Um, personally I think that uh the best

use of you of AI is really as like a

like a pair like someone you're pair

programming with and not someone as not

not a not a tool to just replace the act

of writing code or worse outsourcing

your thinking.

And I think there's a tendency like, you

know, because AI is so exciting, you

want to put AI everywhere and be seen as

someone who's very, you know, um, what

do you call it? Like, uh, on the ball, I

guess, with with AI. Uh, that you feel

this pressure of like, you know, I'm

just going to I got to increase my

productivity. I got to ship like 100 PRs

this week and barely understand what I'm

doing and, you know, vive code my way to

a million ARR or whatever.

Um but I think you know um one of the

things that I've personally seen is that

although when I use AI I save some time

you know writing the code I actually

find myself spending more time reviewing

what the AI did and like correcting it.

So, you know, like I've I've tried the

the thing that a lot of people say like,

you know, you uh instead of prompting an

AI to just build the feature outright,

you sort of get it to uh build a spec

for you first, then you review that

spec, and then you do this whole thing,

then it's like,

>> uh, okay. So, I assume you can see my

screen.

>> Yes. Yeah, we're good. [clears throat]

So yeah, today yeah I think I think the

big theme for me as I've been using

agents to write code and I'm sure a lot

of you have had the same experience as

well is how do you trust it? You know

especially if you are an engineer that's

been writing code for a very long time.

You have a lot of opinions and lessons

that you've learned about doing good

engineering. And when you see agents

just, you know, winging it and, you

know, guessing, hallucinating,

uh, you know, confidently stating that

they found the smoking gun, uh, for the

hundth time, uh, but it's actually not

the real problem. You lose a lot of

trust. And when you lose when you don't

have much trust [clears throat] in your

agents, I feel like you you really can't

get the most out of them. And for me,

the parallel is like with management. Uh

so if I'm an a manager an engineering

manager of a team and I have a bunch of

you know I have a team of engineers uh

on my team and I don't trust them then

the mode of operation I'm going to be in

is going to be like micromanagement

right I'll have to spend a lot of time

looking over my reports shoulders and

checking that they're doing their work

well you know that they're not shipping

bugs to production

and so I drew this chart because uh it's

It's not it's not a very scientific

chart but like this is how I imagine

myself and my journey through using

agents. So you know like fast forward or

back forward uh or fast back uh fast

backwards like a year or so when you

know nobody was or not many people were

using agents to code. Uh I think you

uh you know get into this mode where you

are

in very heavily in the loop with one or

several like a handful of agents and you

find yourself just constantly fig uh you

know trying to understand what your

agents are doing uh and you're very very

in loop. You're watching every single

output. you are sitting there prompting

um and you really can't parallelize

beyond that because you don't again you

don't have that trust right you can't go

to a 100 agents uh like spawn 100 agents

when you don't even trust the output of

one agent

so over the past 5 months I feel like

I've really been able to uh like ascend

this trust curve and now I'm at the

point where uh I actually have this

sounds kind of scary to say And it it

makes me sound like a slop artist, but I

I promise I'm not. But I actually have

my agents now um autoemerging PRs for

me. Uh which is like a wild thing to

say, but um like I woke up today and

there were like 20 PRs landed and I just

reviewed them on main like they were

already landed and they were good. Uh so

how did I get to that point is basically

what I wanted to talk about today.

Uh and again like yeah feel free to jump

in if you have questions. colon. Um but

uh oh yeah, of course I got to show this

this chart. [laughter]

Uh where uh

no do not trust to someone requested to

control my computer. Uh I probably won't

do that. Uh but yeah, so this chart I

think I I'm I'm sharing this chart not

to kind of like flex but to kind of show

like the journey like so you can see

like the curve like it sort of like

inversely matches the contributions I've

been able to land at cursor. So I joined

five months ago and five months ago like

I you know my first month I was like not

very productive because I was obvious

you know I was learning the codebase

didn't know what the heck was going on

and as I got more confident in in my

agents uh I've really been able to kind

of ramp up my productivity uh and again

like yeah like last month I shipped a

thousand PRs which is ridiculous. Uh,

and then this month we're only on the

12th. I'm already at like almost 800 PRs

landed. Uh, so the velocity is

definitely high and you you I'm sure a

lot of you will definitely be

questioning like how how much of this

code is actually good. Um, and I think

yeah, like that's definitely fair to

question.

Um but uh yeah, I think I think if you

set up your agents well, you can

definitely get to a very similar level.

Um and so I'm going to talk about how we

do that.

Uh so for me I think I'm curious like I

guess call in your experience as well

but uh for me I think the most important

skill that you should have in your

toolbox when you work with agents is

verification. Uh and by verification I

mean the ability for an agent to

actually run the code uh or take CPU

traces or heap snapshots or uh you know

open an iOS simulator whatever you know

however your application is exposed to

your users. It can do the same thing and

uh run it for real and actually test and

verify it don't work because that's the

thing that really closes the loop. uh it

doesn't guarantee your agent writes good

code uh but it allows them to at least

write correct code uh which is a big a

really big step forward for being able

to trust your agents. Um

I will I can share one example that we

have uh within cursor. Uh

oops

where let me open this

let's make this uh make full screen.

There you go.

