skill, you need the prompt.

67:28

Okay? And then once you are done with

67:29

the loop, sort of takes us to the last

67:31

step, which is the routine. Now, loops

67:34

are great. The only issue with loops is

67:36

that they need to be run on your

67:37

computer. And so that that decreases

67:41

your ability to share the loops with

67:43

other people in your organization. And

67:45

it also means that the loops are kind of

67:47

brittle. If something changes in your

67:48

computer, if maybe you change your I

67:50

don't know login or whatever, um you

67:53

change the plugins that your clot has

67:54

access to your skills and MCP servers

67:56

and whatnot, the loops can break and

67:58

they cannot work. But if you really want

68:01

to like expand things and make them you

68:03

know institutional and actually add

68:04

things to the organization not just your

68:06

own work space uh you need to take them

68:08

out of your computer and then put them

68:10

on somebody else's computer. So if you

68:12

guys remember we covered routines

68:13

earlier and uh you know like the

68:14

difference between local and cloud

68:16

environments well routines just run in

68:18

cloud environments and the idea is you

68:20

will pay you know anthropic or another

68:22

cloud provider some amount of money

68:25

usually very little to be clear. I mean,

68:26

we're just spending money on tokens at

68:28

this point. And then it will do the task

68:30

and then it will output some

68:31

deliverable, which is, you know, a lot

68:34

more money. And so, maybe this is the

68:37

simplest ROI of your life. You spend,

68:39

you know, $1 in tokens, then you get the

68:42

equivalent in deliverables of um, I

68:44

don't know, let's just say like $100 or

68:46

something like that output. Um, it is

68:48

entirely self-managed. Uh, you don't

68:50

have to touch anything on it. It just

68:52

occurs without your involvement. And so

68:54

this is eventually where we are going to

68:55

go with all of our systems to be clear.

68:58

Um although you do need some sort of um

68:59

input to that like a schedule, API, web

69:01

hook, trigger, whatever the heck you

69:03

want. Um but I'm just going to be

69:04

approaching every single one of our

69:06

steps through this four-step pipeline.

69:08

So you guys are going to see that in our

69:10

very first u automation, which is going

69:12

to be using Cloud Code to create ads

69:14

essentially wholesale. Uh, we're going

69:16

to start by building out a step-by-step

69:19

prompt where I walk Claude manually

69:21

through a process to go and do what I

69:23

want to do. I'm then going to convert

69:25

that into standardized instruction sets

69:27

and then show you guys how that runs

69:28

over and over and over again. We're then

69:30

going to run that a few times to make it

69:31

perfect, make it very low likelihood of

69:33

failing. Then we're going to convert

69:34

that into a loop that works it without

69:36

us. And then finally, we're also going

69:38

to convert that into a routine. And I'll

69:39

show you guys pricing and and stuff like

69:40

that. A couple of simple rules. Uh, in

69:42

general, the moment that you find

69:44

yourself saying the same thing more than

69:46

once, okay? I would turn it from a

69:49

prompt into a skill. So, you guys are

69:51

going to see me do that as we build all

69:53

of these and get really, really hands-on

69:55

and practical rather than you having to

69:57

do the same thing over and over and over

69:58

again, like saying act as an expert

70:00

social media marketer and generate five

70:02

LinkedIn post ideas about AI automation.

70:04

You know, we're just going to save that

70:06

as a skill and then just, I don't know,

70:07

type ad batch or something like that and

70:09

then do it. But um in general, you can

70:12

also just do this like across the stack.

70:14

Anytime that you find yourself asking AI

70:15

to do something more than once uh and if

70:17

it is like virtually the exact same ask,

70:19

you should at least expend a tiny bit of

70:21

time and effort to see if it is even

70:23

turnable into a skill because you know

70:25

not all things are turnable into skills.

70:27

Um some things do require a human being

70:29

in the driver's seat. I mean if they

70:30

didn't, thank god we'd be essentially

70:32

useless tomorrow. Uh so the idea is what

70:35

you want to do is attempt to turn

70:37

everything that you do often into a

70:38

skill. see if it passes that, you know,

70:41

totally autonomous barrier or not

70:42

threshold. If it does, now it's a skill.

70:44

It doesn't really concern you. You just

70:46

run it. And then if not, you know,

70:47

you're still in the driver's seat. In

70:49

this way, you can very quickly and

70:51

easily determine all of the tasks that

70:53

you yourself are not necessary for in

70:55

the business or in your marketing role

70:57

and then ascend up through the ranking

70:59

of abstraction and leverage and get to

71:01

the point where, you know, you're

71:02

overseeing a fleet of agents that do a

71:04

bunch of economically valuable work for

71:05

you as opposed to actually having to sit

71:07

down and do everything yourself. Okay,

71:08

so hopefully you guys are as excited as

71:10

I am. Um, this is just a final review of

71:12

the concepts. Prompts are triggered by

71:15

you. Skills are triggered by you. Loops

71:17

are a timer on your computer. And

71:19

routines are on the cloud. Uh, prompts

71:21

run when you type. Skills run when you

71:24

press run. Loops run while your laptop

71:26

is on. And routines work regardless of

71:29

whether or not your laptop is on. Uh,

71:31

prompts are great for quick

71:32

experimentation and building things out.

71:34

skills are great for repeat tasks that

71:36

you still need to be in the loop for.

71:38

Uh, speaking of loops, loops are best

71:40

for monitoring things that automatically

71:43

scrape a resource and stuff like that.

71:45

And then routines are best for daily

71:46

production. Um, and I think we've now

71:48

done more than enough sort of explaining

71:50

about these concepts, we can actually go

71:52

build something. So, I'm going to head

71:54

back up here and then just mark that

71:56

puppy as done. And now we can build some

71:58

actual creatives with Claude code. Okay.

71:59

So, how are we actually going to put

72:00

this creative generator together? Well,

72:03

the first thing I want to dispel you of

72:04

is the notion that you can just say,

72:06

"Make me some great ads," and then it'll

72:08

automatically work. Um, you need to be a

72:10

lot clearer about what it is that you

72:12

want the model to do. You also need to

72:14

be a lot clearer about how you want the

72:16

model to do things. Like, if you just

72:18

say, "Make me great ads," you'll see

72:19

that the model will instinctively just

72:21

try asking you a bunch of questions to

72:22

narrow down what it is that you want to

72:24

do. Um, but that's not really enough.

72:26

even if you answered all these

72:27

questions, it take you on a wild goose

72:28

chase and it probably wouldn't get

72:29

anywhere despite the fact that it sounds

72:31

like it knows what it's doing. You know,

72:33

it really doesn't. So, my recommendation

72:35

is you need a process that allows you to

72:37

create ads already. Uh, and ideally, you

72:39

would already have a process, right?

72:41

Like you're not trying to come up with a

72:42

new process today. What we are doing is

72:44

we are taking a pre-existing process and

72:46

then we are turning it into or we are

72:48

basically process automating it. And

72:50

sometimes we'll have to shift a few of

72:51

the steps in a process in order to make

72:53

it more amendable to automation. like

72:54

we'll need to provide some sort of input

72:56

data and so on and so forth. But um in

72:58

general uh you know don't come to AI

73:01

without a process. Try and sit down and

73:03

think about the process yourself.

73:05

Actually like whiteboard the process cuz

73:07

if AI just picks the process for you um

73:09

not only is it picking the process

73:11

itself, it's also carrying out that

73:13

process and then maybe it's even

73:15

evaluating the results of that process.

73:16

What you're doing is you're multiplying

73:18

probabilities. And in general, if you

73:20

multiply probabilities where somebody

73:21

else is in control of the quality of the

73:23

output, the end result goes way down.

73:25

Um, just to, you know, demonstrate for

73:26

you guys what I mean by that. You know,

73:28

if I go back to, um, my little drawing

73:31

pipeline here, if I had a couple of

73:33

processes that were 0.8 aka, you know,

73:37

80% likely to work. Let's say I had

73:41

three steps in my pipeline. Okay, that's

73:44

0.8 time 0.8

73:48

8 * 0.8.

73:52

Um, let's say we had, you know, step

73:54

one, step two, step three. Sure, the

73:57

probability that step one resolves

73:59

correctly might be 0.8. The probability

74:01

that step two resolves correctly might

74:03

be 0.8. And the probability that step

74:05

three resolves correctly might be 0.8.

74:07

But if you do the math on all of these,

74:09

okay, the total result is not 0.8. the

74:13

total result is 0.5

74:16

or basically 51.2%

74:20

success rate. So what you want to do is

74:23

you basically want to minimize the total

74:24

number of steps involved in the creation

74:26

of a good prompt, a good skill or a good

74:28

process. And the way you do that is, you

74:30

know, rather than have AI come up with

74:32

the process for you, then rather have AI

74:35

actually maybe judge the quality of the

74:36

outputs for you. You want to do both of

74:38

these yourself. All you want is for AI

74:41

to do the thing in the middle, which is

74:42

actually like carry out the process. And

74:44

in that way, you can go from like a

74:45

51.2% success rate, maybe up to an 80%

74:49

success rate. Okay. Also worth noting,

74:51

you're probably not going to achieve

74:52

100% success rate with any sort of AI

74:55

agent process, at least not of as of the

74:57

time of this recording. Um, really, the

74:59

way that AI works is it allows you to

75:01

take a lot more shots at the net in any

75:03

process, and then you can apply your own

75:04

human intellect to just picking a bunch

75:06

of the correct shots, if that makes

75:07

sense. So, for instance, with our ad

75:09

generator, what we're going to do is

75:10

we're not going to aim to like make this

75:12

thing just pop out perfect ads every

75:14

single time. What we're going to do is

75:16

we're going to have it generate dozens,

75:18

hundreds, thousands of ads, okay? And

75:20

then all you're going to do is you're

75:21

just going to review them and then run

75:22

them. And in that way, uh we're going to

75:24

be able to generate a lot more top

75:26

offunnel ads, which will alleviate the

75:27

creative bottleneck, assuming that that

75:29

is the bottleneck. So, anyway, this is

75:31

the process right here that we're going

75:32

to be um going through. We're going to

75:34

save a bunch of high performing ad

75:36

formats as templates. Then we're going

75:38

to feed in a variable. So a niche, some

75:40

sort of offer, some sort of angle,

75:41

whatever it is for whatever product.

75:42

I'll just come up with a hypothetical

75:44

one. Claude will generate a bunch of

75:46

different combinations of niche times ad

75:48

format, offer time ad format, angle time

75:51

ad format. And then we're just going to

75:52

review, pick, and ship. Um, staying in

75:55

the loop, uh, basically all the way up

75:56

until the end where the taste matters.

75:58

But actually before we even get there, I

75:59

I hope you guys know just based off of

76:01

that bottleneck thinking, um the only

76:03

situation in which this would make sense

76:05

to apply to a business is if creative is

76:08

your bottleneck. Like a lot of companies

76:10

are big on ideas and they also have the

76:12

means to distribute those ideas via paid

76:13

or organic. They just can't actually

76:15

make the ads themselves. So this is this

76:17

is the situation in which that would

76:19

make sense. If your company has lots of

76:20

ideas, if it has lots of distribution,

76:22

but it doesn't have a lot of creative,

76:24

um you could actually meaningfully

76:25

improve the business significantly. um

76:27

you know so if you're currently

76:29

bottlenecked on designing and stuff like

76:30

that like this the system will genuinely

76:32

improve things but if you already have

76:34

the ability to uh I don't know like make

76:36

a bunch of ads or maybe you're really

76:38

short on ideas or maybe you're really

76:40

short on distribution you having the

76:42

ability to create more ads won't

76:43

necessarily improve things really all

76:45

that much. Okay, but let's carry out

76:47

this process. So this is something I sat

76:49

down and I thought a little bit about

76:51

because obviously I wanted to spend our

76:52

time effectively. I didn't want to just

76:54

like do a bunch of whiteboarding on ad

76:55

processes. A lot of people have way

76:57

better ad formats um and and ad systems

76:59

than I do. Uh and I'm not like a

77:01

professional, you know, advertiser by

77:03

any means. I used to run a PPC company,

77:05

but it wasn't super successful. I mean,

77:07

the PPC company probably capped out at

77:08

like 200,000 in a year. Um you know, I

77:11

run ads for my own products now, my

77:12

information products, and then my

77:14

education products, but um I don't know.

77:16

I mean, like 5 to maybe a 10x rowass was

77:18

considered okay, but it's probably not

77:20

considered the best. So, I just want to

77:21

give you guys like a cookie cutter

77:23

process that is probably above average

77:25

in terms of performance, but I also want

77:27

you guys to know that you guys all have

77:28

your own internal and successful

77:30

marketing mix. So, you guys should do

77:31

whatever the heck you want there. Just

77:33

map out the process ahead of time and

77:35

follow the series of steps that I'm

77:36

going to show you to convert this into,

77:38

you know, something that is replicable

77:40

because the first thing is we need to

77:41

find a bunch of high performing ad

77:43

formats and then save them as templates.

77:45

And so, ideally, you know, in your

77:47

company, you would already have high

77:48

performing ad formats. Okay? Okay, so

77:50

you'd actually already have a bunch of

77:52

things that are very good. They've

77:53

worked before. You have high rorowass,

77:55

high uh I don't know, LTV to CAC ratios.

77:58

You know, you'd already have a bunch of

78:00

these. We don't. So, I'm just going to

78:01

go and I'm going to find a bunch. And I

78:03

don't know what the performance of these

78:04

are, but you know, I need to basically

78:06

show you guys the process of getting

78:08

data into the system. So, bear with me.

78:10

What I'll do is I'm just going to open

78:11

up like Facebook ads library, and then

78:14

I'm just going to look through a bunch

78:15

of ads that people are running for a

78:17

variety of products. So, I'm in Canada,

78:19

which is why it says that I don't want

78:21

to stay in Canada, so I'm just going to

78:22

go all. And then I'm also going to go

78:24

all over here. Uh, we don't really care

78:26

about the political ads, so we're just

78:27

going to avoid those. Uh, I don't know.

78:29

Keyword. I'm just going to do um let's

78:31

see. Looks like I searched out Stripe,

78:33

Maker School, Maker School, Survive,

78:34

Nick Surive. That's kind of funny. Um, I

78:36

I I like Stripe. Why don't we take a

78:38

look at how Stripe is running their ads?

78:40

Maybe show you guys a cool pipeline to

78:41

do some poaching. Um, okay, cool. And so

78:44

you can see that they actually have a

78:46

pretty solid format or a pretty solid

78:47

template set up already. Uh looks like

78:49

there's a variety of different languages

78:50

that they're advertising in. They're

78:52

doing things in French. They're doing

78:54

things in a variety of different ones.

78:55

But you can see that they have a pretty

78:57

straightforward format. It looks like

78:58

what they have is they have like a

78:59

highquality almost like gradient image

79:02

that fits their brand colors. They have

79:04

like their logo and then they have text

79:06

that's sort of placed just like this. Uh

79:08

make your you know company in America or

79:11

or whatever. my French ain't so good,

79:13

but something like that. Um, they also

79:15

have another format here where they have

79:16

like a cool gradient sort of background

79:18

and then they have simple text right

79:19

over here with a start now button. Looks

79:21

like they have some videos as well. If I

79:23

just play this without the audio, looks

79:25

like they have a very simple video.

79:27

Offer your customers your preferred way

79:29

to check out. Okay. And then they also

79:30

have a bunch of other languages as well

79:32

as well as people that are advertising

79:34

with Stripe. And so I actually did a

79:36

video with Stripe a while back. Um, I

79:38

don't know where that is, but anyway,

79:40

clearly it's not here. Uh but anyway,

79:42

this is a pretty good I think like first

79:43

step just to show you guys how to

79:45

automate really simple like image

79:46

creative like it's it's super

79:48

straightforward to automate this stuff.

79:49

So maybe we'll start with that and then

79:51

uh we'll get progressively more

79:52

complicated. That probably makes sense.

79:54

Okay, so we you know obviously need to

79:56

grab the ad itself. So how am I going to

79:58

do that? Well, I'm going to try um you

80:00

know opening this ad detail and just

80:02

seeing if I can get it as big as

80:03

possible and I can right here. Okay, and

80:05

you can see the images. This is an image

80:07

ad. That is basically I don't know if I

80:09

open up my little drawing tool. It's

80:11

basically this is the bounds of the

80:13

image, right? Uh probably up to about

80:15

here, then up here. And so this is sort

80:18

of what we need to feed into our model.

80:20

Um if I go to the next ad here, like

80:25

we'll just do why don't we do three to

80:27

start.

80:28

Um it looks like this is actually

80:29

opening this little JavaScript segment.

80:31

So why don't we actually go to Finder

80:32

and I'll just make a folder and I'll

80:33

start dumping all of them in now. And

80:35

I'm full screen, so I just got to unfull

80:37

here. Okay, we'll go downloads. I'm just

80:40

going to make a folder. And why don't I

80:41

call it um why don't I call it add

80:45

formats for claude. Okay. And what I'm

80:48

going to do is I'm just going to zoom in

80:49

because I want this creative to be as

80:51

high quality as possible. Obviously, it

80:52

can't get super perfectly high quality,

80:54

but we can get pretty close. And then

80:56

I'm just going to take a screenshot and

80:58

bump down to maybe here or so. This is

81:01

going to be an ad format that I'm going

81:02

to attempt to replicate. It's not going

81:03

to be perfect as mentioned, but uh we

81:05

will attempt to replicate it and then

81:07

spin it. Kind of do our own style. Okay.

81:09

And then next up, I want to see the

81:11

actual ads themselves. So, I'm just

81:13

going to click on this one here. And

81:15

then you can see, same idea. I'm going

81:17

to zoom way in. And it looks like this

81:19

one is specifically for mobile. So, it's

81:22

a little bit taller, but that's okay.

81:24

This should natively kind of figure out

81:26

that I am attempting to run a little bit

81:28

taller of an ad. Going to paste that in

81:30

as well. Why don't we do just one more

81:32

to start? and then we'll get more

81:34

complicated later on. Uh, we need a good

81:37

format. Yeah, there I am. We need a good

81:40

format. Create a link. Sell anywhere.

81:43

Okay, cool. I like this one. This is

81:44

pretty solid. I mean, these guys

81:45

obviously they crush it with ads.

81:46

They're um and they're they're really

81:48

really pretty, too, which I like. Then

81:51

why don't I take this little photo?

81:54

Okay. And then go back to my folder and

81:55

then stick that in there. Cool. So, you

81:58

would fill this with whatever your high

81:59

quality ad formats are. Um, you know, in

82:01

my case, I'm just I'm assuming that

82:03

Stripe is performing well with these

82:04

ads. I don't know for sure. Uh,

82:06

obviously, you don't want to make a

82:07

bunch of shitty ads, but the idea is

82:08

fill this with like the best formats

82:10

ever. And then we're just going to spin

82:12

these formats as much as humanly

82:13

possible. Uh, okay. So, once we're done

82:15

with that, we can go back to draw. And

82:17

I'm just going to pretend now that I'm

82:19

done with this. The next thing we need

82:21

to do is we need some sort of variable

82:23

angle to feed in. So, here's where, you

82:25

know, you need a bunch of information

82:26

about your business. So in my case, you

82:28

know, uh I'm running uh a couple of

82:30

different products. I'm running like a

82:32

um Claro, which is a software as a

82:34

service product. Uh Clarvo is basically

82:36

a dialer, which improves your ability to

82:38

connect and then have people on the

82:40

other end of the dial actually pick up.

82:41

Also automates the process of dialing.

82:43

So you can dial multiple people

82:45

simultaneously, call 10 people, and then

82:46

just have the one that actually picks up

82:48

routed to an agent. All of these minor

82:50

optimizations, it significantly improve

82:52

the total number of people you can dial

82:53

and the total number of connects you

82:55

get. Um, I also run a product called

82:56

Maker School. And Maker School is an AI

82:59

automation program that basically

83:01

guarantees you a customer in 90 days.

83:03

And so these are things that I can

83:04

market. And uh, you know, I always like

83:06

making courses where I'm marketing like

83:08

my own products because I know the most

83:09

about my own products and I can, you

83:11

know, actually talk about the benefits

83:13

therein and stuff. So why don't we try

83:15

doing some ads for Maker School? I like

83:17

that idea. Pretty straightforward and

83:18

pretty simple. Maybe I'll do Claro ads

83:20

later. Claro obviously being a software

83:22

as a service is going to be different

83:23

from like an e-commerce product that

83:25

people can hold in their hands, but

83:26

that's okay. Um, so what I'm going to do

83:28

next is I'm just going to voice dump a

83:30

bunch of stuff into a cloud and then I'm

83:32

going to have it come up with, you know,

83:33

niches, offers, and angles. And

83:35

actually, just to get ahead of things,

83:36

I'm also going to take a screenshot of

83:38

this picture right here. Okay. Then feed

83:42

that directly into Claude. So what I'm

83:44

going to do here is I'm going to see if

83:46

I can clear this. So we're back to a

83:47

base conversation. I'm going to paste

83:49

this in. And then I'm going to press

83:50

this little voice transcript button and

83:52

then just talk. So I'm going to press

83:53

and hold to record.

83:56

[gasps]

83:56

Hey, my goal is to create a creative

83:59

pipeline skill. Before we create a

84:02

skill, I'm going to prompt with you back

84:05

and forth to determine how best to

84:07

structure the skill and then uh you know

84:09

eventually arrive at a set of

84:10

highquality outputs that we like. So

84:13

here's the situation. Um I have a bunch

84:15

of ads in a folder called ad formats

84:17

that I'm going to feed you. What I want

84:19

you to do is I want you to apply a bunch

84:21

of information about Maker School, which

84:23

is my AI automation program that helps

84:25

people get their first client in 90 days

84:27

or their money back for a service that

84:30

sells artificial intelligence or is

84:32

enabled by artificial intelligence. You

84:34

can think of this as like systems that

84:37

help create ads, the very system that

84:39

I'm getting you to make, uh, for

84:41

instance. So, the way Maker School works

84:43

is people will come in and then I will

84:45

coach them day by day in order to help

84:47

them get their first client. They also

84:49

watch a bunch of videos on a daily

84:50

basis, uh, usually three to four videos

84:52

where I actually show people how to do

84:54

the thing that they're supposed to do

84:56

that day. So, I don't know, setting up

84:57

their cold email boxes. I'll actually

84:59

record a video walking them through that

85:00

whole process and then it's their turn

85:02

to do it. Uh, it's also a big community.

85:04

We give a bunch of discounts and free

85:05

resources, around $21,000 in discounts

85:07

at the time of signing up. And uh yeah,

85:10

if you need any more information from me

85:12

to, you know, produce niches, offers,

85:14

angles, and that sort of thing, just let

85:15

me know. My goal is I want to recreate

85:18

the ads that um are in the ad formats

85:20

library. So, I'm just going to send all

85:22

of that to you and then I'd like you to

85:24

take that information and then use it to

85:26

scaffold out virtually the exact same

85:27

ads. Um I know that it's not going to be

85:30

perfect to start because we're going to

85:31

do so procedurally, but u we're going to

85:33

go back and forth until we get like the

85:34

highest quality um ad version or variant

85:37

as possible. Um, last thing is we can't

85:39

just use, you know, Stripe's logo or

85:42

whatever company's logo that we're

85:43

feeding in as an ad format inspo. So,

85:46

uh, because this is a demo, I just went

85:48

and I found like a random set of ads.

85:50

I'm not using my own ads. Uh, so we're

85:52

going to need to replace that with Maker

85:53

School. But aside from that, I want very

85:55

similar styled ads. I want them to look

85:58

quite similar. Um, we just need to

85:59

change sort of the image up at the top.

86:01

Instead of the stripe gradient, I want

86:03

something different. And then we'll turn

86:04

this into a repeatable engine to

86:06

generate dozens if not hundreds of these

86:07

ads uh very quickly. Okay. And then what

86:10

I'm going to do is I'm actually going to

86:11

go and feed in this folder too. If you

86:13

guys remember, you have the ability to

86:14

feed in a folder. So now I've given it

86:16

the folder. I've also given it a big

86:18

prompt. And at the very end of it, I've

86:20

also given it an image. And hopefully

86:22

you guys can see I'm just talking to

86:23

this thing. Right? So what it's doing is

86:26

uh you know it just did some thinking.

86:28

It's now found the screenshots. It's

86:29

actually going to take a look at them.

86:31

And uh we'll go through and see sort of

86:32

where we land. Okay, so it's breaking

86:34

down the pattern. Bright abstract

86:36

gradient, wave graphic, bold short

86:37

benefit driven headline, tiny wordmark

86:39

logo, single CTA button or text, high

86:42

contrast, very buildable. Answering my

86:44

question for free, do you have an actual

86:46

Maker School logo file or wordark I

86:48

should use or should I type set Maker

86:49

School in a clean senset

86:52

here? And I'll say yes, type set for

86:56

niches. Okay, what I'm going to do is

86:58

continue answering these questions.

87:01

Yeah, in general it's people that want

87:03

to quit their nineto-5, freelancer

87:05

agency owners that want to add AI

87:06

services, uh, and a lot of marketers and

87:08

ex-corporate professionals. So, just

87:10

create a bunch of different niche sets

87:12

based off of this this audience, and uh,

87:14

we'll vary them. The whole idea is

87:16

you're going to help me generate way

87:17

more ad variants than I could ever

87:18

possibly do myself, and then I'm just

87:20

going to select the ones that work. uh

87:22

for offers, 90-day money back guarantee

87:24

to land a first client, $21,000 in

87:26

bundle discounts and resources on sign

87:28

up, and then also include the fact that

87:31

I'm there. Like, I actually show up in

87:32

the community every day and respond to

87:34

almost 100% of every post. Cool. So, now

87:36

I'm, you know, giving it a bunch of

87:38

information about that. And now it's

87:40

actually going through and building a

87:42

template spec. Now, you guys should know

87:44

that Claude does not have an inherent

87:46

ability to generate images. So, what

87:48

we're doing this time is we're actually

87:49

just building out a a series of

87:51

templates. Okay. And these templates are

87:53

basically rules that allow Claude to

87:56

generate um these things on demand using

87:58

very cheap old school image processing

88:00

platforms like image magic and stuff

88:02

like that. So, these aren't going to be

88:04

like the highest quality things right

88:05

off the bat. We're going to have to

88:06

massage them a little bit. But, the idea

88:07

is at the end of it, we're going to have

88:08

a pretty repeatable template and then

88:10

anytime we want to come up with a

88:11

different ad, boom, we're just going to

88:12

like say, "Hey, you know, come up with

88:14

400 ads." And it'll do. So the benefit

88:16

to that as well is it's very cheap. You

88:18

know there are image generation

88:19

platforms out there which I am going to

88:20

show you guys how to do after this. Um

88:22

but these image generation platforms

88:23

also cost a fair amount of money. Uh if

88:25

it costs me $2 let's say to generate an

88:27

ad and I want to generate a 100 of

88:29

those. That's $200 every time. And while

88:31

$200 every time for something that is a

88:33

top offunnel growth lever like ads might

88:35

not make that much difference to like a

88:37

$10,000 a day business. You know it's a

88:40

fair amount I think for most people that

88:41

are probably watching this. And yeah, I

88:43

wanted to make sure that it was about as

88:44

accessible as humanly possible. Okay, so

88:47

what it's doing is it's actually

88:48

generating an HTML template to show me

88:50

what the ads are going to look like. Um,

88:52

and then, you know, after this is done

88:54

in presumably another 20 seconds or so,

88:56

I'll show you guys the results. Okay.

88:58

And so it's actually come up with an

88:59

example here, and it's done so

89:01

underneath test stacked.png. And you can

89:04

see that it did an okay job. I mean, you

89:06

know, it came up with its own gradient

89:07

here, which I think we're going to have

89:08

to significantly improve. Um, but you

89:10

know, it did kind of space out the font.

89:12

Well, it does have a little start now

89:14

button. Um, I would say we're just

89:15

losing a couple things on the spacing.

89:17

Um, you can also see there's this little

89:19

stacked overlay where we have a much

89:21

smoother gradient down here with a

89:22

similar sort of card. And, uh, yeah, you

89:24

know, this isn't perfect, but it's going

89:26

to be pretty clean. So, what I'm going

89:27

to do next is I'm just going to give it

89:28

a bunch of feedback on this and see how

89:30

well it did. I'm also going to have it

89:32

generate me a web page that I can very

89:34

quickly use to uh, make changes and

89:36

alter things. So, first thing I'm going

89:38

to do is I'm just going to hold command

89:39

D because that's how you record.

89:41

Hey, this is an okay job. I thought we

89:44

got maybe 70% of the way there, but we

89:45

do need to significantly improve the

89:46

design and make it much closer to the

89:48

initial stripe one that I showed you

89:50

guys. Um, so we do need to push the CSS

89:52

further with a variety of different

89:54

gradient styles. Um, we could actually

89:56

generate high quality gradient

89:57

background images as well. Uh, and then,

89:59

you know, do a bunch of flips and stuff

90:01

like that. I'll show you or I will use

90:03

an image model later. For now, I want to

90:04

do things entirely procedurally. I also

90:06

think the text spacing is a little bit

90:08

off and uh yeah, I just want to

90:09

significantly improve the quality of

90:10

this first. So, in order to do so, could

90:13

you create me a web page that I could

90:15

use to adjust various sliders to change

90:17

things until the settings are exactly

90:19

how I want them to be? Uh, think about

90:20

it as like me placing something on a on

90:22

a page, let's say. Uh, build me the

90:24

ability to, I don't know, change the

90:25

font width and the different styles and

90:27

and and where they're placed on the page

90:28

and so on and so forth. Um, after we're

90:30

done, we'll save those and make those

90:32

concrete. And then once they're

90:33

concrete, then we can spin, you know,

90:35

hundreds if not thousands of these um

90:37

to, you know, generate a bunch of ads.

90:39

You see it's now actually building an

90:40

SVG filter pipeline to try and reproduce

90:43

what we're seeing here, uh, which is

90:45

some sort of smooth meshed design that

90:48

obviously Stripe used their own internal

90:50

image model or something like that to

90:51

generate. So, we're going to try doing

90:52

this procedurally and programmatically

90:54

just to save a bunch of time and energy

90:56

and money. Okay. And now we have a

90:57

clawed browser, which it's going to open

90:59

just to allow me to actually tune the

91:01

ads. So, it just opened it over here on

91:02

the right hand side as you guys could

91:04

see. It doesn't look like I'm actually

91:06

capable of seeing one of these ads just

91:08

yet. So, I think it's probably going to

91:10

have to do a little bit more work to

91:11

tighten that up. Now, one thing you'll

91:12

find when you just have Claude start a

91:15

task and then, you know, give you the

91:16

results is it'll self-manage a lot of

91:19

errors and debugs. In the past, this

91:21

didn't happen. In the past, you

91:23

typically had to do a lot of this

91:24

debugging yourself. But you'll see that

91:26

it's just passing its own outputs back

91:27

in as an input to the next step and then

91:30

progressively getting closer and closer

91:31

to what it is that you want. Um, we're

91:33

also coming up on like 3 minutes here.

91:34

Uh, there are a lot of prompts that I

91:36

run that now genuinely take somewhere

91:37

between 15 to 20 minutes. And so I will

91:40

fire off a task by dumping in a bunch of

91:42

my demands and want and then I'll just

91:45

step away or, you know, I'll work on

91:47

something else. Or what I'll do is I'll

91:49

just spawn a bunch of these. And then

91:50

eventually this little icon here will go

91:53

from gray to yellow. And the second that

91:55

it's yellow, I know that I, you know,

91:57

it's done and I need to come back in and

91:58

actually check in with it. I think

92:00

sometimes that also turns green. We can

92:02

actually see the filter debug right

92:04

here. It's actually coming up with a

92:06

variety of different filters. Um, I

92:07

think what we could do is we could

92:08

probably just apply noise to this, some

92:10

sort of procedural noise. That would

92:11

probably look even better. Uh, that's

92:13

fairly easy to do. And yeah, as you can

92:14

see, it's even figuring out like the

92:16

streaks, which is neat. So, it'll just

92:18

do this hundreds, maybe thousands of

92:19

times until it comes up with something

92:21

that looks pretty clean. And then once

92:22

we're done, we'll actually be able to

92:23

generate those demand uh those ads on

92:25

demand. And keep in mind, um if anybody

92:27

from Stripe is watching this, I'm more

92:29

than happy to build you guys procedural

92:32

ad generators in 5 minutes. But you

92:34

could also ask literally anybody that's

92:35

watched one of my courses, and I'm sure

92:37

they'd be able to do the same. Not

92:39

exactly rocket science. And as you can

92:41

see, we're getting much closer to that.

92:43

We now just have to deal with a couple

92:44

of little spacing issues. So, I'm

92:47

actually going to jump into the ad tuner

92:48

and do that myself. Okay. And it just

92:50

opened it for me. Um, looks like we can

92:52

land your first AI client in 90 days.

92:54

Looks like we can also change the angle

92:55

of the gradient applied to this, which

92:57

is kind of neat. Um, I don't actually

92:59

need to do any of that cuz I'm I'm quite

93:00

satisfied. While it's working, I'm just

93:02

going to say add a bit of noise or the

93:04

ability to add it so I can test. And the

93:09

reason why I'm going to do that is

93:10

because I think noise just makes it a

93:11

lot cleaner. So, organic streak textures

93:14

and stuff like that. We can come up with

93:15

about as many of these as we want now,

93:17

which is nice. Streak win. That's kind

93:19

of cool. You know, it it looks like

93:20

stripe. It looks like stripe. It's

93:22

obviously pretty sexy. Okay. So, I'm

93:25

just going to change the word mark a

93:26

little bit. Um why don't we make the

93:28

size bigger? And then what we need to do

93:30

if think if you think about it is

93:31

actually change the spacing of some of

93:33

these because um it didn't it doesn't

93:36

really look like you know as clean I

93:39

would say. I don't know. I'm just

93:40

playing around with this a couple of

93:41

different ways but yeah I think we

93:43

should also change the line height a

93:45

little bit maybe. I don't know. Content

93:47

pad X. I don't exactly know how all of

93:49

this works but I'm just going to change

93:51

this around a little bit. Block gap. Oh

93:53

yeah. Yeah. I like that. I like that.

93:54

Cool. Awesome. And then I think what we

93:56

just need to do now is just I don't

93:59

know, maybe find a way to make the font

94:01

at Maker School just a tiny bit

94:02

different. Then we can just rip this

94:03

over and over and over again. Also

94:05

allows you to use plain text or a pill

94:06

button. That's kind of cool. CTA text is

94:08

kind of neat. Maybe I'll say join now

94:11

instead of start now. And then

94:14

maybe we'll do something like maker

94:16

school. Okay, great. So I really like

94:18

this. I'm actually just going to export

94:20

the settings which is right over here.

94:22

This will allow me then to copy and

94:23

paste it into Claude. Uh, and then I'm

94:26

just going to give it one final piece of

94:27

advice. I'll say,

94:30

"Okay, let's find a way to make the

94:33

Maker School logo more interesting."

94:38

Right now, it's uh I don't know why I'm

94:40

not using voice transcription here.

94:43

Just a little too spread out, aka the

94:46

letter spacing makes it look not as

94:48

professional. Um, but the fact that

94:51

we're using a default font also makes it

94:53

sort of bleed into the rest of the

94:55

design. So, come up with like 20

94:57

different fonts that I could use that

94:58

are all very high-end, new, and clean.

95:01

And you can see while I was doing that,

95:02

and this is something that you guys

95:04

should um do uh pretty often. While I

95:06

was doing that, it also allowed me to

95:08

adjust the grain. So, it came up with a

95:10

new way basically of doing this. So, I

95:12

can now change the grain. It's a little

95:13

bit difficult to see here, but if I

95:15

change some of the colors just to maybe

95:18

I don't know, let's see here. Maybe

95:20

instead of white, I want it to be kind

95:21

of like reddish or something. Maybe

95:23

pinkish. Um, you guys could see that I'm

95:25

now adding a little bit of grain. I can

95:27

also change the size of the grain. Maybe

95:28

I'll make it a little bit smaller like

95:30

this. Then I'll just make it just pop

95:33

out a tad. And we can even change what's

95:36

called the seed here. Maybe we'll do

95:37

soft light. I don't know. Multiply.

95:39

Multiply would look a little too strong,

95:40

I think. So, we'll stick with overlay.

95:42

and then maybe do some color RGB static.

95:45

Cool. So, I I really like this. I mean,

95:46

I don't know if you guys could tell, but

95:47

it just adds a tiny bit of additional

95:49

finesse to it. Um, and then, you know,

95:51

I'm just going to wait until it finishes

95:53

with this, and then we can actually

95:54

generate a thousand. It's also added um

95:56

the ability to use radial bursts, which

95:58

is kind of neat. So, you can even see

95:59

like I don't know, you could add the

96:01

gradient right over here or something

96:02

like that. Um, which probably spices it

96:04

up even more. Still, I think I'm going

96:06

to keep it simple with just linear. And

96:08

then I can just like adjust the colors

96:09

and stuff like that as they will. Uh,

96:11

nothing super special. Looks like we

96:13

also now have the ability to change the

96:14

fonts. So, this is what I wanted the

96:16

first time. I think they tend to use

96:18

inter for everything. So, I would I

96:19

would just want mine to look a little

96:20

bit different. Basically, we need to do

96:22

is we just need to wait for this to

96:23

stand out so it doesn't look like, you

96:24

know, the rest of this. Um, I don't

96:27

know. Man rope. Man's such a funny name

96:29

for a Who comes up with that? Maybe

96:32

we'll do this. I would say change the

96:36

letter spacing so it's tight. I don't

96:37

know. Could be distinctive. Sora, that's

96:39

a little too big. Bricklage grotesque. I

96:41

actually really like Red Hat display. I

96:43

think that's probably the best we could

96:45

get here. So, I'm actually just going to

96:47

make it a tiny bit smaller. Maybe I'll

96:49

add a period at the end of it. Okay.

96:52

Land your first client in 90 days or

96:54

it's free. So, we'll just change the

96:55

letter spacing a little bit. Make it

96:57

nice and tight. And then now I'm just

96:59

going to zoom out. And h maybe I'll

97:02

actually just make everything just a tad

97:04

bigger. You know, one of my friends once

97:05

told me that literally like one of the

97:07

biggest problems with ads is people just

97:08

can't see them. Okay, zooming out now.

97:10

You know, people looking at it on their

97:12

phones. Maybe we'll do even a tiny bit

97:14

bigger. Cool. That looks pretty nice to

97:15

me. I like this. Um, cool. So, what I'm

97:17

going to do now is just export this as

97:19

JSON. Copy. And then go back here and

97:23

I'll say, "Great. I really like this.

97:28

Now that we've created a format that

97:30

looks pretty good, uh, I want you to

97:31

apply these settings and then I just

97:33

want you to generate like 50 different

97:35

ads for me. uh all with different CTAs,

97:38

all with different looks and and so on

97:40

and so forth. What I'll do is I'll just

97:41

pick the best ones. Then I want you to

97:43

turn this into an artifact and a

97:44

reusable resource. So anybody on my team

97:46

can just quickly jump in and then

97:48

generate these ads at scale. Uh

97:50

eventually we'll scale it up and then

97:51

make it a little bit different where uh

97:53

you know, we have the ability to change

97:54

which ads we want to generate as well.

97:56

But yeah, for now this is this is pretty

97:57

good. First generate me 50 so I can

98:00

quickly look through and then pick and

98:02

then do work on generating the artifact

98:04

afterwards.

98:05

Cool. So, as you guys can see, we are

98:07

slowly alleviating bottlenecks and we're

98:09

making an internal tool to build these

98:10

sorts of artifacts in just a few

98:12

minutes. Um, really hasn't taken me that

98:13

long to do. I'm just going to cut to,

98:15

you know, when we actually have it. And,

98:17

uh, yeah, let's take a look. Now, under

98:18

the hood, what it's doing is it's

98:19

actually creating a script to do it for

98:21

us. Like, we've, um, used intelligence,

98:24

okay, this really flexible sort of

98:25

intelligence to, you know, create a

98:28

script that is now multiplying and

98:30

iterating through all the different

98:31

procedural things we could do. Um, so

98:34

you know, I hope you guys know like what

98:35

this has done is it's actually gone

98:36

through and then already generated all

98:38

of these things. It's now just going to

98:39

iterate through all of them. So we can

98:40

take a look at what that looks like um

98:42

now because it's gone through and done

98:44

it. And I'm just going to take a look at

98:45

it now. Cool. Taking a look here. You

98:47

can see we've now generated uh something

98:49

like 50 in total. It's even then kind of

98:53

given me some information on the angles

98:56

and stuff like that. So land your first

98:58

AI client 90 days or it's free. Add AI

99:01

services. Get 21k in tools free. I'm in

99:03

your inbox every day until you land a

99:04

client. Your first day a client

99:05

guaranteed in 90 days. I like this one.

99:07

Sounds pretty cool. So, you know, I

99:09

could just note like I want to keep four

99:11

for instance. Daily coaching day one 05.

99:14

Okay. So, we're starting to get a little

99:15

bit more impressive here with the

99:17

various gradients and and looks and

99:18

stuff. Um, I am finding that we should

99:20

probably vary like I think we should

99:22

vary the angle of whatever is going on.

99:25

So, we're not doing that right now. Um,

99:26

I think it's probably just not actually

99:28

doing this with uh I don't know its

99:31

settings. Like it's not actually varying

99:32

the angle. So, I'm just going to go

99:33

back. Going to hold command D. This

99:37

looks great. You should also be varying

99:38

the angle of that cool background

99:41

gradient thing so that it doesn't all

99:43

look vertical. Right now, the vast

99:44

majority of it looks vertical. Also,

99:46

ideally, we'd have a quick and easy way

99:47

for me just to select all the winners

99:49

and then download them, too. So,

99:51

hopefully that gives it some more

99:52

context. Um, but yeah, I mean, it did

99:54

did a pretty good job. Okay, so what

99:56

have we done now? We've basically built

99:57

like a procedural ad generator just

99:59

based off of a scooped template if you

100:00

think about it. Um, and you know, the

100:03

idea is not that you just build systems

100:05

that scoop other people's templates and

100:06

then just like duplicate the hell out of

100:08

them. Obviously, the idea is that you

100:10

get inspired by winning performers um

100:12

either that your own team has created or

100:14

that you have run in the past that do

100:15

quite well and then you kind of scoop

100:17

your own work, right? And then you just

100:19

generate variants of that really really

100:20

quickly. You know, I think these

100:22

variants are pretty neat, but obviously

100:23

there's only so much we could do with

100:25

procedural looks, right? We can only do

100:26

so many things just by varying uh I

100:29

don't know, changing little procedural

100:31

variables in the background with color

100:33

and angle and stuff like that. Even if

100:34

we fixed the angle and we made that look

100:35

a little bit better. Obviously, there's

100:37

only so much we could do. Uh so what I

100:39

want to do next is I want to take this

100:40

from a like procedural image generator

100:44

and then I want to turn it into an AI

100:45

based image generator flow where instead

100:48

of us just using um you know like this

100:51

sort of gradient background or whatever

100:52

we're actually using an AI model to

100:54

generate imagery and maybe we combine it

100:56

with our procedural generator to get the

100:57

best of both worlds and also keep cost

100:58

pretty low. And then after that, what I

101:01

want to do is show you guys how you guys

101:02

could go one step further and you could

101:04

make video ads with, you know, AI. Have

101:07

AI orchestrate all of that. And then,

101:09

um, now, you know, your creative, while

101:12

still costing money, does not cost, I

101:14

don't know, let's say $500 per video ad

101:16

that you're testing. Maybe you can get

101:18

it down to $2 per video ad that you're

101:19

testing. In doing so, test 10x as many,

101:22

100x as many video ads and then have the

101:24

market actually tell you which ones are

101:25

the best. Because I find in general that

101:27

tends to be the approach these days. You

101:29

don't really just want um you know to

101:31

like spend a bunch of time and energy on

101:33

your opinionated idea about what

101:35

performs the best. You know the cost of

101:37

production is now so cheap as you guys

101:39

could see here that it makes sense to

101:40

build systems to produce a lot and then

101:42

just have the market actually select

101:43

things for you. Okay. But first I just

101:45

want to make sure that we do have a

101:47

repeatable process for this. Okay. And

101:49

now we have what looks to be um a

101:51

promptbased way of doing this. And what

101:53

we can do now is we can also select

101:55

maybe the ones that we like. Obviously

101:57

I'm just doing this with like two

101:58

formats. Imagine if you had 50 and

102:00

afterwards it'll actually download them

102:01

all onto your computer. So, for

102:03

instance, I've now downloaded all of

102:04

these which hopefully you guys could see

102:06

are now pretty straightforward and easy

102:07

to like I don't know upload to uh Google

102:10

uh Facebook ads or whatever ad library

102:12

manager that you were using. Um but I I

102:14

just want to turn this into a skill

102:15

really quick uh before we proceed with

102:17

the next step and then show you guys how

102:18

also to loop it. And then we can also

102:19

have like cloud routines do this for us

102:21

too. So, you know, now that we've

102:23

basically accomplished the um prompt

102:26

done, would you run me through the

102:28

skill? Well, first thing is if I go

102:30

maker school ads, you can actually see

102:32

it um now populating down here. So, no

102:35

longer do I need to manually do this.

102:36

You know what I can do is now next time

102:38

I can just write maker school uh ads.

102:40

But if we read the skill.md, this is the

102:43

file that it actually went ahead and

102:44

generated for us based off of my

102:46

conversation with it. So, you know, I

102:47

prompted it back and forth. I figured

102:49

out a prompt that works and then I said,

102:50

"Hey, I want you to consolidate this

102:51

into a skill, right?" So, it went

102:53

through and it did. So, generate stripe

102:55

style minimalistic ad creative with an

102:56

organic gradient header and bold

102:58

headline plus CTA for Maker School,

103:00

Nick's AI automation and coaching

103:01

program. Use this whenever anyone asks

103:03

to create ad creative, add variants, add

103:05

copy with visuals, or run a creative

103:06

pipeline or ad batch. And you can see

103:08

that, you know, we have a bunch of

103:10

layers to this. We even have the scripts

103:11

involved and so on and so forth. So,

103:14

it'll go through and generate copy. You

103:16

know, it it's doing quite a bit here.

