Can you imagine possible features that Python 4.0 might have that would necessitate the creation
of the new 4.0 given the amount of pain and joy, suffering and triumph that was involved in the
move between version 2 and version 3? The following is a conversation with Guido van
Rossum, his second time on this podcast. He is the creator of the Python programming language
and his Python's Emeritus BDFL, Benevolent Dictator for Life. This is the Lex Friedman
podcast. To support it, please check out our sponsors in the description. And now,
dear friends, here's Guido van Rossum. Python 3.11 is coming out very soon. In it,
Cpython claimed to be 10 to 60% faster. How did you pull that off? And what's Cpython?
Cpython is the last Python implementation standing, also the first one that was ever created.
The original Python implementation that I started over 30 years ago.
So what does it mean that Python, the programming language, is implemented in another programming
language called C? What kind of audience do you have in mind here? People who know programming?
No, there's somebody on a boat that's into fishing and have never heard about programming,
but also some world-class programmers. You're going to have to speak to both.
Imagine a boat with two people. One of them has not heard about programming and is really
into fishing. And the other one is like an incredible Silicon Valley programmer that's
programmed in everything. C, C++, Python, Rust, Java. It knows the entire history of
programming languages. So you're going to have to speak to both. I imagine that boat in the
middle of the ocean. I'm going to please the guy who knows how to fish first.
Yes, please. He seems like the most useful in the middle of the ocean. You got to make him
happy. I'm sure he has a cell phone. So he's probably very suspicious about what goes on
in that cell phone, but he must have heard that inside his cell phone is a tiny computer.
And a programming language is computer code that tells the computer what to do.
It's a very low-level language. It's zeros and ones, and then there's assembly, and then...
Oh, yeah. We don't talk about these really low levels because those just confuse people. I mean,
when we're talking about human language, we're not usually talking about vocal tracts and
how you position your tongue. I was talking yesterday about how when you have a Chinese person
and they speak English, this is a bit of a stereotype they often don't know.
They can't seem to make the difference well between an L and an R. And I have a theory
about that, and I've never checked this with linguists, that it probably has to do with the
fact that in Chinese, there is not really a difference. And it could be that there are
regional variations in how native Chinese speakers pronounce that one sound that sounds
to L, like L to some of them, like R to others. So it's both the sounds you produce with your
mouth throughout the history of your life and what you're used to listening to. I mean,
every language has that. Russian has... Exactly. The Slavic languages have sounds like the
letter Z, like Americans or English speakers don't seem to know the sounds.
They seem uncomfortable with that sound. Yeah. Oh, yes. Okay. So we're not going to the shapes
of tongues and the sounds that the mouth can make. Fine. And similarly, we're not going into the
ones and zeros or machine language. I would say a programming language is a list of instructions
like a cookbook recipe that sort of tells you how to do a certain thing, like make a sandwich. Well,
acquire a loaf of bread, cut it in slices, take two slices, put mustard on one, put
jelly on the other or something, then add the meat, then add the cheese. I've heard that science
teachers can actually do great stuff with recipes like that and trying to interpret
their students' instructions incorrectly until the students are completely unambiguous about it.
With language, see, that's the difference between natural languages and programming languages.
I think ambiguity is a feature, not a bug in human spoken languages. Like,
that's the dance of communication between humans. Well, for lawyers, ambiguity certainly is a
feature. For plenty of other cases, the ambiguity is not much of a feature, but we work around it,
of course. What's more important is context. So with context, the precision of the statement
becomes more and more concrete. Right. But when you say, I love you to a person that
matters a lot to you, the person doesn't try to compile that statement and return an error saying,
please define love. Right. No, but I imagine that my wife and my son interpreted very differently.
Yes. Even though it's the same three words. But imprecisely still.
Oh, for sure. Lawyers have a lot of follow up questions for you. Nevertheless,
the context is already different in that case. Yes, fair enough. So that's a programming language
is ability to unambiguously state a recipe. Actually, let's go back. Let's go to Pepe.
You go through and Pepe, the style guide for Python code,
had some ideas of what this language should look like, feel like, read like. And the big idea
there is that code readability counts. What does that mean to you? And how do we achieve it?
So this recipe should be readable. That's a thing between programmers. Because on the one hand,
we always explain the concept of programming language as computers need instructions and
computers are very dumb, and they need very precise instructions because they don't have
much context. In fact, they have lots of context, but their context is very different.
But what we've seen emerge during the development of software, starting in the probably in the
late 40s, is that software is a very social activity. A software developer is not a mad
scientist who sits alone in his lab writing brilliant code. Software is developed by teams of people.
Even the mad scientist sitting alone in his lab can type fast enough to produce enough code
so that by the time he's done with his coding, he still remembers what the first few lines he
wrote mean. So even the mad scientist coding alone in his lab would be sort of wise to adopt
conventions on how to format the instructions that he gives to the computer so that the thing is
there is a difference between a cookbook recipe and a computer program. The cookbook recipe,
the author of the cookbook writes it once and then it's printed in 100,000 copies.
And then lots of people in their kitchens try to recreate that recipe, that particular
pie or dish from the recipe. And so there the goal of the cookbook author is to make it clear
to the human reader of the recipe, the human amateur chef in most cases. When you're writing
a computer program, you have two audiences at once. It needs to tell the computer what to do,
but it also is useful if that program is readable by other programmers. Because computer
software, unlike the typical recipe for a cherry pie, is so complex that you don't get all of it
right at once. You end up with the activity of debugging and you end up with the activity of...
So debugging is trying to figure out why your code doesn't run the way you thought it should run.
That means broad, it could be stupid little errors or it could be big
logical errors. It could be anything from a typo to a wrong choice of algorithm to
building something that does what you tell it to do, but that's not useful.
Yeah, it seems to work really well, 99% of the time, but does weird things 1% of the time on
some edge cases. That's pretty much all software nowadays. All good software, right? Well, yeah,
for bad software then. That 99 goes down a lot. But it's not just about the complexity of the
program. Like you said, it is a social endeavor in that you're constantly improving that recipe
for the cherry pie, but you're in a group of people improving that recipe. Or the mad scientist
is improving the recipe that he created a year ago and making it better or adding something.
He decides that he wants some decoration on his pie or icing.
So there's broad philosophical things and there's specific advice on style.
So first of all, the thing that people first experience when they look at Python,
it is very readable, but there's also a spatial structure to it.
Can you explain the indentation style of Python and what is the magic to it?
Spaces are important for readability of any kind of text. If you take a cookbook recipe
and you remove all the bullets and other markup and you just crunch all the text together,
maybe you leave the spaces between the words, but that's all you leave.
When you're in the kitchen trying to figure out what are the ingredients and what are the steps
and where does this step end and the next step begin,
you're going to have a hard time if it's just one solid block of text.
On the other hand, what a typical cookbook does if the paper is not too expensive,
each recipe starts on its own page. Maybe there's a picture next to it.
The list of ingredients comes first. There's a standard notation. There's shortcuts so that you
don't have to write two sentences on how you have to cut the onion because there are only three ways
that people ever cut onions in the kitchen, small, medium, and in slices, or something like that.
None of my examples make any sense to real cooks, of course.
We're talking to programmers with the metaphor of cooking. I love it,
but there is a strictness to the spacing that Python defines. There's some looser things,
some stricter things, but the four spaces for the indentation is really interesting.
It really defines what language it looks and feels like.
Because indentation, sort of taking a block of text and then having inside that block of text,
a smaller block of text that is indented further as sort of a group, it's like you have a bulleted
list in a complex business document and inside some of the bullets are other bulleted lists.
You will indent those too. If each bulleted list is indented several inches, then at two
levels deep, there's no space left on the page to put any of the words of the text.
So you can't indent too far. On the other hand, if you don't indent at all,
you can tell whether something is a top-level bullet or a second-level bullet or a third-level
bullet. So you have to have some compromise based on ancient conventions and the typical
width of a computer screen in the 80s and all sorts of things. We came up with four spaces
as a compromise. There are large groups of people who code with two spaces per indent level.
For example, the Google Style Guide. All the Google Python code and I think also all the
Google C++ code is indented with only two spaces per block. If you're not used to that, it's harder
to at a glance understand the code because the high-level structure is determined by the indentation.
On the other hand, there are other programming languages where the indentation is eight spaces
or a whole tap stop in classic Unix. To me, that looks weird because after three indent
levels, you've got no room left. Well, there are some languages where the indentation is a
recommendation. It's a stylistic one. The code compiles even without any indentation.
And then Python, really, indentation is a fundamental part of the language.
Right? It doesn't have to be four spaces. You can code Python with two spaces per block or
six spaces or 12 if you really want to go wild. But sort of everything that belongs to the same
block needs to be indented the same way. In practice, in most other languages, people recommend
doing that anyway. If you look at C or Rust or C++, all those languages, Java, don't have a
requirement of indentation, but except in extreme cases, they're just as anal about having their
code properly indented. So any IDE that the syntax highlighting that works with Java or C++, they
will yell at you aggressively if you don't do proper indentation. They'd suggest the proper
indentation for you. In C, you type a few words and then you type a curly brace, which is their
notion of begin an indented block. Then you hit return and then it automatically indents four
or eight spaces depending on your style preferences or how your editor is configured.
Was there a possible universe in which you considered having braces in Python?
Absolutely. Yeah.
What was the 60, 40, 70, 30 in your head? What was the tradeoff?
For a long time, I was actually convinced that the indentation was just better.
Without context, I would still claim that indentation is better. It reduces clutter. However,
as I started to say earlier, context is almost everything. In the context of coding,
most programmers are familiar with multiple languages, even if they're only good at one or
two. Apart from Python and maybe Fortran, I don't know how that's written these days anymore.
All the other languages, Java, Rust, C++, JavaScript, TypeScript, Perl, are all using
curly braces to indicate blocks. Python is the odd one out.
That's a radical idea. As a radical, renegade revolutionary, do you still stand behind this
idea of indentation versus braces? What can you dig into a little bit more? Why do you still stand
behind indentation? Because context is not the whole story. History, in a sense, provides more
context. For Python, there is no chance that we can switch. Python is using curly braces for
something else, dictionaries, mostly. We would get in trouble if we wanted to switch,
just like you couldn't redefine C to use indentation, even if you agree that indentation
in a Greenfield environment would be better. You can't change that kind of thing in a language.
It's hard enough to reach agreement over much more minor details. In the past, in Python,
we did have a big debate about tabs versus spaces and four spaces versus fewer or more.
We came up with a recommended standard and options for people who want to be different.
But yes, I guess the thought experiment I'd like you to consider is if you could travel back
through time when the compatibility is not an issue and you started Python all over again,
can you make the case for indentation still? Well, it frees up a pair of matched brackets of
which there are never enough in the world for other purposes. It really makes the language slightly
sort of easier to grasp for people who don't already know another programming language.
