Hey Crockbot, could you go get that

information off of Twitter for each of

my followers either through the API or

by physically going and looking at their

profile, create a notion database, pull

that information into the database, um,

and then hand it over to me. Now,

typically this is something we might use

AI to write a script for to accomplish

that goal, right? This is typically like

a personal scripting or software

approach. Now, I just had an agent do it

and it's kind of better because it's

completely disposable. Um, it took me

maybe five or 10 minutes and now I have

access to this agent forever. I can

tweak it. I can update it and I can

approve it. Today, we're going to talk

all about Grockbot. Specifically, we'll

talk about what Rockbot is and how it

works. And then we're going to move into

how you can get started and how you can

set up the tool to start automating your

own workflows. And finally, we're going

to dig a bit deeper and I'll show you

some of my favorite workflows and uses

for this tool because really uh the

possibilities are endless, as cliche as

that might sound. Um, and we're all

still kind of figuring out what this

tool is capable of. So, what is

Grockbot? It might be easier to show you

rather than explain. I have a bot open

in front of me here. Under the hood,

Grockbot is just an agent with a

computer. So, an agent, as you're

familiar with, um, is just a model that

has access to some tools and can do

stuff for us. And this agent has access

to a computer. So if I click this

window, we can actually see the

computer. More than that, we can

interact with it like a Ramount desktop.

So behind the scenes, this is running on

a virtual machine in the cloud

somewhere. So none of this is happening

on my laptop. And I have a terminal in

front of me. I could run a command. Um I

have a file system and I have uh X here.

Now, the cool thing is that Grockbot has

access to all of these things the same

way we do. So, more than just an agent

with a computer, it's an agent with a

computer with really good computer use.

And we can actually teach Grockbot how

to do certain things on this computer by

recording and teaching a task to

Grockbot or by prompting and instructing

it. And so today I'm going to go over

some workflows and some things that I

found Grockbot to be really good at. But

just to give you a bit of a taste there,

that might include things like doing

grocery shopping. For example, I asked

Grockbot to look at Costco and look at

Amazon and add certain things to my cart

here and then compare prices, um,

compare delivery, uh, charges, compare,

um, times, etc. And it's really good at

stuff like that. We can control the

computer just like Grockbot can. So when

it needs us to step in to complete

two-factor authentication or log into a

website, it can hand that computer off

to us and allow us to complete that

action. So recap, what is Grockbot? It

is an agent with a computer that runs in

the cloud. So nothing's running on your

machine and that gives us access via

mobile and via desktop that has really

good computer use. And that's really it.

That's the foundation. So while the

inputs and outputs are chat, we're

always typing text. Um those are pretty

simple. Under the hood, there's this

whole like operating system you can

think of like as a server or a really

powerful tool that allows us to get

stuff done. And that's the foundation

for the rest of this video. So, just to

drive this home, I can come over to my

marketplace bot here and ask it to do a

search uh for a variegated monster

elbow. Um, and what we'll see in the

computer use on the right, uh, is that

the bot, because I've already authorized

and logged into Facebook, is going to be

able to open up that machine, uh, go to

a new tab or maybe use the same one and

do a new search. It's going to take all

the actions that we would take. So,

really, it's more than a personal

assistant. It's more than some of the

other AI tools you're familiar with.

It's an agent that can do everything

that you can do on a computer. So, we

can see Grockbot performing searches for

a few different plants um and only

finding a few of them on Craigslist. So,

what can Grockbot do? I want to give you

a few use cases before we dig into how

to use Grockbot and how to get things

set up so you kind of understand why you

might adopt a tool like this if you're

still curious. I've been using Grockmot

for more main four main things which is

first writing software. Um second

building personal software as agents.

This is a little bit different. I'm

actually moving the software into chat.

We'll talk about what that means later

on in the video um for information and

knowledge retrieval. So looking across

the cursor and SpaceX, Slacks, notion,

GitHub, it's been really helpful. It's

actually how I onboarded to cursor.