103:17

What we've done is we basically taken

103:19

our whole prompt, which was a series of

103:20

distributed messages, and we've codified

103:22

it as a instruction set that we can now

103:24

feed in and just have it generate over

103:26

and over and over again. You know, for

103:28

instance, what I could do is I could

103:29

actually just open up a new chat, one

103:31

that has no context, run maker school

103:32

ads, and now, you know, this model is

103:35

now going to run based off of this

103:37

entire long strip. So, you know, this

103:39

has zero context of our prior

103:41

conversation, right? And now it's

103:42

asking, what do you want to do? Generate

103:43

a single ad, run a batch, adjust the

103:44

visual profile? Um, no. I want to run

103:47

let's Gen 10 ads and then I don't know

103:50

maybe instead of maker school ads I want

103:52

to do this for another product but do it

103:54

for Clarvo. My startup is clarvo.io.

103:58

Once done send me contact sheet. So now

104:02

for instance what I've done is and it's

104:06

asking me about Clarvo. I'll say

104:07

research it claro.io.

104:10

Um I'm going to have it research

104:11

clarvo.io for me. So, it's going to go

104:14

find a bunch of information about my

104:15

dialer and stuff like that. And now I

104:16

can actually spin this and I can use the

104:18

skill regardless of whatever, you know,

104:20

product I'm selling. It can go and it

104:22

can find that information for me, apply

104:23

it to the skill, and then it can spin

104:25

it, mutate it in a variety of different

104:26

ways. So, what it's going to do now is

104:28

it's actually going to generate a bunch

104:29

of its own, gone through and just ran

104:32

all of the different ways you could

104:33

pitch things. And now I even have a

104:35

contact sheet, uh, which I'm going to

104:37

open the folder of, and then double

104:40

click on to show you guys. So now, you

104:42

know, I've done this, but I've done it

104:43

for a different software platform

104:44

entirely. How cool is that? Uh, and the

104:47

reason why is because I have that

104:48

reusable instruction set that I can

104:49

change very slightly. So this one's

104:51

cool. Dial leads in seconds. Hit 85%

104:53

pickup rates. See how you paid for the

104:55

lead. Talk to them. Start now. Call

104:57

every lead while they're still warm. Get

104:58

started. I mean, these are all pretty

105:00

cool. Um, so you know, based off

105:01

whatever background gradient I have, you

105:03

guys could see how you could extend

105:04

this.

105:06

How do I turn this from a skill, which

105:08

is that instruction instruction set that

105:10

is reusable, into a loop? Well, I mean,

105:13

this situation is a little bit different

105:15

because we've already built in like an

105:17

ad format, uh, for instance. And, you

105:19

know, that obviously required some

105:20

manual work, but hopefully you could see

105:22

if instead of this set of two ad formats

105:26

that were duplicating, we had 500 ad

105:28

formats that were duplicating, we could

105:30

genuinely create some novel things on a

105:32

daily basis. maybe once every morning,

105:34

you know, I would come in here and

105:36

because we're spinning 500 different ad

105:38

variants times, I don't know, 10

105:40

different brands times 10 different

105:43

formats, 10 different niches or

105:44

whatever, every time I come in here, you

105:46

know, I can choose from like one of like

105:48

50,000 options. Maybe every morning we

105:50

could generate, you know, a big batch of

105:51

them and then have some creative

105:53

specialist that just goes through, finds

105:54

the best ones, and then sends them off

105:56

to the media buy team. Um, that's

105:58

actually fairly easy to do. So, what

106:00

I'll do now is I'm going to go back to

106:01

my main chat, which is general coding

106:03

session, and I'll say, "This sounds

106:04

great. I want you to um put this on a

106:07

loop." The idea is we want to run this

106:09

every morning at 5:59 a.m. So that when

106:12

our creative specialist gets in at 6:00

106:14

a.m., they could take a look through the

106:15

ads and then identify and pick the

106:17

winning um combinations.

106:21

So now what we're going to do is we're

106:22

going to turn this skill into said loop.

106:25

It's going to run unattended every

106:27

morning and ask me for some scope size.

106:29

So batch size, why don't we do 20 copy

106:32

pool, we will generate new copy every

106:35

time. And you can see, you know, it's

106:37

also asking me questions here. I'm just

106:39

going to answer it manually because I

106:40

think that's easier. Output location

106:42

delivery. Um, we're going to generate

106:44

locally. And then old batches. Um, don't

106:48

clean up for now. Maybe later. Cool. So

106:52

now what we're doing is we're converting

106:53

this from that program that we've run

106:55

into a selfgenerating or running program

106:58

that does it all without me. Now in the

107:00

process of converting from a skill to a

107:02

loop, you know, some things will

107:04

probably have to change because we're no

107:06

longer here. If it needs to ask us some

107:07

questions about, hey, you know, how

107:09

should I generate the ads? Um, you know,

107:11

obviously I I I I'm not around to

107:13

actually like guide it at 5:59 a.m.

107:15

Well, knowing me, I probably am, but uh

107:18

we need to give it even more autonomy

107:20

than it already has. And so what we did

107:22

is rather than it using a bunch of fixed

107:25

like pieces of copy that it had created

107:27

and just ran through and iterated

107:28

through a script, now it'll actually

107:30

pass through itself every time when it

107:32

runs. So it'll actually basically say

107:34

like, "Hey, here are all the ads that

107:35

we've generated so far. You I want you

107:37

to make this one different." and it's

107:38

going to come through and then make

107:40

something new, something timebased so

107:42

that it actually runs and varies on a

107:44

day-to-day basis. So, it's done a little

107:46

bit of testing and now it's going to do

107:48

a real batch render to show me how it

107:49

works before actually instituting it on

107:51

my computer uh via a platform called

107:53

cron which you know actually goes

107:55

through and then makes it happen. Here

107:56

it's setting up the daily automation.

107:58

First, it's going to check a scheduling

108:00

skill because cloud actually has a

108:01

built-in skill called schedule which

108:02

outlines how to do all of this. And now

108:05

it's going to actually run this. Okay,

108:06

now it's asking me some final

108:08

confirmation info like does this run

108:10

every day or does it do it, you know,

108:11

Monday to Friday instead. Um, just gave

108:15

it that information and now it looks

108:16

like it's good to go. It's going to run

108:18

tomorrow morning at 559. Uh, just cuz

108:20

right now we're already at 8:52. But as

108:22

we know that is not enough, right? We

108:24

went from prompts to skills, skills to

108:26

loops, and now we need to go from loops

108:28

to routines. The major difference

108:29

between loops and routines being that

108:31

just because this thing runs at 5:59

108:33

a.m., it's still doing so on my

108:34

computer, meaning I'd need to have like

108:35

an office computer, you know, uh, at my

108:38

HQ that somebody signs into every day.

108:40

That's kind of a pain in the ass. Rather

108:42

than doing that, what I want to do now

108:43

is convert this into basically a

108:45

cloud-based skill that runs autonomously

108:47

on a schedule and then dumps the

108:49

information not into a local folder on

108:51

my computer, but some company accessible

108:53

folder that anybody in my company can

108:55

use. The question is obviously, how the

108:56

heck do I do that? You know, everything

108:58

up until now has been done entirely

108:59

locally on my computer, and now I'm

109:01

considering giving it access to

109:02

something else. Well, like most things

109:04

in life, you can just talk to Claude

109:06

about it.

109:07

Hey, now that I have done the prompt to

109:11

skill and skill to loop step, what I

109:13

want to do is convert this from a loop

109:15

into a Claude routine. My idea here is

109:18

rather than generate these locally at

109:19

559, what I want to do is generate these

109:21

in a Google Drive folder and then make

109:23

that Google Drive folder accessible so

109:25

that anybody that has the link can look

109:27

at it. I'm then just going to give the

109:28

link to a couple of team members so that

109:30

when they come in at 6 a.m. they'll all

109:31

have access to the exact same ad pool.

109:34

Uh, walk me through how to do this step

109:35

by step and then set it up for me. Um, I

109:38

know we'll have to give some access to

109:39

some platforms. That's totally okay.

109:41

Okay, cool. Now that it has everything

109:43

that it needs, we're going to convert

109:45

this again up to uh the highest quality

109:47

version of this, which is the

109:49

architecture shift. Obviously, since I'm

109:51

doing this, it's saying, "Hey, okay, we

109:52

should probably kill the local one that

109:53

we're doing and then turn it into

109:54

something on the internet. Um, does that

109:56

sound okay with you? How should we do

109:58

it?" Well, now what I'm going to do is

110:00

because we're on cloud, I need to figure

110:02

out the time zone. It's also going to

110:04

ask about GitHub repos. So, what I'm

110:05

going to do is I will create a new

110:07

GitHub repo. um that for people that

110:10

don't know is just like an online place

110:12

to put code so that code can run without

110:14

my involvement. What should we do? New

110:16

dated subfolder and uh you know anything

110:19

else here it's asking about like some

110:20

routine information on one time setup

110:22

and so on. So I need to give it some

110:24

information in my Google Drive. I'm

110:26

going to start with a private GitHub

110:28

repo. I'm actually already authenticated

110:29

but if I was not authenticated it would

110:31

ask me to sign in. Uh as you guys could

110:33

see it's not very big ask. It would just

110:35

basically say hey do you want to sign

110:36

in? I'd then click yes, sign in, and

110:38

then I'd be in. Uh, if you don't have an

110:40

account, it would then prompt you to

110:41

create an account, which is fairly

110:42

straightforward, not that big of a deal.

110:44

The cool thing is Cloud will just walk

110:46

you through most of this now, assuming

110:47

that you um have a clear defined step.

110:50

Uh, this is now finding the Clarvo

110:52

branded variant of the pipeline that I

110:54

created before in Maker School ads

110:56

conversation. So, it's just kind of

110:58

removing that.

111:00

And now, it's asking me some questions.

111:02

Yeah, I did this earlier. Not a big

111:04

deal. can remove. Eventually, it'll ask

111:07

me about Google Drive. So, what I'm

111:09

going to do is open cloud.ai/customize

111:13

connectors in a browser, connect Google

111:14

Drive, authorize with the right Google

111:16

account, and then come back here. Um, so

111:18

in order to do that, I'm just going to

111:19

go to the top right hand corner, go

111:21

settings, and underneath connectors,

111:24

which is all the way down at the bottom.

111:25

If you guys remember, I'm now going to

111:26

connect to my Google Drive. So, just

111:28

click that button. It'll now say, "Hey,

111:30

can we connect?" So, it'll then open up

111:32

a browser. I'm then going to give it

111:34

access to my Google. I'll just say use

111:36

all uh for now. Okay, now we can go

111:39

back. So, we should be good. It says

111:42

connected to Google Drive. So, I'm going

111:44

to go back here and I'll say connected.

111:48

And it should actually be able to set up

111:49

the list and then create the folder.

111:51

Cool. And it's gone through and actually

111:53

added that information. And we now even

111:55

have a folder called Maker School Ads,

111:57

which I can click and open right over

111:59

here. Uh worth noting we don't have

112:00

information in there yet. The reason why

112:02

is because it's saying the connector

112:04

can't change sharing settings. So you

112:06

might have to do this once and uh that's

112:08

fine. Uh so I'm just going to go to the

112:10

top right hand corner, go to share,

112:12

click share, and then change this to

112:14

anyone with the link can now view the

112:16

ads. So now I'm going to go back here

112:18

and then I'll say good to go. So now

112:21

team members will have access to this

112:23

folder cuz I'm just going to share it,

112:24

you know, within my company or whatever.

112:26

And then it'll actually build the

112:27

routine that generates the batch. And

112:29

the last thing we need to do is just

112:31

give it access to the Google Drive

112:33

itself. Um, and I say Google Drive

112:35

itself, not as we haven't already done

112:37

that because we have done that, but

112:39

we've done that locally. If you think

112:40

about it, what it needs to do is it now

112:42

needs to connect Google Drive in the

112:44

cloud routine instead of here. And it

112:46

looks like it also connected the Google

112:48

Drive connector too, which means we now

112:50

have the routine created. It even kicked

112:53

off a test run. It's going to check on

112:55

the status um you know sort of as time

112:58

goes on. If you go back to routines in

113:00

the top lefthand corner you'll see that

113:02

we now actually have maker school daily

113:03

ad batch. So I'm going to give that

113:05

button a quick click and it's actually

113:07

testing it right now which is why the

113:08

status is still paused. So as you guys

113:11

can see very similar um to the skill.

113:13

It's just sort of done almost in like

113:15

this I don't know cloud way. Generate

113:18

today's maker school ad badge and upload

113:20

it to Google Drive context. This repo is

113:22

a stripe style ad creative generator for

113:24

Maker School. It is a tuned visual

113:25

profile and a batch script that

113:26

generates ads with procedurally varied

113:28

copy each run. Okay, there's a bunch of

113:30

instructions down here where it runs it

113:32

through how to do the thing. We then

113:33

have cloud code remote Google Drive as

113:35

connectors. Then we even have the GitHub

113:37

repository which again is just like a

113:39

it's like a Google Drive but for code

113:41

and that allows it to do things. You can

113:42

see that we've actually started running

113:44

it. If I click on that run button at the

113:45

background, it there's literally like a

113:47

cloud chat going on with Claude. So,

113:50

it's doing all of the work, but it's

113:51

just not doing it on my computer. This

113:52

is now in the cloud. And anytime you see

113:54

this little cloud logo, you know that

113:56

it's not occurring on your computer,

113:57

which means that it also just doesn't

113:59

depend on you. So, it's going through

114:01

this whole process right now. And uh,

114:03

you know, I'll check back in a minute to

114:04

confirm that the routine is done. And

114:06

once it's done, we can move on. Gone

114:07

through and generated the ad

114:09

successfully. Now, it's just going to

114:10

verify the output directory contents

114:12

before uploading it to the Google Drive.

114:14

So, it's creating a dated subfolder in

114:16

Google Drive, which I'm going to take a

114:18

look at right now. Show you guys what's

114:20

going on. You can see it's now created

114:22

that. Okay. Now, if I double click on

114:24

this, it's in the process of uploading

114:26

the ads. So, we'll see them pop up as um

114:29

you know, the advertisements get

114:31

uploaded. Obviously, these are images,

114:32

so they'll take a little bit of time.

114:34

Okay. And you can see the ads are

114:35

starting to populate. Now, if I give

114:37

this one a click, we got some good Maker

114:39

School ads. Uh same ones that we were

114:41

looking at before. So, nothing super

114:43

special or crazy here, but hopefully you

114:45

guys see how easy it is to build even

114:47

like a simple procedural ad generator

114:49

using the the systems that I I talked

114:51

about. Okay, what I want to do next is I

114:54

want to show you guys how to go from

114:55

developing really straightforward um

114:57

kind of simple image ads to using AI to

115:00

generate some of the images. Then, after

115:02

we've generated these images with AI,

115:04

what I want to do is I want to go to

115:05

video generation. And you know, as

115:07

mentioned, none of these are going to

115:08

like knock your socks off. you guys are

115:10

probably going to be better at PPC than

115:12

I am if you guys are established

115:13

marketers that have been working on your

115:14

own companies. Um, but you know, we can

115:16

get pretty far with with where we're at

115:18

and I'm going to run you guys through

115:18

all of that now. Okay. So, how are we

115:20

going to do this in practice? Well,

115:22

first I just want to make it really

115:23

clear what we've done so far. This is a

115:26

Claude logo in case you guys didn't

115:27

know. U basically what we've done so far

115:30

is we have one had Claude,

115:34

you know, generate I'm just going to

115:36

handr write here so I'm a little bit

115:38

faster. um our assets.

115:42

And for those of you guys that don't

115:43

know, Claude doesn't actually have any

115:45

image or or video generation built in.

115:48

What we're doing is we're actually

115:49

having Claude generate our assets by

115:51

using kind of compositing tools. So, for

115:54

instance, um there are variety of

115:55

different tools that allow us to, you

115:58

know, make things on the internet. One

115:59

of them, for instance, might be, I don't

116:01

know, Photoshop, right? Another that you

116:04

guys might be familiar with is Canva. I

116:07

don't know if we have any old heads in

116:09

the building, but uh I don't know. How

116:10

about Microsoft Paint?

116:13

Well, what we had Claude do in that last

116:15

example is we basically had Claude

116:18

drive, you know, these tools or

116:20

equivalents of these tools in order to

116:22

generate um you know, our assets. And so

116:25

Claude literally baked in a set of

116:27

instructions similar to how you know you

116:29

tweak your lighting and your your

116:31

settings in Photoshop or whatever. And

116:33

then it just controlled or steered these

116:36

until eventually we got, you know, a

116:37

bunch of outputs, which is what we

116:39

wanted. And so that is how we got those

116:42

cool stripe images. It literally like

116:44

locked in and hardcoded. Every single

116:47

pixel on the page, right? Every single,

116:50

you know, part of the the headline and

116:52

the text and stuff like that was just

116:53

hardcoded. And this is cool obviously,

116:55

but, you know, we can't really extend

116:57

far past that. um you know obviously we

117:00

can do okay things but they're going to

117:02

be procedurally generated at the end of

117:03

the uh day and you know they're just not

117:05

always going to be of the best quality.

117:06

So, what we're going to do instead is

117:08

we're going to go from this sort of

117:09

like, you know, version one with really

117:11

simple ads, which you guys can obviously

117:13

do and I've done this and it works quite

117:14

well instead to um kind of version

117:17

number two where instead of claw

117:19

generating our assets, okay, what's

117:21

happening is we're using direct

117:25

image

117:26

and video models

117:32

steered by Claude

117:34

just like we were steering our Photoshop

117:38

and our canvas and our Microsoft Paints.

117:41

Turns out we can have Claude steer

117:43

direct image and video models too and

117:44

you can do a pretty good job. But

117:46

there's a big difference between you

117:47

know uh Photoshop let's say which is a

117:50

compositing tool and then um you know

117:54

something like I'm just going to say GPT

117:56

Image 2. For those of you guys that

117:58

don't know you know Photoshop composits

118:00

a bunch of different elements. You kind

118:02

of control them. GPT image 2 literally

118:04

produces the pixels on the page

118:05

directly. Like when you prompt a GPT

118:08

image 2, you are legitimately turning

118:10

your text into an array of pixels on the

118:12

screen. Likewise, um you know there's

118:15

also a bunch of video models out there,

118:16

right? So I don't know how many people

118:17

here do video editing, but I mean I do.

118:19

So Premiere Pro is my chosen tool of

118:22

choice. Well, that is again a

118:23

compositing tool for video. It's a video

118:26

compositing tool. That's pretty

118:27

different from like one of these next

118:28

generation video gen models like you

118:31

know Cance 2. I'm just going to say N

118:33

here, but really I think the um model as

118:35

the time of this recording is like a

118:36

2.5.

118:37

And so like let me show you guys some

118:39

examples here. Um these are a bunch of

118:41

images that I generated in just one of

118:43

the many tools that uh is available to

118:45

me. This one's called Higsfield. And

118:47

what I had was I had to generate a bunch

118:49

of product photography. So product

118:51

photography of a frosted glass jar of

118:52

blue clay overnight face mask minimalist

118:54

label labeling reading midnight clay.

118:57

Sorry. And so this is actually just like

119:00

every single pixel on this page has

119:02

actively been generated by an artificial

119:03

intelligence. So that little like glint

119:06

here, you know, the shadows, everything

119:08

like that. It's not like this was a drag

119:11

and drop sort of tool. Every single

119:13

pixel on this page was like

119:14

independently generated. And it's simply

119:16

because of the intelligence of the model

119:17

that this is capable of of occurring.

119:19

just been trained on a variety of stuff.

119:21

And you can see that idea extends to

119:23

virtually whatever sort of medium you

119:24

want. So, you know, it for some reason

119:27

flagged me when I tried generating

119:28

images of horses, maybe. [snorts]

119:30

Anyway, I'm going to talk about that.

119:32

Um, but uh, you know, you can see, you

119:35

know, I'm doing this for like painting

119:36

style stuff, for old school astrolabes.

119:38

I'm doing this for like jellyfish. I'm

119:40

doing this for cartoons. I'm doing this

119:41

for some futuristic building with

119:43

drones. I animated a bunch of videos of

119:46

a friend and I drinking coffee in Italy

119:50

uh just to send to him. That's a place

119:52

that I visited called Rio Major, by the

119:54

way. That's me making a mocha pot. No

119:56

idea why it made me look so feminine,

119:57

but hey, what are you going to do? Um so

120:00

yeah, all of these are just pixels that

120:01

are generated directly. And what we're

120:03

going to do is we're actually going to

120:04

have AI sort of drive the generation of

120:06

this stuff. And it doesn't just stop at,

120:09

you know, like image models. You can

120:10

also generate videos. And so for

120:12

instance, this video here is generated

120:13

by a model called Sea Dance 2.5, which

120:16

is pretty badass. Like here we have a

120:17

bunch of, you know, ships lying in

120:19

weight. Obviously, none of this is real,

120:22

so to speak. It's all a simulation. But

120:24

the video models are pretty cool because

120:25

it's actually like uh it's a world that

120:27

it is generating. It knows the rules of

120:29

physics and stuff like that. It knows

120:31

consistent characters, shadows,

120:32

lighting, how human beings look, and so

120:34

on. Um, the thing to know about, you

120:37

know, these sorts of direct image models

120:40

and video models is there's a much

120:43

higher likelihood that they're not going

120:44

to work the first time. So, with

120:47

compositing tools, okay, most of the

120:50

time once you build a template,

120:54

[sighs and gasps]

120:54

uh, compositing tools,

120:58

maybe you have like an 80% chance of it

121:00

being okay. And I don't mean okay here

121:02

as like this is perfect and I'm going to

121:04

choose it. just okay is like stuff is in

121:06

the right place. There's no weird

121:08

elements in the image. Uh the shadows

121:09

make sense. There's no odd motions. But

121:12

with direct, you know, gen tools, at

121:16

least right now, and I'd encourage you

121:18

guys probably to think about this even

121:20

later in the future, you have closer to

121:22

maybe a 25% chance. And um the idea here

121:26

is because of the comparative

121:28

differences, you know, you should use

121:29

compositing tools for simple stuff like

121:31

we did earlier and you should use direct

121:33

gen tools for more complicated stuff. In

121:35

addition, compositing tools are

121:37

virtually free because number one, you

121:39

get okay-ish outputs every single time

121:41

or almost every single time. And we're

121:43

also just controlling like procedural

121:45

software like programs, right? But with

121:47

direct gen tools, we are controlling

121:48

these mammoth intelligences. And so

121:50

compositing tools tend to be quite cheap

121:52

as well as accurate um for simple

121:55

things. And then direct gen tools uh can

121:57

actually be quite expensive. And it's

122:00

expensive both more expensive per gen,

122:02

but it's also more expensive because

122:04

it'll take an average of like four gens

122:06

before you get a good one, right? So I

122:09

would just keep all of this stuff in

122:10

mind as we proceed. This is not going to

122:13

be free. And realistically, what we're

122:14

going to do is we're going to generate

122:15

around four times the number of images

122:17

that we want because odds are a lot of

122:19

them are not going to be, you know, make

122:20

sense and whatever. Okay, so I just say

122:23

this to you guys because a lot of

122:25

creators will show like the one shot on

122:26

gens and they'll be like, "Oh, it's

122:28

perfect. You know, this is an amazing

122:29

video model. Changes everything or

122:30

whatever, but it doesn't really do

122:31

that." And uh it's good for us just to,

122:33

you know, dial our expectations back a

122:35

little bit and be more realistic.

122:36

Despite that, you can obviously do

122:38

incredible things. And uh I'm going to

122:39

show you guys a couple of really cool

122:41

examples uh right now. Okay, so the very

122:44

first thing we need to do if you think

122:45

about it, um, is we need to go and grab

122:47

our creative pipeline. And so, same

122:50

thing that we did last time, very, very

122:52

similar. We're going to save our best

122:53

performing ad formats as templates.

122:55

We're then going to feed in a variable,

122:57

which in my case, I'm just going to be

122:59

one of those product images that I

123:00

generated earlier. Then Claude is going

123:02

to generate a bunch of combinations of

123:04

all the different types of ways the

123:06

product can look within the confines of

123:08

the template.

123:09

Then we're going to pass it off to

123:11

nanobanana or GPT image 2. I think I'll

123:13

probably do GPT image 2 to start just

123:15

because um it's easier for me and I have

123:17

the API key. And then uh finally we'll

123:19

review pick and then you know ship just

123:21

like we did before. And the human is

123:23

still in the loop. We're still the

123:24

people that are ultimately making

123:25

decisions whether we're creative

123:26

director or whatever. Like we're going

123:27

to be the people that are in the

123:28

driver's seat. Okay. So how how do we do

123:31

that? Well, I'm going to go to the ad

123:32

library here. You can see I was kind of

123:34

peeping um another image, but uh what I

123:37

want really is I don't know. I just want

123:41

let's just say socks. I don't know. I

123:43

just want to like see static image ads

123:45

of socks. Why? Cuz I'm probably going to

123:48

try advertising some socks or it's like

123:50

some consumer product or something which

123:51

is fairly straightforward.

123:54

So, just zooming in here, what do we

123:56

got? We have uh it's an image of a belt.

123:59

Okay, that is literally an image of

124:01

socks. So you can see like Amazon's

124:03

doing a lot of these and Amazon has

124:04

quite the style. Uh they typically don't

124:06

like provide any text or anything like

124:08

that. So I just want some text cuz I

124:10

think it'll probably be a little bit

124:11

more representative. That is that is I

124:13

don't know that just does not seem like

124:14

a very good ad. This also doesn't seem

124:16

like a very good ad. So I don't really

124:18

want to use that as like my my template.

124:20

Let me find something nice. All right.

124:21

This looks pretty good. Nordic socks.

124:23

Right. So the logo of the company in the

124:25

middle and then we just have like a four

124:28

um sort of panels each that have their

124:30

own socks. So, I'm just going to go over

124:31

here and then see add details for one.

124:34

And then it looks like I can just open

124:36

image a new tab, I would hope, which now

124:39

gives me the ad. Cool. So, I'm just

124:40

going to save this. We'll just call it

124:41

socks one. Okay. And uh you know, this

124:44

time I just want to do a little bit more

124:46

than that. So, this is cool. It's Marvel

124:48

and then some other brand with kids

124:50

wearing a bunch of socks presumably. And

124:52

you know, if this is like some sock

124:55

company,

124:57

then I think as long as I have some

124:59

notorious business that I could like

125:01

claim affiliation with or something,

125:02

then that'd be okay. Maybe we have a new

125:04

line that's coming out or something. I

125:05

don't know, man. I definitely do not

125:07

sell socks if it's not clear.

125:09

[sighs and gasps] This is interesting.

125:11

Uh, it's like some dude putting the

125:12

socks on, moving around. Let me just see

125:15

if we could find another really good

125:16

one.

125:21

I like this one. Sock well feel better

125:23

in style with marino wool. Very similar

125:25

to what I am aiming for here. The only

125:27

issue is the sock images are pretty

125:29

difficult to see. Still, I think that's

125:31

going to be okay. It's just we're going

125:32

to have to like, you know, if I open

125:34

this in a new tab.

125:36

Yeah, like they're pretty small still.

125:37

I'm going to save it. I'll call it socks

125:39

3. Cool. And now that I have a bunch of

125:42

socks, and you know, these are kind of

125:43

my templates. What I'm going to want to

125:44

do is I'm going to open up Finder and

125:46

then I'm just going to grab all these.

125:48

And then what I want to do is I just

125:49

want to pump them into the same sort of

125:51

folder that we had before. And I have

125:53

one called ad formats for image

125:54

generator. So I'm just going to go up,

125:57

select these three. And then I don't

126:00

know why the images are so big here.

126:02

Let's just zoom out. It's a picture Jack

126:04

sent me for our podcast. You can see we

126:07

are we are really ramping up the podcast

126:09

here. the stacked podcast, anybody that

126:11

was wondering, is uh is my life's work

126:13

and I'd highly recommend you guys check

126:14

it out. Okay. Um and now we have

126:17

actually everything in the folder, which

126:18

is nice. Cool. Right. All right. So, now

126:20

that we've done this, okay, the next

126:22

logical step is we obviously need to

126:24

have Claude sort of steer and then

126:29

Okay, so now that we've done this, the

126:30

next logical step is we need to feed in

126:32

the variable. And in our case, the

126:33

variable is just going to be the

126:34

product. Um, I do recommend that you

126:36

guys have products. You know, if you

126:38

guys want to advertise something, you

126:40

should have something that is like very

126:41

visually distinct and it's a product.

126:43

So, I got a couple of products here.

126:45

One's called Cloud Socks. They're marino

126:46

wool sleep socks. Another one here is

126:49

called Linen Tails, which is a waffle

126:51

towel bath sheet. 100% organic linen.

126:54

You know, it's kind of cool, right? I

126:55

like the look of that. And uh what I

126:57

want to do is I basically want to take

126:58

my product and then I want to use that

127:00

proven format that is obviously known to

127:02

work really well. Assuming you're taking

127:04

it from your own company or something

127:05

like that, right? Um and then just

127:06

generate a bunch of variants. So the

127:09

first thing I'm going to do is I'm

127:10

actually just going to go to an image

127:11

generator directly and I'm just going to

127:12

verify that it can do the sorts of

127:13

things that I want it to do. And if it

127:15

can do the sorts of things that I want

127:16

it to do, great. Um that's fine. We can

127:18

move on. If it can't do the sort of

127:20

things that I want it to do, I'm happy

127:23

that I'm testing it at the beginning

127:24

because then I'll know, okay, I probably

127:26

shouldn't pursue this because even if

127:27

Claude prompted, I wouldn't be able to

127:28

do a good job because the model itself

127:29

just is insufficient for my task. So

127:31

that's typically how I treat these

127:32

things. And um as mentioned, the model I

127:34

want to do is called GPT image 2. Uh

127:36

it's a pretty cool model. You know, it

127:37

has a lot of I say highquality detail

127:40

orientedness to it. So you can actually

127:42

like write things. Um, the reason why

127:44

every single one of these uh has text in

127:46

it is because for a while images could

127:49

not be generated with text with most

127:50

image models. Um, now, you know, this

127:53

was like the first time that we had

127:54

coherent image gen uh with like a bunch

127:57

of different topics and stuff like that.

127:58

So, anyway, now I'm right over here and

128:00

what I want to do is I want to create an

128:02

image and we'll see if I could do this

128:03

with the free model. If not, I'll

128:04

actually have to go and uh pay a little

128:06

bit of money for it. But what I want to

128:08

do is I want to take my little finder

128:09

here. And I don't know, I like sockwell

128:12

as like a demo. So I'm going to feed

128:13

this in. Okay. And then I'm also going

128:16

to grab

128:18

this these cloud socks. Actually, maybe

128:20

we should do the lens. No. No. We'll do

128:22

the socks. Call these sock real. Okay.

128:26

They're totally real. I'm g feed this

128:28

in. Looks like I can't just because I

128:31

think the file type is a webp

128:33

technically. So I'm actually have to do

128:34

like webp. Let's do uh WEBP to PNG

128:39

converter. Kind of annoying to have to

128:41

do this step, but sometimes, you know,

128:42

as you are scraping and taking ads from

128:45

various parts of the internet, you'll

128:47

find that the um formats of the ads are

128:49

not what we want. So, [gasps] let me

128:51

just get a real PNG here. Okay, cool. We

128:54

do have it. I'm just going to go back

128:55

here and drag this up. Okay. Anyway, it

128:57

turns out that I cannot upload more than

128:58

one image in a free plan. So, I did have

129:01

to go to my business plan, which is kind

129:03

of unfortunate. uh just kind of how it

129:05

is. So, no major deal here. Uh let's put

129:08

that away. Um yeah, so what we need to

129:11

do is, you know, go and get a paid plan.

129:13

For those of you guys that didn't know,

129:14

ChatGpt has a variety of plans. My

129:16

recommendation for

129:19

um you know, pricing model here is I

129:22

would probably get at least as of the

129:23

time of this recording like the Go plan

129:26

or the plus plan. um you can't upload

129:29

more than one image with the free plan

129:30

and it'll also interfere with your

129:32

ability to communicate with the via API.

129:34

Uh any who so I went through and I got

129:35

the you know chatb plus plan and what

129:38

I'm going to do here is I'm going to

129:39

upload socks real okay which is that

129:41

real image of the socks and then I also

129:43

wanted to upload socks 3 which is um I

129:46

think not findable right now because I

129:49

hid it away. Cool. And I'm just going to

129:51

say, I want you to use the add format in

129:54

the second image, but apply the product

129:58

in the first image to it. I then want

130:00

you to add, you know, value props that

130:03

make sense for what the product is,

130:05

which is a light, fluffy cloud sock

130:09

that's cozy and helps you go to bed. You

130:11

don't need to do the exact same um

130:14

background or setting. I just want you

130:17

to apply the template in so far that you

130:20

know the main focus of the uh static ad

130:24

should be on enumerating the benefits of

130:26

the product in sort of that cute quirky

130:28

way. Uh also make sure the name of the

130:31

company is cloud socks. Then just going

130:33

to put it in. It's going to analyze both

130:35

of these images and then it's actually

130:36

going to try and generate for me. Okay.

130:39

And so here we have our cloud socks ad.

130:43

Softer, cozy, cloud-like, your new

130:45

bedtime essential. We have the ad itself

130:47

feels like a cloud. Lightweight warmth,

130:49

cozy bedtime comfort. Helps you wind

130:50

down. The actual image of the product

130:53

all rolled up. Looks like some of the

130:55

text is a little cut off there. I would

130:57

say this is actually like reasonably

130:58

good. It's not the best ad ever,

131:00

obviously. Okay, we've now generated

131:02

another one. And yeah, I mean, I'm I'm

131:04

pretty confident this is okay enough for

131:06

me to uh want to move forward with it.

131:08

You know, I think obviously the bottom

131:10

is kind of weird and not all of these

131:11

are perfect. Um, like for instance,

131:13

what's going on over here? How is she

131:14

grabbing this thing? And what exactly is

131:16

she wearing if her if her thumb is sort

131:18

of popping out underneath? So, there a

131:19

couple of idiosyncrasies, but it's not

131:21

terrible. And, uh, yeah, this is now

131:23

confirmed to me that I think we can move

131:24

forward. So, all this basically tells me

131:25

is that like this is feasible. You know,

131:27

we got an okay looking at. It's not

131:28

perfect, but it's it's okay. And I'm

131:30

sure you can imagine if we iterated over

131:32

that like 30 times, we had 30 different

131:34

variants all with different font tiles

131:35

and stuff like that. Like, we'd probably

131:36

get something that's okay, right? So now

131:39

what we need to do is we actually just

131:40

need to just connect GPT image 2 to

131:42

Claude code. And then once it's

131:44

connected, we can just have Claude steer

131:45

this for us, which is far easier than we

131:47

actually going into uh you know GPT

131:50

image 2 and then manually trying to do

131:52

it all. So how do you actually do that?

131:54

Good question. Well, you need to do

131:56

what's called the GPT image 2 API. So

131:59

I'm just going to head over here to try

132:00

in playground. Then I'm going to connect

132:03

with the same account that I was using

132:04

before.

132:06

And this will give me access to API keys

132:09

over here. You can see I have a lot of

132:10

API keys. I've been using this account

132:12

for a while. What I'm going to do is go

132:14

create new secret key. I'll say this is

132:16

for YouTube. Okay. And then what I want

132:19

to do is I'm going to create the key.

132:21

And now I have the ability to basically

132:23

copy this at any time. So you copy it

132:26

and now it's disappeared. So then you go

132:28

back to Claude, start a new

132:30

conversation. Then you go back here and

132:31

then you see where it says local. Okay.

132:33

Okay, what you want to do is you want to

132:34

click on this little button and then

132:35

these are your environment variables. So

132:37

this is like all of the stuff that Cloud

132:39

has access to. What you're going to want

132:41

to do is you're going to want to create

132:42

a variable and you can actually call

132:44

this anything you want. I didn't realize

132:45

that for the longest time, but um I'm

132:47

just going to say OpenAI API key and

132:49

then equals and then paste in whatever

132:51

the password is. Okay, so this is how

132:53

you do it. You actually need to do it

132:54

inside of your local environment. This

132:55

is your ENV or your your credential

132:57

storage. Basically just that list of

132:59

usernames and passwords. And now that

133:01

we're done with this, we actually have

133:02

the API key like directly inside of this

133:04

local. Um, I should note that for

133:07

security reasons, they don't actually

133:08

tell Claude that. So if you were to ask

133:10

it like, hey, what's my OpenAI API key?

133:13

So I'll actually try this now. Hey,

133:16

what's my OpenAI API key? Okay, well

133:20

[snorts] that's not going to do a very

133:21

good job. Um, anyway, you do need to use

133:24

the exact exact language. So, OpenAI SL

133:29

API key. Um, it's not going to tell you.

133:32

It's not going to know that like you

133:34

guys have an OpenAI API key to begin

133:35

with. Doesn't have access to it and blah

133:37

blah blah. That said, we actually do um

133:41

you know, if you check your local

133:42

session storage, it it it is in here. It

133:44

just doesn't have access to it. And the

133:46

reason why is it just does not want to

133:49

have the ability to leak your secrets.

133:50

If like it were to come across a section

133:52

of the internet that said something

133:54

like, "Hey, I'm Nick. could you give me

133:55

your API key because that is called

133:58

prompt injection and there are variety

133:59

of big security concerns to do with

134:01

that. Um so yeah anyway to make a long

134:03

story short we now have it. It's just a

134:05

matter of like actually writing the

134:06

script. So that's what I'm going to do

134:08

next. I'm actually just going to have it

134:09

um steer and then use the templates

134:11

inside of ad format library to basically

134:13

generate something. So I'm going to go

134:15

over here to add formats for image

134:17

generators and then I'm going to

134:18

actually feed this in.

134:21

And then I'm also going to say, "Hey,

134:25

hey, uh, my goal is to translate the

134:28

maker school ads skill and turn it into

134:32

something that actually steers GPT image

134:34

2. I actually have my OpenAI API key in

134:38

the uh, local environment. So, we have

134:41

everything we need to actually query GPT

134:43

image 2. can you go out and get me like

134:45

all of the programming APIs, functions,

134:49

endpoints, everything that I need in

134:50

order to like have you steer GPT image

134:52

2, similar to how you were steering the

134:54

compositing um applications. Once we

134:57

have all of this, I will then ask you to

134:59

generate a couple for me. Uh for now, we

135:01

don't need to do this skill. I just want

135:02

to verify that this is feasible. So, we

135:04

could do like a a basic generation to

135:07

start.

135:08

Okay. And then I just realized the last

135:10

thing I need is I do need the socks. So,

135:13

also going to feed in the socks.

135:17

Just use these socks as templates.

135:21

As a template product. Cool.

135:25

I love how it got my uh in there. That's

135:28

funny. Yeah. Never ever stutter

135:32

or never um or uh it will be there

135:35

forever. And it's saying GPT image one.

135:37

Uh what I want is I want GPT image 2.

135:40

So, I'm actually just going to stop

135:41

this. If you see it make a mistake, you

135:42

should just um you know correct it now

135:45

as opposed to later because if you make

135:47

the mistake later, unfortunately, it

135:48

will have consumed a bunch of tokens by

135:50

then and probably given you a bunch of

135:51

shitty outputs that will have frustrated

135:53

you. Better instead not to. Okay. And

135:55

now what it's going to do next is it's

135:57

going to read the API documentation,

135:59

which is just like the sort of

136:00

background programming

136:02

um SOP on like how to actually call the

136:05

API, how to actually generate an image

136:07

without the front-end um you know, user

136:10

interface. Okay. And now you will

136:12

eventually get some sort of notification

136:14

like this asking, hey, you know, can you

136:16

give me access to the directory. That's

136:19

fine. Now it's listing a bunch of files

136:21

in there. Just let this thing steer all

136:22

the way until the end. You can see it's

136:24

actually now running a live test image

136:26

edit, which was part of what I asked it

136:28

to do. Um, it looks like it's going to

136:30

be using the socks photo as an input

136:32

image. Okay, so this thing went nuts. It

136:34

generated five different variants as you

136:36

guys could see here. Maker School cloud

136:38

socks. Uh, that's sort of based off of

136:40

that, I guess, like Marvel Bombas one.

136:43

Same thing over here with the logo up

136:45

top. I I like this one personally. Um, I

136:47

mean, you know, I just gave it maker

136:49

school. Obviously, it's not actually

136:51

related or whatever, but yeah, I like

136:53

how it put it in that box and it it does

136:54

look quite clean. Same thing over here

136:56

with the product. And then same thing

136:58

over here with the product. I mean, the

137:00

products are a little bit different from

137:01

image to image and that's just because I

137:02

gave it a rolled up version of the

137:04

product. If you guys wanted to have a

137:06

lot more, you know, product adherence,

137:07

then you would actually just feed it

137:08

like a unrolled photo, maybe something

137:10

like this, and then it would actually be

137:12

able to get the curves and the folds and

137:13

stuff. But yeah, hopefully you guys see

137:15

like I, you know, we basically have an

137:16

image generator. However, um, the

137:20

probability that this will be exactly

137:21

what I'm looking for has gone way down

137:23

simply because it now has a lot more uh,

137:25

freedom and flexibility. And that, you

137:27

know, takes me to an important point,

137:29

which is that the more freedom and

137:30

flexibility that you give a model,

137:31

typically the more rope you're giving it

137:32

to hang itself. And that's good in many

137:35

ways because it's the freedom and the

137:36

flexibility of these models that

137:38

ultimately allow us to do cool things

137:39

with them. Stuff that you could not have

137:40

imagined doing just a few years ago with

137:42

a Photoshop. Um, but it's also the the

137:44

main downfall at least for business

137:46

because with business you tend to want

137:47

to have like very repeatable consistent

137:49

processes as talked about that you know

137:51

convert maybe a stranger into somebody

137:52

that actually has paid you money for a

137:54

product or service. Hard to do that when

137:56

you have total crazy pixel um variants

137:59

like this. you know, a photo that's on

138:01

like a linen cloth to one where both of

138:03

these are like hanging in the air. If

138:04

you think about it, like these are very,

138:05

very different pictures. The pixels are

138:07

all over the place, very very

138:08

fundamentally opposed. So, we, you know,

138:11

what we want to do is we actually want

138:12

to exert a little bit more top- down

138:14

focus. Um, we want it to be a little bit

138:16

clearer and closer to, you know, the the

138:19

sample image. Uh, we don't want to copy

138:21

the sample image, obviously, but we do,

138:22

you know, we want to we want to extract

138:24

the elements in the sample image that

138:26

make sense to vary and then the ones

138:28

that don't. And so what I'm going to do

138:29

is I'm actually going to talk to it and

138:31

ask it to do that.

138:33

Hey, I thought this was a pretty solid

138:35

job. However, I'm noticing wide variance

138:37

over the input image. For instance, it

138:39

looks to me like the crux of what you

138:42

regenerated was the logo uh rather the

138:46

two logos being opposed, Maker School

138:48

and Cloud Socks. And that's fine, but it

138:52

varies tremendously from that image ad

138:55

that I gave you as an example. I think a

138:58

better way of doing this would be you

139:00

digest the image ad, then identify a

139:03

bunch of different areas that we could

139:04

tweak things, sort of like levers to

139:06

pull, similar to the compositing

139:07

workflow. Then you template out a bunch

139:10

of changes and then we generate them

139:12

that way. However, we do need more

139:14

conformity with the main image. The idea

139:16

is we just want a template that we can

139:18

hot swap any product into and then be

139:20

approximately 80% sure that what comes

139:22

out is going to be okay. Okay, now I'm

139:25

going to feed that in. We're going to go

139:26

back and forth with the model until we

139:28

get something reasonable. And to be

139:30

clear, this is the prompting stage,

139:31

right? This is the whole idea. We are um

139:34

we're not automating this yet because we

139:36

don't have the process in place. If we

139:37

were to try and automate this like build

139:39

a skill essentially, we would not be

139:40

doing a very good job. Um now, what is

139:43

this doing? It's actually coming out

139:44

with the various things that it can

139:46

change. So, the first thing is somewhere

139:48

between 0 to 20% of the height, you

139:50

know, we want some sort of a logo band.

139:53

Okay. Then we need transition or

139:55

negative space. Okay, that's important.

139:57

Then we need uh the product band. Okay,

139:59

and then finally at the bottom we need

140:01

some other thing. So what this this is

140:02

doing now is it's actually logically

140:03

breaking down the template into its

140:05

components and then it's you know

140:06

actually going through and generating

140:08

the stuff. Now it's asking me what would

140:10

you do? Should I do a model plus some

140:12

sort of overlay? Um generally speaking

140:14

that's a bad idea because you are then

140:16

adding you know a procedural compositing

140:18

approach to an AI based generating pixel

140:21

by pixel approach. So, I'm actually just

140:23

going to use a pure prompt template

140:24

basically every time. And you can see it

140:26

even says I lean product option number

140:28

two since text and image is the single

140:30

biggest variant source and you already

140:31

have a proven compositing pipeline for

140:33

exactly that layer. Uh, I would agree

140:34

with that. And so, yeah, it's just going

140:37

to continue creating this stuff and uh

140:38

I'll take a look at its results

140:40

afterwards and then circle back. Okay,

140:42

looks like it has finished one and now

140:44

it is just waiting on the second. Both

140:46

are done. So, I'm going to get to look

140:48

at both of these images now. Let's take

140:50

a look here at these pictures, shall we?

140:53

Okay, you can see we're starting to get

140:54

a lot closer in conformity. Uh, mainly

140:57

we're now kind of comparing uh I think

140:59

the photo was like kids wearing

141:01

Spider-Man socks or something like that.

