Hi listeners, welcome back to No Priors.

Today I'm here with Simon Last,

co-founder at Notion. We talk about

their new vision for notion in the AI

age as a platform for humans and agents

to collaborate, how the engineering and

product org at notion is changing,

[music] and these new tools for thought.

Welcome, Simon. Hey, Simon. Thanks for

doing this.

>> Hey, of course. Yeah, it's really fun to

be here.

>> Notion's at scale. Amazing platform,

lots of users. You did start quite a

while ago. I think of notion as one of

the companies that has really like

braced AI quite aggressively. I was told

you first got your hands on GPT4 uh at a

company offsite in Mexico. Um is that

true? What is the origin story of like

starting to work on this stuff?

>> Yeah, I think yeah, that year that was

2022. Um I I've been watching you know

what's going on in general. I've just

been like super curious about the

technology and fascinated to to try

everything and think about like like how

we can apply it. It wasn't until I

played with GBG4 that it it became

really really real. So, you know, we

when we got access to it, it it was sort

of like a a protogt like interface. Um

and uh my co-founder Ivan and I both got

access and it was just immediately clear

like I would say two big things. One is

that it was just pretty smart. it it

could follow reasonably complicated

instructions. It could write things for

you. You could edit things and and the

second big thing was that uh the scope

of its knowledge was extremely

interesting. Uh super super deep like um

and and broad world knowledge. When we

played with it, it became just instantly

clear to both of us like okay the the

time is now to start thinking about how

to apply this. It's only going to get

better.

>> We were talking about Mexico TPT4. You

guys saw it was like clearly the time.

Did you start with like a particular

vision of like what you should obviously

be able to do with AI and notion or did

you start pulling people from different

teams or recruiting people and say like

let's experiment? How did you begin?

>> I think we immediately had a long-term

and a short-term vision. I would say the

the I'll start with the short-term one.

The the thing that was immediately

obvious was oh it could be like a

writing assistant.

>> Um so it could be in your document. You

could like select some text, have it

rewrite it. You could have it write text

for you. maybe look something up and

then uh you know give you like like

sources or more information. So that was

the thing that we immediately like like

got to work on and you know we sort of

started a tiger team around it and then

we were able to launch it in like like

two or three months after that. And then

the long-term vision that we immediately

had was like oh the thing that looks

like it may be possible is more of like

a general assistant. So what if you

could just give it all the tools inside

notion that a human would have be able

to like create its own databases, query,

manipulate them, create documents, edit

them, uh and sort of weave all these

things together to do like a longer

range task. And so we we sort of uh

immediately started on both. The the

short-term one we're able to shoot very

quickly and then the long-term one

didn't really work yet and so that took

much longer to get working. Are there

like specific

first launch of the AI specific notion

features and products was when last

year?

>> No, it was uh it was February 2023 is

when Okay. It launched. Yeah.

>> My timelines are wrong. Um are there

like a few um specific learnings or

breakthrough moments you think since

beginning to release that are

interesting?

>> Yeah, I mean there's been it's it's it's

been a slog over many years or over

multiple years at this point with with

many many learnings I would say. Yeah. I

mean just to give you a timeline of the

arc of what what we shipped is you know

so the first thing was our our writing

system uh we called it AI writer um

that's the first thing we launched uh it

was the easiest to get working is it's

like singlestep task rewriting editing

text uh there's no like retrieval aspect

it was just like raw access to the model

to write uh uh to write the text the

next

>> uh the next big thing that uh that we

immediately started working on was Q&A

doing a semantic index of the entire

workspace and then letting you ask a

question and and I can give you an

answer that's that's grounded in the

sources. That was also immediately

obvious to us that that'd be super

useful. And so we started work on that.

That one we launched in I think it was

October 2023.

So we started a beta before then, but

then that our our GA was in October.

That was just a much bigger effort to

get working. Obviously, we weren't just

like plugging in the LM. It was actually

doing this like real-time updating

index,

>> right?

>> We had to get much more serious about

the the evals and the quality there as

well. the the Q&A has been a a

multi-year journey. Basically, what what

we did is immed uh as soon as we got the

notion index working, it it was obvious

that okay, we should index everything

else as well and and so we index like

Slack and Google Drive and and we're

launching new ones uh on a on a regular

cadence and and and now we have a uh I

would say fairly complete

>> one could argue that those are like very

difficult problems that you know those

products natively have not solved

perfectly yet. So, how did you think

about taking that on? I don't know if

that's like an offensive thing to other

product teams, but like it's not working

yet.

