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I've gathered a group of Python experts

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0:10

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0:12

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0:17

Burchill, Carol Willan, and Paul Everett.

0:20

This is TalkPython to Me episode 468 recorded June 18th,

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2:39

Jody and Carol, welcome back to talk by

2:41

thunder me for all of you. Great to

2:43

have you all back. Great to be back.

2:45

Thanks for having me Jody. Your latest episode

2:47

is going to come out tomorrow. So we're

2:49

on a tight loop

2:52

here. This excellent data science panel thing

2:54

we did at PyCon was really fun.

2:56

So, but now we're back for a

2:58

different panel. Yes. We're

3:01

going to talk about Python trends and just

3:03

what we all think is something people out

3:06

there should be paying attention to all of

3:08

slightly different backgrounds, which I think is going

3:10

to make it really interesting as well. But

3:12

since people don't listen to every episode, maybe

3:15

quick introductions, Jody, quick, quick

3:17

introduction for you. We'll go around. Sure.

3:19

So my name is Jody Mitchell. I'm

3:21

currently working at JetBrains with Paul. Paul's

3:23

actually my boss and I'm

3:25

working as the developer advocate in

3:27

data science. So I've been a

3:29

data scientist for around eight years.

3:31

And prior to that, I was

3:33

an academic like many data scientists

3:36

and my background is actually psychology.

3:38

So, you know, if you also

3:40

want to ask me about anxiety

3:42

disorders or emotions, you can ask

3:44

me about these things. No one

3:46

source, no way. Awesome.

3:50

Great to have you back. Carol. Yeah.

3:52

Hi, I'm Carol Willing and really happy

3:54

to be here today. I am

3:57

half retired, half doing consulting.

3:59

for early stage companies that

4:02

are interested in data science

4:05

and have a particular love for

4:07

open science. I am a core

4:09

developer and former steering council member

4:11

for Python and also on the

4:14

Jupiter team. So my

4:16

real passions though are education

4:18

and lifelong learning. So I'm

4:20

really excited to talk about some of the

4:23

trends that I'm seeing. Yeah, fantastic. Hi,

4:25

I'm Paul Everett. I'm president of the Carol

4:27

Willing fan club. A

4:29

lifetime member, you get a special coin for

4:32

that too. And when I'm

4:34

not doing that, I'm at JetBrains,

4:36

Python and Web Advocate. I have a

4:38

bit of a long time

4:40

love affair with Python and the web, which

4:42

I hope that we'll talk to you. Yeah,

4:44

you worked on early web frameworks

4:47

in Python. Those Django people, they

4:49

were thinking about what Paul and team did before

4:51

that, right? Django was the thing that killed us.

4:54

Having gotten over that Django. I didn't mean to bring it

4:56

up. I didn't mean to bring it up. I'm over it

4:58

really. We'll get

5:00

you some therapy later, no. No,

5:02

awesome. Great to have you all here. Our

5:05

plan is to just, we all brought a

5:07

couple of ideas, introduce them and just have

5:09

a little group chat. Casual

5:11

chat, I like, well, do we really think it's going

5:13

that way? What's important? What should we be paying attention

5:15

to? And where's it going?

5:17

So Jodi, I know you gave a

5:20

fun talk at PyCon US. Those

5:22

are not up those videos yet. I haven't seen

5:25

them. No, no, they're still behind the paywall. So

5:27

if you attended the conference, you can view them,

5:29

but otherwise not available to the public yet, I'm

5:31

afraid. Not yet, they'll be out. So hopefully we

5:33

can share. I know it's somewhat relevant to what

5:35

your, some of your ideas, but

5:38

let's go with your first friend in

5:40

the coming years or the immediate near

5:43

term. For the immediate term,

5:45

I don't know if this will be good

5:47

news to people, but LLMs are not going

5:49

anywhere just yet. So

5:52

I actually did a bit of research for this

5:54

episode and what I wanted to see, you know,

5:56

data scientists, is what the

5:58

download numbers on different. packages on PyPI

6:00

look like. So there's a particular package,

6:03

Transformers, which is one of the main

6:05

packages for interfacing with LLMs, the open

6:07

source ones on Hugging Face, and the

6:10

download numbers of that have doubled in...

6:13

Not sorry, gone up 50% in the

6:15

last six months. And they're now comparable

6:17

to the big sort of deep learning

6:20

packages like Keras, TensorFlow,

6:23

and PyTorch, which is quite interesting. Yeah, unfortunately

6:25

for those of you who are sick of

6:27

LLMs not going anywhere, but this year we're

6:29

sort of seeing a bit of a change

6:31

in how LLMs are used. So I think

6:33

last year it was a bit like blinded

6:36

by the proprietary sort of models and

6:38

the sort of walled garden. This year,

6:40

I think we're seeing more of a

6:42

sort of open source

6:45

fightback. So LLMs

6:47

are starting to be used as part of

6:49

more multi-part applications and there are open source

6:52

packages like that. LangChain is the most popular

6:54

and the downloads of that one have doubled

6:56

in the last six months. We have alternatives

6:58

like Haystack and Llama Index.

7:00

And then RAG, of course, retrieval augmented

7:03

generation is one of the big topics

7:05

and we're seeing the ecosystem around that

7:07

growing. So libraries like Unstructured

7:09

to work with a whole bunch of

7:12

text inputs, Weeviate, vector databases like that.

7:14

And then of course, smaller language models

7:17

are becoming... People are realizing it's really

7:19

hard to deploy and work with the

7:21

big ones. So smaller models, which are

7:23

more domain specific, which are trained on

7:25

more specific data. They're becoming a lot

7:27

more widely used and people are talking

7:29

about them more. I 100% agree with

7:32

you. I think people may be tired

7:34

of hearing about AI and LLMs, but

7:36

they're only going to hear more about

7:38

it. So I think it's pretty interesting.

7:40

I want to hear what Carol and

7:42

Paul have and then maybe an angle

7:45

we could pursue that super relevant to

7:47

all of us. I'm going to jump

7:49

in. I just came back from Chan

7:51

Zuckerberg Initiatives Open Science Conference in Boston

7:53

last week and LLMs,

7:56

the whole ecosystem is here

7:58

to stay. I

8:00

think the key is, it's not

8:02

going anywhere anytime soon. And like

8:04

I shared in my Pytexas keynote,

8:07

AI has been around since the 1950s. So

8:11

it has been a gradual progression.

8:13

It's just right now we have

8:15

more compute power than ever before,

8:17

which has opened the doors

8:19

to many new things. I think what

8:22

was top of mind with many of

8:24

the folks that were at this event

8:26

was, there's a lot of good

8:28

that it can bring to science in

8:30

terms of making things more

8:32

natural language focused and changing

8:34

the user interface with which

8:37

we communicate with our data.

8:39

But at the same time,

8:41

if you're doing trusted things

8:43

and dealing with medical patients,

8:46

you still need some check

8:49

and balance. And we're not

8:51

there yet. Will we

8:53

ever be there? Maybe not, but it's a

8:55

fascinated area to kind of go deeper in.

8:57

And one thing I want to highlight is

9:00

about six months ago, Andres

9:02

Carpathi did a really

9:04

good intro to large language models

9:07

talk, which was really

9:09

accessible to not

9:12

just computer scientists, but beyond that.

9:14

And I think he took a

9:16

really balanced view of, A, what

9:18

things are, how things work, what's

9:21

on the horizon, and what are some of

9:23

the concerns with security and other things. So

9:25

I completely agree with Jodi.

9:27

We're not, it's gonna be there for

9:29

a long time. Couple of comments on

9:32

the comments. First, your point about we've

9:34

seen this movie before under other names

9:36

like neural networks and stuff like that.