Because sort of one of the things, and I mostly got this from my mentors who
taught me programming language design in the earlier 80s, when you're teaching programming
for the total newbie who has not coded before in not in any other language,
a whole bunch of concepts in programming are very alien or sort of new and maybe very interesting,
but also distracting and confusing. And there are many different things you have to learn.
You have to sort of, in a typical 13-week programming course, you have to,
if it's like really learning to program from scratch, you have to cover algorithms,
you have to cover data structures, you have to cover syntax, you have to cover variables, loops,
functions, recursion, classes, expressions, operators. There are so many concepts if you
sort of, if you can spend a little less time having to worry about the syntax. The classic example was
often, oh, the compiler complains every time I put a semicolon in the wrong place or I forget
to put a semicolon. Python doesn't have semicolons in that sense, so you can't forget them. And you
are also not sort of misled into putting them where they don't belong because you don't learn
about them in the first place. The flip side of that is forcing the strictness onto the beginning
programmer to teach them that programming values attention to details. You don't get to just
write the way you write an English paper. Many of other details that they have to pay attention to,
I think they'll still get the message about paying attention to details.
The interesting design choice, I still program quite a bit in PHP and I'm sure there's other
languages like this, but the dollar sign before variable, that was always an annoying thing for
me. It didn't quite fit into my understanding of why this is good for a programming language.
I'm not sure if you ever thought about that one. That is a historical thing. There is a whole
lineage of programming languages. PHP is one, Perl was one. The Unix shell is one of the oldest
or all the different shells. The dollar was invented for that purpose because the very
earliest shells had a notion of scripting, but they did not have a notion of parameterizing
the scripting. A script is just a few lines of text where each line of text is a command
that is read by a very primitive command processor that then takes the first word on the line as
the name of a program and passes all the rest of the line as text into the program for the program
to figure out what to do with as arguments. By the time scripting was slightly more mature
than the very first script, there was a convention that just like the first word on the line is
the name of the program, the following words could be names of files.
input.text, output.html, things like that. The next thing that happens is, oh, it would actually
be really nice if we could have variables and especially parameters for scripts. Parameters
are usually what starts this process. But now you have a problem because you can't just say the
parameters are x, y, and z. And so now we call, say, let's say x is the input file and y is the
output file. And let's forget about z for now. I have my program and I write program x, y. Well,
that already has a meaning because that presumably means x itself is the file. It's a file name.
It's not a variable name. And so the inventors of things like the unique shell and I'm sure job
command language at IBM before that had to use something that made it clear to the script
processor. Here is an x that is not actually the name of a file, which you just pass through to the
program you're running. Here is an x that is the name of a variable. And when you're writing a script
processor, you try to keep it as simple as possible. Because certainly in the 50s and 60s,
the thing that interprets the script had to be a very small program because it had to fit in a very
small part of memory. And so saying, oh, just look at each character. And if you see a dollar sign,
you jump to another section of the code and then you gobble up characters or say until the next
space or something. And you say that's the variable name. And so it was sort of invented as
a clever way to make parsing of things that contain both variable and fixed parts
very easy in a very simple script processor. It also helps even then it also helps the human
author and the human reader of the script to quickly see, oh, 20 lines down in the script,
I see a reference to x, y, z. Oh, it has a dollar in front of it. So now we know that x, y, z must
be one of the parameters of the script. Well, this is fascinating. Several things to say, which is
the leftovers from the simple script processor languages are now in code bases like behind
Facebook or behind most of the back end. I think PHP probably still runs most of the
back end of the internet. Oh, yeah, I think there's a lot of it in Wikipedia too, for example.
It's funny that those decisions are not funny. It's fascinating that those decisions
permeate through time. Just like biological systems, right? I mean, the sort of the inner
workings of DNA have been stable for, well, I don't know how long it was, like 300 million years,
half a billion years. And there are all sorts of weird quirks there that don't make a lot of
sense if you were to design a system like self replicating molecules from scratch.
Well, that system has a lot of interesting resilience. It has redundancy that results,
like it messes up in interesting ways that still is resilient when you look at the system level
of the organism. Code doesn't necessarily have that a computer programming code.
You'd be surprised how much resilience modern code has. I mean, if you look at the number of
bugs per line of code, even in very well tested code that in practice works just fine,
there are actually lots of things that don't work fine. And there are error correcting or
self correcting mechanisms at many levels. Including probably the user of the code.
Well, in the end, the user who sort of is told, well, you got to reboot your PC is part of that
system. And a slightly less drastic thing is reload the page, which we all know how to do
without thinking about it when something weird happens. You try to reload a few times before
you say, oh, there's something really weird. Okay, or try to click the button again,
if the first time didn't work. Well, yeah, that we should all have learned not to do that,
because that's probably just going to turn the light back off. Yeah, true. So do it three times.
That's the right lesson. And I wonder how many people actually like the dollar sign.
Like you said, it is documentation. So to me, it's whatever the opposite of
syntactic sugar is syntactic poison. To me, it is such a pain in the ass that I have to type in
a dollar. Also super error prone. So it's not self documenting. It's, it's like a bug generating
thing. It is a kind of documentation that's the pro and the con is it's a source of a lot of bugs.
But actually, I have to ask you, this is really interesting idea of bugs per line of code.
If you look at all the computer systems out there, from the code that runs nuclear weapons,
to the code that runs all the amazing companies that you've been involved with, and not code that
runs Twitter and Facebook and Dropbox and Google and Microsoft Windows and so on. And we like laid
out. Wouldn't that be a cool like table bugs per line of code? And what would let's put like actual
companies aside? Do you think we'd be surprised by the number we see there for all these companies?
That depends on whether you've ever read about research that's been done in this area before.
And I didn't know that the last time I saw some research like that, there was probably in the
90s and the research might have been done in the 80s. But the conclusion was across a wide range
of different software, different languages, different companies, different development styles.
The number of bugs is always, I think it's in the order of about one bug per thousand lines in
sort of mature software that is considered as good as it gets.
Can I give you some facts here? There's a lot of good papers. So you said mature software, right?
So here's a report from a programming analytics company. Now this is from a developer perspective.
Let me just say what it says because this is very weird and surprising.
On average, a developer creates 70 bugs per 1000 lines of code.
15 bugs per 1000 lines of code find their way to the customers.
This is in software. I was wrong by an order. Fixing a bug takes 30 times longer than writing
a line of code. That I can believe. 75% of a developer's time is spent on debugging.
That's for an average developer that they analyze. It's 1,500 hours a year and US alone,
$113 billion to spend annually on identifying and fixing bugs.
And I imagine this is marketing literature for someone who claims to have a golden bullet
or a silver bullet that makes all that investment in fixing bugs go away, but that's usually not
going to happen. They're referencing a lot of stuff, of course, but it is a page.
There's a contact us button at the bottom. Presumably, if you just spend a little bit
less than $100 billion, we're willing to solve the problem for you. Right. And there's also
a report on StackExchange and StackOverflow on the exact same topic, but when I open it up at the
moment, the page says StackOverflow is currently offline for maintenance. Oh, that is ironic.
Yes. By the way, their error page is awesome. Anyway, I mean, can you believe that number of bugs?
Oh, absolutely. Isn't that scary that 70 bugs per 1000 lines of code? So even 10 bugs per 1000
lines. Well, that's about one bug every 15 lines. And that's when you're first typing it in. Yeah,
from a developer, but like, how many bugs are going to be found if you're typing it? Well,
the development process is extremely iterative. Typically, you don't make a plan for what software
you're going to release a year from now and work out all the details, because actually all the
details themselves consist, they sort of compose a program. And that being a program, all your
plans will have bugs in them, too, and inaccuracies. But what you actually do is
you do a bunch of typing, and I'm actually really, I'm a really bad typist. I've never
learned to type with 10 fingers. How many do you use? Well, I use all 10 of them, but not very well.
But I never took a typing class, and I never sort of corrected that. So the first time I seriously
learned, I had to learn the layout of a QWERTY keyboard was actually in college in my first
programming classes, where we used punch cards. And so with my two fingers, I sort of backed out
my code. Watch anyone give you a little coding demonstration. They'll have to produce like
four lines of code. And now see how many times they use the backspace key.
Yeah. Because they made a mistake. And some people, especially when someone else is looking,
will backspace over 20, 30, 40 characters to fix a typo earlier in the line. If you're
slightly more experienced, of course, you use your arrow buttons to go or your mouse to,
but the mouse is usually slower than the arrows. But a lot of people, when they type a 20 character
word, which is the most unusual, and they realized they made a mistake at the start of the word,
they backspace over the whole thing, and then retype it. And sometimes it takes three, four
times to get it right. So I don't know what your definition of bug is, arguably mistyping a word
and then correcting it immediately is not a bug. On the other hand, you already do sort of lose
time. And every once in a while, there's sort of a typo that you don't get in that process.
And now you've typed like 10 lines of code. And somewhere in the middle of it, you don't know
where yet is a typo or maybe a thinker where you forgot that you had to initialize a variable
or something. But those are two different things. And I would say, yes, you have to actually run
the code to discover that typo. But forgetting to initialize a variable is a fundamentally
different thing. Because that thing can go undiscovered. That depends on the language in
Python, it will not. Right. And sort of modern compilers are usually pretty good at catching
that even for C. So for that specific thing, but actually deeper, it might, there might be another
variable that is initialized, but logically speaking, the one you meant related. Yep.
It's like name the same, but it's a different thing. And you forgot to initialize
whatever some counter or some some basic variable they're using. I can tell that you've coded.
By the way, I should mention that I use a kinesis keyboard,
which has the backspace under the thumb. And one of the biggest reasons I use that keyboard
is because you realize in order to use the backspace on a usual keyboard, you have to stretch
your pinky out. And like the, for most normal keyboards, the backspace is under the pinky.
And so I don't know if people realize the pain they go through in their life
because of the backspace key being so far away. So with the kinesis, it's right under the
thumb. So you don't have to actually move your hands, the backspace and the deletion.
What do you do if you're ever not with your own keyboard and you have to use someone else's
PC keyboard that has a standard layout? So first of all, it turns out that you can actually go
your whole life always having the keyboard with you. So this, well, except for that,
that little tablet that you're using, so we're not taking right now, right?
Yeah. So it's very inefficient not taking, but I'm not, I'm just looking stuff up.
But in most cases, I would be actually using the keyboard here right now. I just don't anticipate.
You have to calculate how much typing do you anticipate? If I anticipate quite a bit,
then I'll just, I have a keyboard with me. And the same, same with the, I mean,
the embarrassing, I've accepted being the weirdo that I am. But, you know, when I go on an airplane
and I anticipate to do programming or a lot of typing, I will have a laptop that will pull out
a Kinesis keyboard in addition to the laptop. And it's just who I am. You have to accept who you
are. But also it's a, you know, for a lot of people, for me, certainly there's a comfort space
where there's a certain kind of setups that are maximized productivity. And it's like some people
have a warm blanket that they like when they watch a movie. I like the Kinesis keyboard. It takes me
to a place of focus. And I still mostly, I, I'm trying to make sure I use the state of the art
IDEs for everything, but my comfort place, just like the Kinesis keyboard is still Emax.
