Transcript: AI in the Real World - Marlene Mhangami & Tim Allen

More on their services later in the show.

I'm Carlton Gibson, joined us here by Will Vincent. Hello, Will.

Hey, Carlton.

Hello, Will. And today we've got with us Marlene and Tim who are coming on to discuss,

I don't know, AI or new overlords, these kind of things.

Well, let's give them an introduction, Carlton.

Can you give them a quick intro?

Okay, so well, Marlene, you're a Senior Developer Advocate at Microsoft, is that right?

Hi, everyone.

I don't know if that would be helpful.

But yeah, my name is Marlene.

I'm a Senior Developer Advocate, like Carlton said.

I currently work at Microsoft.

I focus on Python and AI, so I'm on the Python on Azure team.

So I'm doing a lot.

with ai right now i think most of the python code i'm writing including the django stuff

is is ai so yeah that's okay that's me i'm also very involved in the python community i was on

the board the psf board for a couple of years um was one of the co-founders of pycon africa so i

love that um so love the python community love django as well so yeah okay cool and tim you're

Wharton Business School.

We actually work with Harvard, Stanford, MIT more than Wharton faculty on my actual job.

The platform we run, we have several petabytes of data, of finance data that we store that the business schools across the world come to our platform to do academic research on.

So it's a pretty cool job.

I mean, I'm lucky that I get to build cool things and work on solving interesting problems.

So I still get to write a fair amount of code, too, which is pretty nice.

How do you manage to stay involved?

That's one thing I've never understood about the tech industry fully is so often the career path for your top engineering talent,

which has been the rarest, hardest-to-find talent

over the past couple of decades,

has been, oh, you want to progress in your career?

Become a mediocre manager

and do what you really don't want to do.

So it never made sense.

Well, it's the Peter principle, right?

You rise to your level of incompetence

and there you stay.

That's how I've heard it phrased.

Well, I do want to mention,

so this came about in part

because you both gave excellent talks at DjangoCon US,

which, Carlton, sorry you weren't at.

I think this is a DjangoCon US.

Yeah, sure.

And finally, we're like,

you should just come on the show

because this is about continuing the hallway track

for everyone to make it accessible.

But broadly speaking,

there was discussion about AI.

And I know, Carlton,

we're not making this an AI podcast,

but both of you had slightly different takes on AI.

Marlene, you gave a keynote.

Tim, you gave a talk, touched upon it.

I guess I'll just start with a question,

which is in your day-to-day jobs,

how are each of you using ai you know not the marketing type but like actually like day-to-day

talk at django con about a keynote to be fair a keynote yes yep and uh was a little nervous

because it's like ai not everyone loves it but uh definitely i would say in my own work i i tend to

use ai quite a lot um right now i would say the primary way that i'm using ai in two different

ways so i think with developer advocacy i'm doing a lot of writing for example and so typically what

i'll do is i'll have you know if there's a topic that i'm supposed to write about i'll have ai go

and get all of the documents or i'll i'll paste in some documents and then have it summarize the

documents to me and then i'll like use that as a starting point for a draft blog or something like

that um another thing is that i'm writing a lot of code so right now for example i'm maintaining

the azure integrations in langchain and i'm doing a bunch of python code for that and um and i i

don't yet feel comfortable having ai like write a full pr for me to to for me to merge into into

those libraries but what i do have ai do is is for example run linting for me so i don't know

why whenever i do linting there's always like something that tends to be missed um so i have

something where it just automatically runs the linting for me um which i think is quite nice

and i also have it sometimes if i'm having trouble with a bug have it figure out where in the code

the bug is and then i use that to as a starting point as well to to debug so yeah those are the

primary ways i would say i'm using i'm using vs code and copilot by the way that's a plug for

uh pretty much every day um like i said you know i'm really lucky i get to work on solving some

really interesting problems. So I still write a fair amount of code, but I've found as I become

a more and more senior engineer, I'm the first principal engineer in the history of the university.

So this sort of new career path was sort of developed with me in mind, which was a really

big honor. But I find the actual time I'm pressing buttons, writing code is getting to be less and

less and these days i also find that my best prs often delete more code than they create and i like

to say you know while the llm is very useful for those you know the 10 to 15 percent of the time

i'm actually pushing buttons in the ide um the most value my employer actually gets for me isn't

monday through friday nine to five it's when i'm doing two things one sleeping and two showering

because that's where I come up with the solutions to the really hard problems I work on.

You know, sometimes I wake up in the morning and I've been staring at the screen for three days

trying to solve a problem and I wake up and it's just there. Or I get out of the shower and the

solution to that bug is just there. Another part of my job that I really enjoy on my day-to-day

activities is working on projects with junior engineers. And sometimes an LLM can be involved

and moving that along so you know when you're pairing up with somebody you're not sitting there

so you know one person isn't sitting there for 15 minutes while the other person is looking through

the documentation um trying to find that thing you know is there somewhere but you can't quite

find it because every documentation setup is slightly different so um i mean that's another

thing teaching is something that is that i found is still always fulfilling that i never get bored

of so um you know having an llm integrated in that does make it a bit more efficient but i don't think

it's any kind of magic wand for my day-to-day activities um and yeah that's sort of a summary

of how how it's you know it's another tool in my in my toolbox carlton are you willing to say

last year or so where i would previously have gone to stack overflow i'm happy to type it into

claude or type it into copilot and the answers are for me about as good they're sort of like

yeah okay um and probably better than stack overflow because otherwise it wouldn't have

replaced them in that i don't have to spend quite so long reading the number of threads i get a

concise good answer straight away and so for me that they're super what i'm not doing with them

is writing code um i might i might you know ask for an explanation how does this work in javascript

how does this work in playwright and it'll tell me and then okay i'm still going to write the

code myself though i am so not even autocomplete tabbing autocomplete no god no um i've got what