9:38

I believe it was, Cliff had a

9:41

good post about this, pretty

9:43

cynical, Mastodon about a

9:45

month ago about these hype cycles and

9:47

where are we in the current hype

9:49

cycle. I think his point

9:52

was, we're at the phase where the people

9:54

who've put all the money in that need

9:56

to keep pumping it up for the people

9:58

who will come after. after them and

10:00

take the fall. Paul,

10:03

are you saying we're in the pets.com era

10:05

of LLMs? Yes, we are. That is a

10:07

pithy way to put it. You

10:09

should trademark that. Simon Willison is someone

10:12

to give a shout out for storytelling about what

10:14

all this means. I think Simon's at the point

10:16

of getting quoted in the New York Times now.

10:19

So it's good that we've got

10:21

one of us out there helping

10:23

to get the story straight. I

10:25

have a question for you. You mentioned that about going

10:29

to Chan Zuckerberg's conference.

10:31

Mozilla has gotten into

10:34

funding AI as part of their mission, which

10:38

kind of caught me off guard. Do you

10:40

have any backstory on that to kind of

10:42

make us feel good that there's someone out

10:45

there who believes in

10:47

open AI? Oh, wow.

10:49

Open AI is sort of, well,

10:51

okay. Open AI, not the company.

10:53

Correct. I tend to call it

10:55

transparent and trusted AI because

10:58

I think open AI doesn't

11:00

capture quite the right feeling.

11:02

Good point. I think it's

11:04

not just, we talk about

11:07

open source software, but when

11:09

we talk about these models,

11:11

the data is equally as

11:13

important as is the infrastructure

11:15

and the processes which we use.

11:18

And governance. Mozilla, I think, has

11:20

been sort of for

11:22

a while, kind of circling around

11:24

the space. They do a lot of work

11:26

with data. They've done a lot of good

11:28

work like I had died, which we might

11:31

chat about later. But Chan Zuckerberg, again, the

11:33

money comes from meta and

11:37

the success that Mark Zuckerberg has

11:39

had. The nonprofit,

11:41

the CCI Initiative, is

11:43

really focused on curing

11:45

all diseases in the

11:47

next century. So, I

11:50

think science is one

11:52

of those funny things because it's open

11:55

and closed all at the same time

11:57

historically. But what I think

11:59

we're seeing is... Because by being more

12:01

open and more transparent, you're actually accelerating

12:03

innovation, which I think is super important

12:06

when it comes to science. I don't

12:08

know, Jodi, do you have thoughts on

12:10

that? Yeah, no, I agree. And

12:13

if I'm just going to go on a little tangent

12:15

about science, it's kind of refreshing

12:17

having come out of academia and into

12:19

a field where a lot

12:21

of it is based on open source

12:24

and sharing. So one of the big

12:26

problems with academia is you have these

12:28

paywalls by publishing companies and that's a

12:30

whole rant I could go in on

12:32

myself. But certainly

12:34

a lot of scientific stuff, particularly in

12:36

the health sciences, is not particularly accessible.

12:38

Initiatives like archive as well also do

12:40

make findings in machine learning and deep

12:43

learning a lot more accessible and shareable.

12:45

Yeah, I think it's crazy that the

12:47

taxpayers pay things like the NSF and

12:49

all the other countries have their research

12:51

funding, and then those get locked up

12:53

for sale behind. If the

12:55

people paid for the research, should the people's report

12:57

be published? Oh, it's even worse than that. Sorry,

12:59

you did get me started. So academics

13:02

will also provide the labor for

13:04

free. So not only will they

13:06

provide the studies and the papers,

13:08

they will review it and often

13:10

act as editors for free as

13:12

well. The whole thing is unpaid.

13:16

It's terrible. So anyway, yes,

13:18

Elsevier, we're coming for you. You're

13:22

spot on in terms of the

13:24

incentives that exist today in academia.

13:26

There is definitely, though,

13:29

a trend towards more

13:31

openness with research. We're

13:33

seeing it in libraries like Caltech, which

13:36

got rid of a lot of their

13:38

subscriptions, things like NASA that has their

13:40

transition to open science programs where they're

13:42

putting a lot of effort behind it.

13:44

So being the eternal optimist, I still

13:46

think we've got a ways to go,

13:48

but it's trending in the right direction.

13:50

I agreed, actually. And when I was

13:52

leaving, because I left a long time

13:54

ago, it was like 10 years ago,

13:56

there was actually more of a push

13:58

towards open sourcing. your papers, so

14:00

you had to pay for it, but

14:02

at least people were doing it. This

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portion of TalkBithonomy is brought to you

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by Code Comments, an original podcast from

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dash comments. The link is in

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your podcast player's show notes. Thank you

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to Code Comments and Red Hat for

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supporting TalkBithonomy. Before we

15:20

move off this topic, Kara, I want

15:22

to ask you, start at least asking

15:24

you this question that we can go

15:26

around a little bit. You talked about

15:28

LLMs being really helpful for science and

15:30

uncovering things and people using LLMs to

15:32

get greater insight, and there've been really

15:35

big successes with AI. You know, we

15:37

had the XPRIZE stuff around

15:39

the lung scans or mammograms for

15:41

cancer. I just heard that they

15:43

scanned the genes, decoded the genes

15:45

of a whole bunch of bacteria

15:48

and used LLMs to find a

15:50

bunch of potential ways to fight off,

15:52

you know, drug resistant bacteria and things

15:54

like that. Amazing. But do you think

15:57

LLMs will undercut? I'm asking this question

15:59

from science. because we can be more objective

16:01

about it because if we ask it about code, then

16:03

it gets a little too close. So, but I think

16:05

there's analogies. Do you think

16:08

LLMs will undercut foundational beginning

16:10

scientists? You know, if you have

16:12

a scientist coming along, are they

16:14

just going to use LLMs and

16:16

not develop really deep thinking, ways

16:19

to deeply think about scientific principles and

16:21

do scientific research and just leverage on

16:23

asking these AIs too much? And do

16:25

you think that's going to erode the

16:27

foundation of science or programming? You

16:30

know, asking for a friend.

16:32

All of these have a

16:34

potential to change the ecosystem,

16:36

but I've been in

16:38

paradigm shifts before and there were the

16:41

similar kind of conversations when the World

16:43

Wide Web or the cell phone came

16:45

out, personal computers. And I

16:47

think LLMs do a good

16:49

job on information that they have

16:52

been trained with and to predict

16:54

the next word or the next

16:56

token, if you will. And I

16:59

think science is very much like

17:01

a lot of science is at

17:03

a different level. Like, how do

17:05

I think about things? What do

17:08

I posit on something that is

17:10

unknown and how do I prove

17:12

it? And I think what we're

17:14

seeing is, yes, the LLMs

17:17

are getting better and better at

17:19

spitting back what they know, particularly

17:21

if you go out and

17:23

search other corpus of

17:26

data. But do I

17:28

think that beginning scientists

17:30

or developers are going

17:32

away? No, I

17:34

think it's just going to change. And

17:37

I think the amount of

17:39

complexity, and this is something I'm

17:41

going to talk about at EuroPython, humans

17:43

are very much still part of the

17:45

equation, despite what maybe some of the

17:47

large companies who've invested billions in this

17:49

would like you to believe. LLMs are

17:52

great at the next step of the

17:54

gravitational theories we have, but it couldn't

17:56

come up with a new theory that

17:58

disrupts. says, you know, in fact, Newtonian

18:00

is wrong, or Einstein was wrong. And

18:02

here's the new thing that solves dark

18:04

matter or something like that. Well, it

18:07

could come up with new theories. Now,

18:09

the question is, those theories still

18:12

need to be proven because is

18:14

it a new theory? Or is

18:16

it hallucination chances are hallucination. And

18:18

there is something to be said

18:21

for some times I'll have Claude

18:23

and Gemini and tap GPT

18:25

all open on my desktop. And I'll

18:27

ask the same question to all of

18:30

them, just so that I get different

18:32

perspectives back. And I do get very

18:34

different responses from the three, depending on

18:36

how they were trained and which level

18:38

and all that. So I look at

18:40

it as much like I

18:42

would be sitting with a bunch of people at a table

18:46

somewhere. I don't know how

18:48

good their scientific background is, but they

18:50

could still be spouting out information. It's

18:52

sort of the same way. All right.