141:02

Now we have something that's a lot

141:04

closer to what it is that we wanted

141:05

initially. Now we have people basically

141:07

with their feet up wearing these socks

141:09

showing off how cozy they are, which is

141:11

kind of cool. Um, so this is more more

141:13

the idea. I didn't want just a bunch of

141:15

people doing whatever the hell they were

141:17

doing back over here, which looks to me

141:19

drinking coffee sort of solo because the

141:22

only real element that was taken was the

141:24

the logo in opposition. What we have now

141:26

is we have something a lot closer to

141:27

that initial, which is, you know, the

141:29

feet on a couch or something like that

141:31

comparing the two. Presumably, you know,

141:33

in advertising, this gets closer to the

141:35

core of what the product is about, which

141:36

is, you know, getting cozy with somebody

141:38

that you love or something, right?

141:40

That's the whole idea. Just doing a

141:41

little bit more tweaking here. Uh, we

141:43

got a reasonable looking variance. Uh,

141:45

this is more close to I think what I was

141:47

looking for, which is I just want to

141:49

take the product cloud socks and then

141:51

apply them to a template. Uh, it's not

141:54

one for one, but it's pretty good. And

141:55

what I like is we basically have like a

141:56

rainbow of different skin colors. Um, we

141:59

have really cute looking socks with our

142:00

logos and everything like that. Um, this

142:02

this looks good. It's like it really

142:04

took the horizontal idea, which is

142:06

valuable. So, I'm still iterating on the

142:09

generator, of course. Um, but you know,

142:10

the generator is getting pretty close to

142:12

where I want it to be. This is going to

142:13

take some back and forth no matter what

142:14

it is that you are doing. Hopefully you

142:16

guys see now just how much more variable

142:17

AI can be versus, let's say, a

142:19

compositing sort of flow. Cool. And now

142:21

we are building out the rest of the

142:22

generator, which is just ensuring that

142:24

this can work across the board. Um, this

142:26

one looks like it kind of put this in

142:28

tennis. I don't know if that's

142:29

necessarily the way to do things. We'll

142:30

see. Allight comfort and you're out

142:32

playing tennis. This one over here

142:34

though is basically exactly what we

142:35

wanted. We wanted some sort of like

142:37

collage based flow where we have like a

142:39

bunch of different things. So we can see

142:40

the texture of the sock. We can see a

142:42

couple of the socks laid out. And then

142:43

yeah, we have that other one. So what

142:46

I'm going to do now is I'm just going to

142:47

turn this from um this like built-in

142:49

flow here which is occurring locally to

142:51

a flow very similar to our HTML one

142:53

where you could actually like click on

142:55

the ones that you want. So that's what I

142:56

did right here. just gave it a prompt

142:58

with an abundance of ums and o's.

143:01

Basically saying, now that we have the

143:02

flow working inside of cloud code, turn

143:04

it into that HTML flow we had with the

143:06

little check boxes and the ability to

143:07

mark the ones that we want. Also,

143:08

instead of just generating three,

143:10

generate a lot more variants. So, uh,

143:12

kind of misunderstood me there, but

143:14

every variant should or each template

143:16

should generate at least five variants.

143:18

So, that means if we're going to do

143:19

three, we should have 15. And then we

143:21

should actually like feed very different

143:22

instructions into each so that you know

143:24

a taste person, some sort of creative

143:26

director can just select the winners. So

143:28

that's what we're doing. Um we're adding

143:30

a bunch of different like variation

143:32

pools per template so that we can adjust

143:34

uh the prompt that we're sending. Maybe

143:36

some of them will be warm, some of them

143:37

will be cool, some of them will be

143:39

inside, some of them will be outside.

143:40

But again, you know, we're just taking

143:42

that template and adhering to it quite a

143:43

bit. So you can imagine how for this

143:45

template we'd probably have four

143:47

distinct sections. This one here would

143:49

be some sort of feet arrayed

143:50

horizontally most likely. This one here

143:52

maybe would show it in action in some

143:54

way. Uh and then you know we just add as

143:56

many of these ad variant inspir

143:58

inspiration ads as humanly possible.

144:00

We're done generating 12 out of 15 of

144:02

the first batch. Okay, now we have our

144:04

own little generator very similar to

144:05

what we had before. It's like uh like

144:07

another mini app. And the idea behind

144:08

these mini apps of course is you know I

144:10

want to show you guys how to build cloud

144:12

codebased flows but the cool thing about

144:13

cloud code is you can use them to build

144:15

apps. And uh this could be a really cool

144:16

internal tool for team members obviously

144:18

based off of you offering the correct

144:20

formatted um you know ad initially.

144:23

Yeah, you can see how we have maker

144:25

school cloud socks. We have these sort

144:27

of brands uh opposed to each other. Now

144:29

all of them are slightly different in

144:31

their own way. We have a background

144:33

colors. We have people's different skin

144:35

colors. We have uh the socks themselves

144:38

that look a little bit different from

144:39

one to one. We have our little like ad

144:41

collage here. If this was a really high

144:42

performing template and we were just

144:44

like ripping that template, you can

144:46

imagine how we can just generate a

144:47

thousand of these really easily and

144:48

really straightforwardly. Uh none of

144:50

these are going to win any awards. I

144:51

think in the way that I've shown them

144:53

here or done them, but suffice to say,

144:55

you guys understand that I don't have

144:56

like really high performing ad templates

144:58

selling socks. You guys might for

145:00

whatever your own company is. So this is

145:01

how you would rip that format and then

145:03

really expand the top of funnel uh to be

145:06

able to select, you know, the the the

145:07

the best variants for you. So, you know,

145:09

this app allows you to very quickly and

145:11

easily scaffold out, let's say, 20 and

145:13

then you can just download selected. Um,

145:15

maybe you you fill your template folder

145:17

with like 500 ads or something and then

145:19

just consistently rip the best ones

145:20

until you get something that's nice. And

145:22

you can change adherence, too. I mean,

145:24

like in our case, we have it pretty

145:26

strongly adhering to the same look, but

145:28

we could also decrease that if we wanted

145:29

to add diversity. So, I don't know,

145:31

maybe instead of having these on like a

145:34

uh the top right hand corner every time,

145:36

maybe we just change it so that we can

145:37

actually experiment with maybe triple

145:39

collages instead of quadruples and so on

145:41

and so forth. All right, so where do we

145:43

go from here? Well, now we need to turn

145:44

it into a skill. This looks great. Um,

145:47

let's turn this into a skill. Looks like

145:49

I was voice transcribing without

145:50

realizing it. This into a skill now. Um,

145:54

the whole idea being that we're going to

145:55

go from prompt to skill, skill to loop,

145:57

and then loop to routine. Skill's just

145:59

about done. What I'm going to do while

146:00

this is um wrapping up is I'm also going

146:02

to turn it into a loop. So, I'm going to

146:04

press and hold this to record. Hey, now

146:05

that you've created the skill, I'd like

146:07

you to turn this into a loop. I want the

146:08

loop to fire at 5:59 a.m. every morning.

146:10

Very similar flow to our Maker School

146:12

ads flow. The idea is being uh we will

146:14

have a link by you here that anybody can

146:19

use to access a page that includes let's

146:22

say five renders per variant in the ad

146:24

format. Sorry, I don't know where I got

146:26

10 from. If we had 10 images, it' be

146:28

time five or 50 in total. Generate three

146:32

* 5 or 15 in total. Cool. And uh now

146:37

that it's, you know, running this on a

146:39

single image smoke test, I'm just going

146:40

to feed this in. Uh what happens when

146:42

you feed in something while it's working

146:43

on something else is it finishes this

146:45

thing before it starts this thing. So uh

146:48

you know, now that this is sort of in

146:50

the background here, it can start on the

146:51

other task. Basically, just fire that

146:53

off and it's now going to turn it into a

146:54

loop. So that's kind of neat. Um

146:56

obviously you could do the exact same

146:57

thing now again by telling it to turn it

147:00

into a routine once we've verified that

147:01

the loop works. Now for the routine we

147:03

have to go just one step further here.

147:05

Um the reason why is because we are now

147:07

going to be running a cloud environment

147:09

that includes an API key. We don't uh

147:12

currently have the ability to provide to

147:14

cloud. So uh if you go back to your API

147:17

keys here I'm just going to create a new

147:18

one just out of simplicity and so that

147:20

you know I forget I don't forget. This

147:24

is going to be routine API key inside of

147:26

GPT image. Just going to copy this. What

147:29

I'm going to do is I'm going to go back

147:30

to cloud, open up that routines tab, and

147:32

then um as you see, there's a local

147:34

routine, which is kind of a skill.

147:36

There's also a cloud routine, which is

147:38

kind of the one that we want. [gasps]

147:39

And what we need in order to make this

147:41

work is we need to set the environment

147:44

such that it includes our OpenAI API key

147:48

just like this. Okay, so that's in ENV

147:51

format. This is our default environment.

147:53

Uh this default environment will apply

147:54

to all new sessions. So I'm just going

147:56

to save changes. Now we actually have

147:58

all that. And then if you think about

147:59

it, what we need to do in each session

148:01

is we just need to uh basically do the

148:04

same thing that we did before just in

148:06

the maker school ads. It's just we need

148:07

to do this in um the GPT image

148:10

non-compositing workflow. So you know it

148:13

has the key. It has everything that it

148:14

needs in the default environment. If I

148:15

go back to routines then try creating a

148:17

new one on cloud and then check the

148:19

default environment you'll notice the

148:20

key is still in there. So, it's now like

148:22

inside of our thing, and now we can

148:23

actually go back and like ask it to

148:25

create the create the actual flow. So,

148:27

I'm just going to jump back in here and

148:29

then say uh well, uh what kind of ads

148:32

did we have? We had I think product ad

148:34

genen. Yeah, we did right over here. So,

148:36

turn product ad genen into a routine. um

148:41

for the routine mirror the maker school

148:44

ads system that generates a Google drive

148:48

folder and then a dated subfolder and

148:50

then populates it with the gens do so

148:54

using this structure for every ad in the

148:58

template ad folder which is currently

149:01

hosted at generate five variants.

149:05

First take the ad formats for image

149:07

generator and put it somewhere on my

149:09

Google drive. Then uh build the

149:11

structure that I talk about mirroring

149:13

the maker school ad system. Um the maker

149:16

school ad system is a routine. So query

149:18

that routine first, get all the

149:20

information and use it to scaffold out

149:21

the new gen. I don't know why I can't

149:23

type to save my life today, but

149:24

hopefully this is going to be more than

149:26

enough. Um oh and you know I should

149:28

probably give it some context. I have

149:30

also added the API key in the same

149:33

format as our original skill.

149:37

It's in the default environment under

149:40

OpenAI

149:42

API key default cloud environment. There

149:45

you go. Um I'm just giving this it just

149:48

in case it knows sort of how we're going

149:50

to structure things. uh we need to be

149:51

100% clear because as mentioned it will

149:54

not have access to the API key inside of

149:56

the cloud environment simply by simply

149:59

because rather of um you know security

150:02

doesn't want to like show you the API

150:04

keys everywhere because every single

150:05

push it's not going to make any sense I

150:07

think it's probably misunderstanding me

150:09

too check the routine first there is a

150:12

routine called you know maker school I

150:14

don't know let me actually double check

150:16

this what this thing called maker school

150:17

daily ad batch daily ad batch

150:20

This is what I want you to replicate

150:22

just for our product aden skill.

150:27

Yeah. And then we're just going to turn

150:28

it from, you know, a local flow into a

150:30

routine. Cool. And now it has the full

150:32

spec. And it's just going to continue

150:33

going down the top to bottom. And just

150:35

like we did last time, we're going to

150:36

create a new GitHub. And then did I

150:39

already put a file on my Google Drive?

150:42

Uh, no I do not. No, I don't. So you

150:45

will create one. And voila. If I move my

150:48

fat head and super cool Clairvo hat out

150:50

of the way, you guys can see we now have

150:51

both the product aden daily batch and we

150:53

also have the Maker School daily batch.

150:55

So, next run tomorrow at 5:59 a.m. If I

150:58

jump in here, you guys will see we have

150:59

more or less the exact same layout. You

151:02

know, install the dependencies, run the

151:03

script. All the stuff does not really

151:05

require us. We don't have to be pros at

151:06

programming to know what's going on. Um,

151:08

has some scheduled runs. You can also

151:10

trigger it manually. API web hook. I'll

151:12

cover more about that later when we talk

151:13

about weaving it into a pre-existing

151:15

automation flow. But yeah, it has the

151:16

Google Drive. It also has the clock code

151:18

remote. And then it um has our API key

151:20

again stored inside of the environment

151:21

itself. So that's important. It's tough

151:23

to get stuff in the environment unless

151:25

you know how all of that works. Um I

151:28

don't think they make the environment

151:29

feature as straightforward as it could

151:30

be. But anyway, that's that for both um

151:34

you know, compositing workflows, which

151:35

hopefully you see are pretty

151:36

straightforward if you use a simple ad.

151:38

Um image based workflows, which

151:39

hopefully you guys can also see is

151:40

pretty straightforward if you guys use a

151:42

good image model. Why don't we talk a

151:43

little bit now about video models? Now,

151:45

video models need to be done quite

151:46

differently. If I go back over here and

151:49

then open up something like uh

151:50

Higsfield, which is where the vast

151:52

majority of all video models are

151:54

currently hosted and uh you know people

151:56

sort of use them, you guys could see

151:58

that there is just tremendous tremendous

152:01

um possibility for really high quality

152:03

like adgen. The one issue I would say

152:06

with most of this stuff is there is

152:07

currently no like there's no captions on

152:10

here. There's also no text and the

152:11

reason why is because text just tends to

152:12

perform kind of poorly. Um, also this

152:15

model is like kind of an advanced model.

152:16

It's not technically here yet and so on

152:18

and so forth. Um, it's starting tomorrow

152:20

as of the time of this recording, so I'm

152:22

sure by the time you guys use it, it'll

152:23

be really good. They're offering free C

152:25

dance 2.0 4K, which is kind of neat. Um,

152:28

core things to know before we get into

152:29

this is, you know, like video models,

152:31

just like image models, there's a lot

152:33

more frames, and because there's a lot

152:34

more frames, there's a lot more

152:35

opportunity for it to screw up. And so,

152:37

you just need to understand that what

152:38

you are going to generate is not going

152:39

to be perfect. And so long as you're

152:41

okay with that, that's fine. Um,

152:43

generally speaking, the flows that tend

152:45

to work really well are like kind of UGC

152:47

style, uh, user generated content style,

152:49

uh, either testimonials or product

152:51

reviews or that sort of thing. What I'm

152:53

going to do is I'm going to show you

152:54

guys how to use this particular vendor,

152:55

Higsfield, which is quite a high quality

152:57

vendor. It allows you to basically

152:59

streamline the process of putting in

153:00

like e-commerce style ads. You can uh

153:02

add a product as well as a consistent

153:04

character um to generate like UGC style

153:07

advertisements. And I want you guys to

153:09

know that like you can make this better.

153:10

You could make this better by hard

153:11

coding captions. You could make this

153:13

better by, I don't know, like hard

153:15

coding some sort of visuals and and

153:16

having them pop up on the page. You

153:17

could automate the process of like the

153:19

dubbing and stuff if you wanted to be

153:20

better. You could add noise layers to

153:22

the background to make it more

153:22

realistic. I have a few friends that

153:24

spend several tens of thousands of

153:26

dollars a day right now running like

153:27

totally AI generated ads using the flows

153:30

that I'm going to show you. They just

153:31

touch them up a little bit more. And for

153:33

time purposes, I'm not going to get into

153:34

all of that, but it is very

153:36

straightforward. You literally just talk

153:37

cloud code through what it is that you

153:38

want and then yeah, that's that's more

153:39

or less it. So, let me show you guys

153:42

more or less how how this is going to

153:43

work. Um, yeah, for those of you guys

153:45

that don't know, Higsfield is just like

153:46

a major model aggregator. Basically, it

153:48

just combines a variety of other models

153:50

that you guys could use. So, all the

153:52

video models, all the image models,

153:53

everything that we've already touched at

153:55

uh touched on, as well as even like text

153:57

models and stuff into one place. And it

153:59

kind of makes sense in an environment

154:00

where there's just more models than most

154:02

human beings know what to do with that a

154:04

point of value would actually be in the

154:06

organization and um making available of

154:09

those models to the general public. Like

154:12

think about it, there's like a bajillion

154:13

different models you could choose.

154:14

There's a bajillion different platforms

154:15

you could sign up to. Um you know, in an

154:18

environment where it's just really

154:19

confusing for people to get started,

154:20

what they do is they basically just

154:21

combine all that onto their platform and

154:23

then you just need one API key and then

154:24

it does it all. So this is what I'm

154:26

going to be using. Uh you guys don't

154:27

have to use it, but I really like them.

154:29

It's a cool, you know, set of of people.

154:31

Um it's also just uh yeah, it's a pretty

154:34

straightforward platform. And I think

154:35

Claude Code is really really good at

154:37

driving this puppy um because they have

154:39

like a really simple and easy sort of

154:41

API endpoint structure called a model

154:43

context protocol server. So if you guys

154:45

don't end up using this, then just know

154:47

that you'll probably have to do a little

154:48

bit more work there if you want it to

154:50

work like very similarly to what I'm

154:51

showing you guys. But that's okay,

154:53

right? Um, and you know, hopefully we've

154:55

established now that video costs more

154:56

money than photo. And, uh, I haven't

154:58

made you guys feel like this is super

155:00

unrealistic or anything like that.

155:01

[gasps] Okay, cool. So, what do you do?

155:03

Uh, well, first things first, you got to

155:04

like make an account. So, I'm just going

155:06

to open up an incognito window here and

155:08

then go to higsfield.ai. What you have

155:10

to do is you have to click sign up. They

155:12

give you guys like a bunch of discounts

155:13

and stuff right now. So, I'm just going

155:14

to let's exit out of that. Sign up. I'll

155:16

go continue with Google and I'm just

155:17

going to add my email address. So, I've

155:19

signed in. It's going to say verify that

155:21

you are a human. So, I just did that.

155:23

Keep in mind the interface is going to

155:24

look very very different um by the time

155:26

that you guys are using it probably. But

155:27

anyway, we want to do UGC videos and

155:29

we're maybe beginners so we want

155:30

oneclick presets and I don't know uh

155:32

what do we want to try? Probably MCP and

155:35

then I'll just leave it at that because

155:37

yeah, again this is just like a just an

155:39

onboarding and there's many different

155:40

ways you could do this.

155:43

Okay, cool, cool, cool, cool. So yeah,

155:47

we've now received some big fat offer

155:49

and they'll give us a bunch of money

155:50

off. That's cool. Um I'm just going to

155:52

you know proceed without that account

155:54

understanding that you do have to

155:55

purchase some credits but um yeah anyway

155:58

so what we want is basically we want two

156:00

things. The first thing we want is uh we

156:02

want to generate this locally just so

156:03

you guys understand what is going on

156:05

under the hood all the different

156:06

parameters and stuff like that that you

156:07

could use to tweak in order to generate

156:09

your like high quality UGC. And then

156:11

after that we're going to do this via

156:12

program uh you know claude code via

156:15

what's called MCP model context

156:17

protocol. And this is where Claude can

156:19

actually just like drive this whole

156:20

thing. So we can actually just copy over

156:22

a prompt here and basically say, "Hey,

156:24

install this whole thing." And it'll

156:25

work perfectly fine with no issues at

156:26

all. So yeah, pretty cool. Uh I'm going

156:29

to head over to video first and then I'm

156:30

going to generate something pretty cool.

156:39

You can see I've already done a couple

156:40

of different gens here before. Um you

156:42

know, one of the gens that I did was uh

156:45

uh ash falling in a city, which is kind

156:47

of cool.

156:48

show you guys what that looks like. You

156:50

can see it's slowly sort of going down

156:52

to the ground. That was for a website

156:54

that I made. This is sort of like a cool

156:56

wheel that's zooming in and out.

156:59

That's kind of neat, right? It's like a

157:00

like an old church style theme. I've

157:02

done a couple of other pretty badass

157:04

ones, too. Here's a bunch of like people

157:06

charging in like a more medieval

157:07

battleground. You'll notice that some

157:09

things are kind of weird, right? Like

157:10

why are the horses going backwards?

157:12

Well, it's because this model isn't

157:13

perfect. And uh you know every now and

157:15

then one of the gens will be sort of

157:17

trash. So you just got to keep that in

157:18

mind. Uh anyway, so what do we want to

157:20

do? We basically want to create a video

157:22

using some media over here, some user

157:25

generated content stuff um of somebody

157:27

talking about a product. And I think

157:30

what a lot of people don't understand is

157:31

that you can't just do this in one shot

157:33

and expect it to work really well. What

157:35

you need to do is you actually need to

157:36

like vary it and have multiple different

157:38

shots. And so what I'm going to do is

157:39

I'm going to feed it an image of a

157:41

product and then I'm also going to feed

157:43

an image of like an influencer and then

157:44

I'm going to get the influencer to like

157:45

talk about the product. But if you think

157:47

about that that's like that's actually

157:48

multiple steps. Um so what I'm going to

157:50

do first is I'm going to go actually not

157:51

over to video but to image and this

157:53

allows you to generate things using um

157:56

image models. So as you can see that

157:58

midnight clay thing that I generated is

157:59

right over here. So, I'm actually going

158:01

to reference this in an image flow. And

158:04

then I'm actually going to go and I'm

158:06

going to find some like UGC style

158:08

influencer on Pinterest. So, I don't

158:10

know. I'm just going to go like, as you

158:12

can see, there's a lot of like UGC style

158:14

influencers here talking about products

158:15

and stuff. Um, I don't know. Let's use

158:18

this one. You know, obviously most of

158:21

the time, and I I don't want to stress

158:23

this too much, but most of the time it's

158:24

like a woman that is demoing a product

158:27

of some kind. And that's just because

158:29

mathematically they found that when they

158:31

have women demo products, they do a lot

158:33

better. So a lot of these UGC's are

158:34

going to be um you know images and

158:36

videos and stuff like that of women. But

158:38

anyway, I'm just going to feed in this

158:40

woman. And then what I want is I

158:41

actually want to go to uh what do we

158:42

have? GBT image 2. I don't know. I think

158:45

we had GBT image somewhere here. So let

158:47

me find it. There you go. 4K images. And

158:49

uh we have a bunch of settings here. We

158:50

can go high, best visual fidelity, 2K,

158:52

1K, 4K, auto size, which is important.

158:55

So this is going to depend on how we how

158:57

we're going to generate our videos. For

158:59

the purposes of simplicity, I'm just

159:00

going to go one one and assume that

159:01

we're generating everything one. And

159:03

then batch size. Batch size is just the

159:04

total number of gens that I am going to

159:06

create here with these two images. And

159:08

so what I want to do first is I want to

159:10

generate a woman that's kind of like

159:11

this woman. Okay. Actually, so let me

159:13

just go back. Generate a woman that

159:15

contains all of the features that this

159:17

woman has, but is ultimately different.

159:20

Um I don't know, change her hair a

159:21

little bit. Uh and then instead of

159:23

outdoors, put her indoors. Uh the reason

159:25

for that is I'm going to use her in like

159:26

a product shoot later.

159:30

So I want to generate I don't know three

159:32

options which is going to cost me 21

159:33

credits. And the workflow is you know

159:35

we're just going to take this woman who

159:36

is I don't know if she's real. Maybe

159:38

she's already an AI generated image or

159:40

something. She might be. But we're going

159:41

to take her and then we're going to

159:43

stick her in a fake environment that

159:45

I've made and it's going to be indoors

159:46

and we're going to use this woman who is

159:48

now fake. It's not like a real woman to

159:50

advertise our products by turning that

159:52

into a video. So that's sort of the

159:53

three or fourstep flow. And uh yeah, I

159:56

always like doing this manually at least

159:57

once because it's important for you guys

159:58

to understand how it works manually

160:00

before you guys move to fully automating

160:01

it with cloud code. If you guys don't

160:02

know how to do the process manually, I

160:04

find a lot of the time you're just

160:05

misunderstanding the the different

160:07

features and the different options that

160:08

are available. Uh I generated this in

160:10

2K. You could just generate this in 1K,

160:12

which is just 1024 pixels. Uh, but I

160:14

find that, you know, if we want really

160:15

high quality input images to be used for

160:18

the purposes of video, it's better to,

160:20

uh, you know, do this with like a high

160:22

2K. Um, yeah, and then, you know,

160:25

there's a bunch of different models you

160:26

could choose here, too. There's

160:27

Higsfield Soul, Higsfield Soul Cinema,

160:29

Cream 5.0 Pro. This one's actually

160:30

pretty good. Um, they're also offering,

160:32

at least as the time of this recording,

160:33

a bunch of free usage. So, that's pretty

160:36

neat. Uh, it's just like an advertising

160:38

play where they're trying to, you know,

160:39

make as much money as humanly possible

160:40

by incentivizing models that are last

160:42

gen, pretty cheap inference-wise to run.

160:44

And it's kind of like a Costco chicken.

160:46

You come in through the door, buy

160:47

yourself a Costco chicken, and then

160:48

you're like, well, I'm at Costco, I

160:49

might as well buy myself some tomatoes.

160:51

Yeah, that's their whole idea. This is

160:53

not occurring instantly because it is

160:55

quite computationally intensive. We are

160:57

using GBT image 2 and generating, you

160:58

know, 20 48 pixel x 20848 pixel images.

161:01

So, I'm just going to circle back when

161:02

this is done. Okay. And so, we now have

161:04

a bunch. Uh, I like probably this one

161:06

the best. I think it's like it's a

161:08

little different from the original,

161:09

right? It's a little different. I mean,

161:11

like, yeah, the lips are a little bit

161:12

different. She's wearing the same

161:14

outfit. So, I'm looking at this one more

161:16

and like this is obviously an AI image

161:18

as well. So, we are just putting AI on

161:19

AI on AI.

161:22

But, uh, yeah, this is fine. So, I'm

161:23

just going to download this now. Well,

161:25

actually, you don't even need to

161:26

download it to be honest. Uh, and then

161:28

what I want to do is I want to use this

161:29

as a reference. So, I'm going to

161:32

reference this here. And then over here

161:34

is going to be my generated woman. Okay.

161:37

And then what I want is um make it so

161:40

that the woman is holding my product. Uh

161:42

maybe in like a bathroom or something

161:44

like that where it would make sense to

161:45

put on a midnight clay mask. She should

161:47

be enthusiastic about using the product.

161:49

Uh but don't like oversell it.

161:52

The reason why last time we generated

161:54

three is just because I wanted to

161:55

provide myself a lot of um and I'm just

161:58

going to do this 2K again is because I

162:01

wanted to give myself a bunch of

162:02

different options just to make sure that

162:03

like you know we had diversity, but I'm

162:05

finding that I don't really need that

162:06

diversity. It does a pretty good job.

162:07

So, I just one-shoted it and I'm going

162:09

to see how it looks now when she's

162:10

holding the element. Um yeah, and this

162:12

is like a little bit different of an

162:14

influencer than this chick. You can see

162:15

like the features are slightly

162:16

different. Very very slightly different.

162:18

obviously hyper hyper exaggerated and

162:21

stuff like that. Uh but yeah, we'll

162:22

we'll we'll check out when see what the

162:24

gen looks like. While we're waiting for

162:26

that, I'm actually just going to go back

162:27

to Claude. I'll go to new and then I

162:30

will give it um a picture of this

162:33

product and then I'll have it generate

162:35

me a script for my VSSL. So, just

162:38

scrolling down here to uh this midnight

162:41

clay mask. Okay, actually I don't think

162:42

I have the midnight clay mask set up

162:44

yet. So, let me go and grab that.

162:48

We're actually going to go here,

162:49

download this. So now we've downloaded

162:51

it. Go back to Claude Code. Going to go

162:54

to Finder. And then this midnight clay

162:57

mask is jumping in here. This is a

163:00

product that I am about to record a UGC

163:03

style ad for. I want you to generate me

163:05

three scripts similar to what a real

163:09

influencer would say talking about this

163:11

product. Pretend the influencer is a

163:12

young woman that's found a lot of value

163:14

in our midnight clay mask. and then make

163:16

it somewhere between 30 seconds or so.

163:19

Okay, so now uh it's going to read the

163:21

image and then generate me a bunch of

163:23

different options.

163:26

Okay, so I need to talk about this cuz

163:27

I've been sitting on it for like 2 weeks

163:28

and I'm mad I didn't try it sooner. This

163:29

is the Midnight Clay Overnight Mask and

163:31

I have combo skin that's oily in my

163:32

T-zone but flaky everywhere else. So

163:34

most clay masks either dry me out or

163:36

don't do anything. This one's different

163:38

cuz you can literally sleep in it. I put

163:40

it on like a thin layer before bed, wake

163:41

up, rinse it off, my skin just looks

163:43

calm, like less redness, my pores look

163:45

smaller, and it's not stripped at all.

163:47

So, I mean, like, this is, you know,

163:49

it's not really all that good. Yeah, I

163:50

think I'm just going to like edit it

163:52

slightly. So, I need to talk about this

163:54

cuz I've been sitting on it for like 2

163:55

weeks and I'm mad I didn't try it

163:56

sooner. This is the midnight clay

163:58

overnight mask. I have combo skin that's

163:59

oily in my T-zone but flake everywhere

164:01

else. So, most clay zones either dry me

164:03

out or don't do anything. But, this one

164:05

changed the game for me. I can literally

164:07

sleep in it. Or maybe you can literally

164:09

sleep in it. You just put on a thin

164:11

layer before bed like this. Then wake up

164:14

and rinse it off. Your skin just looks

164:17

really calm. Way less redness. My pores

164:19

look smaller and it's not stripped at

164:21

all. This is going to be my little

164:22

script for the video. Uh let's go back

164:24

and see how the gens are looking. Cool.

164:27

So, she's now holding this, you know, in

164:29

a in a bathroom supposedly. So, that's

164:31

nice. I'm now going to turn it into a

164:32

video just like this. Okay. All right.

164:34

Okay. And what I want to do, there are a

164:36

variety of different models you could

164:37

choose for this. Uh, let's see. I think

164:41

I'm probably going to do the Well, we

164:43

can actually just go test a bunch of

164:45

this to be honest. Uh, I'm going to try

164:46

C dance 2.0 fast, but yeah, I'm just I'm

164:49

going to test a bunch. So, this is

164:53

a woman doing, let's say, a woman doing

164:57

a UGC

164:59

style. Let's say a woman influencer

165:02

doing a UGC style ad talking about a

165:05

product. Okay. And I think just for

165:07

starters, I'm probably going to make it

165:10

really small. And there's also a bunch

165:12

of elements that you can reuse. So, what

165:13

I'm going to do is I'm just going to

165:14

upload that media from before. The

165:18

reason why is because um if you don't

165:21

upload media, sometimes it will like not

165:24

actually keep the midnight clay mask in

165:27

the right place. So, I'm just going to

165:28

add this to my element. Here it is.

165:30

Midnight clay mask. This is a clay mask

165:33

used for product videography. Okay, so

165:36

now it now it actually has access to the

165:37

midnight clay mask for the video. So,

165:39

we're just going to use this as our

165:40

prop. It's actually added to the prompt

165:43

box. Uh, and then cool. I mean, I I see

165:45

cling here. I'm just going to try a

165:47

bunch of different models. We'll see

165:48

which ones work best. Okay. And now what

165:49

I'm going to do is I'm going to turn on

165:51

sound and then I'm just going to

165:53

generate multiple. So, I'm going to do,

165:54

you know, generate 10 with cling. What

165:56

I'm going to do here just so I can test

165:58

out a couple of different approaches. So

165:59

I'll do the same thing with Cling Turbo

166:01

and then I'm going to do the same thing

166:02

with Cance 2.0. Okay. And after a little

166:05

bit of playing around, uh, we now have a

166:06

couple of different ads. So I'm going to

166:07

turn this on. Hopefully you guys can

166:08

hear this.

166:09

>> Okay. So I need to talk about this

166:10

because I've been sitting on it for like

166:11

2 weeks and I'm mad I didn't try it

166:12

sooner. This is the midnight. I have

166:14

combo.

166:15

>> So as you guys can see, we generated a

166:16

pretty short one here and I did that

166:18

just as a test. I wanted to make sure

166:19

that we could actually hear, you know,

166:20

audio and it didn't seem super crazy.

166:22

Um, but suffice to say, you can actually

166:24

do Yeah, you can do a fair amount with

166:26

this. assuming the woman's moving her

166:27

mouth. She's like promoing the product.

166:28

There's even a little zoom in which is

166:29

kind of neat. Um, so I'm going to try

166:31

this on a variety of different models

166:32

and just see what the best one is, at

166:33

least as the time of this recording. Uh,

166:35

you can expect to spend maybe like $10

166:37

to $20 doing this initial test round.

166:39

The whole idea is you just want like a

166:40

reusable pipeline, right?

166:42

>> Okay, so I need to talk about this cuz

166:43

I've been sitting on it for like 2 weeks

166:45

and I'm mad I didn't try it sooner. This

166:46

is the Midnight Clay overnight mask. I

166:48

have combo skin that's oily and

166:50

>> Okay, so this is good. I mean, I like

166:52

what we what we had here. This is

166:53

probably a little bit better than Cling

166:55

3.0 Turbo. The issue is obviously the

166:57

time here. Um, you know, that's just not

166:59

enough time to actually do the ad. So, I

167:00

think what I'm probably going to do is

167:02

I'm just going to max it out at like 15

167:03

seconds. What was the Gemini Omni Flash

167:06

good for? I think it's only 10 seconds.

167:08

Yeah, it's only 10 seconds. So,

167:09

basically, we need to like we need to

167:10

make this a little bit shorter. Maybe

167:12

instead it'll just be like you need to

167:16

try this. This is the Midnight Clay

167:17

Overnight Mask. It fixes oily skin

167:21

without flakes. 10 out of 10. would

167:24

recommend change the game for me. Sure.

167:26

I don't know. Something like that. And

167:28

looks like I'm doing 16 by9 which is

167:29

kind of unfortunate, but I'm just going

167:30

to do a couple of these now. Um, and

167:33

then see if we can actually make it

167:34

through the entire script. Then once we

167:36

have that, we'll have the rule which we

167:37

can use to generate using Gemini Omni

167:39

Flash. All right. So, while that's

167:40

working, let's scaffold out the skill

167:42

that'll be used to generate these on

167:44

demand. If you think about it, the idea

167:45

is I'm going to feed it in a product.

167:47

That's going to be like my initial step.

167:49

It's then going to, I don't know, use

167:51

some influencer that I've already sort

167:53

of chosen um to represent that product.

167:56

And then all we really need to do is we

167:58

just need to come up with a bunch of,

167:59

you know, scripts. And those scripts

168:00

need to be pretty short. Um, you know,

168:02

probably somewhere around, I don't know,

168:03

like 150, 200 characters. So, just

168:05

checking back on, you know, the gens

168:07

here. It looks like

168:10

>> you need to try this. This is the

168:11

Midnight Clay Overnight Mask. It fixes

168:14

oily skin without flakes. 1010 would

168:16

recommend. Change the game for me.

168:19

Okay. And this is just a little bit too

168:20

long. You need to try this. This is

168:21

>> She's looking all like, but I don't

168:24

really need to just wait three hours.

168:25

>> You need to try this. This is the

168:27

Midnight Clay Overnight Mask. It fixes

168:29

oily skin without flakes. 1010 would

168:32

recommend. Change the game for me.

168:34

>> Okay, so that one's much better. I think

168:36

we could probably just rip this ad over

168:38

and over and over again. Um, so yeah,

168:40

this is this is good. I think what we

168:42

need to do is we just need to time this.

168:44

So, let's check out word counter and

168:46

then figure out exactly how many words

168:47

are here. [sighs and gasps] Looks like

168:49

it's about 146 characters. I think we

168:51

could probably go to like 170

168:53

characters. So, I think that's what I'll

168:55

do. I'll say 170 character. Uh, well,

168:57

why don't we just do words instead? 27

168:58

to Yeah, 27 to maybe like 30 words or

169:01

so. So, I'm going to go back to Claude

169:03

and I'm just going to voice dump all of

169:05

this in as the process.

169:08

Hey, I'm going to give you an image of a

169:10

product. I then want you to use the

169:13

provided image of the influencer to

169:15

generate a picture or a starting frame

169:17

of a woman holding the product uh

169:20

somewhere in an environment that it

169:21

would make sense. Then I want you to

169:23

generate 10 script candidates. A script

169:27

candidate is a script that espouses the

169:30

virtues of the product, talks about the

169:32

benefits in an influencer UGC style

169:34

fashion. Uh, the script needs to be

169:36

under 35 words or so in order to fit our

169:40

time limit. After you're done, I want

169:43

you to show me the uh, generated product

169:46

image with GPT- image-2. And then I also

169:50

want you to show me the scripts so that

169:52

I can verify everything's good. After

169:54

that, I'm going to add a couple more

169:55

steps to the flow and we'll turn that

169:56

into a skill.

169:59

Cool. So, that is what we're going to

170:02

do. Clearly, we verified that this works

170:03

if we do it manually. So, let's make

170:05

this work um digitally as well. Here's

170:08

the influencer and here's the product.

170:10

I'm going to feed both of these in.

170:11

Voila.

170:13

And you can call GPT image 2 using open

170:17

AI API key. It's in the local

170:20

environment. Okay.

170:23

Uh and then going to circle back and

170:24

just double check that now that we can,

170:26

you know, we verified we can do this

170:27

through like manual usage, we need to

170:30

verify that we can also do this through

170:32

claude. And then we we're just going to

170:33

scale the hell out of this thing. I'm

170:35

sure you guys can imagine we could take

170:37

this further. Um, as this is building

170:38

it, uh, just some brainstorming out

170:40

loud. Rather than just using one woman,

170:43

you could have, let's say, 20 or 30

170:44

people, men, women alike, people from

170:46

different geographies, different ages,

170:48

different ICPS, different like personas

170:50

all all over the place. You would do

170:52

this by using AI to pre-generate a bunch

170:55

of images of or videos of influencers

170:57

that you like. uh and then you would

170:59

also kind of multiply or cross that by

171:02

all the different products that they're

171:03

holding in a bunch of different

171:04

locations. So now what we're doing is

171:06

we're just multiplying uh a bunch of

171:07

different options. you then multiply

171:09

those by a bunch of different scripts.

171:10

And in that way, what you could do and

171:12

what you know I I I like thinking about

171:14

AI as what you could do is um you could

171:18

basically choose let's say 10

171:20

influencers, okay? Multiply them by I

171:23

don't know 10 settings, multiply them by

171:26

10 different scripts. Uh the resulting

171:29

thing is you'll have 1,000 possible

171:31

options.

171:33

Now, it kind of sucks to look and watch

171:34

through each, right? So, what you could

171:36

also do, and I'm just brainstorming. I'm

171:38

not going to do anything as complicated

171:39

as this because I don't want to kind of

171:41

put the cart before the horse. What you

171:42

could do is you could have some sort of

171:43

automated image check where every single

171:46

one of these videos, which are all 10

171:48

seconds or so, you break down into 1

171:50

second frames. So, now it's like 1

171:51

second frame, 1 second frame, 1 second

171:53

frame. You take screenshots of each. You

171:55

feed that into an AI model like, you

171:57

know, GPT uh or Claude Code. Have Claude

172:01

Code take a look at the image and tell

172:02

you, is there anything malformed that

172:04

doesn't make sense? If so, just delete

172:06

it from that problem set or from the

172:08

option set entirely. After this process

172:10

is done, maybe you have like I don't

172:11

know, let's say 250 to 500 that are

172:14

remaining and each of these are 10

172:16

seconds. So, what you can do is you can

172:17

have a human being watch them all.

172:19

That's 2500 to 5,000 seconds of watch

172:23

time. And then, you know, if you do the

172:25

math on two 2,500 seconds, I think

172:27

that's like 40 minutes to, I don't know,

172:30

80 minutes. If you have somebody that

172:32

then chooses the top, let's say 20

172:34

performers of those in the 40 to 80

172:36

minutes, you've effectively replaced an

172:38

entire creative department for, you

172:40

know, a thousand times the cost of

172:41

generating this, which is not trivial,

172:43

right? Uh, but now you have literally 20

172:45

new pieces of creative that can make it

172:46

to the next step. So, I say all this

172:48

just to show you guys, uh, this is

172:50

extraordinarily possible right now. It's

172:52

very economical and, you know, you can

172:53

then test 20 with some small little ad

172:55

budget, figure out what makes the most

172:57

sense to scale. So, just going to take a

173:00

look at this. Yeah, this is her now

173:02

holding the product, which is more or

173:03

less exactly what I wanted. Cool.

173:04

Midnight clay overnight face mask. Fully

173:06

kind of comprehensible and digestible.

173:08

Everything here is good. What we want to

173:10

do now is we want to take all this and

173:12

then just like we did with Higsfield

173:13

earlier, uh we just want to have that

173:16

generated through uh I think like an

173:18

initial start frame uh which we did

173:20

primarily using Gemini Omni Flash. So

173:23

the question is how do we you know take

173:25

uh Gemini Omni Flash and then actually

173:27

make it kind of accessible within Cloud

173:30

Code. Well, it involves the MCP and CLI

173:33

feature that we did before. So if I go

173:35

to MCP and CLI, you'll see here we have

173:36

Hixfield MCP and CLI for an AI. I'm just

173:38

going to go to cloud code. Then what I'm

173:40

going to do is I'm going to copy this

173:42

and then go back to my cloud code

173:43

instance here. And then I'm just going

173:45

to paste this in. Okay. And so I think I

173:47

actually already have, you know, some

173:49

form of Higsfield MCP on. So that's

173:53

okay. I'm just going to say run it. What

173:54

it's going to do now is install the

173:56

Higsfield CLI globally.

173:58

And what you have to do after you

174:00

install a command line interface or a

174:02

CLI and make it accessible to cloud

174:04

codes. You have to log in. And that's

174:05

just how we're going to authenticate. If

174:07

you think about it, previously we did so

174:08

in kind of an inefficient way. We had to

174:10

get the API key from Google uh from GPT

174:13

uh image 2 directly, right? And then we

174:14

had to like add that to our environment.

174:16

Well, this is just a simpler and easier

174:17

way of adding stuff to our environment.

174:19

So, anytime there is like a CLI option,

174:21

I would highly recommend it. CLI options

174:23

are awesome. They also save you just a

174:24

tremendous amount of time. And uh, you

174:26

know, it's a very easy and

174:27

straightforward way of doing this. And

174:29

so, one thing you guys don't see is it

174:31

just asked me to sign into Higsfield

174:32

right over here. So, here's where I'm

174:34

going to do the sign in. I'm just going

174:35

to go click um, you know, sign in and

174:37

then go from there. Now, it's saying,

174:39

you know, hey, we want to access this.

174:40

Is this authorized? Yes, it's

174:42

authorized. I can actually close this

174:43

out. And now it's saying authenticated.

174:45

Now, the skills can all install. So now

174:47

we've authenticated inside of Higsfield

174:49

and we have everything that we need in

174:50

order to actually use it. Um so yeah,

174:52

this is fantastic. Now we can actually

174:54

run this. So what is the next step after

174:56

this? Well, the next step is we're just

174:58

going to cross them. So what I'm going

174:59

to do, okay, is I'm just going to voice

175:01

transcribe.

175:02

I want you now to take the provided

175:05

image, the hero frame, and then I want

175:08

you to use this as a start frame for the

175:12

Gemini Omni Flash model. Set it to

175:15

10second total gen on a 16x9 aspect

175:19

ratio. The prompt I want you to provide

175:22

is as follows. Eg. A woman influencer

175:26

talking about a product. Ensure the

175:29

provided scripts are reasonably short so

175:31

we can get them in the whole 10-second

175:33

gen. Um, every time that I ask you to do

175:37

this, I'd like you to generate three

175:38

candidate options. And for those of you

175:40

guys that are probably wondering what's

175:41

going on here with the voice

175:42

transcription tool, I like using

175:44

Claude's built-in voice transcription

175:45

tool, but um I also have my own. If I

175:48

just hold Fn on my computer, it'll pop

175:50

up and it allows me to paste text into

175:52

any box. So I keep on trying to use the

175:54

Claude one because I'm trying to

175:55

showcase Claude code features, but I

175:57

also just oftent times forget and then

175:59

use my own. Um same deal. So it's going

176:02

to go through this whole process and

176:03

it's actually going to select things for

176:04

me. It's going to build them and so on

176:05

and so forth. Um, you know, it's double

176:08

checking with me that, you know, I'm

176:09

okay with getting build for this.

176:10

Obviously, I am okay getting build with

176:12

this. The first time I think it'll

176:14

probably realistically take uh a little

176:16

bit more, you know, trial than you'll

176:18

think. It'll take more credits. It'll

176:20

just not be necessarily perfect. But

176:22

over time, you'll be able to make this

176:24

cheaper. And then once you consolidate

176:25

it as a skill, uh, you can go. So, I'm

176:28

going to say yes. And now it'll actually

176:29

do the generation. And yeah, I mean,

176:31

like check out these scripts. You need

176:33

to try this. This is the midnight clay

176:35

mask. Fixes oily skin without any

176:36

flakes. 10 out of 10. Would recommend a

176:38

change of game for me. Okay, I have to

176:39

talk about this. Midnight Clay Overnight

176:41

Mask. You put it on before bed. You wake

176:42

up glowing. No effort, just results.

176:45

Genuinely obsessed with this thing. Like

176:46

for instance, I don't like that tone of

176:47

voice, but we could tweak it. [gasps]

176:49

Uh, if you have oily skin, you need

176:50

this. Midnight clay works overnight. So,

176:52

you wake up matte, not greasy. It's the

176:54

best skincare purchase I've ever made

176:55

all year. Hands down. I could absolutely

176:57

see, you know, some woman talk about it

176:59

on like a UGC UGC style video at Crush.