Uh so for the for cursor's agent window

uh so this is actually an interesting

story but uh when I joined cursor five

months ago uh they're actually uh well I

was supposed to join a different team I

was supposed to join like the cloud

agents team uh but then since I have a

lot of experience working on react and

agents window is a react application uh

I was

uh I was asked to basically help out

with the agent window work. Uh but um

there wasn't really a lot of like skills

to help me. So I just found myself that

okay uh agents window is going to launch

in like week right we have a really

tight deadline and um there was uh you

know I was just sitting there like okay

I'm going to open up the performant the

the chrome dev tools and just like take

a trace look at it myself and try to

make sense of this flame graph. And keep

in mind I was just like in my first week

so I had no idea what I was looking at.

No idea where you know I mean I had some

idea but you know the code base was

completely fresh to me. Uh and I

realized like my agent had no idea

either you know like I would take a

screenshot of the tree I would download

trades I would send it to it and it be

like yeah it kind of looks like this you

know uh and it would like confidently

state like it's this thing and then I

try to fix that and turns out that's not

the actual thing.

So this was very very slow process and

if you've ever done any like performance

work yourself or you know just even

development with an agent where you

don't have a verification skill you are

the verifier right you you're the

bottleneck you you you tell your agent

to do something and then it goes off and

write some code then you open up your

you know local dev build and then you

start to say oh you know doesn't work

then you got to copy paste screen uh you

know screenshots or console errors or

whatever uh and then your agent like

slowly kind of like uh you know works

with that and then tries to understand

it and um fix the thing but then you're

constantly just in the loop and and

being a bottleneck. So there's really no

way to parallelize. So the control glass

skill is like one of the first skills I

built uh for cursor. Uh and glass by the

way is the code name for agents window

that we use internally but it's just

cursor I guess. Um, and so this skill uh

is I guess the the the code itself is

not super interesting. Your agent can

very easily make one for you. Uh where

if if you're building an electron app or

a web app or even iOS uh applications,

uh you can teach your agent how to use

like the Chrome DevTools protocol or

through uh Apple has some utilities as

well for running the simulator and

taking traces and controlling

programmatic control as well. Uh so

that's really useful. Uh but one thing I

want to talk about is uh the

this thing

uh where is the read me? Uh so this

skill comes with this very unique

feature called or not feature uh unique

file called a feature map. And so the

story then is like I built this skill

and so now the agent was able to uh

actually run the agent window and take

traces and whatnot. Uh but it had no

idea what what the agent's window was.

So um you know like someone would say

like oh the the left sidebar is like

laggy or something like that or you know

the right side the the PR tab is not

working and the agent would just be like

kind of flailing around. it would spend

a lot of time trying to like look up the

code and you know where is this feature?

How do I actually get to it on the UI

which made it basically completely

useless. Uh you know like we would I

would run this skill locally and you

know it would spawn a dev build uh but

then it just be turnurning like I just

try to click here. It wouldn't know how

to get to things um and it was just an

awful experience. Uh so who's putting

arrows on my screen? Um, so, uh, yeah,

this this feature map has been really

useful, uh, because it teaches the agent

how to get to all of the features that

you have. Um, and in PAC, the plugin

that I I've made, uh, if you search for

PAC cursor on Google, you you'll find

it. Uh, but there is a create

verification skill in that plugin where

it actually helps you set up something

like this for yourself. Um, including

the feature map. So it will actually

explore the code and build up this

initial feature map that tells your

agent how to get to all of the different

features that you have. Uh and this is

extremely powerful because now like you

have these user reports that come in uh

you you can actually map even like a

vague report or even a screenshot. So we

have this uh internally at cursor where

uh we have a slack channel with you know

lots of people giving us feedback on the

agents window and rockbot and whatnot.

Uh, and often times the report is very

bad, like very low quality, like someone

just put very often we get like a

screenshot like and then someone just

says question mark question mark

question mark like what is this?

[laughter] And you know like the without

this your agent is like I have no clue,

right? But with a feature map like this,

it has a lot more context and

understanding of how to actually

navigate how to get to all of the

different features. Uh so like you know

example like I guess like the sidebar

like what is the sidebar uh you know

like all the different sub features that

are present in it um like from the user

point of view here's where to how do I

get to it all the different keyboard

shortcuts

uh even like the the what do you call it

the DOM elements or yeah like the

attributes that you use for selecting

things through the CDP uh are all there.

So uh again yeah this is like really

really powerful uh for for agents

>> uh and piece stack ships uh that create

verification skill but also a maintain

verification skill uh so you can keep

this up to date.

>> Cool. Yeah, I was just going to ask how

you created that. So do you mind sharing

a little bit more about um that that

process in the context of Pstack and

maybe just what Pstack is for the folks

who aren't familiar?

Yeah. So, Pstack is pretty interesting

because uh well, first of all, the name

is kind of goofy. Like the P the P in P

stack is like potato potato stack

because I um so uh uh there's a pretty

uh famous person Gary Tan who is the CEO

of Y Combinator and he's come up with

this plugin called GStack uh Gary Stack

and uh funnily enough we share the last

name. We have no relations. Uh, but I

thought it would be funny to kind of,

you know, poke fun at Gary and make

Pstack my version of of of his plug-in.

Uh, but kind of tailor it to my own set

of p engineering practices.