>> Yeah, it's it's kind of true. Yeah, this

has been something we talk about a lot

because it's like, you know, it's like

almost like what right do we even have

to do this? But but it turns out that

most of the companies are pretty bad at

making their indexes somehow. It's

honestly kind of baffled us a little

bit,

>> right? But I I think my take after

dealing with all of this and you know

working with the team to try to get it

working is there's a little bit of just

AI pilled savviness that's pretty

important and then and then I think most

of it is honestly just like a bit of

like like craft and attention to detail.

I think like in in particular with this

like indexing retrieval stuff in order

to really get it working you you have to

be quite empirical and iterative and

actually be like like trying queries

like you know like each each uh data

source is a little bit special like you

know you can't just apply a

one-sizefits-all to like quering Slack

versus quering Google Drive let's say

they're they're completely different

kinds of information and we found that

there's just a little bit of like like

craft and love that has to go into it in

terms of like actually trying a bunch of

different queries actually using it

every day and constantly

iterating and rethinking and and and

tuning how the retrieval works.

>> How did you um think about the diversity

of how people organize their workspaces

and just I mean even notion is not use

of it is not homogeneous, right? Like

I'm probably part of 15 workspaces as an

investor and so I look at them and I'm

like well mine's a mess and these people

are really organized and the workflow is

reflected in how their notion works.

>> Yeah, totally. I would say I mean the

interesting thing is that with

embeddings it almost doesn't matter as

much. anymore. The the AI doesn't really

care what the what the the tree

structure is. For example, the all the

AI cares about is that there's a snippet

of text that has the the context you

need and then it can retrieve it. And

so, actually, we kind of advise people

now like don't worry as much about

organization. Just just just find a way

to get it all piped in and like like

thrown in there.

>> You still make decisions that could

change performance quite a bit like

chunking strategy or whatever.

>> Yeah,

>> that's super important. But but that's

sort of not that's sort of transparent

to the user and sort of in independent

of their their particular method of

organizing things.

>> Mhm. It just seems like still a

difficult technical challenge given how

different the content bases are.

>> Yeah. Yeah. Yeah. I think yeah that took

a lot of iteration. Yeah. The chunk

sizing, how retrieval works, the

different like steps in the pipeline of

retrieval. Um yeah, there's a lot of

iteration on that. Ivan said I should

ask you um how many times you've rebuilt

Notion and rebuilt your harnesses.

>> Yeah. Yeah. It's kind of a running joke

almost. I mean we we we rewrite our AI

harness probably every six months or so

and it and the time to rewrite has kind

of been been decreasing just because I

think like like progress has been

accelerating. I think this is honestly a

a really key thing and something that a

lot of companies get wrong is just like

doing one thing and then just like like

sticking with it. you really do have to

keenly aware of what the current state

of the models and the technology is and

then designing the harness the system

and the product deeply around that and

it basically means you have to rewrite

it every six months and um I find it

pretty fun. It's part of the process. Um

you know you get to get to restart and

and and and rethink it. You know we're

working on we're about to release a new

version of a harness like in the next

week or two. Uh and then and then we're

already thinking about the one after

that as well. I I think that leads to a

a set of questions I had for you on just

like how does not as an engineering and

product and research organization work

now that you have the power of um coding

agents as well because I imagine like

your willingness to rewrite the harness

goes up dramatically if you're like

agents are going to help me do it.

>> Yeah, that's extremely true. Yeah, I

mean yeah, it's been it's been really

fun to use the coding agents. I think

the ambition of what I even consider

building has has has gone up a lot. What

do you think has most dramatically

changed in how you think about how um

engineering and product should work at

notion over the last two three years?

>> Yeah, I mean it's it's definitely

changed multiple times. I mean in terms

of the coding agents, we kind of went

through multiple eras. There was kind of

like the tab autocomplete era and then

we and then we got into sort of

inserting rewriting some code u but but

it wasn't really until the the agents

started working. I I would say like

early last year we started to adopt the

agents like I started using cloud code I

think around April of last year that was

a huge unlock like I would say the the

the big shift there is that you know you

can really push on getting these agents

to end to end you know implement and and

verify and maintain stuff but it but it

requires pretty significant thought in

terms of how you architect things and

what is the verification loop um but but

but the upshot is I think if you do it

well you can be much more ambitious

about what you're building and also make

it much more robust than you could have

done uh with with with humans writing

it. And then the flip side is if you do

it badly, it's all slop.