So I still use, I still, I mean, that's one of some of the debates I have with myself
about everything from a technology perspective is how much to hold on to the tools you're comfortable
with versus how much to invest in using modern tools and the signal that the communities provide
you with is the noisy one because a lot of people year to year get excited about new tools
and you have to make a prediction. Are these tools defining a new generation of something
that will transform programming or is this just a fad that will pass? Certainly with JavaScript
frameworks and front and the back end of the web, there's a lot of different styles that came and
went. I remember learning, what was it called, action script? I remember for Flash,
you know, learning how to program in Flash, learning how to design, do graphic animation,
all that kind of stuff in Flash, same with Java applets. I remember creating quite a lot of Java
applets thinking that this potentially defines the future of the web and did not.
Well, you know, in most cases like that, the particular technology eventually gets replaced,
but many of the concepts that the technology introduced or made accessible first
are preserved, of course, because, yeah, we're not using Java applets anymore, but the notion of
reactive web pages that sort of contain little bits of code that respond directly to something
you do, like pressing a button or a link or hovering even, has certainly not gone away.
And that those animations that were made painfully complicated with Flash, I mean,
Flash was an innovation when it first came up. And when it was replaced by JavaScript equivalent
stuff, it was a somewhat better way to do animations, but those animations are still there.
Not all of them, but sort of, again, there is an evolution. And often, so often with technology,
that the sort of the technology that was eventually thrown away or replaced was still
essential to sort of get started. There wouldn't be jet planes without propeller planes. I bet you.
But from a user perspective, yes, from the feature set, yes. But I, from a programmer perspective,
it feels like all the time I've spent with ActionScript, all the time I spent with Java on
the applet side for the GUI development, I, well, no, Java, I have to push back. That was useful
because it transfers, but the Flash doesn't transfer. So some things you learn and invest
time in. What, yeah, what you learned, the skill you picked up learning ActionScript
was sort of, it was perhaps a super valuable skill at the time you picked it up,
if you learned ActionScript early enough. But that skill is no longer in demand.
Well, that's the calculation you have to make when you're learning new things. Like today,
people started learning programming. Today, I'm trying to, to see what are the new
languages to try, what are the new systems to try that, what are the new ideas to try to,
to keep, keep improving. That's, that's why we start when we're young, right?
When we're, but that seems very true to me that, that when you're young, you have your whole life
ahead of you and you're, you're allowed to make mistakes. In fact, you should, you should feel
encouraged to, to do a bit of stupid stuff. Try not to get yourself killed or seriously maimed,
but try stuff that deviates from, from what everybody else is doing. And like nine out of 10
times, you'll just learn why everybody else is not doing that or why everybody else is doing it
some other way. And one out of 10 times, you sort of, you discover something that's better or that,
that somehow works. I mean, there are all sorts of crazy things that were invented
by accident, by people trying, trying stuff together. That's great advice to try, read
them stuff, make a lot of mistakes. Once you're married with kids, you're probably going to be
a little more risk averse because now there's more at stake and you've already hopefully had
some time where you, where you were experimenting with crazy shit.
I like how marriage and kids solidifies your choice of programming language.
How does that, the Robert Frost poem with the, the, the road less taken,
which I think is misinterpreted by most people, but anyway, I feel like the choices you make early
on, especially if you go all in, they're going to define the rest of your life's trajectory in a way
that like you basically are picking a camp. So, you know, there's, if you invest a lot in PHP,
if you invest a lot in.net, if you invest a lot in JavaScript, you're going to stick there.
That's, that's your life journey. It's very hard.
Only as far as that technology remains relevant. Yes. Yes. I mean, if, if at age 16, you learn coding
in C and by the time you're 26, C is like a dead language.
Then there's still time to switch. There's probably some kind of survivor bias or whatever
it's called in, in sort of your observation that, that you pick a camp because there are many
different camps to pick. And if you pick.net, then, then you can coast for the rest of your life
because that technology is now so ubiquitous, of course, that it's, even if it's, if it's bound
to die, it's going to take a very long time. Well, for me personally, I had a very difficult,
and in my own head, brave leap that I had to take relevant to our discussion, which is most of my
life at programs in C and C++. And so having that hammer, everything looked like a nail.
So I would literally even do scripting in C++. Like I would create programs, I do script like
things. And when I first came to Google, and before then it became already before TensorFlow,
before all of that, there was a growing realization that C++ is not the right tool for machine
learning. We could talk about why that is, it's unclear why that is, a lot of things has to do
with community and culture and how it emerges and stuff like that. But for me, they decided to take
the leap to Python, like all out, basically switch completely from C++, except for highly
performant robotics applications. There's still a culture of C++ in the space of robotics.
That was a big leap. I had to, people have existential crises or midlife crises or whatever.
You had to realize almost like walking away from a person you love. Because I was sure that C++
would have to be a lifelong companion. For a lot of problems I would want to solve, C++ would be
there. And it was a question to say, well, that might not be the case. Because C++ is still one
of the most popular languages in the world, one of the most used, one of the most dependent on.
It's also still evolving quite a bit. That is not a sort of a fossilizing community.
They are doing great innovative work, actually. But their innovations are hard to follow if you're
not already a hardcore C++ user. Well, this was the thing. It pulls you in. It's a rabbit hole.
I was a hardcore. The meta programming, template programming, I would start using the modern
C++ as it developed. Not just the shared pointer and the garbage collection that makes it easier
for you to work with some of the flaws. But the detail, the meta programming, the crazy stuff
that's coming out there. But then you have to just empirically look and step back and say,
what language am I more productive in? Sorry to say, what language do I enjoy my life with more?
And readability and able to think through and all that kind of stuff. Those questions are
harder to ask when you already have a loved one, which in my case was C++. And then there's Python,
that meme, the grass is greener on the other side. Am I just infatuated with a new fad,
new cool thing? Or is this actually going to make my life better? And I think a lot of people
face that kind of decision. It was a difficult decision for me when I made it. At this time,
it's an obvious switch if you're into machine learning. But at that time, it wasn't quite
yet so obvious. So it was a risk. And you have the same kind of stuff with them. I still,
because of my connection to WordPress, I still do a lot of back ended programming in PHP.
And the question is, no JS, Python, do you switch back into any of those
programming? There's the case for no JS for me. Well, more and more and more of the front end,
it runs in JavaScript. And fascinating, cool stuff is done in JavaScript. Maybe use the same
programming language for the back end as well. The case for Python for the back end is, well,
you're doing so much programming outside of the web in Python. So maybe use Python for the back end.
And then the case for PHP, well, most of the web still runs in PHP. You have a lot of experience
with PHP. Why fix something that's not broken? Those are my own personal struggles, but I think
they reflect the struggles of a lot of people with different programming languages with different
problems they're trying to solve. It's a weird one. And there's not a single answer, right? Because
depending on how much time you have to learn new stuff, where you are in your life, what you're
currently working on, who you want to work with, what communities you like, there's not one right
choice. Maybe if you sort of, if you can look back 20 years, you can say, well, that whole
detour through ActionScript was a waste of time. But nobody could know that.
Nobody could know that. So you can't beat yourself up over that. You just need to accept
that not every choice you make is going to be perfect. Maybe sort of keep a plan B in the back
of your mind. But don't overthink it. Don't try to sort of don't create a spreadsheet with like
where you're trying to estimate, well, if I learn this language, I expect to make X million dollars
in a lifetime. And if I learn that language, I expect to make Y million dollars in a lifetime.
And which which is higher and what which has more risk and where's the chance that it's like picking
a stock? Kind of kind of. But I think with stocks, you can do diversifying your investment is good
with productivity in life. Boy, that spreadsheet is possible to construct.
Like if you actually carefully analyze what your interests in life are, where you think you can
maximally impact the world, there really is better and worse choices for programming language.
They're not just about the syntax, but about the community, about where you predict the
community is headed, what large systems are programmed in that. But can you create that
spreadsheet? Because that's sort of you're mentioning a whole bunch of inputs that go
into that spreadsheet where you have to estimate things that are very hard to measure and even
harder. I mean, they're they're hard to measure retroactively. And they're even harder to predict
like, what is the better community? Well, better is is one of those incredibly difficult words.
What's better for you is not better for someone else.
But we're not doing a public speech about what's better. We're doing a personal spiritual journey.
I can determine a circle of friends, circle, circle one and circle two. And I can have a bunch
of parties with one and bunch of parties with two. And then write down or take a mental note of what
made me happier, right? And that you have, if you're a machine learning person, you want to say,
okay, I want to build a large company that does that is grounded in machine learning,
but also has a sexy interface that has a large impact on the world. What languages do I use?
You look at what Facebook is using, you look at what Twitter is using, then you look at
performant, more newer languages like Rust, or you look at languages that have taken that most
of the community uses in machine learning space as Python. And you can like think through you can
hang out and think through it. And it's always a invest and the the level of activity of the
community is also really interesting. Like you said, C++ and Python are super active in terms
of the development of the language itself. But do you think that you can make objective choices
there? No, no. But there's a gut you build up. Like, don't you, don't you believe in that gut
feeling? Oh, everything is very subjective. And yes, you most certainly can have a gut feeling.
And your gut can also be wrong. That's why there are billions of people because they're not all
right. I mean, clearly, there are more people living in the Bay area who have plans to sort of
create a Google sized company, then there's room in the world for Google sized companies.
And they're going to have to duke it out in the market, the space. And there's many more choices
than just the programming language. Speaking of which, let's go back to the boat with the
with the fisherman who's tuned out long ago. Talk to the programmer. Let's jump around and go back
to see Python that we tried to define as the reference implementation. And one of the big
things that's coming out in three out of 11, what's the right way to. We tend to say three
dot 11, because it really was like, we went three dot eight, three dot nine, three dot
10, three dot 11, and we're planning to go up to three dot 99. 99. What happens after 99?
Probably just three dot 100. What if I make it there? Okay. And go all the way to 420. I got it.
Forever Python v three, we'll talk about four, but more for fun. So three dot 11 is coming out
one of the big sexy things in it is it'll be much faster. So how did you beyond hiring a great team
or working with a great team, make it faster? What are some ideas that may makes it faster?
It has to do with simplicity of software versus performance. And so even though C is known to
be a low level language, which is great for writing sort of a high performance language
interpreter, when I originally started Python or C Python, I didn't expect there would be
great success and fame in my future. So I tried to get something working
and useful in about three months. And so I sort of I cut corners. I borrowed ideas left and right
when it comes to language design as well as implementation. I also wrote much of the code
as simple as it could be. And there are like, there are many things that you can code more
efficiently by adding more code. It's a bit of a sort of a time space tradeoff where you can
compute a certain thing from a small number of inputs. And every time you get presented with
a new input, you do the whole computation from the top. That can be simple looking code. It's
easy to understand. It's easy to reason about that you can you can tell quickly that it's
correct, at least in the sort of mathematical sense of correct. Because it's implemented in C,
maybe it performs relatively well. But over time as sort of as the requirements for that code
and the need for performance go up, you might be able to rewrite that same algorithm using
more memory, maybe remember previous results. So you don't have to recompute everything from
scratch. Like the classic example is computing prime numbers. Like, is 10 a prime number?