Uh, but I honestly actually never set

out to build PAC. Uh, it just started

with a bunch of skills, right? Like I

started with that control glass skill

and then I started with another skill

like called how which I also noticed

through like observing agents. Um so

like you know in the early days of me

you know trying to climb this ladder I

was like super in the loop and I was

basically nitpicking my agents to an

extreme degree. I was like I would tell

it um you know this feature has stopped

working. Here's a bug report like why

isn't it working?

And very often the agent would just like

confidently state like, "Oh, it has to

be this, right? It has to be this

thing." And I noticed like when I looked

at the actual tool calls, I noticed it

wasn't actually reading the code that I

thought should be affected. And that

made me just extremely suspicious. And

at that point, I was like, I'm not going

to I can't trust any this agent anymore

cuz it's just it's just completely

hallucinating. And I think

I think it's very easy to just, you

know, like build up that distrust and

not and kind of feel helpless like, you

know, you don't know how to help your

agents succeed. But like again, I think

the the the management analogy is super

helpful because like imagine if you were

a manager of an engineering team and you

had an engineer on your team who was a

really good coder, no business context

whatsoever. you know they they just you

just hired them and they they onboarded

you know like 5 seconds ago. Uh and so

how do you actually teach that person to

be effective? So how you do that is

through a skill. uh skill being just you

know it's just markdown right but you

know it encodes a lot of information

instructions a lot of uh you can really

draw out a lot of intelligence from an

agent by well some people on Twitter

call it like you know pull the agent to

a different latent space which is kind

of like a fancy way of just saying like

since uh you LLMs are sort of like they

predict the next token uh when you give

it some high quality tokens uh to begin

with then you know it it can kind of

pattern match on like a higher space

that's you know smarter.

Um so that's like a very interesting

model there. But yeah I built PAC very

very incrementally. Uh so uh started

with just really observing how agents

you know all the fail different failure

modes of of that agents were having and

every time I saw that I just okay I'm

just going to make that a skill right

like stop hallucinating actually go and

search up look up the code use a lot of

sub agents uh and yeah stop guessing

>> yeah that makes sense one one kind of

followup question here both from myself

and from a bunch of people in the chat

so

>> I guess it's two two parts So one is

like how do you maintain these skills?

So like the product changes over time.

Obviously there's a lot of people who

are shipping against the codebase. So

how do these skills get maintained? Uh

and then second to that is like how do

you know when your verification is is

good enough? Uh like and you know you

can trust that the ver verification

loops that you've built are going to I

guess you trust that the outputs uh when

they're done.

>> Uh yeah maybe I'll talk about um I think

somewhat related. Maybe I'll start with

this one first. So like how do I

maintain these skills?

So um if you're not familiar with this

concept, an eval is essentially like a

way to uh well I think the mental model

I have is like it's like a unit test for

an agent. Um and uh you can actually

make your own eval. You don't need like

a special framework for them. You can

build you can you can build one

depending on like you know how

scientific and how rigorous you want to

be. Uh, my screen is red.

>> Yeah, there's a little button. Um,

sorry. Do

>> you mind like disabling the drawing or

something? I I can't see my screen.

>> Yeah, sorry. If you guys could not draw

on the screen, that'd be great. But, um,

there's a little button in the

>> Is that a troll?

>> Yeah, the little drop down.

>> Um, how do I clear?

>> Yeah, you got it. Perfect.

>> Yeah. Yeah, evals are a way to unit test

your skills basically. And actually in

Pstack, we ship uh under potato mode

there's a playbook if you search for it

called eval playbook. Um and it's uh

uh it's like not it's actually pretty

pretty rigorous the way it's done. Uh

but um essentially what I do is I spawn

a lot of different sub agents. I have

like my main coordinator agent uh come

up with a rubric for uh what I want the

skill to do. Um and then it spawns all

these sub agents and it it creates

individual directories for them uh which

are cleverly named to not let the sub

agent know that it's being evaluated

because uh agents can actually tell and

when they do they change their behavior.

Uh, but it does a bunch of stuff like

that to um essentially yeah like test

whether or not the skill I'm making or

changing is actually doing what I think

it does. Um, and one of the really nice

things about cursor is that we are we we

support so many different models. So you

can actually eval your skill across all

sorts of different models. Um, and you

know get a sense of how well it performs

across that different matrix. um

especially for the models that you use.

Uh so I do this a lot. Every time I I

modify a skill, I will run one of these

uh like the Ebal playbook uh and make

sure that you know it's actually leading

to a result I want. Uh but I will say

like maintaining skills is actually

pretty hard. Uh it requires I think a

lot of taste and observation. So you

kind of need to be very good at being a

backseat driver. You know what I mean?

Like if you do pair if you ever done

pair programming for example uh and you

watch a coworker code and you just like

you you could probably do this better

you know you could do you know like why

did you not do this right you you ask a

lot of questions to your coworker and

it's kind of a similar thing here you

like you don't want to just be a passive

observer of your agent you want to be

very in the driver seat in the initial

stages when you're building up your own

set of skills uh you know obviously you

can use something like PAC but if you're

building your own set of skills it's

very I think you opening up the all the

tool calls and like reading the code and

reading all the to uh the agent behavior

and their thinking blocks is a really

great way to see where they they fail,

right? like what what you know where are

they being done and then you can go and

build a skill for that and then with

verification how you trust it is it's I

think it's also a very similar iteration

loop uh where you know like I actually

did the same process for verifying the

verification skill where I actually get

um so one thing that's interesting about

eval is that you can sort of hill climb

them meaning that uh your eval can

produce a score right uh a score that

you can get your coordinator to produce

uh but also comp uh you can have a judge

agent of a different model to uh kind of

cross reference and make sure that the

first model is not being biased, right?

