Everyone, I am Lauren Lauren Tan. I

guess not many people know my last name.

Uh, I am Potato on Twitter. Uh, potato

with spelled with an E. Um, and I have

been at Cursor for about 5 months. Uh,

previously I was at Meta where I worked

on the React team. Uh, specifically

working on the React compiler, uh, which

was a whole lot of fun. Uh I'm still on

the on the core team and and uh

contributing to open source here and

there. Uh so that that's really nice

that they still let me do that. Uh and

before Meta, I was at Netflix uh where I

was uh both a tech lead and uh I

transitioned to be be an engineering

manager um for about two years.

So I've had a I've have had a lot of

experience going between engineering

management and being an individual

contributor. Uh and I think something

I've noticed actually which is quite

interesting is that there are so many

parallels with you know management

skills and how to like manage agents. Uh

and that's actually a big part about

what I wanted to chat with you and

everybody else about today. Um but yeah

that's that's me. Uh I do have some like

very light slides but uh it's not going

to be uh just rambling. So let me just

share my screen

and hope that I don't leak anything. Uh

oh no I need to allow permissions.

>> No worries. Oh take your time. There's

always tech tech trouble. Uh give me one

second to rejoin.

>> Yeah go for it.

I see many of you already know Lauren

from from the looks of the chat here.

Um, so yeah, it's exciting to to get a

chance to chat with her uh and and go

through some of her her recent work. As

you guys heard, you know, a lot of

recent experience from from Netflix to

to Meta and then now over at Cursor. Uh,

we're going to chat a little bit about

Grockbot as well. So, uh, that'll be

exciting. I don't know if you guys saw

that was a recent release. I think

literally maybe yesterday or the day

before uh from the cursor team which is

kind of like um let's call it like

agents for everyone. You can go check it

out if you want and learn a little bit

more about the product but um but yeah

we'll we'll explore that a little bit

today as well.

All righty. Welcome back.

And Lauren, you're just on mute there if

uh you want to hop off of mute if you're

chatting.

>> Yeah, sorry.

>> No worries.

>> It's 2026 and I still don't know how to

use Zoom.

>> That's [laughter] all good.

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

screen.

>> Yes. Yeah, we're good. Um, 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 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 cuz uh 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 this and it

makes me sound like a slop artist, but I

I promise I'm not, but I actually have

my agents now um automerging 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 Maine 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 Colin. Um but

uh oh yeah of course I got to show this

this chart. Uh where uh

no do not trust to someone requested to

control my computer. Uh 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 ob 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 me full screen. There

you go.

Uh so for the for cursors agent window

uh so this is actually an interesting

story but uh when I joined cursor 5

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 uh the agent window work. Uh but um

there wasn't really a lot of like skills

to help me. So I just found myself like

okay uh agents is going to launch in

like a 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 what you know I mean I had some

idea but you know the codebase 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 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 ChromeDev tools 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 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 churning 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 is 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

plug-in 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 where 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 will 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? And you

know like without this your agent 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 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 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 pstack 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, P stack is pretty interesting

because uh well, first of all, the name

is kind of goofy. like the P the P and P

stack is like potato potato snack

because I um so uh uh there is 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 peace

stack my version of of of of [laughter]

his plugin. 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 uh 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 a 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 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 Pstack 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 sign agents, uh, and yeah, stop

guessing.

>> Yeah, that makes sense. One, one kind of

followup question here, uh, 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 the outputs uh when

they're done.

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

some are 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 [clears throat] is

essentially like a way to, uh, well, I

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.

>> How do I clear?

>> Yeah, you got it. Perfect.

>> Yeah. So, eval

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 aents 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 co-orker 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 know

opening up the all the tool calls and

like reading the code and reading all

the 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 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 in 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 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 followup 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. start thinking about

automations that automatically do things

like uh I I get I 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, is this not fixed. So you you you

gain back so much time. Uh, but you

know, everybody on my team benefits from

this. Everybody at the company benefits

from this. Uh, so definitely think that

uh, you know, keeping these 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 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

a hundred of thousand 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 and give us

back a PR. And then maybe the last step

is like automerging the PRs, which uh is

where you're at, maybe not where

everyone is at.

>> Um, and then and reviewing them on main,

but um, is that is that about right?

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

I drew this this uh this 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

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 Pstack, 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 PA

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'll I'll pick up more

questions as I go. But

>> 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 codebase 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 there 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 But

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 monora 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.

[laughter] Um, and so I often joke that

like, you know, before AI sloth, we had

human sloth. 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 sounds that

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. Um 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

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 Grockot 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 is 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

>> 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 this is fine

>> benchmark

>> but like we have so okay this is not

that interesting but uh well fun fact is

that virtualization in grabbot 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 pro 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 there 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 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 like what

like why 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 right your 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 uh 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 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. 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 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 buggbot 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 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 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 have a couple minutes

left. Lauren, was there anything else

that you wanted to to highlight? I

obviously there's lots of questions so I

can grab more but I want to give you a

few minutes uh if there's anything else

you want to talk about

>> I think I've been yapping for quite a

lot so 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 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 code base 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 or

because I mean that's a cool problem to

have, but also you 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 no 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 Do you hire

someone to do this or do you spend the

tokens to set up a codebase 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 graph

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? 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 Grothbot has

been actually exceedingly powerful. Uh

where so before grabbot 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 developerentric 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 used

cursor uh to do their work but it

definitely wasn't like a delightful

experience for them. Um I think now with

Grogbot

uh it's become Grogbot 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 is

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

oftent times they will just say oh

here's a bug I fixed 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

stuff.

>> Yeah, that's awesome. Uh well, we are at

time. So, uh I guess Lauren, if if folks

want to support you, maybe go try out

Grockbot, try out uh 46 and uh you know,

get provide some feedback. But yeah,

this was awesome. Really appreciate you

taking the time. Uh thanks everyone for

all the messages in the chat. Lots of

good questions. I know we didn't get

through everything, but as kind of said

at the top, way more questions than than

we could get through, but uh yeah,

really really thanks thanks for for

joining. Thanks everyone for joining and

hopefully you enjoyed the session.

>> Yep.

>> All right. Yeah, I see your man. Thanks

for having me. And uh if you have any

more questions yet, just DM me on

Twitter. I'll I'll open them up. I guess

I'll let the let the

>> You're going to get a lot of DMs.

>> Yeah, I'll open the updates. So, yeah,

DM me. Maybe I'll do like a Twitter

space at some point. That's all for more

questions. But

>> really appreciate everyone for showing

up uh you know, taking an hour out of

your day.

>> Yeah. All right. Thanks all. I'll see

you the next one.

>> Okay. Thanks everyone. And bye.