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I think the like dystopian view of AI or
AI as like taking all our jobs and all
of that is like not correct. And I think
the future of work is more human, is
more interactive, is more multimodal.
Once the making of things gets easier,
the bottleneck goes back to to to like
how many ideas you can have. I wouldn't
put learning to code as the top thing. I
would put learn to make things. Learn to
make things with code. Learn to make
things with video. learn to make like
anything with AI.
[Music]
>> Welcome back to the breakdown. I'm Dave.
This is Tom. And today we're joined by
Amjad Msad. Amjad is founder and CEO of
Replet, which was a YC 2018 company. Uh
welcome to the show.
>> Thank you.
>> So today, uh we want to talk about kind
of your beginnings in helping people
learn to code. what you're doing now in
replacing a lot of that work and then
where we see the future going in terms
of how people make things. So, Replet
started um I guess in 2016, you were in
YC in 2018. You started out as a tool to
make it really easy for people learning
to program to set up development
environments purely on the web. Um but
now you're really taking off in AI
assisted coding. Tell us tell us the
latest. Yeah. So, um the mission has
always been to make programming more
accessible. Uh then we sort of uh you
know after YC we sort of updated it to
be a little more ambitious. We started
talking about a billion software
developers and it just sounded absurd at
the time and
>> this is all prel right.
>> Yeah. Prelm. There was a moment of time
I don't know if you remember that but
like 20 in the like 2015 AI hype. There
was like a bit of a NLP natural language
hype as well. There's a bunch of AI
companies that ended up being just like
humans behind the scene. They all went
broke, but um there was like a a glimpse
of potentially doing NLP on code. So, we
had an idea that, you know, it's it's
probably coming. I I even had it in my
in my seed deck that like at some point
like we'll collect enough data to like
train models. It wasn't until like GPG2
in 2020 that I felt like it was was
going to be possible. So we you know we
had we had built all these um primitives
like the development environment the
sort of hosting environment all the
other stuff around it. Uh and I felt
like if if we just like added um like AI
agents like it will be able to
orchestrate all the stuff and it'll be
it'll be really great. agents were like
every time we tried them. Uh I think the
first time we tried it like 2021 didn't
work, 22 it didn't work and then 2024
like early early in the in the year we
just felt like it's getting close.
>> Even GPT40 could like be coherent for
like um 2 minutes somehow just ended up
doing a big bat. The company was not
doing super well.
>> Yeah. was I was about to ask was there
like a bet the company moment where it's
like we were teaching people to code now
we're no longer doing that now we're
going to enable everyone to build apps
>> yeah yeah yeah we had grown the company
quite a bit um and we were burning way
too much money uh and we decided to do a
layoff
>> and so we we um cut like perhaps 50
people and then like another like 15 20
people left and so we were like less
than half of the company and so I just
put everything on on replet agents I
just felt like this is the thing it has
to work. It felt like it was close.
Yeah. I don't know. I was just like burn
the boat boats kind of moments. It's
just like we got to do this. I mean,
it's I felt like this was the this was
the the thing that would make the
company work. And honestly, like if claw
3.5 hadn't come out mid US building
agent, we would have probably failed.
>> Yeah.
>> Because um like I said, like GPT40 would
like stay coherent for 2 3 minutes. Quad
3.5 was the first one that like could
work for like you know five to 10
minutes and actually actually work and
the code generations
>> and that's sort of the approach that I
think is most successful for startups
kind of building on the very edge of
what is possible you know you start out
on a mission it's the techn is not quite
there yet you start building and you
sort of you you skate where the puck is
going you know it sort of catches up
with you
>> yeah and you you want to keep keep doing
that
>> totally
>> yeah I remember you came to speak at YC
like 6 months ago and right after you
had launched this and at the time you
said we're very far from fully automated
software development.
>> Do you still feel that way?
>> No, not at all. I mean, I I feel like
I've, you know, every time I make a sort
of some kind of prediction, it feels
like bold and like, you know, but I've
been I've been consistently wrong and
like uh the way things are moving uh is
a lot faster. Just the level of
autonomy. Uh like I said, like maybe 3.5
was like 5 to 10 minutes. Like 3.7
perhaps can get up to like four five
minutes to an hour. The uh 4.0 No, in
the in the system card and opus, they
said they made it work for seven hours.
>> Wow.
>> 7 hours.
>> That's incredible.