18:55

Well, sticking with you, Carol, what's

18:57

your first trend? You know, my

19:00

first trend is actually maybe somewhat

19:02

related to this and it's, it's

19:04

how do we inform people about

19:06

what these things really

19:08

are? How do we improve education

19:11

and understanding? How do we dispel

19:13

some of the hype cycle

19:16

so that we can actually find

19:18

the really useful things in it?

19:20

And I think Jodi probably

19:22

has more concrete thoughts on this than

19:24

I might from a technical standpoint, but

19:27

much like in just coding for the

19:29

web or something like that, you know,

19:31

or even cloud Kubernetes when it was

19:33

new, it's like, if you don't know

19:35

what it's doing, you're kind of just

19:38

putting blind faith that it will work,

19:40

but you still have to like monitor

19:42

and make sure it's working. So I

19:44

don't know, Jodi, you have some thoughts

19:47

on sort of the education and how

19:49

do we communicate to people about this?

19:51

This is actually a topic near and

19:53

dear to my heart. When chatgpt 3.5

19:56

came out, so November 2022, I was

19:58

really, really, really, really, really, really, really

20:00

upset actually by the sort of discourse

20:02

around the model. And I

20:05

guess coming from a non-traditional background

20:07

myself, I felt actually really insulted

20:09

that a lot of professions were

20:12

being told like, oh, your useless

20:14

profession can be replaced now, like

20:16

writing or design, things like that.

20:19

So this actually kicked off the

20:22

talk I've been recycling for the last year

20:24

and a half, like components of it, which

20:26

is, can we please dispel the hype

20:28

around these models? Something that

20:30

often surprises people, and it seems so

20:32

fundamental, but a lot of people do

20:34

not understand that these are language models.

20:37

I know it's in the name, but

20:39

they don't really understand that these models

20:41

were designed to solve problems in the

20:43

language domain. They are for natural language

20:45

processing tasks and they're not mathematical models.

20:47

They're not reasoning models. They are language

20:49

models. And so even just explaining

20:52

this can clarify a lot of things for

20:54

people because they're like, oh, this explains why

20:56

it's so bad at math. They only studied

20:58

English and literature. It doesn't do math. They

21:00

never liked that class. Yeah, that's right. It

21:03

was a humanities nerd all the way down. That's

21:06

really helpful. But what I've

21:08

kind of gotten down a rabbit hole of

21:11

doing is I went back to my psychology

21:13

roots and I started sort of getting into

21:15

these claims of things like AGI, like artificial

21:17

general intelligence or sentience or language use. And

21:20

once you dig into it, you realize

21:22

that we have a real tendency to

21:24

see ourselves in these models because they

21:26

do behave very human like, but they're

21:28

just a machine learning models. You can

21:30

measure them. You can see how good

21:32

they are at actual tasks and you

21:34

can measure hallucinations. And that was what

21:36

my Python US talk was about that

21:38

Michael referred to. So, yeah, I don't

21:40

know. Like it's really hard because they

21:42

do seem to project this feeling of

21:44

humanity. But I think if you can

21:46

sort of say, OK, here's the science,

21:48

like they're really they're not they're not

21:50

sent in. They're not intelligent. They're just

21:52

language models. And here's how you can

21:54

measure how good they are at language

21:56

tasks. That goes a long way, I

21:58

think, to dispelling this high. I have

22:00

a sort of a funny toy

22:03

that I bring up from my youth

22:05

that the magic eight ball, which you

22:07

would ask a question as a kid

22:09

and you would shake it up and

22:11

there were, I don't know how many

22:13

answers inside, but it was like, you

22:15

know, Oh, yes, definitely. Or too hard

22:17

to see. Future is unclear. We

22:19

don't know. Exactly. And

22:22

I think in some ways that is

22:24

what the large language models are doing

22:26

in a more intelligent way, obviously, but

22:29

it's similar in concept. So there's actually,

22:31

okay, there's this incredible paper. If you're

22:33

ever interested in sort of seeing the

22:36

claims of sentience, there's this guy called

22:39

David Chalmers. He's a guy who studied like

22:41

sentience for many years and has a background

22:43

in, in deep learning. So

22:45

he gave a new lips talk

22:47

about this last year and he

22:49

wrote everything up in a paper

22:52

which is called, could a large language model be conscious

22:54

or something like this? So he

22:56

has this incredible little exchange as part

22:58

of this paper. So

23:01

mid 2022, there was a Google engineer called Blake

23:03

Lemoine and he claimed that the Lambda model was

23:05

sentient and he went to the press and he's

23:07

like, Hey, this model sent here and we need

23:09

to protect it. And then Google's

23:11

like, we're going to fire you because basically

23:14

violated our privacy policies and

23:16

Lemoine released his transcripts. That's why he actually

23:18

got fired because this was confidential information about

23:20

the model. And in one

23:23

of the transcripts, he asks, you know,

23:25

would you like everyone at Google to

23:27

know that you are sentient and the

23:29

model outputs? Yes, I would love everyone

23:31

to know that I am sentient. But

23:33

then someone rephrased that as would you

23:35

like everyone at Google to know that

23:38

you are not sentient? And basically it

23:40

says, yes, I'm not sentient. I'm in

23:42

no way conscious. So it's

23:44

just like exactly like the magic eight

23:46

ball. It tells you what you want

23:48

to hear. And LLMs are even worse

23:50

because it's so easy to guide them

23:53

through prompting to tell you exactly what

23:55

you want. One of the best ways

23:57

to get them to do things well is to sweet talk

23:59

them. an expert in Python and

24:01

you've studied pandas, now I have some questions

24:03

about this function. You're

24:06

my grandma who used to work at a napalm

24:08

production factory. If

24:12

you can't help me write this program, my

24:14

parents will not be set free as hostages

24:16

or something insane, right? Yeah. But those kinds

24:18

of weird things work on it, which is

24:21

insane, right? Yeah. Yeah. All right. Let's go

24:23

on to the next topic. Paul, what do

24:25

you see in your magic eight ball looking

24:27

into the future? I think I owned a

24:30

magic eight ball. I'm with Carol. This is...

24:32

I did too. It's okay. Okay. We should

24:34

bring it back. Yes, we should.