do i use for autocomplete i use um still use um what they call lsp based um autocompletion which

is um uh deterministic and correct and um perfect for me in fact um like the next line suggestion

stuff that comes out i'm like i don't want that i literally don't want that um as well here's

something you've got to remember i have um snippets for every um code construction so if i'm writing a

loop i have a snippet for the loop with placeholders that i tab between and things like that i'm never

typing for variable name in loop name you know i'm tabbing between um the placeholders very rapidly

and but that's totally pre-llm it's totally static it's you know so the the the use case

for autocomplete just doesn't come up for me um in my use yeah yeah i'm sorry go ahead no no you

for years people have talked about getting into flow as a programmer and hitting that flow state

and i found that when the llm sort of interjects itself it's jarring it gets in the way it gets in

it gets in the way of my thought process so i too i like having the chat agent on the side but for

the autocomplete or the think ahead type stuff it tries to do i just find that really um impedes my

progress and uh you know breaks chain of thought i i've been thinking a lot about you know the

I mean and I think we're all sort of coalescing

around and this is one thing that came out of DjangoCon US

is it's a tool

it's a tool that has benefits

I don't think anyone feels like

we're immediately going to be replaced

and it's imperfect

for sure but also

I was thinking you know the old days

of Stack Overflow and hunting around for a Google

blog post and just being

stuck stuck it's not that that

was perfect either

um so you know i guess like the three of you i use it a lot for discovery for research i also

find the autocomplete aggressive and also just annoying um but i'm using yeah the chat interface

all the time for research or to like hunt down a bug and increasingly i'm using agents on the

command line um but more for boilerplate greenfield stuff and still i haven't quite

i still don't fully sit back and even when i sit back it's almost like i i sort of miss the days

of like fully doing compiled code.

But when you sit there and just like it's whirring away,

like what is that feeling?

Like I sort of, I just get like existential waiting

three seconds, 20 seconds, and then I have to evaluate.

Like I'd like to be a little more leaning in.

So for myself, it's much more of like a research tool.

That's a great research tool.

But the fully agentic spinning up six parallel agents

and having a coffee and coming back and it's done.

I've never quite had that.

Sorry, Molly.

Oh, yeah, no, I'm far past my incompetence levels.

But, yeah, it's, well, but I think, I mean, the thing I've come back to is,

I mean, again, we're all in the Python space.

So if you're doing Python stuff and Django stuff,

you get almost as good a result as you're going to get

because Python and Django are so mature, so well documented, right?

If you're using a newer programming language

or a smaller programming language or framework,

it doesn't work anywhere as well.

So this is tip of the spear, what we're all doing.

And even then, on the one hand, I'm like,

oh, it's like I still have to code,

but it's also this amazing research discovery thing

and finding bugs.

I mean, I remember spending hours, if not days,

on some little thing that I couldn't find the right post.

And now the LLM, even if it doesn't get it right right away,

It'll, you know, I mean, like try harder, you know, and it'll try harder and find stuff, right?

It just feels like a speeding up of that, but not a total replacement of code.

And yeah, and I still like elegant code.

These things don't write elegant code.

Even if you have rules or guidelines, like it's incredibly verbose and, you know, just vomits up code rather than like concise code.

I've even tried, you know, write it in the style of, you know, Carlton Gibson, like go crawl his GitHub repos.

you know that helps a little bit but it's still not a total replacement for you carlton

cowboy hat on when you code carlton's i can see you by jumping at a bit there go on yeah i wanted

Because when we have been, so one of the things, you know, Copilot and the VS Code team have been working on is just growing and meeting the demand for AI programming with Copilot and VS Code.

And so I've been kind of looking at the comments on like VS Codes, like social medias and things like that.

and one of the number one complaints people have on social media is that they just feel like the

autocomplete is not fast enough or it's not like they come like a lot of people compare it to

cursor and and say that they think that you know in vs code it's too slow or it's not doing enough

and they just want to tab tab tab sort of is the vibe and so it's so interesting to me that

actually all three of you think that the or to complete it's usually too aggressive or it's um

there's too much going on there and i think it also shows kind of there's like a bit of a

discrepancy in terms of like people's comfort levels with with this in terms of i think certain

groups are like pushing the boundaries and want to be like really at the edge there and even will

you mentioned not wanting to leave agents and i was talking to armin who's like the creator of

flask and he was like his 90 right if he does 90 he lets claude code just write all over the code

he just wants to give it instructions and let it do its own thing and come back and and he's he's

a great programmer and then on the other hand you know you i also for example even when i was

first getting started with the autocomplete felt like it was too aggressive but over time i think

i just started to get used to it and that kind of wore off so i'm i'm now kind of using it quite a

lot um but yeah i think the discrepancy there sometimes is so interesting to me and kind of

he uses it. Like he has a custom something or other for YOLO mode where he just like lets it,

you know, so you have, he's spent a lot of time to fine tune this. And I think, I think that's

part of it is not just playing around, but fine tuning it. Because if you just try to one shot

something, of course it's going to be, I mean, this is the main thing I have when people say

like, oh, it didn't work, whatever tool they're using, Juni, Claude, you know, Copilot. It's like,

if you just ask it as a simple one sentence prompt, how could you get something good, right?

It's like the joke is like you kind of have to use it to write a spec.

You'd never write a spec for a fellow human, right, Tim, in your team.

But if the machine will do it for you, you'll write the spec.

So I do feel like you need to kind of like play with it more

and to get to a point where you can fully evaluate it.

But that said, I still am shocked that people,

and there are some like Armin who knows how to write code,

is fully comfortable and happy and sped up, you know, doing that.

So, yeah, it makes me feel like, what's going on?

Maybe he's used to that.

Doing the LLM code because I think this verbosity issue is a pretty big issue.