>> That's always been the limiting factor
for making agents is like, can they stay
coherent? Can is the context length
actually useful to reason over. If an
LLM can work for for seven hours, that's
basically like a human worker.
>> Yeah. And presumably they're working at
much faster speed than a human would do.
So they're kind of completing perhaps a
week's work in those seven hours. The
only thing that I think is is missing
and a big limiting factor for actually
automating a lot of work is computer
use.
>> Computer use kind of sucks.
>> You know, I'm sure you've you've tried
it. And this is the difference between
Replet agent really uh giving it one
prompt going all all the way in an app
versus having to kind of babysit it a
little bit and like test with it.
>> And we had a company in the last batch
called browser use that's um that's
working browser automation.
>> Browser Use is great. And another one
called pig which is doing the same for
kind of windows desktops.
>> And uh I think advice I would give
founders today is um taking either
browser use or windows automation with
pig and trying to apply that into
enterprise into vertical
>> into a vertical industry.
>> The moment this technology worked those
two companies are just going to
>> totally and I think I think we're like
weeks or perhaps single digit months
away from it working really really well
and so now is the time to get started
using those technologies.
>> Absolutely 100%. And this is this is
what we're focused on. So you know
replet agent v1 to v2 was a huge jump in
autonomy. Uh v3 is the most autonomous
thing. So we're already working on it
and um the interesting thing for us is
the underlying technology and how it
would enable autonomy. There are like a
few important things. One is uh uh you
know being transactional. The ability to
roll back is is very important. So you
you would want you'd want it to be safe
for agents in the same way that gits
made it safe for human programmers to
kind of experiment and create branches
whatever you want the same thing for for
agents like if an agent kind of messes
up a DB migration it should be able to
roll back and also it should be able to
like sample
>> uh across different different paths. Uh
I think this is very very important for
autonomy. If you look at uh like for
example when when uh Anthropic publishes
their Swedbench score they publish one
with that sampling and one with sampling
and it goes from 70% to 80%.
>> And so this is the idea that you sort of
you spawn multiple agents each one will
take a a shot at it and then you figure
out which one works and and choose that
that branch effectively. We built a
infrastructure that is um uh fully
transactional and moves in lock step
meaning the file system is a snap
snapshot fail based file system the DB
is a snapshot based uh u DB and so a as
you're we're making commits uh to the
entire system including the virtual
machine as you're going uh and so what
we can do is also we can we can fork it
and branch it out. So if if we had
computer use that's working well you can
you can just like right now what they do
with the sampling is they do they have
some kind of judge that does ranking
>> which you know it's not a true verifier
a true verifier is a is a is a test
right and a computer use test so you you
can sample out and really pick the best
branch that's actually working
>> that's incredible
>> and and repeat that on and on and
reliability gets really really high
>> so fast forward six or 12 months you're
not spawning one agent you're spawning
is Is it five or 10 or a million? Like
how
>> I think this is where it's going to get
interesting, which is you'd want to give
the user the ability to set compute
budgets. I I think we're starting to see
that with um with the O style models of
like here's here's how much budget. But
but look, I mean, if if you give us
$1,000, we'll spend them if you know.
So, uh
>> that's really cool. I've not thought of
that before. Um but the idea of like
spawning multiple branches then just
picking the best one and being able to
do that in parallel. I just the human
brain doesn't naturally work like that.
We think sequentially. We can't.
>> It reminds me of this legend. I don't
know if this is actually true, but at
Apple when Steve was running it, he
would intentionally have teams doing
basically the same things and then see
which one did the best job. It's like
>> I've heard OpenAI does that now.
Interesting. Uh I I've heard that like
the Codeex project like had multiple
different different teams. So in the
literature, you'll see that uh small
small models sampled uh will beat larger
models. So Sonnet sampled is probably
better than than Opus companies. uh
haven't tried that where instead of
hiring one senior engineer you hire like
10 junior engineers and like give them
the same task and then
>> it's just very expensive with humans but
with LM it's relatively cheap you can
just do that do a hundred of them and
just pick the best one every time
>> so how are people using replet agent
like and and who are the people using it
>> you know it goes back to sort of our
early vision that if you make
programming easy uh then uh then more
and more people would want to would want
to do it actually this is one of the
first thing uh that that PG PG and I
sort of connected on um before we got
into YC actually PG um found us on
HackerNews and so we started this email
relationship um and so he he told me
there's like a super linear relationship
with how easy programming is versus how
many people would want to do it. The
optimizing function of rapid has always
been like just keep lowering the the
barrier to entry and and that's how you
grow users and you grow uh customers.