The model that's judging all of the sub

aents that are running the thing. Uh but

you can also like hill climb. So meaning

that you can you can use like /loop in

cursor and you can say okay keep looping

on this eval right until everything is

10 out of 10 as an example. Uh and I did

the same the basically the same approach

with the control skill. And so I kind of

it was very it was very hands-off

actually. Uh so you know I uh I kind of

built I built that skill that way like

the CLI and that skill. Um and over time

it's gotten really good. Uh but yeah, it

was definitely not super smooth at the

beginning. It required a lot of

iteration and I think there's an analogy

here for me which is um well I make this

analogy later in a different slide on my

drawing here. Uh but I think of it like

uh you know as a as a engineer now

you're sort of more like you uh like

maybe a manager or the analogy I like is

like you're like a a chef in a

restaurant. uh you know, you're the head

chef. Uh you're not cooking all the food

yourself anymore. You have a team of

cooks, right? You have line cooks, you

have a sue chef, you have, you know, all

these different stations.

Um and it's your job to really design

the environment. You know, you you're in

charge of setting up the kitchen. You're

in charge of, you know, like giving

tasks to different people. So,

um yeah, it's a very interesting way of

working. Uh but yeah, that's that's how

I've basically built uh these

verification skills.

>> Yeah, just just one fault there on like

to go try to go one layer deeper. So are

you let's say we wanted to build um an

eval or a skill for for something and we

wanted to kind of get better on its own

which is is what I think you're

suggesting. Uh are you doing that in

like a work tree kind of isolated with

like the sub aents and and then the

reviewer agent and and all that? Is it

happening like in some type of cloud

hosted environment? like what's the the

more the practical steps if I wanted to

go do this uh and like set up a

verification system for something? What

would I what would I do or where would I

start?

>> Um I think that uh the best place to

start is local because you can observe

you can definitely observe what your

agents are doing. So, uh, if you're

building a verification skill for

yourself, uh, I would definitely start

local and just have your agent bring up

the application, whether it's like a CLI

or, uh, desktop app or whatever. And so,

you can actually observe, right? You can

see how the agent is interacting with

the the application. You can see it, you

know, how it calls like the different

APIs that that allow it to interact with

the uh the application.

Um but uh for me personally uh I have

basically been kind of all in mostly all

in on cloud agents because they're

extremely powerful. Uh and the really

powerful thing about cursor is the the

cloud agents actually where if you spend

a little bit of time setting up your

environment

these control skills these verification

skills pay a huge amount of dividends

because it's not just something that

makes you as a single engineer better.

It actually levels up your whole team uh

and even your whole company because uh

you can actually start thinking about

cloud agents. You can start thinking

about automations that automatically do

things like uh I I get I kind of talk

about this a bit later but I'll just

kind of get into it. Uh where where you

know for example like I talk a lot about

this agent we have called Benny, right?

who who uh you know takes all of the bug

reports that we get and it automatically

goes off in the cloud, opens up a cloud

uh it's you know its desktop. It runs

cursor in its own computer and it uses

the same control skills to interact with

the application and try to reproduce the

bug uh or the user report, right? And

this is so so powerful because at once I

can immediately I I get so much

information from this automatically like

here in this example you can see that uh

the Benny actually reproduced the bug uh

but it's already fixed on main. So it

actually confirms that we fixed this

problem already and all I need to do is

just release another build of of cursor.

Uh so that's like huge information there

that I didn't have to go off and sit

with an agent you know and spend an hour

trying to figure like is this fixed?

this is not fixed. So you you you gain

back so much time. Uh but you know

everybody on my team benefits from this.

Everybody in the company benefits from

this. Uh so definitely think that uh you

know keeping these uh using cloud agents

is super powerful. Uh but yeah it's like

a journey. You have to trust it first

right before you you get to this point.

And that's it goes back to what I was

saying here where you know it's very

hard. It's it's almost impossible. And I

would definitely encourage you not to

try to jump from, you know, like if

you're still in this zone, you don't

want to jump to like I'm going to spawn

hundred of thous or thousands of cloud

agents right now because you're just

going to waste a lot of tokens. Um and

it's going to be extremely expensive.

>> Yeah. So just to kind of recap so far,

basically the if we wanted to go on the

journey that you've kind of gone on, it

would be to start with verification,

building some some skills and some some

ways of determining that the agents are

producing at least like correct code.

Whether like you said, whether it's good

code or not is maybe a separate

question, but like it's it's technically

solving the problem by looking at, you

know, stack traces, looking at, you

know, the the actual behavior in the app

and so on. Um, and then once we trust it

locally, then we can start to think

about scaling into the cloud and running

more agents that are picking up signals

I guess on their own, right? So whether

that's like a bug report that comes in

or something, they can go and pick it up

and solve the problem and give us back a

PR. And then maybe the last step is like

automerging the PRs, which uh is where

you're at, [laughter] maybe not where

everyone is at.

>> Um, and then reviewing the one main, but

um, is that is that about right?