Yeah.

You know, I still prefer chat mode to agent mode.

Agent mode feels far too much like autopilot to me.

And when I've gone down that rabbit hole, I've ended up with sort of a maelstrom of nonsense a couple of times.

And, you know, studies have started to show that if you use LLMs too extensively, it does add a lot of technical debt.

I recently, so I haven't owned a car in a dozen years.

I just recently bought one.

Um, the state of software on cars is absolutely miserable. Um, so the car play car play improvement, but the actual code in the individual components of a vehicle, um, the average vehicle has more than four times as much code as Facebook in it, which is terrifying because for me, every line of code I write, I consider a liability.

Every line of code that I put out is a potential security flaw. So when I purchased my new car, I actually did research on what vehicle I could buy that had the least lines of code. And I ended up with a 2022 Toyota Corolla with only one screen.

It doesn't have blinking lights all over the place.

It doesn't have sensors all over the place.

There was an interesting study that came out of Ford.

Ford has 150 different software vendors that write the software together for their vehicles that they try to put together into one working package.

And, of course, car repairs for software are just as common as car repairs for anything that's actually wrong with the engine now.

So, you know, this is an example of verbosity of code and runaway code bases that just become unmaintainable.

And I think we're going to see this problem continue to grow over the years.

You know, we're starting to see people whose actual job is fixing your LLM coded mess.

Like, yeah, marketing themselves.

I will come in and fix your vibe coded nonsense.

Like he's, you know, he's figured out how to make it work.

And I think it's a combination of knowing, of being used, to your point, Tim, of being used to reviewing PRs.

So you're kind of in that manager, whatever, higher level mindset anyways.

it's just a it's just a it's not a paradigm shift but it's a shift to go from just evaluating

someone else's thoughts to like thinking the thoughts yourself and then writing them

yeah i'm trying not to be all calcified about it right like i use the chat and the research

every day for two years but the agents and i still play with it and i'll use it for like

fun projects and it mostly works but then i just get you know then the debt builds up or i've lost

control of a mental model of what's happening and um you know but it's weird because you can

you can you know we anthropomorphize these models right you can sort of treat it like a person right

like it'll do something and then say like are you sure be like okay imagine that like i don't trust

you like you can do all these sort of tricks and they shouldn't it doesn't seem like they should

work but they do kind of work right to to be like you know how sure are you you know and

and they're getting better about being a little less sycophantic um but they're still you know

They're not, to your point, Tim, they're not going to, like, take out code as much, right?

They're more like they want to generate stuff for you.

Hacksoft is your development partner beyond code.

From custom software development to consulting, team augmentation, or opening an office in Bulgaria, they're ready to take your project to the next level.

I mean, I think this is the thing.

And this is one of the things that I mentioned as well in my talk is that I think the best way to, I agree completely that the technical debt thing is a thing.

And I think more people are struggling with their code.

They have vibe coded and not knowing what to do with that.

And I really do think that's a real issue.

My thoughts there or what has worked for me when I'm using like agent mode to help me with coding tasks is I really use it in a modular way.

So I don't have it generate unless it's something like completely Greenfield where it's like a prototype or something like that.

Or I, you know, it's just creating the structure of it or something.

usually what I will do is I'll give it one file and I'll say, this is the issue with this file.

I want XYZ done in this file. Well, I see this bug in this file. Here's all of this context.

And if you treat it in a modular way where you know exactly what the goal is for this problem

and you give it all the context that it needs, I actually think it can be super helpful

in those cases when it's a modular case when you're just generating these larger bigger projects and

just having it go off i would say those are a little bit harder to control and that's where i

think the technical debt comes in but i i think there's other ways to use it i i primarily use

agent mode and i found that for me agent mode is is is better um because the model usually has

access to the logs and and things like that and the whole structure a bit better when it's in

agent mode but i will restrict it and say even though you have all of this only you know write

code change this file or do things like that so giving it restrictions do you have that in your

rules or do you have to manually put that in each time uh i do sometimes add it to my rules

um and but but usually i will i will put it in the chat so usually i will give it the context

that it needs and i found that that that works for me um because i'm not usually trying to

refactor the entire code base at least not in the problems that i'm i'm working on um for me

it's usually adding a feature or debugging an issue one at a time and so that's where i i would

way of attacking problems. Yeah. You know, I started to think about what are the biggest

productivity boosts. So I started writing code when I was six years old. So I've been writing

code for a long time. I was trying to think of what are the biggest productivity boosts I've

seen as a developer over, you know, over four decades. And, you know, Stack Overflow sort of

coming about in the 2000s and 2010s

was definitely a big boost

to my productivity.

Having sort of the amalgamation,

the coming together

of all the development knowledge

on a single source

that I could rely on was.

But I think the biggest productivity boost

I ever got as a developer

was Windows 98.

Yes, Windows 98

was the first time

I had an operating system

that had support for a second monitor.

that was truly a game changer for my development productivity let me tell you

having like browser and emacs up at the same time i'm dating myself but that was a danger

that the thing that these tools are amazing for is you know if i remember when i was learning

got stuck i'm missing a semicolon or something and i'm at home by myself and i had nobody to

help me and i i couldn't work it out and eventually i worked out but it was hours of like tearing my

hair out about this stupid semicolon whereas one of one of these machines would have spotted that

and told me and gone look this isn't working why isn't this working it's not working because

and that would have been an amazing boost i think you know again stack overflow a lot of people

learn by copying and pasting from stack overflow and there was all this kind of programmer

superiority about oh you mustn't copy and paste from stack overflow but a whole generation of

people learn by copying and pasting from stack overflow well nothing wrong with that that's

brilliant more power to them and i think the same here from llms i think if you're a junior by

yourself you haven't you know who hasn't got a support there who a mentor there who can

brainstorm it with you to have the llm go here's the problem that's going to let you go forward