>> Does that change your lens of like what

teams should look like at notion like

size, seniority, anything like that?

>> Yeah, I mean I would say I mean the

fundamental effect is that you know

everyone's individual impact in terms of

their output can be much higher. um and

your output increasingly depends on your

ability and willingness to use the

tools. I I think that's the fundamental

thing that's happening. And then like

like how does that play out? I think I

don't think we've seen that much impact

on the the team size really. I think we

we like to work in like smalish tiger

teams for the most part. Um I think if

you can make teams small, it's almost

always better. That was true before and

I think it's still true. Uh maybe

increasingly a little bit but but not

that much. I I think yeah the main thing

is to just like like really harness the

tools.

>> Do you think something different happens

to the median engineer in an

organization versus the 10x engineer or

the engineer 10x more willing to use the

tools?

>> Yeah, I think the the gap is bigger. You

can be like a 100 or thousandx engineer

if you're using the tools right now. I

think I think the the gap is much bigger

like the the the minimum bar has not

changed but the maximum bar has has

extremely increased. One impact it's had

internally I would say is like broadly

things feel like a little bit more messy

and chaotic I would say like but I kind

of love that I mean it's like there's

there's more pro there's way more

prototypes uh you know people are like

for example our uh uh design team made

an made an entire uh git repo they

called it the design playground and it's

essentially like a simplified notion uh

with a bunch of like UI primitives in it

>> and they've made it like really

sophisticated you it it it has like an

agent in there and like um and it's it's

pretty cool because it allows them all

the designers can can spin up like super

high fidelity prototypes

>> really quickly and so it's no longer

like like pointing at a mock and being

like like you know like like how will

this look like they'll give you like a

URL to a prototype that's that's that's

been deployed and that sort of thing is

true all the way up and down the stack

you know for all of engineering just

like a little bit more chaotic more

stuff happening um all the PRs are more

ambitious

>> do you draw a line somewhere about like

stuff that is more dangerous to touch or

sensitive like ah there's could be risk

of data loss over here or and not or is

it kind of you look at it all is it's

fair game

>> we still do reviews on all the pull

requests and and I would say and you

know all the pull requests are now

written by agents they're often like

larger and and and more complex that's

like the worst part but the better part

is that they're often like a much better

tested and we can demand sort of a much

better testing for the things that merit

I never produce a PR that like hasn't

been like fully ant tested anymore. And

so it's like you can get to a pretty

high degree of confidence that it that

it works, but it requires like you're

not just vibe coding by by saying the

thing you want. You're sort of thinking

carefully about like what is the thing

I'm like what is the change I'm trying

to make and like and and how can it be

verified and how can it be deployed

safely and then enlisting the agent to

to help you with that process.

>> When you think about where you said the

general assistant like doesn't quite

exist yet. Um, what's the what do you

imagine notions agent agents being able

to do like over the next year or two

that are still unblocked? They're still

blocked by either capability or your

harness work.

>> We struggled for a few years to build an

agent. Um, and you know, it always like

like sort of worked but then you know

wasn't that useful largely just it was

too early. So we you know we we tried to

to build an agent I would say actually

three or four times and then uh we

finally launched it uh last fall so like

last August September. Um so the you

fuse notion AI now it's like the full

agent that has access to everything in

notion pretty much. Um so that that that

totally works. I would say like the a

lot of the original vision that we had

totally works now. Um and it you know

it's like like fully shipped. Last

August or September, we shipped our

personal agent. U so it's pretty much

every user in notion has an agent and it

basically it has access to all all the

things that the user has access to. So

you know it can create a database for

you. It can update things, create

documents, it can search search the web,

do research and then the second big

thing uh that we just launched last week

actually was u custom agents. So you can

basically you can create a new custom

agent give it a name and unlike the

personal agent uh by default it doesn't

have access to anything. Uh so you have

to grant it access but then once you do

it can actually run autonomously in the

background. So for example you can give

it access to its own database to file

tasks let's say and then you can attach

it to a slack channel and then it will

start responding to people on Slack and

filing tasks. That's that's that's one

use case. Another one is maybe you could

um you could give it access to a

database of like weekly reports and then

and then let it search the web or search

your workspace. And so it's sort of a

custom agent sort of represents some

work or job some some knowledge work

task that you want to be done

autonomously. One thing I'm really

excited about this going forward is is

um we want it to be extremely good at

sort of bootstrapping its own

capabilities basically from an initial

kernel allowing it to basically

bootstrap itself to do anything right.