right now. Um, basically people from
kind of every walk of life, we've seen
users, product managers tend to be tend
to be a very a great um use case and
users for us and we've had customers uh
product managers uh who uh are able to
make significant impact on the business
without talking to engineers at all like
you know running AB tests or
optimizations or or things like that. I
mean it's empowering. I mean it gets us
to think about the really the seams
between these roles like you know
product manager, designer, engineer. We
actually just created a new new product
group and and and typically you know
product uh you know head of product is
have has a bunch of like product
managers reporting to them but we're
actually having uh this this group have
has designers, engineers and and PMs and
the the idea is they're all using AI all
the time to to prototype in some cases
go all the way to production. there
isn't this sort of waterfall model where
you know there's a lot of inefficiency
you know like communication problems
between these different teams they can
move incredibly fast and I think that
that starts to change how tech companies
work my experience of this was when
whenever I was working in a startup the
list of ideas and the backlog would
always would be infinitely long
basically and the bottleneck was always
engineering time and now I'm doing my
own personal projects I write my to-do
list or my idea list and then I get to
work and my the ideas just get done and
suddenly the bottleneck is like my
ability to have ideas
>> and it's just such a weird experience
looking at a to-do list. It's just empty
being like what do I do next? I've heard
from a team that has like a really large
uh replet deployment in their company
that their founders using Replet and
that's stressing the engineers out
because oh I did this in a weekend like
can't you do it in a
>> what do you guys show for yourself and
so are these previously technical people
or non techchnical people they're
building the first version are they
deploying that production or are they
giving to engineers and say hey build
this like what what do you see typically
>> we advise to like work with with
engineering but that doesn't always
happen I think It's um understandable
that a lot of PMs and designers want to
go straight to users. What we're seeing
is they go to like beta users and test
users and I think this works really well
but in some cases they just like put it
in production. Uh and so right now we're
having discussions with all these
companies especially the engineering
leaders are unhappy about this like
who's on call for these services you
>> who's responsible for the bug
>> who's responsible for it. There's a lot
of questions that are coming up. The
obvious answer to all these questions is
like agents are responsible.
>> So what's the limiting factor today? I
buy code a thing and I'm like yoloing it
into production. Like what are what do
the engineers typically object like?
What what's going wrong?
>> Security is is is a big one. Like um
LLMs are fallible like like humans are.
They tend to write some uh there's some
components that they do terribly at like
for example off
>> uh like they all kind of suck at it. all
use like you know old methods of of
assaulting and hashing and so that's
been that's been a big sticking point
and we've seen a lot of examples out
there right now of some catastrophes
luckily it hasn't been like there hasn't
been a major catastrophe I think it's
coming but uh there's been uh like solo
founders who would leak um you know API
keys or would make it really easy to get
around the login security protections
and there are a lot of tools out there
that are like really not trying to take
responsibility for that and saying, "Oh,
it's it's the user's problem, the user's
fault." For us, we think that as a
platform that is marketing uh for the
non-developer, you actually have a
responsibility. We're trying to take
away
uh some of the things that we think LMS
should not do today. Uh so O for
example, we have a built-in O. So if you
go to wrap and just say add o, it will
pull in an o component that we built
from scratch. It has capture on it. It
has all the security you know bells and
whistles. So you don't have to worry
about all of that.
>> We integrated with your database. You
have a user management portal on on your
site as much as possible. Building in
these components that are tricky. I
think payments is the other one. I don't
think you'd want the LLMs to kind of do
do the payments.
>> And they're not that different, right?
like the sort of checkout. You know, you
might have a one time checkout, you
might have subscription, pay as you go,
but there are not unlimited versions of
payments. There's like two or three or
four.
>> And this is the analogy is today humans
don't write their own version of
payments or their own version of O. They
use providers that have built these
components already. So, seems like we're
seeing the same thing play out and
that's the best way to do it.
>> Yeah, 100%. And then the other thing we
added uh we partnered with is a a great
security company called Samrip. And so
right now uh when you go to deploy a
replet app, we run a security scan. We
run a code security scan and we give you
like a report of warnings and errors and
things like that and the agents can try
to fix them for you.