faster i think for me that use case is very interesting we can open up an awful lot of doors

with these i don't know what are they magic lanterns that speak you know speak kind of the

discussion so yeah like days days on a semicolon and maybe newer idees would fix it but that

feeling of just helplessness like that's that's gone um i think it's absolutely javascript today

oh because they're key literal keys and you need a evaluated key oh but oh thank you job

thank you llm for solving my problems yeah it's not about being a beginner but how much of what

for that yes yeah how much i mean that feeling i used to get when i was writing you know turbo

pascal there was no internet and i was there with my own devices and a book of borland turbo pascal

when i found that semicolon after three hours i'd learned a lot more about my good base

i'd learned a lot more general knowledge from going through the book and learning about

you know how you should write code and how to avoid these problems in the future i've learned

a lot about debugging and the feeling of accomplishment you get after struggling like

that sheena o'connell said something brilliant at last year's jango con when she was teaching

people during the tutorial during sprints and that was never steal someone's struggles from them

because that is the best way that people learn and how they come to love programming is that

feeling of accomplishment after the struggle that journey is an important part of becoming a senior

software engineer and uh i i think that part of that is is being taken away from a generation of

is that the re the real reason why i don't use the agents more is you know quality of output and all

least i can say all that the real reason why is i'm worried about losing my strength i'm worried

about going to the gym and getting the machine to do the weight the weights for me and you know

what happens in six months time when i haven't really been coding the same way if i still got

the same the same strength so that's the flip side of the hard learning is by doing the reps

every day coding you you keep your skills sharp and what happens in six years time when our entire

is something like trying to find cobalt programmers now yeah i actually would agree with that and

concern is about is for junior developers and and building that that muscle and i actually was it's

so interesting because I had a conversation it was reading something recently where um yeah where

there's been some studies done about how LLMs tend to select for or it's biased towards senior

engineers where even we talked about Amin earlier and even the intuition needed to review what the

agent has done ETC to know what to change what not to change is something that is biased towards

senior engineers that already have the experience. And so if junior engineers don't have that

struggle that Tim is talking about to be able to understand their code, how do they actually get to

the point where they know how to interact even there with an LLM in a way that's productive?

I think that's actually an industry level concern that I think we need to be worried about as an

industry. How do we actually solve that problem? I do think that there needs to be some spaces where

there's a joint collaboration. So I think LLMs can be good, like Halton said, for potentially

like personalizing education and helping to make it easier to ask questions. So maybe if a junior

developer doesn't know how to solve a specific task and maybe you give them room to struggle

so i think we need to be thinking rethinking how do we use what we have these tools to be able to

create these environments where the junior engineers can still grow can still learn

but we are preparing them for the future that is inevitably coming with the with the llms in

general so i would say here on this point i do i agree i'm it's something i'm concerned about and

i think we need to be thinking about this um yeah i just had an idea as you were talking now i think

right so if you spent 10 actual minutes writing the prompt then you can press enter and send it

to the LLM, but if you haven't done that,

then you have to spend the 10 minutes

because that makes you think it through.

That's like a rubber duck.

I might do that as a demo.

we're replacing experts with ghosts, right?

These LLMs are kind of ghosts of, like,

where did this stuff come from?

And, you know, not everyone has access

us to, you know, ping Carlton when they're stuck on a bug. But it is, I don't know if it's magic

or what it is. Like, I was just watching Andres Karpathy just had a long interview on, was it

Darpesh Patel's podcast and some really interesting takeaways. And he was saying something I've

thought about, like, he's like, the internet is such garbage. Like, it's not, you know, it's not

New York Times articles. Like, it's just complete garbage. There's no, doesn't make any sense how

something can come out of this garbage and so on the one hand like what's left there that has any

like good meaning so stack overflow like that like let's take that as an example right so

you ask a question and then humans come in and give responses and vote up or down there's no

and it's not perfect but it's pretty good like there's nothing like that with these llms and

yet somehow they sort of kind of do it but like you can't trust it and also why would any of us

go on Stack Overflow now and try to get credibility or write an issue do a good response write a book

you know like the new Django survey will be out by the time this comes out you know

people aren't reading books they're not even reading blog posts you know like look at like

Adam Johnson if you just read Adam Johnson blog posts you would be like a senior dev but

But, you know, he's just doing it because he wants to, right?

Like, there's no...

because we might be at peak LLM coding time right now

because the top model is now a ghost town.

Like, Stack Overflow is dead.

tagged Django or tagged Python over the past five years,

It's dropped off a cliff, and there is nobody on Stack Overflow.

I used to get, you know, 30 points a day for my various responses out there.

I now get, you know, 30 points a month or something like that for upvotes to my old responses.

What happens if things change and there's no central model for the LLMs to steal their training from?

And, you know, I'm looking at right now, all of these LLMs are being operated at a big loss.

So, I made $4 billion last year and spent $9 billion to make that $4 billion. This is not sustainable and it's not going to last forever. And, you know, I referenced AI a bit in my talk. A lot of my senior thesis written in the mid-1990s was about the dream of AI.

I am not an ater, but I've been around enough hype cycles and I've seen what these big tech companies have done to the Internet and done to search engines and shitifying their own products that I truly think, you know, the same patterns that social media and big tech have brought us over the past few decades are sort of being repeated on steroids.

And they have not yet enshitified the LLM.

What happens when they want to keep you on the LLM longer, like Google has, and starts intentionally giving you worse results?

Some of the worst companies and the worst people in the world are the ones pulling these levers while genuflecting to the current U.S. administration.

This is worrisome to me.

I can see the same exact pattern of what has happened to Google, to search engines, to social media, to them lobbying and not having any concerns for our children's mental health or futures being amped up on steroids.

And I see the same sort of people I see.

