Transcript: Building a Django API Framework Faster than FastAPI - Farhan Ali Raza

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I'm Carlton Gibson, joined as ever by Will Vincent. Hello, Will.

Hey, Carlton.

Hello, Will. And today we've got with us Fahan Ali Raza, who I had the pleasure with spending this summer working on the Google Summer Code project to bring template partials into Django Core.

Hi, Fahan. Thank you for coming on the show.

So come on, Fahan, let's talk about template partials and the Google Summer Code project, because that was the big thing.

And I've mentioned it a few times on the show, but you were on the sort of coalface doing the hard work there.

So how was that for you? Describe the summer.

But like I was just...

When I was applying, I was just thinking like, it is just a package. I'm just going to copy paste things.

But like when doing like I find out like it was not just copy pasting.

So it was amazing experience. I learned a lot of things. It was great.

But that's pretty.

That's pretty impressive that your first Google Summer Code is like a top three feature in a major release, I would say.

I was just going to ask Fahan, was there a moment when you panicked?

Was there any moment where you were like, oh, no, this is going wrong?

But like first moment, like when I realized like this is not like what I thought it will be.

Was like when I was handling...

Like I think it was the first PR in Django packages or Django tablet partials when we were handling render time and compile time things.

So I did not like I did not even think about that when applying.

So I was thinking like this is something else like there is kind of there is a kind of thought that kind of you have to think before making changes.

And stuff like that.

So that was the whole process was cool.

Like I enjoyed the whole process.

There was no panic per se, but it was just a surprise there.

I mean, so the way I've described it when we talked about on the show is that without your help, I would never have been able to get it in.

Not because I couldn't physically do it, but I just didn't have the time.

I didn't have the capacity.

It really does need like somebody dedicated.

You know, a 12 week project does need someone to dedicate a good chunk of time to make actually make it happen.

Because there's a lot of things which were the third party package was okay, but not up to Django standards.

And like there, there are so many kind of what can we say like a side quest that happened when we emerging something into Django.

The thing that I remember is the like we...

Merged the like that thing like to find partials, the regex solution that was merged into Django template partials and then that was merged.

So we thought that regex was the solution.

And we, I think there was five or six reviews for that regex.

They're like, we have to fix the regex part, regex part, regex part.

And in like in the next PR, we removed that regex.

So...

Exactly.

There are many side quests that happen.

Yeah.

So, okay, we'll move along there.

But tell us a bit more about yourself.

What did, like, were you, are you a student?

Were you a student?

What are you up to now?

I, now I'm like just freelancing and...

Stuff, like, working part-time, like that.

Before that, I, I was a pre-med student.

So I was not, like, a computer science student.

But, like, it, it, like, there is, I don't want to go that deep into it.

But, like, there is a process in, like, Pakistan called MCAT.

So there is a, like, competitive exam for medical.

Admissions, like MBBS student admissions.

So that was a goal when I was doing pre-med.

But I discovered kind of programming in my, like, 11th grade.

So there is, like, it's different.

I don't know, like, what is in Spain or America.

But, like, it's first 11th grade, 12th grade.

And after that, you start your bachelor's degree.

So I discovered programming in, like, 11th grade.

I was just...

Like, I, I downloaded a book, Head First, like, HTML, CSS.

That, then I discovered HTML, CSS.

And, like, I thought that was very interesting.

So, like, like, it was lucky.

Like, the, like, I could not clear the competitive, like, exam.

And, but I could become, like, a doctor of pharmacy or something like that.

So I thought I...

I don't want to do medical at all.

I want to do computer science.

So I switched the whole thing around.

Do you use Python?

Is it Java?

Or what is the formal...

Or what are the languages that are used?

Like, every university have different way of teaching.

Like, most commonly, I think, they start with C++.

Like, in first...

After there is introduction to programming, that is C++.

And after that, Python comes at, like, I think, fourth or fifth semester.

