How many people here know what poolside

is and does? Anyone? Anyone? Yeah. So,

let's talk about that real quickly.

Poolside exists to close the gap between

models and human intelligence. That's

literally it. That's what we're here to

go do. We're building our own models

from scratch to do this. We're based on

the idea 2 and a half years ago that we

thought next token prediction was an

amazing techn technological

breakthrough, but it need to be paired

with reinforcement learning really to

make that leap. So that's what we've

been doing for the past 2 and a half

years. So we're on our second generation

of models now, Malibu agent. And instead

of kind of like walking you through some

slides and all that, we just thought

maybe I don't know, let's kind of show

you what we're doing here. So

are you there?

>> I got you, Jason.

>> So as I said, you were supposed to see

him today, but there's

I don't know. Our airline system kind of

works sometimes, maybe. So he's stuck in

California, but uh we thought we'd just

walk you kind of through some um some

demos here today. So what you're looking

at here is a very modern programming

language that the government uses to run

all the world's critical infrastructure

called ADA. Anyone familiar with ADA?

>> Yes. Yes. Okay. So everyone I saw put

their hands up for ADA either has no

hair or gray hair like me. So that

should tell you what's going on here. So

ISO, why don't we uh why don't we figure

out what's going on with this codebase

here?

>> Well, let's start asking what the

codebase is about.

>> That's great. And what you're seeing

here is obviously our assistant in in

Visual Studio Code backed by poolside

agent, a model we train from scratch

using our proprietary techniques. Um,

and you can see what's going on here.

Kind of the stuff you expect from an

agent. Uh and obviously the form factors

of all of these things are going to

change a couple of times over the next

couple of years, but you know people

seem to like VS Code. Uh so we're going

to you know show you this demo here

today. So you can see from this it kind

of went through told you what this

codebase is all about but um you know

these things run in our satellites and

uh I don't know anything about ADA but I

do know a lot about a couple of other

programming languages. So uh ISO what do

we want to do here? Why don't we uh see

what this thing might look like in Rust?

Let's do it. Let's ask it convert this

database to rest.

>> So obviously you're going to see what's

going on here. Again, if you guys have

used other tools, you're not going to

expect too much of the difference for

what's happening here. Except that

again, we're backed by our own model.

We're not using Open AI. We're not using

Anthropic. This is poolside. And

poolside is a bottom and top stack that

is right now if no one's touched it and

I know no one in this room has touched

this unless you work for a three-letter

agency, a defense contractor, or you've

sent missiles somewhere that we're not

going to talk about in this session. Um

because that's where we're working.

We're working in high consequence code

environments for the last year inside

the the government and the the defense

sector. Um as you can see from this

demo. Um so what you see here is is kind

of going through doing the conversions.

What you see in the middle pane is

something that we built to kind of show

you as the streams come through all the

different changes that are happening.

Um, one of the tricky parts about

working on inside the defense sector and

things like that is you can't have an

agent that's just going to run around

and do stuff. I mean like I can't walk

into half of these buildings. You can't

give an agent access to these data

source and just say, "Hey, go nuts." You

need to have the right permissions. You

got to actually really ratchet these

things down to do things inside those

environments that you know they feel

comfortable with. So, uh, where are we

on this now? What is is it trying to fix

itself yet? Yes, it's it wrote about

1152 lines of code. Uh, and it just

popped up a command start and tested,

excuse me. Uh, so we see here all the

files on the left hand side that it

created. Uh this is essentially our live

diff view that's available.

Uh and as we see it's currently starting

to actually test it out.

So this is the part where we just sit

here and watch this for 3 minutes and I

see nothing.

>> No. What you see

>> the good thing is that this is a very

fast inference.

>> Yes.

>> So 1100 lines of code.

>> Did it task completed?

>> Do we know if this works yet?

>> Well, let's have a look. So it actually

wrote some bell commands to test it. And

when we check out the output of those,

this actually looks pretty good.

>> We ask

>> can we verify that

>> to run it? Let's go verify it. So of

course our agent came back and gave a

summary of what it did. But let's just

ask how to run this.

Okay.

So,

I'm going to go open up now. So, it says

this is how I can run the ADA version

and this is how I can run the Rust

version. Let's run the Rust version.

Perfect. Let's have a look at

We might be hitting an actual

>> an actual demo bug.

>> Let's have a look.

>> Let's see what happens.

>> I know. No, no. Just warnings. Just

warnings.