>> Okay. And so security is one of the big
kind of bottlenecks to fully deploying
to production. I can imagine there must
be other things that coming down the
line like sort of scalability, you know,
looking for like N plus1 database
queries, uh performance bottlenecks.
What do you have in your mind like a
clear set of of blockers that need to be
overcome before this is truly like one
click to deploy?
>> Yeah, I I mean the big the biggest one
is just going to be humans like social
like you know there's just going to be
mistrust and I think it's just going to
have has has to play out. So you know
leaving that aside like enterprises has
to adapt to all of that stuff having
some way to also scan for scalability
like figuring out you know uh like doing
fuzzing or whatever it is or having some
kind of adversarial agent that's you
know trying to to to break your app I
think um you know that's one big thing I
think integrating with a company's
ecosystem so one thing we're adding is
the ability to bring in your design
system
>> that's cool
>> so in addition to kind of us providing
these components if you go to any
company they have a lot of these
components built out as raplet is
getting deployed into these large
companies how can we hook into their
their internal systems
>> this makes me think about the kind of
spectrum of these different coding
tools. Um, on one end of the spectrum,
you have kind of what I would call the
the power tools, the cursors, the wind
surf that let developers use this as a
way to amplify their efforts. And on the
full other end of the spectrum, you have
more of the like consumerf facing, hey,
if you want to make an app, like you can
now make an app. And it sounds like you
guys are kind of in the middle. Um,
you're helping companies get stuff done,
but you're doing it for people who don't
look like the tra traditional developer.
How do you see this world playing out?
Are there going to be like n different
tools or will we converge to one place
on that spectrum?
>> AGI is convergence obviously but but
leaving that aside, it's really hard to
plan for that world. I think that the
battle for how to incrementally make
product uh engineers more productive is
just it's an obvious one. The market
there is obvious. There's a lot of
companies going after it. Both the
application companies like cursor, the
underlying model companies are trying to
go there. I mean um you know claude code
is competing with cursor. Cursor uses a
cloud. I think it's a bit of a blood
bath there but it's the market is
obvious and the market is really large.
I would guess that there's going to be
more of a consolidation there.
>> Maybe it's not you know one but it's
probably two or three uh at best. I
think the sort of the the market we're
in is a lot larger. Uh so the
addressable humans is a lot more. You
know, we're talking about a billion, but
it could be more. Like really any
knowledge worker should be able to like
solve problems with software. My
conception of replet right now is the
what we want it to be is a universal
problem solver. It'll solve problems in
your personal lives or you know solve
problems in uh in your work and and all
of that. And so I think that that market
is uh will probably be like a little
more diverse and I think companies will
kind of figure out where um where they
slot in trying to solve uh autonomous
programming with the focus on the
non-engineer.
>> Uh we want you to not worry about
security, not worry about systems, not
worry about any of that. We want you to
really come in with your ideas to to
Replet and be uh an agents manager. And
we're trying to do it in a way that is
like as human as as possible and kind of
fits into into into the workflow. One
big difference between engineers and
sort of non-engineers, be it executives
or product managers, you're not on your
desk like 8 hours a day. Mobile is a big
part of that. We have like a really
great mobile app. And we're thinking
about this way of like ambient building.
Maybe you start an app on your uh in
your desktop. You go away with your
phone. You're in boring meeting. You get
a notification from the agent saying you
I'm done with this. Do you want
something else? You got texted. And so
we're trying to kind of build that
thing.
>> Yeah. A question I had um related to
that point is is about the user
interface. So for something like cursor
or wind surf, it's pretty obvious. The
primary UI element is like the code. You
you see code, you have a little chat
window, but primarily it's about diffs.
It's about kind of changes to code. And
for a tool like Replet, the primary
interface is like the graphical user
interface. You know, it's the it's the
buttons and the the wizzywig like you
see what you're building.
>> Exactly. And that's great for building
user interfaces, but when you're trying
to build more complicated sort of
logical flows, I found it a little bit
difficult cuz I couldn't there's no way
I couldn't see the code. I can't
visualize what's going on behind the
scenes. And it's sort of it's almost a
black box. Fast forwarding here, if
you're trying to build more complex
internal workflows, how do they how does
a product manager or an operations
manager at a big company visualize that
workflow and the kind of logical
branching that happens?