Well, you sort of is it divisible by two? Is it divisible by three? Is it divisible by four?
And we go all the way to is it divisible by nine? And it is not well, actually 10 is divisible by
two. So there we stop at say 11. It's divisible by 10. The answer is nine is no 10 times in a row.
So now we know 11 is a prime number. On the other hand, if we already know that two, three,
five and seven are prime numbers, and you know a little bit about the mathematics of
how prime numbers work, you know that if you have a rough estimate for the square root of 11,
you don't actually have to check is it divisible by four? Or is it divisible by five? You all you
have to check in the case of 11 is is it divisible by two? Is it divisible by three? Because take 12,
if it's divisible by four, well, 12 divided by four is three. So you, you should have come
across the question, is it divisible by three first? So if you know, basically nothing about
prime numbers, except the definition, maybe you go for x from two through n minus one,
one is n divisible by x. And then at the end, if you got all nos for every single one of those
questions, you know, oh, it must be a prime number. Well, the first thing is you can stop
iterating when you find a yes answer. And the second is you can also stop iterating when you
have reached the square root of n. Because you know that if it has a divisor larger than the
square root, then you also have a divisor smaller than the square root. Then you say, oh, except
for two, we don't need to bother with checking for even numbers because all even numbers are
divisible by two. So if it's divisible by four, we would already have come across the question,
is it divisible by two? And so now you go special case, check is it divisible by two,
and then you just check three, five, seven, 11. And so now you sort of reduced your search space by
50% again by skipping all the even numbers it kept for two. If you think a bit more about it,
or you just read in your book about the history of math, one of the first algorithms ever written
down, all you have to do is check, is it divisible by any of the previous prime numbers that are
smaller than the square root? And before you get to a better algorithm than that,
you have to have several PhDs in discrete math. So that's as much as I know.
So of course, that same story applies to a lot of other algorithms, string matching is a good
example of how to come up with an efficient algorithm. And sometimes the more efficient
algorithm is not so much more complex than the inefficient one. But that's an art, and it's not
always the case. In the general cases, the more performant the algorithm, the more complex it's
going to be. There's a kind of trade off. The simpler algorithms are also the ones that people
invent first. Because when you're looking for a solution, you look at the simplest way to get
there first. And so if there is a simple solution, even if it's not the best solution, not the fastest
or the most memory efficient or whatever, a simple solution and simple is fairly subjective. But
mathematicians have also thought about sort of what is a good definition for simple in the case of
algorithms. But the simpler solutions tend to be easier to follow for other programmers who
haven't made a study of a particular field. And when I started with Python, I was a good
programmer in general. I knew sort of basic data structures. I knew the C language pretty well.
But there were many areas where I was only somewhat familiar with the state of the art.
And so I picked, in many cases, the simplest way I could solve a particular sub-problem,
because when you're designing and implementing a language, you have to
like, you have many hundreds of little problems to solve. And you have to have solutions for
every one of them before you can sort of say, I've invented a programming language.
First of all, so C Python, what kind of things does it do? It's an interpreter. It takes in
this readable language that we talked about that is Python. What is it supposed to do?
The interpreter, basically, it's sort of a recipe for understanding recipes. So instead of a recipe
that says, bake me a cake, we have a recipe for, well, given the text of a program,
how do we run that program? And that is sort of the recipe for building a computer.
The recipe for the baker and the chef. What are the algorithmically tricky things that
happen to be low-hanging fruit that could be improved on? Maybe throughout the history of
Python, but also now, how is it possible that 3.11 in year 2022, it's possible to get such a
big performance improvement? We focused on a few areas where we still felt there was low-hanging
fruit. The biggest one is actually the interpreter itself. And this has to do with details of how
Python is defined. So I don't know if the fisherman is going to follow this story.
He already jumped off the boat. He's bored. Yeah, stupid.
Python is actually, even though it's always called an interpreted language,
there's also a compiler in there. It just doesn't compile to machine code. It compiles to
bytecode, which is sort of code for an imaginary computer that is called the Python interpreter.
So it's compiling code that is more easily digestible by the interpreter or is digestible at all.
It is the code that is digested by the interpreter. That's the compiler. We tweaked very minor bits
of the compiler. Almost all the work was done in the interpreter. Because when you have a program,
you compile it once, and then you run the code a whole bunch of times.
Or maybe there's one function in the code that gets run many times. Now, I know that
that sort of people who know this field are expecting me to at some point say
we built a just-in-time compiler. Actually, we didn't. We just made the interpreter a little
more efficient. What's a just-in-time compiler? That is a thing from the Java world, although
it's now applied to almost all programming languages, especially interpreted ones.
You see the compiler inside Python not like a just-in-time compiler, but it's a compiler that
creates bytecode that is then fed to the interpreter. The compiler, or is there something
interesting to say about the compiler? It's interesting that you haven't changed that
tweak at all or much. We changed some parts of the bytecode, but not very much. We only had to
change the parts of the compiler where we decided that the breakdown of a Python program in bytecode
instructions had to be slightly different. That didn't gain us the performance improvements.
The performance improvements were like making the interpreter faster in part by sort of
removing the fat from some internal data structures used by the interpreter. But
the key idea is an adaptive specializing interpreter.
Let's go. What is adaptive about it? What is specialized about it?
Well, let me first talk about the specializing part, because the adaptive part is the sort of
the second-order effect. But they're both important. So bytecode is a bunch of machine
instructions, but it's an imaginary machine. But the machine can do things like call a function,
add two numbers, print a value. Those are sort of typical instructions in Python.
If we take the example of adding two numbers, actually in Python the language, there's no
such thing as adding two numbers. The compiler doesn't know that you're adding two numbers.
You might as well be adding two strings or two lists or two instances of some user-defined class
that happen to implement this operator called add. That's a very interesting and fairly powerful
mathematical concept. It's mostly a user interface trick because it means that
a certain category of functions can be written using a single symbol, the plus sign,
and sort of a bunch of other functions can be written using another single symbol, the multiply
sign. So if we take addition, the way traditionally in Python the add bytecode was executed is
pointers, pointers, and more pointers. So first we have two objects. An object is basically a
pointer to a bunch of memory that contains more pointers.
Pointers all the way down.
Well, not quite, but there are a lot of them. So to simplify a bit, we look up in one of the objects,
what is the type of that object and does that object type define an add operation?
And so you can imagine that there is a sort of a type integer that knows how to add itself to
another integer. And there is a type floating point number that knows how to add itself
to another floating point number. And the integers and floating point numbers are sort of
important, I think, mostly historically, because in the first computers,
you used the sort of the same bit pattern when interpreted as a floating point number had a
very different value than when interpreted as an integer.
Can I ask a dumb question here?
Please do.
Given the basics of int and float and add, who carries the knowledge of how to add two integers?
Is it the integer?
It's the type integer versus?
It's the type integer and the type float.
What about the operator? Is the operator just exist as a platonic form possessed by the integer?
The operator is more like it's an index in a list of functions that the integer type defines.
And so the integer type is really a collection of functions, and there is an add function,
and there's a multiply function, and there are like 30 other functions for other operations.
There's a power function, for example.
And you can imagine that in memory, there is a distinct slot for the add operations.
Let's say the add operation is the first operation of a type, and the multiply is the
second operation of a type.
So now we take the integer type and we take the floating point type.
In both cases, the add operation is the first slot and multiply is the second slot.
But each slot contains a function, and the functions are different because the add to
integers function interprets the bit patterns as integers.
The add to float function interprets the same bit pattern as a floating point number.
And then there is the string data type, which again interprets the bit pattern as
the address of a sequence of characters.
There are lots of lies in that story, but that's sort of a basic idea.
I could tell the fake news and the fabrication going on here at the table.
But where's the optimization?
Is it on the operators?
Is it the different inside the integer?
The optimization is the observation that in a particular line of code,
so now you write your little Python program and you write a function, and that function sort of
takes a bunch of inputs and at some point it adds two of the inputs together.
Now, I bet you even if you call your function a thousand times, that all those calls are likely
all going to be about integers, because maybe your program is all about integers, or maybe
on that particular line of code where there's that plus operator, every time the program hits
that line, the variables A and B that are being added together happen to be strings.
And so what we do is instead of having this single byte code that says, here's an add
operation and the implementation of add is fully generic.
It looks at the object from the object, it looks at the type, then it takes the type and it looks
at, looks up the function pointer, then it calls the function.
Now the function has to be, has to look at the other argument and has to double check
that the other argument has the right type, and then there's a bunch of error checking
before it can actually just go ahead and add the two bit patterns in the right way.
What we do is every time we execute an add instruction like that, we, we keep a little note
of, in the end, after, after we hit the code that, that did the addition for a particular
type, what type was it, and then after a few times through that code, if it's this, if it's the same
type all the time, we say, oh, so this add operation, even though it's the generic add
operation, it might as well be the add integer operation.
And the add integer operation is, the add integer operation is, the add integer operation
is much more efficient because it just says, assume that A and B are integers, do the addition
operation, do it right there in line, and produce the result.
And the big lie here is that in Python, even if you have great evidence that in the past,
it was always two integers that you were adding, at some point in the future, that same line
of code could still be hit with two floating points or two strings or maybe a string and
an integer.
It's not a great lie.
That's just the fact of life.
I didn't account for what, what should happen in that case in, in the way I told the story.
There is some accounting for that.
And, and so what we actually have to do is when we have the add integer operation, we
still have to check, are the two arguments, in fact, integers, we applied some tricks
to make those checks efficient, and we know statistically that the outcome is almost always,
yes, they were, they are both integers.
And so we quickly make that check, and then we proceed with the, the sort of add integer
operation.
And then there is a fallback mechanism where we say, oops, one of them wasn't an integer.
Now we're going to pretend that it was just the fully generic add operation.
We wasted a few cycles believing it was, was going to be two integers.
And then we had to back up, but we didn't waste that much time and statistically, most
of the time.
We were sort of hoping that most of the time we guess right, because if we, if it turns
out that we guessed wrong too often, or we didn't have a good guess at all, things might
actually end up running a little slower.
So someone with armed with this knowledge, and a copy of the implementation, someone
could easily construct a counter example where they say, oh, I have a program and now it
runs five times as slow in Python 3.11 than it did in Python 3.10.
But that's a very unrealistic program.
That's just like an extreme fluke.
It's a fun reverse engineering task though.
So there's people like fun, yes.
So there's some presumably heuristic of what defines a momentum of saying, you know, you
seem to be working adding two integers, not two generic types.