We'll use that as an example. And fourth

for automating workflows. So, very

similar to what I just showed you with

this grocery example, you can think of

any workflow, any sort of knowledge work

you might have to do, whether that's

going on LinkedIn and reaching out to

people or finding information if you're

a salesperson, bring that into your CRM,

reaching out to these clients, drafting

emails, etc. A lot of our go to market

team actually does just that with

Grockbot. Similarly, for our most recent

launch, I actually used Grockbot to

coordinate across all the different

feedbacks receiving for our launch in

Slack in notion and then to sync all the

content between notion to PRs to

incorporate notion comments, PR

comments, Slack feedback, and then to

kick off cursor cloud agents to make

sure that all the feedback was

integrated and it saved me a tremendous

amount of time and it was a really great

use case. So, we'll dig into those four

things, but first I want to show you how

to set things up. So, what are these

agents and how do they work? Well, let's

take a look at an example. And this is

one of my favorite, my tech demos

example. Um, and if I open up the

settings here inside the bot, we can see

there's a name, a title, um, and a

description. The name is technically um,

optional and doesn't actually influence

the behavior of the bot. The title is a

description of what the bot does. And

then the actual description is a longer

form um explanation. So this bot looks

at my Twitter bookmarks. I call it like

uh colloquially the Devril bot. And each

day um comes up with something that's

cool out of those bookmarks. From there,

it drafts up a prompt in my style, sends

it over to me, and then I can approve or

deny that prompt. Behind the scenes,

it's going to take that and kick it off

to a cursor cloud agent through the

cursor integration. And that brings me

to another really powerful part of

Grockbot which is that it supports all

of the integrations MCP servers and

plugins that cursor does as well as

skills etc. So we can add things like

Gmail, Google calendar, Google drive um

some of the more complex ones and

internal things that our team are using

here as well as other SASes like

Amplitude for data analytics, Twitter

for accessing uh more structured data,

one password etc. Uh and the nice thing

is that we can even add multiple

accounts. So I have multiple Gmail

accounts there. If I go to um actually

notion, which I think is the better

example. I can have my SpaceX cursor and

personal accounts. We can retrieve

information across all those sources. So

this has cursor as a connection. And

what's happening is that it said, hey,

you know, there's this example shader

lab from Basement Studio. If I went and

looked at the screen, I can even see

that it was looking in my bookmarks and

it was scrolling and found this um

shader lab example probably down here.

Yep, there we go. And Crockpot said,

"Hey, I can build a demo for this in

your demo repository." It ran the prompt

by me. I tweaked it a little bit and

then approved the build. From there it

actually started a build in cursor which

if we take a look at cursor cloud in

chrome we see that the build is running

and this is the really nice part about

the integration with cursor which is

that if you run things in cloud agents

they sync across cursor web but then

also cursor desktop. So this automation

kicks off cloud agents and starts

building software and I have access to

that immediately. Now, I think one thing

to call out which is really interesting

is that there are some really unique

feedback loops to this workflow, which

is that if I have these automations

running on these different triggers,

we'll get into that. I could basically

build software that's kicked off via new

information, via actions that I take,

via any external sort of data source.

And I think that opens up the

possibility for some really cool

software factories and really cool um

workflows. So, we'll talk about that a

little bit more, but this um bot is

going to kick off that build and then

notify me when it's ready and actually

send along some screenshots and videos.

We can take a look at a previous run

here where I was uh demoing some like

prompt uh benchmarking um or eval uh and

so Grockbot built this website, took

some screenshots when I was done and

opened a PR on this repository. So,

that's just one example. Now, the thing

to call out here is that there are three

main ways you can run a bot. The first

way to run a bot is by chatting with it.

So, if I say, "Hey, you know, I want you

to do something." The second way is by

clicking in uh to this routine here and

um scheduling one. Now, the easiest way

to schedule a routine is just to tell

the bot, "Hey, I want you to run on

weekdays at 9:00 a.m." And it'll build

the the trigger here. You can also build

a trigger by clicking in via the guey

and setting it on a schedule, setting a

Slack message, setting an event from

GitHub, etc. And that's also an easy way

to sort of modify the name instruction

set, run history, run a test run, delete

the routine, or disable the routine. So

that's the basics. When we create a new

bot, we're really just going to get a

chat interface. So let's say I create a

new bot here. It's an empty chat

interface. we can start to build this

bot by simply explaining to Rockbot what

we want to happen. Um, and it'll even

kind of prompt that out of us. So, the

TLDDR here and the thing I want you to

remember is that if you're wondering if

you can do something with Crockbot, just

ask. It has a ton of different tools

that it can fetch that information for,

and it's going to proactively help you

accomplish your goals. So, I started

talking about this tech demos bot as

sort of an example um and an explanation

of what bots are and how they work. I

think a good follow-up question is

probably, well, how do I know that this

thing is going to do what I ask it to?