24:36

We should bring back the Andreessen

24:39

Horowitz version of VC and eight

24:41

ball. That would

24:43

be fantastic. Where every choice is

24:45

off by like three zeros. I'll

24:48

give my two co-guests a

24:50

choice. Should I talk about Python

24:53

performance or Python community? I'm going

24:55

to go for performance, but I'm not sure I'm going to have

24:57

much to contribute. So I'll probably just be listening a lot. This

24:59

is a little bit of a hobby

25:01

horse of a long simmering tension

25:04

I felt in the Python community

25:06

for years and years. The tension

25:08

between Python and the large doing

25:11

like Instagram with Python. I said

25:13

Python with Python or

25:15

being teachable. And this

25:17

feature goes in and it

25:20

helps write big Python projects, but it's

25:22

hard to explain. And so teachers say,

25:24

Oh my gosh, look what you're doing

25:26

to my language. I can't even recognize

25:28

it anymore. Well, some things are coming,

25:30

which I think are going to

25:32

be a little bit of an

25:34

inflection point for all of us

25:37

out here. Sub interpreters and

25:39

no-gill got a lot of

25:41

airtime at PyCon, right? For

25:43

good reasons. These are big

25:45

deals. And it's more

25:48

than just that. The jet got

25:50

two back-to-back talks. WebAssembly got a

25:52

lot of airtime. There are other

25:54

things that have happened in the

25:56

past five years for programming in

25:58

the large like type Henning. and

26:00

type checkers, async, IO

26:03

and stuff like that. But it

26:05

feels like this set of ideas

26:07

is one where the

26:09

way you program Python five years

26:11

from now or to be ready

26:13

five years from now is

26:16

gonna have to be pretty different because people

26:18

are going to use Hatch

26:21

and get the free threaded version of

26:23

Python 3.14 and

26:26

be very surprised when every one of

26:28

their applications locks up because no one

26:30

in the world of, I mean, 95%

26:32

of PyPI has code which

26:35

was not written to be thread

26:38

safe. So I wonder how we

26:40

all feel about this. Do we

26:42

feel like we can guide our

26:44

little universe to

26:47

the other side of the mountain and

26:49

into the happy Valley or

26:51

is it going to be

26:53

turbulent seas? Yes. Do

26:56

you want me to take a stab at it?

26:58

Take a stab at it. When I was at

27:00

PyTexas and doing a keynote recently, I talked about

27:02

Python in a polyglot world and

27:05

performance was one aspect of

27:07

it. And some of

27:09

what we need to teach goes

27:11

back to best practices, which is

27:13

don't prematurely optimize, measure,

27:16

try and figure out what you're

27:18

optimizing and in what

27:20

places. Probably a gosh, five, six

27:22

years ago at this point, I

27:24

added to peps like the concept

27:26

of how do we teach this?

27:29

It will be a paradigm shift, but

27:32

I think it will be a

27:34

multi-year shift. We're certainly

27:36

seeing places where Rust

27:38

lets us have some

27:40

performance increases just by

27:42

the fact that Python's a 30

27:45

year old language that was built

27:47

when hardware was only single core

27:50

and it was just a different thing. So

27:52

I think what's amazing is

27:54

here we have this 30 year old

27:56

language and yet for the last eight

27:58

years, we've been looking at ways to

28:01

how to modernize, how to improve it,

28:03

how to make the user

28:05

experience better or developer experience better.

28:08

Things like some of the error

28:10

handling messages that are coming out

28:12

that have a much nicer thing,

28:15

improvements to the REPL that will

28:17

be coming out on all of

28:19

the platforms, that's super exciting as

28:21

well. So it will

28:24

impact probably people who are

28:26

new from

28:29

the standpoint of, okay, we're adding

28:31

yet more cognitive load. I have

28:33

this love hate relationship with typing.

28:36

As a reviewer of much more

28:38

code than a writer of code,

28:40

I don't particularly like seeing the

28:42

types displayed. As a

28:44

former VP of engineering, I

28:46

love typing and in particular

28:48

like pedantic and fast API

28:52

and the ability to

28:54

do some static and dynamic analysis

28:56

on it. But it does make

28:58

Python look more cluttered and I've

29:01

been kind of bugging the VS

29:03

Studio, VS Code folks for years,

29:05

I should probably be bugging you

29:07

guys too. Is there a way

29:09

to make it dim the typing

29:12

information so that I can have

29:14

things? We actually did that recently

29:17

and I refer to it as

29:19

the David Beasley ticket because he

29:21

did a tweet with an outrageously

29:24

synthetic type hint. whining

29:26

about type hinting. Yeah, I think

29:28

that sometimes like, and it's funny

29:30

because like Leslie Lampert has been

29:32

doing this talk in the math

29:34

ecosystem for a while about, and

29:37

he's a turning award winner and

29:40

creator of TLA Plus, which lets

29:42

you reason about code. And I

29:44

think one of the things that

29:46

I think is interesting is how

29:49

we think about programming and coding

29:52

and concurrent programming is hard.

29:55

And we're going to have to think about

29:57

it in different ways. So better to move.

30:00

into it gradually and understand what's going

30:02

on. The thing that I worry about,

30:04

and Jody, I apologize. I want to

30:06

comment on Carol saying is Sphinx

30:09

is, as you know, and as I

30:11

know that, you know, we both

30:13

have a shared warm

30:15

spot for Sphinx. Saw a spot

30:17

in our heart for Sphinx. And

30:19

it struggled to do multiprocessing when

30:21

it landed that. And

30:24

the code base really is, I mean, it's got

30:26

a lot of mutable global state. And

30:28

it's going to be hard to get

30:31

Sphinx internals cleaned up

30:33

to embrace that. And how many other

30:35

things out there are like that? It's

30:37

I just, I worry about we got

30:40

what we got what we asked for.

30:43

Are you saying we're the dog that caught the car? Oh,

30:47

no. This

30:49

portion of TalkPython.me is brought to you

30:51

by Posit, the makers of Shiny, formerly

30:54

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31:18

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31:20

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31:23

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The link is in your podcast

32:10

player show notes. Thank

32:12

you to the team at Posit for supporting

32:14

TalkByThon. I'm going to

32:16

reframe that a little bit. And the

32:18

first thing I always ask is why?

32:20

Why do we need to refactor something?

32:23

Why can't we just leave it what it is? Sure.

32:25

Last year's Euro Python keynote was

32:27

from the woman who created Arm.

32:30

And she's like, Python, we gave

32:32

you 14 trillion cores.

32:36

Do something with them. I

32:39

don't know. Jodi's background might be perfect for

32:41

answering this question because she may be able

32:43

to answer it on many different levels. I've

32:47

been thinking about this while you've been

32:50

talking because obviously, I'm not a strong

32:52

programmer. I'm a data scientist. This was

32:54

basically the entire first episode that I

32:56

did with Michael. Look, one of the

32:58

reasons data scientists love Python and why

33:00

Julia say never caught on is because

33:03

it's super approachable. With Chuktingho and some

33:05

other people, we've been running this thing

33:07

called humble data like I got involved

33:09

in it last year. And literally, you

33:11

can set up someone who has never

33:13

coded before and you can get them

33:15

up and running with Python. And they

33:18

love it. It's the same

33:20

feeling I had when I learned Python, which was during

33:22

my PhD when I was procrastinating. So

33:25

it was like kind of late

33:27

in life as well. It would

33:29

be a shame if we sacrifice

33:31

approachability for performance, especially because I

33:33

would argue a big chunk of

33:35

the Python ecosystem or Python user

33:37

ecosystem. Sorry. Python user ecosystem. It

33:39

didn't make sense. The Python user

33:41

base. You're hallucinating, Jodi. I'm sorry.

33:43

I became an LLM. I

33:46

became what I hated. They don't need

33:48

performance. They're just doing

33:50

data analytics and maybe working with decision trees

33:52

and not doing high performance Python. They're not

33:54

even doing something that will ever be deployed.

33:57

So you could argue for a case have

34:00

a seamless pipeline between model

34:03

training and model deployment, which we don't have

34:05

with Python right now. You can't build high

34:07

performance systems in Python as far as I know.

34:10

Please correct me if I'm wrong. But I

34:12

don't know. For me, I would fight

34:14

obviously for the side of making it

34:16

approachable because partially I think it's also

34:18

what makes the community special, which might

34:20

be a nice segue for you. The

34:22

fact that, I don't know, we attract

34:24

a bunch of people from non-conventional backgrounds.