After that, like, Python and stuff.

Because in the U.S., they don't.

It's like an elective course, usually.

But they have made it, like, it is not elective.

Like, you will have to take it.

So...

Ha ha ha.

Because in, like, smaller universities, they don't have, like, a lot of teachers.

So, they just enforce even elective subjects.

So, there is one development or web development course.

And is that...

So, I remember, like, we did HTML and CSS in 2021, I think.

Like, HTML.

And CSS just in six months or so that semester.

That's not...

That's the case also in some U.S. universities.

So, it really depends.

Sometimes the web development course is designed for people who are not computer science majors.

Actually, often it's not.

So, that's why it will be HTML, CSS, a little bit of JavaScript.

But I...

There's not that many places that have a, you know, Django course or a Spring Java course.

For some reason, it seems...

Seems important, right?

Because everyone gets trained and then goes to work on the web.

But I guess there's a databases course.

But there's really...

There's...

You know, even MIT, just down the street from me, they have a missing semester.

It's like a January term where they teach, like, how to use Terminal, how to use GitHub,

how to do web development.

Because in the core MIT curriculum, you're not going to take web development unless you

do it on your own.

Right?

So, it's everywhere.

There's a course that Harvard gives.

Like, that is called CS50.

Like, I learned my Python from that course.

Did you actually...

This is the David Malin course.

Did you...

Yeah, yeah.

...work through the whole thing?

Because, like, famously, people start, but then it's a lot of work to do on your own.

And it just sort of keeps ramping up the demands.

So, I just...

Like, watched the stuff that I wanted to learn.

There was, I think, a Django video for one hour.

And, like, the Python video, there is for one hour.

So, I just saw, like, what I wanted to learn from that.

I did not, like, follow the whole course.

I just, like, learned the small parts that I wanted to learn from that.

But that was very, like, a very good course.

I want to ask you about all the things you've been doing since the Summer of Code project.

Because you've been quite prolific.

Can you...

You know, I mean, I've got a whole little bullet point list of projects to mention.

But can you talk us through your open source work around Django for the last few months?

I would say, besides the fact Carlton knows you, when I look and see who's doing really interesting stuff,

it's, like, you again and again and again and again.

So, it's not just with Carlton.

I'm having discussions with people.

We're sitting back going, well, who is this mad scientist doing all these...

All these crazy things.

So, it's...

Just so you know, it's really exciting to see someone smart with new ideas just doing stuff because.

So, it's exciting for the community that you're doing this.

So...

Because I was...

There was a, like, side project I was building that required...

Web sockets or, like, a server...

Like, SSC.

Server sent events.

So, when I went to, like, the forum, it said, like, you will have to use, like, Daphne for...

Because, like, some, like, long views take time.

So, you will have to do that or use channels, Django channels.

So, there was a fear of fast API and Django channels.

So, I thought I should build a new framework to solve that.

So...

I built...

Go on, carry on, carry on.

I...

There, like, for some reason, I just wanted to have something fast because I really enjoyed ORM part of Django.

Like, it is amazing.

So, I just wanted to have something fast that is built for Django.

So, and then I...

I saw Robin.

There is a framework called Robin.

It is also, like, Rust and Python mixture.

So, I thought, like, it should be possible to, like, build something that handles request in Rust and does, like, business logic in Django part of the things.

So, that was...

Gives me, like, inspiration to, like, go all in.

I started that just as an experience.

So, I wanted to see if, like, that was possible or not.

But when I, like, tried to implement it, its performance was very good.

So, I thought, like, I can spend, like, months after that to, like, make it something better.

And you've mentioned the phrase performance was very good.

It's one of my pet peeves on the show that people talk about fast, but they never give any numbers.

And you've been laying down benchmarks from day one, which is what makes it so exciting.

So, can you give us some idea of the performance?

The performance, the throughput you're seeing on Django Bolt?

Like, performance is subjective.