And second, what permissions does it

have? Because maybe I just logged into

my Twitter account here. Theoretically,

that means that the bot can go to the

homepage and start tweeting things,

right? That might not be uh any good.

How do I know that the bot won't do

that? And the short answer is that the

bot can do anything you give it

permission to do. However, the

architecture of Grockbot makes it such

that we can trust the way that it's

working to um sort of align with what

we'd like. And we can see this if we

click our profile here and go into

settings. Under the bot settings, there

is a allow and deny list. So you can set

rules for Grockbot that it will adhere

to. And basically the way that this

works is that there's a reviewer agent

that looks at every single action

Grockbot put could take and it either

says I'm going to allow Grockbot to do

this thing. I'm going to deny this thing

or I'm going to escalate it to the user

and I'm going to ask. You can add rules

here such as uh when Grockbot wants to

send a tweet uh it should not allow it

automatically but first ask. And that's

going to prompt Grockbot to ask me

before it does something. Uh, so if I

was to say like, hey, send a tweet,

it'll ask me before it it takes that

action. Under the hood, the way this

works is that actually the review list

or the review agent um has these lists

in context and it's proactively looking

at every single message and then

checking to see if um it matches one of

those rules. The other thing to call out

is that if I log into a website on one

bot, because the underlying computer is

the same across all these different

bots, it will be um active on the other.

So, if I open, for example, this weekly

product digest bot and I open up our

Chrome instance and go to x.com, I'll be

logged in on this bot. It has access to

Twitter the same way the other bots do.

And that's just um fundamentally the way

that the bots work. So, this bot, for

example, is scanning Facebook

Marketplace and Craigslist for espresso

machines. Um, and all of my bots now

have access to those two sites because I

logged in with them. So, I mentioned

that bots can be run in three different

ways. And those are first if you send a

bot a message. Second, if you define a

routine or a trigger that kicks off the

bot, but third, bots can also be

triggered by other bots, which means

that they're composable. And this has a

couple different implications. The first

is that you can actually create a group

chat with different bots. So if I wanted

the cursor product expert, the cursor

product digest and then I don't know my

prioritization bot in a group chat, I

can do that. Now an important thing to

mention, if I just say hey in this group

chat, each bot is going to take turns

responding. So that's one thing you

should be aware of if you want to send

messages to multiple bots. A better way

to trigger bots sequentially or sort of

on demand is to poke into one bot. So,

say I jump into my writing bot and I

say, "Hey, uh, look at what shipped in

cursor via the um cursor product expert,

then write a short tweet, uh, about um,

it using my writing voice." And this is

how I use that sort of composability of

bots to chain actions together. So, my

writing bot is essentially acting as an

orchestrator and it's going to go over

to the cursor product expert and say,

"Hey, you know what's new?" And then

it's going to bring that back into uh

the um the chat here. And so we get a

little message. We can see the product

expert got um pinged. And then the

product expert is going to start working

on the query that was sent across by the

writing bot. And so really that has

implications for how we define workflows

because we should be thinking about

these bots as specialists or maybe

employees that work in certain domains.

And so you could have a routine, for

example, that runs every weekday and

routes requests through different bots

to accomplish a goal. This is sort of

what I meant earlier when I said there's

so many possibilities that this is a

totally green field product and there's

a lot of different stuff to try. I can't

possibly come up with all the different

ideas and it presents an opportunity for

you to come up with amazing ideas and

share them with everyone. So we can

actually see here that the cursor

product expert shared a change log from

um earlier today. Uh and now it's

passing it over to the writing bot to

draft up some responses. And that's

exactly some of the workflows that I've

been using this for. Okay, so to recap

some of the most important features,

well bots can be triggered by messages,

by routines or triggers or by other

bots. And that makes them composable. It

means you can have group chats with

bots. It means that um you can have

chains of bots that work together to

accomplish workflows. Bots have access

to a computer under the hood that stays

logged in. It has persistent sessions to

the accounts you give it access to. If a

bot needs you to log in, it can hand you

that computer or it can request a secure

input. I didn't mention that, but if a

bot just needs a password, it can

actually send you a secure form where

you just type your password in. That

means that you can take action to get

bots over the hump, over uh certain

difficult actions so that they can

continue doing work for you.