>> Yeah, exactly. I think yeah, that's why

I drew this this uh this curve, right?

because that this this basically

describes my journey of you know when I

started barely could use a couple agents

and I was just observing every single

thing. I think there's really no

shortcut for going from here to there

because this is really about your

personal level of trust in agents,

right? Um obviously, you know, as a as

an engineer, you don't want to just slop

code into production. So, how do you

actually build up that trust? Takes um a

lot of uh I guess taste and judgment. Um

but uh you know, like I think plugins

like Pstack definitely kind of help you

uh get up to speed much quicker. Uh and

so I guess it's like if you trust me and

you trust PAC then in by extension you

can maybe trust your agents but if you

don't trust me and I I definitely would

not encourage people to blindly trust me

uh uh you know if you build up your own

set of skills that you can obviously you

know take a look at PAC and kind of fork

it make it your own improve the skills

definitely encourage that uh but for me

it's really all about it just keeps

coming back to trust you know every one

of us here in this chat have a different

standard for engineering. Uh and there

are different things that are important

for us in our codebase and uh when you

are able to encode all of that into

skills and you can verify that your

agent is actually doing them that allows

you to really kind of ascend this curve

and um uh you know start automating

things. Uh there's another piece I

wanted to talk about um if there's more.

>> Yeah, go for it. I I'll pick up more

questions as I go. Yeah, I think there's

a third part to this which I haven't

talked about yet which is kind of an

interesting one which is like

refactoring and rewriting. Like one of

the uh I guess most controversial one of

the most controversial topics in the

industry I think is like should you

rewrite your app or not? Um because I

think engineers are very prone to this

where especially when you join a company

you come in and you see like the code

base and you're like oh man this is

like who wrote this code you know it's

terrible I want to rewrite the whole

thing is a very common inclination and I

think a lot of you know before agents um

and I guess arguably even now people

will definitely discourage you from re

rewriting stuff but I'm actually here to

make a case for why you might want to

consider it.

Um because

I think it really depends. Uh you know

uh brownfield applications I think are

actually in a pretty good spot

especially if they're set up well

already. Uh and like recently I've been

talking to some people but uh you know I

I was just observing I I just noticed

this parallel which is that a lot of big

tech company problems are now

everybody's problems. Um, and the big

tech company problem, you know, like

when I was working at Meta, like we had

this giant monor repo, we had like, I

don't know, tens of thousands of

engineers just, you know, like banging

on their keyboards and and shipping

code. And

a lot of really great engineers at Meta.

Uh, but, uh, I'll say like, you know,

you'll be surprised that the code

quality is actually not that good. Um,

and so I often joke that like, you know,

before AI slop, we had human slop. Um

and so uh you know I think a lot of big

tech infra like uh like what Meta has or

Google you know you know really big tech

companies are actually designed for that

where you you're sort of like you're

catering to the the you know like uh

this sounds so bad to say but like the

the least capable engineer on your team

right you build you build frameworks you

build conventions you build guard rails

you know you restrict credentials so

that you know your intern doesn't wipe

your production database Um

there's uh you know if you have that

level of infra already I think your

agents can actually already do a very

solid job right because they have the

the guard rails are already in place for

agents to not cause havoc or not cause

too much havoc uh in your codebase. Um

and you can always add more you know

guard rails. Uh but I think like green

field applications especially are you

know like the brand new applications are

like the biggest risk in my opinion. Uh

and also the greatest opportunity

because you know if you vibe code a

project uh a prototype um like we did

for Grockbot you know Grockbot was spun

up very very very quickly. Um and if you

if you haven't heard of of Grockbot it's

like our a new application we just

launched yesterday. Uh it's it's really

cool. uh lets you orchestrate your

create like individual agents that have

their own identity and you can kind of

orchestrate them. It's super cool.

Definitely check it out. U but yeah,

that was it's like a very it was a very

green field application like most

prototypes are so like vibe coded very

quickly. Humans were not reading the

code at all. And uh I had this tweet

recently uh where I said something about

organic architecture. Um

maybe I'll find it. Uh but the idea is

that uh when you have a completely vibe

coded application, you essentially have

no guard rails whatsoever. So uh your

agents

when you give them a task, they will

just solve it in whatever method is the

most convenient. And over time you get

into this uh situation where you have a

codebase that is spiraling out of

control because you don't understand it.