Like, it will all depend what you are doing in views.

So, like, I...

And there is, like, a whole Reddit discussion when I, like, launched Django Bolt on that.

So, everybody was saying these benchmarks are, like, just wrong.

Like, this does not approve anything, prove anything and stuff like that.

So, like, my whole point was, like, when I am building something, I will have to use something to measure something.

Like, what I will do, like, build a whole application and deploy it on a VPS to measure.

I cannot do that.

I will have to measure, like, small JSON, small database queries, small, like, stuff.

So that I know, like, if I make a change, it makes a positive impact on my, like, base.

So, what I am getting in, like, if we use one worker.

So, one worker in Django Bolt is one Actis thread.

Like, Actis is a Rust framework that I use.

And that one Actis thread is bound with one, like, Python event loop.

So, Actis handles the Rust part and Python event loop, like, kind of, like, execute the whole Python thing.

So, that makes one worker.

So, that one worker, I get about 27,000 RPS on Lexi.

if like if we return a simple hello world json so if we do like a database query so that like

the orm becomes kind of the bottleneck for that so that like the performance that becomes

that is still faster if we use sql alchemy with sqlite so i have not measured with possible sql

or like faster uh orms with fast api but i have measured with like sql alchemy so that i was

getting about uh five thousand nearly i think five thousand rps uh with like uh i think there

this vanilla django i guess would be the question the performance difference yeah

if like we uh like take account like database queries if we compare database queries then

the performance difference uh is like i think uh 1000 rps on django like if we use some faster

serialization and like uh yeah i i have forgotten the actual numbers but like it i think it was 1000

rps uh on django like raw django and like i think uh

i got about like uh three four about four thousand i think five thousand rps in django bolt so

you did a project called django rapid which was using diff which is just python-based vanilla

vanilla django but then changing out the serializer layer and you can comparing rest

framework serializers to i don't know um pydantic serializers to meshi spec serializers um and what

was interesting i thought there was that you were getting fast api like um throughputs just with

kind of equal it like i think um it will uh like the performance is kind of equal it like i think

um it will uh like if we have because django or or fast api orms are async so if we have a lot of

queries and uh like a lot of business logic then the performance i think the performance difference

will increase i think and but like if we compare simple read queries the performance difference is

benchmarks is you've got them all up on github and everyone can go and look at them and everyone can

pull them locally and everyone can run them themselves and they can see that these ideas

well actually you know django slow is well no it's not the same speed as everything else what you're

doing yeah you mentioned you mentioned async orm then today i think or yesterday you put out um

a kind of uh turbo orm package which is um an async query manager for the django rm right talk

uh time he he was comparing the whole like the request response like time

cycle so he he he wanted to measure that with like the async back end that uh free created so

i he also like i think he does not said like um another i forgot his name

he said like uh you should be able to use that async backend with uh django bolt and

my year like what performance difference you get so i when i tried to use it because

it is just a async backend so we can just use raw sql so i thought like it will not be that hard

just because in my in my mind i was thinking i'm gonna overload some like execution of django

queries and like uh use that async backend and just create it but like when i tried to create it

like that was a whole another process it took me like four five hours of work uh to just like

create something working

because it worked like in in one request it worked fine like it loaded the thing but when i

wanted to benchmark it with the bombardier the like i was getting um like connection

exhaustion errors so i had to fix uh polling issues and stuff like that and i did not know

like the full it it's a naivety of like a person so i because i did not know that i had to fix it

because i did not know like the full it's a naivety of like a person so i because i did not know like the full it's a naivety of like a person so i because i did not know like the full it's a naivety of like a person so i because i did not know like the full it's a naivety of like a person so i because i did not know

because i did not know like the full it's a naivety of like a person so i because i did not know how the full orm and database