So even for example maybe u uh building

an integration that we don't support yet

deploying that and then and then using

it.

>> So you imagine that notion agents are

actually the broader definition of agent

where like writing code is a tool it's

pretty close to yeah

>> I think it's pretty key. Yeah I think I

I think of coding agents as like the

kernel of AGI. AGI will be a coding

agent. Um and and and and code is just a

really really useful uh a primitive for

representing like deterministic logic.

The thing that's really exciting about

it um we're applying it to to a

knowledge work agent is that it can

bootstrap a capability you know so yeah

like I said if integration doesn't exist

it can build it um if if it needs to uh

you know connect itself to a new data

source it can do that

>> given you have a you know notion is at

scale but is operating in a landscape of

productivity and platform players that

are at even more scale right um many of

these will end up with their own agents

lots of people from the labs to the

Microsoft world are trying to integrate

other data sources. So you have this

like cross attempt to integrate and

index like how do you think that plays

out? Like what do you what do you

imagine that notion agents are best at

or what they have the right to go do?

>> If you look at the landscape like I I

would sort of say there's the labs and

then there's maybe the the the software

platforms and then there's maybe like

infrastructure. In terms of the labs,

you know, we see ourselves as kind of

like the the Switzerland for models. We

think and our customers they, you know,

they don't want to be locked into a

certain certain labs model. They're

always uh releasing new versions any

given month. One is better than the

other. Um so we want to be a a place

where basically you can you can easily

get access to all the best models um at

any time and you can easily switch

around.

>> Do you think open source plays into that

as well?

>> Yeah. Yeah, absolutely. I think the open

source models are actually getting

really good. There's like the four

different Chinese models now that are

that are quite good. We actually just uh

released one of them in our agent uh

last week and and we're going to do all

four for sure. Um they're they're

actually quite good and they're and and

they're way cheaper than the the

frontier models. So I think there's

there's a lot of use cases where where

where you'd want that and we want to

give that as an option in terms of like

the other you know so you know we think

of our role as sort of taking all the

best models that we can creating a

really high quality state-of-the-art

agent implementations where where people

can easily and conveniently get access

to them and then making sort of a

collaborative workspace that is really

good for for humans and for the agents

uh to to to coordinate on. I think it's

it's something that's that's very needed

in the world and we're just trying to do

it in a really tasteful wellexecuted

way.

>> You were describing you need the index

to make the agents good. Um you give the

agents access to the tools that we

humans have in notion. How do you think

about um the structure of notion and

like where it's like useful or even like

not useful or relevant for agents like

blocks and databases and such?

>> It's all still pretty useful. Um

extremely useful. it uh there there's

been a a challenge to sort of you know

we want to make it really convenient for

the agent. I think that's that's a new

thing that that didn't exist. You know,

in in the past it was convenient for

humans and then we also made APIs

convenient for humans writing code

>> to use our API. Uh so we essentially

have a new customer which is the agent.

At first that was definitely a problem

you know. So for example like our our

API uh uses this this crazy JSON format

for blocks that by default is like crazy

verbose and like like horrible for the

agent. But we basically took on that

challenge and um designed uh just really

convenient APIs for the agent. We

created sort of a markdown dialect that

um looks like the default normal

markdown but it's sort of enhanced with

uh all the notion blocks. Um and the

models are really good at it. It works

really well. Uh so so that's how it

reads and writes to pages. And then uh

for databases uh we we use a SQLite. Um

so so basically it's the guess the speak

and SQLite which also works really well.

So the default thing did not work really

well. Uh but then we just like like took

that on as an engineering challenge and

and I would say now we have like

extremely convenient APIs that the

agents are are really naturally good at.

>> How did you uh understand or figure out

what would make the API better for

agents?