uh your agents understand it I guess in

a way but like they've built something

that is you know optimized for short for

shortcuts uh and uh you know it will you

will suffer you'll have a lot of of

issues with that application

uh so I think starting your codebase

with uh like very strong constraints is

very much needed uh because like when

you have a codebase that you can trust

right when you have guardrails that

actually help you uh uh help your agents

write good code. You can get into the

you know like into this part of the

curve where I I where like I I said you

know I woke up today and I had like 20

PRs merged u by my agents and that's

because I invested a lot a lot of time

uh over 600 PRs I I I calculated

yesterday uh when I refactored all of

Grockbot to this new architecture that

I've been building. Um,

and yeah, I've gotten to a point where I

I don't really look I really don't look

at the code anymore. And um, I say that

not just, you know, to sell you tokens,

but because I, you know, it it it took a

lot of work to get to that point. I

spent a lot of tokens to get the

codebase to this point where I no longer

have to look at it. Uh, but I'm very

excited because, you know, of the

potential where, you know, it's not just

this doesn't just benefit me. It

benefits everyone contributing to

Grockbot and it also empowers you know

designers and product managers and you

know pe uh even GTM people to add

features to Grockbot and I don't have to

worry you know I don't have to to wake

up at night in in the middle of the

night and worry like oh someone's

just merged a perf regression right I

have a ton of constraints and CI like

it's actually very annoying to write

code in in graph web but like agents

absorb all of that annoyance

Um but yeah, I'm happy to talk about

what exactly that is. Um

>> yeah, I think one question um

>> yeah,

>> before we get into the this part here is

just around that element of like what

your your your CI looks like or maybe

some of the constraints and then also

like the average PR size. I saw a

question about that earlier just to give

people you know kind of a a glance. It

doesn't have to be like mathematically

average but just uh you know like what

generally the size of the a PR is. um if

it's only a couple lines of code or you

know um yeah

>> um

I think it depends uh let me

>> I'm trying to do this in a way where I'm

not going to like

>> you yeah you don't have to share the

actual number like an actual average

>> I think this is fine

>> benchmark

>> but like we have so okay this is not

that interesting but uh well fun fact is

that virtualization in grockbot and in

uh cursor is actually powered by uh

Pretext uh which is a sort of new

library that someone's built. Um that's

really interesting. You should you

should check it out, but that's not

really that important. Uh I think the

average PR size I actually don't know. I

I don't know if I want to click on

these. Uh I probably can, but I would

say like they can range anywhere from a

few hundred lines or 50 lines to like a

thousand depending on what the thing is

doing. Uh, so like here I'm actually

like deleting a bunch of files. So I

expect that it's just like mostly

deletion. Uh, but yeah, it kind of

varies.

>> There's no like Yeah,

>> there's no like hard cap or hard limit.

Are they're all like 50 line PR?

>> There's no hard cap. Yeah, there's

definitely no hard cap, but I I do

encourage my agents to split up their

work into multiple PRs. Uh I do that

mostly because uh I like I like the idea

of the I guess maybe this is much harder

to do now as in the world of agents and

you have like so many commits but I like

the idea that you know the git history

is a very rich source of context. Uh,

and I like the I like each PR to sort of

atomically describe what that small

piece of thing is doing, which also

makes it easier for me to revert changes

and like figure out, you know, oh, I

shipped a bug and it's just it's here,

right? It's not in this 40,000 line PR

where you who knows what landed in

there.

Uh, but I don't have a hard cap on PR

size.

>> Cool. And then um yeah, also quick

question on like CI. So again, you don't

have to go into like uh the screen share

of like your CI does, but just generally

would you describe what the CI kind of

looks like uh or how strict it is?

>> Uh yeah. So uh well specifically for

Grockbot. So Dune is the is the sort of

cheeky code code name for the

architecture that we've built for

Grockbot. Um the CI looks pretty

annoying because there's checks for

everything. So like literally I have um

uh well if you've written any react for

example you know you know that one of

the biggest foot guns in react is use

effect. Uh so in

uh Dune and in graphbot we've banned use

effect. So Dune is just you can the the

mental model of what Dune is uh you can

kind of think of it as like Nex.js JS

for uh electron apps and it's designed

for agents to write uh and it's like

custom for you know our agent powered

applications. Um so the CI checks are

very like specific to that like you know

don't use use effect it's it's it's

banned like CI will fail uh and yell at

you. We have like some of the more

interesting ones that people might raise

eyebrows is like I actually ban code

comments as well uh which is very

interesting. Uh, but I've noticed that

99% of the time agents just write code

comments that kind of describe some

historical thing that is actually

totally irrelevant to the code. Um, like

it will often say like, you know, oh,

Lauren said you should never do this and

it's now in in a code comment. I'm like,

what? Like why? What? That was I didn't

say that as like a durable, you know,

global rule. I just meant like your this

PR sucks and you should change that

part.

agents don't really understand us that

well surprisingly uh and or they kind of

assume too much and they kind of do

things in like very stupid ways. So like

yeah we just ban everything everything

you can imagine like the agents are bad

at we ban. Uh so one example that we

actually suffer a lot in the agents

window is we have uh you know you if

you've used the agents window you've

definitely seen performance issues and

you know we're constantly trying to fix

them. Uh but it's like a it's a never-

ending struggle because there's so many

pull requests that get merged. Every any

one of them could just regress

performance or stability or reliability.

Uh you know the agents window doesn't

have this architecture yet. I plan to do

bring this learning back there and kind

of refactor everything there. uh but uh

it just regresses super often uh because

uh there's just one example is like we

have very poor um isolation between

processes. So like on you know on on

Electron you have a renderer thread that

renders your UI but you also have like a

main thread that you can run other code

that you know doesn't need to block the

renderer.

Um, but we do a poor job of separating

those things and so often times you just

accidentally have code that gets pulled

into running on the renderer thread and

then all of a sudden you're competing

with the the renderer that you know that

has a very if you want like 60 fps you

have to every frame that gets drawn has

to be done in 16 milliseconds. So very

very small you know deadline per frame

uh if you want you know a very smooth

product. Uh and when you start building

bringing in accidentally bringing in you

know things that are like very

computationally heavy or they have a lot

of IO uh then you just get into like a

lot of jank your FPS really drops. You

start uh you know losing frames. You get

long tasks that take more than 16

milliseconds and you just get this

really choppy experience.