know how the full orm and database

things works i did not even think about like

how the connection management works

and how the stuff like that things work

so in my naivety i started and after that

i have to solve the whole connection

things and stuff like that

because uh in a django hand the sync

version of django handles the connection

in a very different way

it's like i think threads context was

and stuff like that

so it like i think it when you do not use

polling it closes after request i don't

know

for fully uh is if that's the case but

for fully uh is if that's the case but i could not find anything

i could not find anything

i could not find anything for async that like we were discussing

for async that like we were discussing

for async that like we were discussing before this podcast

before this podcast

before this podcast so i will try the async signal thing

so i will try the async signal thing

on that what i love is the way you just

on that what i love is the way you just like not just but the way you dive in

like not just but the way you dive in

like not just but the way you dive in you're like yeah i'm going to take this

you're like yeah i'm going to take this

on i'm gonna do it and then you discover the massive locker and you're like right i'm gonna

fix that as well and you just keep going so i mean for me looking through the the the rust front end

for the the request response plan i think that's a really exciting um development looking at the

different serializers we talked about that on the show before but like rest framework serializers

are kind of last generation and there's caters there's pydantic there's message spot there's all

these um much quicker options out there and so to demonstrate those with django is like a real

service to the community it's like yes look this is how you do it and look how much faster they are

wow and then for me the the um async properly fully asyncifying the orm is like the last piece

of the async story you know if we go all the way down to the database connector then you know

whatever you want to do with async whether you want to use it or not it's it's the stories then

at that point finished so like you're really

hitting kind of all the all the tick boxes of the things i'd love to see

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for some reason like that thing is fast that thing is fast that thing is fast so as a

beginner i kind of like that my thing is fast kind of stuff so that was a whole kind of process if

like if we have orm that is full async that will make the orm very fast because there is like a

sync to async part and the whole stuff it is it it i when i was using i was trying to handle sync in

vault um the performance drop for sync to async part like becomes very like stark at that if you

are getting like some even if you don't use the orm the performance difference if you are getting

22 000 rps and you want to execute the synchronous views you like uh create a like a wrapper using

sync to async on the bus and you want to execute the synchronous views you like uh create a like a

the performance difference drops like 22 to direct like 10 12 000 so there is like a 50 percent drop

there because of the third part of the whole thing so if we have the whole orm async i think that

will be that will i think be the performance difference will become i think us to talk like

that where the

because obviously there's a cost to um spin up the extra thread but is it is it the fact that the

python threads aren't able to um execute in parallel um it's causing a lot of the blockage

there do you know i mean you may not know you may not have been able to profile because like how the

gill part so if we have something that execute using thread

it kind of blocks a gill for some reason so this is also a gill part and also a latency part of like

spinning up a new thread for handling that request i think but do you think i mean have you have you

it is not kind of ready or something like that so i think it's kind of blocking the gill for some

the performance difference like decreases it like decreases a lot like not like normal decrease

it decreases a lot i tried it with like i think fast api and the jango bolt the performance

difference decreases a lot i think there is some issues or like i was not able to and also because

um the pi o3 it does not like it has different like

for it is a it has a different method of handling uh the non-threading python so i was not using the

whole things that it provides to handle like non-threading python so i think that also causes

an issue so i was not able to like properly benchmark that okay that's not quite ready yet

yeah no no i'm happy to listen i i mean

your early projects but is gets a lot of usage what what can you say about that project so that

so uh that is a project that i have been working when i was studying uh that project is like a

partnership with uh like a project that i have been working with and i have been working with

another person uh he's like i i build stuff and like he manages the whole selling part of the

stuff so it's an email validation site like for email marketing people come in and validate their

email like this is a valid email this is a invalid email this is a catch-all email and

like people take that result and do email marketing with that so did he come to you with that i

i mean i wouldn't think of that unless i was in the email marketing world he's an email marketing

so i i was gonna build and like we have like a partnership kind of thing there so yeah that we

carlton has mentioned that you do you use um llms and ai in your coding at all i use a lot of it

tell us about it because i feel like a dinosaur like because i we use them but i use them to sort

on to my existing learning profile but it seems like you're just sort of straight lining llms and

doing it in a better way than than i do so what's your process how do you think about that

puzzles kind of that so but typing was never my thing when like i started there was no llm was i

i did not enjoy typing the code so

so llm part solves that part for me so i use cloud code so it depends on like what i am trying to do

i usually start with plan mode because it is so hard to predict what they are gonna do

so i usually start with plan mode for cloud code like if i am for example like if we discuss like