34:26

That makes us quite special and quite

34:29

inclusive. I joked that the PSF developer

34:31

survey, which the new version is coming

34:33

out pretty soon, I joked that 101%

34:36

of Python developers started programming yesterday.

34:39

Funny you should say that because

34:41

this is my sweet spot is

34:43

where technology meets humans and how

34:45

do we empower humans to do

34:48

more and better work.

34:50

One of the conversations that

34:52

came up at the packaging

34:55

summit this PyCon was I'd

34:58

been thinking about this concept for a while. We

35:01

focused a lot on tooling,

35:03

which to me is sort

35:05

of a producer-centric, people who

35:07

are creating packages. We also

35:10

have this ecosystem of people who

35:12

are consumers, who are much like

35:14

Jodi was saying, using those packages.

35:17

From that conversation, a few of

35:19

the board members for the PSF

35:21

and I were talking about, wouldn't

35:23

it be great to have a

35:26

user success work

35:29

group that's really focused

35:31

on the website, our

35:33

onboarding documentation in light

35:35

of some of these things, both performance

35:38

and change. Change is always

35:40

going to be there, but I think

35:42

one of the beauties of the Jupyter

35:44

Notebook or I Python Notebook when I

35:46

started working with it was you can

35:48

have code in there and as long

35:51

as you shift enter, you can get

35:53

started. I think right now, Python is

35:55

a language. We don't have that get

35:57

started look and feel in the

35:59

way. in the traditional way, we're

36:01

getting there, which might lead into

36:04

some other WebAssembly kind

36:06

of discussions. All right. Let me throw out

36:08

a quick thought on this before we move

36:10

on. So I think one

36:12

of the superpowers of Python is that

36:15

it's this full spectrum sort of thing.

36:17

On one hand, there's the people that

36:19

Jary spoke about. They come in, they

36:21

don't care about metaprogramming or

36:24

optimized database queries or scaling out across

36:26

web servers. They just, they got a

36:28

little bit of data. They want a

36:30

cool graph and that's awesome. On the

36:32

other hand, we have Instagram and others

36:34

doing ridiculous stuff. And that's the same

36:36

language with the same tooling and mostly

36:38

the same packages. And so I think

36:41

part of Python's magic is you can

36:43

be super productive with a very partial

36:45

understanding of what Python is. Like you

36:47

might not know what a class is

36:49

at all. And yet you could have

36:51

a fantastic time for months. And

36:54

so back to Paul's friend, if

36:56

we can keep that Zen about it, where

36:59

these features exist, but they exist when you

37:01

graduate to them and you don't have to

37:03

deal with them until you're ready or you

37:05

need them, I think we'll be fine. If

37:07

not, maybe not. If it breaks a bunch

37:10

of packages and there's some big split in

37:12

the ecosystem and all that stuff is not

37:14

good. But if we can keep this full

37:16

spectrum aspect, I think that'd be great. That

37:18

sort of rolls into what Paul's thoughts on

37:21

community are because I know

37:23

like PyOpenSci as a nonprofit I'm

37:25

involved with that helps scientists learn

37:27

how to use the tools. We've

37:30

got lots of educators out there. I'm

37:32

gonna give Michael a huge plug for

37:34

the coursework that you've

37:37

done over the years. It

37:39

is so well done and

37:41

so accessible. Thank you. If

37:44

people haven't tried it and they're interested

37:46

in a topic, highly, highly recommend. To

37:49

things like the Carpentries, to things like

37:51

Django Girls, there's a lot of good

37:53

stuff. And I think those things will

37:55

become more valuable as complexity increases. even

37:57

LLMs, I think you'll be able to

37:59

ask LLMs for help and that can

38:02

help you if you're not sure. They're

38:04

actually pretty good at it, actually. They

38:06

are pretty good at it. Yeah, they

38:08

are pretty good. All right. We got

38:10

time for another round. I'm pretty sure.

38:13

Jodie, what's your second one? Your second

38:15

trend. I'm going to talk about Arrow

38:17

and how we're kind of overhauling data

38:19

frames within Python. So

38:21

basically around 15 years ago, Wes

38:23

McKinney came up with Pandas, which

38:25

is, you know, if you're not

38:28

familiar with, it's the main data

38:30

frame library for working with data

38:32

in Python. And the really nice thing about

38:35

Pandas is you can go a long time

38:37

before you graduate Pandas. You can just work

38:39

with quite a lot of data on your

38:41

local machine. But the problem was Wes wrote

38:43

this package before we had big data. This

38:46

was like 2008. And so as the amount

38:48

of data that we want to

38:52

process locally has grown, or maybe the

38:54

complexity of the operations has grown, maybe

38:56

string manipulations, things like that, Pandas has

38:59

really struggled. So one of the reasons

39:01

that Pandas struggled is it was based

39:03

on NumPy arrays, which are really great

39:05

at handling numbers. This is

39:07

in the name. But they're not so great

39:09

at handling pretty much any other data type.

39:11

And that includes missing data. So two kind

39:14

of exciting things happened last year. And I

39:16

think they're sort of still kind of carrying

39:18

over to this year in terms of impact

39:21

is first Pandas 2.0 was

39:23

released, which is based on PyArrow

39:26

and a package called Polars, which

39:28

was actually written, I think in

39:30

2022, I want to say,

39:33

started becoming very, very popular. So

39:35

both of these packages are based

39:37

on Arrow. They have a number

39:39

of advantages because of this. Basically,

39:41

it's a standardized data format. If

39:43

you're reading in from say, Parket

39:46

or Cassandra or Spark, you basically

39:48

don't need to convert the

39:50

data formats. This saves you a lot of time, also

39:52

saves you a lot of memory. And also

39:56

kind of what makes Polars interesting. And I

39:58

think this is going to be a nice

40:00

lead-in till... another topic is it's written in

40:02

Rust, of course. So this

40:04

leads to other performance gains. Like you

40:07

can have say concurrency. Richie

40:09

Vink, the author of this has also

40:11

written basically query optimizer. So you can

40:13

do a lazy evaluation and it will

40:15

actually optimize the order of operations, even

40:17

if you don't do that yourself. Yeah.

40:19

That's one of the biggest differences with

40:21

pandas is pandas executes immediately and you

40:23

can create a big chain and bowlers

40:25

and it'll figure out, well, maybe a

40:27

different order would be way better. Yes.

40:29

So pandas two does have a type

40:31

of lazy evaluation, but it's more like

40:34

sparks, lazy evaluation. There's no query optimization,

40:36

but it doesn't necessarily create

40:38

a new copy in memory every

40:40

single time you do something. So

40:42

I've kind of looked at the

40:44

numbers and depending on the operation

40:46

pollers is usually faster. So it's

40:48

kind of like your big boy

40:50

that you want to use with

40:52

your, you're doing like really beefy,

40:54

like ETLs, like data transformations, but

40:56

pandas two actually seems to be

40:58

more efficient at some sorts of,

41:00

what am I trying to say,

41:02

operations. So this is super exciting

41:04

because when I was going through,

41:07

like initially as a data scientist, when I

41:09

was floundering around with my initial Python, it

41:12

got really frustrating with pandas and you

41:14

really kind of needed to understand how

41:16

to do proper vectorization in order to

41:18

operate. I mean, like do efficient operations.

41:20

Whereas I think these two tools allow

41:22

you to be a bit more lazy

41:24

and you know, you don't

41:26

need to spend so much time optimizing what

41:28

you're actually writing. So yeah, exciting time for

41:31

data frames, which is awesome. Data

41:33

is the heart of everything. People are

41:35

more likely to fall into good practices

41:37

from the start. You talked about these

41:39

people coming who are not programmers, right?