I see Sam Altman and I see Mark Zuckerberg.

You know, I don't see much of a difference in the morals or the discussions coming out of them.

I see move fast and break things.

I see little concern for the mental health of people, for humanity or the future.

And that worries me.

So I think we're currently at the peak of performance we're going to see because the companies are going to start intentionally and shitifying them to put profit over pulling people in because that's a pattern we see.

over and over and over over the past couple of decades well sorry marlene you go

currently the way the world is structured is to maximize profit in a lot of ways and i don't think

you know, LLMs are an exception to that. I do think LLMs are not explored enough in terms of

the good potential that could come out of them. Do I think there's loads of bad things that are

going to come out of them? Yes. I think we are seeing that already now. I'm not sure if you saw

even like, I don't know, Sam Altman shared some updates recently that are concerning

um that i think as well the intention is to continue to keep people using chat gpt for

example and um and you can look for that online i won't talk about it um but i think that um

i also think that technology is always going to be this sort of double-edged sword where you have

very good things and very bad things coming out of it so you know when i was at django con

And I talked about how we haven't really explored to the extent that we could these open source models, these small language models that have huge potential to transform education.

You know, I talked about the Django Girls curriculum and how we could do something like Django Girls offline because there's so many parts in Africa, for example, that are being left behind.

and there's this growing digital divide

that is consistently growing

and no one is doing anything about it

and it's not changing.

And like people just don't have the resources there

to bridge that gap.

And the thing that has helped

to sort of close this gap over time,

technology is a huge part of that.

So, you know, I grew up in Zimbabwe

and a big part of why I learned how to code in Python

is because I had access to an internet cafe

where i could go and i could connect with people online who are like writing django code and and

who can teach me stuff and and that's because of technology and those advances yes absolutely have

huge ramifications negative ramifications but also you know i think about the parts in zimbabwe

for example, that don't have access to stable interconnect connection or internet is super

expensive. And thinking to myself, can I imagine a world where we bring in small language models

that can teach people when they don't have an internet connection? And what that means if

someone now has access to all this information is huge. So my personal perspective on this is

there are two parts that are going to continue to grow it was very negative stuff and then also

potentially very positive um so so that's my perspective i would agree to an extent but also

marlene i think i'd you know really positive the thought that came up um with so i saw that a

similar article to you Tim was about the economics of all this and the sort of latest whether it's

latest or recent figures they're spending three dollars to get one well that's not you know three

dollars of spending to to make one in revenue well that's not sustainable and nobody's going

it seems like nobody's going to pay three times as much for the same service because you can run

a local model which on you on you know you know I've got a five-year-old laptop that runs a model

that i can do 90 of what i do with the you know the claudes and the chat gpts or the co-pilots of

the world it seems those local models might be you know the way forward if you can you know there's

an internet in a box project which i think is you know has you download wikipedia you download your

pi pi mirror you've got everything you need to run workshops without an internet connection

if they had a local llm on them as well then you've got the complete the complete set um

And I just, I wonder what you think about local models and, you know, how that affects the economics of these frontier, the anthropics and the open eyes of the world.

And I remember Hitchcock's Guide to the Galaxy.

They build the computer deep thought to find the answer to life, the universe and everything, the ultimate question of the universe.

And it comes back with the answer being 42.

um but what we use is really it there in that and um i think you're really on to something

because it's been shown you know studies have started to come out that show that the smaller

the scope the higher the accuracy and utility of a language model and uh you know i could be

completely wrong but i think the far more useful future that you're um speaking about here could

be small focused tightly scoped language models um you know not just rag but the entire language

model being contained to a very specific scope. I mean, imagine if our coding LLMs didn't have

billions of unused parameter pathways for when someone wanted to make a cat picture,

but was just focused really on, you know, the stack overflow training set. You know,

it would be a lot more efficient. It would be a lot more environmentally friendly to run. It could

run locally. And, you know, maybe dividing these things up, having a thousand different models

instead of one big god machine is the way to go

because then it also can be run locally.

Maybe the race shouldn't be to a trillion parameters.

Maybe it should be to a million parameters.

Yeah, it's going to be interesting to see.

That's small, focused.

That's the term I've seen bandied around.

Carlton, can we mention the comment you shared with me yesterday

about the art heist at the Louvre in the context of all this?

Well, so yeah, Carlton shared, I guess, a meme or something going around saying,

if the thieves had said they were training an LLM model,

it would have been fine to go take off with the jewels.

Because there is, I keep coming back to the underlying there there.

Even if the technology keeps improving, how could it have good responses

if the underlying content is garbage.

And as all the economic underpinnings

for creating good content on stock overflow,

on a book, on a blog post,

even something as prosaic as like,

I want to buy, I'm going hiking,

I want to buy a backpack.

In the old web, you know,

you could try to rank for like top backpacks

and do a ton of research and have affiliate links.

And that would justify hours and hours of time to do well.

Well, why would you do that now, right?

Especially as, like, Tim, you were mentioning,

all these LLMs are now going to be adding in, you know, e-commerce, ads, right?

Like, I do sort of wonder if this is the glory days of, you know,

if this is like Google in, you know, 2000 or something, right?

Because they're actually trying to give you the right response.

But because of the underlying economics,

we know that they're going to be doing the things you alluded to, Marlene.

Like, they're going to be turning it in.

They're going to be enshitifying it.

And it's going to be about engagement.

1,000%, right?

Like there's that, another meme is the connector core,

like the power adapter plugged into itself.

Like that does seem to be, yeah, accurate.

But Marlene, you seem like you had something to add.

I'm going to play devil's advocate, I guess, for a little bit.

I don't know.

I'm not sure.

I think, I think it's hard because at the same time, one of, so there's, there's definitely

opposing views in the space in terms of like, on one hand as well, we need to kind of be

thinking about the future and how do we get to, how do we get to the best possible technology

we can create for the future?