>> Yeah. So, if if you look back in the
history of computing, there was always
this vision of visual programming, it
never worked uh very well because
ultimately it's about Turing
completeness like these systems are not
universal computing devices. And now we
go to codegen. Obviously codegen is is
turing complete. Um but you're
interfacing with it primarily via
natural language. Natural language is
fuzzy. It's really hard to know whether
it's doing the right thing. I think the
synthesis of these two things is
probably coming where you are
interfacing with natural language but
you can instead of like just staring at
code uh there's maybe an interface or
like a different view on top of code.
You can imagine being able to do you
know small talk.
>> Yeah. So small talk is this uh
>> it's the first object-oriented um uh
programming um system and you know Alan
K would say it's like it is actually OP
where everything comes after it is not
um but the interesting thing about it in
small talk you can actually the way you
interact with code is not via files but
via objects via like logical objects and
so there's some kind of prior arc there
and I think the world we're headed in
where there's some kind of abstraction
over code that allows people to like
understand it.
>> Yeah, I think that's really interesting.
I there's some there's like an open
space there whether it's like pseudo
code is like looks like English but it's
a little bit more structured or it's a
visual drag and drop. I don't know. It's
>> Yeah, I think back to building products
with a team of engineers, designers, and
other folks. The interactions that I
would have as the like product lead with
those teams was verbal. It would be
written like abstracted ideas. We would
draw stuff on the whiteboard together.
We'd make system diagrams. We'd look at
the results of the app and we would test
it and point out like oh this thing is
broken that's too slow and it feels to
me like that sort of interface which is
very multimodal and very flexible is
probably the best end result like I
think we will get to something like that
where the author of products will be
doing that but the teams they're talking
to are not other humans they're agents
doing these things. Has has there been
an attempt in in sort of PM land to have
a little more formalism around
communication or
>> Yes. And I would not say it's been good.
The main thing is just like the PRD,
right? The product spec, right? And it's
just become, in my opinion, uh
oftentimes a performative
>> work artifact that you create just so
that you can have a thing to get your
promotion, right? If you're at a big
company. Uh but they're not actually
that useful. To me, the most useful
interactions are just these like
whiteboard conversations, right? What do
we wanted to do here? Oh, we got to
think about that. Oh, we didn't consider
this. Okay, let's re rethink this whole
thing. Like those sorts of
conversations.
>> AI can play a role in that as well. So,
I um you know this startup granola uh
that allows you to kind of record
meetings and they released this like
team version that like all the meetings
uh gets transcribed and and go there and
they have a mobile app right now where
like you can put on the table and like
it's trying. So I I was thinking maybe
we should go gr granola maximalism where
look you shouldn't fight the trend in
which companies are becoming
increasingly oral as opposed to like
written um and because like you know
people are talking on Slack people are
in meetings people like are
communicating via prompts with with
agents uh but you you would want a set
of AI tools that is actually like
creating that that that record and in
background that's like searchable and
organizable and and all of that.
>> Yeah. I wonder when we'll have our first
AI in like oral meetings. You know, you
you're jamming with your designer and
you the AI like chips in and says,
"Well, how about this idea?"
>> Yeah. And and this is where I think the
like dystopian view of AI or AI as like
taking all our jobs and all that is like
not correct. And I think the future of
work is more human, is more interactive,
is more multimodal, is more fun in my
opinion.
>> YC's next batch is now taking
applications. Got a startup in you?
Apply at y combinator.com/apply.
It's never too early and filling out the
app will level up your idea. Okay, back
to the video. All right, so so last time
we chatted, uh, you had launched Replet
agent and things were growing really
crazy. I imagine that has continued.
Any, anything you can share there? Maybe
soon we'll we'll share some numbers but
uh since replet agent launch we're
growing 45%
compound monthly uh average.
>> These are like metrics that we tell YC
companies during the batch when they
have a base of like no users to try to
achieve and you're doing this at larger
scale.
>> Yes. But you know it put a lot of strain
strain on the company and our systems
were still relatively relatively small.