Furthermore, if you want to record a

workflow, you can click record and just

click through the screen to get the bot

to do that. You can also provide allow

or deny list rule sets to make sure that

bots don't take actions you don't want

them to or always take actions you do

without asking you for prior approval.

Setting up bots as is as easy as just

sending through a prompt or a voice

note. And that's often the way that I

go. So from start to finish, that's kind

of everything you need to know to get

running with Crockbots. Oh, and we also

have the plugins which allow you to

connect accounts, connect services with

a couple clicks and some OOTH has been

rock solid for me so far. So, with that,

now I want to talk about some of the

workflows I've been using this tool for

and give you examples um of both how I

think it can be helpful and also what I

think the future of software development

or workflow automation looks like. Okay,

so the first workflow I want to talk

about, this one's actually pretty cool.

I whipped up this morning on my phone.

So, I have a view of it here, but we're

actually going to cut over to my phone

and I'll walk you through exactly what I

did to build this out. I had this idea

that um I've made a lot of friends on

Twitter and a lot of them don't actually

live in San Francisco where I live. They

live all across the United States,

across the world, but most of them have

their location in their descriptions.

That means it's public information,

right? I was wondering, hey Crockbot,

could you go get that information off of

Twitter for each of my followers, either

through the API or by physically going

and looking at their profile, create a

notion database, pull that information

into the database, um and then hand it

over to me. Now, typically this is

something we might use AI to write a

script for to accomplish that goal,

right? This is typically like a personal

scripting or software approach. Now, I

just had an agent do it and it's kind of

better because it's completely

disposable. Um, it took me maybe 5 or 10

minutes and now I have access to this

agent forever. I can tweak it. I can

update it and I can approve it. So, you

were looking at the mobile view. Um,

I'll pop open Notion right here. And I

follow roughly eight or nine00 people.

Now I have um a list of all of them here

with profile images, which is kind of

nice. Uh, their descriptions and where

they're located, the country, the um a

geo confidence here and then like kind

of a fuzzy location. So, you know, say

uh, you know, I'm a San Francisco guy

and I'm going to New York. I could

search here New York um, and I could get

all of my followers that reside in New

York. and then I could ping people and

be like, "Hey, you want to grab a

coffee? I'm the annoying coffee guy out

here asking people to grab a coffee."

This, I mean, quite literally, I spun up

in 15 minutes. Uh, actually, I think

closer to 10 minutes um with my phone

this morning. And that means it's less

about uh what you can build and more

about what you can think or what you can

sort of dream up, what your imagination

contains.

Basically, take anything that you could

do manually. Hey, I'm going to go like

go through all my LinkedIn follow or

LinkedIn connections and do this thing.

And just think what if I gave this to an

agent and just added to it manually. And

I think that's really amazing and really

cool. So that's one workflow example

that I did on my own. Okay. Second, and

I think this one is actually a little

cooler. In front of me is a piece of

software. I didn't build this with

Grockpot. I actually vibecoded this

mostly with Fable 5, but I spent a lot

of time building out this uh personal

training coach. So it allows me to

period periodize my training and use

some principles of uh you know

scientific um hypertrophy and strength

training which is something I'm very

passionate about to build a program and

then from that program I can go through

and log my exercises I can get a

projection for the next week and that's

great but what I realized is that you

know this is a piece of software and if

you build software then you have to

maintain it and a lot of times this

breaks or there'll be stuff that's

incorrect and the way I got to this app

was by like weeks and honestly months of

building building on top of what was

relatively simple progression logic and

a shared data source and that brings us

a little bit back to like what software

is. So if you think about software, it's

really like three things. It is logic.

I, you know, have some business logic or

some computations or things I want to

happen with some data, which is the

second piece that I'm storing

persistently either in a database or a

JSON file or, you know, object storage,

but really data can be anything. It can

be a CSV, it can be something in Google

Drive, it can be in GitHub, it can be on

your computer. So I'm storing some data

and I'm performing logic on that and

then I'm interacting with that software

through an interface.