>> Do we have an unwrap in there that we

need to take care of? I heard that those

things are dangerous.

>> So, right now there's a ripple.

Uh, let's hit help. See what we're able

to do. So, it looks like we have a set

of commands. I'm going to be lazy. I'm

going to copy paste these queries.

So, create table users. Okay. So far so

good.

Let's insert a record.

Okay. Well, let's find out if it

actually did its job. Select start from

users. Okay, we've got a record here.

>> That's nice.

>> Now, now I want to actually

uh you see if I use the up arrow,

it doesn't actually allow me to cycle

through commands. Let's ask it to add a

feature.

Uh

allows me to use the up arrow to cycle

through.

I think it will understand my center.

>> The one thing we know about ISO is he

actually does know how to read and write

but he can't type. So all those errors

that you're seeing in there. Uh yeah.

>> So it looks like the agent's identified

a package that we can use. Let's just

quickly look here. Compare this to the

Virgin one.

And it looks like it's adding a library

called rusty line and changing the files

accordingly.

It's currently built it and it looks

like the build output is successful.

There's some warnings. We'll ask it to

clean those up later on. And it's now

starting to test it.

Okay, apparently it works. It's going to

It wrote itself a little bash script to

test the history.

It's wrote itself a little final demo

script.

So let's let it Okay. So, and it gave us

the summary. Well, now how do I rerun

this? I do kind of know that, though.

So, let's just

>> should know that. That was 30 seconds

ago.

>> Let's build it. And let's run it again.

Okay, let's do a help.

And oh yeah, that's the up arrow. It

works.

>> Very nice.

>> Now, our models aren't just capable

coding agents. They're capable in lots

of areas of knowledge work. They're also

emotionally intelligent. They're fun.

They're great to write bedtime stories

with for the kids. So, I'm going to ask

you to write me a poem about all these

changes, but that's just more for fun.

So, as Isa was saying, this is just an

interface into our platform. There's

other interfaces into it if you're

inside one of those organizations that

has adopted poolside. So this is the

coding interface into it but we also

have other ways in which you you can

interact with it web as well as an agent

that you can download on your machine

but um yeah we don't really tout the

poem writing or the songwriting though I

did send this to my wife to see and I

have been sending her love letters

written by poolside so I kind of hope

that she did not enter this session to

know exactly how I've been doing that

for the past 6 months but uh yeah so

this is kind of poolside this is what

we've been up to Um, so as I said,

Malibu agent is as a second generation.

We've got a ton more compute coming

online and that's when we're training

our next generation. That is be going to

be the one that comes out publicly to

everybody very early next year. We're

going to have it behind our own API.

It'll be on Amazon behind the bedrock

API. Anybody in the world who's building

out any sort of on a one side the

engineering assistants like the cursors,

windsurfs, cognitions, replets of the

world, you can use ours. or if you use

building out on any other side of the

fence, the Harveys, the writers, the

whatever applications of the world,

there's going to be a fifth model out

there that's going to be at that level

that you can you can consume. But we're

dead set on doing this and bringing this

out to everybody in the world and kind

of advancing that state-of-the-art and

we're just going to keep pushing that

out. So, that's kind of who we are. Um,

and uh you can find out very little more

at our website since we don't put much

out there.

But Iso, anything else you want to say

before you uh try to go make your flight

this time, please?

>> So, I would say that it's been a pretty

incredible journey for the last 2 and

1/2 years of starting entirely from

scratch and now building to a place

where we see our models have grown up to

become increasingly more intelligent.

And the kind of missing ingredient that

we had was compute. And now that it's

unlocked for us and and with a large

number of over 40,000 GB300s coming

online, we see how we can start scaling

up some of those models uh to get even

further uh in in their level of

capabilities and software development

and other types of long horizon

knowledge work. What I think is exciting

about this conference and this audience

is of all the work that's happening of

evolving the form factor. Right? Right

now what we looked at was this

asynchronous way of of operating with

agents. You know, Jason, you and I, we

have agents running that are doing tasks

for for hours, and I think in the near

future, we can see a world where they're

able to start doing tasks in days in the

coming years. And so, I think the

interface will continue to change. Uh,

we're really focused on the

fundamentals, building intelligence, and

being able to scale up and serve it. And

it's why we go full vertical. It's why

we go from our multi gigawatt campus in

West Texas where we're building out data

centers building out models. And the

interface that you saw today is just our

version of an expression. But I think

this audience is going to do an

incredible job of building lots better

versions of how to express using that

intelligence uh into actually, you know,

valuable, economically valuable work.