I I feel like it can get to to your head
and you can start optimizing for for the
wrong thing. It's very easy in AI to
increase AR while users are not happy
>> because they're spending a lot more and
like not getting the results and in some
cases maybe shouldn't grow that that
fast because like you'd want users to
get a better experience for less less
money and so it's one thing that we try
not to obsess we actually don't have AR
goals at at Replet we have like more
product goals retention goals just like
other methods
>> yeah because the the sort of bad pattern
with some AI companies you grow topline
revenue very very quickly the churn is
like approaching 100% and eventually
that just catches up
>> and the gross margins are horrible too
and so it's like
>> you know the more growth you have the
worse the company's doing financially
and so
>> so how do investors see this space you
you must have talked with a bunch can
they tell the difference
>> it's all kind of a blur for them because
investors when they I mean I'm just
going to generalize here but when they
start looking at the space they'll use
everything for three minutes and
everything for three minutes looks the
same so I I think I think it will it
will start to clarify and these products
will start to uh I suppose to converge
diverge more with the different focuses
in the areas we're talking about. I
think over the next year it'll be like a
little more clearer but I think a lot of
them are are just like you know super
when we talk to them they're super
confused. They don't understand these
systems. They don't understand where
they're going.
>> I think that's a great segue into um the
the sort of technology underlying some
of these tools. I'd love to dig in a
little bit deeper. We've we've seen some
announcements from cursor and wind surf
about how they're kind of layering
whether it's um Claude or Gemini or um
the OpenAI models with then their own
layers on top the fast apply APIs stuff
like that fast apply models I guess can
you give us an overview of how it works
at Replet?
>> Yeah, a lot of it is you're patching
problems with the underlying frontier
models. Yeah.
>> So the reason fast supply was important
because none of the models uh were doing
very well at diffs. Can you explain what
what supply is?
>> When you're trying to like edit a file
as an LLM, the best thing to do is to
like uh create a create a diff. Uh but
these models are actually not very good
at creating diffs. They're actually not
very good at countering the lines in the
source code. So they get confused about
a lot of these things. So for a while, a
lot of companies were just like
rewriting the entire entire file.
>> And so diff for for nontechnical users
is sort of like remove these three lines
and inject these other three lines
instead. like find and replace almost.
>> That's right. That's right. And so you
needed some way to um make the model
generate some something of a diff,
>> but it's actually not good enough to
merge it. And so you need another model
to actually do the merge. So
>> instead of rewriting like it's a
thousand line file, just like output the
entire thousand lines,
>> it's very slow. It's very expensive. So
you prompt the model to be as lazy as
possible.
>> But in that case, it's really hard to
apply. Okay, so you need another model
that's like doing the application. Okay,
>> you can train a model or you can use
Gemini Flash or some of these smaller
smaller models. And so in some cases,
you need to to train a model or
fine-tune a model to do a better job at
it. In other cases, you just also patch
a bunch of other models to to do it.
It's engineering. It's engineering as
opposed to like research, I would say.
>> And I noticed um you guys don't expose
the underlying model to the user. With
other tools like cursor and wind surf,
there's a drop down. It's like I want to
you know let's see what Gemini thinks of
this problem. You don't do that. Why is
that?
>> A a big part of our research efforts is
in eval
>> and I think this is like an underrated
part of like you know co AI coding. So
we spend a ton of time evaling new
models writing evals generating evals
just data crunching trying to figure out
what the users are are getting or are
feeling. We built a lot of systems try
to understand um how these systems are
are performing the moment a new frontier
model uh lands we are evaluating almost
immediately. Jim and I for example like
uh you know couple months ago were
making the rounds really great model
really awesome at like oneshotting in
some cases better than claude agentic
work wasn't at like a tool calling at
you know some of the things like that
but users just see the hype and they're
like oh like yeah give me Gemini that's
>> how much notice do you have do they like
drop it on you and you like scrambling
or do you have like days or weeks before
that to to try to you know test it out
>> well we we have good partnerships with
these companies we We have a great
partnership with Google. We have a great
partnership with uh with Anthropic, even
with OpenAI. We have a close
relationship. So, a lot of them give us
give us a heads up, give us some early
checkpoints and we we try we we kind of
play with all of them. And in the case
of anthropic, we're always like the
first day kind of launching because we
end up building. A lot of times we're
sort of anticipating where they're
going.
>> Yeah.
>> Because you kind of like you can tell
like 3.5 3.7 there there's some
direction you can tell like where 4.0 is
going to land. So you start architecting
the systems but you know ultimately a
lot of our engineering efforts still
infrastructure like the the distributed
network file system that snapshot based
network file system like it took us 2
years to build like there's nothing off
the shelf to do that a lot of the
security stuff is really really
difficult like replet is one of the few
places in the world where you can get
like a virtual machine in the cloud um
you know by just like creating an
account and that's like a it's really
hard to kind of run this and protect
against this. You have crypto miners,
you have all sorts of stuff like that.