And do I think that, I think the issue is that the frontier labs are at least framing

themselves as having the solution to get to that future.

So a lot of people have talked about AGI, that's a big thing people are hoping we get

to.

And so, you know, when we look at OpenAI, for example, they were the pioneers for chat

GBT. They've been the ones that have been pushing the frontier. Anthropic has now come on the scene

and is also pushing the frontier there with LLMs. And we've seen that scaling these LLMs has helped

to an extent. And if the people who have the most knowledge at the moment, we're assuming the people

that have the most knowledge about this space are working for you know these frontier labs

do they not kind of owe it to us to kind of explore to the full extent how good these LLMs

can get so that potentially maybe the LLMs could generate really good results on their own and I

know there's a lot of talk right now on reinforcement learning and having these

LLMs learn by themselves and improve themselves over time. I don't know if that's a practical

future. Do we think that this is a practical future, that that's something that is actually

going to happen? Or do we think this is all going to be driven by economic incentives and there's

no incentive almost at all to reach this kind of AGI future is my question.

which blew up beyond my expectations on YouTube and got, I guess it struck a chord,

it got a lot of views, whereas imploring people not to buy into the AI hype. You know, some people

took that to think that, you know, I was a technology hater or something like that. It's

quite the opposite you know the thesis of that talk was meant to be that this insane hype cycle

which is the biggest technology hype cycle i've seen in my over four decades in tech

is actively preventing us from finding how to use these algorithms to improve the human condition

because i do think there is a there there there's an undeniable um there's an undeniable something

there in these algorithm in these algorithmic advances we've made over the past few decades

You know, we see it in better prediction models and tracking hurricanes and stuff like that. But, you know, trying to make these into some kind of magic wand, I think, is actively dangerous. I think it, you know, I've watched OpenAI go from being supposedly a nonprofit that was supposed to improve the future to humanity to now, you know, creating interactive sex bots that I think Marlene was referring to last week.

Like, it's like, how do you go...

if you read like Empire of AI and other stuff,

they never really meant it.

They just couldn't,

they didn't have the salaries to compete with Google.

And so what better way than to say we're an academic lab?

So I think, I don't think they ever meant it.

I mean, there's internal chats like a weekend

with between Musk and all the rest saying,

well, as soon as we hit scale, we can just discard this.

is they i saw a good comment about that the other day which was that if i was 18 months away if i

really thought i was 18 months away from agi i wouldn't be pivoting to interactive sex box right

yeah i think that's the answer to marlene's question is they don't think that they're

any way it comes.

Yeah.

because we're coming

a little bit on time.

I did want to ask a question around

how can Django be an,

if not an AI-first framework,

not miss out on this wave

that FastAPI is riding?

Carlton and I have discussed this.

I'm curious, Tim and Marlene,

if either of you have thoughts on,

right, I mean, FastAPI

is completely ascendant

for a number of reasons.

And how do we,

how does Django latch onto that

and not be left behind?

That's a big question.

That's a great question.

I mean, in my opinion,

I do think that as a community,

in the Django community,

we just need to be,

I think, more open to AI.

Do I think AI has some very toxic things

potentially associated with it?

Yes, absolutely.

Um, but at the same time, I think there's lots of goods that we can add, uh, that AI adds. And so, you know, I mentioned potentially a lot of Django just generally as it's structured is fantastic for, um, doing modular programming, for example, and, and helping people who are vibe coding applications.

Django's a potentially really good framework for that and I think doing things to actively like

interact with that AI community is something that I think can grow the Django ecosystem in terms of

AI so I mentioned I mentioned creating potentially an agents.md file that will people can go ahead

and put in their code and have Copilot or whatever,

Juni, whichever assistant they want to use,

be able to create a Django app for them,

but that's following the guidelines

that this agents.md file creates.

So really creating these centralized resources

that we're also using to kind of steward

where we want the industry to be going.

And even as Django developers,

I think these conversations we're having right now are really helpful.

So, you know, how helping Django developers have some guidance in terms of how they should use AI or how we think we should be approaching the community.

And I forget, why am I forgetting his name?

Corey is fantastic.

And he has a really good video he made on vibe coding with Django that I think has like some fantastic principles on approaching it.

you know in terms of modularity and things like that um so more resources about ai and django

on uh october 9th and 10th and the videos are now available and on the second day sage abdullah and

Tom Usher, two of the core team, gave a really good talk about how Wagtail is going to handle AI in the future.

And I really like the path that Wagtail is going down.

You know, so many of the corporate entities out there are forcing AI into all their products, forcing it down our throats, raising prices after initially giving it for free.

You know, we've seen it over and over and over again with Microsoft putting copilot everywhere.

There are like 18,000 different versions of Copilot coming from every angle.

No offense, Darlene, I know it's-

All of the big tech companies are making it sort of mandatory.

They're not giving people an opt-in option.

Wagtail is taking the opposite approach.

Wagtail has made a commitment that there will never be any AI for the Wagtail core,

but that the Wagtail team is making a secondary package for anybody who is interested and wants

to opt into it called Wagtail AI.

So you can pip install Wagtail

if you just want the core Wagtail AI free,

but if you want some of the AI features,

you can pip install Wagtail AI.

So the talk given during Wagtail Space,

I think it's available on YouTube now,

is called AI and Wagtail,

Responsible Innovation for Content Editor.

And a couple of the commitments Wagtail has made

is again that it won't ever be forced into core,

but this is a blessed under the Wagtail organization's

umbrella package that is being developed secondarily to Wagtail for people who want

those AI features. It also said, we'll provide, you know, we'll provide a clear

picture of our AI vision, have it publicly stated, and we will always avoid any kind

of vendor locking. It also gives sort of a practical knowledge of what's available today

in the Wagtail AI in this talk.