Couldn't have said it better. Can't wait

to see what you guys build on this uh in

the future when it's publicly available.

And if anyone really does want to build

a data center campus, we are hiring for

that. Um it is weird to be putting

shovels in ground again like we did in

the '9s and early 2000s, but that's what

you got to do to scale intelligence

these days. So,

>> I would make one other non-scheduled

statement if you're going to be okay

with this one, Jason.

As as our models are are getting more

capable, we'd love to also see who wants

to build with them. Right now, the the

vast majority of of you know, companies

that are doing additional reinforcement

learning and fine-tuning on top of

models are are doing it on what I would

consider right now the you know,

best-in-class open source models, the

the Quens and Fumies and Miniaxes of the

world. And uh we'd like to start

figuring out how we can you know partner

with you with our our models anywhere

from any checkpoint early on to where we

are today for you to be building closer

together with us on top of things. Uh we

haven't really figured out the approach

to it yet. Uh but I think since we have

this audience it's uh it's not a bad

place to put it out there and so

definitely reach out to us. Uh we think

the world till date was built by

intelligence. The world in the future

has been built on top of intelligence

and so be a great way to partner.

>> Well thanks ISO. Thanks everybody here.

And now we do have 5 minutes left. I

don't know if we're supposed to take

questions, but I'm happy to. So, if

anyone does, but if not, I'm just going

to go that way.

>> What was that?

>> Sort of. I mean, I think of him that

way. Here, here's a fun story. Here's

how I met ISO. I like to tell this story

because um ISO is a fun fun dude. I met

ISO because started with a failed

acquisition at GitHub. So back when I

joined GitHub in 2017 as a CTO, I wanted

to take GitHub from a kind of

collaborative collaborative code host

with open source bent and turn it into

an endto-end software development

platform infused by intelligence. And so

you know the the products that we

launched from 27 on or 17 on GitHub

actions, packages, alerts,

notifications, eventually code spaces,

um and then co-pilot was the last thing

that the office of the CTO did before I

left with Nat Friedman, Uga De Moore,

and a couple of other folks inside

there. But ISO in 2017 when I joined uh

he had working code completion before

the transform architecture had landed

fully. He had on LSTMs and so I quickly

tried to acquire his company and he just

he just said no. So he just said no to

me. Uh but we had that was a long drawn

out process talking about what we

thought neural networks were going to

mean for the world. And so during that

process, which was a lengthy one, we

became really good friends and we'd

stayed in close contact over the years.

And then 22 rolled around, obviously

Chat GPT comes out, Anthropics out, and

we kind of saw the endgame at play and

we said, "Do we jump back in or not?"

And of course, yes, we jump back in. But

I like to tell that story about how he

just kept saying no to me and I just

kept asking him questions and eventually

he said, "Yes, we should found a

company." Cuz by the way, when I asked

him if we should do this, he said, "Oh,

god damn no." That were his exact words.

He's like, "No, we should just learn how

to paint and sail." But here we are.

So,

>> yeah,

>> it's it's been a great journey together.

Jason, I I think the reason we ended up

doing this is because of our our

opinionated view on what it was going to

take to build more capable intelligence.

And in the first 18 months of this

company, you know, obsessing and

focusing on reinforcement learning

combined with LMS felt like one of the

most contrarian opinions in the world,

but I think today it's absolutely not.

And it's super exciting to see the the

progress that's continuing to make like

we're in the coming years we're going to

see the world that started in

completions and went to chat and is now

at a gentic increasingly approach more

autonomous and we're all of it is

stemming effectively from the

combination of bringing highly capable

models that are constantly evolving

together with real world problems and

and I think what we're starting to see

now is we're entering these kind of

awkward teenage years ahead of AGI where

everybody in this room is building out

incredible companies and applications is

bridging this gap of what it really

takes to make intelligence that in its

raw form actually be valuable and we uh

we want to be a small humble part of

that. We've got a lot of work still

ahead of us. Uh the team is growing. Uh

but hopefully what you've seen today uh

is what our our customers and

enterprises have been having access to

and seeing for a while is that we're you

know hard at work at uh at really

pushing those capabilities. We also want

to make sure we make them available to

build together with others.

>> Well, that's it. Thanks everybody.