177:03

And what's cool is um you can

177:04

parallelize these. So what we've done

177:05

here is we've actually sent three jobs

177:07

simultaneously. So we have job A in the

177:09

queue at the same time as job B in the

177:11

queue and job C. And we're just going to

177:13

grab all three of these once they're all

177:15

done and then combine them back into the

177:17

output. Uh it's then going to present

177:19

the output to me just to verify that

177:20

like this did what I wanted it to do.

177:22

And once I have verified that it has

177:23

done what I wanted to do, I'm going to

177:25

turn this into a skill. Anyway, we now

177:27

have the jobs. So we have the videos

177:29

right over here.

177:30

>> You need to try this. This is the

177:31

midnight clay mask. Fixes oily skin

177:33

without any flakes. 10 out of 10. Would

177:36

recommend. 10 out of 10. Would

177:37

recommend. It changed the game for me.

177:39

>> Okay, so we could see we had some weird

177:41

things there cuz uh the script was a lot

177:43

shorter than the 10-second video length

177:45

that tried to like come up with stuff.

177:46

>> Okay, I have to talk about this midnight

177:48

clay overnight mask. Put it on before

177:50

bed. Wake up glowing. No effort, just

177:53

results. Genuinely obsessed with this

177:55

thing.

177:56

>> Cool. So, that looks pretty solid. If

177:58

you have oily skin, you need this.

178:00

Midnight clay works overnight so you

178:02

wake up matte, not greasy. Best skinare

178:05

purchase I have made.

178:06

>> Okay, so see this is the sort of stuff

178:07

that I'm talking about. That's like kind

178:08

of weird. So we just went from having

178:10

the product like physically like what

178:12

happened there?

178:13

>> So you wake up matte, not greasy. Best

178:16

>> it just disappeared like

178:17

>> in purchase

178:18

>> vanishes. So I mean like this is the

178:20

sort of thing you don't want to happen

178:21

which is why you're going to need some

178:22

sort of Q&A. Um that said, I mean people

178:24

still rip it, right? Like it's not that

178:26

crazy. Um, maybe she's just a a magician

178:28

that can summon the midnight clay mask.

178:30

>> Your hands

178:32

>> like I'm more interested, girl, in how

178:33

you summon the midnight clay mask to

178:35

your hands. Anyway, um, cool. So, now we

178:38

we we basically have the skill, right?

178:40

So, we're pretty good. Um,

178:44

this is fantastic. We now have

178:46

everything that we need in order to

178:47

create a skill. Um, only one tiny

178:50

change. I'm finding the script for the

178:52

first video was just a little bit too

178:54

short. So, the woman had to repeat

178:56

herself a couple of times. Uh, we need

178:58

to

179:00

make shorter scripts just a tiny bit

179:03

shorter on the video side as well. Eg.

179:06

candidate A could have been done in 8

179:08

seconds instead of 10. This will also

179:10

make us a lot more credit efficient as

179:12

well. Oh, and then once I'm done with

179:13

that, I'm going to say, let's now turn

179:15

this into a repeatable skill. I should

179:18

be able to simply say, "Generate me uh

179:22

videos of this using the AI video

179:26

generator product ad skill." And then

179:28

you should be able to take the products

179:30

that I supply you, the influencer that I

179:32

supply you, and then turn that into a

179:34

set. Okay. And now it's just going to

179:35

reconfirm the product pipeline with me.

179:37

So what is the pipeline? We're going to

179:38

take product images and influencer

179:39

images as input. Prep resize each.

179:42

Generate hero frames. Generate 10 script

179:44

candidates. Show the hero frames and

179:46

scripts for approval before spending

179:47

video credits on confirmation. Generate

179:49

three video candidates per approved

179:51

script via this model using the hero

179:53

frame um 16x9 8second duration not 10.

179:58

And then download and deliver all video

179:59

candidates. So it's not actually

180:01

correct. Uh we don't want it to uh we

180:04

don't actually care about this step. So

180:06

we'll say no step five self select the

180:11

winner scripts no fixed 8second

180:15

duration.

180:16

Sometimes

180:18

if the uh script is a little longer use

180:22

10 seconds if it's a little shorter use

180:25

8 seconds. And then if I supply multiple

180:28

influencers in a folder plus multiple

180:32

products cross them. eg have influencer

180:36

A on both product A and product B and

180:41

influencer B on both product A and

180:44

product B. Then generate them

180:49

autonomously and deliver to me in a

180:52

folder.

180:54

No human oversight until the last step

180:58

and parallelize where possible. Okay.

181:02

So, what I'm now going to do is I'm just

181:03

going to make a folder here. And I'm

181:05

just going to call this um I don't know,

181:08

video add gens video add assets. Okay.

181:12

And then we're just going to combine

181:13

both of these in here for now. And then

181:15

the duration rule. What's the actual

181:16

word count threshold? So, less than 22

181:19

words, 8 seconds. More than 22 words, 10

181:21

seconds. Script A was 28 words and

181:24

worked fine at tech 10 seconds, but felt

181:25

short. Okay. So, as part of me now

181:28

testing the skill on multiple gens, um

181:30

we have two products. We have the woman

181:32

with the midnight clay, but we also have

181:34

a man with uh what looks to be some

181:36

rosemary scalp oil. So, the idea is

181:38

we're going to cross them. Um she's

181:39

going to do this and she's also going to

181:41

do this and then he's going to do this

181:42

and he's also going to do this. This

181:44

should allow us to test what the kind of

181:46

cross functionality is like and whether

181:47

or not that works. So, I'm just going to

181:49

feed this into the skill that we just

181:50

generated and make sure that this is

181:52

fine, that we have the skill. It's a

181:53

simple product uh byproduct of just

181:55

checking to see, hey, you know, can you

181:56

do this on the video add assets folder

182:00

and it's just going to look at each

182:01

image and classify them. I could

182:02

probably actually have created a

182:03

subfolder structure that does this. So

182:06

yeah, we'll give that a try and then

182:07

have it run. It looks like it

182:09

misunderstood me the first time. It

182:10

wanted to generate nine videos per

182:12

person instead of three, which would

182:14

have been like,00

182:16

credits or something like that. So we've

182:18

uh cut that down to 360. And it's

182:20

actually going to be less than 360. I

182:21

think it's probably going to be like 300

182:23

cuz we're going to do a bunch at 8

182:24

seconds instead of 10 seconds. So, we'll

182:26

see how all that goes. But, yeah, we

182:27

have woman and mask, woman and scalp,

182:29

man and mask, man and scalp. And we're

182:31

going to do three of each of these hero

182:33

frames. It's now just going to run this

182:34

through top to bottom. Um, and then also

182:36

generate uh the scripts, too. Sorry

182:39

about that. My fire alarm just went off.

182:40

That was pretty fun. Anyway, where we

182:42

at? Woman x mask. You need this.

182:44

Midnight clay pulls out all the gunk out

182:46

overnight. No effort. I just wake up

182:47

glowing. Generally, the best thing in my

182:49

routine right now. When in scalp oil, my

182:51

hair stylist asked what I've been doing

182:53

differently. It's this rosemary and mint

182:54

scalp oil. A few drops for a bed and my

182:56

scalp actually feels alive again. So,

182:58

this is kind of neat. You can see we're

182:59

actually implementing that logic where

183:00

the shorter word ones are 8 seconds, the

183:02

longer word ones are 10. So, yeah, we're

183:04

we're going through and generating. It

183:06

looks like there was some issues with

183:08

some sort of concurrency limit. Looks

183:10

like you can only have eight jobs at the

183:12

same time, whereas it is firing 12 at

183:15

once cuz we did four pairings times

183:16

three candidates. So, that's okay. We're

183:18

just going to kind of update the skill

183:20

to reflect that. And it looks like

183:22

that's what it is doing. Um, candidates

183:25

within a script should submit and wait

183:26

in parallel for speed capped at three

183:28

concurrent. So, we're already six done.

183:31

We're just going to retry the two failed

183:33

man pairings one at a time to avoid the

183:35

concurrency cap, which is kind of neat.

183:37

[gasps] Um, I'm just going to take a

183:38

look at this right now. Cool. We

183:40

actually have the videos up here now

183:41

that I could test. So,

183:43

>> you need this midnight clay pulls all

183:45

the gunk out overnight. No effort. just

183:47

wake up glowing.

183:48

>> Cool. Thank you.

183:50

>> You need this. Midnight clay pulls all

183:52

the gunk out.

183:53

>> And see how we're testing different

183:54

angles. I should move my fat head

183:56

around. See how we're testing out

183:58

different angles now? Uh we have

183:59

different like product shots. You know,

184:00

this time we're actually showing this.

184:03

Her hair looks a little bit different.

184:04

She's actually putting this on in this

184:06

one. This is the sort of creativity that

184:08

we want.

184:08

>> You need this. Midnight clay pulls all

184:10

the gunk out.

184:11

>> Cool. And I like how we're having a lot

184:12

more adherence to the thing. Uh I think

184:14

Do we have anyone with men? My hair

184:16

stylist asked My hair stylist asked what

184:18

I've been. It's this rosemary and mint

184:20

scalp oil. A few drops before bed and my

184:23

scalp actually feels alive again.

184:25

>> That's cool. Yeah. Yeah. And the timing

184:27

is basically perfect.

184:28

>> My hair stylist asked what I've been

184:30

doing different.

184:31

>> Okay. And so some of these are just

184:32

going to sound weird, kind of more

184:33

staccatoy, but that's okay. We're just

184:35

frontloading again the the gens. Um so

184:37

I'm just going to go and find the ones

184:39

of the dudes. Looks like we got them

184:41

right over here.

184:41

>> I was skeptical about clay masks, but

184:43

this one's different. Midnight clay

184:44

works overnight. There is no mess. No

184:47

mess at all. And I wake

184:49

>> No mess. No mess at all.

184:50

>> Wake up looking like I

184:51

>> Anyway, obviously that

184:52

>> I was skeptical about clay masks, but

184:54

this one's different. Midnight clay

184:55

works overnight. No mess. And I wake up

184:57

looking like I actually slept well and

185:00

startardy. I was

185:02

>> don't know what the hell that was. You

185:03

know what's really interesting? I think

185:04

there's just less training data out

185:06

there on men. Um and so as a result, the

185:08

the male ads tend to always

185:09

>> I was skeptical about clay

185:10

>> kind of suck more. This audio is really

185:12

good

185:12

>> masks, but this one's different.

185:14

Midnight clay works overnight, no mess,

185:17

and I wake up looking like I actually

185:19

slept well.

185:21

>> That's cool. Yeah, I like this one the

185:22

best. So, you can see how, you know, we

185:23

need to generate multiple, otherwise

185:25

we're not going to get like good good uh

185:27

shots. And who knows, maybe this works

185:29

better. Maybe having some handsome dude,

185:30

you know, show off the clay mask is

185:32

going to work better for a specific core

185:33

audience. You never really know, right?

185:35

Anyway, so what we have now, we actually

185:37

have a reproducible skill uh that does

185:39

this. And uh as mentioned, you know, I

185:41

think you could realistically run this,

185:42

I don't know how many times it was. I

185:44

don't know what our math was, but a lot.

185:46

Uh you could have run this realistically

185:48

like every morning, let's say, on

185:50

something like 10 gens, and then by the

185:52

end of the month, you would have

185:53

significantly alleviated the bottleneck

185:55

in a lot of ad uh production flows. And

185:58

yeah, you know, well, you can experiment

185:59

with making this better. You could have

186:00

like some slight audio track in the

186:02

background. You could make it a little

186:03

bit noisy. You could add like grain to

186:04

the videos to maybe remove some of the

186:06

more characteristic AI components. Like

186:08

I'd say his skin is just a tiny bit too

186:10

smooth. It's almost like a little too

186:12

real. Um yeah, you could have like

186:14

automated processes that check to see,

186:16

hey, you know, is the text totally

186:18

legible on the midnight clay mask? Hey,

186:20

anything weird going on with the person?

186:21

What's the start frame here? Right? Like

186:23

cuz it looks like for whatever reason it

186:24

screws up the start frame on this one.

186:26

Maybe it's like, oh, every video cut in

186:29

like three frames so that we always

186:30

start at a good one. Or I don't know,

186:32

like if a person doesn't have anything

186:34

to say, just kind of smile and like

186:35

wave, right?

186:37

>> [sighs and gasps]

186:37

>> I'll leave it there because I think you

186:38

guys probably know where to go. Um, all

186:40

I'm going to do now is do the same

186:42

thing. Transform it from a loop uh from

186:43

a skill into a loop and then from a loop

186:45

into a routine. Exact same idea just

186:46

with the Higsfield connector instead.

186:48

And um yeah, then when we're done uh I

186:51

will have successfully walked you guys

186:52

through the process end to end of

186:54

generating a very in-depth series of

186:57

prompts, skills, loops, and routines.

186:59

Okay. Now, in order to connect HGfield,

187:01

what you have to do is go up here to MCP

187:03

and CLI, copy this URL. Okay. Then go

187:07

back to Claude. What we'll do is we'll

187:09

scroll all the way down to connectors.

187:11

Under add, we'll go add custom

187:12

connector. Add this as the URL and call

187:15

it Higsfield. And then you will now

187:18

essentially automatically connect to

187:20

Higsfield via their OOTH flow. That just

187:22

means it's going to open up a little

187:24

window, which it just did in my other

187:26

screen. So I'm just going to bring that

187:27

puppy over here. Then you can actually

187:29

proceed with the login. And now this is

187:31

just another version of what we did

187:32

earlier. It's just now we actually have

187:33

the ability to connect to this through

187:36

um our cloud routines as well. Okay,

187:38

great. So now that we have this uh if we

187:40

go back to routines and then at the top

187:44

right hand corner go new routine can

187:47

then go cloud. And what's cool is we now

187:49

have the ability to connect our

187:50

Higsfield connector. It'll actually

187:51

automatically populate um every time we

187:53

do this. And so then I can go down here

187:55

to our prompt and then I can just ask it

187:57

hey make this a routine. Awesome. And

187:58

now I just told it make me the routine.

188:00

What it's doing is creating a tople

188:02

Google Drive folder for me. This is

188:04

where all of this information is going

188:05

to be stored. Now, it's actually

188:06

uploading the raw source images. Um, the

188:09

ones that I was showing you guys earlier

188:10

of the men and the woman and then the

188:12

clay mask and then the rosemary oil. So,

188:14

what it's doing is it's going to upload

188:16

via some tool. Uh, this is I guess some

188:19

base 64 representation of my file. I

188:21

don't know. You can turn images into

188:23

like big long strings and then you can

188:25

send them over over the wire. Um, and

188:27

that's what this tried to do. It didn't

188:29

do it a very good job though. Uh, and

188:31

now it's actually downsizing the images,

188:33

sending them over, and then it's going

188:34

to, you know, base 64 encode, upload

188:36

each. All right. And now we have

188:38

generated both the uh, loop and the

188:40

routine. And so you guys could see that

188:42

um, right over here. If we go back to

188:45

routines, we have UGC video ads daily

188:47

batch. So we can connect, you know, the

188:50

platforms that we need via custom

188:52

connectors right over here. We also have

188:53

cloud code remote, which I think is just

188:55

what Cloud Code always inserts now if

188:56

you want to be able to contact it. Um,

188:58

and you can see the step-by-step file.

189:01

So, list the files in the asset drive

189:02

folder. Uh, for simplicity, that is this

189:05

folder right here. So, you can see that

189:06

like they're images that we are

189:08

providing of the rosemary oil of the

189:09

product and the people. So, maybe we

189:11

could make it easier by subdividing

189:12

that. I don't know. Determine which

189:14

assets are new since the last run. So,

189:15

it actually checks to see is there a

189:16

dated uh subfolder that exists within

189:19

the AI video gens in drive. So, it's not

189:21

this folder, it's another folder. If

189:23

there is no dated subfolder, it'll

189:24

actually go through and then build the

189:27

cross of every influencer times product.

189:29

So two influencers, two products means

189:30

four pairings. For each pairing, it'll

189:32

generate a hero frame, write 10 adcript

189:35

candidates, select the best ones,

189:38

determine the video duration by word

189:39

count using a rule, generate three video

189:42

candidates for the pairing, then

189:43

actually insert all of that into the

189:45

Google Drive link. So this is how you

189:48

develop a small granular application in

189:50

your company using cloud code. Uh this

189:53

is going to run for cents on the dollar

189:55

every day. The credits that we're

189:56

spending on Higsfield gens are going to

189:58

cost more than that obviously. Um but

190:00

that's okay. Uh you know this is still

190:02

way cheaper than actually having like a

190:03

production studio go find influencers to

190:05

do this with, right? And uh yeah you

190:07

know you guys now have essentially a

190:09

full endto-end pipeline allowing you

190:10

guys to do this for whatever product you

190:12

guys want. So that is a end to end

190:16

step-by-step walkthrough of how to

190:18

basically go through that full like

190:19

prompt to routine process for you know

190:22

creative ad generation. I want you guys

190:25

to know probably looking at the time and

190:27

you're like looking at the skills that

190:29

we still have to build and and so on. Um

190:31

I decided to go really in depth on that

190:32

first one cuz I wanted you guys to see

190:34

everything involved in the actual

190:35

production process. Future ones are

190:37

going to be a lot faster because now

190:38

that you guys understand how to go from

190:39

a prompt to a skill, a skill to a loop,

190:41

and a loop to a routine, we don't

190:42

actually need to do this whole process

190:44

every single time. Instead, what I'll do

190:46

is I'll math it out, make it a skill,

190:48

and then just automate the process of

190:49

turning it into a routine, just using a

190:51

little prompt template. I'm also going

190:53

to just proceed a little bit faster, and

190:54

maybe explain things a little bit less,

190:56

and just show you guys what the process

190:57

of building it looks like. Um, just so

190:59

that you know, you guys aren't sitting

191:00

here forever waiting for an agent to

191:02

finish. Uh, and now that you guys

191:03

understand how kind of realistic the

191:05

weight times are and stuff like that, I

191:06

think we'll all be able to proceed a

191:08

little bit faster. Hopefully you guys

191:09

have appreciated this so far. Let's move

191:11

on to our next major step. What is that

191:13

next major step, you might be asking?

191:15

Well, now that we're done with creative

191:16

generation, which is that top offunnel

191:18

sort of step, I want to work our way

191:21

down just one more substep before we get

191:23

to middleunnel with the generation of

191:25

personalized copy. So, text, templates,

191:28

and really outreach. And uh I'm going to

191:30

show this to you guys in the context of

191:32

two major you know assets. The first is

191:34

going to be like a newsletter campaign

191:35

where we're using AI to automat automate

191:37

the process of filling out variables.

191:39

And the second is going to be like an

191:40

outbound email campaign where we're

191:42

using AI to automate the process of

191:44

entering in little icebreaker snippets

191:47

and like custom assets and stuff like

191:48

that. So all of this is in the pursuit

191:50

of making copy sound like you wrote it

191:53

yourself even though you probably

191:55

didn't. And so if you think about it,

191:58

okay, these newsletters that we're

192:00

usually sending out now for um flash

192:03

sales and, you know, multi-step sort of

192:06

long-term campaigns, nurturing sequences

192:08

and whatnot. These are pretty great from

192:10

a marketing perspective, of course, but

192:11

they suffer from the major problem that

192:14

usually the only real personalization

192:15

data you have is you have like a first

192:17

name. So it's like, hey, Nick, sometimes

192:20

it's not even hey Nick, sometimes just

192:21

like hello. um you know we're running a

192:24

last second flash sale from this time to

192:26

that time and you know you need to sign

192:28

up now to get 20% off [gasps] and this

192:31

works okay but what if I told you you

192:32

could immediately make all of these

192:33

campaigns work like 5% better right off

192:35

the bat just through some very basic AI

192:37

personalization obviously you'd probably

192:39

do it right the cost of the tokens used

192:41

to you know personalize these emails is

192:43

way lower than whatever you're currently

192:45

missing by not doing so so uh yeah you

192:48

know I'm going to show you guys how to

192:49

do that and I'm going to show you guys

192:50

that as an example on my own data which

192:52

I collect through Maker School and Maker

192:54

Zero, which is my free community where I

192:56

host all my resources. So, I have a big

192:58

newsletter and sort of like mailing list

192:59

set up for those people. And I'm going

193:00

to show you guys how to automate that

193:02

just in the context of my own business.

193:04

The second big thing is cold outreach.

193:06

So, you know, instead of somebody

193:08

signing up and entering a bunch of

193:09

information into some form, right, to

193:11

sign up to your newsletter. With cold

193:12

outreach, typically what happens is

193:13

you'll scrape databases, you'll scrape

193:15

places like LinkedIn Sales Navigator and

193:16

so on and so forth. And in doing so,

193:18

you'll get a bunch of information on uh

193:22

you know, people that they may or may

193:24

not have actually given you and that you

193:25

may have just like stolen from the

193:26

internet. And so, this is a good example

193:28

of that. Um here's a big list of people

193:30

with email addresses that I'm not going

193:32

to leak. They're on the right hand side.

193:33

But I've basically gone through and I've

193:35

and I've scraped, you know, about a

193:36

hundred of these. And all of them have

193:37

email addresses, all the first names,

193:39

last names, and everything. And there's

193:41

a really important column right over

193:42

here. And the column that I want to

193:44

point out is this company description

193:46

column. You see how it says Waltz is an

193:48

e-commerce company that helps brands

193:49

grow revenue on Amazon. Advertwise is a

193:51

digital marketing agency which empowers

193:53

local businesses to do blank. Should you

193:54

hire an SEM agency or an in-house

193:56

person? What if you could get the best

193:57

of both? Lean SEM offers full-time SEM

194:00

managers. ROI works, Net Booster, Flexi

194:02

Webb, and so on and so forth. These are

194:04

all PPC companies that I scraped really

194:06

simply and easily. And imagine if you

194:09

will a moment that you wanted to reach

194:11

out to these businesses at scale with

194:13

personalized outreach. You didn't just

194:15

want to say, "Hello, I would like to

194:16

sell you something." Instead, you wanted

194:18

to say like, "Hey, Walt, I love Walt go

194:20

and I've been following you guys for the

194:21

better part of the last like couple of

194:23

months. I uh think I could probably

194:25

solve a major problem for you. Here is

194:27

the problem that I can solve and here's

194:29

how I would solve it. Let me know if

194:30

there's any interest in your end. I'd be

194:31

happy to do it, you know, upfront for

194:33

the first 30 days, no charge." Which one

194:35

do you think would do better? Obviously,

194:37

the latter, right? So, AI affords you

194:40

the ability to do that. And sorry, my

194:42

camera just died. But um yeah, I'll I'll

194:44

loop back around after I've spent some

194:46

time charging this thing. And that's

194:47

that's what I want to do here. I just

194:48

want to show you guys how to essentially

194:50

use AI both to customize not only, you

194:52

know, outbound, um not only inbound, but

194:55

both of them as well, because they're

194:56

both very powerful levers. Right? So,

194:58

let's start with the very first thing

195:00

we'll be doing today, which is

195:01

automating newsletter customization. I'm

195:04

going to be using a platform called Kit

195:06

for this, which is a very

195:07

straightforward email newsletter

195:09

platform that, as you guys can see, a

195:10

lot of cool people use. What's going on,

195:12

Ali? Um, essentially what this does is

195:16

the same thing that most other email

195:18

newsletter platforms do. Just allows you

195:20

to, you know, add your subscribers, get

195:22

a bunch of information from them. You

195:24

know, first name, email address, last

195:26

name, why they signed up, all this

195:27

stuff, and then send blasts and email

195:30

campaigns. So, yes, that's right. I am

195:33

among Matthew McConna, Ali Abdal, and

195:35

the rest of the greats. Uh, it's the

195:37

platform I'm going to be using today.

195:38

I'm not affiliated with them at all.

195:39

Just um, you know, it's one of the many

195:41

you guys could use obviously for

195:42

marketing. So, yeah, I'm signed in right

195:45

over here and I don't want to scroll

195:46

down anymore because if I do, you guys

195:48

will be able to see actual live people's

195:49

email addresses. Um, so the goal is

195:52

obviously I don't want to show you guys

195:53

that, but um, it's this is a real

195:55

account and we're going to be doing

195:56

something that I'm legitimately doing in

195:57

my business. Just before we dive super

196:00

deep, I just wanted to give you guys

196:01

some context. Um, we have 38,000 almost

196:04

38,100 people on the mail list. We

196:06

started this, I don't know, 6 months ago

196:07

or so. So, we've grown at like an okay

196:09

pace. The way that we currently do our

196:12

email newsletter growth for the most

196:14

part is through LinkedIn and through

196:16

Instagram. On Instagram, I'll publish

196:18

videos that are uh basically like lists

196:21

of tools that you could use. So, like,

196:22

hey, here's a free AI tool that attacks

196:24

your vibecoded app and does all this

196:26

cool crazy stuff. And then up here I

196:28

have a typical many chat flow where I

196:30

say comment code to get this free and

196:32

open source to secure your vibecoded

196:34

app. You then scroll down, you can see a

196:36

bunch of people are commenting things.

196:38

Uh I think we might have changed the

196:39

name in this particular case because

196:41

we're not getting open codes. We're

196:42

getting um I don't know like hands or

196:45

something like that. [gasps] Uh yeah, so

196:47

this you know agent and then what we'll

196:48

do is we'll actually comment just

196:50

message you to the person immediately

196:51

after. And then um we then start a DM

196:53

flow with them where we will give them

196:55

the asset usually in exchange for like

196:57

their email address or something to my

196:58

mailing list. Okay, so that's that's

197:01

kind of the context. And then um we also

197:03

have a bunch of other things that occur.

197:05

Uh you know the second that they sign

197:07

up, what we'll typically do is we'll

197:08

send them an email blast. And the email

197:10

blast is like a Maker School welcome for

197:12

instance, which you know Maker School is

197:14

my product. So uh we'll do like a Maker

197:17

School welcome. A Maker School welcome

197:19

just looks like this. Um, hi. Welcome to

197:21

Maker School. This won't be one of those

197:24

bloated stuffy onboarding emails. Just

197:25

the essentials. First, thanks for

197:27

joining. Make sure to read through our

197:28

onboarding guide. You'll see them in

197:30

Maker School every day. Just the

197:31

classics, right? Sort of like the post

197:34

purchase email that you would see in any

197:35

major big business. Okay, great. And I I

197:38

show all you guys this because I find it

197:40

annoying when I'm watching a course or

197:41

whatever and we're just doing isolated

197:43

cherrypicked tasks that don't really

197:45

kind of weave into a real business. You

197:46

know, this is my real business. We do uh

197:48

I think we did $280,000 selling this

197:50

exact product um last month, which is

197:53

like pretty damn chunky if I do say so

197:55

myself. We did so with an extremely lean

197:56

team. Um and the vast majority of it was

197:58

just through content and marketing like

198:00

I'm showing you guys how to do right now

198:01

with cloud code and stuff like that

198:02

optimizing it. Okay, so what are we

198:05

actually going to do today? Do you guys

198:06

see this email here? Hi-ash, welcome to

198:09

Maker School. Extremely excited to have

198:10

you with us. Is there anything

198:12

personalized or customized about this

198:13

email? No, there's nothing personalized

198:15

or customized about this email. This is

198:16

essentially just a mass email that we

198:18

are sending people. It's the same

198:20

goddamn thing every time. And you know,

198:23

somebody that is at the forefront of the

198:24

AI revolution, um that's kind of

198:25

annoying because with AI, we don't need

198:28

to suffer, you know, crappy templated

198:30

emails anymore. We actually collect a

198:32

fair amount of information about the

198:33

people that we work with. Um this isn't

198:36

uh exactly accurate, but you can imagine

198:38

how if somebody signs up to your mail

198:39

list, you would probably have

198:41

information like this from them. You'll

198:42

have their first name. You obviously

198:44

have their email address. Maybe you'd

198:46

have their company name. Maybe you'd

198:49

have reasons why they signed up. And so

198:51

I just added a bunch of like fake data

198:53

here with fake people. So none of these

198:55

people are real. At least not that I

198:56

know of. If they are, then uh it's a

198:58

bunch of monkeys on a typewriter making

199:00

Shakespeare. But you know, I just wanted

199:03

to give you guys an example of like all

199:04

the data that you that you could collect

199:06

and what you could do with some of that

199:07

data. So, what you could do with some of

199:09

that difference is instead of just

199:11

saying, "Hi, welcome to Maker School,"

199:12

you could obviously go, "Hi, Marcus.

199:13

Welcome to Maker School. That's neat."

199:16

Instead of just saying, you know, you'll

199:17

really like it here, it's like, you

199:19

know, I know you want to grow your

199:21

agency to 20K a month plus. Um, I will

199:24

help you with this through Maker School

199:27

or I don't know, hey Sally.

199:30

um you'll love Maker School specifically

199:33

because especially because you mention

199:36

um tech stuff being a bottleneck right

199:38

now. I will solve that for you, right?

199:40

Like Maker School will deal with that.

199:42

We're like, "Hey, you know, no, you use

199:43

HubSpot. Want to make sure you have XYZ

199:46

cool asset for HubSpot." So this is like

199:48

basic email segmentation, which most

199:50

marketers will know, but then we go one

199:51

level up because rather than just

199:53

inserting the variables verbatim like

199:54

most people will do, we're going to use

199:55

AI to customize the variables so that it

199:57

makes sense in the context of our mail

199:59

campaign.

200:00

>> [sighs]

200:00

>> And so this is essentially what I am

200:02

showing you guys how to do today. I'm

200:03

showing you guys how to use fuzzy

200:04

variables. That is sort of the the term.

200:07

So if I go back to my little draw app

200:09

here, um the whole idea is, you know, if

200:12

you guys use hard variables, it's stuff

200:15

like first name, company, city. These

200:18

are usually copied straight from the

200:19

data. And you know, this is stuff that

200:21

you already see in a lot of like email

200:22

campaigns and stuff like that. That's

200:25

fine, but we don't have to suffer that

200:27

anymore because hard variables are just

200:29

sort of like they're they're like a

200:30

byproduct of just how difficult it is

200:32

logistically to make high quality

200:34

personalized emails to scale. We have a

200:36

solution for that. So what we do is

200:37

we're just going to pass all these hard

200:38

variables through AI basically. And then

200:43

in doing so can actually just use my

200:45

damn pen tool. We're going to pass this

200:47

through AI. And in doing so, it'll turn

200:49

into I mean that probably wouldn't

200:52

actually because it's just first name,

200:53

but it'll turn into a much more enriched

200:54

variable like why their company is

200:56

interesting, right? So rather than like

200:58

taking the verbatim hobbies, Pottery and

201:00

Indie films, it it'll be like, "Hey

201:02

Pete, saw you love snowboarding and

201:03

drone flying. I live uh in X location a

201:06

couple of minutes away from like a

201:07

massive mountain and I go snowboarding

201:09

really often." So warmed my heart to see

201:11

that. Anyway, wanted to talk to you

201:13

about X today. like you can actually do

201:15

stuff like that um at scale and that

201:17

sort of personalization is leverage. So

201:20

yeah, that's what we're going to be

201:20

doing. We're going to be turning all

201:21

these hard variables into fuzzy

201:23

variables and then inserting them in an

201:24

email. Um and I have a couple other kind

201:26

of things I want to show you here um

201:30

that'll help drive home the point of

201:32

value. Okay, so here's what I want my

201:35

email to look like after all is said and

201:37

done. And this is different from how it

201:38

looks right right now. So just bear with

201:40

me. What I want to do first is I'm just

201:43

going to actually call them by their

201:44

name. So instead of just hi, it's going

201:45

to be like hey Nick. Uh second, I'm

201:47

going to remove the n dash because it's

201:48

kind of AIE and I don't want anything

201:50

that that's even remotely AI here. So

201:53

now it'll say hi Nick, welcome to Maker

201:54

School. Extremely excited to have you

201:56

with us, especially because short

201:59

plausible reason why they signed up.

202:03

What this is is a fuzzy variable. You'll

202:07

see that I use what's called uh camel

202:09

case where the first letter is lower,

202:12

right? And then eventually all the other

202:13

ones are high. It's kind of like a camel

202:15

in that the head is low and then the

202:16

there's a bunch of humps underneath

202:18

after that. Kind of interesting. It's a

202:20

programming convention. Um and then I

202:22

typically make them pretty long and

202:24

they're also quite descriptive. So short

202:25

plausible reason why they signed up.

202:27

That's actually for the AI agent. That's

202:28

not for me. Anyway, so extremely excited

202:30

to have you with us, especially because

202:32

I know you struggled with client uh or

202:34

like lead genen in the past or

202:36

especially because you mentioned you

202:37

struggled with lead genen in the past.

202:39

Boom. Now it's like, whoa, whoa, hold on

202:41

a second. What? You guys are reading all

202:43

these customized like whoa. Obviously,

202:45

it's like a it's a strong effect. First,

202:48

this won't be one of those blood bloated

202:49

stuffy onboarding emails, etc. Um, okay.

202:53

It covers what to do first, how to get

202:54

the most out of your group, and our

202:55

event structure. particularly useful in

202:57

your case because of short custom reason

202:59

why it's useful. So what what are we

203:01

doing here? We're doing the same thing.

203:03

We're just providing a short custom

203:04

reason why the onboarding guide will be

203:06

useful to you. Uh what we're going to do

203:09

is we're going to take those variables

203:11

that I just showed you earlier. Okay,

203:12

pretend that this is just all the data

203:14

we're getting from people. You know,

203:14

it's a little much. We're probably not

203:15

actually going to get that much data,

203:16

but I could see you get like why you

203:18

signed up and first name, last name

203:20

probably or maybe biggest challenge, one

203:21

of the two to minimize friction. They're

203:23

probably not going to add like 10

203:24

questions to your thing. And then down

203:26

over here, paraphrase challenge. I know

203:27

a big challenge for you is paraphrase

203:29

challenge. It's like, whoa, damn. Is

203:30

that Nick speaking to me seriously? Hm.

203:32

They got their AI stuff unlock, right?

203:36

So, yeah, this is more or less what

203:37

we're going to be doing. The question

203:37

is, how do you do it? Um, so first

203:40

things first, you know, get an email

203:41

template and then make the email

203:42

template really high quality like this.

203:44

Customize it with variables that you

203:45

could realistically fill based off of

203:47

whatever you are currently collecting as

203:49

part of your email flow. That's sort of

203:50

step one. Okay. But I'm assuming that

203:52

you guys are with me here and we've

203:54

already done this. So what's step two?

203:55

So I'm going to copy this and I'm going

203:57

to paste this up over here at the top of

203:59

a prompt. And then let me read out the

204:01

rest of this prompt to you. I'm not

204:02

going to read out the entire thing, but

204:03

I'm going to read out like a fair chunk

204:04

of it so you see how I'm going to do

204:06

this. First, I need you to personalize a

204:08

Google sheet of leads for an email

204:10

campaign. Here it is. The sheet has six

204:14

The sheet has columns. first name, last

204:16

name, email, company name, job title,

204:17

city, hobbies, why signed up, favorite

204:19

tool, and biggest challenge. This is

204:22

context for an email template that uses

204:24

these merge variables. That's just

204:26

another way to say what this is. Short

204:29

plausible reason why they signed up,

204:31

short custom reason why it's useful, and

204:33

paraphrased challenge. This email is a

204:36

welcome email for Maker School, Nixar's

204:38

AI automation coaching community, which

204:40

is school-based. It teaches people how

204:41

to get their first AI automation agency

204:43

client. Their email's tone is casual,

204:45

direct, no fluff, and first person from

204:47

neck. Task. Do this in one step without

204:50

pausing to ask clarifying questions.

204:52

First, read every row of the source

204:54

Google sheet. Use whatever drive or

204:56

sheets read tool you have to pull the

204:58

full row data. Do not guess or sample.

205:00

Second, for each row individually, write

205:02

three new column values that are custom

205:05

to that specific person, not pulled from

205:07

a shared lookup table or, you know,

205:09

keyed on some sort of repeated thing.

205:11

Even when two rows share the same text,

205:13

the generated copy should read

205:14

differently because you're allowed to

205:16

draw on their other fields to make each

205:17

one feel individually written. Vary

205:19

sentence structure and word choice row

205:21

to row. Avoid falling into a small set

205:23

of repeated template sentences. Then it

205:25

actually talks about the uh variables.

205:27

So short custom reason, short plausible

205:29

reason, paraphrase challenge. Do not

205:32

modify the original sheet. Instead,

205:33

create a new Google sheet. Upload via

205:35

drive. Do not attempt to write directly.

205:37

Title it this. include all original

205:39

columns plus the three new ones appended

205:41

at the end in this exact order. So this

205:43

is highly specific. I don't know if you

205:44

guys could tell but obviously AI helped

205:45

me write this and then before uploading

205:48

validate everything and then give me the

205:50

link to the sheet. Cool. So what I'm

205:52

going to do is I'm just going to give

205:53

this to the model. And if you guys

205:56

remember in a previous module we um

205:58

actually gave AI a Google Drive and so

206:01

it has access to our Google Drive and it

206:03

can do all of that stuff. It does not

206:05

have the ability to um you know create

206:08

new Google Sheets I don't think but what

206:10

it can do is it can use drive and drive

206:12

is you know a way that drive essentially

206:14

allows you to communicate with Google

206:16

sheets. Um so what we can do is we can

206:17

actually like we can spin up a Google

206:19

sheet despite not having access to edit

206:21

a Google sheet if that makes sense.

206:23

Yeah. To make a long story short the

206:24

Google API is highly confusing but um we

206:26

have the ability to make a new one. We

206:29

just can't edit it. And that's fine. So,

206:31

I picked this specific example and I'm

206:33

doing in this specific way so that I

206:34

don't have to walk you guys through

206:36

authorizing Google Sheets, which can

206:38

also be kind of a pain in the ass.

206:40

Anyway, what's kind of going on under

206:41

here? Well, it's writing the full data

206:43

set with handcrafted per row copy. Given

206:45

100 rows and three fields, it's going to

206:47

build this in a Python file as a list of

206:49

dict writing genuinely distinct copy for

206:51

each row using their specific job title,

206:52

city, hobbies, favorite tool, context.

206:55

So, whatever that means, uh, basically

206:57

what it's going to do is it's not going

206:58

to like do 100 sets. let's say um one at

207:02

a time. It's just going to rate like a

207:03

big huge database um just like regular

207:06

text by reading the job title, the city,

207:09

the hobbies, the favor tool, and then

207:11

just like doing it as a as a little

207:12

variable. So, yeah, this is probably

207:14

going to take a couple minutes. I'm not

207:15

going to waste your guys time. So, let

207:17

me cut when it's done. Okay. And it's

207:18

looking like it's now done all of the

207:20

rows that we need. Um just going to

207:23

build and validate the CSV itself. And

207:25

now it's doing a Google Drive create a

207:28

file. So, I want you guys to see this is

207:30

taking a fair amount of time. It's

207:30

taking a fair amount of time because I'm

207:32

doing this in in a session. Um, we

207:33

haven't actually built a skill for this

207:35

yet. What we're doing is we're just

207:36

doing this like the first time. And you

207:38

can imagine the first time would take

207:39

way way longer than any other time.

207:41

Eventually, the idea is uh we would just

207:43

run daily based off of the data that's

207:45

in our kit. And so, you know, people

207:47

fill things out, they're in our kit once

207:49

a day. You know, AI would look through,

207:51

see all the new signups in the kit, you

207:53

know, generate all these new variables

207:55

for them, and then send the send the

207:57

batch blast. That's a much simpler and

207:59

easier way of doing it. And then what's

208:00

cool is we don't actually have to wait

208:01

and do it in a session because it's

208:02

happening via a cloud routine. Um, we'll

208:05

get to that soon. But what's really

208:07

important, and I figure I'll take a

208:08

second just to talk about this, is you

208:11

should verify that you can actually do

208:12

the thing you want to do first. Like

208:14

always, like don't just trust that

208:16

you'll be able to do the thing that you

208:17

want to do. Like actually start at the

208:19

end, do the thing, and then work your

208:20

way backwards and systematize it after.

208:23

I don't know if you guys have noticed,

208:23

but in this course, that's basically

208:25

what we've done every time. We start

208:26

with the prompt, then we do the skill,

208:27

then we do the loop, then we finally do

208:29

the routine. And every time we verify,

208:31

is this thing actually doing what we

208:32

want it to do. And the reason why is so

208:34

that we don't end up in the shitty

208:35

situation, which I used to end up in all

208:37

the time when I started automating

208:38

stuff, where you have this grandiose

208:40

idea about all this amazing stuff you

208:41

want to do and all these great systems

208:43

and then you build them, but then the

208:45

output just isn't very good. [snorts]

208:47

And that's because you didn't start at

208:48

the end. You started at the beginning.

208:50

So, best just to start at the end, work

208:52

your way backwards after you've verified

208:53

that you can get the sort of output that

208:54

you want. like with our ad generator and

208:57

then worry about automating it. If you

208:59

can't get the output that you want, then

209:00

there's actually no point to automate in

209:02

the first place since you're automating

209:03

a process that sucks. So, you can see we

209:04

actually have the Google sheet right

209:06

over here or the the CSV rather not a

209:08

Google sheet. So, Marcus Hayes at marcus

209:10

northparters.com. If we go to short

209:12

plausible reason why they signed up,

209:14

you're trying to push north partners

209:15

past that 20k ceiling and you're betting

209:17

a automation is the lever. Short custom

209:19

reason why it's useful. We spend a lot

209:21

of time in repeatable atband systems,

209:22

which is exactly what fixes lead flow

209:24

that drives up between projects.

209:25

Paraphrase challenge. Marcus' biggest

209:27

challenge is that leads show up in burst

209:28

instead of a steady monthly rhythm.

209:31

Okay. And I think that actually probably

209:32

doesn't work. Blake's biggest challenge.

209:34

Blake's biggest challenge. Um, let me

209:36

just go through and read the thing.

209:39

I know a big challenge for you is

209:41

paraphrase. Yeah, that doesn't actually

209:42

make sense. So, clearly there was

209:43

something lost in translation. I'll have

209:45

to edit that. Um, I would say these are

209:47

okay variables right now. They're not

209:48

the best. They're a little too long. I

209:50

think um realistically we would we would

209:52

have written this like 1 2 3 4 5 6 7 8 9

209:56

10. Probably 10 words or so. So I think

209:58

I'm just going to like double back. Hey,

210:00

these look fantastic. Um we just need to

210:02

make them shorter. So short plausible

210:04

reason why they signed up should be

210:05

closer to 10 words. Short custom reason

210:08

why it's useful should also be closer to

210:10

10 words. And paraphrase challenge

210:12

should be maybe five words or so.

210:14

>> [sighs]

210:14

>> However, in paraphrase challenge,

210:16

instead of saying the person's name, you

210:19

need to write it in such a way that I'll

210:21

be able to insert it into the email copy

210:23

itself. As a reminder, here is the

210:25

actual email section that that text will

210:27

be inserted into.

210:30

I'm just going to go and copy this. Then

210:33

I will go back to pasting this. I'm just

210:35

going to add a slash here and then paste

210:37

it. So now it'll just redo that last gen

210:40

part and make it a lot shorter. So I'll

210:42

circle back when that's done, too.

210:43

What's cool too is this allows you to

210:44

essentially send a single template and

210:47

then turn it into a thousand private

210:49

letters. [sighs] So for instance, hi

210:52

Sarah, some templated stuff up here. Saw

210:55

you finished the cold email module. This

210:56

week's issue builds right on top of

210:57

that. Hi Mike, some templated message

211:00

right over here. I know you mentioned on

211:02

your call you wanted to leave your day

211:04

job by June. This one is for you.

211:07

Imagine if you had quality like that.

211:10

only this text is ultimately what's

211:11

going to change too, which allows you to

211:13

um build the rest of the template out

211:15

such that it's very high converting.

211:16

It's like a high quality offer. And this

211:18

is different from just having AI write

211:20

your entire email, right? AI writing

211:22

your entire email usually sucks. So

211:24

instead of that, what we're doing is

211:26

we're having a human being write the

211:28

entire email using practices that work.

211:30

Then we're just inserting a couple of

211:32

these variables here or there. So this

211:34

is a pretty solid strategy and it

211:35

doesn't really matter where you get your

211:36

data from. Like for instance, um I'm

211:38

going to show you guys how to do this in

211:39

cold outreach in a second. But

211:41

basically, like the only difference

211:42

between a newsletter and then cold

211:43

outreach is, you know, our first step is

211:45

not data collected by a newsletter

211:48

signup form. It's data collected or

211:50

scraped by usually on LinkedIn Sales

211:52

Navigator or an alternative. So,

211:55

wherever way you're getting your data,

211:56

um you guys can do quite a lot with this

211:58

method and uh hopefully you guys will

211:59

see it as valuably as I do. Okay, so

212:02

looking over here at the update. So you

212:05

guys could see now a short plausible

212:06

reason is you're pushing North P

212:08

partners past 20K a month. You want to

212:10

run lead genen for LuminOps that runs

212:12

while you sleep. You and your co-founder

212:13

want a lead genen engine. You're

212:15

launching Harbor Digital's automation

212:16

agency. You got curious how Claude could

212:18

sharpen your cold emails. These are all

212:19

far better. They're also way shorter.

212:21

And typically the fewer AI generated

212:22

words you have in your email as a

212:23

proportion of total words, the lower the

212:25

probability is. Anybody would be like,

212:26

"Wait a second, did AI write that?" Like

212:28

your biggest challenge is keeping up

212:29

with how fast AI changes. That's really

212:31

cool, right? If you consider that in the

212:33

context of our U template, it's like, I

212:37

know a big challenge for you is keeping

212:39

up with how fast AI moves. So, treat

212:41

this as an opportunity to solve it. Work

212:42

hard, put the energy in, and let's just

212:44

see how high you can climb. That's

212:45

pretty cool, right? And I mean,

212:46

obviously that's pretty custom. Okay.

212:49

So, uh yeah, now we are now we actually

212:51

have a prompt that can reproduce this.

212:52

Um what I am going to do is I'm just

212:54

going to um basically upload this list

212:57

and then like pre preview this so that

212:58

we could see, you know, whether or not

212:59

this is what we want it to look like.