So all of those patterns that we've

learned basically building electron apps

we've encoded into this framework and it

becomes like a hard failure. So I

literally in in grabbot we literally

have a directory called electron main

electron renderer and we have a import

uh CI guess where we actually check the

dependency graph to make sure you're not

accidentally importing code from one

directory to another. Uh so that's

enforced by CI um as well as bug bots uh

which is our which cursors um like code

review tool that runs on CI uh you know

in our agents MD it's everywhere like so

I I I I um I have this thing here where

I I talk about like um you know like

there are multiple layers I think for

building a good codebase. Uh obviously

the codebase is one where uh if you have

an architecture like this where it's

extremely strict uh you know the the the

way to build features is very

conventional that's like the strongest

strongest level of enforcement because

agents just love to copy existing

patterns. So uh one example of this in

Rockbot is like we have this these

concepts called like a feature and we

have entry points and transcript cards

like oh you know the cards that you see

in the chat these are all like like

nouns I guess in in the framework and so

there's a very conventional way of

creating them and so like a feature is

all in in a single directory as an

example and so all of the code that

contributes to that feature lives in one

directory so it's all collocated in one

place makes It's super easy. You know,

agents don't have to like uh grap around

and try to figure out like where all the

things are. It just looks at the feature

and like, oh, okay, I'm working on the

onboarding feature in Grockbot. Uh, I'm

just going to work in this directory.

And for 80% of the work, it's mostly

just very encapsulated there.

[clears throat] But, uh, like it's like

very it's like designed again for you

know like the dumbest agent like you

don't have to think, right? the the the

one of the key principles I have for

this framework is like the shortest the

shortest path is the best path.

So uh because that plays exactly to how

agents love to write code is like they

like to take shortcuts really you know

they they'll find the quickest way to

solve the problem. So why not make that

the best way to solve the problem? Uh so

I I probably won't get into all the

specific details. Um and uh the the this

framework is really more of a collection

of ideas and principles rather than

something that will open source. Uh you

can you can you know screenshot this I

guess if you want and uh tell your agent

to uh do some build build something like

this for you too.

Um yeah, but it's really all about the

layers uh you know like the the codebase

is one part with features uh and

directories and you know import or

blocking import dependencies uh that

shouldn't be imported uh but and and it

all enforces that and static analysis.

So like uh there's CI checks, we have a

lot of lints for bad patterns that we

observe. uh compiler diagnostics

uh there's also rules and bugbot which

are um I think like three four five are

more soft right these two actually make

make CI red right so that you know

there's a hard constraint where the

agent can't just write crappy code

for rules and skills and bogbot your

agents can still forget right you can

still or it may not always consistently

apply them So, I like to layer them, but

I don't I don't like to rely on them as

the only source of enforcement because

it's very very soft, right? And if you

if you only have rules and bug bar and

skills and a style guide for your code,

you will it's only a matter of time

before your codebase looks like complete

trash. I'm sorry to say that, but uh I

definitely recommend yeah like you know

investing in you know things that can be

hard and forced, right? And this is why

you know maybe uh the choice of text

stack that you use is also very

important. Um like I think for example

Rust is sort of making you know it's

like getting super popular again. Uh

because the compiler is so strict right

the compiler enforces so many different

things. you know, there's a borrow

checker that you have to appease and if

as long as you make sure your agents

don't write unsafe code blocks, uh you

can more or less feel somewhat confident

that if the code compiles, it's probably

works and it's good. Uh but you see, it

gives you that level of trust and

confidence that you as a human engineer

no longer need to go and check it

yourself. you know, you you rely on code

and static analysis to actually make

that uh a lot smoother. Um, and I I

guess the worst part, the worst place to

be in is if you are stuck in code review

land where you actually enforce all of

the constraints, the invariance in your

codebase by literally the human person

saying, you know, reading the code and

like, okay, you should not do this,

right? Every time you have to do that,

you should consider that as a code

smell, like a anti- pattern and you

should say, "Okay, instead of me

commenting on the PR, how do I turn this

into a hard rule, right? How do I turn

this into a lint rule? How do I turn

this into a CI failure? Or how do I even

categorically eliminate this problem uh

entirely?" Uh I I can talk about another

migration I've done, but I'll probably

pause here.

>> Sure.

>> Yeah. I feel like that's that's where I

am to be honest. is is what you're

describing right now, which is that like

I don't have all of these rules. So, I

have some things to go do after this

session in terms of being able to scale

my agents. I'm I'm definitely on like

the uh you know, maybe a couple of

parallel ones locally stage. So, like

two to three locally, and I'm sure most

people here are on the same. So, uh

yeah, I know we only couple minutes

left. Lauren, was there anything else

that you wanted to to highlight?

Obviously, there's lots of questions, so

I can grab more, but I want to give you

a few minutes if there's anything else

you want to talk Well, I think I've been

yapping for quite a lot, so I'm maybe

let's just do questions.

>> Okay, cool. Uh, one question that had uh

a couple of uh came up a couple times

was just around like token usage.

>> So, the the question is like is is what

you're describing a realistic thing for

people who are on, you know, uh a normal

set of token usage. They don't have, you

know, basically unlimited tokens uh to

work with.

>> I think that's a really good point. I

mean like obviously you know I work at a

AI lab where we have unlimited tokens.

So uh I definitely cannot

say that you know this is something

everyone should do in the exact same way

that I did it. I think it's possible to

get to this point without you know

breaking the bank.