So you could think about it as well like

there is a server that is performing

those computations. There's a persistent

data store and there's a UI client

server database right that's that's

software kind of in a nutshell most full

stack applications. Well, I thought,

what if I brought that software into

chat? And I think what I've started to

realize is that agents are software. And

so, what I did with this um Arnold bot

here is that under the hood, this is my

strength and hypertrophy programming

coach. Now, it's possible many of you

have just used chatbt or grock or codeex

or whatever claude for this, but the

reason this is different is because I

gave this um agent access to all of the

logic that was in my app. So, I took my

app, I broke it down into a set of MCP

servers into a set of skills and

plugins, and then I prompted Grockbot uh

to build all of this. And I actually did

all of this from inside Grock. So, if we

go back up here, you can actually see

I'm adding periodization plugins and

skills incursor. There was an error

there, but took one more try. And I

could open that in cursor as well and

take a look at the actual PR and see

what I built and then committed to

Grockbot.

And then I'm just using the same

interface to chat with Grockbot. And

it's sending through all of my different

workouts. Under the hood, it's logging

these in git as commits to, I believe, a

JSON file. But what I have is a piece of

software with logic and a computer. The

agent is the computer and the interface

is chat. So, while this might not look

as beautiful or as fullfeatured as what

I had before, this is much more easily

maintainable. And if there's an error,

the bot is actually going to be able to

diagnose that, write code, commit to the

GitHub repository, and publish that. And

that's a much faster feedback loop than

my training app, right? Because in my

training app, I'd be basically have to

fire off cursor cloud agents. I'd have

to go home. I'd have to, you know, maybe

look at things, approve, deny, merge,

set up this like preview, deploy,

feedback loop, which is maybe a little

bit tricky depending on the technologies

that you that you use. And so this is a

fundamentally simpler approach to

software whereby the feedback loop of

iterating on that software is in the

chat. Another thing I kind of wanted to

build AI chat into my training app. I

wanted to be able to chat with it as if

it was a coach. With Crockpot, I already

have that out of the box. So, depending

on what the software is that you're

trying to build, it might just be an

agent. And we're very soon going to ship

multiplayer for tools like Rockbot

that's going to allow you to basically

share this software with all your

teammates. So, this is the personal

software approach and something to keep

in mind. If you can break your app down

into a client, a server, and a database,

consider attaching some sort of data

source to Grock, either by connecting it

to Google Drive, giving it access to a

git repo, or just giving it some files.

It has a computer and then using the

agent itself as the computer, either by

giving it skills, and you can also just

define skills for Grockbot. Something we

didn't talk about, um, you can attach

files, teach a task, but then Grockbot

also has access to skills if you define

them. um MCP servers tools plugins um

and then using chat as the input and

output that may be a replacement for a

lot of the personal software or personal

scripts you've been building. Okay,

another way I've been using Grockbot is

to write code. Now, we already talked

about the sort of Xbookmarks example

where I'm building demos off of my

Twitter bookmarks and I'm dispatching

agents that way. Now I want to talk

about a bit more of an advanced concept

which is doing actual software

engineering from inside Grockbot and I

think Lauren part of the cursor team

really amazing engineer um stated this

best. I sat down with her at a

conversation and she described Grockbot

to me as the perfect outer loop. And so

I actually call this agent outer loop.

Basically, what we want is some outer

loop where we're gathering context,

understanding problems, pulling in

context from these different systems

before we kick it off to a cloud agent

that acts as our inner loop and actually

builds the software. Why do we do this?