So...

what i i will do with that is i i will start with plan mode it does not produce any code

with that plan mode it just produces a markdown file so i will go i will discuss the whole plan

with it so if i wanted to build like a sync orm so i will discuss uh like because i did not know

so i i i tell him like this is the repository the django backend and this is uh what i want to build

it like researches the whole thing and i usually have like a cloned django repository in in the

project folder so i say like this is the django repository like tell me like how can i achieve

that thing so it like goes all in and like at the end it produces a plan like what we

want to achieve i read that and if like that makes sense or if i want to change something

because like

once you enter like if you accept the plan and you want to change something

they forget that like they have added a function or class and they are gonna just like create a

new thing so you will have a lot of code that that has no like purpose in there so

that is kind of an annoying part so i usually spend a lot of time planning and stuff um i i

research if like uh i i'm not sure if like the lm is telling something so i i will go and

like read my code myself and like tell it like you are thinking it is wrong or you're like

your hypothesis is wrong yeah stuff like that so

that is kind of the whole process so after that like testing is a whole another thing with llms

like it is i think it is impossible to produce like correct test with llms because they think

like and you are like they don't think like we humans do so like their tests are like

made up things they are gonna test what like they are gonna say it is working but i can see it is not

working so the testing part is a whole another thing i i usually like tell it to like test but

if you remove that git commit the test should fail so that i followed from django so uh like

django i think uh um

i forgot her name natalia natalia yeah he said like if you create a test the test should fail

without the fix so i i took that thing from that so i i like tell llm to like test should fail

but it still produces a lot of bogus tests so that is a very it removed the commit then put

an assert false in the test and goes look the test i think that is like it is a problem with

reinforcement

learning that they too optimize for passing of the test whenever i i will say like create a failing

test for this case it will create a test that is passing but in the like in the in the test it will

have a if condition like it is failing but it will pass the test for some reason so they are very

afraid of like failing tests so i think they are ocd kind of personality they have they don't want

the way to do it you're you're i think you're i know you're a better user of them than i am but

you have to plan with them in advance and i mean so one of my colleagues uh paul everett he's been

doing a bunch on spec driven development and so basically what you described like having talking

through planning things out and i suppose you're too young to know but the joke is you could never

get a human like a senior software architect to do that so i think that's the way to do it

to write specs for something but now that the llm will do it for you everyone suddenly is excited

about specs whereas they never were excited about specs before so it's forcing maybe good behavior

on humans so there's an irony in there for those of us who are a little bit older

but i completely agree that's yeah i i wonder if there's if we're going to have different because

they're so eager to write code so eager to pass things i don't know if it's a filter or it's a

different retraining but

it feels like they do need to be harsher at least in a coding context right it would i wonder you

know yeah can the companies get there can we get there with better prompts is can some third-party

package come in with a whole thing of like you know how do we get there because i agree it's a

like uh months some like few weeks ago at claude

like it for some reason it is like very eager to write code like it is very eager to produce a

hypothesis even if like it is a wrong hypothesis like it is very eager to say oh i found it but

it it like it now in whole conversation like it will be say two and three times i found it

but it never found it like it just say i found it but like it it will create a like wrong

hypothesis and it will say oh i found it but it will create a like wrong hypothesis and it will say

hypothesis and like start working on it and after that it will oh i did this thing wrong i but it

will forego to revert to the changes that it made because of that wrong hypothesis so there is a lot