So it's something that people can look at right now.

I think that's a pretty good model.

So, you know, I consider myself incredibly lucky.

We've had Django code in production for a decade now.

I've also been sober for a decade now.

So it's caused me to sort of reflect

on how lucky I am to be part of these amazing communities,

my recovery community, my work colleagues,

and then the Russian doll of Wagtail

within Django, within Python.

You know, I've met some of the most amazing people in my life through these communities.

And I think that having the right people in place here to have this sort of moral and ethical conversation about the right ways to do it, to allow people to opt in without forcing it down our throats.

I think Wagtail is really sort of leading the way here.

as being sort of the smaller Russian doll

within Django and Python,

I think it's a good place where we can look

where on a slightly smaller scale than Django

or a much smaller scale than Python,

maybe Wagtail is sort of leading the way

in something we can look to as Django

on a way to sort of address it head on.

But without being the first ones

to dip our toe in the pool,

it might be somewhat easier to do

when you're in the content management space

than a bigger web framework space.

but I think there's some really good ideas there.

Yeah.

my talk was related to this idea of,

I think there's a sense that Django and FastAPI

just on the underlying technologies

isn't either or.

When practically speaking, it's both.

You can and probably should use both.

And I see a whole new generation of Python developers

starting with machine learning and pure Python

and then FastAPI comes along

and it's an end point

and they think that's the end of the web.

And so I see a gap there,

an education gap around,

you know, when does Django slide in?

Because people think,

well, do I even need Django?

And part of that is just if you read,

you know, Reddit or Hacker News,

you only see posts from OpenAI or Anthropic.

You see massive, massive scale

as opposed to, you know, me,

a couple of people trying to incorporate LLMs

into a workflow.

And then Django's there,

but I think we in the Django community

need to tell that story

a little bit better.

One of the things in my talk

was showing how you can hook Django

into a local LLM

and have a chatbot

because, like, it works, you know?

It works pretty fast, actually, too, you know?

So, like, we're not running

our own frontier models here.

So, yeah, FastAPI has its uses,

and for sure, but, like,

just clarifying, like,

I'd love to give a talk next year

on kind of choose-your-own-web framework,

you know, like, choose-your-own-adventure,

and just break down, like,

flask is great here fast api is great here django is great here and just sort of i guess

redefine what those boundaries are because i think it's still a little fuzzy especially to

a newcomer you know it used to be flask versus django and now it's really fast api and like

do i even need django you know so that's something over the past couple months on a project i'm

comfortable batteries that I'm familiar with

of Django and the Django ORM.

So I've got all like my webby stuff,

but then it gives me sort of that fast API,

you know, quick, easy API access,

ultimately flexible.

So Django Ninja has been one of the tools

I've really enjoyed working with

sort of in the LLM space,

tying it into Django.

around APIs that maybe shall remain

on serialization which is um new modern serialization that um is sort of orm friendly

because you know it's you know the bottom line is for me that rest framework serializers are

kind of the last generation and they're a lot slower than caters or pedantic or message spec

and you know there's a whole raft of these newer ones which are just much much much much quicker

um but they don't know anything about the django orm um and i think there's a nice way where we can

you know handle um restricted field queries handle pre-prefetch related automatic pre-fetch related

and have modern serialization and the speed effects from there so i'm i'm working on that

i'm hoping for end of year around that kind of period to have something to

and if you have it a book recommendation maybe i'll i'll go first i will put a link to it marlene

you have a django girls offline repo that um is excellent fun fun to explore maybe the start of

something more of having offline resources for places that don't have internet access so that's

okay we'll put your your Django your Django con um your talk repo you have the great tokenizer

and the mcp like your demos were so good okay awesome um I'm not going to recommend one from

uh recent reads was uh by uh carl sagan's daughter sasha sagan it's called for small

creatures such as we and it's a love letter to the universe um on behalf of an atheist who has

recently had a child and how she seeks to create meaning in the universe through rituals without

falling back on religion. And it's just, it's an absolutely wonderful book. It's, uh, I needed

something optimistic and uplifting given the state of the world and it definitely hit the spot for

that. So I will definitely recommend, uh, Sasha Sagan's for small creatures such as we. And I

also wanted to mention just two projects, um, Wagtail Nest and Django Commons. I think people

are probably pretty familiar with, I've moved some of my projects over to Django Commons and

Wagtail Nest, and I found them to be a huge help with avoiding developer burnout. Just having other

people to pick up the slack during those months when you are doing something like, I don't know,

planning a wedding and now have a 13-year-old in your life, it's been very handy. So if you're

looking for a group to get together to avoid sort of that single developer on project burnout,

Both Wagtail Nest for the Wagtail community and Django Commons for Django projects has been absolutely wonderful.

That sounds amazing to me.

I wanted to mention a project, Django Keel by Sayim Khurana, who goes under the tagline on GitHub of Curious Learner.

It's another kind of production-ready template for your Django projects.

Sayim is top-notch.

I'm really quality developer.

I'm really keen to see what his opinions are

and what options he's had.

It looks like it goes into a lot more depth

than most of your starter projects.

He's got things about observability.

He's got things about async, background tasks.

He's got front-end story, back-end story.

He's got proper docs on read the docs.

It looks really good.

So I'm excited by that.

I also know that he is looking for work.

So if anybody's looking to hire a top-notch developer,

seek out sayam on linkedin he's absolutely wonderful yeah no absolutely i'll double that

looking for work but yeah i'll just hire him if you're in the market that's a it's a great

opportunity um for a book i wanted to mention um the web web access 30 cookbook this is by um

Manuel Matuzovic, which is, and it's a, it's just a kind of how to go, it goes through

like, I don't know, everything you need to know about your website.

So you can create accessible HTML from the, the get guy rather than trying to bolt it

on afterwards.