Well, we do it to protect the context of

the inner loop, the cloud agent, um, and

to give us a place to explore. Why is

this important? Well, LLM's work on next

token prediction, right? That means that

everything that's in context is going to

influence the outcomes. So if you're in

an agent and you're like, "Oh, read

these files. What should I build? Should

I build this? Help me think through

these problems." And then from that

point, you start delegating fixes. That

agent is actually going to have a bunch

of dirty context and a bunch of

information that might not be relevant

to what you're trying to build. So with

this outer loop in loop uh architecture,

we can protect the context of that inner

loop and focus on collecting information

with essentially an agent that's

specialized for that. So this agent I

have here uh and if we look at the

description, this is an expert on the

cursor codebase, documentation, and

marketing repos. So I gave it access to

all of those things. And its goal is to

help me look through sources of context

and come up with perfect prompts to then

delegate to cloud agents with the skills

that I use to build at cursor. One of

those is Lauren's um Pstack uh plugin,

which is amazing. So an example prompt

that I might send through here is do

research on user credit grants. this is

a problem I've been thinking about. Look

across notion and Slack and see how our

company engages with granting user

credits. Then come up with a plan to

improve the system and kick off a prompt

to a cloud agent. So what this bot is

going to do is going to go through all

of those sources, start to understand

the problem. Ideally, it would come back

to me. We'd chat through it and then the

same thing we did um down here with our

tech demos, it's going to fire off uh a

cursor cloud agent to go out and

actually build the thing. Again, the

goal of this and why we might do it is

to priorit prioritize and protect the

context um of

that interloop coding agent and keep it

more specialized. So, a bit of like meta

discussion here, a bit of feature of

building, but I've been using this flow

to basically kick off the the bots that

I have working in cursor. So, a lot of

my cursor um bots end up started from

Crockbot. We're not replacing the code

coding harness. We're just augmenting it

and staging for that coding harness.

Another really great use case, I won't

dive too deep on this one, is just a

cheap of staff or prioritization bot.

So, I use this kind of for a catch-all

of like, hey, here are some really good

links on Devril. Can you put these in my

notion? We'll just write to my database.

It also has access to my uh Gmail and my

calendar, so I can have it draft

responses to emails, which has helped me

automate a few of those things. And then

I do a weekly review and a Monday weekly

kickoff as well and it prompts me to do

those because otherwise I just don't

actually do it, you know, so I have a

bot keeping me accountable essentially.

So that's another really great use

there. So we talked a little bit about

coding, we talked about personal

software, we talked about workflows, now

I want to talk about information

retrieval, which is maybe one of the

simpler ways to use Grockbot, but

honestly insanely helpful. And this is

how I onboarded to cursor. Um, if you

want to see a write up of this, I have a

Twitter article that I shared out um,

not too long ago. We could go to x.com

and the article is chat is all you need.

And I kind of talk about, you know, um,

lower on down here. Uh, we have

information retrieval. I talk about how

difficult it can be uh, to access

information across different resources.

You might have felt this if your company

has a notion, a Slack, a different wiki,

uh you know, you're responding to

emails, it can be really hard to find

stuff. And so my onboarding at Cursor

was largely driven by Grockbot. And I

used um actually some combination of

this Cursor product expert bot um number

one to answer questions about the

product, but then also to help me answer

how the company works. So, you can even

see when I was doing research for this

for this um video, I said, "Hey, how do

Grockbot group chats work?" It has

access to the codebase. It could just go

and find that for me and tell me that uh

all the agents that you add join the

room. There's an orchestrator, but it's

not one of them. And then the host runs

a round robin after you send a message.

So, it wakes each member in turn. That's

how I knew what to tell you all, right?

And I've been using this to learn about

the products, but also to search across

Slack and Notion. My onboarding really

took like a couple of days. Uh,

and it's still ongoing, right? But I

feel like I can answer all these

questions because all of that

information is accessible and saved off

somewhere. All right, so I've been

talking a lot, but the main things to

remember here are that one, Grock is an

agent that's running in the cloud. Uh,

Grockbot is an agent with a computer,

and it can kind of do anything you can

do. So you really have to kind of expand

what's possible here and think about

writing code, running code, running

software, doing workflows, controlling a

computer the same way you can. And if

you delegate appropriately, you're going

to be able to accomplish quite a bit

with this tool. You can control what

Grockbot is able to do or not do. And

you can access Grockbot through your

phone or your desktop computer. It runs

in the cloud, so you can close your

laptop. You don't have to worry about it

shutting off. It's a persistent Linux VM

uh under the hood on a server that's

running in the cloud. The main

difference between Grockbot and many of

these other personal agents you see out

there are that there's zero setup and

you don't have to do any server

management. You don't have to do any

configuration. You can just jump in and

start building and that's really

impactful. So, I highly recommend giving

it a shot. Check it out. Let me know

what you think. Let me know if this

video helped you. But until next time,

I'm Matt with SpaceX. Peace.