of code that is like uh just uh like unowned code so that is just a junk that kind of lives there

ai teams work a lot of times the benchmark is user accepted completion rate so they want their

they want you to say oh that looks good and move on and so i suspect that writing more code or

writing things that pass even though it's not good is better than them like they're not incentivized

to say don't do that right i think you know that sounds simple but i really do think it's it's

almost like i can see like we say oh what's our you know completion rate user acceptance rate

whatever for juni versus claude code versus co-pilot like that's that's the benchmark that

internal ai teams are using which would lead to that behavior i think that is why like it is going

just going to pass the test which is working hey like congratulations yeah you should be able to

introduced simply code simplify i have not tried it uh like very significantly but it is kind of

does that like it it is gonna see the commit that you made for the solution it is gonna go that go

over all the changes that it is it is gonna simplify the code and like try to remove if like

it thinks it is it does not belong i think that is a step in the right direction i think

the difference that i found i find in a human code and an lm code it's also like a viral

tweet the human code is beautiful like everything that it produces it looks like it is like it was

to do that thing the llm code is like a kid coding

is just gonna solve the thing like it is not gonna see like if i'm putting that functions there

why i'm putting there like it should belong to that file or not the a lot of comments that it

adds so like beautification or the beautiful code like uh if you think like a coding as an art

like that is human and if you think like nlm coding that is like a kid coding

like it is gonna solve the task it like it it is gonna solve like solve the issue but like

the code will be everywhere and like stuff like that the difference we can see if like we see a

professional code base like django and a solution provided by an llm so the difference is very clear

was only four or five days ago that that came out but that's that's really interesting so my question

is how do you stay informed about all this i mean because carlton and i like we pass tips on like rss

feed readers because we love to see like blog posts and i feel like we're multiple generations

back like are you on so like what are your news sources to stay up to date i think you also have

part of social media is very eager for vibe coding mastodon is like 180 degrees of that

they don't want like ai in their code so that's where carlton and i hang out with our django

degree like twitter there is like less than 10 percent of code like llm right and in mastodon

like there is like i think larger than 50 percent code i write myself so there is a difference there

is like the extreme kind of like two things i i kind of believe that like both like any extreme

i don't know the word for that like any extreme

what is the word like that like any extreme thought a person have like any extreme opinion

sorry and any extreme opinion a person have like extreme opinions are never right so the truth will

always be like some something in like in between so if like coding changes it will be like kind of

50 percent you're going to 50 percent at a time then stuff like that i i guess um my question here

it's probably loops back to what you've already said but i think these machines are very good

at creating um stock code you know boilerplate that you've you know seen written 100 times

it seems to me they're less good when you're trying to push the boundaries into something

that's not directly in the training data that's not that's new right and you seem to be doing a

lot of things that are new so how do you how do you find i mean does is it the planning mode

spending enough time in the planning mode helps to pin it down and i think that's a really good

like give me options to solve this thing so it is gonna say like it is gonna be an extreme option

there it is gonna be like something that like that i think like can work so i i will say try that

that is also i think a very good point for llm because we can try a lot of option because the

the

price of code is very cheap that way so if like that thing does not work i i can just like

discard those whole changes so i try different options

the it there is

there is like two extreme uh one time you are gonna say like llm is oh

llm this solution was very intelligent and sometime it is gonna be stupider than a dog

solution is very like stupid it thought something else and produced something else so there is like

extreme cases there so i think we can never like predict llm stuff that makes them dangerous kind

of that way so there will always be human for coding i think there will be less human but there

being wired because for me like i had to think about how your brain is being wired because for

me i had to type everything and you're making me almost rethink the advice i would give people

which is before you start coding make sure you're you're a really good typist because you don't want

any friction between typing and thinking but so for me like if i don't type something this is my

biggest problem with llms if i don't type it it's not i'm missing a pathway in my brain so i'll have