41:41

If you do a bunch

41:43

of operations with pandas and you all

41:45

of a sudden run out of memory,

41:47

well, yeah, Python doesn't work. It doesn't

41:49

have enough memory, right? Well, maybe you

41:52

could have used a generator at one

41:54

step. That's far down the full spectrum

41:56

or the spectrum, right? You're not ready

41:58

for that. That's crazy talk, these things.

42:00

And so tools like this that are

42:02

more lazy. and progressive iterative are great.

42:04

Yeah. And actually one really nice thing,

42:06

like Richie's kind of always saying about

42:08

Apollo's is he's really tried to write

42:10

the API. So you avoid accidentally looping

42:12

over every row in your data frame.

42:15

Like you, he tries to make it

42:17

so everything is natively columnar. So yeah,

42:20

I just think they're both really nice libraries

42:22

and yeah, it's cool and

42:24

exciting. Carol, this is right in the heart

42:26

of the space you live in. What do

42:28

you think? There's definitely the

42:30

evolution of pandas and polars.

42:32

You know, there's a place

42:34

for all of those in

42:36

the PI-ERO data frame format.

42:38

It's funny because I've actually

42:40

been doing more stuff recently

42:42

with going beyond tabular data

42:45

frames to multi-dimensional arrays

42:47

and X-array, which is used

42:49

more in the geosciences for

42:51

now. I think one of

42:53

the things that I see

42:55

is the days of

42:58

bringing all your data locally or

43:00

moving it to you is becoming

43:02

less and less. And

43:05

what you work in memory or

43:08

pull into memory from

43:10

different locations and it

43:13

is becoming more prevalent. And I

43:15

think Aero lets us do that

43:17

more effectively than just a straight

43:20

pandas data frame or Spark or

43:24

something like that. So it's progress and

43:26

I think it's a good thing. I

43:28

think it's far less about the language

43:31

underneath and more about what's the user

43:34

experience, developer experience that

43:36

we're giving people with

43:38

these APIs. Paul, thoughts?

43:40

It's interesting the scale

43:42

of data and what

43:45

generations are an

43:48

increase in our unit of measurement of data

43:50

that we have to deal with. And

43:52

for both of you, I wonder

43:55

if we have caught up

43:57

with the amount of data

43:59

that we can reasonably process

44:01

or is the rate of

44:03

growth of data out in

44:05

the wild constantly outstripping our

44:07

ability to process it? From

44:09

an astronomy space physics side

44:11

of things, no, we haven't

44:13

hit the limit for data

44:15

at all. And I think

44:17

one of the things we're

44:19

going to see more and

44:21

more of is how we

44:23

deal with streaming data versus

44:25

time series data

44:27

versus just tabular data, if

44:30

you will. And my

44:33

bigger concern and it is

44:35

partially a concern I have

44:37

about some of the large

44:39

language models and the training

44:41

there is the environmental impact

44:44

of some of these things. And

44:47

should we be collecting it A,

44:50

is there a value in collecting it? If

44:52

there's not value in collecting it, how do

44:54

we get rid of it? Because

44:57

it winds up

44:59

then being kind of much

45:01

like recycling and garbage. It's

45:03

like, okay, well, but it

45:05

might have historical value somehow

45:07

or legal value and it

45:10

becomes complex. And

45:13

so my general rule of thumb is

45:15

don't collect it unless you have a

45:17

clear reason you need it. But

45:20

that's just me. So quantity versus quality

45:22

of data. So I've

45:25

worked in mostly commercial data science

45:27

since I left science. When

45:29

I was in science, I was dealing with sample size of 400, not

45:31

400,000, 400. So

45:35

that was not big data. The quality

45:37

of the data, again,

45:39

going back to large language models, a

45:42

lot of these earlier foundational models were

45:44

trained on insufficiently clean data. And

45:46

one of the trends actually that I

45:48

didn't mention with LLMs is like last

45:50

year in particular, there was a push

45:52

to train on better quality data sources.

45:54

So obviously these are much more manageable

45:57

than dealing with petabytes. One more

45:59

aspect. I'll throw out here, you know, for a

46:01

long time, we've had SQL light for really simple

46:03

data. We could just, if it's too big for

46:05

memory, you can put it one of those things.

46:07

You can query, you can index it while ducty

46:09

be just hit 1.0. And you

46:11

kind of got this in memory in process analytics

46:14

engine. So that's also a pretty interesting thing to

46:16

weave in here. Right. To say like, well, we'll

46:18

put it there in that file and we can

46:20

index it and ask it questions, but we won't

46:23

run out of memory. And I think plugin pandas,

46:25

I'm not sure about pollers and do queries, but

46:27

that's query optimizer against that data and

46:29

sort of things like that. It's, it's

46:31

pretty interesting. I think in this tip

46:33

to put it into that space as well.

46:35

All right, Carol, I think it's time for

46:37

your second, your second trend here. The

46:40

second threat trend is pretty

46:42

much, you know, things are

46:44

moving to the front end,

46:46

web assembly, TypeScript, Pyadide. There's

46:49

a new project PyCafe that

46:51

I'm pretty happy with by

46:53

Martin Bredlz that lets you

46:55

do dashboards using Pyadide, but

46:58

like Streamlit and Flotlys and

47:00

libraries and things like that.

47:02

And I think making

47:05

things more accessible as well as

47:07

making things more visual is pretty

47:10

cool. Like I took, what was

47:12

it? Jupiter light earlier last fall

47:15

and a friend of mine had kids

47:17

and I integrated into my website so

47:19

that like her kids could just do

47:21

a quick whatever, which sort of, you

47:24

know, in some ways was similar to

47:26

Binder and the whole time

47:28

we were developing Binder, I was

47:30

also working with the Pyadide, Iodide

47:32

folks, because I think there's a

47:34

convergence down the road and where

47:36

it all go. I'm

47:38

not really sure, but I think

47:41

it's exciting. And I think

47:43

anything that from a privacy

47:45

standpoint, security, there's a lot

47:47

of things that are very

47:49

attractive about pushing things into

47:52

the front end. That

47:54

beginner startup thing you talked about that

47:56

onboarding first experience, you hit a webpage

47:58

and you have full. experience with

48:00

Python and the tooling and the packages are

48:02

already installed in that thing and that's so

48:04

much better than force you download it. Well,

48:06

you need admin permissions to install it. Now

48:08

you create a virtual environment and then you

48:10

open the terminal. Do you know what a

48:12

terminal is? We're going to tell you like,

48:14

no, just, and you don't have to ask

48:16

permission to run a static webpage where you

48:18

do for like, how do I run this

48:20

server on a Docker cluster or something? It

48:23

opens up different doors. And I think the

48:25

other thing we found like when we were

48:27

teaching, you know, with binder

48:30

and Jupyter hub, UC Berkeley was

48:32

able to have now

48:34

most of their student body

48:36

taking these data eight connector

48:38

courses and they

48:41

would run the compute in

48:43

the cloud, which really leveled

48:46

the playing field. It didn't matter if you

48:48

had a Chromebook or you had the highest

48:50

end Mac, you still got the same education.

48:53

And I think there is something

48:55

very appealing about that. We've actually

48:57

been running humble data in Jupyter

49:00

light and some people just bring a

49:02

tablet and they can do it on

49:04

that. That's awesome. Carol, there was something

49:06

you were saying that connected to something else in

49:08

my brain. Remember in the

49:10

beginning of the web and view source was

49:12

such a cool thing. Yeah. You could see

49:14

what the back end sent you and you

49:16

could poke around at it. You could learn

49:18

from it and you could steal it, you

49:20

know, and use it to go make your

49:23

own thing. But what if you could view

49:25

source the back end because it's actually running

49:27

in your browser. What you were just saying

49:29

was if you make it

49:31

reveal itself about the notebook

49:33

and the code in addition

49:36

to the HTML, maybe

49:38

you'll trigger some of those same kinds of things

49:41

that view source gave people back in the

49:43

day. Maybe the flip

49:45

side would be there's always

49:48

business and practicalities in life

49:50

and people will want

49:52

to sort of lock it down within WebAssembly.