So how do you figure out that heuristic?
I think that the heuristic is actually, we assume that the weather tomorrow is going to
be the same as the weather today.
So you don't need two days of the weather?
No.
That is already so much better than, than, than guessing randomly that.
So how do you find this idea?
Hey, I wonder if instead of adding two generic types, we start assuming that the weather tomorrow
is the same as the weather today, where do you find the idea for that?
Because that ultimately, for you to do that, you have to kind of understand how people
are using the language, right?
Python is not the first language to do a thing like this.
This is a fairly well-known trick, especially from other interpreted languages that had
reason to be sped up.
We occasionally look at papers about HHVM, which is for Facebook's efficient compiler
for PHP.
There are tricks known from the JVM, and sometimes it just comes from academia.
So the trick here is that the type itself doesn't, the variable doesn't know what type
it is.
So this is not a statically typed language where you can, you can get afford to have
a shortcut to saying it's ints.
This is a trick that is especially important for, for interpreted languages with dynamic
typing because if, if the compiler could read in the source, these X and Y that we're adding
are integers, the compiler can just insert the single add machine code, that hardware
machine instruction that exists on every CPU and Ditto for floats.
But because in Python, you don't generally declare the types of your variables.
You don't even declare the existence of your variables.
They just spring into existence when you first assign them, which is really cool and sort
of helps those beginners because there is less bookkeeping they have to learn how to
do before they can start playing around with code.
But it makes the, the interpretation of the code less efficient.
So we're, we're sort of trying to, to make the interpretation more efficient without losing
the super dynamic nature of the language.
That's always the challenge 3.5 got the PEP 44 type hints.
What is type hinting and is it used by the interpreter, the hints, or is it just syntactic
sugar?
Type hints is an optional mechanism that people can use.
And it's especially popular with sort of larger companies that have very large code bases
written in Python.
Do you think of it as almost like documentation saying these two variables are this type?
More than documentation.
I mean, so it, it, it is a sub language of Python where, where you can express the types
of variables.
Here is a variable and it's an integer.
And here's an argument to this function and it's a string.
And here is a function that returns a list of strings, but that's not checked when you're
on the code.
But exactly there, there is a separate piece of software called a static type checker that
reads all your source code without executing it and thinks long and hard about what it
looks from just reading the code that code might be doing and double checks if that makes
sense.
If you take the types as annotated into account.
So this is something you're supposed to run as you develop.
It's like a linter.
Yeah.
That's definitely a development tool, but the type annotations currently are not used
for speeding up the interpreter.
And there are a number of reasons, uh, many people don't use them.
Even when they do use them, uh, they sometimes contain lies where the static type checker
says everything's fine.
I cannot prove that this integer is ever not an integer, but at runtime somehow someone
manages to violate that assumption and the interpreter ends up doing just fine.
If we started enforcing type annotations in Python, many Python programs would no longer
work and some Python programs wouldn't even be possible because they're too dynamic.
And so we made, we made a choice of not using the annotations.
There is a possible future where eventually three, four, five releases in the future,
we could start using those annotations to sort of provide hints because we can, we can
still say, well, the source code leads us to believe that these X and Y are both integers
and so we can generate an add, an add integer instruction, but we can still have a fallback
that says, oh, if the, if somehow the code, code at runtime provided something else, maybe
it provided two decimal numbers, we can still use that generic add operation as a fallback,
but we're not there.
Is there currently a mechanism or do you see something like that where you can almost
add like an assert inside a function that says, please check that my type hints are
actually mapping to reality, sort of like insert manual static typing.
There are third party libraries that are in that business.
It's possible to do that kind of thing.
It's possible to, for a third party library to take a hint and enforce it seems like a
tricky thing.
Well, what we actually do is, and this, I think this is a fairly unique feature in Python,
the type hints can be introspective at runtime.
So while the program is running, they mean Python is a very introspective language.
You can look at the variable and ask yourself, what is the type of this, this variable?
And if that maybe that variable happens to refer to a function, you can ask, what are
the arguments to the function?
And nowadays, you can also ask, what are the type annotations for the function?
So the type annotations are there inside the variable as it's at runtime.
They're mostly associated with the function object, not with each individual variable,
but you can sort of map from the arguments to the variables.
And that's what a third party library can help.
Exactly.
The problem with that is that all that extra runtime type checking is going to slow your
code down instead of speed it up.
I think to reference this sales pitchy blog post that says 75% of developers time is spent
on debugging, I would say that in some cases that might be okay.
It might be okay to pay the cost of performance for the catching of the types, the type errors.
And in most cases, doing it statically before you ship your code to production is more efficient
than doing it at runtime piecemeal.
Yeah.
Can you tell me about my py project?
What is it?
What's the mission?
And in general, what is the future of static typing in Python?
Well, so my py was started by a Finnish developer, Yucca Lettucello.
So many cool things out of Finland, I gotta say.
Just that part of the world.
I guess people have nothing better to do in those long cold winters.
Yeah.
I don't know.
I think Yucca lived in England when he invented that stuff, actually, but my py is the original
static type checker for Python and the type annotations that were introduced with PEP
484 were sort of developed together with the static type checker.
And in fact, Yucca had first invented a different syntax that wasn't quite compatible with Python.
And Yucca and I sort of met at a Python conference in, I think, in 2013, and we sort of came
up with a compromise syntax that would not require any changes to Python, and that would
let my py sort of be an add-on static type checker for Python.
Just out of curiosity, was it like double colon or something?
What was he proposing that would break Python?
I think he was using Angular brackets for types like in C++ or Java generics.
Yeah.
You can't use Angular brackets in Python.
It'll be too tricky for temporal.
Well, the key thing is that we already had a syntax for annotations.
We just didn't know what to use them for yet.
So type annotations were just the sort of most logical thing to use that existing dummy
syntax for.
But there was no syntax for defining generics directly syntactically in the language.
Mypy literally meant my version of Python, where it refers to Yucca.
He had a parser that translated mypy into Python by doing the type checks and then removing
the annotations and all the Angular brackets from the positions where he was using them.
But a preprocessor model doesn't work very well with the typical workflow of Python development
projects.
That's funny.
I mean, that could have been another major split if it became successful.
If you watch TypeScript versus JavaScript, there's a split in the community over types.
That seems to be stabilizing now.
It's not necessarily a split.
There are certainly plenty of people who don't use TypeScript but just use the original
JavaScript notation, just like there are many people in the Python world who don't use
type annotations and don't use static type checkers.
No, I know.
But there is a bit of a split between TypeScript and old school JavaScript, ES, whatever.
Well, in the JavaScript world, transpilers are sort of the standard way of working anyway,
which is why TypeScript being a transpiler itself is not a big deal.
And transpilers, for people who don't know, it's the exact thing you said with mypy.
It's the code, I guess you call it preprocessing code that translates from one language to
the other.
And that's part of the culture, part of the workflow of the JavaScript community.
So that's right.
At the same time, an interesting development in the JavaScript slash TypeScript world at
the moment is that there is a proposal under consideration.
It's only a stage one proposal that proposes to add a feature to JavaScript where just
like Python, it will ignore certain syntax when running the JavaScript code.
And what it ignores is more or less a superset of the TypeScript annotation syntax.
So that would mean that eventually, if you wanted to, you could take TypeScript and you
could shove it directly into a JavaScript interpreter without translation.
The interesting thing in the JavaScript world, at least the web browser world, the web browsers
have changed how they deploy and they sort of update their JavaScript engines much more
quickly than they used to in the early days.
And so there's much less of a need for translation in JavaScript itself because most browsers
just support the most recent version of ECMAScript.
Just an attention of attention, do you see if you will recommend somebody use a thing,
would you recommend TypeScript or JavaScript?
I would recommend TypeScript just because of the strictness of the typing.
It's an enormously helpful extra tool that helps you sort of keep your head straight
about what your code is actually doing.
I mean, it helps with editing your code.
It helps with ensuring that your code is not too incorrect.
And it's actually quite compatible with JavaScript.
Never mind this syntactic sort of hack that is still years in the future.
But any library that is written in pure JavaScript can still be used from TypeScript programs.
And also the other way around, you can write a library in TypeScript and then export it
in a form that is totally consumable by JavaScript.
That sort of compatibility is sort of the key to the success of TypeScript.
Yeah, just to look at it is almost like a biological system that's evolving.
It's fascinating to see JavaScript evolve the way it does.
Well, maybe we should consider that biological systems are just engineering systems too,
right?
Yes.
Just very advanced with more history.
But it's almost like the most visceral in the JavaScript world because there's just so
much code written in JavaScript that for its history was messy.
If you talk about bugs per line of code, I just feel like JavaScript eats the cake or
whatever the terminology is.
It beats Python by a lot in terms of the number of bugs, meaning like way more bugs in JavaScript.
And then obviously the browsers are developing, just there's so much active development, it
feels a lot more like evolution where a bunch of stuff is born and dies and there's experimentation,
debates versus Python is more, all that stuff is happening.
But there's just a longer history of stable working giant software systems written in
Python versus JavaScript is just a giant, beautiful, I would say, mess of code.
That's very different culture and to some extent differences in culture are random,
but to some extent the differences have to do with the environment.
And the fact that JavaScript is primarily the language for developing web applications,
especially the client side, and the fact that it's basically the only language for developing
web applications makes that community sort of just have a different nature than the community
of other languages.
Plus the graphical component and the fact that they're deploying it on all kinds of shapes
of screens and devices and all that kind of stuff, it just creates a beautiful chaos.
Anyway, back to my pie.
So what, okay, you met, you talked about a syntax that could work.
Where does it currently stand?
What's the future static typing in Python?
It is still controversial, but it is much more accepted than when my pie and PEP484 were
young.
What's the connection between PEP484 type hints and my pie?
My pie was the original static type checker.
So my pie quickly evolved from Yucca's own variant of Python to a static type checker
for Python and sort of PEP484, that was like a very productive year where like many hundreds
of messages were exchanged debating the merits of every aspect of that PEP.
And so my pie is a static type checker for Python.
It is itself written in Python.
Most additional static typing features that we introduced in the time since 3.6 were also
prototyped through my pie.
My pie being an open source project with a very small number of maintainers was successful
enough that people said this static type checking stuff for Python is actually worth an investment
for our company.
But somehow they chose not to support making my pie faster, say, or adding new features
to my pie.
But both Google and Facebook and later Microsoft developed their own static type checker.
I think Facebook was one of the first they decided that they wanted to use the same technology
that they had successfully used for HHVM because they sort of, they had a bunch of compiler
writers and sort of static type checking experts who had written the HHVM compiler and it was
a big success within the company and they had done it in a certain way, sort of.
They wrote a big, highly parallel application in an obscure language named OCaml, which
is apparently mostly very good for writing static type checkers.
Interesting.
I have a lot of questions about how to write a static type checker then.
That's very confusing.
Facebook wrote their version and they worked on it in secret for about a year and then
they came clean and went open source.