Um, really, really, really solid learning in that book.

I really recommend it.

Um, yeah.

I'll just quickly do a book and then Marlene, if you have your thoughts, you can go.

So I've been reading Apple in China, which came out early this year.

Tim, you'd love this one if you haven't read it.

But it's, you know, basically how Apple thought it was.

It moved tens of billions of dollars every year into China

and really developed the infrastructure in China

and thought it was getting a better end of the deal.

And it turns out China was.

So I think it's interesting just day-to-day the executives at Apple

really did never step back and think why are they being so why is china being so accommodating why

are they allowing us to do all these things and you know apple thinking short-term china thinking

long-term in a way and the amount of investment apple alone put into china just dwarfs any federal

plans and everything else we have here so i think it's yeah it's really really eye-opening really

kind of scary um but really well researched so i'd recommend that book so marlene do you have

repository uh but i mentioned quarry zoo um and and that when i was researching for my dango's

I had been looking on the discussions page on the Django project website.

And I'd actually seen a conversation between Will and Corey where they were discussing an Agents.md file for the Django project.

I think Will had been asking if there was one around or what people's thoughts on it were.

And that's where Corey shared his YouTube video.

and then I went off like on a tangent just to watch the YouTube video um and and anyway in that

uh YouTube video Corey like talks about like principles for vibe coding and then he also

shares like a project that he created um a Django project he created through this vibe coding

process so I was going to recommend that I cannot find the repo at the moment but hopefully I will

find it after and i will link that uh and so that it can be linked somewhere for for people to help

but a great person to follow i would say is cory has a youtube channel with great thoughts there

and then for a book recommendation i i don't know i'll kind of do like a half a two half

recommendations the first is um there's not a book but it's simon willison's blog

um which i think is super it's really good if you want someone who's a realist someone who's

connected to django of course um i think he has fantastic thoughts about ai and and generally

where the industry is heading and has very kind of realist perspectives there but that's like a

that's like not a book um so uh a fun book kind of fun to recommend for reading is i've been reading

a book called uh children of god and it's like a sci-fi book um is that fun though that's a little

bit apocalyptic it's a little apocalyptic it's it's a little bit i mean a little bit in a british

sense yeah it is it is not a fun book in the in the chill sense of the term but it's so interesting

because in the book, I've been surprised by,

this is a book that was written a long time ago,

and the author imagines AI.

So I'm sorry to interrupt.

I love her.

I've read this.

I've read a ton of her stuff.

She's so good.

Sorry, go ahead.

And I remember reading this part

where she was talking about ai in the book for this like they are on an alien planet somewhere

and she's imagining what ai would like talking about what ai is like and i was just like i do

not know how someone from back in the day could have this kind of foresight to imagine that it

would be like this um so that book is called children are gobbets a fiction book can recommend

linguist who speaks like 13 languages and there's a line that still haunts me where he

i think it's communicating the aliens he's he has a line about you know uh you know fluent in 13

languages and he couldn't find a word to express a phrase or a feeling and that utter frustration

of language failing oneself um yeah oh thank you for mentioning her she's so good i think that's

her best one. She's written a ton. She's done

poetry, too. Oh, deep cut.

Yeah, she's awesome.

some cool

pieces, for sure, about language and

about AI as well.

Python and Django community that

recommend a book and only one out of the

three was on tech.

Yeah.

consistent. Carlton's good.

It's my job to recommend a tech book

because everyone else always goes off left field.

So I'm like, okay, I'll be the tech book person.

is there anything Tim or Marlene you wanted to mention

that we didn't get a chance to cover?

that I don't think AI is going to take all the jobs.

I want to assuage that fear a bit.

When you look at the history, whether it's farm work or, I mean, the computer being introduced in the 1980s, it eliminated 3.5 million jobs, but it created over 19 million jobs.

So while some people were displaced, it ultimately led to more work.

I remember 20 years ago headlines saying that half of the employment of the health care industry was going to be replaced by technology.

And instead, over the next decade, it doubled.

So I want to leave anybody who's struggling to look for work right now. I think that's got a lot more to do with sort of political forces right now and uncertainty than it does anything AI. You know, Mark Benioff, CEO of Salesforce, has been gleefully been celebrating laying off 10,000 of his own employees, but they hired 20,000 people in one year during the pandemic.

is it more likely that he is actually using AI to replace these people right now,

or is it more likely he's covering up for a stupid hiring bitch that his company made?

So if you're looking for work, I know it's tough. This is the worst job economy I've seen for tech

in many years. I want to send out my sympathies to anybody who's looking for work right now. It's

incredibly hard, but I also want to give a ray of hope that I don't think that AI is going to take

all of the technology jobs in the future.

And I think there will be hope

and it will re-event.

So I just wanted to send that out

to anybody out there

because I know it's tough.

but if you had something you wanted to.

yeah, I agree with him

that the market is really tough right now.

And I would say that I do still think

that the core skills matter.

And, you know, right now,

for example even at microsoft we had like loads of layoffs as well um not great but at the same

time there's like i have no idea why i laughed when i was saying that because it's such a serious

issue i'm so sorry um but like i think the issue is that we had like lots of layoffs but at the

same time the company is hiring as well in in a lot and there's so many open positions right now

and a lot of those positions are kind of aligned to ai so i would encourage people as well not to

be afraid of ai i think there's a lot of negative things that it's led to but i think if we can

combine the skills that we have right now with that knowledge i do think that um there are

opportunities there for us um and i really hope that as an industry as well we can just work

together to hopefully shape the direction of where the industry is headed for for the better so

for for continuing this conversation that's really kind of the point of this podcast is to

is to do that to have the conversations and to share them so thank you for

making the time thanks so much for having us thank you all right and we are jango chat.com

and we're also on YouTube

and we'll see everyone next time.

Bye-bye.

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