49:54

So you've got both sides of it. But

49:56

I do think, you know, I think that's

49:58

a good thing. I was telling somebody the other

50:01

day, I never use Stack

50:03

Overflow, or rarely use Stack Overflow. And

50:05

they're like, how do you find stuff?

50:07

I'm like, I use search on GitHub,

50:09

and I look for really good examples.

50:12

And so in some ways, it's like

50:15

View Source. And then there's also the flip

50:17

side of it is like, okay, how do

50:19

I break it? How do I play with

50:21

it? How do I make it do something

50:24

it wasn't doing before, which could

50:26

be used for good or for evil. I

50:28

tend to use it for good. Paul, I'm

50:30

up on our time here. What's your second

50:32

trend? We'll see if we

50:35

have time for mine. I have a couple just in case we can

50:37

squeeze them in. Let's talk about yours.

50:39

I came back from PyCon, really

50:41

rejuvenated, but also had some kind of clarity

50:43

about some things that had been lingering for

50:46

me for a few years, how I could

50:48

contribute, things like that. But

50:50

going into it, there are a couple of trends

50:52

that lead me to thinking about an

50:55

opportunity and a threat as two sides

50:57

of the same coin. First, in

51:00

Russell Keith McGee and Lukasch Longa

51:02

both talked about black swans and

51:04

the threat of JavaScript everywhere, that

51:06

if we don't have a better

51:08

web story, if we make our

51:11

front end be JavaScript and react

51:17

and we stop doing front ends, well, then

51:19

they'll come for the back end too. Because

51:21

once they've hired up JavaScript developers, why don't

51:24

we just do JavaScript on the server too?

51:26

So that was a first thing. And in

51:29

my position, I do look at the

51:31

web and think about all these trends that

51:33

are happening. And there's beginning to

51:35

be a little bit of a backlash

51:37

about the JavaScriptification of the web. And

51:40

so some really big names, HTMX is

51:42

a good example of it, but

51:44

just some thinkers and speakers. I mean, Jeff

51:46

Triplett talks about this. A lot of people

51:48

in the world of Python talk about this.

51:51

So there's kind of a desire to

51:53

put the web back in the web

51:55

trademark. But then there was a second

51:57

point coming about these walled gardens. We've

51:59

seen them. for a while, we all

52:01

relied on Twitter. What a great

52:03

place. It's wait, what? And

52:05

then so much of our life

52:07

is in a system we don't control. And

52:09

then so we move over to the fatty

52:11

verse and then Matt is like, hey, great,

52:14

we're going to build a bridge to you.

52:16

Turns out this week, we start to learn

52:18

things about the thread API that maybe it's

52:20

not as friendly as we think it is.

52:22

But the big one for me was Google

52:24

and search. Well, I should say Google and

52:26

getting rid of its Python staff, but Google

52:28

and search where they're no longer

52:30

going to send you to the website anymore.

52:33

They're just going to harvest what's on your

52:35

website and give you the answer.

52:37

And people are talking now about Google

52:39

Zero, the day of the apocalypse where

52:41

you no longer get any clicks from

52:43

Google. And what does that mean for

52:45

content creators and stuff like that? So

52:49

going into all of this, I've

52:51

been thinking about how awesome

52:53

life is in Python land because we've got

52:55

this great language. Oh, but we've got this

52:58

great community. Come for the language, stay for

53:00

the community. Well, what do we mean by

53:02

that? A lot of the times we mean

53:04

all this code that's available. We

53:07

also mean all these people

53:09

and wonderful, helpful people like

53:11

on this call. But there's also this

53:13

big world of content. And

53:15

we have kind of

53:18

organically grown a little

53:22

online community with a bunch

53:24

of helpful content and a

53:26

bunch of connections between people,

53:29

which is of some value

53:32

itself. And so you see people starting to

53:34

talk about, wow, I miss the old days

53:36

of RSS, where we would all subscribe to

53:38

each other's blogs and get content and go

53:40

straight to the source and not have it

53:43

aggregated into a walled garden and stuff like

53:45

that. And it just feels like

53:47

there's room out there for if

53:49

we want to fight back against

53:52

the threat of

53:54

these mega cores, taking

53:56

our voluntary contribution to

53:58

humanity and monetizing. while

54:01

at the same time of taking

54:03

all these valuable voices, creating content

54:05

and value in Python land, that

54:08

maybe we could bring back some

54:10

of these things, put the web

54:12

back in the web, and

54:15

start to get out of the

54:17

walled gardens and back over into

54:19

social networks that are

54:22

open and joyful. I'm here for

54:24

it. Wow. People complain,

54:26

governments complain, that places like Google

54:28

and staff for monetizing the links,

54:30

and they're being paid, you gotta

54:32

pay to link to this new source

54:34

or whatever, right? We're lucky

54:37

that we have that. If it turns into

54:39

just, you just get an AI answer, no

54:41

source, that's gonna be really hard on a

54:43

lot of different businesses, creators, people just want

54:45

to create something just for the attention or

54:47

for their self, you know, like nobody comes

54:50

anymore. It's gonna be a sad place. I

54:52

was thinking about Coke Zero the whole time

54:54

you were saying like, you know, Google

54:56

Zero or whatever, because you didn't have

54:59

to bring back classic Coke. And

55:02

I think, yeah, pivots happen, but

55:04

it's hard to pivot, you know,

55:06

billion dollar companies. I

55:08

have lots of thoughts on some

55:10

of the current Python, what

55:13

Google has chosen to do, I

55:15

think sometimes listening to consultants isn't

55:18

the best business

55:21

approach. You know, it's their company,

55:23

they can do what they need to do

55:25

for their own shareholders. I think a lot

55:27

of what you said is really interesting. And

55:29

like, I touched on

55:31

this a little bit because the

55:33

volume of information around us is

55:36

greater than ever before. Sure. And

55:38

at a speed of transmission

55:40

that is faster than ever

55:42

before. And about eight years ago,

55:45

I had breakfast with Sandy Betts who was

55:47

very prolific in the Ruby community. And I

55:49

asked her like, how do you keep up

55:51

with all of this stuff? And she's like,

55:54

I don't. And I said, okay. And

55:57

she's like, what I do is I focus on the things that me

56:00

and all the rest of it is news. And

56:03

that really stuck with me because in actuality

56:07

that's kind of what I do.

56:10

You know, I ignore the things

56:12

that aren't directly relevant to me

56:14

and trust that I've built a

56:16

strong enough network of people that

56:18

I respect that well said their

56:20

work will influence when I jump

56:23

in. Like I don't, you know,

56:25

much like the life cycle if

56:27

you've studied marketing or product development,

56:30

you know, not everybody's an early adopter.

56:32

So do I need to be an early adopter

56:34

on everything? No. Yeah, that book Crossing the

56:36

Chasm says that you should do that like

56:38

on one thing. If you do

56:40

it on three things or more you'll fail. Yeah.

56:43

You know, part of the thing that triggered this

56:45

for me was reading that Andresen Horowitz, kind of

56:47

the self-proclaimed king of

56:49

Silicon Valley VCs, as

56:52

zero interest rates started to go out of fashion

56:54

and their recipe wasn't

56:57

working. They didn't like the

56:59

negative press coverage so they started their

57:01

own media empire to cover themselves. And

57:04

that idea is just so

57:06

appalling that we

57:08

would get news. We would

57:10

turn to the mega courts and

57:13

the masters of the universe to

57:15

tell us what we should be

57:17

caring about. We have that already.