llm code and it part of it is i didn't fully think it through but i think a lot of is i just didn't

type it um and so you're making me wonder maybe that's just a

step leap that the newer generation of people you know won't have that problem like they'll

just be like oh this this is normal this is how i i do things um because i don't know carlton is

that the case for you for me if i don't physically type it it doesn't quite get in there but that's

because that's how i learned it learned how to code originally there's something in that yeah

works but i don't fully understand it 100 like i don't understand it fully so the difference kind

of becomes you have to be a good reader of the code if you like read it and some like and you

will have to because sometimes like because i i was new to rust so sometime it produces like key

words or something like that that i don't understand so there is like a skill to have like

you should have a kind of a way to like you want to learn so if it uses rw lock you don't know

rw lock you should ask it to like what is rw lock if you just become a like um just enter enter

enter like everything except like it just works and stuff like that so that you will not never

like understand like what it it is doing there is a thing like if you code it yourself you

understand it better when i was working for jsoc i i was very aware of that thing so i usually

if i i i used llm i used it for like small commits uh there is a mod in cloud code where you like

you don't accept changes it it just shows you that commit so if uh if

like i uh see that commit i will say like this this is not django standard so rewrite it this

way or like if i wanted to do it myself i i'm gonna rewrite it that way so if you want to

understand the code it is uh like a requirement that you will have to read it and if you type it

then like you will be a better understander than uh just a simple

person that uses llm to just push everything and i'm really interested here where the um

taps yes and accepts you know except they're going to be in trouble they might be able to

produce something very quickly but you know code is more maintenance than it is production

and then at the other end i'm i'm as much as it might pay me just to think i i don't think we can

do every character anymore um because of the just the economics of it aren't going to make any sense

and so between those two extremes that it's got to settle somewhere and maybe there are a range of

sustainable positions that an individual developer could take up but i mean how you find you know how

are you finding in your freelance work that you have to you have to balance these things

most of the people come with a like kind of an mvp of the project so i want or i want

like stuff like that that is already there like solved kind of things so llm is very good at those

like if you tell it to do oath in django it will like does it like 100 good actually kind of so

in freelance work if like problem is something interesting or something

complicated that kind of like a recent project that i am doing it is a like uh

software for ships like uh there is a like a task assigned to people on ships like uh

like ocean ships kind of that so it does not get it like it does not understand it like if a task is

the whole thing is that it does not understand world like ai company says it does like it does not

so if like it it creates a task it like makes wrong hypothesis about the real world

stuff like that so that way you have to guide it like you like uh if a delivery comes a person

have to accept that delivery it is going to put the all the buttons on the top

to accept delivery accept accept accept i i i i saw that because i built that it's like

if a delivery comes i have to see the product and like receive that delivery because that's

how the real world works but it created like every button on the top to accept delivery and

it like it asks nothing like like what is the delivery id what is the like

there is a whole process there is a difference the whole like how the real world works and how

llm thinks it works because i think that this this is the whole context problem also because

if it is working on a small solution it kind of forgets a lot of how the other things work or

something like that so it it has a narrow focus kind of that way so there is a difference if if

like a problem is a kind of real like so we i will have to like or like explicitly say

like you don't want to do that you you have to do this like in this way that's the kind of the

authentication examples like how many real world django shipping container options are there like

is there a point where it um and actually that's i'm going to pose you my philosophical questions

to get your take uh are you familiar with the term model collapse have you seen

model collapse

this idea out there um i can describe it it's it says basically that um as the data goes from

being human generated to being computer synthetic generated um there's there was an academic paper

out of oxford uh 2024 showing that over time if you train on synthetic data the llms get worse

right so in my mind i think of it as we know the underlying data is kind of getting worse like it's

even as the models are somehow getting better so but where does that all end up it feels

to me like maybe the whole foundation is crumbling beneath us um but i'm curious what your take is on

about i just don't remember the keyword uh so yeah yeah i agree like i agree 100 percent uh it is also