213:01

So, I'm just going to go over to

213:03

subscribers and I'm going to hide the

213:05

rest of this from you. Now, in order to

213:06

do that in Kit, you have to add what's

213:08

called a custom field. And I think the

213:10

same might be true for Mailchimp. Um,

213:12

kind of annoying, but anyway, we're

213:14

going to go to the subscribers tab here.

213:16

Okay. Now, I'm going to go and add a

213:17

custom field here. And if you guys think

213:19

about it, we needed how many did we

213:21

need? Okay. And then eventually, you

213:23

will have all of these fields. So, first

213:25

name, just going to be first name. Uh,

213:27

last name. We don't actually have a last

213:28

name, which is unfortunate. Probably

213:29

shouldn't have added that. There's a

213:30

bunch of this stuff down here. What we

213:31

really want is we just want these,

213:33

right? Like we want short plausible

213:34

reason why they signed up. We want a

213:37

short custom reason why it's useful. And

213:38

then paraphrase challenge right here.

213:40

Then I'm going to go next. And you can

213:43

see that we can identify this import

213:44

with a new tag. Sorry for zooming in so

213:46

much. I just don't want this to, you

213:47

know, leak a bunch of personal

213:49

information. Okay. And now what I'm

213:50

going to do is I just want to preview

213:52

this so I can make sure that we're

213:53

actually getting the email. It looks

213:56

like there's just some issue here with

213:58

the variable. Well, it did get the first

213:59

name, but it didn't get any of those

214:00

expanded ones. So, I'm just going to

214:02

double check why. Probably the variable

214:04

syntax is a little bit different. Uh, I

214:06

think if I go here. Yeah. So, short

214:08

plausible reason why they signed up is

214:10

almost certainly different. I think they

214:11

just happen to use a different one. So,

214:13

that's fine. There you go. Looks like

214:14

there's just a space between the two and

214:16

it's uh subscriber and then all of it is

214:19

lowercase. This is short custom reason

214:22

why it's useful. We'll add that in. And

214:25

then this one here is paraphrase

214:27

challenge. Cool. So now if I go preview

214:30

and now if I preview as Marcus at North

214:32

Partnersh you guys could see it says hey

214:35

Marcus welcome to Maker School extremely

214:37

excited to have you with us especially

214:38

because you're pushing North Partners

214:39

past 20K a month with automation. This

214:42

down here is a second one particularly

214:44

useful in your case because we build

214:46

repeatable outbound systems that fix

214:48

inconsistent lead flow. So we should

214:50

remove the of. Then finally I know a big

214:53

challenge for you is getting consistent

214:54

leads every month. So treat this as an

214:56

opportunity to solve it. Boom. So,

214:58

pretty solid. We obviously just have one

214:59

thing we need to fix it because, right,

215:02

short custom reason goes here. Just

215:04

going to exit out of that. And then then

215:07

we'll do one more test just to make sure

215:09

that this does indeed work. Uh, where's

215:11

our newsletter? Rachel at LuminOps. So,

215:13

we'll go back to kit and then I'll go to

215:17

the preview button. Then, always good to

215:19

test these things, right? Especially

215:21

because you want lead genen for Lumops

215:23

that runs while you sleep. particularly

215:25

useful in your case because our

215:25

day-to-day structure turns lessons into

215:27

shipped automations. I know a big

215:29

challenge for you is finding time to

215:30

implement what you learn. Cool. That

215:32

looks fantastic to me. Awesome. So, what

215:35

we've done is we've done sort of the

215:36

proof of concept and we verified that we

215:38

can do this through a prompt. So, we

215:39

have to do now is we have to systematize

215:41

it. How do you systematize it? Well,

215:42

there are like a majillion different

215:43

ways you could. Um, but a simple one

215:45

that I think would make sense is if we

215:47

connect the kit API, okay, so whatever

215:50

uh their API key is in the back end to

215:53

Claude and what the skill does is every

215:55

day, okay, we pointed towards the new

215:57

email subscribers and like the previous,

215:59

I don't know, let's just say like 10

216:01

hours or something like that, 24 hours,

216:02

maybe once a day, uh, 24 hours in a

216:05

loop, I think would make sense. And then

216:07

it goes through and actually enriches.

216:09

So, it adds the custom fields, these why

216:12

they signed up, the biggest challenge,

216:14

the, you know, reasons why it's useful.

216:16

And then it is now in the campaign set

216:19

that publishes that broadcasted message.

216:22

So, I don't know entirely if this is

216:23

possible, but we're going to have Claude

216:25

help us. So, I'm actually just going to

216:27

go back here, close this out, clear this

216:29

out. Okay. Actually, no. Instead of

216:31

clearing this out, I think I'll keep

216:32

this. And then I'm just going to voice

216:34

dump.

216:35

Hey, my goal is now to turn this into a

216:37

skill. However, instead of doing this in

216:40

a Google sheet, I actually want to query

216:42

the kit API directly. Can you go and

216:44

find if there are all the endpoints that

216:45

I would need to do the following? One,

216:48

every 24 hours, check and see all of the

216:50

new subscribers to the mailing list.

216:52

Two, for each subscriber that is new,

216:55

run the query that we just did to create

216:57

those three variables. Then update the

216:59

custom fields. Three, um add them to

217:03

the, you know, welcome maker school

217:05

broadcast. Is this possible?

217:08

So now uh what I wanted to do is just go

217:10

out and do a little bit of research for

217:11

me. So it's come up with a couple of

217:12

ways to get what I asked for done. First

217:15

it'll create a tag called maker school

217:16

welcome pending. It'll then tag new

217:18

subscribers with it via some API call.

217:20

Then it'll update the draft broadcast to

217:22

filter on that tag and then send the set

217:24

the send at to now and then in doing so

217:27

it'll send after it's done it'll just

217:29

remove the maker school welcome pending

217:30

tat. That's kind of neat and I think

217:32

that like prevents double messages and

217:34

stuff like that. So here's what it is

217:35

doing uh every single day generating

217:37

these short fields from the profile

217:38

data, same logic as the sheets exercise

217:40

and so on and so forth. It will require

217:42

my kit API key and you know a bunch of

217:44

other information about it. So I'm just

217:46

going to wait until it's done and then

217:47

give it all that info. So that's fairly

217:49

easy to do. You just go to settings and

217:51

then I think we could probably do this

217:53

via MCP too now that I think about it.

217:55

Okay, never mind. I'm just taking a look

217:56

at this and it's saying that the MCP

217:58

does not actually cover what we need it

218:00

for which is kind of annoying. So, we

218:02

are going to have to do that um you know

218:03

API key thing. Unfortunate. Just one of

218:05

those things we tried. Uh we'll go back

218:07

to the dev tools. And as you guys can

218:09

see, I have an API key here. I'm just

218:10

going to go V4. We're going to call this

218:12

for YouTube. And I'm calling it that

218:14

because I am going to change this

218:15

immediately after uh the video. Okay.

218:17

So, first we need to disconnect the kit

218:19

API. Unfortunate, but you just have to

218:21

make sure that it's not part of your

218:22

flow if you want to use the API key

218:24

route. Then, uh I'm going to go add new

218:26

conversation. Go to local. I can click

218:28

on this little thing and I'll call it

218:29

kit API key equals that. Save it. I'll

218:33

say okay. Added kit. Let's just say

218:38

what did I actually call this thing for

218:40

Christ sake.

218:42

Kit API key. Added kit API key as a

218:45

local credential.

218:47

See if you can use it for something.

218:51

Um we'll see whether or not it actually

218:53

has access to that. So it's going to try

218:54

hitting a harmless readonly kit

218:56

endpoint.

218:57

If it is not in this shell, the

219:00

probability of this occurring uh sorry,

219:02

the situation in which it does not have

219:04

access to these things is probably just

219:07

because it's in the same conversation

219:09

thread as it was before and we just

219:11

added this in technically a new

219:12

conversation thread. And then it will

219:14

say, can you verify if kit API key

219:17

exists? Make a random query using

219:21

that variable. I added it to local

219:24

environment. you won't natively be able

219:27

to see it. So, just make a kit query.

219:31

And now what it's going to do is it's

219:33

going to just try to call the kit API

219:35

key and then um you know it'll

219:38

essentially allow us to verify in our

219:41

new thread whether or not that's fine.

219:42

So now it's testing a query against the

219:44

kit API to confirm that it is valid.

219:46

Cool. There you go. So it just actually

219:48

did so and everything is working fine.

219:49

What I'm going to do now is I'm just

219:50

going to paste our entire conversation

219:52

history from the last one which I can

219:54

find right over here all the way down to

219:57

the bottom. Then go all the way back up

219:59

here and paste that in. And in that way

220:01

I have continued the conversation. I've

220:03

just sort of taken it out of one thread

220:05

and then pumped it into another thread.

220:08

So uh what are we going to do here? Um

220:11

code review first then a readonly

220:12

verification pass. Yeah, sure. That

220:14

sounds good. Okay. And we got it. Um, we

220:16

now have the system that automates the

220:17

process of adding these paraphrase

220:19

challenges, short custom reasons why

220:21

it's useful, and short plausible reasons

220:23

why they signed up to emails. Um, I will

220:25

note that I did add a bunch of fake

220:27

emails in order to do this and I don't

220:29

want to blast a bunch of fake emails cuz

220:30

that is how you hurt your

220:31

deliverability. Uh, but this is now

220:33

actually pulling real data based off

220:35

challenges from my list and then it is

220:37

enriching it in this way. Um, so I have

220:39

that set up right now as a skill, which

220:41

is cool, but obviously I want to go past

220:43

just the skill and then I want to

220:45

eventually turn this into like a fully

220:46

automated system that just fires once a

220:48

day on my command. Um, and so, you know,

220:51

obviously we know how to do that via

220:52

loops because I just showed you guys how

220:53

to do that. Um, you could very easily

220:54

extend what I did via skill that runs

220:56

locally to a loop that runs locally as

220:58

well, just on a schedule. But, uh, now

220:59

we just need to turn it into a routine.

221:01

And if we head over to the routines tab,

221:03

you can see that we now have the routine

221:04

created. Run today's Maker School

221:06

welcome email personalization batch

221:08

against Kit contacts. This finds new

221:10

Maker School subscribers, generates

221:11

three personalized merge fields per

221:13

subscriber, writes them into Kit custom

221:15

fields, and sends the existing welcome

221:16

broadcast to exactly that day's new

221:18

signups. Uh, you have no local file

221:20

system access, no blah blah blah. And

221:22

then it gives me a bunch of information.

221:23

It gives it a bunch of information

221:24

rather about the kit API_key

221:27

which I just provisioned it access to by

221:29

heading over to you know a new routine

221:32

going into the cloud environment and

221:33

then um actually like manually

221:36

provisioning it that access. So you can

221:37

do so just by clicking on cloud and then

221:39

same thing that little like gear icon.

221:41

Hold on a second so I can move my head.

221:43

That little gear icon just to the right

221:45

of that and that'll open up a thing that

221:46

allows you to select your API key. Add

221:48

them in just like we did locally and

221:50

then uh yeah you're you're good to go.

221:52

So yeah, I mean like we we've done it

221:54

we've done this now I think a couple of

221:56

times and most people here are probably

221:57

significantly more familiar with the

221:59

workflow than they were when they

222:00

started. This is how I would recommend

222:02

you build these automated systems. You

222:04

have to hammer it out time and time

222:06

again manually over and over and over

222:07

until you eventually get what you want.

222:09

But when you do get what you want,

222:10

turning it into like a repeatable

222:12

process is quite simple. Start locally,

222:14

eventually you'll have it on the cloud.

222:15

But that's newsletter based. How about

222:17

cold outreachbased? Well, as mentioned,

222:19

the only major difference in the

222:21

pipelines that I built here between uh

222:23

warm newsletter and cold outreach is it

222:25

just changes where we're getting the

222:26

data. So, you know, over here in our

222:28

newsletter example, we got the data

222:29

through just people signing up on our

222:31

website, right? Or, you know, me sending

222:32

them a many chat link and them opting

222:34

into an offer. Well, over here on our

222:37

cold outreachbased flow, which is

222:38

another form of marketing, um what we do

222:40

is we just scrape their data from

222:41

services like LinkedIn Sales Navigator.

222:44

Then, you know, we connect them to a

222:46

bunch of these different services. Vein

222:47

is one of them. Air scale is another

222:48

one. Amplify is another one. You get a

222:49

bunch of raw rows and then cloud will

222:51

just read each row and write the fuzzy

222:53

variables before inserting into some

222:54

sort of templated platform. Big

222:57

difference is the templated platforms

222:59

are just different. Right? So um as

223:01

opposed to me using kit for instance

223:03

today I'm going to use a cold email

223:04

platform called instantly. Okay. And

223:08

this is just the exact same thing that

223:09

kit does. It's just instead of sending

223:11

it to people that have already opted in

223:13

uh to a mailing list and they want to

223:15

get and receive email from me. This is

223:17

for people that have never met me in

223:19

their entire lives and they don't know

223:20

who I am and I'm trying to sell them

223:22

regardless. And so I used to do this way

223:24

back in the day um quite a bit. You

223:25

know, I would go doortodoor and then

223:26

when that eventually failed, I would

223:28

pick up the phone and I would dial and

223:29

then when that would eventually fail, I

223:31

would start sending like cold outreach

223:32

via emails and DMs on X and Instagram

223:35

and all this stuff. And um the degree to

223:38

which you personalized your comps was

223:40

like the number one determining variable

223:42

to how likely people were to reply. You

223:44

know, if I sent a 100 DMs or emails and

223:47

they were totally templated and there

223:48

was nothing personalized. It was

223:50

literally just like, I don't know, even

223:51

like, "Hey, Nick, I have something that

223:53

could help you or something," I would

223:55

get maybe 1 to 2% of people replying.

223:57

But if I said, "Hey, Nick, love your

224:00

last vid, man." Or, "Big fan of XYZ

224:02

business, man. I want to help you with

224:04

this thing, and I think it makes sense

224:06

given what you're currently focusing

224:07

on," I would get like a 5% reply rate.

224:10

And so, that's like 200 to 250% of the

224:12

revenue if you just zoom out. uh because

224:14

every dollar I made was entirely

224:15

contingent on cold outreach. If I got

224:17

2.5 times better at cold outreach, I

224:19

would make 2.5 times the money. And you

224:22

think about that proposition with a lot

224:23

of companies that are doing doortodoor

224:24

cold emails, cold DMs, cold videos,

224:26

whatever. Um by weaving some form of

224:29

personalization into your process, you

224:31

can massively increase the profitability

224:33

of that business. And it is very much an

224:35

R or reachbased activity, too. So that's

224:38

what I'm going to show you guys how to

224:39

do here. Um, to make a long story short,

224:41

you basically start with the same flow.

224:43

You will usually have their name almost

224:45

certainly. Um, we'll usually have some

224:46

information about their company.

224:48

Sometimes we'll have scraped bios and

224:50

posts on social media platforms like

224:52

LinkedIn, but I'll show you guys how to

224:53

do this without it. And then we'll have,

224:55

you know, the icebreer icebreaker

224:56

actually written by. So, I don't know,

224:58

let's say this is some, you know, dental

225:00

firm and there are a bunch of posts that

225:02

uh talk about hiring hygienists or

225:05

something like that and how important it

225:07

is to do X, Y, and Z. Will you hire a

225:09

hygienist? Is that how you say that? Or

225:11

hygienists. Okay. Anyway, um what you

225:13

can do is you can have AI take that

225:14

information and then write a short

225:16

punchy icebreaker that's literally like,

225:17

"Hey, loved your post on hiring

225:18

hygienists." Boom. And then it just goes

225:20

right into the rest of your copy. Or in

225:22

this case with Omar at Peak Reefer, uh

225:24

Peak Roofing. Congrats on the second

225:26

crew. Imagine you see that immediately

225:28

after hiring a second crew and making a

225:30

post about it. You're like, "Hey, this

225:31

person knows who I am. This person's

225:32

actually watched and read my content.

225:34

This isn't totally cold outrage. This is

225:35

somebody that like meaningfully has a

225:36

connection with me." your open rates and

225:38

your reply rates are going to be through

225:39

the roof. Ain't no way you're getting an

225:41

X on that message. Uh I mean obviously

225:44

you still will because you know people

225:46

are quite discerning these days, but

225:48

it'll certainly improve the odds with

225:50

which you see success. My best email

225:53

copy on planet Earth so far has had over

225:55

a 20% reply rate. And uh you know I was

225:58

using a method like this combined with

226:00

conference leads which are leads that

226:02

are going to the same conference as you.

226:04

I had a bunch of information about their

226:05

companies. I was able to turn that into

226:07

a very convincing, very personalized

226:08

flow. And yeah, I had like a 20

226:10

something% reply, a positive reply rate,

226:12

which means like one in four people that

226:13

I emailed would send me back something

226:14

like, "Hey, Nick. Yeah, sure. That

226:16

sounds great, man. Let's chat. Do you

226:17

have any idea how much money I made from

226:19

that list?" It's it's insane. I've also

226:20

had cold email campaigns that are much

226:22

more conservative at a 2 to 4% reply

226:24

rate, and I've still made hundreds of

226:25

thousands of dollars from them. So, it's

226:27

not that you can't at a sufficient scale

226:29

make money from cold email, even if your

226:30

reply rates are kind of poor. It's just

226:32

you have to be smart about how you do

226:33

this sort of fuzzy variable generation.

226:35

Okay. So, yeah, that's what we're going

226:37

to do and it's going to be sweet. So,

226:39

first things first, I'm just going to

226:41

exit out of these kit pages because uh

226:43

Oh, and I should probably resend my API

226:45

key now that I think about it. So, I'll

226:46

do that after. Um, first things first,

226:49

you know, we got to get leads from

226:50

somewhere. A really simple and easy

226:52

place to get leads from is this service

226:53

here on uh a scraping platform called

226:55

Amplify. I use these ones all the time.

226:57

It's Pipeline Labs Leadfinder with

226:59

emails, Apollo, Zoom Info. Basically,

227:02

it's like a big database of leads and

227:03

then you just look for the leads based

227:05

off your filters. So, you'll have

227:06

filters like job title, I don't know,

227:08

I'm looking for founders. Seniority,

227:10

okay, I'm looking for seuite people.

227:12

Email status, you know, needs to be high

227:14

quality or deliverable. Phone status,

227:16

okay, this needs a phone so I can also

227:17

cold call them and so on and so forth.

227:19

And then you end up just paying for um I

227:22

guess a $1.50 to $180 per 1,000 leads in

227:25

order to, you know, actually like

227:26

connect them and talk to them. So,

227:29

pretty cool stuff. I'm not going to walk

227:30

through actually using it. Uh so if I

227:32

say pretty easy, you just try it and

227:33

then you enter in some of these filters

227:35

yourself and then boom, you know, you're

227:37

you're good. Now we're scraping a bunch

227:39

of leads. What I've done here in this

227:40

example lead list is I actually went

227:41

through and I did scrape real leads and

227:43

all of these are well not all of these

227:45

but a lot of these are actual real leads

227:48

and uh you know I just anonymized their

227:50

information so it's not actually real

227:51

people. So what you can expect with a

227:54

scrape like this is you can expect

227:55

something like this. So you'll have

227:56

fields like annual revenue, company

227:58

audience, company city, company country,

227:59

company description, company domain,

228:02

company industry,

228:04

uh LinkedIn URLs. This is like not real

228:06

LinkedIn URLs but fake ones. Company

228:09

name here, company specialties,

228:12

how cool is that? And then I think all

228:14

the way on the right you even get uh

228:16

their location. So their states, full

228:19

names of the person, functions, their

228:22

cities, and even the technologies that

228:24

they use in their company, which is

228:26

pretty wild. [gasps] So imagine, you

228:28

know, that you'd put all of this

228:30

information into a super smart

228:32

personalization sequence. I mean, you

228:34

really think you can't make something

228:35

that'll at least have the person on

228:37

either end raise an eyebrow and be like,

228:38

do I know this guy? Like, of course you

228:40

can. And that's the value in doing this

228:42

for marketing uh at scale. So the flow

228:44

here is going to be really, really

228:46

similar. nothing different whatsoever.

228:48

Um, just head back to this and then I'll

228:51

make a new a new chat. [gasps]

228:54

Um, yeah, very very similar. All we're

228:56

going to do is instead of doing this

228:58

via, you know, some sort of like warm

229:02

list, we're just going to do this via

229:03

cold list. And usually the only thing we

229:06

need in this email to be honest is just

229:08

like a an icebreaker. So, I'm just going

229:11

to whip up a really quick email for you

229:12

guys, showing you guys the sort of thing

229:14

that you can send that can get people to

229:16

click on it. Um, and then we'll kind of

229:18

go from there. And to be clear, this is

229:19

more sales than marketing, but I'm still

229:21

just going to write it out for you guys

229:22

cuz I think it's valuable for you to

229:23

see. So, usually it'll be like, hey,

229:26

first name or maybe yo, first name or

229:28

something like that. Um, saw

229:32

um thing in common.

229:35

I'm thing in common

229:38

too. Okay. So, okay. Email campaign

229:41

here. Uh, yo, Nick, saw you liked

229:45

Labradors. I have one myself and wanted

229:48

to say hi. I run B2B outbound for

229:50

agencies that do 5 mil a year and up. We

229:53

work with Meta and just help them close

229:55

500K in the last 45 days. Real talk,

229:57

I've done my research on you and I think

230:00

Leftclick is fairly close to that

230:01

number. If so, please hear me out. I

230:03

want to guarantee you an additional 20

230:04

book meetings every month with qualified

230:07

uh MSP vendors. It is probably another

230:09

one to two million a year if we play our

230:10

cards right. Would cost you $0 upfront

230:12

and I would do all the work myself. And

230:14

if I didn't hit that number, it would be

230:15

free, no cost whatsoever. And past that,

230:17

I get paid mostly a commission. I can

230:18

guarantee you 20 appointments a month in

230:20

perpetuity. Are you open to this? If so,

230:22

let me know and I can start generating

230:23

them for you right away. It would take

230:24

no more than 30 minutes over the phone.

230:25

As as mentioned, I'm serious and would

230:28

guarantee you results. Thanks, Nick. So,

230:30

is this going to, you know, win me any

230:32

awards? No, but this sort of email is

230:34

actually far better than the current

230:35

state of affairs. Uh maybe by the time

230:37

that you're watching this, you know, the

230:39

trends will have changed again. But

230:40

right now, um like lowercase, very

230:42

casual language tends to do well. And

230:44

the reason for that is because people

230:45

are really used to AI spam where all of

230:48

the wording and terminology and and

230:50

vocabulary in the email is like perfect.

230:52

They're used to like lots of M dashes.

230:54

They're used to perfect grammar. So when

230:55

they see stuff that's like intentionally

230:57

or unintentionally not perfect, they

230:59

start thinking like, okay, this is

231:00

probably a person, you know? Sure, man.

231:02

Maybe they suck at spelling, but it's

231:03

probably a person. And so, they'll

231:05

actually like read through your email

231:06

and not just think it's AI garbage. Uh,

231:08

which is kind of cool. So, believe it or

231:10

not, what they don't know is we actually

231:12

used AI for this. Their thing in common,

231:14

my thing in common is very valuable

231:16

here. So, uh, you know, there's a lot of

231:18

data in this example lead list, right? I

231:20

[snorts] don't know, man. I saw you guys

231:22

use Google Ads or something or like I

231:25

saw you guys have double click or I saw

231:27

you guys are, I don't know, in Chicago,

231:30

right? And like I spent some time in

231:32

Chicago. Like like you could talk

231:33

about whatever the heck you want.

231:34

Obviously, make it relevant. Make it

231:36

real. Just give AI like a big blurb

231:38

about all the things about your life and

231:39

let it do the matching. Uh, but yeah,

231:42

that's that's about it. So, what we do

231:44

is the exact same thing as before. I'm

231:45

just going to copy this and then I'm

231:47

going to dump this in.

231:50

Then I'm going to say [gasps] above is a

231:53

cold email template that I've been

231:55

sending. It's been working quite well.

231:56

However, I want to kick it up a notch

231:58

with AI based variables. Anything in the

232:01

two curly brackets here is an AI based

232:04

variable. And what these fuzzy variables

232:07

do is they naturally weave into the

232:10

context of the email. They don't seem

232:12

like variables. They just seem like real

232:14

human personalized segments. What I want

232:17

to do here is I want to feed you a bunch

232:19

of information about five leads and I

232:21

want to see how well you can weave in uh

232:24

their information into an email like

232:25

this. Okay, you'll be generating these

232:27

fuzzy variables based off of a bunch of

232:29

data basically.

232:32

Okay, so now what I'm going to do is I'm

232:34

going to go and actually grab like a

232:35

bunch of our leads. So [sighs and gasps]

232:38

I don't know, let's say we'll do one

232:40

here.

232:42

Okay, fine. Why don't we do one here?

232:48

Then I'll do one here.

232:51

Then I'll do one here.

232:54

and we'll do another one here. Okay, so

232:57

I'm just going to paste this in. Just

232:58

space this out a bit. Um, here are the

233:02

five leads that I want you to test this

233:04

out on. Um, create five email variants

233:07

so that I can take a look at them and uh

233:08

I will let you know if the quality is

233:10

okay. [gasps] You already know

233:12

everything you need to know about me in

233:13

order to make this happen. Nick Sarif,

233:15

feel free to do a little bit of research

233:16

on me if needed. These are hypothetical

233:18

demo prospects, so no amount of research

233:19

on them will yield any benefit. So,

233:21

don't worry about that. just because

233:23

I've noticed sometimes it does

233:24

additional research on them too. Okay,

233:26

so what are we doing? We are having it

233:29

start doing the search. The leads are

233:30

fine to test the fuzzy variable concept.

233:32

Quick flag. The templates numeric claims

233:35

are the kind of thing that gets outbound

233:36

account suspended and can cross into

233:37

deceptive claims territory if the

233:38

underlying numbers aren't real or

233:39

substantiated. Yes, yes, yes. Of course,

233:41

the idea is you can't just pull numbers

233:43

out of your ass. Like this is just a

233:45

demo, right? But um you need to make

233:47

sure that you actually have case studies

233:49

and and proven uh growth and stuff like

233:51

that. [gasps] Okay. Yo, Luke, saw you're

233:54

running performance marketing out of

233:55

Tampa. I've spent a lot of time in the

233:56

Florida agency scene, too, and wanted to

233:57

say hi. I run B2B Outbound for agencies

233:59

that do run $5 million a year. We work

234:01

with XCO and just helping close 500K in

234:03

the last 45 days. Real talk. I've done

234:05

my research on you and the Haven Group

234:06

is fairly close to that number. If so,

234:08

please hear me out. I want to book you

234:10

an additional blah blah blah blah and

234:12

blah blah blah blah. So, this looks

234:14

pretty solid. Um, let me just see if

234:15

there were any additional variables here

234:17

that I didn't notice. Yeah, qualified

234:20

prospects, right?

234:23

Qualified western brands entering China.

234:25

Qualified fintech wellness brands

234:27

wanting SEO and PPC. Qualified agencies

234:28

looking to automate their PPC

234:30

management. Qualified hiring managers

234:31

looking to build out search and social

234:33

media teams. And qualified SEO and PPC

234:35

clients ready to buy. Cool. So, do you

234:37

see kind of the idea here? Um, we just

234:39

have AI personalize it, but do so in a

234:41

very fuzzy way. And I'm sure you guys

234:43

can imagine if I fed in a big block of

234:45

information about who I was, all of my

234:47

life experiences, hell, my LinkedIn

234:48

profile, a bunch of other stuff, and I

234:50

said, "Here's data that you can use to

234:51

pull similarities between me and the

234:52

person, it would do quite quite well.

234:55

Uh, I'm just not because I don't really

234:57

want to showcase all of that right now."

234:59

And, uh, hopefully it's clear. You know,

235:01

you'd be sending this to people, but you

235:02

wouldn't really be publishing on the

235:04

internet, which I think is a little bit

235:05

different. So, yeah. I mean, um, pretty

235:08

cool, huh? Pretty cool. So, where do we

235:10

go from here? Well, we actually need to

235:11

weave this into, as mentioned, the

235:13

Google sheet itself. So, what I'm going

235:14

to do is I'm going to go back and I'm

235:16

going to say, "Okay, great. This is the

235:18

Google sheet."

235:20

I'm going to paste it in.

235:22

This is exactly what I wanted. However,

235:25

I now need you to actually and actively

235:27

update a Google sheet uh with those

235:30

variables. Add these columns to this

235:33

Google sheet and then customize them for

235:35

every uh row. The idea is I should be

235:38

able to insert those variables directly

235:42

into the email via merge variables and

235:44

have them be 100% readable and

235:47

contextually relevant. It's the exact

235:48

same thing that you just did. I'm just

235:50

giving this to you in a more structured

235:51

format.

235:53

Cool. And now it's going to run through

235:54

a flow very similar to what we were

235:56

doing before with the newsletter um and

235:58

the Google sheet except now we're doing

235:59

it via outbound. Okay. So, it just

236:01

actually wrapped that up and I ended up

236:02

proceeding saying great work on the lead

236:04

enrichment flow. It was quite nice. Uh

236:06

what I want to do now is set up a skill

236:08

where when I provide you a list of leads

236:09

like this, you'll go through and do the

236:10

entire process automatically. Basically,

236:13

input to the skill is a lead list.

236:15

Output of the skill is, you know, I want

236:17

a totally enriched lead list. Enriched

236:19

meaning um you know, a lead list that

236:21

has these four additional columns. So,

236:24

additionally, there were a lot of fields

236:26

in the CSV. I don't know if you guys can

236:27

tell, but like man, are these a lot of

236:29

fields. And I don't really like going

236:30

through all these fields all the time.

236:32

So, in addition, I said we currently

236:34

have a lot of fields in the CSV that

236:35

make it a pain in my butt to upload. So,

236:37

I'd like you to cut it down. The fields

236:38

we need are the four customization

236:40

fields as well as their first name, last

236:42

name, email address, company name,

236:43

company size, and so on. So, um yeah, I

236:46

mean, I sent that out. Uh I did it about

236:48

an hour ago. I was actually recording

236:49

the video and for whatever reason, my

236:51

audio cut off. Um so, I'll just give you

236:53

guys sort of a transcript playbyplay.

236:55

And [snorts] then it ended up asking me

236:57

a bunch of questions like, "Hey, what

236:58

sort of fields do I want? Location

237:00

fields? what's the input format that I

237:01

should do? Should it be a Google Sheets

237:03

link or should it be CSV or pasted text?

237:05

I ended up walking through it all the

237:07

way up until we actually ended up

237:09

getting um you know a trimmed version of

237:11

this which is a lot shorter and a lot

237:13

higher quality. And so this is what that

237:16

trimmed version looks like that now runs

237:18

via skill. First name Marcus, last name

237:21

Hayes, Marcus and North partners. As we

237:22

scroll through to the right, you'll see

237:23

that most of the superfluous fields have

237:24

now been removed and now we just have,

237:27

you know, my thing in common, their

237:28

thing in common, paraphrase, company

237:29

name, prospects, but their specific

237:31

terminology. It's very straightforward.

237:33

Okay. So, you know, we have the skill, I

237:35

guess, is what I'm trying to say. Um,

237:37

but I I wanted to extend it and kind of

237:39

take it a little bit further than just

237:40

that. And the reason why is because I

237:42

don't think it's sufficient for me

237:44

personally just to have like an input

237:46

output where I give it a lead list and

237:47

then, you know, it does all the work on

237:49

the lead list and returns it to me. I'd

237:51

actually like to go further. And I want

237:52

to go so far that in addition to

237:55

generating me the lead list, it actually

237:57

scrapes the leads to begin with. And

237:59

that sounds kind of crazy, but it's

238:01

actually fairly straightforward. If you

238:02

go back over here to Ampify on one of

238:05

the services that I was showing you guys

238:06

earlier, um I'll show you guys can

238:08

actually scrape the leads really easily

238:09

u manually. And so I'm just going to

238:11

pipe this in to the flow that we have so

238:14

that in addition to enriching the leads

238:16

with all this AI customized stuff. It

238:18

actually goes and finds the freaking

238:19

leads for me too. We do this thing

238:21

inside of LeftClick all the time. Uh

238:23

which is my AI automation agency. I

238:24

basically build things with cloud code

238:26

that economize various parts of growth

238:29

mostly marketing based flows. And as you

238:31

can see I just pumped in a brief query

238:33

looking for founders and owners at

238:35

businesses that were like agencies. So

238:37

SEO agencies and so on and so forth. You

238:39

see, we we actually just got a bunch of

238:41

emails. I mean, I just did this. I just

238:43

spent 18 cents and then I got a 100

238:45

freaking email addresses. What the heck

238:46

can I do with that? I can do a lot with

238:48

that, right? Anyway, so to make a long

238:51

story short, um what I want to do is I

238:52

want to have AI do all this for me. It's

238:54

not enough for me to like give it a lead

238:56

list. Um I prefer just to bypass the

238:57

lead list entirely. And so what I did in

239:00

addition to that is um you know, I gave

239:03

it some instructions saying I do all of

239:05

my lead scraping through a platform

239:06

called Ampify. What I want you to do is

239:08

actually steer Apify to scrape the leads

239:10

for me and then perform this enrichment

239:11

flow on the leads so that I can just

239:12

have all of them done in one shot and

239:14

ready for my email campaign. I'll give

239:16

you all the information you need in

239:17

order to connect it with Apify like the

239:18

MCP and the scrapers themselves. And

239:20

then all I want you to do is just bundle

239:22

that into the skill and test it on a

239:23

small subset. Let's do 100 leads to

239:25

start scrapes because I think that's the

239:27

minimum. And then you know I just wanted

239:28

it to enrich 10 of them to tell me um

239:30

whether or not it it actually ended up

239:32

working. And so pretty crazy but you can

239:35

just do this. Um, in my case, the way

239:36

that you do this with Ampify is, uh, I

239:38

think you have to go via API, uh, or

239:41

maybe you have to do, if I go back to

239:43

this actual scraper here, and then I go

239:45

over here to where it says MCP

239:47

configurator, it'll give me a URL. So,

239:49

all you have to do is you actually just

239:50

copy that URL, go back to cloud, then go

239:54

settings under cloud codes, connectors.

239:56

You can then create a new custom

239:58

connector called Appify and paste that

240:00

link in. And so, this will now just log

240:02

into Appify for you. And you can see

240:03

that I actually already ended up doing

240:04

this earlier. It's the same link right

240:06

over here. Okay, so that's about how

240:08

easy it is. It's really not that

240:10

difficult. Um, fairly straightforward as

240:12

long as you have the the MCP folder set

240:14

up. Immediately after it said a

240:16

connected, it can see the MCP tools.

240:18

Now, let me get the inputs for this

240:20

scraper and then it actually steers

240:22

scraper for me. So, I gave it some more

240:25

instructions as to the sorts of roles

240:27

that I look for. I usually look for

240:28

founder, CEO, co-founder, owners,

240:30

co-owners, partners, co-partners. You

240:32

can see I kind of went all over the

240:33

place and I settled on a new idea and my

240:37

new idea um was as follows. You know, I

240:41

was thinking about the best way to

240:42

structure this and I said, you know, how

240:44

do I typically do it? And it's always a

240:46

good idea to kind of think about the way

240:48

that you do it. What I do anytime I'm

240:50

scraping leads for marketing purposes is

240:52

I'll create a bunch of filters. I'll set

240:54

them myself, right? Then I'll scrape a

240:57

list.

241:00

And then what I'll do is I'll check the

241:03

list

241:05

to ensure

241:07

that the leads in the list

241:10

um match my ICP which is you know your

241:13

ideal customer persona and then usually

241:16

if maybe less than 75%

241:20

of the leads don't match my ICP

241:25

you know maybe sorry if less than 75% of

241:28

leads match basically more than 25% of

241:30

leads suck, then I'll just redo it and

241:33

I'll do it over and over and over again.

241:34

And I've realized that this is how I

241:36

work. So what I did is I explained this

241:38

to Claude. And then I said, "What I want

241:40

you to do is scrape a list of 100, then

241:43

go through 20 out of those 100 leads and

241:44

determine how many of them are in my

241:45

ICP. As long as more than 15 out of 20

241:49

are in my ICP, we're good. If it's less

241:51

than 15 out of 20, I want you to go back

241:53

and I want you to redo it." So what we

241:54

did is I basically took this SOP and

241:56

workflow and then I just turned it into

241:58

my skill. Okay? Okay. And you can

241:59

actually see that right here. It got

242:01

through. It asked me some questions

242:02

about this PPC agencies. And then it

242:04

went it went ahead and did it. And so I

242:06

ended up actually running this like I

242:07

think this ran four or five times just

242:09

redoing it over and over and over again.

242:11

The first time was 9 out of 20. I think

242:13

the second time was 9 out of 20. The

242:16

third time was 15 out of 20. And then

242:18

finally the fourth time and it just did

242:20

all this entirely on its own.

242:21

Self-driving was 19 out of 20. So I just

242:23

sat back. I ran the skill once and then

242:24

went and actually scraped a giant list

242:26

of leads for me. In addition to that, I

242:28

went through and enriched a big list of

242:29

leads for me with this. And so now we're

242:31

at the point where it's like, okay, what

242:32

the heck do we do with all of these

242:34

leads? Where do we put them? Um, and

242:36

that's the final step, which I'll show

242:37

you guys how to do right now. And, uh,

242:40

hope to god my dang microphone is

242:41

working this time. Okay. [snorts]

242:43

So, you know, I was talking about an app

242:46

that's used to deal with leads like

242:47

this. And, uh, the one that we like

242:49

using is called Instantly.

242:51

Instantly is basically a tool that

242:54

allows you to send emails unass um for

242:57

various campaigns entirely to a cold

243:00

audience, people that haven't basically

243:01

ever heard of you before. And that's

243:03

different from like the warm audience

243:05

campaigns which uh are typically managed

243:07

via things like you know Mailchimp kit

243:10

and so on and so forth. So, I have a

243:11

bunch of email campaigns set up here for

243:13

Clarvo, which is the startup, the

243:15

namesake of this hat. And uh, you know,

243:17

we're doing legal intake, staffing and

243:19

recruiting, automotive, BDC, and so on

243:20

and so forth. Up here, I set up a demo

243:22

campaign just to show you guys what it

243:24

would look like if we populated this

243:25

sequence. And so, what we have here is

243:27

the same copy that I put together

243:29

manually is now here in the sequence.

243:32

Subject line is first name. I have my

243:34

variables in here. And you're probably

243:36

wondering, okay, where do we go from

243:37

here? Well, it's actually pretty easy.

243:38

you can preview um the the forms or

243:41

rather preview the variables that are

243:42

filled. And so what I did was I imported

243:44

that list that I just generated, okay,

243:46

and enriched. And then I just plugged it

243:48

in here cuz I wanted to see what it

243:49

would look like. So Marcus receives an

243:51

email in his mailbox called Marcus. It

243:54

says, "Saw you're running a full service

243:55

Amazon marketing shop. I spent a lot of

243:58

time around Amazon seller agency circles

243:59

2 and wanted to say hi. Looks like our

244:02

campaign has an extra two. So, I'm

244:04

[clears throat] just going to remove

244:05

that extra two. Preview that and then

244:07

see how that how that goes. And that

244:08

looks pretty good. Maybe I'm going to

244:10

test somebody else. Um, so you're a

244:13

Google badge partner running out of

244:14

downtown Reno. Okay. How about Maya?

244:17

Performance-based SEO for businesses

244:19

with customuilt sites. How about rich

244:21

marketing? Onetrack mind of clients, not

244:23

just clicks. That one's kind of rough.

244:25

That one sounds kind of AI, but you're

244:27

never going to be able to fully escape

244:28

this. saw you're running an SEO SEM shop

244:31

out of uh

244:33

Bissi. I've worked with a few agencies

244:35

operating out of that region too. Wanted

244:36

to say hi. So, we now have this actually

244:38

like in our campaign. And what we could

244:40

do now is we could just send it. And so,

244:41

that's the skill. Now, obviously, we

244:43

could turn this into a loop,

244:45

[clears throat] but it depends on, you

244:46

know, like would you even need it to be

244:48

a loop? The only situation which would

244:50

actually make sense to turn this into a

244:51

loop is if you knew every single day you

244:53

were going to make a new campaign. So,

244:54

every day you needed a bunch of leads

244:55

available. But this is a good example of

244:57

like bottleneck thinking. To be honest,

245:00

I don't actually need this all the time.

245:02

Like I don't need one of my lead lists

245:04

to be ready for me every morning. Um I

245:06

could just do it on demand myself.

245:07

Hopefully you guys see how quick it is.

245:09

So yeah, you can very quickly and easily

245:11

turn it into a routine. Cloud routine.

245:12

You just tell Claude, "Hey man, turn

245:14

this into a routine. I want it to launch

245:15

at 5:59 a.m. every morning. Here's the

245:16

Appify connector. Here's everything

245:18

else." And I did so um you know in that

245:20

previous video uh and you know it works

245:22

well. Routine's right over here. Daily

245:25

Clervo Legion, it's called. Here's this

245:27

uh big block runs every day at 5:59 a.m.

245:30

PDT, [gasps and sighs] but I did that

245:32

more as just like a demonstration that,

245:34

you know, you can turn anything into a

245:35

routine. Not necessarily it makes sense

245:38

for us to turn this one into a routine.

245:41

Okay, so that that is it in a nutshell.

245:43

That's how to go basically from a

245:44

totally manual lead scraping process for

245:47

marketing services to actually

245:48

generating these marketing lists

245:50

entirely autonomously. Um, it's also how

245:52

to go from like standardized newsletters

245:54

using kit.com and then turning into like

245:56

a customized AI fuzzy variable-based

245:59

newsletter. All very straightforward if

246:01

you know what you're doing. And

246:02

hopefully I've shown you guys how easy

246:03

it is to to get out there and do it. And

246:05

that takes us to, if you think about it,

246:07

drum roll please, our next uh massive

246:12

build. So we are now done with our

246:14

personalized copy, which means we are

246:17

done with the top of funnel. Nice job

246:19

everybody. From here we can go down to

246:21

middle of funnel which is speed to lead

246:23

for appointment booking via email, SMS

246:26

and voice. And I just want to outline

246:29

the problem very quickly because I don't

246:31

think enough people appreciate just how

246:33

large of a problem it is. So the problem

246:35

in marketing is a lot of the time you

246:38

spend tons of money on leads.

246:42

We're talking like thousands, tens of

246:44

thousands of dollars. You do so in a

246:47

variety of ways. I mean, one, you buy

246:49

lead list, like I just showed you guys

246:51

earlier, you can do with platforms like

246:52

Ampify, but [clears throat] probably

246:54

what you're doing more more likely is

246:56

you're running ads, okay? And then after

246:59

a certain number of clicks to that ad,

247:01

somebody actually ends up booking a

247:02

meeting with you and and doing some sort

247:03

of intent, right? So, this is like a

247:05

typical setting. It's like a booked

247:06

meeting flow. And uh the issue is you

247:08

spend lots of money on leads, okay?

247:13

And then you only get a small fraction

247:15

of that money back.

247:19

The reason why is because most companies

247:21

that run advertisements of any kind,

247:24

hell, even organic brand-based

247:26

advertisements like I do on my YouTube

247:27

channel, is they do not treat their

247:29

leads with the time, energy, and respect

247:32

that they need in order to, you know,

247:34

make a ton of money with it. I want you

247:37

guys to think about leads, okay, this is

247:39

just some lead as basically being

247:42

directly convertible

247:44

to money. This is money, man. And this

247:48

is me drawing my little, you know, I

247:52

don't know how to do it, but the dude's

247:54

face, Thomas Jefferson or whatever. This

247:56

is money. This is just a giant stack of

247:59

bills. Um, this is a currency that is

248:02

directly convertible to, you know,

248:05

freedom dollars, Benjamins, Jefferson's.

248:09

I'm not American. If it's not clear, I

248:10

don't actually know what y'all call your

248:11

money. Um, but you can convert gleeds to

248:15

money. um if you know how to squeeze out

248:18

every last drop of it. The issue is most

248:20

people are just converting at a very low

248:21

exchange rate right now. So they spend,

248:24

you know, let's say $10,000 on leads a

248:26

month and then they will receive maybe

248:28

$20,000 on their leads a month, which is

248:30

a 2x ros, which is quite poor. Uh means

248:33

that you're spending 50% of every dollar

248:35

you earn basically on the marketing to

248:37

acquire that. It means your cost uh to

248:39

acquire, you know, a lead is about 50%

248:43

of what you make. [gasps] The the

248:44

reality is if you treat your leads

248:46

better, if you respond to them faster,

248:48

if you make them think it's more

248:50

customized and personalized, if you do

248:51

everything in your power to give them

248:52

the best experience possible before they

248:54

pay you, ideally after they pay you,

248:56

too. But that's definitely not

248:58

marketing's job. You can turn that

248:59

single solitary dollar sign into a lot

249:02

of dollar bills, y'all. And so this is

249:04

more or less what um I actually do all

249:07

day. And when I mean all day, I mean,

249:09

you know, like probably like 20 30% of

249:12

my day. Um, I do it so much that I

249:14

actually ended up starting a whole

249:15

business around this with a friend of

249:16

mine, uh, called Clearville. And the

249:18

idea behind this is very similar to what

249:20

I just talked about. You know, you paid

249:22

for the lead. You just paid, I don't

249:24

know, like a $30 cost per meeting or

249:26

something, okay? And if they're in your

249:27

calendar, you spent $30 on them. You

249:29

might as well make as much money out of

249:31

that person as humanly possible, right?

249:33

Just makes sense. So, you know, check in

249:36

with them immediately. Actually call

249:37

them ASAP within a few seconds of them

249:40

filling out your form. send them text

249:41

messages, follow up and squeeze them

249:43

like a damn wet rag. I mean, they gave

249:45

you your information and you bought

249:46

them, you might as well get the most

249:47

value out of them. In our case, the way

249:49

that we do it, and this is just a highle

249:51

explanation cuz again, I want to make

249:53

this contextually relevant for people

249:54

that are watching. um is we will weave

249:57

in flows that call leads immediately.