>> That's a good question. Yeah, I would

say it's a it's a combination of just

trying things. It's it's it's very

empirical. So, so we're just playing

around and like like noticing, oh, it's

not very good at that. Oh, that's way

too many tokens. How can we make this

smaller? And then a little bit of just

like like first principles thinking of

like, you know, what is it the models

are being trained on and what's what's

in their prior? What do they know? And

what what do we think it would naturally

be good at? And and like like how does

the agent loop work and like what what

would be the convenient efficient

pattern for for accessing these things?

Um, and so and then just you know a lot

of playing around. I hear user research

where the user is actually agent and

then you know ongoing eval.

>> Yeah. I mean user Yeah. You just chat

with it.

>> The user is always there. It's ready to

talk to you.

>> Yeah. Actually, that is wonderful where

you have infinite access to it.

>> You have infinite access to it. Yeah.

And and you can you can script and scale

the access as well.

>> I assume you have actually I know you do

because you walked in. You're like,

"Hey, I need to get access to the Wi-Fi.

I need power. We can't block the agents

while we're doing this." Um what do you

have running right now? Tell me about

your setup. I'm working on a new

prototype and so I have a couple agents

I'm working on that. Um and then yeah,

my setup these days is just um either

claw code or codeex. I like the the the

CLI tools. Um they're they're they're

super simple and like work pretty well.

I'm I'm pretty comfortable in the CLI.

So, and then yeah, my my

>> you don't need my generated game CLI

commands.

>> It's it's a very cool idea. Um, I would

say, yeah, my my my whole goal these

days is essentially to just have as many

running as possible and to run them all

the time. And you know, so for example,

like every night before I go to bed, I'm

I'm like, "Okay, I

>> Let's go, guys.

>> Yeah. Basically, what I have to do is

make sure that I've given it enough

stuff that by the time I wake up in the

morning, it it will still not be done.

And so I've maximum

>> That's victory.

>> Yeah, that's that's victory. Yeah.

>> So, yeah, like I've I've I've done that

I would say last last five nights pretty

well. My personal record is that I've

had a a coding agent running for I think

it was 13 days straight uh without

stopping and just just basically working

through like tasks.

>> Well, well prompted. Yes. I I admit to

having woken up in the middle of the

night at least multiple times this week

and just being like, "Are you still

going?"

>> Yeah, I know. Yeah. It's it it's kind of

nerve-wracking. I I always like there's

always like I I'll check it one last

time before bed and just really make

sure that it's still spinning.

>> What about on the notion agent side?

Like do you have a workflow there that

is core to daily work?

>> Yeah. I mean I mean I I use our personal

agent all the time. So it's it it has

all the context about about our company

and everything that's going on, you

know. So like for example, last night I

was asking it about um how the custom

agents launch was was going and like

like like what the what the signals were

getting from it. We're super useful for

that. And then for I I have many custom

agents that are that are running. U my

my my personal favorite is I have a

email triage agent. So it has access to

all of my work and personal emails. Um

and it just uh wakes up every day and

just archives all the stuff I don't need

to see. Train it over time to uh to to

learn my preferences.

>> Do you actually label data for it?

>> It's pretty to do actually. So all you

have to do is you make the agent and

then you give it access to email and

then you you can make a blank page. It's

like it's memory

>> and you let it edit that page and then

you just say okay now go look at my

emails and then interview me ask me

which things you know so sort of it will

like propose things that it thinks it

should archive

>> and then you can kind of correct it and

then we'll use that to essentially

generate like a list of rules about like

like what it thinks are correct or not.

And so for the first couple days I was

sort of like like like uh correcting it

on things. After a couple weeks or so, I

I I I dropped the approval entirely and

it just automatically archives all the

things I need to see now.

>> Wow.

It It completely solved my email

problems cuz for me, like I don't I

don't use email that much for work

stuff. Like it's it's mostly in Slack.

95% of the personal emails and working

emails that I get, I don't need to see

at all. And so it's just a waste of

time. Uh and so it it it completely

solved that. So now when I have my

inbox, it's like only stuff I need to

see. I've got lots of uh custom agents

running. Uh there's another one um that

I built that uh uh triages uh customer

fe u all all internal feedback and and

and

bugs. So we have a Slack channel where

basically people just just uh post

random like like product feedback and

bugs. In the past it was it would sort

of sometimes get answered but then

sometimes like like half-hazardly get

ignored just because you know there's so

many teams where things uh so its entire

job is just to route it to the right

place. uh and and it it uses a similar

sort of like like memory pattern where

it sort of learns on the fly uh where

it's supposed to file bugs uh and then

over time it's built up like like

hundreds of roles that it just um sort

of like like learned over time, you

know. So for example like if there's a

there's a bug about the mobile app, it

knows to route to the mobile team and

then a file a task in their database.