And Replet also uses um uh Nyx OS under
the hood. Nixos is a like fully
declarative also transactional uh
operating system generator as it were.
Uh we have like this multi- terabyte uh
hard drive in all the different regions
that we have compute. um that that that
cached all the packages in the in the
world and that gets attached to every
container and and again all of that I
mean I keep coming back to this idea of
transactionality like you would want the
system to be fully functional. You would
want it to be um you would want it to be
safe in order to like experiment with
something go back do the sampling with
with agents. So really a lot of the a
lot of the work is just like the
engineering of this infrastructure and
it's like not as a parent not as sexy as
sort of like we're trained this model
here's the um but but I think this is
where you can build like a uh a bit of a
lead.
>> Yeah. I when VCs talk about moes uh the
thing that I translate it to in my head
is what is the compounding advantage
that you might be able to achieve.
Right. It's not really a defensive mode.
It's more just I'm ahead and by being
ahead in some vector it allows me to
continue to move faster. That's right.
>> And it sounds like this is a great
example of that.
>> True modes are often not obvious until
like decades perhaps many decades into
into the company. Like you wouldn't know
what like Netflix's mode. Uh but
obviously they have one like Disney
tried to like compete with them and
everyone was like you know down on on on
Netflix but turned out they have a mode
that is this uh content production
system that they that they built.
>> So Amad you you started with the mission
of like making it easier for people to
learn to code. You've accomplished a lot
of that. Now you're pushing the envelope
of what it even means to code. I've got
young kids. I want them to be productive
creators in the world. What should I
tell them to do? Should they learn to
code? What does that even mean?
>> Look, I think if you want to go the like
professional software developer route, I
think getting a computer science degree
and like learning fundamentals makes
sense. But if you want to be a a
creator, if you want to be a generalist
in this world, I don't think it's
necessary anymore to learn to code in
the more like traditional ways of
learning to code. I think you pick it up
by osmosis almost like like go go go to
replet and you know and start using it
and at some point you're going going to
run into some issue where you're going
to have to look at code or you're going
to have to look at logs just by being
resourceful having to Google around and
all that. You'll start picking it up.
And by the way, this is how our
generation kind of learned how to code,
right? when you were talking I'm like
that's what I did.
>> Yeah. And somehow uh it just became very
industrial and very formal over the
years like the way we made web apps is
like you just like start a notepad with
a you know HTML file and now you have to
like learn like webpack or whatever you
know it's like so um I think the future
of work is not really clear what is you
you know we can sort of like have some
like you know idea of like where where
the world is headed and so with with my
children I want them to have like as
broad uh based knowledge as possible. Uh
I want them to be as generalist as
possible. I want them to be as
generative as possible like being able
to like create a lot of ideas because
once the making of things gets easier.
The bottleneck goes back to to to like
how many ideas you can have.
>> Um and so I I I wouldn't put learning to
code as the top thing. I would put learn
to make things. Learn to make things
with code. Learn to make things with
video. learn to make like anything with
AI.
>> And so what do you think happens with
SAS generally if we're very soon able to
say create me a version of Google
Calendar or clone Docyign? What happens
to SAS? Do you think
>> we have stories today of um a lot of
people replacing hundreds of thousands
of dollars worth of SAS um with Replet?
The other day I heard a story from
someone who um you know they got quoted
$150,000 for a piece of software. He
went and made it in replet sold it to
his employer for $32,000
cost him $400. Companies that have a
platform developer community around it
and and plug-in ecosystem and things
like that. I think those are safe.
>> You're not going to be able to vibe code
Salesforce. I think the the vertical SAS
is is in trouble and I think it's my
guess it's already probably showing in
some of the metrics.
>> Okay, last question. What advice would
you give to um founders starting out
right now?
>> You know the best advice is what we what
you what you actually pointed out
earlier is work on the edge of what's
possible.
uh because uh one evolution of of AI or
models will make your business valuable
and suddenly you're first on market. I
find it rare to see founders that are
actually like sitting down trying to
actually predict the future and and and
maybe that's something that was you know
ill advised in the past but I think
right now trying to actually figure out
where things are headed is very very
important uh skill to have. So like make
some prediction uh figure out like how
to create like a crappy product that
would get better immediately as you
switch switch the model. I mean computer
use is a is is a great example.
>> Oh it's been an absolute pleasure. Thank
you so much for coming on.
>> Pleasure. Thank you.
>> See you next time. Thanks.
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