But you know if you're like an

engineering leader or you know you're

you have a startup that you lead um I

think to me it's a question of ROI. Um,

and it's like, uh, yes, you spend a lot

of money on tokens in the upfront stage,

you know, like refactoring a codebase is

going to take a lot of tokens. Uh,

adding all these things uh, is going to

take a bunch of tokens. But if we're

heading to a world where agents are

writing all the code and, you know, you

want to be very lean, right? You don't

want to have to hire, you don't want to

be, you don't want to become like meta,

right? Like I mean like in terms of you

don't want to become a 10,000 person

engineering org because I mean that's a

cool problem to have but also you have

so much overhead. There's like planning

you know like you it's it's a personally

I I wouldn't uh it it's not super fun

but um I think you want to stay very

nimble right and you want to you want to

be like agents are all about allowing

you to do things that you couldn't do

before. That's really to me like the

value of agents, you know? It's not just

storing tokens on every single little

thing, but um to me like the thing I

couldn't do before is like enforce this

level of constraints in a codebase by

myself, right? Like I'm just a single

person, you know? Uh it would have taken

me years to build this framework uh and

do all the refactoring and test

everything myself and verify, you know,

like run imagine if there it was just

me, right? you know in in pre- agent era

just like running you know by it would

take me so long right and my salary is

pretty high right like so you know the

the question I think an engineering

leader might have is just then you know

like what is there's a trade-off of do

you hire someone to do this or do you

spend the tokens to set up a code base

so that even the the most naive right

the dumbest agents can do a good job and

when you actually get to this point like

even agents that are not you know fable

size do an excellent job of writing code

and this pays a lot of dividends as well

for me personally where I've empowered

not just myself but again like PMS

designers engineers who are not familiar

with Grockbot to just contribute in a

way that is sustainable

so I think yeah it's definitely like a

trade-off for sure you know like nothing

is like free for sure uh and tokens are

pretty expensive Uh but oh actually uh I

I I don't know how many of you have seen

this but we actually announced Grock 4.6

today. So very exciting finally out. Um

so yeah, GRO 4.6 would be like a great

it was very very smart. Uh it's really

good on the on the benchmarks. Uh and

it's the same the tokens uh well uh I

hopefully I'm not saying this

incorrectly, but uh I believe the cost

per token is the same as 4.5. So you're

actually getting more intelligence for

the same cost. Uh I think this is an

area that cursor tries to cursor and

SpaceX AI try to really optimize for

like that heredto frontier of you know

cost versus intelligence. Uh you know we

don't necessarily want to build the

biggest model ever because that is

extremely expensive to run. It's really

about like how do you find that sweet

spot right? you don't you don't need a

giant model, but it's just super smart,

right? And it's not very expensive for

inference.

Uh but um yeah, I think to kind of round

it up, um I think it's like a it's it's

there's a if you do your own analysis, I

feel like it's pretty positive. It it'll

be pretty positive that the ROI you get

from investing in stuff like this uh

just empowers not just yourself, but

your whole team to be so much more

productive, right? Like imagine if you

have an army of engineers like me who

are shipping so much improvements and

and bug fixes uh you know every day,

right? Like that is pretty exciting.

>> Cool. Uh one last question before we

wrap up. This one is for the people in

product on the on the call.

>> So let's say we do have an army of

engineers who are shipping like Lauren.

I'm just curious like how is the product

team or other functions of your company

keeping up given that like if you're

shipping so quickly, have are they using

AI more to do their jobs? Like as much

as you can speak to that obviously you

don't have like you're not in that role

but just curious about how that works.

>> Um I think this is where grathbot has

been actually exceedingly powerful. uh

where so before graphbot like you know

uh obviously cursor only had cursor like

we only had agents window we had a CLI

we had an IDE and these are really like

power user tools right like de they're

designed for developers so it's very

very developer centric you can do

knowledge work in them but it like the

UI is not really optimized for that so

we actually didn't really have uh well I

think like a lot of people like you know

GTM product like they might have use

cursor uh to do their work but it

definitely wasn't like a delightful

experience for them. Um I think now with

grabbot

uh it's become Grockbot is basically

like the kusher moment for people who

are not in tech in my opinion like it's

like it's like a very very accessible

way to use agents in a very comfortable

very familiar interface. It looks like

iMessage. Um, and it's very fun to, you

know, you can give your agent a fun

name. Uh, you can have you can kind of

do orchestration with in a very like

natural way where you can sort of, you

know, each agent's like a person, right?

Now, you got a team of agents like

working on you have one one agent per

account that you manage as an example.

Or if you're a PM, you have, you know,

you can have an agent that summarizes

all the work that Lauren did last night

and then now you know what I did, right?

So I think our PMs are leveraging that a

lot and they're shipping code too. Uh so

you know like often times they will just

say oh here's a bug I fixed it can you

look at it and then I'll go review it

and actually it's just perfect. I'm like

okay stamp. Uh so uh that I think that

shows that you know the the Dune

architecture is holding up right the all

the the really strict constraints allow

people who are not experts in

engineering to contribute at a high

level. Uh so I'm I feel like I'm already

seeing that pay off a lot where uh you

know designers and PMs are just able to

to to ship features directly. Um and

that just makes the Grockbot team super

fast, right? Where we can ship so

quickly. Um and we have a lot planned.

So, I'm very excited uh to, you know, uh

to to ship more ship more