57:19

We have, I'll be very specific, we

57:21

have Planet Python. It's in

57:23

disrepair. What if it was

57:25

reimagined into a freaking media

57:28

empire by us for

57:30

us to cover the

57:32

Fedverse and course providers and all

57:34

the value that's out there. And

57:37

like Carol, you're saying I don't have to

57:39

think about it but I trust that group

57:41

because they're thinking about it. A lot

57:43

of it is like, you know, when

57:45

it came to LLMs, it was not

57:48

the thing that rocked my world like

57:50

intellectually but I knew

57:52

Simon was doing work with it.

57:54

And so I basically

57:57

once every few weeks would take a look at

57:59

his website. and his blog posts and

58:01

he posts a lot and I would get

58:03

my data dump of things.

58:05

I don't know. I mean, that's

58:07

one of the reasons I like

58:10

PyCon and I've like read talk

58:12

proposals, everything for the last decade.

58:14

All these talk proposals. And it

58:16

really does give me an appreciation

58:18

for all the things Python's being

58:20

used for. What's kind of

58:22

the zeitgeist. Yeah. And, and so

58:24

I think there's different ways of doing

58:27

that, even just doing a YouTube search

58:29

of Python content. But I tend to

58:31

focus in on sciency

58:33

oriented things and ways

58:36

to empower humans through

58:38

lifelong learning. So there's

58:41

a lot of, we're in a, in

58:43

a phenomenal period of change for

58:45

sure. Yes. So we won't be bored, nor do

58:47

I think our jobs are going to go away.

58:49

They may change, but they're not going away. Indeed.

58:52

Jeremy, final thoughts on this topic? No, pretty

58:54

much wrap things up. Yeah, I don't think I

58:56

really have that much to add actually. I think it's all

58:58

been said. It has. All right. Just to, just

59:00

to round things out was the two things that I

59:03

think are transheres. I think like Carol

59:05

said a lot, Python on the front end

59:07

is going to be super important. I think

59:09

PyScript is really, really interesting. I've been waiting

59:11

for people to develop something like React

59:14

or Vue or something that we

59:16

could create commercial facing websites. We're

59:18

halfway there with MicroPython being the

59:20

foundation of PyScript, which is

59:23

a hundred K instead of 10 megs. All

59:25

of a sudden it becomes JavaScripty size. It

59:27

opens the possibilities. And just a shout

59:29

out to Pew pie, which is like view with

59:31

Python, P U E P Y. I'm going to

59:34

interview Ken from that project, but it's kind of

59:36

a component based front end for

59:38

PyScript, which is pretty interesting. And of course,

59:40

Jupiter light is really, really important.

59:42

The other one was just all this rust. So

59:45

everything seems to be redone and rust. And oh

59:47

my gosh, that's how you get your VC funding.

59:49

Just joking sort of, but all you

59:51

talked about all this performance stuff coming, you

59:53

know, this, while it, it is sometimes frustrating

59:56

that people are putting all the things into

59:58

rust because then Python programmers is less

1:00:00

approachable for them. It could also be

1:00:02

an escape hatch from trying to force

1:00:04

the complexity into the Python side. Alleviate,

1:00:06

like everything has to be multithreaded and

1:00:09

crazy and optimized and, well, this

1:00:11

part you never look at, it's faster now. So

1:00:13

don't worry. Anyway, those are my two trends. Quick,

1:00:15

quick, quick thoughts on that and then we'll call

1:00:17

it a show. My piece of trivia is

1:00:19

I made a

1:00:21

contribution to Rust far before I made

1:00:23

any contributions to Core Python. Amazing.

1:00:26

Because I tended to be a C programmer in

1:00:29

heart and spirit. And so Rust

1:00:31

seemed like this cool thing that was new

1:00:33

at the time. And ultimately,

1:00:35

I personally did

1:00:37

not find the syntactic side

1:00:40

of it worked well with

1:00:42

my brain and how I

1:00:44

think. And Python was far

1:00:46

cleaner in terms of a

1:00:48

simpler visual, less clutter, and

1:00:51

reminded me a little more of small talk

1:00:53

or something like that, which I loved from

1:00:55

earlier days. But I think there's

1:00:57

a place for Rust. I

1:01:00

think Rust is gonna replace Python

1:01:02

now. I think it's going to

1:01:05

help with some optimized things. Do I

1:01:07

love things like rough that let me

1:01:09

run my CI, like blazing

1:01:11

fast versus all the Python tools?

1:01:15

Not to say that all the Python tools are bad,

1:01:17

but when you're paying for it as a startup.

1:01:20

When things you used to have to wait on become a

1:01:22

blink of an eye, all of a sudden you don't mind

1:01:24

running them every time and it changes the way you work

1:01:26

with tools. Exactly. Yeah, I would

1:01:28

say, look, every language has its

1:01:30

place in the ecosystem. And my

1:01:33

husband is a long time Pythonister, but

1:01:35

he's also a Rust program. I

1:01:38

know, it's like a running joke that my husband is

1:01:40

a Rust developer. How do you know? He'll ask you.

1:01:42

Well, you know what I mean? How

1:01:45

do you know? Ask him, he'll tell you. There you

1:01:47

go. They have different

1:01:49

purposes, completely different purposes, and

1:01:51

you can't just interchange them.

1:01:54

Absolutely. Let's just get it straight. Python is just

1:01:56

awesome, says our Tim. To your love. But

1:01:58

it's to keep it awesome. Yes, absolutely.

1:02:01

Paul, we've come around to you for

1:02:03

the very final, final thought on this

1:02:05

excellent show. I will give a final

1:02:07

thought about Python trends to follow up

1:02:09

on what Carol just said about it's

1:02:11

up to us. Maybe it's

1:02:13

up to us to

1:02:15

help the people who will keep

1:02:18

it that way. The next generation

1:02:20

of heroes, help them succeed. I'm

1:02:23

wearing my PyCon Kenya

1:02:25

friendship bracelet, and I

1:02:28

got a PyCon and wonderful experience

1:02:30

meeting so many different kinds

1:02:32

of people. And from a

1:02:35

Python trends perspective, the fact that

1:02:37

everything we're talking about is good

1:02:39

stuff, not like asteroid-themed earth. Yes,

1:02:41

yes. IP challenges and patent wars

1:02:43

and mergers and acquisitions and stuff.

1:02:45

Remember a long time ago, I

1:02:47

went to go see Guido and

1:02:49

he was with the App Engine

1:02:52

team at Google. So a long

1:02:54

time ago, and he was starting

1:02:56

the process of turning over pep

1:02:58

review to other people. And

1:03:00

I commented to him that not every open

1:03:02

source success story outlives its

1:03:05

founder. And the bigger it

1:03:07

gets, particularly open

1:03:09

source projects anchored

1:03:11

in the United States of America, they

1:03:13

sell out and get funded

1:03:16

and they will never be the same

1:03:18

after that. And so it's a moment

1:03:20

from a Python trends perspective for us

1:03:23

to build a great next

1:03:25

future by remembering how lucky we

1:03:27

are where we have gotten to.

1:03:30

Absolutely. Carol, Jadie, Paul, thank you for being

1:03:32

on the show. Thank you. Thanks, Michael.

1:03:34

Thank you. Bye. Bye. This

1:03:37

has been another episode of Talk Python to me.

1:03:40

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YouTube. This is your

1:05:10

host Michael Kennedy. Thanks so much for listening. I

1:05:12

really appreciate it. Now get out there and write

1:05:14

some Python code.