249:59

And then we'll also build what are

250:00

called dispositions, intelligent

250:02

dispositions, which are outcomes such

250:03

that if we call somebody and maybe they

250:05

don't pick up, we route them in the

250:06

right ways to the right places to

250:08

maximize the probability of them picking

250:10

up in the future. We also do things like

250:11

leave voicemails intelligently. We dial

250:14

them with good phone numbers that

250:15

maximize the possibility and probability

250:17

they'll pick up cuz how many people here

250:18

get random spam calls a day? Like we all

250:20

do now. Um we brand them. uh we you know

250:24

allow call centers uh visible monitoring

250:28

and stuff like that so we could see sort

250:29

of what people are saying what they're

250:31

doing and why it's not working. We

250:33

customized the information on our

250:34

immediate post uh post ad for form fill

250:38

calls such that we actually have like AI

250:40

that can help coach people. We do a lot

250:42

of stuff and um this is how we deliver

250:44

absolutely absurd outcomes for a lot of

250:46

the clients that we work with like a

250:48

home services company that uh I've

250:49

mentioned over and over and over again

250:50

on the channel but they're just probably

250:51

the best example of this. They were

250:53

spending god knows how much money on

250:54

leads before. I don't actually know the

250:56

figure. We took the revenue from 3 mil a

250:58

month to literally $9 million a month

251:01

which is 300% revenue growth simply by

251:03

treating their leads like gold. you

251:05

know, simply by actually calling these

251:07

leads ASAP, calling them consistently

251:09

and repeatedly, and then dialing them

251:11

with like really good dial practices.

251:13

[clears throat] Okay, so this is what a

251:16

speedtole system does really. Okay, and

251:18

it's um affectionately referred to as

251:20

S2L

251:22

in the industry. Those of you guys that

251:24

just want that spelled out, that's speed

251:26

to lead. It allows you to squeeze

251:30

significantly more juice out of leads.

251:33

If before,

251:35

[clears throat] let's say you get a new

251:37

lead, somebody that fills out a form or

251:38

an ad or something, and then I don't

251:40

know, you have an average wait time of,

251:43

let's say, 10 minutes before somebody

251:46

picks up the phone and dials them after

251:48

a speed to lead system. Okay? They'll

251:51

fill out the form and then on average,

251:52

they'll, I don't know, wait maybe like

251:54

30 seconds.

251:57

In doing so, the person that filled out

251:59

the form or the person that signed up to

252:00

your offer, opted in, they'll still be

252:02

in the same mindset that they were when

252:03

they actually did it. The longer that

252:04

they wait, the lower the likelihood is

252:06

that they're actually going to want to

252:07

engage in the offer cuz how many times

252:09

have you filled out a form for something

252:10

you don't actually need, but you just

252:11

kind of felt like you did in the moment,

252:13

right? Like we all do this sort of thing

252:14

all day long. So, yeah, that's um that's

252:17

sort of the idea here. And uh today,

252:20

what I'm going to do specifically with

252:21

you guys is is I'm going to show you a

252:22

simple system. Sorry, somebody's blowing

252:24

up my phone. They do not have branded

252:26

numbers though, so I'm not picking that

252:27

puppy up. Is I'm going to show you guys

252:29

a system that basically you're going to

252:31

have some sort of, you know, event. So,

252:33

I'm going to call this intent

252:35

demonstrated. And this intent can be

252:36

demonstrated in any way, whether it's a

252:38

form fill, whether it's a phone call,

252:39

whether it's an an email to your brand,

252:41

whether it's an SMS. And instead of it

252:43

taking, you know, like 10 minutes, um,

252:47

we're going to cut that down and we're

252:48

actually going to we're actually going

252:49

to get them talking to you within like

252:52

30 seconds. Hell, probably even less.

252:53

probably like 5 seconds. Uh what we're

252:56

going to do is immediately after we get

252:57

the message, okay, we're going to send

252:59

them a text/ SMS.

253:03

We're also going to send them a email.

253:07

Both of these are going to be AI

253:10

customized just like we customized our

253:12

outreach a moment ago based off the

253:14

information in the form fill. The

253:17

information in the form fill is going to

253:19

be stuff like I don't know their name.

253:21

It's going to be stuff like why they

253:23

signed up. It's going to be stuff like

253:24

their challenges, whatever sort of stuff

253:26

you collect on your um opt-ins for ads.

253:29

Then once we have AI do this, then we

253:32

can even extend this. And I don't know

253:33

if I'll get to that part today cuz I

253:34

don't know if that's super important,

253:35

but we can even extend this to some sort

253:37

of like merged call. And there are a

253:39

bunch of services out there that can

253:40

actually assist in the merging of call.

253:42

That's that's what we do over at

253:43

Clarabo. But it but it is more kind of

253:45

intense software that we would have to

253:47

build where you know there's somebody in

253:48

a call center or something and they just

253:50

constantly get merged into calls with

253:52

people that just filled out a form. Uh

253:54

and you know because they're getting

253:55

merged like the second that it happens

253:56

the person on the other end of the line

253:57

doesn't have to wait. Like the second

253:58

they fill out the form 5 seconds past

253:59

they get a phone call. It's like what?

254:01

You know back in the day I lived with uh

254:04

this guy Jonno and we were both running

254:06

kind of like wedding based videography

254:07

based photography based uh companies.

254:09

And I remember one day he he made over

254:12

like $20,000 one month and I was really

254:13

broke at the time, you know, eating rice

254:15

and beans for the most part. So I was

254:16

just like listening through the wall. He

254:17

was my roommate and I was just like,

254:18

"What the hell is going on? How's he

254:19

how's he doing this?" And he had like a

254:21

thing on his website where it would say

254:23

if you fill out this form, I will call

254:25

you within 30 seconds or something like

254:27

that. Maybe it was 60 seconds. And

254:29

people would fill it out and they would

254:30

be like, "Well, obviously he's not

254:31

actually gonna call me in 60 seconds."

254:33

But he had like a proprietary new, this

254:35

was six years agoish at the time,

254:37

version of this. He would text them

254:39

immediately, be like, "Hey, you know,

254:40

just saw you filled out the form. I'm

254:41

going to give you a ring in a second.

254:42

Stay tuned." He'd send them an email.

254:44

Hey, just following up on the text I

254:45

sent you a moment ago. Wanted to make

254:46

sure you had everything. And then he'd

254:47

immediately call them, too, based using

254:49

like an automated uh phone merge thing.

254:51

[gasps and sighs] So, very low hanging

254:52

fruit, right? But you'd just be

254:53

surprised at how many companies don't do

254:55

this. You'd be surprised how many

254:56

companies make $30,000 a month when they

254:57

could be making $100,000 a month or 50k

255:00

instead of like 150 or 200k. And what I

255:02

want to do is I want to show you guys

255:04

how to do that. um you know using uh

255:07

speed to lead. Okay, no more perishable

255:09

leads. Uh we're going to try and reach

255:11

other people in just a few minutes.

255:13

We're definitely not going to wait an

255:14

hour and we're definitely not going to

255:15

wait the next day. Um the amount of

255:17

money that your business makes is

255:19

directly contingent on all of this. So

255:21

it's very important that we we reach

255:23

out. So now that I'm done yapping, how

255:27

the hell are we going to do this? Well,

255:28

as I'm sure you can imagine, we're going

255:30

to use Claude. So what I'm going to do

255:31

is I'm just going to set up like a

255:32

hypothetical form fill. And this

255:34

hypothetical form fill I'm going to

255:35

build using cloud just to show you guys

255:36

that you can also build demos with cloud

255:38

and little artifacts. And we're just

255:40

going to pretend for a moment that that

255:42

uh form fill is like you know an ad

255:44

based form fill. And what do I mean by

255:46

ad- based form fill? I mean, like you

255:47

you literally go on a website like I'm

255:49

just going to type in like I don't know

255:50

like HVAC or something and you see that

255:52

there are uh I don't know people running

255:55

well actually just interestingly enough

255:56

there's like only a couple people that

255:57

are actually running ads here which is

256:00

so so fascinating and they're not even

256:02

running good ads. Okay, why don't I do

256:04

this? Let's just go I don't know like

256:06

air conditioning

256:08

installation hypothetically. Cool. So

256:11

you see we have an ad right here, right?

256:13

Let's say I'm on this page and I'm

256:15

filling out this form. So, we're

256:16

basically going to rebuild the flow that

256:17

would occur immediately after this. And

256:18

these are exactly the sorts of companies

256:20

that a speed to lead flow like crushes

256:21

for, by the way. Like, they tend to be

256:22

home services. They tend to be blue

256:24

collar. They tend to be really boring

256:25

businesses that you never would have

256:26

considered. Uh, but yeah, these guys

256:28

these guys would crush if they had

256:29

something like this. And I guarantee you

256:30

they don't. Maybe I'll fill it out just

256:31

to see how long it takes for them to

256:33

reach out to me. But no, I guess I'd

256:34

have to put my phone number on here, and

256:36

I don't really want to. Okay, so uh

256:39

yeah, let me let me run you guys through

256:40

this. First thing I'm going to do is I'm

256:41

going to go back to Claude. I'm going to

256:42

go new and I'll say, "Hey, I'm creating

256:45

a demo to show people how speed to lead

256:48

works. Essentially, I want to make a

256:51

simple form fill for a hypothetical

256:54

error conditioner installation service.

256:56

Pretend that I'm running an ad for this.

256:58

When I fill out the form, I want to

257:00

receive an email and my email address.

257:02

I'm then going to build a speed to lead

257:03

system that monitors that email address

257:05

and then um, you know, emails and then

257:08

SMSes them immediately after they fill

257:10

out the form.

257:12

So, help me build this HTML artifact,

257:16

whatever works, needs to actually send

257:19

me an email. Yep.

257:25

Okay. Now, in order to actually receive

257:27

these emails, we're going to need to

257:30

um actually fill this puppy out. So, I

257:32

don't know. I'm just going to do some

257:33

password. Can I use the term

257:36

password?

257:38

We'll say, "Hey, baby cakes.

257:42

Okay. And I'm going to register.

257:44

Perfect.

257:46

And uh what I'm going to do is I'm going

257:47

to use this as like the form fill

257:49

backend. Basically, you can't actually

257:50

set up a demo like this unless you have

257:52

a [sighs and gasps] something set up.

257:54

Maybe we'll say suggested by AI. What

257:57

prompted use, dude? Nothing. Let me sign

257:59

up for the love of God. Okay, we're

258:01

going to add a new form.

258:04

Okay, and we're back over here. Um I

258:07

just gave it a little form fill. uh

258:09

basically something that like wires in

258:11

an endpoint to allow it to connect to my

258:13

email. If you don't have something like

258:15

this, it can't actually send an email

258:16

directly from this page. So, it's kind

258:18

of [clears throat] annoying, but u I

258:19

just use a service called Form Scree.

258:21

It's totally free. It takes like two

258:22

seconds to sign up. Anyway, you can see

258:23

we actually have the the form here,

258:25

which is nice. Um it's nice. Like, it it

258:28

actually looks pretty good. I'm just

258:30

going to move my gigantic

258:32

handsome face out of the way. Um, my

258:36

girlfriend caught me calling my head

258:37

really big and fat, so now I call it a

258:40

handsome head. Uh, Summit error. New AC

258:44

installed in as little as 24 hours.

258:45

License insured and local. Get a fixed

258:47

price quote on a new high efficiency

258:49

system. No sales pressure in a surprise.

258:50

Sure, whatever. Expect a text and email

258:53

within 60 seconds. That's pretty cool.

258:55

So, now what I'm going to do is I'm

258:56

going to set up a system that actually

258:57

does this thing. Um, so yeah, let's do

259:00

it. Uh, it looks like I just need to

259:01

confirm some form fill service and then

259:03

it'll actually get sent. And it'll get

259:05

sent like this, which is about as clear

259:07

a speedtolebased system as possible.

259:14

And and if I go back to my email, you

259:17

can see it right over here, which is

259:18

cool. So, we've now verified that this

259:20

is entirely possible. And we also have

259:22

information like source, notes, service,

259:26

home, email, you know, zip code and and

259:29

all that stuff. [gasps] So, if I refresh

259:32

this, anything else we should know?

259:33

Cool. I can put some information in

259:34

there. Condo, new install, two twotory

259:37

units about 15 years old. I think what

259:39

I'll do

259:41

um make the anything we should know

259:44

field mandatory and then change it

259:46

because I want to use AI to take that

259:48

information and then weave it into a

259:50

cool email reply template.

259:53

[snorts]

259:54

So I'm just going to feed that in and

259:56

then Okay, cool. We now basically have

259:57

everything we need in order to make this

259:59

work. So what is the flow actually going

260:00

to look like? Uh well the flow is going

260:02

to be fairly straightforward. We're

260:03

going to start, okay, with

260:09

the lead filling out the ad. Immediately

260:12

after, I'm going to build a polling

260:14

system, which is going to be new to you

260:15

guys, but it's going to be a system that

260:17

essentially checks my email constantly

260:19

to see if emails exist that look like

260:21

this. And if they do, we are going to

260:24

send an email to that address

260:25

immediately. We're also going to

260:27

personalize an SMS. Now, there variety

260:29

of different ways to do SMS

260:30

personalization, uh, and stuff like

260:31

that. I'm going to show you guys a

260:32

simple way that I do it and then at the

260:34

end um I don't think we're actually

260:36

going to do the full call merge, but uh

260:38

I'll show you guys what you need to do

260:39

if you did want to do a full call merge

260:41

like let's say for a company that you're

260:42

working with that does phone based

260:43

marketing. And uh yeah, I mean like we

260:45

can do all this thing. We can also do

260:47

this in like 5 seconds. We can do this

260:48

not in 5 minutes, in 5 seconds. Um but I

260:51

think I'll probably spread it out just a

260:52

little bit so it doesn't seem like too

260:53

crazy.

260:56

And that is something worth noting like

260:58

if you do this too fast people will

260:59

think it is fake. So, you do have to

261:02

sort of be in the bounds of reason. Uh,

261:05

could be like 30 seconds. You could

261:06

insert like a little spelling mistake.

261:07

That's something that a lot of people

261:08

will do where they'll say like, "Hey,

261:10

Pete, just coming back from the office.

261:12

You know, just driving into the garage

261:14

right now. I just saw this and I will

261:15

call you. Can you give me like 3

261:17

minutes?" They do stuff like that to

261:18

make it seem more realistic. It just

261:20

depends on on sort of how you want to to

261:22

do it. Um, anyway, cool. So, what's

261:26

going on with your current system?

261:27

Awesome. Why don't I just take this and

261:28

load this in a new browser? So, we'll

261:31

actually go file speak demo. So, this is

261:33

actually like live. Well, not live. It's

261:35

just on my computer. Um, and then I'm

261:38

going to fill this out. So, Nikki wiki,

261:40

we'll go. There you go.

261:44

And then we'll go nick at

261:46

leftclick.aair. That is my business

261:47

name. And we'll do condo service needed

261:50

new install. And then I'll say

261:53

um hm I don't know. Can't make the top

261:57

floor cold. Please help get my free

261:59

quote. Cool. You're on the list. Summon

262:01

air specialist is reviewing your request

262:03

now. I'm just verifying that this 100%

262:05

works.

262:06

You can see here in my email that we did

262:08

just receive it, which is great. So,

262:10

what am I going to do now? I'm going to

262:11

go back to Claude and now that we built

262:13

this demo, I'll say, "Excellent. Now

262:16

that we have this system, I need a way

262:17

to pull my mailbox for new form fills."

262:22

Okay. And next up, it is checking my

262:24

Gmail to see if it can connect to um you

262:27

know my my address and then grab all the

262:29

information. I would recommend just

262:31

doing this through a Gmail connector

262:33

directly. So you can just go to

262:34

connector and then go Gmail. I'm going

262:36

to connect this directly.

262:39

Then I'll go down to continue. I'll

262:41

select all. And now I can read, compose,

262:43

and send emails from my Gmail. So then I

262:46

will go back here. Then I'll just stop

262:49

this cuz it's on a wild goose chase.

262:50

Hey, I just added a connector. So, you

262:52

can uh take a look at that now. Cool.

262:54

That looks pretty solid to me. And now

262:56

that we have this information, looks

262:58

like it's skipping the whole OOTH dance.

263:00

At least that's what it's saying. So,

263:02

it's going to connect that to

263:04

nicholas@gmail.com, which just happens

263:05

to be one of my many emails. And it's

263:08

even going to go and full pull one full

263:10

message to see the body format.

263:11

Hopefully, it's going to pull the nicks

263:14

one. Let's see. Looks like it's also

263:16

parsing this out correctly, which is

263:17

nice. And now it's going to O. Just

263:20

verifying that the OOTH client is

263:22

usable. Should just try and log me in.

263:24

Okay, cool. I think it did. Okay. And it

263:26

looks like we also now have a list of

263:28

all the leads. And it's even giving me

263:29

the number of seconds since they got

263:30

sent, which is awesome. All right. So,

263:32

from here, we need to build some sort of

263:34

polling system that actively checks the

263:36

mailbox uh once every minute to see if

263:39

there are new entries that aren't

263:40

already part of, you know, the old list

263:42

of entries. So, we need two things. We

263:44

need one, a way to verify that these are

263:46

new. And then two, we need a way to

263:49

basically take that information and then

263:51

immediately send an email back to them,

263:53

you know, asking them questions and then

263:55

verifying and making it seem real human.

263:57

I'm going to give you the copy in a sec.

263:58

Okay, so it's going to go and do that.

264:00

Um, while it's doing that, I'm just

264:01

going to go to Google Docs and just

264:03

write a couple of messages. So, email is

264:06

going to be a little bit longer, but I

264:07

think what I'm going to do is I'll just

264:08

say re first name because that makes

264:11

sense. And if you get an email, it's

264:12

like repeater and then I'll say air

264:15

conditioning. That's going to be the

264:17

subject line. Um, and then I'll say,

264:19

"Hey, Peter, you know, first name,

264:22

sorry, just got your request.

264:25

I hear paraphrased version of their

264:28

request."

264:30

Just got your form or just saw you

264:33

filled out a form. I hear a paraphrased

264:36

version of your request. Will happily

264:38

help you with that. I or someone on my

264:41

team will call you in the next I don't

264:44

know let's just say 5 minutes.

264:47

Thanks Nick. And maybe just you know for

264:51

my own sanity I'm not going to use Nick.

264:52

I'll pretend that the person's name

264:54

that's calling is Jonah. So hey Nick,

264:57

just saw you filled out a form. I hear

264:59

your upstairs is getting pretty hot

265:00

these days. We'll happily help you with

265:02

that. I or someone on my team will call

265:04

you in the next five minutes. Maybe I'll

265:06

say we'll call you in the next five

265:08

minutes.

265:10

to book a time to come. Or maybe I won't

265:13

even mention that

265:16

and sort this out. Cool. Why don't we do

265:18

that? Now, um for SMS, typically you

265:21

want something way shorter. Um there's

265:22

like 160 character constraints on SMS.

265:25

So, I'm just going to go here uh and say

265:29

okay, ooth off me. I'm going to go back

265:32

here and I'll say SMS limit characters

265:35

because I just want to see how many. So,

265:36

it's 160. So, we have to be very very

265:38

careful about how many characters we're

265:39

writing in order for this to make any

265:41

sense whatsoever. Um, just while uh

265:43

we're doing this, it is going to ask me

265:44

to authenticate. So, I'm just going to

265:46

verify that it can. And then I'm going

265:48

to say something like, "Hi, first name.

265:53

Why don't we start with my email?"

265:54

Actually, this this is probably already

265:56

100 characters to be honest. 176. Yeah.

265:58

So, it's just a little bit too much.

266:00

I'll say, "Hi, first name.

266:04

Just got your request.

266:08

I know paraphrased version of their

266:11

request

266:12

calling you in the next five men

266:17

to sort this out.

266:19

Jonah, right? So in the worst case, hi,

266:24

you know, Antonius,

266:27

just got your request. I know

266:29

paraphrased version of request and this

266:31

is quite long as is. So realistically,

266:33

like this will probably be about 20 or

266:34

30 characters anyway. We'll say short

266:37

paraphrase version of this request

266:39

calling you in the next five men to sort

266:42

this out and then it'll be Jonah.

266:47

Maybe we'll actually just make that even

266:48

shorter. Basically, we want to minimize

266:50

the probability that this will spill

266:52

with the AI variable fills. And you

266:53

know, there is a potential case of them

266:55

having a really long name. So, if that

266:56

is the case, that would suck. Okay, so

266:59

this looks pretty good to me. I'm now

267:00

just going to fill this in. This is

267:02

going to be like email copy. This is

267:05

going to be like SMS copy.

267:08

And then what I'm going to do is I'm

267:09

going to feed this in to AI and I'll say

267:14

so this is the copy that I want to send

267:15

back immediately. Um because we're just

267:17

working on email right now. Just do the

267:19

email one. For a paraphrased version of

267:21

the request right at maximum like 10 or

267:24

maybe 15 words or so. Keep it really

267:25

short. The shorter it is the more human

267:27

it seems. And when you send the email,

267:29

make sure to send it like really

267:30

casually formatted just like I provided

267:32

to you. not stray from this template.

267:34

Um, the 60-cond scheduleuler sounds

267:36

awesome.

267:38

Okay. And then we'll fire that off.

267:39

Okay. And it's looking like we are now

267:41

sending and receiving emails. This one

267:43

just came in a moment ago as a test. I'm

267:45

just going to It looks like for whatever

267:47

reason, it's like pretty short. I don't

267:49

know if this is normal. Yeah. Okay. And

267:52

just while I was doing this, I was

267:53

experimenting with SMS. So, there's a

267:54

service out there called Qo. It's

267:57

formerly called Open Phone. And uh I've

267:59

had Open Phone for a long time. They've

268:01

since changed their name and stuff like

268:03

that, but uh it's just a requirement to

268:04

like have a a b an American business and

268:07

then set up an American bank account.

268:09

You need to have like a phone number for

268:10

legitimacy purposes. And I was just

268:12

curious to see like, hey, if I had one

268:13

of these, can I text from it? And I can.

268:15

Um so, I'm just going to use that as my

268:17

example because I want to show you guys

268:18

what an SMS and email combined flow

268:20

looks like. But if you guys don't have

268:23

one of these, you can also set them up

268:24

using Twilio. you do have to wait and

268:27

actually set up like a phone number via

268:29

um what's called A2P verification or

268:31

registration. This takes a while. Um and

268:34

you know I think people on average have

268:36

waited probably somewhere between like 2

268:38

days to maybe like 7 or 14 days. You can

268:42

also get rejected which is kind of

268:43

annoying and it's a relatively new um

268:46

it's a relatively new requirement by the

268:48

American government to ensure that there

268:50

isn't just like tremendous amounts of

268:51

phone spam. So you know it's not a bad

268:54

thing per se. It's not necessarily

268:55

horrific, but uh yeah, because of these

268:59

spam message increases, they've now sort

269:01

of made it so that you have to wait a

269:03

little bit. And I had to do A2P2 with

269:04

Kuo, but uh you know, mine's done, so

269:06

I'm just going to use that as example.

269:08

Anyway, everything I'm about to show you

269:09

with Qo is the exact same thing with

269:12

Twilio. It's just the interface is going

269:13

to look a little bit different. The same

269:14

core principles apply. just um you know,

269:16

whatever platform you use, whether it is

269:18

Qo or Twilio, which is the main one that

269:20

most people use, or some other thing,

269:22

you're just going to go by the API key

269:24

and just give it to your model. Anyway,

269:25

just running through the test here, you

269:27

could see that we just got a new form

269:29

submission from Dana Whitfield. It

269:31

includes my email address despite the

269:33

fact that it is Dana cuz I'm I'm sending

269:34

it back um to me and I obviously want to

269:37

make sure that email is going to be

269:38

fine. The upstairs bedrooms never cool

269:40

down no matter what we set the

269:41

thermostat to. Unit is maybe 12 years

269:43

old. So, this is all the information

269:44

that we need. Let's see what happens.

269:46

Hey, and if we check my sent mailbox,

269:48

you can see that we just replied saying,

269:50

"Hey, Dana, just saw you filled out a

269:51

form. I hear the AC at your house needs

269:53

a look. We'll happily help you with

269:54

that. We'll call you in the next 5

269:55

minutes and sort this out. Thanks,

269:57

Jonah." Again, you know, keep in mind,

269:59

Jonah, Dana, Nicholas, we're all

270:02

blending together into one formless

270:04

entity. Um, I'm just using my own email.

270:07

Ideally, this would be something

270:08

different. One more thing. Now, I want

270:10

to integrate that phone number flow. So,

270:12

how am I going to do that? Um, I'm going

270:14

to go and check to see if they have an

270:16

API or an MCP that I can plug into. Just

270:18

looking up MCP. Looks like they do have

270:21

integrations with Claude and Chat GPT.

270:24

So, let's see. Do we have an MCP here?

270:26

It looks like we do. Just looking up the

270:29

service. Um, oh, and it looks like

270:31

they're actually part of Claude now. So,

270:33

that's good. I can go back here and then

270:34

go to settings. Go down to connectors.

270:38

I'm actually going to type Qo. And it

270:41

doesn't look like it has my connected

270:42

one. And so I think that's cuz I need to

270:44

search through their default ones

270:46

instead. So I'm going to go back to

270:47

connectors.

270:48

Then what I want is

270:51

browse. So I'm looking at theirs. I'm

270:54

typing qo. I'm seeing this one. I'm

270:57

connecting now. And this should allow

271:00

the firing of SMS messages even without

271:02

me. So there we go. Qo cloud connector

271:06

is requesting permission to access my

271:08

business workspace. It's now connected

271:10

which looks great.

271:12

Okay. And if I go back to this

271:15

conversation, I'll also say, could you

271:17

check to see if you have Kuo access? And

271:21

if so, can you send a number, a text to

271:25

my number? Okay. And I don't know if you

271:28

guys could see this, but I just got a

271:29

message from this number. U basically

271:32

pretending that I was Dana. Um, hey

271:34

Dana, just got your request. I know the

271:35

AC house needs a look. Calling you in

271:37

the next 5 minutes, Jonah. So, uh, this

271:40

works, right? We just actually sent the

271:42

SMS and it only really took me a few

271:44

seconds. That's how easy it is to

271:45

integrate different platforms if they

271:47

are available in the Claude MCP sort of

271:49

connector list. So, I'm going to go

271:50

ahead and turn that from a series of

271:52

prompts into a dedicated skill and then

271:55

I'm going to show you guys how to loop

271:56

this. Um, you guys can loop this

271:57

locally. You can also do it via routine

271:59

since we're cloud connected uh aka we

272:01

have connector integrations. All of this

272:03

stuff is capable of occurring entirely

272:04

autonomously without any of us now.

272:06

Cool, cool, cool. It's now doing one

272:08

final test. I just had to reroute it

272:10

away from building like a script. Uh it

272:13

just usually wants to do this because

272:14

it's more computationally efficient. But

272:16

if you think about it, the whole idea

272:17

here is we are going to run this like in

272:18

a loop. We're just going to do it via

272:19

routines. And speaking of routines, um

272:22

we are going to set one up here. It's

272:24

going to be cloud-based. As you can see,

272:25

we have a Gmail connector. Uh we also

272:28

have a Google Drive connector. We have a

272:32

Gmail app. I'm going to take that out. A

272:34

Higsfield. Going to take that out. And I

272:36

think that's it. We just need Gmail. We

272:37

need kuo. So with all that in mind, um

272:41

I'm not going to set this up entirely

272:42

just yet. I want to see the results of

272:43

that last test, but I'm just going to

272:45

have it generate copy for the routine

272:46

and then run it. We just tested this on

272:48

a new lead called Priya. Hey Priya, just

272:50

saw you filled out a form. I hear

272:52

upstairs is stuck at 80 even with the AC

272:55

running. We'll happily help you with

272:56

that. We'll call you in the next 5

272:58

minutes and sort this out. I also just

273:00

got a text message saying, "Hi Priya,

273:02

just got your request right over here."

273:04

So both of these were basically done

273:06

immediately, right? And ideally they are

273:08

done in like a humanized way like this.

273:10

Some snippet that is short, you know,

273:13

not super overly long, but still written

273:15

like a human being would write it. And

273:16

now what it's also doing is creating me

273:18

a routine. Um, I asked it to upgrade

273:20

this and basically turn it into a clawed

273:22

thing. So all of this is good.

273:25

Everything looked basically perfect. And

273:27

we are now actually generating the

273:28

routine.md, which is right over here.

273:31

I'm then going to paste this in. Just

273:32

don't want to scroll too far down so I

273:34

don't leak my damn phone number for the

273:36

4 days time. We'll call this speed to

273:38

lead SMS and email. And then yeah, the

273:40

connectors I want are um Gmail and Quo.

273:44

And here we're going to want to do a

273:45

couple different things um in addition.

273:48

So I mean like we could do it scheduled

273:50

where like every single time it'll pull,

273:51

but routines are a little bit different.

273:53

Like routines

273:54

um they are capable of getting run via

273:57

API. What that means is we can basically

274:00

wait for a trigger and then only respond

274:02

when we get a request. So if you think

274:05

about it, what we really could do is we

274:06

could trigger every time somebody fills

274:09

out that form. Okay, wait a few seconds.

274:11

That'll land in our inbox and then we'll

274:13

fire the routine only then. And it's a

274:15

lot more efficient uh to do it that way.

274:17

So that's what I'm going to do. I'll go

274:19

call via API. You could still run the

274:21

polling approach like I was showing you

274:22

guys how to do uh locally on your

274:23

computer. Uh but I think this is

274:25

probably like a much simpler way. can't

274:26

really do this API thing if it's local,

274:28

which is why it's more of a routine

274:30

based thing. Yeah, this looks pretty

274:32

solid to me. I'm just going to click

274:33

create. Now, we have a token that'll

274:35

allow us to fire the routine via post.

274:37

So, just going to copy it here. I'm

274:39

going to say here's the token. Please

274:42

set this up. Cool. And you can actually

274:44

see these runs working right now. Um,

274:46

this is actually like literally firing

274:47

off and being sent. So, initializing the

274:50

session, starting Cloud Code. Here's a

274:52

bunch of the the data and the

274:53

information. And no, I did not leak my

274:54

phone number. Thank goodness. Um, so

274:56

it's just going to double check the

274:57

connectors responsive and actually go

274:58

ahead and fire them off. Searching for

275:00

the send and label tools, confirming

275:02

Gmail. Looks like it does have Gmail.

275:05

And uh, yeah, I mean now we have our

275:06

little speed delete system set up as a

275:08

cloud routine and it runs um, via some

275:10

remote trigger um, sort of API as

275:13

opposed to something different. So

275:16

that's that. I mean like we just built a

275:17

speed delete system. You can obviously

275:19

take this further if you want to by

275:20

doing things like automatic call

275:22

merging. The way that automatic call

275:23

merging works um just for your

275:25

convenience is it'll call both of us

275:27

simultaneously. So there'll be some

275:29

third phone number that'll call me and

275:30

it'll call them at the same time and so

275:32

we're both going to get rings on our

275:34

phones. So ideally you would pick up

275:35

fast, right? It's trigger based and the

275:38

first person to pick up is going to be

275:39

in the call. Um that way there won't be

275:41

latency like when the other person comes

275:42

onto the call. But yeah, you could do

275:44

this with like big setting teams and

275:45

stuff. And uh the presence of this

275:47

versus not having the presence of like a

275:49

call merge system for something that

275:51

like makes sense to be dealt with over

275:53

the phone and you know a lot of mid to

275:55

high ticket offers do makes a real big

275:57

difference. But think about it. You're

275:59

now slapping them with an email. You're

276:01

slapping them with an SMS. You're

276:02

slapping them with a phone call. Really

276:03

the only missing piece here before you

276:05

get something that is kind of like

276:06

Clarvo is just like tracking

276:08

dispositions and then doing follow-ups.

276:10

So, pump pumping this into some sort of

276:12

CRM um you know, scheduling follow-up

276:15

calls by people on your team and so on.

276:17

That is more sales based. So, I'm not

276:19

going to talk about that in today's

276:20

course, probably in a future course. Um

276:22

but yeah, I just wanted to make sure

276:23

that we all knew how to squeeze the

276:24

juice out of the leads to the biggest

276:26

and best extent possible.

276:29

And if you think about it, that now

276:30

takes us to our next task, which you

276:34

know, now that we've done our middle

276:36

ofunnel speed to lead for appointment

276:37

booking and I want to do middle ofunnel

276:40

data collection and tracking, uh things

276:42

like UTMs, things like actually building

276:44

a dashboard and so on. All skills that

276:45

are very important and will serve you

276:46

quite well in your marketing journey. Uh

276:48

but first, I am going to go eat a big

276:50

fat dinner. I'll be right back. Okay, so

276:53

dashboards. What the hell is the purpose

276:55

of a dashboard? It's obviously to

276:56

visualize [clears throat]

276:57

a data. in our case, marketing data. And

277:00

you know, a big question is how do we

277:02

make dashboards that both showcase

277:04

marketing data in like an effective and

277:06

simple way while also making use of all

277:08

of the really cool features of cloud

277:10

code and uh AI agents in general um to,

277:13

you know, make them not absolutely ask

277:15

to look at. So, you know, this is like a

277:17

naive approach that I think most people

277:18

would probably do. They'd basically just

277:20

pump in a bunch of their marketing data

277:21

to cloud code, say visualize it, and

277:24

then end up with something like this.

277:26

We're going to make something that is

277:27

far far better and far higher quality.

277:29

And we're going to do so in a couple of

277:30

ways. Um, you know, this is for a

277:32

hypothetical company called North Beam

277:33

Digital. If I zoom in here so you guys

277:35

can maybe see a little bit better. It

277:37

tracks uh the total ad spend, the

277:39

sessions, the leads, the blended CPL,

277:41

closed one deals, one. Um, but the

277:43

problem is, you know, like when people

277:44

visualize stuff like this, they're not

277:46

really treating it like what it really

277:47

is, which is a web design project. Uh,

277:50

you know, dashboards nowadays are just

277:51

like HTML. And HTML is hypertext markup

277:53

language. It's essentially the language

277:55

of websites and you go a lot further if

277:57

you treat you know what you are building

277:59

as uh like literally just like a

278:01

glorified website um than you know is

278:04

like necessarily a data showcase tool.

278:07

So yeah, I'm going to show you guys how

278:08

to make stuff like this way way better

278:10

and I'm also going to show you guys how

278:11

to visualize data in ways that allow you

278:13

to share them with virtually everybody

278:15

in your organization. If I scroll way

278:17

down here to my little marketing um

278:19

section, like you have access to

278:22

virtually everything that you could

278:23

possibly need already. You know, meta

278:25

ads, your spend and clicks, Google

278:27

Analytics, G4, Stripe, your revenue,

278:29

CRM, email platforms, stuff like that.

278:31

Your issue is they all tend to be spread

278:33

between multiple platforms. And so, you

278:35

know, like all of these realistically

278:37

will live in different places. The whole

278:39

idea of like a unified cloud code build

278:41

dashboard is we get to create a bunch of

278:43

connectors that connect to these

278:45

platforms which hopefully you've already

278:46

seen is fairly straightforward, right?

278:47

You just get an MCP or something and go

278:50

for it and then build what is

278:52

technically referred to as an ingestion

278:55

pipeline.

278:58

Um, and then your ingestion pipeline

279:00

just runs on like a daily, weekly or

279:02

monthly sequence to pump into some sort

279:04

of dashboard. So that you know that's

279:07

sort of the idea here. U we're just

279:09

going to get out of like the super

279:10

disconnected dashboards and build like a

279:12

single source of truth. Um what's really

279:14

cool is you know back in the old days

279:16

and I say this as somebody that used to

279:17

run a marketing agency. It was our

279:18

primary deliverable. Um you know we'd

279:21

have like a report day once a month and

279:24

uh the whole idea was you know at the

279:25

end of the month we'd go through all our

279:26

spreadsheets and stuff like that and put

279:28

together really nice reports send those

279:30

reports to the clients and the reports

279:31

are almost like sales mechanisms. You

279:33

know we we we try and tune them so that

279:35

they look as sexy as possible. try and

279:37

get the client to, you know, review it

279:39

and be like, "Wow, I'm really getting

279:40

good return on investment and stuff like

279:42

that." Um, it was a massive pain in the

279:43

butt to import data from all these

279:45

different places. But now the idea is

279:47

we're not necessarily going to have like

279:48

a report day. It's just going to be a

279:49

dynamic dashboard that does the

279:51

ingestion automatically and then, you

279:52

know, shows the data um pretty

279:54

constantly. And I think you guys

279:55

realistically have everything that you

279:57

need in order to do that, right? Like

279:58

we've now built loops. We know how to

280:00

build routines. We know how to build

280:01

systems that can essentially log into

280:02

services and grab data without us. So,

280:05

how far of like a leap is it

280:07

realistically to like have an online

280:08

asset that is automatically updated?

280:10

It's really not that big of a leap at

280:11

all. Um, in addition, I think would be

280:13

really cool, at least for us, is to take

280:16

the dashboard that we're building and

280:17

then provide some sort of like AI

280:19

questionnaire functionality where not

280:21

only can we like, you know, talk to our

280:22

AI about it, but maybe, you know, a

280:24

client or something can come in on a

280:26

dashboard and then query an AI maybe

280:28

locally on the dashboard to tell us

280:29

stuff about it. So, uh, I'm going to

280:32

extend our own little marketing

280:33

dashboard, which is going to look much,

280:34

much better, into something like that.

280:36

Make it a little bit more dynamic in

280:37

that way. We'll have basically like an

280:39

like an like an AI assistant. I'm

280:41

capable of analyzing stuff. The actual

280:44

flow itself up here is going to be

280:45

fairly straightforward. Um, we're just

280:47

going to build dashboards for a client

280:50

based off of the information that, uh,

280:52

we're tracking for them. So, all the

280:53

KPIs of interest, you know, like meta

280:55

ads, uh, total number of sessions of

280:57

their website and stuff like that. It'll

280:59

then read all the API docs on all the

281:01

platforms, write the tests and the

281:02

queries and then generate a dashboard

281:03

page which is refreshed by a cloud

281:06

routine. And yeah, I mean I don't want

281:08

to make the whole basis of you know this

281:10

part just a bunch of AI generated

281:11

visuals. Hopefully you guys understand

281:13

we are going to go from super sad

281:15

frustrated guy over here on the left to

281:17

super cool chill guy over here drinking

281:19

his coffee uh with a cute little robot

281:21

pointing at it. Okay, so that's the

281:22

idea. How do we actually do so? Well,

281:25

step one realistically is like

281:26

assembling your data. Okay. So, um let

281:29

me just move this a little bit over here

281:31

otherwise it starts getting kind of

281:32

laggy. Step one is assembling your data.

281:36

So, I am going to use default sort of

281:40

templated data here. Um our data is

281:42

going to include a variety of sources.

281:44

We're going to have like hypothetical

281:45

Facebook ads. We're going to have

281:47

hypothetical like website visits. Um

281:50

we're going to have hypothetical sales

281:52

data as well. Um, I know it's not

281:53

marketing data necessarily, but we're

281:55

going to have like logs from

281:57

salespeople. I think we'll even have

281:58

like email stats and we're going to

282:01

combine all of this into um, you know,

282:04

claude as mentioned. Worth noting mine

282:07

is not going to exist on the cloud. Mine

282:09

is going to exist in basically like a

282:11

big CSV. CSV just stands for

282:13

commaepparated value. It's basically

282:14

like a Google sheet. And the reason why

282:16

is because I I just don't want to

282:17

actually pull all of my stats for you

282:19

guys because a lot of that stuff uh as

282:20

we saw with the kit marketing emails is

282:23

sort of proprietary and it's also sort

282:24

of private. So what I've done is I've

282:25

just dumped in a bunch of fake data to

282:27

you know a bunch of files that will just

282:28

pretend to be our databases. Um now and

282:32

then once we've assembled the data we're

282:33

then going to visualize it. Um

282:37

and what's really cool is we're actually

282:39

going to generate like I don't know five

282:41

or six different variants.

282:44

Uh, and so, you know, I think the way

282:46

most people do this is they'll just try

282:47

and oneshot it and then they end up with

282:48

like a really ugly design like this,

282:50

right? Um, I don't know. I just I don't

282:52

think this is a very efficient layout. I

282:53

don't think this is very good for a

282:55

client. I think you can do way better

282:56

than that. So, what I'm going to do is

282:58

I'm going to take that idea and then I'm

282:59

actually just going to generate a

283:00

variety of different variants. And then

283:01

we're going to pick a variant that we

283:03

really like, like B. And then what B is

283:04

going to do is it's going to make a

283:05

bunch of other variants. Maybe A, B, C,

283:08

D. And then maybe we'll pick C. Then C

283:10

will do a couple variants like A and

283:12

then B. And then maybe we'll pick A. So

283:14

now what we've done is we've basically

283:15

gone through, you know, three different

283:16

iteration periods to find like the best

283:19

dashboard ever. And what's cool too is

283:21

you can just repeat this process anytime

283:23

if you wanted to visualize different

283:24

data. Um, so that's going to be sort of

283:25

my web design process. And then third,

283:28

we're going to add some sort of like AI

283:29

assistant functionality.

283:32

[gasps] So why don't we start with the

283:35

first, which is fairly straightforward,

283:37

just assembling all of our data. Okay,

283:38

so this is all of the data in uh my

283:40

quote unquote database. And as you guys

283:42

can see, we have a lot. We have DIM

283:45

channels, clients, sales reps, fact

283:47

activities, fact ad creatives, agency

283:49

financials, attribution paths,

283:50

backlinks, budget pacing, call tracking,

283:53

uh, number of calls, channel daily. I

283:55

mean, these are these are massive kind

283:57

of data dumps, right? U we even have

283:59

like client summaries and monthly time

284:00

series. I will show you what a couple of

284:02

these look like just for your own

284:04

sanity, but uh you know just for you

284:07

know I think the sake of time and and

284:09

whatnot. Um I would recommend you just

284:12

let's find something reasonably big

284:13

activities. I'd recommend you just

284:15

assume this is like Google Analytics or

284:17

something like that. Just going to

284:18

import this and show you guys what the

284:20

spreadsheet realistically looks like. So

284:21

this is fact activities which is going

284:24

to show a bunch of activities um that

284:26

include different reps that are doing

284:28

things. This is total number of calls,

284:29

emails replied and so on and so forth.

284:31

This is basically like a time log. And

284:33

so in this way, you know, I'm sure you

284:35

guys can imagine if you guys have some

284:36

sort of time logged data within your

284:37

company. You could figure out, okay,

284:39

like, you know, how much time has rep

284:41

four spent, let's say, in the last 24

284:44

hours. Well, what I can do is I can sort

284:46

or filter by activity date. I could

284:48

clear all the values except for uh well,

284:50

I guess this is about a year ago, but

284:52

let's just hypothetically say it's

284:53

today, which is um August the 6th,

284:56

right? And then we can filter by, I

284:58

don't know, rep one. And then we can

285:00

actually add up their total time here.

285:01

2,940 seconds to determine how long

285:05

they've actually spent. 2940 divided by

285:08

60 means they've actually only really

285:10

logged 49 minutes of activities. Maybe

285:12

then we can make some determination as

285:13

to their productivity, right? Okay. So,

285:15

this is the sort of data that we have.

285:16

Uh a lot of other data as well, like um

285:19

let me show you just one more that I

285:21

think is probably a little bit more

285:22

representative. Um, you know, we have a

285:24

bunch of sales rep stuff, but I don't

285:26

know, maybe we'll do G4 data. That

285:27

probably makes more sense. This is going

285:28

to be a really big one, mind you. 6.1

285:31

megabits. So, just uh bear with me here

285:34

as I actually do the upload. I'm going

285:35

to append this to the sheet just so that

285:37

I could visualize both of these

285:38

simultaneously. We just scroll down a

285:40

tiny bit. You'll see what that actually

285:42

looks like. We have over 10,000 rows,

285:46

right? And so, this activity data was I

285:48

think up to like 30,000 or something.

285:51

Yeah, approximately. right about here,

285:53

50,000, sorry. And then here's where all

285:55

the Google Analytics data starts. Check

285:57

this out. I mean, we have like the

285:58

client ID, the channel, we have the

286:01

country, the number of sessions on their

286:03

website, the number of engaged sessions,

286:05

the bounce rate, the pages per session,

286:07

and so on. I mean, we we have a lot of

286:08

stuff here, right? Um, ideally, this

286:10

would be interpretable to you, but just

286:12

wanted to make sure you guys see saw

286:13

like the degree of data we were working

286:14

with here. It is honestly pretty

286:16

freaking insane. [gasps] Okay. Anyway,

286:18

so where do we go from here? Um, first

286:19

things first, if I want to visualize

286:21

stuff, I'm going to open um up Claude.

286:23

And then what I'm going to do is in a

286:25

new chat, okay, I'm going to go and find

286:27

all of this data, which is in marketing

286:29

dashboard data. Okay, and then I'm going

286:31

to hold this button here, and I'm

286:33

actually just going to feed it a brief

286:34

description of all of the data, as well

286:36

as the data folder itself. So now I'm

286:39

going to say, hey, this is a super

286:41

detailed um database that contains all

286:44

of the information on a hypothetical set

286:46

of companies that I am using to develop

286:49

a dashboard for for a YouTube video that

286:51

I'm recording. I want you to go through

286:53

all of the data and for every bit of

286:56

data, I want you to see if there are

286:58

ways to combine the databases logically

287:00

or if the databases are discussing, you

287:02

know, things that are naturally linked

287:03

together. Uh then create a highle

287:05

structure using this. We're going to use

287:07

this to create the pages of our

287:08

database. For now, we'll scaffold them

287:11

really simply. Just provide me a few

287:12

candidate structures that I can look at

287:14

and then determine, you know, how to how

287:16

to lay it out. Okay. And just before I

287:17

do that, um I want to talk a little bit

287:19

about model intelligence. For most of

287:20

the course, I've actually been using

287:21

Sonnet 5, which is like a last

287:23

generation model. It's not a very

287:25

intelligent model whatsoever. Um the

287:27

smarter that you know your needs get,

287:29

the more intense like your design needs

287:31

and stuff like that get typically the

287:32

more intelligent the model you have to

287:33

use. So I think like last video or maybe

287:36

the module before I went from Sonnet 5

287:38

to Opus 5 just because I was I was

287:40

wanting to see how much of the errors

287:42

that the model was making was due to

287:43

just like its model intelligence and it

287:45

really wasn't all that much. However,

287:46

for high quality design purposes, I

287:49

really do recommend using the best

287:50

models possible and they'll just save

287:51

you a tremendous amount of time and

287:53

energy. And so that's what I'm going to

287:54

do with Fable 5. I'm just going to run

287:56

it on usage credits. You can see that

287:58

it's asking me to buy a certain number

287:59

of credits over here. So, I don't know.