Google in the meantime was developing something called PyType, which was mostly interesting
because it, as you may have heard, they have one gigantic monorepo.
So all the code is checked into a single repository.
Facebook has a different approach.
So Facebook developed Pyre, which was written in OCaml, which worked well with Facebook's
development workflow.
Google developed something they called PyType, which was actually itself written in Python
and it was meant to sort of fit well in their static type checking needs in Google's gigantic
monorepo.
So Google has one giant got it.
So just to clarify, this static type checker philosophically is a thing that's supposed
to exist outside of the language itself and it's just a workflow, like a debugger for
the program.
It's a linter.
For people who don't know, a linter, maybe you can correct me, but it's a thing that
runs through the code continuously, preprocessing to find issues based on style, documentation.
I mean, there's all kinds of linters, right?
It can check that.
What usual things does a linter do?
Maybe check that you haven't too many characters in a single line.
Linters often do static analysis where they try to point out things that are likely mistakes,
but not incorrect according to the language specification.
Like maybe you have a variable that you never use for the compiler that is valid.
You might be planning to use it in a future version of the code and the compiler might
just optimize it out, but the compiler is not going to tell you, hey, you're never using
this variable.
A linter will tell you that variable is not used.
Maybe there's a typo somewhere else where you're meant to use it, but you accidentally
use something else, or there are a number of sort of common scenarios, and a linter
is often a big collection of little heuristics where by looking at the combination of how
your code is laid out, maybe how it's indented, maybe the comment structure, but also just
things like definition of names, use of names.
That'll tell you likely things that are wrong.
In some cases, linters are really style checkers.
For Python, there are a number of linters that check things like, do you use the PEP 8-recommended
naming scheme for your functions and classes and variables?
Because classes start with an uppercase and the rest starts with a lowercase.
There's differences there.
The linter can tell you, hey, you have a class whose first letter is not an uppercase letter,
and that's just, I just find it annoying if I wanted that to be an uppercase letter.
I would have typed an uppercase letter, but other people find it very comforting that
if the linter is no longer complaining about their code that they have followed all the
style rules.
Maybe it's a fast way for a new developer joining a team to learn the style rules, right?
Yeah, there's definitely that.
But the best use of a linter is probably not so much to enforce team uniformity, but to
actually help developers catch bugs that the compilers, for whatever reason, don't catch.
There's lots of that in Python, but a static type checker focuses on a particular aspect
of the linting, which, I mean, it, my py doesn't care how you name your classes and variables.
But it is meticulous about when you say that there was an integer here and you're passing
a string there, it will tell you, hey, that string is not an integer.
So something's wrong, either you were incorrect when you said it was an integer or you're
incorrect when you're passing it to string.
If this is a race of static type checkers, if somebody winning, as you said, it's interesting
that the companies didn't choose to invest in this centralized development of my py.
Is there a future for my py?
What do you see as the, well, one of the companies went out and everybody uses like a py type,
whatever Google's is called.
Well, Microsoft is hoping that Microsoft's horse in that race called PyRite is going
to win.
PyRite, right, like R-I-G-H-T?
Correct.
Yeah.
My, all my word processors tend to typo correct that as PyRite, the name of the, I don't know
what it is, some kind of semi-precious metal.
Oh, right.
I love it.
Okay.
That's the Microsoft hope, but it, okay, so let me ask the question a different way.
Is there going to be ever a future where the static type checker gets integrated into the
language?
Nobody is currently excited about doing any work towards that.
That doesn't mean that five or 10 years from now, the situation isn't different.
At the moment, all the static type checkers still evolve at a much higher speed than Python
and its annotation syntax evolve.
You get a new release of Python once a year.
Those are the only times that you can introduce new annotation syntax.
And there's, there are always people who invent new, new annotation syntax that they're trying
to push.
And worse, once we've all agreed that we are going to put some new syntax in, we can never
take it back.
At least a sort of deprecating an existing feature takes many releases because you have
to assume that people started using it as soon as we announced it.
And then you can't take it away from them right away.
You have to start telling them, well, this will go away, but we're not going to tell
you that it's an error yet.
And then later it's going to be a warning.
And then eventually three releases in the future, maybe we remove it.
On the other hand, the typical static type checker still has a release like every month,
every two months, certainly many times a year.
Some type checkers also include a bunch of experimental ideas that aren't official standard
Python syntax yet.
The static type checkers also just get better at discovering things that sort of are unspecified
by the language, but that sort of could make sense.
And so each static type checker actually has its sort of strong and weak points.
So it's cool, it's like a laboratory of experiments, Microsoft, Google and all, and you get to see.
And you see that everywhere, right?
Because there's not one single JavaScript engine either.
There's one in Chrome, there's one in Safari, there's one in Firefox.
But that said, you said there's not interest.
I think there is a lot of interest in type hinting, right, in the PEP 484.
Actually, how many people use that, do you have a sense?
How many people use, because it's optional, it's sugar.
I can't put a number on it, but from the number of packages that do interesting things with
it at runtime and the fact that there are like now three or four very mature type checkers
that each have their segment of the market.
And then there is PyCharm, which has a sort of more heuristic based type checker that
also supports the same syntax.
My assumption is that many, many people developing Python software professionally for some kind
of production situation are using a static type checker, especially anybody who has a
continuous integration cycle probably has one of the steps in there.
Their testing routine that happens for basically every commit is run a static type checker.
And in most cases, that will be myPy.
So I think it's pretty popular topic.
According to this web page, 20 to 30% of Python, three code bases are using type hints.
Wow.
I wonder how they measured that.
Did they just scan all of GitHub?
Yeah.
That's what it looks like.
Yeah.
They did a quick, not all of, but like a random sampling.
So you mentioned PyCharm.
Let me ask you the big subjective question.
What's the best IDE for Python?
And you're extremely biased now that you're with Microsoft.
Is it PyCharm, VS Code, Vim, or Emacs?
Historically, I actually started out with using Vim, but when it was still called VI.
For a very long time, I think from the early 80s to I'd say two years ago, I was an Emacs
user.
Nice.
Between, I'd say, 2013 and 2018, I dabbled with PyCharm mostly because it had a couple
of features, PyCharm is like driving an 18-wheeler truck whereas Emacs is more like driving your
comfortable Toyota car that you've had for 100,000 miles and you know what every little
rattle of the car means.
I was very comfortable in Emacs, but there were certain things it couldn't do.
It wasn't very good at that sort of, at least the way I had configured it, I didn't have
very good tooling in Emacs for finding the definition of a function.
When I was at Dropbox, exploring a five million line Python code base, just grabbing all that
code for where is there a class foobar?
Well, it turns out that if you grab all five million lines of code, there are many classes
with the same name and so PyCharm sort of once you fired it up and once it's indexed
your repository was very helpful, but as soon as I had to edit code, I would jump back
to Emacs and do all my editing there because I could type much faster and switch between
files when I knew which file I wanted much, much quicker and I never really got used to
the whole PyCharm user interface.
Yeah, I feel torn in that same kind of way because I've used PyCharm off and on exactly
in that same way and I feel like I'm just being an old grumpy man for not learning how
to quickly switch between files and all that kind of stuff.
I feel like that has to do with shortcuts, that has to do with, I mean, you just have
to get accustomed just like with touch typing.
Yeah, you have to just want to learn that, I mean, if you don't need it much.
You don't need touch typing either.
You can type with two fingers just fine in the short term, but in the long term, your
life will become better psychologically and productivity wise if you learn how to type
with 10 fingers.
If you do a lot of keyboard input.
Before everyone, emails and stuff, right?
Like you look at the next 20, 30 years of your life, you have to anticipate where technology
is going.
Do you want to invest in handwriting notes?
Probably not.
More and more people are doing typing versus handwriting notes.
So you can anticipate that.
So there's no reason to actually practice handwriting.
There's more reason to practice typing.
You can actually estimate back to the spreadsheet the number of paragraphs, sentences, or words
you write for the rest of your life.
You go again with the spreadsheet of my life.
All of that is not actual like converted to a spreadsheet, but it's a gut feeling.
Like I have the same kind of gut feeling about books.
I've almost exclusively switched to Kindle now for ebook readers, even though I still
love and probably always will the smell, the feel of a physical book.
And the reason I switched to Kindle is like, all right, well, this is really paving.
The future is going to be digital in terms of consuming books and content of that nature.
So you should get, you know, you should let your brain get accustomed to that experience.
And that same way, it feels like PyCharm or VS Code.
I think PyCharm is the most sort of sophisticated, featureful Python ID.
It feels like I should probably at some point very soon switch entire, like I'm not allowed
to use anything else for Python than this ID or VS Code.
It doesn't matter.
But walk away from Emacs for this particular application, because I think I'm limiting myself
in the same way that using two fingers for typing is limiting myself.
This is a therapy session.
I'm not even asking a question.
But I'm sure a lot of people are thinking it's all right.
To help you, I think that sort of everybody has to decide for themselves which one they
want to invest more time in.
I actually ended up giving VS Code a very tentative try when I started out at Microsoft
and really liking it.
And it took me a while before I realized why that was, and I think that actually the founders
of VS Code may not necessarily agree with me on this.
But to me, VS Code is, in a sense, the spiritual successor of Emacs, because as you probably
know as an old Emacs hack, the key part of Emacs is that it's mostly written in Lisp.
And that sort of new features of Emacs usually update all the Lisp packages and add new
Lisp packages.
And oh yeah, there's also some very obscure thing improved in the part that's not in Lisp.
But that's usually not why you would upgrade to a new version of Emacs.
There's a core implementation that sort of can read a file and it can put bits on the
screen and it can sort of manage memory and buffers.
And then what makes it an editor full of features is all the Lisp packages.
And of course, the design of how the Lisp packages interact with each other and with
that sort of that base layer of the core immutable engine.
But almost everything in that core engine in Emacs case can still be overridden or replaced.
And so VS Code has a similar architecture where there is like a base engine that you
have no control over.
I mean, it's open source, but nobody except the people who work on that part changes it
much.
And it has sort of a package manager and a whole series of interfaces for packages and
an additional series of conventions for how packages should interact with the lower layers
and with each other.
And powerful primitive operations that let you move the cursor around or select pieces
of text or delete pieces of text or interact with the keyboard and the mouse and whatever
peripherals you have.
And so the sort of the extreme extensibility and the package ecosystem that you see in
VS Code is a mirror of very similar architectural features in Emacs.
Well, I'll have to give it a serious try because as far as sort of the hype and the excitement
in the general programming community VS Code seems to dominate.
The interesting thing about PyCharm and what is it, PHPStorm, which are these JetBrains
specific IDs that are designed for one programming language.
It's interesting to when an ID is specialized, right?
They're usually actually just specializations of IntelliJ because underneath it's all the
same editing engine with different veneer on top, where in VS Code, many things you
do require loading third-party extensions.
In PyCharm, it is possible to have third-party extensions, but it is a struggle to create
one.