>> Do you look at that um like the

generated and updated memory to like

because it's legible to you to say like

did that make sense to me? I think I did

it I did at first. Uh but then sort of

once you trust it's kind of working, you

just you kind of ignore it and then if

if it ever breaks, I'll I'll go fix it.

Yeah, it it'll break every now and then

and then um

>> but the benefit reading your email is

>> here.

>> Yeah, just not read it. So yeah. Um

yeah, I I mean generally I would say

yeah the the general pattern I follow is

sort of I I build it as a prototype. I

have it in sort of like an approval mode

where I'm sort of, you know, watching it

closely and then but then after it runs

a bunch of times, you kind of trust that

it's working. And then

>> is there anything you do internally at

notion to um make sure non-technical

teams have the intuition for how to

build agents or how to like express that

productivity too?

>> Yeah, it's a great question. I mean, we

do uh sort of workshops and hackathons

pretty frequently. So like for example

like a month ago I did a I did a

hackathon with uh the the people team

and sort of sort of got them the the

people team has been amazing. They're

actually one of the the highest adopters

of custom agents.

>> You know they do all these kind of

workflows in like Slack and notion kind

of like like manual work like that and

um and yeah I would say yeah like like

people are super excited to to try it

and sort of like like maybe just need

like a little bit of a push in terms of

intuition and like like getting them

started. Um, but then honestly I've been

super impressed like I I think the

concept is like kind of intuitive sort

of like like once you get once you get

past sort of a little bit of the

technical barrier of like what is a

prompt and like what is the agent and

how does it get triggered and woken up

and like like how does that even work?

But then once you sort of get past that,

I think it's actually a very humanlike

interface.

>> Yeah. Maybe the maybe the biggest

barrier is actually just getting people

to try and assuming it's going to work

at all. Right.

>> Yeah. Yeah. You and Ivan originally met

on the internet tools for thought

community. Um it feels like you know the

tools we have for thinking are very

different now. Has your like core

conception of notion changed over the

last few years because of all the AI

stuff like what what is the what what

thinking does the tool do for you?

Should agents do for you? What do you

get to do?

>> Yeah, I mean it's I would say changed

quite a lot. I mean, broadly speaking,

before AI, our our our our goal was to

create the best tool for humans to

directly perform their work.

>> And then now the goal is to create the

best tool for humans to manage agents to

do the work for them.

>> That's a big shift.

>> That's a pretty big shift. Uh it's it's

pretty fundamental. Um but it it turns

out that you need most of the same

primitives. uh you actually all the

primitives that we built are actually

still extremely useful. It's it's more

that we just needed some some new

primitives like like representing what

is an agent and you know how does it

interact with your pages and databases

but you know you still need the same

primitives. You still need a document.

It's an unstructured way to you know to

write stuff. Uh agents love to write

markdown documents. So

>> yeah,

>> it's still very relevant and you still

need a database. It's um you still need

structured data. you know, if you're

working with your your swarm of like 100

background coding agents, you don't want

to have 100 chat threads. You want a

kemb board. It's, you know, the same as

before.

>> Makes sense. You still need the uh the

coordination structure. What is one

thing that just because you're ahead of

the on this stuff and then trying to

figure out how to bring, you know,

notion and then users along with you.

What is something that's really changed

about um how you personally like build

even in the last six months? I mean,

it's completely changed. I haven't

written code since like last summer. I

don't type code anymore.

>> Yeah. It's it's it's completely shifted.

I mean, we went from humans type all the

code to like we're still typing, but we

like tab complete to sort of like we

talk to the agent and it sort of does

little tasks for us, but we are still in

the outer loop. And then now it's more

like I I design a endto-end task that

involves like making some change and end

to end verifying it. And then I'm just

the the outer you the outer verifier

sort of like like double checking at the

very end that it that it's correct and

if it's going off the rails kind of like

like monitoring it. Um so it's a it's a

complete shift is you know I'm I'm now

like the agent manager instead of the

coder.

>> Amazing. Well um thanks Simon. This has

been a super great discussion about how

we're all going to become Asian managers

and uh uh hopefully in notion.

>> Cool. Yeah.

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