288:01

I'm just going to buy, let's just say,

288:03

$20 in credits. Uh, make my life a

288:05

little easy. It'll initiate the purchase

288:07

and go through. Uh, and you don't need

288:09

to do this if you have, I think, um,

288:11

like a max plan. You only need to do

288:13

this if you're on a pro plan. I actually

288:14

have like a max pro plan. It's just I've

288:16

decided not to use it today cuz I want

288:18

you guys to have the same experience

288:19

that I do. And I presume most of you

288:21

guys are probably going to be on, you

288:23

know, the smaller plan, not like the

288:24

super major plan. So, this is what that

288:26

UX looks like if you actually buy one.

288:28

Um, okay, cool. So, we have that. And I

288:30

don't know, my monthly credit usage

288:32

limits probably going to be 20 bucks.

288:33

I'm not going to turn on auto reload.

288:34

And then what I'll do is I'm just going

288:37

to um basically rerun this with Fable 5.

288:40

And then instead of medium, I'm going to

288:42

go to high. Okay, which tends to be like

288:44

reasonable. I just swapped to Fable 5.

288:47

I'd like you to revisit these what makes

288:50

the most logical sense. Assume the

288:52

analysis done before was by a dumber,

288:56

less capable model. And so what I've

288:57

done is I've taken all of the stuff that

288:59

we had before and I've just fed it to

289:01

Fable 5 um because you know these models

289:04

share context. And so this is like the

289:06

Opus 5 recommendations which are okay.

289:09

Did so off of medium reasoning. Uh I

289:11

want Fable 5 on high which is just again

289:13

the absolute most highquality data

289:16

humanly possible.

289:18

Okay. So working through it again the

289:19

structure is actually more constrained

289:21

than a matter of preference. Three

289:22

properties of the data set force the

289:23

answer. The anomaly distribution

289:24

dictates the primary axis of the seven

289:26

stories fiber client scoped. One is

289:28

global, one is rep scoped. A funnel

289:30

ordered layout scatters each client

289:31

story across five pages. The deal the

289:34

death spiral shows up as a traffic blip

289:36

on one page. They're talking about a

289:37

specific client. A clientcentric layout

289:39

is the only one each planted story is

289:40

visible as a story on a single screen.

289:42

Since the anomalies are the entire demo,

289:44

this alone settles the primary axis.

289:46

Two, the coverage gaps punish global

289:48

pages. E-commerce exists for 4 out of 12

289:50

clients. Attribution for 44% of leads.

289:52

GBP is local business flavored and then

289:54

the grain mismatches dictate what cannot

289:56

be client tabs. So here is what it

289:58

recommends. Overview portfolio grid

290:00

alert feed performance traffic pipeline

290:02

search presence commercial and this is

290:04

client broken down by ID. Um specific

290:06

channels sales teams and then signals.

290:10

So I like this idea and I'm going to say

290:12

yes. And you know if there was something

290:14

here that I didn't like obviously I

290:15

don't just have to take the model's word

290:17

for it. This model despite being super

290:18

smart makes a lot of issue uh a lot of

290:20

errors as well. Um I just think that

290:22

this is a reasonable breakdown of the

290:24

data that I was um u working with here.

290:26

They are like client-based uh and so you

290:29

can see opus did not do the same thing.

290:31

Funnel oriented overview acquisition

290:32

traffic and site client centric agency

290:35

overview client detail channel analysis

290:38

candidate C persona question. Are we

290:39

hitting the number? Where's the money

290:40

going? Is the site converting? These are

290:42

all very different approaches and uh

290:44

Fable basically just jumped right in and

290:46

then determined the the highest quality

290:47

approach immediately. So, it's going to

290:49

scaffold those five pages for me and I'm

290:51

just going to loop around when it's

290:52

done. Once it's done the scaffolding, uh

290:55

then I'm actually going to like turn

290:56

this from just like a scaffold project

290:58

into like a design project. And I'll

290:59

show you guys what it looks like when

291:00

you do cool high quality web design uh

291:03

with, you know, Fable and these other

291:04

models. Actually, while I'm at it, I

291:06

mean, like this is um Fable Design 25. I

291:10

made this website a while back. Uh it's

291:12

called Fable 25. And basically, I had

291:14

Fable just like create a bunch of

291:16

extremely highquality, well-designed

291:18

sites. And I say well-designed here in

291:20

like a relative term. Um these sites are

291:22

not really the sorts of sites that you'd

291:23

realistically probably want, you know,

291:25

to build a dashboard off of, but they

291:27

are very impressive and they they

291:29

showcase just how high quality Fable

291:31

really is at stuff like this. So, um I

291:34

don't know, how about this one here?

291:35

Right. This is like extraordinarily

291:37

interactive website where we actually

291:39

are modeling all of these and we're

291:41

doing it while also flipping between

291:43

different font sizes uh and also

291:45

autocasting them. You know, this over

291:47

here is like a sexy architecture website

291:49

with like cool effects, right? You can

291:51

do pretty crazy stuff these days. This

291:52

one's like cool typography stuff. So, we

291:54

don't need to feel constrained by design

291:56

really to any extent and um Fable 5 can

291:58

do a really really good job. It's just

292:00

important to approach it in a specific

292:02

way. Get the data on the page. Once you

292:03

have the data on the page, then you skin

292:05

it and select high-quality iterations.

292:07

You can see the example that it has put

292:09

together uh which is very in-depth. It's

292:11

an extremely extremely detailed

292:13

database. And I mean the design is far

292:15

better than whatever we uh did before

292:16

with that much dumber model. As you can

292:19

see, you also have the ability to click

292:20

into a specific client, which is kind of

292:23

neat. And you can see the leads per day.

292:25

You can see the traffic collapse. Uh

292:27

what they do is they have naive SQL

292:29

threshold detection over the engagement

292:31

window. Basically, if something drops

292:32

off or comes up really really quickly,

292:35

um you can see it right here. It'll

292:37

actually show you like it'll say, for

292:39

instance, hey, there's a weird lead flow

292:41

collapse happening for CL10, that's

292:43

client 10 on the week of, I don't know,

292:45

March uh I don't know, 9th, 2026. On

292:49

average, you guys normally get 57 leads.

292:51

Now, you guys are getting three leads.

292:53

So, I think it's like a 5x difference

292:54

threshold or something like that. And uh

292:56

that's how you determine whether or not

292:58

something is weird. But then you also

293:00

have a breakdown of areas that it kind

293:02

of makes sense to point out. So, hey,

293:04

you know, here's a conversion rate jump.

293:05

The baseline percentage is 1.65. Okay,

293:08

so 1.65% and now it's 2.23. So, what

293:10

we've done is we basically 1.25 or

293:13

1.5xed total results. You can see that

293:16

sort of law hold across the board here.

293:18

And so, this is like good information

293:20

for you to like present to a client.

293:22

Then down at the very bottom, you can

293:23

see they're also doing a bunch of stuff

293:24

on rep rate wins. So, you know, like

293:26

these are sales representatives and uh

293:29

you know, this guy has, I don't know, a

293:31

win rate of 15.6% on deals, so about one

293:33

in seven calls. Um, team average

293:36

percentage is, oh, sorry, this guy's not

293:39

a winner. He's a he's a loser. The team

293:41

average percentage is 24.2%. His win

293:43

rate is 15.6%. So, it's like, hm, okay,

293:45

maybe maybe I should do something about

293:47

that. So, Fable is going through this

293:49

with me and basically doing the

293:50

evaluation to try and figure out what's

293:52

going on. Um, we're clicking through all

293:54

these different clients. Just making

293:55

sure that like nothing here is is weird.

293:57

End of all of this. It has gone through

293:58

and done all of the testing. Um, I just

294:00

want to verify that I haven't actually

294:02

ended up using more than my fable usage

294:05

limit. [sighs and gasps] So, it's

294:07

looking like the cost of this session

294:09

was 94% fable $13. I just want to make

294:13

100% sure that as something that um just

294:16

worked out.

294:18

Let me actually go through usage

294:20

credits. you know, we realistically

294:22

didn't burn through them. And it looks

294:23

like for that dashboard, which to be

294:25

clear is, you know, this controls like

294:28

20 clients or something like that and

294:30

tens of thousands upon tens of thousands

294:32

upon tens of thousands of rows of data.

294:34

Like, it's not a it's not a trivial

294:35

build. That cost me $18 Canadian dollars

294:38

or about 13 12 or 13 American, which I

294:42

think you guys are probably more used

294:43

to. So, that's big, right? I mean, we

294:45

just spent a lot of money. But the

294:47

reason why I did this with Fable is

294:48

because now we have basically the

294:51

scaffold.

294:52

We have the architecture here. And what

294:55

we can do is we can take this

294:56

extraordinarily detailed super in-depth

294:59

um dashboard and now we can just spin it

295:01

into like 20 different variants. What do

295:03

I mean by this? Well, what I'm going to

295:05

do now is I'm going to go from Fable 5

295:07

to Opus 5. Okay. And I'll keep it at um

295:09

high just for simplicity. And let me

295:12

just see if I can make this fast. I

295:13

don't know if I can. Yeah, it doesn't

295:15

look like I can. That's okay. Fast mode

295:17

just allows your model to operate a

295:18

little bit faster. Now, I'm going to go

295:20

back to Opus 5, which um is not being

295:22

built as part of my usage limits. Uh

295:24

sorry, it's not being built as like

295:25

extra credit. It's being built as part

295:27

of my usage limits. And now I can spin

295:28

it.

295:30

Hey, now that we've built this extremely

295:33

um highquality scaffold, what I want to

295:35

do is I want to spin this in five

295:36

different designs. Aka, I want you to

295:38

generate five highly different variants

295:40

each. I'd like you to spawn five sub

295:43

agents all working on this project

295:44

simultaneously using HTML. Um, go nuts

295:47

here. Really demonstrate the diversity

295:49

and range of your visual uh design

295:52

appeal and give me something that just

295:53

looks really really awesome and

295:55

interesting on all of them. Key focus

295:56

should be interpretability obviously uh

295:59

usability and practical UX. UX that

296:02

actually does serve uh the user. Okay,

296:04

sorry it turns out you can't use extra

296:06

high on Opus 5, I don't think. Um, in

296:10

case you guys didn't know, you can

296:11

actually spawn multiple sub agents

296:13

simultaneously that are all working on

296:15

things for you at the same time. This

296:18

consumes vastly more usage limit than if

296:21

you were to do it in serial or a linear

296:23

manner like most of us are used to, but

296:25

it also allows you when you're doing

296:27

something like creating five different

296:28

designs to design things far faster. The

296:31

whole principle here is parallelization.

296:34

And I'm glad we're doing a design

296:35

project cuz I I did want to talk about

296:36

this at some point in the program. Um,

296:38

but basically what we want to do is, you

296:40

know, if you think about it, the way

296:42

that we were doing things before is we

296:43

had one process. Okay, maybe make this a

296:45

little bit bigger. And why don't I

296:47

actually draw this on the uh dashboard

296:49

here? That'll allow you guys to see this

296:51

um in perpetuity and then we can come

296:52

back to it. You know what we're doing

296:55

before is we have one process, okay? And

296:58

then we want to do another process, we

297:00

want to do another process, and then

297:03

maybe we want to do a couple more.

297:06

What we were doing is we were proceeding

297:08

in like a linear manner.

297:10

And when you do things in a linear

297:12

manner with agents, there is a certain

297:14

time cost. So maybe in order to go from

297:16

1 to 2 takes 5 minutes. In order to go

297:19

from 2 to 3 takes 5 minutes. In order to

297:22

go from 3 to 4, it takes 5 minutes. In

297:23

order to go from 4 to 5, it takes 5

297:25

minutes. What that means logically

297:27

speaking is if you add up the total

297:29

amount of time

297:31

that is 5 + 5 + 5 + 5 20 minutes in

297:34

total. Well, when you have um

297:37

parallelizable

297:38

uh processes and I define a

297:40

parallelizable process with agents as a

297:44

process where you can spawn multiple

297:46

agents working on the same data resource

297:48

but whose actions are independent and do

297:50

not impact the actions of other agents.

297:52

Then you can do something really cool.

297:53

You could start with step one, but then

297:56

you know what you can do? You can spawn,

297:59

you know, two, three, four. Well, I

298:02

guess probably just one more here. And

298:05

then you can immediately combine those

298:07

into some next process six such that

298:10

okay, from 1 to 2 3 4 and 5, it's only 5

298:12

minutes. And from 2 3 4 5 and 6 to 2 3 4

298:16

5 26, it's only another 5 minutes. So

298:18

now if you think about it, this whole

298:19

process here, okay,

298:23

this is only uh 10 minutes long. And so

298:26

we're comparing it to a process that's

298:28

previously 20 minutes that has a lot of

298:29

weight times. Well, now we're just doing

298:31

that waiting kind of in one shot. We're

298:32

just immediately generating all of the

298:34

variants that we want. And this second

298:36

half might not even be necessary as we

298:38

will see. But generally speaking, what

298:40

I'll do is I'll I'll try and combine the

298:41

best portions of each of these variants

298:42

and then um stick them in one. So the

298:45

upside obviously okay if we look at the

298:47

pro of parallelization it's it's way

298:50

faster okay it also allows you to search

298:53

a lot of what's called the solution

298:55

space which is the space of all possible

298:57

solutions to your problem the cons are

299:01

you know it's much more expensive so I'm

299:03

just going to say not cheap [laughter]

299:05

because you are doing a lot of this and

299:08

two um in order for you to be able to do

299:10

this each of these processes need to

299:12

inherently be like non-moniitored

299:14

Um, and what that means is,

299:17

you know, it's like

299:19

higher I'm going to say higher risk of

299:22

failure. But it's okay if there's a

299:25

higher risk of failure because logically

299:26

speaking um we don't really mind uh you

299:29

know as long as the models are capable

299:31

of I don't know going out and doing

299:32

these things um you know reasonably

299:35

autonomously like it's okay if we do it

299:38

five times only like one of those

299:40

actually need to work right or be

299:42

sufficient to our taste. But anyway yeah

299:44

that's that's more or less the process.

299:47

Okay so checking back in on our model

299:48

you can see that we've now spawned five

299:50

sub agents. One is doing an editorial

299:52

style design. Another is doing a

299:54

terminal style design. Another is doing

299:56

a Swiss style design. Another is doing a

299:58

dense operation style design. And the

300:00

last is doing a warm print style design.

300:03

And two things that built into the brief

300:04

that matter for the video that I'm

300:06

recording. Every variant must find the

300:07

same seven anomalies, which is pretty

300:10

interesting. I guess it's trying to like

300:11

improve my ability to make this video

300:13

because I'd be able to cut between each

300:14

of these and show you guys what each of

300:15

the data sources are. [gasps] Okay, so

300:18

this is the reference scaffold right

300:20

over here. This is what we were looking

300:21

at before. Okay. And it's just like bare

300:23

bones. It's kind of a very neat and kind

300:25

of sleek design. It's not doing a lot of

300:27

BS here, uh, which I like. But, uh, I

300:29

wanted I want to kick this up a notch

300:31

and I want to show us all of the same

300:32

information just at much higher quality

300:34

and levels of granularity. Now, what's

300:36

really cool here on the right hand side,

300:38

you can see we actually have a monitor

300:40

that's showing all of the current agents

300:43

that are in progress. Um if I move my

300:46

head just a little bit so you guys could

300:49

see this my my luscious head. Um we can

300:52

see here we have one called Northbeam.

300:54

Now this is actually the main agent.

300:56

This is build editorial variant that's

300:58

agent one. Terminal variant agent two.

301:00

Swiss variant agent three. Dense ops

301:02

variant agent four and warm print

301:03

variant agent 5. So we actually have

301:06

transcripts of all of these. We can see

301:07

the tools that and we can actually just

301:09

jump into this at any point in time to

301:10

see what's going on.

301:12

And you can see sort of what's happening

301:14

here, right? It's actually designing

301:15

like this editorial thing as like a

301:17

quarterly review style, um, you know,

301:21

uh, design, which is kind of neat. You

301:22

can see how deep we're going with some

301:24

of these designs. Um, I mean, look at

301:26

that. That is next level. We have

301:28

basically the same data. It's just being

301:30

shown to us and sort of portrayed a

301:32

little bit differently. Dare I say, you

301:34

know, a little bit better. Um, so I'm

301:36

looking forward to seeing all these when

301:37

they are done. I will be real with you.

301:39

This doesn't occur immediately. I mean,

301:40

I was saying 5 minutes there, not as a

301:43

uh prescription. Like, it's not going to

301:45

take exactly 5 minutes. In this case,

301:46

it's 854, 841, 827, 812, and 756.

301:50

They're all just like lagging behind

301:51

each other a little bit. Um, but yeah,

301:53

that's more or less where we're at.

301:54

Okay. And it's now just doing some final

301:56

checks. You can see down over here, we

301:58

are approaching our usage limit, which

301:59

means we have used a lot of cloud. Um,

302:02

however, this one is basically finished.

302:06

and see the same thing happening for

302:09

I think this terminal variant as well.

302:10

It's just finishing up now. Uh we can

302:13

actually see the the website sort of

302:14

live if I make this bigger. So we can

302:17

jump to a client very easily just by

302:19

using this little terminal structure.

302:20

You know, I think I think this is

302:22

probably one of my better ones. This one

302:25

here is pretty interesting. It's almost

302:26

like an auto group. This one here is

302:27

like the north beam report. This one

302:29

here is almost like a I don't know. It's

302:31

like a consulting style channel.

302:33

Although you could see one of these is a

302:34

little bit off. And I think this is that

302:36

original one at the very beginning. So

302:37

I'm going to got 1 2 3 4 5. Cool. Okay.

302:41

So up here we actually can see the

302:43

different variants. Um I think I'm going

302:45

to see if I can go back to number one.

302:47

But there's now V1's, V2s, V3s, V4s, and

302:50

then V5s. Um they're unfortunately

302:53

titled kind of annoyingly. So let me see

302:55

if I can just copy and paste this. This

302:57

is dense ops. Take a look at this. I

303:00

like dense ops. uh looks far better in

303:02

light mode. I'd say what we see here is

303:04

overview on the lefth hand side channels

303:06

underneath. So we can see all the

303:08

different channels that are contributing

303:09

or not contributing to uh the economics.

303:12

And this is across all of our clients,

303:14

not just one of them. Our sales, so our

303:16

team win rate, total deals won, revenue,

303:18

closed, average sales cycle, and then

303:19

signals as well. These signals are the

303:21

ones that I was mentioning pop up

303:23

essentially anytime that you um you know

303:24

have a major increase or decrease in

303:26

something. So there's wins with

303:28

conversion rate jumps, but also losses

303:30

with traffic collapse. Then you have

303:32

specific client pages. You can actually

303:33

see their performance, their traffic,

303:35

you can see the pipeline, you could see

303:37

um you know rep performance on the

303:39

account, see who's the best, who's the

303:40

worst, and then search. Like I I hope

303:42

it's clear this is like a full product

303:44

in and of itself. It is extraordinarily

303:46

high quality. And um you know this you

303:48

can set it up so that it pulls from the

303:50

database every morning. Um, I've even

303:53

seen a particular uh company, a big

303:55

company, you know, they make, I don't

303:57

know, probably somewhere between 20 to

303:58

$30 million a year, uh, actually take

304:00

one of these dashboards and just put it

304:02

up on their televisions. They, instead

304:04

of structuring it like this, do so in

304:06

like a like a slideshow kind of way

304:08

where the metrics kind of populate as

304:10

the day goes on and then the reps that

304:12

are responsible for specific things

304:14

constantly see it in front of them. I

304:15

mean, you know, maybe that's a little

304:16

bit more basic and we've been doing

304:18

stuff like that for a while, but yeah,

304:20

you can do you can do fantastic work

304:21

with um Fable nowadays and cloud code.

304:23

This one over here is that terminalbased

304:26

style one. So, you can go overview, see

304:28

everything in front of you here. You can

304:30

then click into a specific client, which

304:32

is cool. I like the color scheme of this

304:34

one a lot. You know, you have LinkedIn

304:36

ads, Google ads through search, organic

304:38

search. You can drill down like pretty

304:40

far with this stuff. And obviously, we

304:41

can add filtering functionality if we

304:42

want to.

304:43

>> [gasps]

304:43

>> Um, I like that you can also just go 1 2

304:45

3 4 5 6. So that's what I'm doing here

304:46

with that smooth scroll. So maybe you

304:48

load up, you deliver a presentation

304:50

using this dashboard, and you go to two,

304:52

traffic, you go to three, pipeline, for

304:54

search. This one over here is much more

304:56

punchy and engaging, I'd say, but also

304:58

more like a visual portfolio project. So

305:00

here you can see the client portfolio.

305:02

You can click into the client itself and

305:04

actually see the growth over time. I'm a

305:06

little bit zoomed in, so let me just

305:07

zoom out, make it look a little bit

305:08

better. That's what we got here. Um, you

305:11

could see active, churned, health, MR,

305:13

all of that. And, uh, yeah, I mean, this

305:15

one is looking at it, I think, on a

305:18

30-day timeline, but yeah, you can

305:20

obviously change that, too. Pretty

305:22

clean. Now, the last thing is now that

305:23

we built a dashboard, we actually need

305:25

to get this somewhere, and I want to put

305:28

this on the internet and make this

305:29

accessible to people that are within my

305:31

organization. So, how do you do so?

305:33

There are variety of different ways. Um,

305:35

the way that I'm going to do it is using

305:36

a platform called Netlify, which

305:38

essentially allows me just to put static

305:39

information on the internet for

305:40

virtually free. Uh, in my case, I think

305:42

it is free. I think they have like a

305:43

free plan where it's free forever. You

305:45

can deploy however many websites you

305:46

want. Uh, or maybe you can do 300

305:48

websites a month or something like that.

305:50

But still way more websites than I ever

305:52

possibly need. So, I already have the

305:53

site. I'm just going to click login, but

305:55

you guys can obviously um, you know,

305:56

sign up to a new one. Once in, we need

305:58

our Netlefi remote MCP server. So,

306:01

similar to how we did before, I'm now

306:03

going to go up here, go to settings.

306:05

I'll then go to connectors, add add

306:08

custom connector, add this, and then I'm

306:11

going to call this netifi. And if it

306:13

starts looking weird like that, you just

306:14

kind of pull this back and just do um

306:16

you don't add the npx sections here. All

306:18

you need is this. Once you're done,

306:20

click add. It'll then check the

306:22

connection, ask you to connect to it.

306:24

So, that's what I'll do here. And now

306:25

I'll authorize this so it has access to

306:27

my client projects now. And I can go

306:29

back and so here are all the things that

306:31

it can do. It can uh I don't know import

306:33

a cloud design from a URL. It can, you

306:36

know, set up a new job, create, deploy,

306:38

and manage my websites. And uh that's

306:40

virtually what I want. So yeah, as I was

306:42

doing this, it uh said usage limit was

306:45

reached. So I did go and I bought I

306:47

think another $15 of it or something

306:48

like that. Uh keep in mind that you guys

306:50

don't have to do this. I have just been

306:52

making like a 5 hour course in basically

306:54

a day. So 5 hours of a probably much

306:57

longer course in basically a day. So,

306:59

the probability that you guys yourselves

307:00

would breach these loot usage limits as

307:02

quickly as I did quite low. Um, but what

307:04

I'll do here is uh just voice discate.

307:07

My goal is I want to get this on the

307:09

internet. My favorite was V3. I just

307:12

connected the Netlefi MCP. I would like

307:14

you to add this and upload this to

307:16

Netlifi and then password protect it.

307:18

Make the password 1 2 3 4 5. Okay. So,

307:21

here is the finished app. Uh, it has

307:23

both a light mode and a dark mode. this

307:25

is a great internal thing to be able to

307:27

send to you or your clients. Uh I'm sure

307:29

you guys could see how easy it would be

307:30

to just gate it so that this is just

307:32

like for one specific client. Maybe

307:34

you'd use a URL parameter or something

307:36

like that like you know client equals X

307:38

and maybe there'd be a special password

307:39

for them, a different password for

307:41

somebody else. What we just did is we we

307:42

basically recreated like agency

307:44

analytics or another one of those sorts

307:45

of marketing uh visualization softwares

307:48

and we did so for realistically about

307:50

like $15 in tokens. Um, but I'll be

307:52

honest, this is way more data than you

307:54

would ever ever have on any one of

307:55

those. You're not just dumping the stuff

307:57

into a Google sheet here. This is like,

307:58

as you guys saw, over 50,000, maybe a

308:00

100,000 rows of data. So, you also have

308:04

like a little client health score on the

308:05

uh side here with like a health thing

308:07

that has been generated. Um, you could

308:09

see, you know, their their whole

308:11

pipeline, their funnel, and where they

308:12

dropped off. So, this would be

308:13

considered like a warning or a dead

308:15

zone. And then you can also filter based

308:17

off of CPL. You can also see there's a

308:18

big bump in CPL here from an average of

308:20

about 100 bucks to literally like 1,100.

308:23

And that whole idea is just so that you

308:24

guys could see like, hey, you know,

308:26

what's going on? Oh, there's some sort

308:27

of alert basis for this client. This

308:29

client is, uh, you know, suffering for

308:32

whatever reason. And so, uh, let me see

308:34

which one was that. Torque Auto Group

308:35

with CL10. If we go back to signals

308:37

here, you can see CL10 on the week of

308:39

March the 9th, um, was critical. It got

308:42

three leads versus its usual 58. uh you

308:45

know and there were also CPC spikes for

308:47

10 as well. Um but I think they were

308:49

like so great that we're probably not

308:50

seeing them on this dashboard. Point

308:52

that I'm making is you guys can design

308:54

basically your own apps at this point

308:56

very easily and very um

308:58

straightforwardly. I would encourage you

308:59

guys have something like this at least

309:00

for your own companies. Uh it's very

309:02

easy to put together along with a data

309:04

ingestion pipeline. You know you could

309:05

do this thing in I don't know probably

309:06

like 30 minutes or so just like I did

309:08

here. Um and keep in mind I was also

309:10

just using cheap models and I was being

309:12

quite rate limited as well. um you know,

309:14

like for my own companies because tokens

309:16

are not a concern. You know, it's like a

309:18

business expense and I'm happy to spend

309:19

a tremendous amount. I think one month I

309:21

spent like 14 or $15,000 on them. Um I'm

309:24

I'm fine just like blasting everything

309:26

with Fable 5, spawning multiple Fable 5

309:28

sub agents and then even using fast

309:29

mode, which is a feature that allows the

309:31

model, I think, to achieve somewhere

309:32

around a 2x speed multiple. Um it

309:35

doesn't really matter to me, but you

309:36

know, in our case, I was trying to do it

309:38

the way that everybody else does it

309:39

because I want you to understand and be

309:40

able to learn. Um, we still got

309:42

something extraordinarily economically

309:44

valuable done in just half an hour. And

309:45

that takes me to that middle of funnel

309:48

data collection and tracking item now

309:50

done. All we really have left on the

309:52

project side is a automation of

309:54

highquality follow-ups again via email

309:56

and SMS. We'll use the same um tools

309:59

that we used back here to do our speed

310:01

delete. It's just now instead of it

310:02

being event driven, it's going to be

310:04

schedule driven. We're basically going

310:05

to come up with the cadence by which we

310:07

are following up entirely on our own.

310:09

And I'm going to show you guys that

310:10

tomorrow. Okay, so today we're going to

310:12

take everything that we learned in the

310:14

previous set of builds and then apply it

310:16

to a really practical marketing

310:17

activity. And this one also does kind of

310:19

verge on sales similar to the uh uh not

310:21

the last one which is the dashboard one

310:23

before which was speed to lead, but it's

310:25

still really important and yeah, I think

310:26

it's worth you guys spending at least a

310:28

little bit of time internalizing that.

310:30

Um what it's going to be is we're going

310:31

to automate high quality follow-ups via

310:33

email and SMS. And so just like last

310:35

time I used this platform called Kuo,

310:37

which I'm not affiliated with at all.

310:39

You guys can do whatever the heck you

310:40

want. I'm going to use Kuo for this one.

310:43

Um, but I'm going to walk you guys

310:44

through how to set it up on Twilio as

310:46

well. And then over here, I'm just going

310:47

to use my email connector, which I've

310:50

shown you guys how to do, too. And this

310:51

is a really, really high ROI sort of

310:53

setup. What it looks like for people

310:55

that are unacquainted with this sort of

310:57

thing is um, most of the time sales and

310:59

marketing functions within a company

311:01

will work out of a CRM and that just

311:04

stands for customer relationship

311:05

management platform. customer

311:07

relationship management platforms just

311:08

have a bunch of leads that are arrayed

311:11

sort of uh including all of the

311:13

conversation history between you and the

311:15

prospect and so on. And so I've actually

311:17

like built out a really simple customer

311:19

relationship manager right over here. As

311:22

you guys could see, we have a bunch of

311:24

different fields. Let me just make this

311:25

a little easier to see. Um our

311:27

hypothetical CRM, and this is actually

311:28

pretty close to my real CRM. Um although

311:31

it, you know, I made it a little bit

311:32

different. I'm not just giving

311:34

everything away. But basically, you'll

311:36

have like a company name, let's say.

311:37

You'll have a certain amount of money

311:39

that the deal is pending for. So, maybe

311:41

this is a $4,200 deal for Silver Birch

311:44

Wellness. Uh you have the contact. You

311:46

have, you know, the date that the

311:48

invoice was sent. Maybe um I don't know,

311:50

uh number of days outstanding before

311:52

people pay you. Uh your due date, you

311:55

know, when the [snorts] invoice was due

311:56

and and when it eventually got paid, the

311:58

various services therein, and so on and

312:00

so forth. And what it really is, if you

312:01

just think about it, is this is a

312:03

glorified Google sheet, right? In our

312:05

case, it's happening on a platform

312:06

called ClickUp, but obviously you can do

312:07

this on whatever platform you want.

312:08

Typically, you'll separate them into

312:10

different stages. So, these are clients

312:11

that are actively in progress. These are

312:13

clients whose invoices are upcoming, who

312:15

we sent a while ago, and you know, we're

312:17

like waiting for them to pay for us to

312:18

start. And, you know, the question

312:20

behind the CRM is basically, you know,

312:22

after a while, man, this gets pretty

312:23

complicated. There are a lot of people

312:24

and a lot of them have different due

312:26

dates. A lot of them are for different

312:27

amounts and stuff like that. um how can

312:30

we stay on top of it and actually make

312:31

sure to actively follow up with all the

312:32

people that need to be followed up with

312:34

that are paying us and or not paying us.

312:36

And that's where a system like what I'm

312:37

proposing really comes in handy. So the

312:40

reality is most people are not following

312:42

up on their invoices anywhere near as

312:44

robustly as they should. Also, people

312:47

aren't checking in with prospects for

312:48

any uh part of the marketing stack,

312:50

whether it is beginning the business

312:52

relationship or it's even just like,

312:53

"Hey, are you making it to the to the

312:55

meeting that we booked last week?" Um,

312:57

so if you guys just follow up more,

312:59

you'll usually make significantly more

313:01

money. And I don't mean to say that

313:02

you're going to like, you know, make 100

313:04

times the money or anything like that,

313:05

but you can genuinely add 20 to 30% of

313:07

the revenue of your organization um,

313:09

simply by solidifying your follow-ups.

313:11

And you can kind of think about this

313:13

similarly to how we were thinking about

313:15

speed to lead before. The whole promise

313:17

of speed to lead, right? And I cannot

313:19

draw to save my life today because it's

313:22

kind of laggy because I'm doing a lot of

313:23

stuff with agents in the background. The

313:25

whole promise of speed to lead if you

313:27

think about it is that we can squeeze

313:29

out way more juice. So if you know

313:32

before I would get 10 leads and then

313:37

only I don't know three of them would

313:39

come onto the call. Okay, that's a call

313:41

rate of 30%. After because we're

313:44

contacting them really really quickly

313:45

and we're following up consistently and

313:47

stuff like that. Maybe we can make it to

313:49

six calls. Well this system basically

313:51

does the same thing. If before, you

313:53

know, we had 10 potential clients, okay?

313:57

Uh and now we were only getting, I don't

313:59

know, two clients, let's say, because I

314:00

don't know, you know, only 20% of people

314:02

actually end up signing the paperwork or

314:03

something in the first place. That's

314:05

before. Uh after the whole idea is

314:08

because we're following up so robustly

314:09

and everything like that, maybe we can

314:11

turn 10 leads into three clients. This

314:14

right over here is literally 150%

314:18

improvement in rev. You know, this over

314:20

here is a 200% improvement in Rev. If

314:25

you attach these two together, 200* 150

314:29

uh or 1.5* 2 rather is 300% improvement

314:33

in rev, which uh hopefully I'm, you

314:36

know, not overstating is pretty insane.

314:38

And if a company was previously making,

314:39

I don't even know, let's just say

314:41

$250,000

314:43

a month on average, and you apply these

314:46

systems and take it to $750,000

314:49

a month on average, you are producing

314:53

500k a month in economic value. And you

314:57

know, whether you're working for the

314:58

company's marketing department or it is

314:59

your company we're talking about here,

315:01

um, you're going to be compensated a

315:02

percentage of that. Even 10% of that, 50

315:04

grand a month is pretty huge, right? And

315:05

so this is this is how I make my money.

315:07

Hopefully I'm just showing you guys all

315:08

the sauce because yeah, you can use

315:10

automations like this to do tremendous

315:12

tremendous uh things for companies.

315:14

Okay. Anyway, so yeah, how does this

315:15

work? We query the the CRM and basically

315:17

we'll read through all the conversations

315:19

between me and the prospect historically

315:21

within my um um email. Then we'll check

315:24

trigger conditions. So trigger

315:25

conditions are just a set of things that

315:27

we're going to put up like, you know,

315:28

does this person actually need a nudge?

315:30

Have they given us all the information

315:31

that uh we need in order to determine

315:33

that they're going to pay or they're not

315:34

going to pay? What is outstanding? Then

315:37

we'll just pick and choose a draft from

315:38

a template library that we've created

315:39

ahead of time. And then we'll send. And

315:41

so we're just going to repeat this

315:42

process over and over and over and over

315:43

again. Um on my case, I like doing it on

315:45

a daily schedule. So once a day, we're

315:47

going to run a system that just loops

315:48

this over. And as I'm sure you guys can

315:50

imagine, we are literally going to use a

315:51

loop for this. And in that way, we'll

315:52

just check to see, you know, hey, who

315:54

still needs a nudge? Who still needs a

315:55

nudge? Who still needs a nudge? Because

315:57

we're using a template library instead

315:59

of just raw dogging it like most people

316:00

will do with AI. The quality of the

316:02

follow-ups tend to be a lot higher and

316:04

they're closer to your tone of voice,

316:05

too. So you'll find there are way fewer

316:07

catastrophic screw-ups because you know

316:09

the AI is just pulling from like your

316:10

TVO all the time. And then you also get

316:12

to send the cadence of how often you

316:15

want. So for instance, let's say um I

316:17

don't know if they've gone quiet and

316:19

there's no reply 7 days after the

316:20

proposal, you can actually check and

316:21

see, okay, have they replied in the last

316:23

7 days? If they haven't, you know, sent

316:25

any emails or something, then and only

316:27

then I will send a brief nudge and I'm

316:28

going to pick from my nudge pool. Um if

316:30

they ghosted the question, we asked them

316:32

and they didn't answer, then I'll send a

316:33

nudge. the nudge will be a little bit

316:34

different because now it's, you know, to

316:35

do with them ghosting the question. Hey

316:37

Pete, let me know if there's anything

316:38

more I can clarify on this. Thank you.

316:40

If they ask for later, okay, they said

316:42

follow up next month and it is next

316:43

month, then we will actually catch that

316:44

and then we'll follow up then. Then if

316:46

there's a warm signal, they open the

316:47

last email a bunch of times, we can send

316:48

them a nudge, too. These are all just

316:49

ideas for how detailed you can make the

316:51

system. Um, and yeah, I think I've just

316:54

more or less given you guys everything

316:55

that you need in order to make it work.

316:56

Um, you can make the template libraries

316:58

as complicated as you want. Um, so yeah,

317:01

I'm going to do pretty simple ones to

317:02

start, but hopefully you guys see just

317:04

how you could evolve a system like this

317:06

and turn it into somewhere really cool.

317:07

Okay, so what do we actually do? Well,

317:09

the first thing we need to do is we

317:10

actually need to connect this CRM to

317:12

Claude. And as mentioned, I'm using

317:14

ClickUp, which is one of many CRM that

317:15

you could use. Uh, you guys might be

317:17

doing this in Notion, might be doing

317:18

this in ASA, you might be doing this in

317:20

Pipe Drive, you know, whatever it is

317:21

that you're using. I want you to know

317:22

that's okay. Um, processes are going to

317:24

be quite similar. either you're going to

317:26

connect to the MCP or you're going to

317:28

connect to the API. And I'm going to try

317:30

connecting to the MCP since obviously

317:31

that's preferential. So, first thing I'm

317:33

going to do, and uh this is just what

317:35

I've gotten in the habit of doing with

317:36

all my automations, is I'm going to go

317:38

down to the connector library, and I'm

317:39

just going to see, do we have a

317:41

pre-created ClickUp connector somewhere

317:43

here? I'm going to type ClickUp. If we

317:45

have a ClickUp connector, we're just

317:47

going to oneshot connect to that. If we

317:48

don't have a ClickUp connector, then

317:50

we'll have to do a little bit more

317:51

complicated work. And as you guys can

317:52

see here, we actually do have a oneshot

317:54

ClickUp connector. So I can now just

317:55

press this button and it'll immediately

317:57

just ask me to log in. Okay. And so what

317:59

I'm going to do now is I'm just going to

318:00

log into Claude. And once I've logged

318:02

into Claude, it will now try and do the

318:04

same connection with ClickUp. So this is

318:06

my workspace here called NSM.

318:09

And I'm now going to connect to that.

318:12

And now, if you guys think about it, we

318:13

now have our MCP connector all good to

318:16

go. It actually just connected to

318:17

ClickUp, right? Cool. So what I also

318:19

want to do is I want to find the

318:21

specific place of this list of mine and

318:24

uh you know in my CRM where this is is

318:26

it's inside of the operations space and

318:29

it's called invoice collection CRM. So

318:31

what I'm going to do is I'm just going

318:32

to copy this. I'm going to go back to

318:35

Claude and then I'm going to say I just

318:37

added and let me voice transcribe this.

318:39

I keep on forgetting that I can do that.

318:41

I just added a ClickUp connector. I want

318:45

you to query a specific list within a

318:47

space and then get me some of the

318:49

information therein just to verify that

318:51

you can actually connect and everything

318:52

is okay.

318:54

So as mentioned I always want to start

318:57

at the end just by verifying can I

318:59

actually get that information. This is

319:01

the list and I'll say get me everyone

319:05

that hasn't been let's see how do I

319:07

frame this inside of the CRM. Get me

319:10

anything with a days outstanding of

319:12

seven days. let's say with a days

319:15

outstanding equal to 7 days. So the

319:18

whole idea will be this will now um demo

319:21

my flow and sorry I just reached my

319:23

usage limit for fable because I don't

319:24

have any more usage credits if you guys

319:26

remember from yesterday. So I had to

319:27

switch that back to 5. Um what it's

319:29

going to do is it's going to see can we

319:31

pull tasks that are 7 days late. Uh 7

319:33

days late obviously is just like one of

319:35

the many benchmarks I will use one of

319:36

the many thresholds. So what it's doing

319:38

is it's going to figure out how to do

319:40

this and then we'll sort of have that

319:42

prompted. We'll then be able to use this

319:43

to build our skill later. Okay. And as

319:45

we can see, we pulled a bunch of

319:46

information here on the specific people.

319:48

So, we have Erica Doyle, amount due on

319:50

receipts, Dr. Nina Patel, and then

319:52

Marcus Ben. And these are all different

319:54

deals. So, what I want to do here is I'm

319:56

just going to ignore this middle chunk

319:58

here because there's a bunch of

319:59

information uh that it looks like it

320:01

misunderstood surrounding what field I

320:03

wanted it to pull. I just want to

320:04

verify, can I also get their email

320:06

addresses? What are their email

320:08

addresses?

320:10

So, if I can get their email addresses,

320:12

then I can actually send a check-in. And

320:14

it looks like their email address is

320:16

nickleclick.ai.

320:18

The reason why is because I just added

320:20

one of my emails. So, all contact emails

320:22

route to my own email for demo purposes

320:23

cuz I want to email them, right? Okay,

320:25

cool. So, now that I have them, send

320:27

Erica Doyle this message. Just going to

320:29

verify this is possible. Hey, Erica,

320:32

just wanted to check in. How are things

320:35

going on the invoice? Let me know if you

320:37

have cues. and I'll say thanks Nick. So,

320:41

we should have an email to Erica aka

320:44

Nickleftclick.ai

320:46

with this. Now, it's saying we can't

320:48

actually do this. We don't have the

320:49

ability to send from my email. I'm just

320:52

going to double check why. So, I'll say

320:54

is there no sending

320:57

uh tool inside of the Gmail connector.

321:00

Ask it that question. We'll see how it

321:01

goes. Let me actually check rather than

321:04

rely on the tool name list. So, now it's

321:06

actually going to look through the

321:07

tools. I checked and my claim holds up.

321:10

The Gmail connector exposes these tools.

321:12

So, create draft, update draft, no send

321:14

message, no send draft. Um, so, okay,

321:18

after finagling it, it's given me three

321:19

options that we could use to create

321:21

emails and then actually send them. I

321:23

was unaware. I thought you could do this

321:24

in the Gmail connector. Um, doesn't look

321:26

like you can, but that's okay. Uh, you

321:27

know, the question is not will I get a

321:30

bug or will I not be able to do it the

321:31

way I want to do it, but when I will not

321:33

be able to do it. So, let me show you

321:35

guys how I deal with that. Um, first I

321:36

asked, so what are our options? I had to

321:38

do a bunch of research and now it's

321:40

giving me Gmail API with your own OOTH

321:42

client. Let's do Gmail API. What it's

321:44

going to do now is build a brief

321:46

pipeline that allows us to do this. It's

321:48

then going to open up OOTH, which is

321:50

that like, you know, check-in thing

321:51

where it like stores my credentials. So,

321:54

it's went it's found some libraries.

321:55

It's now going to write an

321:56

authentication script. After it's done

321:58

authenticating me, I will have to um log

322:01

in. Basically, still doing just a little

322:03

bit of testing. Okay. Hey, and I'm now

322:05

logging in to my email account. So, it

322:07

was fairly straightforward. The model

322:08

just asked me a couple questions, gave

322:10

me a link to click to like give it my uh

322:12

Google Cloud Console access. And now

322:14

it's actually going to send a message to

322:15

my own address to prove that the whole

322:17

path works without actually touching a

322:18

client. So, it sent one to me. Let me

322:21

now take a look. And you can see that it

322:22

has actually done this. Hey Nick, just

322:24

wanted to check in. How are things going

322:25

on with the invoice? Let me know if you

322:26

have cues. Now, we said Erica, but uh

322:28

you know, it's actually done uh uh you

322:30

know, Nick. So, a couple things. I'm

322:31

going to change the subject line now and

322:33

then I'm going to change the message

322:33

that we're sending. First, I'm going to

322:34

say write 10 simple messages like the

322:37

one you just sent me for checkin

322:40

purposes. Use very light language.

322:43

Nothing hard, just super soft nudges

322:46

that fit on one line. Okay. And then um

322:51

for subjects use straightforward ones

322:54

like first name

322:57

or maybe I don't know like invoice first

323:01

name

323:04

or you know how are things going etc. So

323:07

these aren't you know the most pushy of

323:09

follow-ups but the idea is it's going to

323:11

run completely automatically on a loop

323:12

so you might as well um you know be

323:14

nicer as opposed to be super crazy

323:17

harsh.

323:18

I've done a lot of following up in my

323:20

time. There's really smart ways to

323:21

follow up and there's also kind of hard

323:23

ways to follow up. So, all of these have

323:25

m dashes. I'll say no m dashes. We're

323:28

just going to fix all of that right now.

323:30

Just checking on invoice. Uh, we don't

323:32

need the number. Also,

323:35

don't mention the invoice number.

323:39

So, what else do we got?

323:43

Cool. And then sign off all of them with

323:47

thanks dashnick.

323:50

Cool. That looks good. So, first name,

323:51

how are things going? Invoice, first

323:53

name, quick check-in, invoice, following

323:54

up, hey, first name, anything you need.

323:55

Check in, invoice. So, now it's just

323:58

going to write all of these. And the

323:59

idea is I'm going to I'm going to loop

324:00

all 10 of these into a a follow-up flow.

324:05

Cool. Looks good. Looks good. We're not

324:07

seeing any of that invoice information.

324:08

It's just all pretty simple.

324:11

So, excellent.

324:15

This will be our template pool. The idea

324:17

is we can never send two of these in a

324:20

row if they're the exact same. They

324:22

always need to be different templates.