Yes.
And it's not part of the culture, all that kind of stuff.
Yeah.
I remember that might have been five years ago or so, we were trying to get some better
MyPy integration into PyCharm because MyPy is sort of Python tooling and PyCharm had
its own type checking heuristic thing that we wanted to replace with something based
on MyPy because that was what we were using in the company.
And for the guy who was writing that PyCharm extension, it was really a struggle to sort
of find documentation and get the development workflow going and debug his code and all
that.
So that was not a pleasant experience.
Let me talk to you about parallelism.
In your post titled, Reasoning About Async I.O.
Summer 4, you talk about a fast food restaurant in Silicon Valley that has only one table.
Is this a real thing?
I just wanted to ask you about that.
Is that just like a metaphor you're using or is that an actual restaurant in Silicon
Valley?
It was a metaphor, of course.
Okay.
I can imagine such a restaurant.
So for people who don't, then read the thing you should, but it was an idea of a restaurant
where there's only one table and you show up one at a time and you're prepared and actually
looked it up and there is restaurants like this throughout the world.
And it just seems like a fascinating idea.
You stand in line, you show up, there's one table, they ask you all kinds of questions
they cook just for you.
That's fascinating.
It sounds like you'd find places like that in Tokyo.
It sounds like a very Japanese thing.
Or in the Bay Area, there are pop-up places that probably more or less work like that.
I've never eaten at such a place.
The fascinating thing is you propose the fast food, this is all for burger.
It was one of my rare sort of more literary or poetic moments where I thought I'll just
open with a crazy example to catch your attention.
And the rest is very dry stuff about locks and semaphores and how a semaphore is a generalization
of a lock.
Well, it was very poetic and well-delivered and it actually made me wonder if it's real
or not because you don't make that explicit.
And it feels like it could be true and in fact I wouldn't be surprised if somebody like
listens to this and knows exactly a restaurant like this in Silicon Valley.
Anyway, can we step back and can you just talk about parallelism, concurrency, threading,
asynchronous, all of these different terms?
What is it?
Sort of a high philosophical level.
The fisherman is back in the boat.
Well, the idea is if the fisherman has two fishing rods, since fishing is mostly a matter
of waiting for a fish to nibble, well, it depends on how you do it actually.
But if you're doing the style of fishing where you throw it out and then you let it sit for
a while until maybe you see a nibble, one fisherman can easily run two or three or four
fishing rods.
And so as long as you can afford the equipment, you can catch four times as many fish by small
investment in four fishing rods.
And so since your time, you sort of say you have all Saturday to go fishing, if you can
catch four times as much fish, you have a much higher productivity.
And that's actually I think how deep sea fishing is done.
You could just have a rod and you put in a hole so you could have many rods.
But is there an interesting difference between parallelism and concurrency and asynchronous?
Is there one subset of the other to you?
Like, how do you think about these terms?
In the computer world, there is a big difference.
When people are talking about parallelism, like a parallel computer, that's usually really
several complete CPUs that are sort of tied together and share something like memory or
an IO bus.
Concurrency can be a much more abstract concept where you have the illusion that things happen
simultaneously, but what the computer actually does is it spends a little time running some
this program for a while and then it spends some time running that program for a while
and then spending some time for the third program for a while.
So parallelism is the reality and concurrency is part reality, part illusion.
Yeah, parallelism typically implies that there is multiple copies of the hardware.
You write that implementing synchronization primitives is hard in that blog post and you
talk about locks and semaphores.
Why is it hard to implement synchronization primitives?
Because at the conscious level, our brains are not trained to sort of keep track of multiple
things at the same time.
Like obviously, you can walk and chew gum at the same time because they're both activities
that require only a little bit of your conscious activity.
But try balancing your checkbook and watching a murder mystery on TV.
You'll mix up the digits or you'll miss an essential clue in the TV show.
So why does it matter that the programmer, the human is bad?
Because the programmer is, at least with the current state of the art, is responsible for
writing the code correctly and it's hard enough to keep track of a recipe that you just execute
one step at a time, chop the carrots, then peel the potatoes, mix the icing.
You need your whole brain when you're reading a piece of code, what is going on?
Okay, we're loading the number of mermaids in variable A and the number of mermen in
variable B and now we take the average or whatever.
I like how we're just jumping from metaphor to metaphor, I like it.
You have to keep in your head what is in A, what is in B, what is in C, hopefully you
have better names.
And that is challenging enough if you have two different pieces of code that are sort
of being executed simultaneously whether it's using the parallel or the concurrent approach.
If like A is the number of fishermen and B is the number of programmers.
But in another part of the code, A is the number of mermaids and B is the number of mermen.
And somehow that's the same variable.
If you do it sequentially, if first you do your mermaids, more people computation and
then you do your people in the boat computation, it doesn't matter that the variables are called
A and B and that is literally the same variable because you're done with one use of that variable.
But when you mix them together, suddenly the number of more people replaces the number
of fishermen and your computation goes dramatically wrong.
And there's all kinds of ordering of operations that could result in the assignment of those
variables and so you have to anticipate all possible orderings.
And you think you're smart and you'll put a look around it and in practice, in terms
of bugs per thousand lines of code, this is an area where everything is worse.
So a lock is a mechanism by which you forbid only one chef can access the oven at a time.
Something like that.
And then semaphores allow you to do what?
Multiple ovens?
That's not a bad idea because if you're sort of, if you're preparing, if you're baking
cakes and you have multiple people all baking cakes, but there's only one oven, then maybe
you can tell that the oven is in use, but maybe it's preheating.
And so maybe you make a sign that says oven in use and you flip the sign over and it says
oven is free when you're done baking your cake.
That's a lock, that's sort of, and what do you do when you have two ovens or maybe you
have 10 ovens, you can put a separate sign on each oven or maybe you can sort of someone
who comes in wants to see at a glance and maybe it is an electronic sign that says there
are still five ovens available or maybe there are already three people waiting for an oven.
So you can, if you see an oven that's not in use, it's already reserved for someone
else who got in line first and that's sort of what the restaurant metaphor was trying
to explain.
Yeah.
And so you're now tasked, you're sitting as a designer of Python with a team of brilliant
core developers and have to try to figure out to what degree can any of these ideas
be integrated and not.
So maybe this is a good time to ask, what is a sync IO and how has it evolved since
Python 3.4?
Wow.
Yeah.
So we had this really old library for doing things concurrently, especially things that
had to do with IO and networking IO was especially a sort of a popular topic.
And in the Python standard library, we had a brief period where there was lots of development
and I think it was late nineties, maybe early 2000s and like two little modules were added
that were the state of the art of doing asynchronous IO or sort of non-blocking IO, which means
that you can keep multiple network connections open and sort of service them all in parallel
like a typical web server does.
So IO is input and outputs, you're writing either from a network connection or reading
and writing to a hard drive to storage.
Also possible.
And you can do the ideas you could do to multiple while also doing computation, so running
some code that does some fancy stuff.
Yeah.
Like when you're writing a web server, when a request comes in a user sort of needs to
see a particular web page, you have to find that page maybe in the database and format
it properly and send it back to the client and there's a lot of waiting, waiting for
the database, waiting for the network.
And so you can handle hundreds or thousands or millions of requests concurrently on one
machine.
Anyway, ways of doing that in Python were kind of stagnated and I forget it might have
been around 2012, 2014, when someone for the umpteenth time actually said these async chat
and async core modules that you have in a standard library are not quite enough to solve
my particular problem.
Can we add one tiny little feature?
And everybody said, no, that stuff is not too, but you're not supposed to use that stuff.
Get your own using a third-party library and then everybody started a debate about what
the right third-party library was.
And somehow I felt that that was actually a cue for, well, maybe we need a better state-of-the-art
module in the standard library for multiplexing input output from different sources.
You could say that it spiraled out of control a little bit.
It was at the time, it was the largest Python enhancement proposal that was ever proposed.
And you were deeply involved with that?
At the time, I was very much involved with that.
I was like the lead architect.
I ended up talking to people who had already developed serious third-party libraries that
did similar things and sort of taking ideas from them and getting their feedback on my
design.
And eventually we put it in the standard library and after a few years I got distracted.
I think the big thing that distracted me was actually type annotations.
But other people kept it alive and kicking and it's been quite successful actually in
the world of Python web clients.
So initially what are some of the design challenges there in that debate for the PEP?
And what are some things that got rejected?
What are some things that got accepted to stand out to you?
There are a couple of different ways you can handle parallel IO and this happens sort of
at an architectural level in operating systems as well.
Like Windows prefers to do it one way and Unix prefers to do it the other way.
You have an object that represents a network endpoint, say a connection with a web browser
that your client and say you're waiting for an incoming request.
Two fundamental approaches are, okay, I'm waiting for an incoming request, I'm doing
something else, come wake me up or sort of come tell me when something interesting happened
like a packet came in on that network connection.
And the other paradigm is we're on a team of a whole bunch of people with maybe a little
mind and we can only manage one web connection at a time.
So I'm just sitting looking at this web connection and I'm just blocked until something comes
in and then I'm already waiting for it, I get the data, I process the data and then
I go back to the top and say, no, sort of, I'm waiting for the next packet.
Those are about the two paradigms.
One is a paradigm where there is sort of notionally a threat of control, whether it's an actual
operating system thread or more an abstraction in async IO, we call them tasks.
But a task in async IO or a thread in other contexts is devoted to one thing and it has
logic for all the stages, like when it's a web request, like first wait for the first
line of the web request parse it because then you know if it's a get or a post or a put
or whatever or an error, then wait until you have a bunch of lines until there's a blank
line and parse that as headers and then interpret that and then wait for the rest of the data
to come in if there is any more that you expect, that sort of standard web stuff.
And the other thing is, and there's always endless debate about which approach is more
efficient and which approach is more error prone, where I just have a whole bunch of
stacks in front of me and whenever a packet comes in, I sort of look at the number of
the pack that there's some number on the packet and I say, oh, that packet goes on this pile
and then I can do a little bit and then sort of that pile provides my context.
And as soon as I'm done with the processing, I sort of, I can forget everything about what's
going on because the next packet will come in from some random other client and it's
that pile or this pile.
And every time a pile is maybe empty or full or whatever the criteria is, I can toss it
away or use it for a new space.
But several traditional third party libraries for asynchronous IO processing in Python chose
the model of a callback.
And that's the idea where you have a bunch of different stacks of paper in front of you
and every time someone gives you a piece, gives you a new sheet, you decide which stack
it belongs to.
And that leads to a certain style of spaghetti code that I find sort of aesthetically not
pleasing and I was sort of never very successful and I had heard many stories about people
who were also sort of complaining about that style of coding.
It was very prevalent in JavaScript at the time at least because it was like how the
JavaScript event loop basically works.
And so I thought, well, the task-based model where each task has a bunch of logic, we had
mechanisms in the Python language that we could easily reuse for that.