becoming a problem for me i think it's a problem for me i think it's a problem for me i think it's a

race for the benchmarks that they have set up like it is like they want to optimize for those

benchmarks so if like you compare a gpt five point something to cloud code even if like

cloud code is better but like gpt is gonna be like uh higher on the benchmark or something

something like that and the data problem is real like that is also i think that's

that data problem and the architecture problem because like you can say it is agi but it is

just a probabilistic like uh prediction of the next world you can say it is more intelligent but

it will never be it is just a prediction of the next world but like the like people who are like

more intelligent than me but like they're gonna say like your word your brain also predict the

next world so i think it's a problem for me i think it's a problem for me i think it's a problem

but it does not like we cannot explain it like what our brain does but it does not predict the

next world because if i like if i walk on a like an icy road or something i know i'm gonna fall

because i have like fallen when i was four or something so the context of that 20 years is

there with me so llm the

like i agree with that uh your statement like 100 percent the like artificial data part and

like the optimizing for the benchmark part like it is gonna be it is never gonna be like

more intelligent than this is my opinion it is not gonna be more intelligent than

human in like general tasks you can train it for a specific task in specific environment and

specific like in a box it is gonna be more intelligent than a human but if you break that

box and everything's gonna fall like stuff like that carlton you look like no no no i'm just i'm

lack of world models these are the stuff like the criticisms and the the llm architecture has gone a

you know if nothing substantive changes then it looks okay that's a very clever trick and it's a

very useful trick but it's not agi as you know was promised to us by now by lord well let's let's

benchmarks i mean this is something that uh jet brains again i'm just going to read it out they

have a developer productivity ai arena dp ai so they're trying to do an an open stage

standard because right now there are these kind of closed standards for the major models and it

just leads to you know optimizing for the standard right when it's like well who set the standard

what is the standard right so i think there is some work being done there to have something

better that hopefully makes the models better but it's like as complex and intelligent as these

things are it's sort of like we have we all have narrow incentives as humans and as ais so so i

career is under threat you're sort of at the other end of your career how do you feel about them i

in the world kind of like that we live in um corporates are gonna optimize for the cost that

is like stuff that's going to be used for the cost of the cost of the cost of the cost of the cost of

like that so if i am like if i am trying to get a job in a small city like where i live but i like

there is a fear like in short there is a fear like the whole thing is going to change and

they uh like there will be less jobs but the the thing that is like

i am hopeful like because like i like to learn and like to know stuff so only those people who

kind of have that uh like there are some people who are gonna do computer science just for the job

so there is a difference between uh like a programmer who want to do is for like because

like they love programming and they want to do computer science and they want to do computer

programming and there is a difference because he like someone said it to like computer science is

a good like job or good pay and stuff like that so i think there will be less programmers but

we don't know because the costs now are very optimized and stuff like that we don't know

how much the llm actually cost because like not everybody is gonna be able to like

use them if like something like not a doomer but like something the bubble crash or something like

that so if the cost is gonna be the same or something like that so there is a fear in like

normal computer science student like there there is gonna be no jobs or less jobs well speaking of

status

i'm open to freelance contract or job anything works for me

him in action since he is you know he's a good he's a good find you should snap him up thank you

is i'm going to put a link to everything i guess if somebody does want to get in touch

i guess email on your personal site linkedin what is the best way for someone to contact you

thoughts and whether in an llm world everything is pushed that way but um i guess i don't know

is there's i always like to ask uh wait carlton are we doing are we doing book reviews oh i don't

we haven't mentioned it's django ripple which um oh yeah yeah it's a full django environment in

the browser which is an amazingly built on is it it is typescripts welt on the front end and using

you know check that out okay so far hand i've really really enjoyed chatting with you and

working with you you know over the last year and i'm so excited for all the things we're doing um

in the django ecosystem so thanks for coming on the show it's been an honor thank you for inviting

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