324:24

So the flow will work like this. To

324:27

start once per day, you will query

324:29

ClickUp. You'll determine whether people

324:32

are 1 day outstanding, 2 days

324:35

outstanding, 3 days outstanding, 7 days

324:38

outstanding, 14 days outstanding, 21

324:41

days outstanding, 28 days outstanding,

324:44

uh 56 days outstanding,

324:47

um 84 days outstanding.

324:50

And what happens is every time that you

324:53

find a person that is one of those days

324:57

outstanding, you send an email template.

324:59

um you fill in the information provided

325:02

in the merge variables with the

325:04

customer's actual or rather the

325:05

prospect's actual uh information. So if

325:08

it's being sent to a woman named Erica

325:11

for instance, you should say hey Erica

325:12

you should not say hey first name and we

325:15

will slowly and reliably continuously

325:19

nudge them towards the finish line. One

325:22

more step just so that we don't end up

325:23

sending the same email. After you um

325:27

verify

325:29

that it is matching one of the days

325:32

outstanding thresholds, go through all

325:34

of our email conversation history with

325:36

the prospect to verify and see what sort

325:39

of follow-ups we've um sent them. That

325:42

way, we're not going to send them the

325:43

exact same message twice.

325:45

Okay, so kind of all over the place

325:47

there, but I think I got what I wanted

325:48

to say. Let's see how it performs.

325:53

Looks like it's going to start by

325:54

confirming the scopes of my quote

325:55

unquote token. And uh the history check

325:58

can't work with the token. So, we're

326:00

just going to have to make a new one.

326:02

So, I'll say add a read scope

326:07

to verify. Basically, what we want to do

326:09

is we just want to like actually check

326:10

my the real live email to make sure that

326:13

we're not sending the same thing twice.

326:15

There are some situations as well in

326:16

which it might make sense not to send

326:18

the email at all. Like for instance, if

326:20

a person says, "Please stop emailing me.

326:21

I've received tons of emails from you. I

326:23

don't want another one or something like

326:24

that," then obviously I probably

326:25

shouldn't email them. So, we'll uh we'll

326:27

add that functionality in later just uh

326:29

to verify that, you know, it's actually

326:31

it's actually working. Cool. Anyway, we

326:33

did that authentication. Let's double

326:34

back. So, this is now going through and

326:36

building uh the flow that I want. It's

326:38

not actually a skill just yet, but yeah,

326:41

let's actually run this thing and see

326:42

what goes on. We're going to send to my

326:44

email. Um I'm going to go back here

326:46

first and then open up that. Okay. And

326:48

so now we have, let me see, just running

326:52

a manual check. Sunhill Property, Pace

326:54

Orthodontics, Ridgeway Autodetailing.

326:56

These are the three that should be

326:58

followed up with. Okay. So I'm going to

327:00

go back to Claude and I'll say, "Okay,

327:02

let's run this on a 7day threshold.

327:06

Again, I want you to go through the

327:07

whole process end to end and actually

327:09

tell me everything that you see so that

327:11

I can verify if the flow is occurring

327:13

the way I want it to occur." One more

327:15

change before we do this. There will be

327:17

some situations in which it doesn't make

327:19

sense to continue it continue the

327:21

nudging process. If the prospect

327:23

explicitly tells us not to follow up

327:25

with them for whatever reason, then flag

327:27

that on the actual ClickUp task and then

327:29

don't send the email logically. So now

327:31

it's actually going to go through the

327:33

opt- out detection flow and then narrate

327:35

everything that it is doing. So what

327:37

ClickUp fields are available for

327:39

flagging, a notes, and a chase stage

327:41

with a closed option. Looks like it'll

327:43

use both of those, which is nice. And

327:45

it'll actually start updating my CRM.

327:46

Okay, now it's running the full flow.

327:48

It's found a bunch of my own emails um

327:50

to myself. So, it's just going to verify

327:52

kind of what's what. And it looks like

327:55

clean path earlier is working. So, it

327:57

correctly remembered templates one and

327:58

two from earlier selected number three.

328:00

Now, it's testing the opt out path. Need

328:02

a real inbound reply containing a stop

328:03

phrase. So, now they've added that in

328:05

there basically saying, "Hey man, please

328:06

don't send me emails. Um, please stop

328:09

emailing me about this. We are disputing

328:10

this invoice through our lawyer." Okay,

328:12

so that's what it came up with. If there

328:14

are situations where that would occur,

328:16

you should probably not continue

328:18

emailing the person, right? Kind of

328:19

makes sense. Um, if that does happen,

328:22

then you've done something wrong.

328:25

Okay. Chase stage to close a note and a

328:27

comment for the audit trail. So now it's

328:28

actually updating the flow. Let me

328:30

verify the flag actually landed on the

328:31

record. So now it's just going to double

328:32

check that its job was done correctly.

328:36

And now we should actually see what's

328:39

going on. So it did match the 7day

328:42

records.

328:44

It then searched Gmail.

328:46

It then selected the template high net

328:49

quick nudge on the invoice. Then had an

328:52

opt out check just to verify whether or

328:54

not that worked. And then which

328:57

recipients got uh went through this flow

329:01

just cuz I want to verify that was

329:02

actually those three. Um, why one? There

329:06

were three with sevenday flags

329:12

because I never put there were three

329:13

real contacts. Yes, of course. I want

329:16

you to run this for real using the info

329:19

in the CRM. You'll find cloud will do

329:21

stuff like this fairly often because it

329:22

doesn't want to use tokens, which is

329:24

generally reasonably positive behavior.

329:26

So you guys could see here we're 44% of

329:29

the way through our 5h hour limit which

329:31

resets in 1 hour and 27 minutes. Um and

329:35

that's you know just cuz we're on Opus 5

329:36

which is a little bit smarter than

329:37

sonnet 5 which is the model we were

329:39

using at the beginning of all this. And

329:41

let's just take a look. How are things

329:43

going? Hi Nick. How are things going on

329:44

the invoice? Happy to help if anything's

329:46

unclear. And then this one here is hey

329:48

Nick. Just checking in on the invoice.

329:49

Let me know if you have Q. So these were

329:51

these were sent to our three records

329:52

basically. Um, it actually went through

329:54

and it found those three that each have

329:56

7-day days outstanding sent to them. Um,

329:59

it's also then logged a bunch of

330:01

information about them, I believe. So,

330:03

this was the uh fake, hey, Nick, please

330:05

stop emailing me about this. So, we

330:07

obviously don't run into like crazy stop

330:08

conditions. And now it's kind of letting

330:10

us know, hey, you know, Chase stage is

330:11

set to close, so we're not going to

330:12

continue following up with them. And we

330:14

did all this pretty easily just using

330:16

like a demo CRM that I set up in just a

330:18

few seconds. If you guys run your own

330:20

real CRM through cloud like the way that

330:21

I did so today, you can obviously get

330:23

tremendous alpha. So, what are we going

330:25

to do now? Well, I basically want to

330:26

extend this and turn this into a full

330:27

endto-end flow. So, uh I want to check

330:30

all of those dates. Hey, this sounds

330:32

great. I now want you to convert

330:34

everything that we've talked about so

330:35

far and all of the fixes therein into a

330:37

skill. The idea is I will run the skill

330:39

once every morning. For now, I'll do it

330:40

myself, not as a loop. But we'll run the

330:42

skill once every morning. And then it

330:44

will go through uh 1 2 3 7 14 28 56 uh

330:50

84 and so on and then match to the

330:54

templates as appropriately. Um we'll

330:57

also make sure to do that opt out check

330:59

and everything else that we talked

331:01

about. Uh convert it into a simple

331:03

bulleted SOP so that a future cloud

331:05

instance that does not have the context

331:06

of our conversation will still be able

331:08

to execute this reliably.

331:10

Excellent. So now I'm going to go from

331:12

that prompt to a skill stage. And then

331:14

I'm going to retest this. And it's

331:15

mentioning that the connector is an MCP

331:17

integration that only works inside a

331:18

cloud session. That's great. That's

331:20

basically exactly what we want. The

331:21

skill is now a standard operating

331:23

procedure that every time I run it is

331:25

just going to look for that script. It's

331:26

going to use its own built-in

331:27

integration. It's going to run it and

331:29

then we're basically good to go. Um, if

331:31

you think about it, what we need to do

331:32

now is we need to turn it into a loop.

331:33

So this looks great. I'd like now to

331:35

turn this into a loop that fires every

331:37

morning at 5:59 a.m. On the local

331:40

credential side, how could we turn this

331:42

into a routine where we can run this

331:44

using a cloud instance as opposed to

331:46

locally on my computer? You mentioned

331:48

the MCP integration for ClickUp. I know

331:52

we can add that as a connector in the

331:55

cloud environment. How can we do the

331:57

same with the local uh script? Okay. And

331:59

after all is said and done, we now have

332:01

a routine set up for the daily

332:02

invoice/proposal chase. I'm sure you

332:05

guys could see how you could extend this

332:06

virtually arbitrarily to whatever the

332:08

heck you want. Um, I just did mine on

332:10

that 1 2 3 74 218 56 and 84day schedule.

332:15

Um, granted, if you are chasing people

332:16

for more than 84 days and they still

332:18

haven't got back to you, you probably

332:19

got bigger issues like, are they even

332:20

going to sign the deal? Are they even

332:22

going to pay the invoice? Probably not.

332:24

Anyway, we did all that with clawed code

332:26

and we did it just a few minutes

332:28

realistically. Um, so that's that. Some

332:30

ways to make this better. We could

332:32

absolutely do some sort of higher

332:35

quality nudges. We could, you know, give

332:38

people specific types of value like

332:40

email sequences in newsletters that drop

332:42

like case studies and assets. Um, we

332:45

could we could give them insights. You

332:47

know, we could go pre or postmeating. I

332:49

just did proposal here because I thought

332:50

it was probably the easiest to to verify

332:52

and see. We could do some sort of

332:53

customer story. We could also just rip

332:56

and rinse and repeat a bunch of these

332:57

gentle price objection check-ins that

332:59

I've been doing. Um, but yeah, I mean

333:01

like this is fairly straightforward,

333:02

which officially marks us as having

333:04

finished the last demo of our course. So

333:08

hopefully you've learned a lot. Just to

333:10

give you guys a brief uh revisiting, we

333:13

started with creative generation. So I

333:15

showed you guys how to generate ads,

333:16

organics, uh, images, and videos all

333:19

using AI. We started off with like very

333:21

standardized um compositing workflow

333:24

that used HTML to like generate

333:26

something that looked really similar to

333:27

Stripe and uh later I showed you guys

333:29

how to do AI image generators like GPT

333:32

image 2 and then video generators like

333:34

you know Gemini Omni and then Cance and

333:37

stuff like that. I also showed you top

333:39

of funnel uh for personalized copy. So

333:41

how to take like email newsletters and

333:42

then personalize the hell out of them at

333:44

significantly higher quality outreach to

333:46

these people than just like hello

333:47

welcome to my product. I actually then

333:50

did that for a product that I'm

333:51

currently running, Maker School, my AI

333:52

automation community that teaches you

333:54

how to sell this stuff. And uh as you

333:56

guys saw, it was fairly straightforward.

333:57

Then I showed you guys how to apply the

333:59

same idea of automating text and

334:00

templates through AI, which is something

334:01

that I had a lot of experience with way

334:03

back in the day when I was going door to

334:04

door. Then uh for middle of funnel, I

334:07

showed you guys how to incorporate

334:08

speedtolebased email, SMS, and voice uh

334:11

into a flow. Um very straightforward and

334:14

easy to do as you guys saw earlier. All

334:17

we really do is we just hook up um sort

334:19

of an email polling system that checks

334:21

our mailbox and then if you want to turn

334:23

that into like a digitally run or like

334:25

cloud-based routine um we do that via

334:27

API call. And so we set that up on both

334:29

email and SMS. Then for middle of funnel

334:32

we did data collection and tracking and

334:33

then visualizing all that in this cool

334:34

sexy dashboard which you could host for

334:36

your own business or you could sell to

334:37

people. And then finally bottom of

334:39

funnel automating high quality

334:40

follow-ups via email. And I'm realizing

334:43

now it actually I I said SMS. So, why

334:45

don't I just round that out for SMS. Um,

334:47

in order to do something like that, all

334:49

you would have to do is basically add

334:51

the Kuo connector, which I will jump on

334:53

right over here, do some editing, and

334:56

then um actually add that QUO connector.

334:58

And then [snorts] over here, I'll also

335:00

check for the phone number. So, I'm

335:02

going to go here, hold this down. In

335:04

addition to sending an email, I also

335:07

want you to um draft and then send a

335:10

message to their phone numbers using kuo

335:13

quo.

335:15

Awesome. You'll get the phone numbers

335:17

from the local

335:21

ClickUp

335:23

scrape. Cool. I mean, that is really how

335:25

easy it is to modify these sorts of

335:27

flows to be clear. Um I will have to

335:29

obviously test that if I did want to use

335:31

that at scale. can't just assume that

335:33

it's going to work perfectly. As you

335:35

guys saw, a lot of bugs do crop up

335:37

throughout the process. Okay, great. So,

335:39

now that we've made it all the way to

335:40

the end of the practical builds, where

335:42

do we go from here? Well, there are two

335:44

things that I want to talk about. The

335:45

first is the maintenance and then the

335:47

upgrading of these systems over time.

335:48

And to be clear, when I talk upgrading

335:50

of these systems, I mean like wholesale

335:52

making this better um basically every

335:54

time you run it. And there are a couple

335:56

cool things that I'm going to touch on

335:57

there in a second, but um for now uh I'm

336:00

just going to give you guys probably

336:01

like the 8020 and then you guys can

336:03

apply these advanced tips if you want.

336:04

And then speaking of advanced tips, the

336:06

second is a bunch of advanced tips. Now

336:08

the advanced tips are more about how to

336:09

actually um run this sort of stuff in

336:11

real organizations, less so academic. So

336:15

uh yeah, I'm just going to give you guys

336:16

more or less everything right now. And

336:18

I've arranged my little worksheet here

336:20

to essentially work like this. So, my

336:22

first major uh point of advice on

336:24

maintenance is, you know, if you're

336:26

doing this for a client or your own

336:28

business, it's going to be a little bit

336:30

different, but I'm assuming right now

336:31

that you're maintaining these systems,

336:32

let's just say for somebody else. Um,

336:34

what you're going to want to do is

336:35

you're going to want to get all of your

336:37

login and authentication upfront.

336:41

And the reason why is because u login

336:44

and authentication in practice actually

336:46

end up being something like 70% of all

336:48

of the maintenance requests and and

336:50

problems that I've ever had. Um

336:51

typically a pro you know a problem will

336:53

go like this. Uh let me not use one.

336:56

it'll be like service upgrades

337:00

and you know when a service let's say

337:01

some sort of Gmail or something upgrades

337:04

you lose uh your connector

337:08

after your connector breaks right

337:12

then and I'm thinking about this in the

337:14

context of a client client gets angry or

337:17

client thinks system doesn't work

337:21

that's not how you spell system

337:25

so Then they call you in the middle of

337:26

the night being like, "Hey, this thing

337:27

isn't working." You build something that

337:29

is inherently broken or wrong. And then

337:31

you have to sort of do a fix last

337:33

second. And then in doing your fix last

337:35

second, you kind of have to get them on

337:36

the call. You have to get some sort of

337:38

communication with them. Um meaning you

337:40

have to reauthenticate. So number one,

337:42

it's a it's a bad look for you, but

337:43

obviously it's also a bad look because

337:45

this whole maintenance and upgrade loop

337:47

could have been handled assuming you

337:48

actually had the core credential um that

337:50

we're talking about. What I mean by that

337:52

is despite the fact that it is far more

337:53

secure not to have credentials for all

337:56

of your clients or all of the team

337:57

members within your team's um login and

338:00

so on and so forth, it is way easier to

338:02

handle stuff like this on the fly if you

338:04

actually have the core or root login. So

338:06

much so that you can actually do what's

338:08

called a debug flow.

338:11

So, I don't know if you guys remember

338:13

inside of Claude, just going back here,

338:16

um, there was a, if we go to new and

338:20

then go down to our local environment,

338:23

okay, there is a section where you can

338:25

actually store keys. And one thing that

338:28

you could also do inside of this local

338:30

environment is you could store usernames

338:32

and passwords. So, what I mean by this

338:34

is let's say, you know, we have some

338:35

ClickUp fail that it just continues to

338:37

occur. We could actually store clickup

338:38

user equals, you know, nick at

338:42

whatever.com.

338:43

We could also store clickup_pw

338:46

equals nick blah blah blah blah blah

338:48

blah fan whatever you know 45 I don't

338:53

know cuz nowadays they all require

338:54

numbers and then what happens is you can

338:57

actually store a line in your cloud

338:59

routine or in you know your your your

339:01

local environment because this isn't a

339:03

local environment. So probably your loop

339:04

in this case if you wanted to do in a

339:05

cloud routine it would be the cloud

339:06

environment. You could store a line that

339:08

basically says um you know if

339:12

o issue

339:16

log in via whatever the creds are. Okay,

339:23

navigate

339:25

to

339:27

you know location where all the stuff is

339:30

and then ultimately regenerate API key.

339:33

I don't know if you guys know, but these

339:35

these models now have browsers, right?

339:37

And so, as we saw with Claude, when it

339:39

develops a website, it literally opens

339:40

up a little browser on the right hand

339:41

side. Well, it can legitimately use that

339:42

to log into services for you. And so,

339:44

what you can do is you can have like

339:46

sort of a debug flow where if there is

339:48

an issue, it can self-manage that issue.

339:50

Um, but the only situation in which that

339:52

is doable is if you already have all the

339:54

login and authentication. And

339:56

unfortunately, as mentioned, this is

339:57

like 70% of all the problems with these

339:59

sort of recurring loginins. And if you

340:00

want something to be long-standing

340:01

business infrastructure, a lot of the

340:02

time you just sort of have to let go a

340:04

little bit and say, "Okay, like take the

340:05

login, log into the service yourself and

340:07

then do everything you need to do."

340:09

Okay, so that's the first thing. The

340:11

second thing is um called self-healing.

340:14

And I'll show you guys an example of

340:15

self-healing in a second. But if other

340:17

issue

340:19

you know my recommendation is you give

340:22

the model

340:24

basically total cart blanch

340:29

autonomy

340:30

to fix

340:34

and then rerun.

340:38

This is called selfhealing

340:41

and it's a really important part of like

340:44

robust um skill loop or routine

340:47

development. To make it much simpler for

340:49

you guys to visualize, you can think of

340:51

it as giving your AI systems the ability

340:54

to rewrite their own instructions.

340:58

If a step keeps failing in the skill,

341:00

update that skills instructions and try

341:02

again. So, for instance, you know, let's

341:05

say I'm doing my usual, uh, creative

341:08

generation flow or something like that.

341:10

Um, and for whatever reason, it just

341:12

can't access, [clears throat] you know,

341:13

one of the API routes that it used to,

341:15

uh, access. So, it calls this route over

341:18

and over and over and over and over

341:19

again, and then, you know, it says it's

341:21

just tells me like, "Hey, Nick, so I'm

341:22

sorry, this can't work." Well, rather

341:24

than force the model to go through a

341:26

bunch of the same attempts over and over

341:28

and over again, if you just add a line

341:29

somewhere at the very bottom of your

341:31

skill or somewhere at the bottom of your

341:32

looper routine that just says if you've

341:34

tried doing this more than three times

341:36

and it does not work, then odds are

341:38

there's an issue with the actual core

341:39

service, figure out what the issue is,

341:41

see if maybe a route has changed or

341:43

something like that, and then actually

341:44

go through and update and add a log of

341:46

everything that you've done in order to

341:47

do it so future runs can work

341:49

successfully. If you do that, um, the

341:51

model will virtually never cause any

341:53

problems for you again. So long as it

341:55

has all the resources it needs to

341:56

actually fix the problem, like maybe

341:58

some login or something like that, it

341:59

will heal itself. I've called this

342:00

multiple things over the uh last year or

342:02

so called a self annealing. That's a

342:04

pretty cool term, but I think

342:05

self-healing probably makes more sense.

342:07

It's like kind of how Wolverine's body

342:09

regenerates when somebody slashes him,

342:11

right? Like that's that's sort of the

342:12

idea here. Uh, and so typically what'll

342:15

happen is you should have a phrase, and

342:17

I'm not going to give you the exact

342:18

phrase here because I don't think my

342:20

phrase is perfect, and there are a

342:21

variety of different things you could

342:22

say, but basically if at any point you

342:25

run into the same error more than three

342:28

times, odds are something has materially

342:31

changed in the systems

342:34

that you are interacting with. Evaluate

342:38

these changes, explore, and see if you

342:41

can solve the problem. You have full

342:43

agency autonomy. After you've solved the

342:46

problem,

342:48

report back and update your own skill or

342:52

loop or routine, whatever it is that

342:53

you're running, with a change log of

342:56

upgrades,

342:57

including the problem, your solution,

343:01

and then update the actual skill itself.

343:05

Append to this change log as you

343:08

continue doing this. So we always have a

343:11

breakdown of what happened and why.

343:14

Okay. So this is just something you know

343:16

this is one of those things you can just

343:17

copy and paste and stick into literally

343:19

every single uh window virtually ever

343:22

and you will have a significantly better

343:24

and higher performing system as a

343:25

result. U so this is in practice how you

343:28

sort of maintain the systems. You get

343:29

ahead of the brakes by letting the model

343:32

do more and more and more heavy lifting

343:33

on its own. And I'm not going to say

343:35

that this is like 100% foolproof. I've

343:37

had some situations where I give them a

343:38

little too much self-healing access and

343:40

because of some spurious issue, maybe

343:42

like a weird rate limit, it

343:43

fundamentally changes itself and then it

343:44

screws up. But those issues are far less

343:47

common than um issues where, you know,

343:49

the model just screws up due to some

343:52

temporary rate limit and then ends up

343:53

just calling it quits. The whole point

343:55

of automation really is that it's

343:57

hands-off, right? It's a system that

343:59

just functions without humans.

344:00

Obviously, we want to eliminate human

344:03

labor completely where possible. We

344:04

don't necessarily just want to minimize

344:05

it. as such that we can redirect that

344:07

human labor towards areas of the

344:09

business that are more profitable and

344:10

then ultimately probably better and more

344:12

fulfilling for a human being. Like think

344:14

about, I don't know, weaving a basket by

344:16

hand 5,000 years ago. It's like, well,

344:18

why would you weave a basket by hand uh

344:21

and you know, try and make a livelihood

344:22

off of if you could just build a basket

344:23

weaving machine, you go up one level of

344:25

abstraction, now you monitor the basket

344:27

weaving machine, fix it, make

344:28

improvements, and stuff like that.

344:29

You're you're producing a net good for

344:31

society, right? That's sort of how

344:32

automation is. Imagine if that basket

344:34

weaving machine could now clean itself

344:36

and like avail itself of little bugs

344:38

here or there if it had its own hands.

344:39

That's basically what we're doing. So

344:40

that's one of the changes. The second

344:43

major thing I want to talk about on the

344:44

maintenance end is error logging. You

344:47

know because we're moving away now in

344:49

automating you know marketing stuff from

344:51

the old school platforms like you know

344:53

make.com nadn these sort of drag and

344:55

drop tools. Um, we're currently in like

344:58

an erroring apocalypse where these

345:01

things are failing constantly and we're

345:02

just not seeing them because we don't

345:03

have simple like notification routes

345:06

available to let you know when something

345:08

is or is not working. So, what do I mean

345:10

by this? If you guys were using an old

345:11

school tool, it's funny that this is old

345:13

school cuz it's kind of where I started

345:14

on YouTube talking about this platform

345:16

which I was using to to automate my own

345:17

marketing. But if you guys are using an

345:19

old school platform like you know

345:20

make.com or something like that

345:22

basically what you get with a tool like

345:24

this is you get automated error logging.

345:27

And so every time there's an error

345:29

inside of like one of these flows that's

345:31

drag and drop based like this. You'll

345:33

actually have like a full history log.

345:35

You'll be able to see every change that

345:36

has ever been made. And then you'll also

345:37

be able to see all the errors,

345:38

incomplete executions um physically

345:41

literally on the diagram. You'll be able

345:42

to see the errors and uh you'll also

345:44

receive notifications. And you can set

345:46

those notifications to send you emails.

345:47

you can set those notifications to, I

345:49

don't know, do push noteps or whatever.

345:51

Now, same thing with like N8 even if

345:53

there are issues on this more kind of

345:55

modern uh builder, you know, they they'd

345:58

be able to see these things. You'd be

345:59

able to see tickets, you'd be able to

346:00

have error logging flows, you'd be able

346:01

to get emails. What I'm trying to say is

346:03

like you would know when something

346:04

screwed up. And the whole idea is

346:06

because you know that something screwed

346:07

up, you'd be able to fix it

346:08

significantly faster, right? It's much

346:10

more like observable and transparent um

346:12

than than most things that we're doing

346:14

now. Unfortunately, because AI has just

346:16

moved so quickly and because we're all

346:17

building slightly different things

346:19

nowadays, we don't have that same

346:20

standardized observability, we don't

346:22

actually have the ability to like log

346:24

errors. And so, what I like to do as a

346:27

minimum is I basically like to give all

346:28

of my actual routines, the ones that are

346:30

running my business, the ability to log

346:33

errors to a uh de facto error channel

346:36

inside of my workspace. And so, what do

346:38

I mean by this? What does this actually

346:39

look like in practice? Well, um I'll

346:42

show you guys. I'm actually going to

346:43

wire up one of our flows to to do this.

346:45

Um, but first of all, let's just call

346:46

this error handling. And the very first

346:50

kind of most important thing about error

346:51

handling is you just have to make it

346:53

visible.

346:55

So, you know, you dump this stuff where

346:58

you work. If your business works in

346:59

WhatsApp, then dump it in WhatsApp. If

347:02

your business works on Slack, dump it in

347:04

Slack. If your business works on

347:06

Discord, literally just dump it in

347:07

Discord. Uh but basically every time

347:09

that an error occurs, I recommend

347:11

finding some place specifically for

347:14

errors, like an errors channel, okay?

347:16

And then wiring it up via um some logic,

347:18

which I'll give you guys in a second,

347:20

that just says like error with, you

347:23

know, XYZ. And then what you can do is

347:25

you actually weave in this self-healing,

347:28

right? Air with XYZ tried XYZ

347:32

to fix, you know, succeeded or failed,

347:36

right? If you know it's a failure,

347:38

something like this is totally fine and

347:40

more than enough. Now every person in

347:42

your organization will have basically

347:44

full um top to bottom access and

347:46

visibility into processes that you guys

347:47

are doing. Okay. So how do you actually

347:49

do this in something like you know um

347:52

claude? It's actually fairly

347:53

straightforward. What you do is you go

347:54

down here

347:56

and then right over here you see how you

347:58

can add a connector. What you do is you

348:00

just add slack and then at the very

348:01

bottom you say something like

348:04

if you have any errors send the error

348:06

notification to the you know # error

348:10

slack.

348:12

Let's do this

348:15

um slack chat.

348:18

Okay. And so once we have the connector

348:20

it'll be very very easy to do. Let me

348:21

show you guys a quick example. Um just

348:24

go back here and then down to the bottom

348:26

I'll go settings. Then I'll scroll all

348:29

the way down to connectors. And what do

348:30

we want? We want Slack, right? So you

348:32

can see it's one of the most popular

348:33

ones to connect to because we can then

348:35

send error messages. I'm not going to

348:36

connect to it. Okay, now we're back

348:38

here. Okay, so what I've done is inside

348:39

of my Slack, I actually have a errors

348:42

channel. Okay, and I just set this up

348:43

here as a demo. Um, my actual errors

348:45

channel is private, but um we're just

348:47

going to use this public one for the

348:48

sake of uh discussion. And I'm just

348:51

going to go errors and we'll go public.

348:52

The reason why we're doing that is

348:54

because I just want to make sure I have

348:55

the public version of that and I don't

348:57

forget it. And then here, what I want to

348:59

do is I basically want to say, "Hey, I

349:01

want you to send a sample error

349:02

notification to and uh I'm going to do

349:06

errors public in Slack."

349:09

Okay. And then this will now have the

349:11

ability to basically dump a brief little

349:12

error message and log as it works. So,

349:14

it's gone through the errors. Okay.

349:18

I'm just going to say send it to show

349:20

you guys what this looks like and then

349:21

we can actually wire it into a real

349:22

flow. So boom. Now we have error sample

349:25

notification service invoice chase

349:27

worker environment production time this

349:28

cannot read properties of undefined

349:30

count one occurrence. This is a test

349:31

message to verify errors are alerting.

349:34

Okay. So now that we've verified that

349:35

this does work with the connector. All

349:36

we have to do is I'm just going to say

349:39

give me all the info to ensure you don't

349:42

have to search this channel up again. I

349:46

will add this to a routines prompt

349:49

verbatim. Now we can actually just copy

349:51

this whole message. Go to a routine. And

349:53

why don't we do this first one here?

349:55

Maker school daily ads batch. I'll just

349:57

go up here and then um add my Slack

350:01

notification.

350:03

Then I'm going to paste this.

350:06

And then I'll say

350:08

for the sake of this demo, I want you to

350:11

pretend there was an error in the flow.

350:14

then report to Slack. Okay,

350:18

I'm going to save this and then I'm just

350:20

going to run this now. I don't know if

350:21

you guys know, you guys can actually run

350:22

these live like manually. So, that's

350:24

what I'm doing here. Um, once you're

350:26

done running it manually, you can

350:27

actually go to the bottom here, go today

350:29

at 10:30 a.m. And you'll see the very

350:31

first thing it's going to do the second

350:32

that it runs is it's going to clone the

350:33

repository cuz that was part of our um

350:35

our action. And then it's going to

350:37

initialize the session. Uh you can also

350:39

add a a setup script here if you want to

350:42

that you know includes all the

350:43

information in addition to the

350:44

environment variables. So you can

350:45

actually have like a script that runs

350:46

every single time one of these things lo

350:48

loads or launches which is kind of cool.

350:50

We don't have to do that though. Um the

350:52

thing that's worth noting is this will

350:54

work slower than our uh physical cloud

350:56

code session like the one that's local

350:58

on our computer. And now it's saying I'm

351:00

generating the ad batch uploading to

351:02

drive first. Let me look at the

351:03

structure. Okay, cool. So that's looking

351:05

pretty good. It's going to install all

351:06

the things it needs. Okay. And you can

351:08

see we've just triggered an error here

351:10

from our flow. Drive upload blocked for

351:12

maker school ad batch service maker

351:14

school ad batch render environment

351:15

scheduled task. And then we don't have a

351:17

time. So now we're saying the bulk

351:19

upload of the PGs plus contact sheet

351:21

HTML stalled. The drive connector only

351:22

accepts blah blah blah. So now now what

351:24

I'm trying to say is we have uh we

351:25

actually have an error flow. And what's

351:28

cool is because we have this now we have

351:30

observability and we have logability. Um

351:32

you can stop a lot of problems before

351:34

they actually become problems. I mean,

351:35

you know, if we're running a routine at

351:36

5:59 every morning and it just screws up

351:38

once and the average person gets into

351:40

the office and we'll need the outputs of

351:42

that routine at, I don't know, 8 a.m.,

351:44

you know, you have like a solid 2 hours

351:45

and 1 minute to actually solve that

351:47

problem. Um, so if you didn't have the

351:49

logging, you wouldn't have known until

351:50

somebody does come into the office. then

351:51

you would have had to bug fix and

351:53

actually fix and uh eventually implement

351:55

some sort of production update and then

351:57

maybe it still would have taken an extra

351:58

hour and before you know it now you've

352:00

lagged that core person's um key

352:03

deliverable the thing that they require

352:04

in order to move forward their job like

352:06

five or six hours. So yeah, that's

352:08

that's my debug flow and um that's how I

352:10

personally deal with these sorts of

352:11

systems. Um I basically treat them as

352:14

highly autonomous agents, hence the name

352:17

that have the ability to rewrite their

352:18

own code if needed. Now, on the advanced

352:20

tips side of things, just want to share

352:22

with you guys some learnings that I've

352:24

had over the course of the last maybe

352:26

twoish years of using this sort of AI

352:28

agent workflows in real production

352:30

systems for clients ranging from very

352:32

small to mid-size businesses, maybe a

352:34

few tens of thousands of dollars a month

352:35

in revenue, all the way up to companies

352:36

that legitimately make billions of

352:38

dollars per year. The first is that

352:39

effectiveness beats efficiency. So I see

352:42

a lot of people on the internet trying

352:45

everything in their life to be as

352:48

efficient as possible. You know, they'll

352:50

shave off tiny percentage points here

352:52

there in a process um by I don't know

352:54

like readjusting how they work. They

352:56

will try and save tiny little 100

352:58

millisecond latencies in some system

353:01

because they think that you know the

353:02

more efficient this thing can work the

353:04

better. They will reduce their margins

353:06

as much as humanly possible for some

353:08

core process because they're like well

353:09

I'm saving more money. I'm spending less

353:11

money in order to do it. And you know,

353:13

at the end of the day, they look at

353:14

their KPI metrics, whatever their logs

353:16

are for marketing activities, and

353:17

they're like, "Wa, this is way more

353:19

efficient now than it ever used to be."

353:20

But then they check out their revenue

353:22

and they're like, "Wait a second, why is

353:24

my revenue so low?" The reason why is

353:26

because what you guys are doing is you

353:27

are automating processes that probably

353:29

aren't all that beneficial to your

353:31

business in the first place. Efficiency

353:33

is just doing things fast and cheap.

353:36

Effectiveness on the other hand is what

353:38

you should really be going for and

353:39

that's about just doing the right things

353:41

allocating your resources in places that

353:44

actually you know move the needle of

353:45

your organization. So I just want to

353:47

give you guys a brief example. Um you

353:49

know we do a lot of automation projects

353:51

for a variety of small to mid mid-sized

353:52

all the way up to enterprise businesses

353:53

and I had this one private equity firm

353:55

that had reached out a while back and

353:57

for those of you guys that don't know

353:57

you know in private equity a lot of the

353:59

time what you're looking to do is you're

354:00

looking to book meetings with qualified

354:01

business owners that are either

354:02

interested in investment or want to

354:04

sell. And so this was a private equity

354:06

company that works with um kind of

354:08

bluecollar businesses and that just

354:10

means like kind of home services, trades

354:12

businesses, that sort of thing. Think

354:13

electricians, think junk removal

354:15

businesses, movers, that sort of deal.

354:18

And so they came to me being like, "Hey,

354:19

Nick, I want to automate stuff." And I'm

354:20

like, "All right, sweet, man. What kind

354:22

of work do you want to automate?" and

354:24

they said,"Well, right now we're

354:26

spending about an hour every week on the

354:29

phones uh with prospective business

354:32

owners trying to do due diligence on

354:35

their business, meet them, and then

354:37

build relationships with them. We want

354:40

to automate that with a voice agent

354:42

because we think it would save us a lot

354:44

of money." And I remember thinking in my

354:46

head about this and I was like,

354:48

"Brother, you're going to be automating

354:50

a single hour of your week. It's like uh

354:53

I don't know that old trade offer meme.

354:55

I don't know if you guys have like seen

354:57

the trade alert meme, but basically it's

355:00

like new trade offer detected. You know,

355:02

I receive

355:05

one hour per week of time back

355:09

and then you receive literal like

355:13

devastation

355:15

to your business.

355:17

What I mean by this is if you're

355:18

automating literally the core thing that

355:21

makes you money like this private equity

355:23

firm is, you better ensure you're going

355:25

to do a goddamn great job. And the harsh

355:29

reality is if the only way you make

355:32

money is by jumping on calls with

355:34

prospective bluecollar businesses to

355:36

determine their eligibility and to feel

355:38

them out. If that is the only way that

355:40

you actually make money in your

355:41

business, it makes no sense to automate

355:43

that for a small time savings gain of

355:46

one hour a week. It's like if you can't

355:48

spend one hour a week doing the most

355:50

important thing in your business, the

355:52

only thing that makes you money, you

355:54

know, uh because you want to save a few

355:56

bucks, then do you even have a business?

355:58

No, I don't think so. So, what's really

356:00

interesting is just in my own business,

356:02

for instance, I do a lot of stuff

356:02

manually. And people are always like,

356:03

"Nick, why are you doing so many things

356:04

manually?" And it's not that I'm doing

356:06

everything manually. It's just the only

356:08

work that I am actually doing is work

356:10

that requires my full attention. It is

356:12

recording videos like this for instance.

356:13

It is going on sales calls with

356:15

mid-market and enterprise businesses

356:17

where my presence makes a material

356:19

change to the probability of that sales

356:21

call working out for us. Uh you know

356:23

it's like applying my human meat brain,

356:25

my stuffy sort of fluffy mind to real

356:29

problems within my business. Uh and then

356:31

thinking about those not thinking about

356:33

how to do my bookkeeping or how to

356:35

scrape the leads. sort of these wrote

356:37

monotonous tasks but actually applying

356:39

myself to things that are ultimately

356:41

effective for my business. So if you're

356:44

a business that is looking to integrate

356:45

AI and automation, try to avoid

356:48

efficiency. And I mean that seriously.

356:51

Don't look for cost. Don't look for

356:53

speed. Look for um hey, what can I

356:56

automate that is currently pulling away

356:58

from my time

357:00

uh from being more effective. what can I

357:02

automate away so that I have more time

357:03

to actually spend on things that drive

357:06

the the the top line of my business and

357:09

ultimately the bottom line of my life.

357:10

And on that vein, here are a bunch of

357:13

things that usually fall under those

357:15

camps. So things that you should

357:17

automate, things like data entry, you

357:19

know, think about it. We just automated

357:21

a fair amount of my scraping flow,

357:23

right? We built um a flow that automated

357:25

the process of enriching personalized

357:26

ice breakers with things that we have in

357:28

common and stuff like that. That's

357:29

pretty routine data entry. I'm not

357:31

exactly winning any awards there if I do

357:33

it or if I pass that off to some team

357:34

member. And it takes a fair amount of my

357:36

time. So yeah, I'm happy to automate

357:37

processes like that. Why? Because it

357:39

frees up maybe 40 to 50 minutes of my

357:41

day and then I can spend that time

357:42

actually focused on, you know, things

357:44

like um sales conversations and client

357:46

relationships. You can automate things

357:48

like your first draft copy. You know, we

357:50

automated a lot of ad creative here. I

357:52

showed you guys how you guys could do

357:53

this at scale and automate tens if not

357:55

thousands of images, videos, and pieces

357:58

of copy uh across the board. That's

358:00

totally okay to have AI help you with.

358:02

What you don't want to automate is you

358:03

don't want to actually automate the

358:04

production of the final asset itself.

358:06

What you want to do is you want to use

358:07

AI as an ideation and um sort of concept

358:10

spec tool. Okay? And then you take those

358:12

concepts and then ultimately you pick

358:14

the ones that make the most sense. You

358:15

know, upgrade them a little bit and then

358:16

as mentioned push them live. So I would

358:19

not automate client relationships, but I

358:21

would automate first draft copy. How

358:23

about reporting? As you guys have seen,

358:24

you know, a lot of that reporting stuff

358:26

that you guys typically previously had

358:27

to do was pretty manual. was like

358:29

logging into 5 million different

358:31

platforms and then taking the data and

358:33

like copying and pasting stuff from one

358:34

sheet over to the other over and over

358:35

and over again. Well, you you shouldn't

358:37

do that. What you should do is you

358:38

should, you know, have AI or uh some

358:40

sort of procedural automated system

358:42

manage the automation of that. Okay? And

358:43

that's fine. Um so you can automate

358:46

reporting, but what you should not do is

358:47

you should not automate, you know, bad

358:49

news from those reports. A terrible

358:52

thing to do would be use that signals

358:54

feature that we developed in the

358:55

dashboard and then have that

358:56

automatically sent to the client. Hey,

358:58

here are your signals for the month.

359:00

It's like no, no, no. That's probably

359:01

one of the core things that you as a

359:02

human being make or lose money on. You

359:05

should be in charge of that and you

359:06

should actually go sit down and meet

359:07

with the client. And then ultimately

359:09

scheduling. You can absolutely automate

359:10

scheduling. Although I don't actually

359:12

automate that much of my scheduling. Um,

359:13

but what you should not do is automate

359:15

anything where a client, a prospect,

359:16

somebody that pays you money must feel

359:18

valued. And a lot of the time that's not

359:20

just like here is my calendar. Go ahead

359:22

and book, you know, like a good

359:24

appointment setting flow actually asks,

359:26

"Hey, yeah, I think we have time on

359:27

Tuesday or Wednesday. Uh, does 2 p.m.

359:29

work for you?" Right? Cuz at least that

359:30

that that makes you feel human. It's not

359:32

disrespectful. It's not a robot

359:34

answering uh, you know, and sort of

359:37

weighing the costs of an interaction

359:39

against what you stand to gain. The

359:40

whole idea really is that automation

359:43

should buy you more time with people. It

359:45

should not replace the time that you

359:46

have with people. This is sort of I mean

359:48

this is an AI generated graphic and I

359:50

think this is a good example. These AI

359:51

generated graphics that I've been

359:52

creating throughout this entire course.

359:54

You know there some of them are kind of

359:55

funky and cute here. There they save me

359:57

a tremendous amount of time cuz I don't

359:58

actually have to go through and make the

359:59

thing. Do you know what that means? That

360:00

means that I can actually spend my time

360:01

talking to you and delivering value in

360:03

that way which is probably the core way

360:04

I deliver value. Contrast that with this

360:06

fell over here who gets a text message

360:08

from some happy smiley robot which you

360:10

know took two cents in tokens to send.

360:13

We value you. It's like no you don't

360:15

really value me. And uh that's inherent

360:17

in the medium and the way that you send

360:18

me that message. You know, if you really

360:20

did value me, you as a salesperson come

360:22

down to the coffee shop and actually sit

360:23

down and chat with me. A simple filter

360:25

that you guys could use is just if you

360:27

have a process you want to automate, ask

360:29

yourself, does the customer actually

360:30

feel this process? Is this like a

360:32

firstline thing where we sit down and we

360:34

like we we talk? Is is a relationship um

360:37

at risk here? If the answer is no,

360:39

you're usually good to automate it.

360:41

Likewise, if it involves a real

360:42

relationship like between you and your

360:44

customer, then you should automate it,

360:46

but you can QA it by hand. You know,

360:49

you're happy I'm happy to have you

360:50

automate processes up until some final

360:52

filter like with ads and stuff like

360:54

that, but there should still be a human

360:56

element. You should have your taste

360:57

applied to it. You should have your

360:58

ability to actually uh uh uh, you know,

361:00

be there for your clients if needed. You

361:02

know, is a slight little like, "Hey,

361:04

Pete, can you check in on the invoice?"

361:05

Is that like a relationship based thing?

361:07

Does the customer feel it? Probably not.

361:08

That's more logistical. but a hey Pete

361:10

I'd love to schedule a call with you to

361:12

talk about X Y and Z I think I can

361:14

really help you and really make a big

361:15

deal you know big bottom line impact to

361:16

your business that over there is

361:18

something that's worth keeping human

361:21

okay hopefully everything that I'm

361:22

saying here makes sense um obviously I'm

361:24

a big fan of automation that's kind of

361:25

my whole business model right to be

361:27

clear but as somebody that has done a

361:29

lot of automation now I have realized

361:30

that people will overmate tremendously

361:33

and I've just seen a lot of downsides of

361:35

that recently thank you guys so much for

361:37

making it to the end of the course I

361:38

really do hope that this was valuable

361:40

and helpful. Uh, you know, I've worked

361:41

with a lot of companies up to this point

361:43

in my career. So, this course is

361:44

basically me just trying to compress

361:45

everything that I know into just a few

361:47

hours for you. I I really hope I did a

361:49

good job. Uh, and I'd love to hear your

361:51

feedback. So, if you guys did enjoy,

361:53

please do two things for me. The first

361:55

is please subscribe to the channel. I

361:57

put a lot of effort into these and for

361:58

whatever reason, most of the people that

361:59

watch aren't actually subscribed. I

362:01

will, of course, continue doing this

362:02

regardless of how many people are

362:04

subscribed. So, don't think that I

362:05

won't, but it obviously helps if I have

362:07

support. And second, if you guys are

362:09

considering using these tools to help

362:10

other people, like other businesses or

362:12

organizations, uh, please check out

362:14

Maker School. It's my 90-day AI business

362:16

program that guarantees you one customer

362:18

or your money back. It includes a very

362:21

comprehensive curriculum literally day

362:22

by day about how to sell this stuff. And

362:24

I will show up, you know, 7 days a week,

362:26

365, every single day, and respond to

362:29

almost every post in the community. I do

362:30

that literally every day. It's the very

362:31

first thing I do when I wake up in the

362:32

morning. I consider Maker School easily

362:34

the most impactful thing I've ever done.

362:36

So, if you guys want to be a part of

362:37

that, then then check out the link in

362:38

the description. Anywh who, thank you

362:40

guys very much for sticking around.

362:42

Hopefully, I'll catch you all in another

362:43

course. I'm going to do a lot more of

362:45

these. Thank you again and good luck.

362:48

Get out there and crush it.

Interactive Summary

This video is a comprehensive "0 to 1" masterclass on automating marketing and sales pipelines using modern AI tools, primarily focusing on Claude Code and the Claude desktop app. The presenter, Nick, shares how his marketing business generated over $500,000 in a month utilizing these systems. Throughout the course, he explains the RACE framework (Reach, Acquire, Close, Expand) and outlines the progression from simple prompts to reusable skills, scheduled local loops, and finally autonomous cloud routines. Nick walks viewers through several practical builds: generating image and video creatives using procedural HTML and models like GPT Image 2 and Higsfield; setting up hyper-personalized email newsletter and cold outreach campaigns with fuzzy variables via the Kit and Apify APIs; implementing instant "speed to lead" email and SMS auto-responses using Gmail and Quo; and building interactive marketing analytics dashboards deployed to Netlify. Lastly, he shares advanced advice on error logging with Slack, designing "self-healing" agents, and prioritizing effective human relationships over minor efficiencies.

Suggested questions

5 ready-made prompts