And I thought, I want to build a whole library for asynchronous networking IO and all the
other things that may need to be done asynchronously based on that paradigm.
And so I just chose a paradigm and tried to see how far I could get with that.
And it turns out that it's pretty good paradigm.
The people enjoy that kind of paradigm programming for asynchronous IO relative to callbacks.
Okay, beautiful.
So how does that all interplay with the infamous GIL, the global interpreter lock?
Maybe can you say what the GIL is and how does it dance beautifully with the async IO?
The global interpreter lock solves the problem that Python originally was not written with
either asynchronous or parallelism in mind at all.
There was no concurrency in the language, there was no parallelism, there were no threads.
Only a small number of years into Python's initial development, all the new cool operating
systems like SunOS and Silicon Graphics, IRIX and then eventually POSIX and Windows all
came with threading libraries that let you do multiple things in parallel.
And there is a certain sort of principle which is the operating system handles the threads
for you and the program can pretend that there are as many CPUs as there are threads
in the program and those CPUs work completely independently.
And if you don't have enough CPUs, the operating system sort of simulates those extra CPUs.
On the other hand, if you have enough CPUs, you can get a lot of work done by deploying
those multiple CPUs.
But Python wasn't written to do that.
And so as libraries for multithreading were added to C, but every operating system vendor
was adding their own version of that.
We thought, and maybe we were wrong, but at the time we thought, well, we quickly want
to be able to support these multiple threads because they seemed at the time in the early
90s when they were new, at least to me, they seemed a cool, interesting programming paradigm.
And one of the things that Python at least at the time felt was nice about the language
was that we could give a safe version of all kinds of cool new operating system toys to
the Python programmer.
Like I remember one or two years before threading, I had spent some time adding networking sockets
to Python.
And they were very literal translation of the networking sockets that were in the BSD
operating system, so UNIXBSD.
But the nice thing was if you were using sockets from Python, then all the things you can do
wrong with sockets in C would automatically give you a clear error message instead of
just ending up with a malfunctioning hanging program.
And so we thought, well, we'll do the same thing with threading.
But we didn't really want to rewrite the interpreter to be thread safe because that was like, that
would be a very complex refactoring of all the interpreter code and all the runtime code
because all the objects were written with the assumption that there's only one thread.
And so we said, okay, well, we'll take our losses, we'll provide something that looks
like threads, and as long as you only have a single CPU on your computer, which most
computers at the time did, it feels just like threads because the whole idea of multiple
threads in the OS was that even if your computer only had one CPU, you could still fire up
at many threads as you wanted, well, within reason, maybe 10 or 12, not 5,000.
And so we thought we had conquered the abstraction of threads pretty well because multi-core
CPUs were not in most Python programmers' hands anyway.
And then, of course, a couple of more iterations of Moore's law and computers getting faster,
and at some point, the chip designers decided that they couldn't make the CPUs faster, but
they could still make them smaller, and so they could put multiple CPUs on one chip,
and suddenly there was all this pressure about do things in parallel, and that's where the
solution we had in Python didn't work.
And that's sort of the moment that the GIL became infamous because the GIL was the solution
we used to sort of take this single interpreter and share it between all the different operating
system threads that you could create.
And so as long as the hardware physically only had one CPU, that was all fine.
And then as hardware vendors were suddenly telling us all, oh, you got to paralyze.
Everything's got to be paralyzed.
People started saying, oh, but we can use multiple threads in Python, and then they discovered,
oh, but actually all threads run on a single core.
Yeah.
I mean, is there ideas in the future to remove the global interpreter law, GIL, like maybe
multiple sub interpreters, some tricky interpreters on top of interpreters kind of thing?
Yeah, there are a couple of possible futures there.
The most likely future is that we'll get multiple sub interpreters, which each run a completely
independent Python program.
Nice.
But there's still some benefit of sort of faster communication between those programs.
But it's also managing for you this running a multiple Python programs.
Yeah.
So it's hidden from you, right?
It's hidden from you, but you have to spend more time communicating between those programs
because the sort of the attractive thing about the multi-threaded model is that the
threads can share objects.
At the same time, that's also the downfall of the multi-threaded programming model.
Because when you do share objects, and you didn't necessarily intend to share them, or
there were aspects of those objects that were not reusable, you get all kinds of concurrency
bugs.
And so the reason I wrote that little blog post about semaphores was that concurrency
bugs are just harder.
It would be nice if Python had no globally interpreter lock, and it had the so-called
free threading, but it would also cause a lot more software bugs.
The interesting thing is that there is still a possible future where we are actually going
to or where we could experiment at least with that, because there is a guy working for Facebook
who has developed a fork of Cpython that he called the no-gill interpreter, where he removed
the gill and made a whole bunch of optimizations so that the single-threaded case doesn't run
too much slower, and multi-threaded case will actually use all the cores that you have.
And so that would be an interesting possibility if we would be willing as Python Core developers
to actually maintain that code indefinitely.
And if we're willing to put up with the additional complexity of the interpreter and the additional
sort of overhead for the single-threaded case.
And I'm personally not convinced that there are enough people needing the speed of multiple
threads with their Python programs that it's worth to sort of take that performance hit
and that complexity hit.
And I feel that the gill actually is a pretty nice Goldilocks point between no threads and
all threads all the time.
But not everybody agrees on that, so that is definitely a possible future.
The sub interpreters look like a fairly safe bet for 312, so say a year from now.
A year.
So the goal is to do a new version every year for Python.
Let me ask you perhaps a fun question, but there's a philosophy to, will there ever be
a Python 4.0?
Now before you say it's currently a joke, and probably not, we're going to go to 3.99
or 3.99999.
Can you imagine possible features that Python 4.0 might have that would necessitate the
creation of the new 4.0?
Given the amount of pain and joy, suffering and triumph that was involved in the move
between version 2 and version 3.
Yeah, well, as a community and as a core development team, we have a large amount of painful memories
about the Python 3.0 transition, which is one reason that sort of everybody is happy
that we've decided there's not going to be a 4.0 at least, not anytime soon.
And if there is going to be one, it will sort of plan the transition very differently.
Because clearly we underestimated the pain the transition caused for our users in the
Python 3.0 case.
And had we known we could have sort of designed Python 3.0 somewhat differently without making
it any worse, we just thought that we had a good plan, but we underestimated what sort
of the users were capable of when it comes to that kind of transition.
By the way, I think we talked way before like a year and a half before the Python 2.0 officially
end of life.
End of life.
Oh, yeah.
What was that?
What was your memory of the end of life?
Did you shed a tear on January 1, 2020?
Was there were you standing alone?
Our team had basically moved on years before.
It was purely, it was a little symbolic moment to signal to the remaining users that there
was no longer going to be any new releases or support for Python 2.7.
Did you shed a single tear while looking out over the horizon?
I'm not a very poetic person, and I don't shed tears like that, but no.
We actually had planned a party, but the party was planned for the US Python conference that
year, which would never happen, of course, because of the pandemic.
Oh, was it like a march or something?
Yeah, the conference was going to be, I think, late April that year.
So that was a very difficult decision to cancel it, but they did.
Anyway, if we're going to have a Python 4, we're going to have to have both a different
reason for having that and a different process for managing the transition.
Can you imagine a possible process that, so I think you're implying that if there is a
4.0 in some ways, it would break back compatibility?
Well, so here is a concrete thought I've had, and I'm not unique, but not everyone agrees
with this, so this is definitely a personal opinion.
If we were to try something like that no-guild Python, my expectation is that it would feel
just different enough, at least for the part of the Python ecosystem that is heavily based
on C extensions, and that is like the entire machine learning data science scientific Python
world is all based on C extensions for Python.
And so those people would likely feel the pain the most because even if we don't change
anything about the syntax of the language and the semantics of the language when you're
writing Python code, we could even say, suppose that after Python say 3.19 instead of 3.20,
we'll have 4.0.
Because that's the time when we flip the switch to 4.0 will not have a gill.
Imagine it was like that.
So I would probably say that particular year, the release that we named 4.0 will be syntactically,
it will not have any new syntactical features, no new modules in the standard library, no
new built-in functions, everything will be at the Python level will be purely compatible
with Python 3.19.
However, extension modules will have to make a change, they will have to be recompiled,
they will not have the same binary interface, the semantics and APIs for some things that
are frequently accessed by C extensions will be different.
And so for a pure Python user, 4.0 would be a breeze, except that there are very few pure
Python users left because everybody who is using Python for something significant is
using third-party extensions, there are like, I don't know, several hundreds of thousands
of third-party extensions on the PyPI service.
And I'm not saying they're all good, but there is a large list of extensions that would have
to do work.
And some of those extensions are currently already low on maintainers, and they're struggling
to keep afloat.
So there you can give a huge heads up to them if you go to 4.0 to really keep developing
it.
Yeah, we'd probably have to do something like several years before, who knows, maybe five
years earlier, like 3.15, we would have to say, and I'm just making the specific numbers
up, but at some point we'd have to say that Nogil Python could be an option.
It might be a compile time option.
If you want to use Nogil Python, you have to recompile Python from source for your platform
using your tool set, all you have to do is change one configuration variable and then
you just run make or configure and make and it will build it for you.
But now you also have to use the Nogil compatible versions of all extension modules you want
to use.
And so as long as many extension modules don't have fully functional sort of variants that
work in the Nogil world, that's not a very practical thing for Python users, but it would
allow extension developers to test the waters, see what they need to syntactically to be
able to compile at all, maybe they're using functions that are defined by the Python 3
runtime that won't be in the Python 4 runtime, those functions will not work.
They'll have to find an alternative, but they can experiment with that and sort of write
test applications and that would be a way to transition and that that could be a series
of releases where the Python 4 is more and more imminent.
We have supported more and more third party extension modules to have solid support that
works for Nogil Python for that new API and then sort of Python 4.0 is like the official
moment that the mayor comes out and cuts the ribbon and now Python, now the sort of Nogil
mode is the default and maybe the only mode there is.
The internet wants to know from Reddit, it's a small and fun question, there's many fun
questions, but out of the PyPy packages, do you have ones you like, in your opinion,
or there must have PyPy libraries or ones you use all the time constantly?
Oh my, I should really have a standard answer for that question, but like a positive standard
answer, but my current standard answer is that I'm not a big user of third party packages.
When I write Python code, I'm usually developing some tooling around building Python itself
and the last thing we want is dependencies on third party packages.
So I tend to just use the standard library.
That's where your focus is, that's where your mind is.
But do you keep an eye of what's out there to understand where the standard library could
be moving, should be moving, it's a good kind of landscape of what's missing from the standard
library?
Well, usually when something's missing from the standard library, nowadays it is a relatively
new idea, and there is a third party implementation or maybe possibly multiple third party implementations,
but they evolve at a much higher rate than they could when they're in the standard library.
So it would be a big reduction in activity to incorporate things like that in the standard
library.
I like that there is a lively package ecosystem and that sort of recent trends in the standard
library are actually that we're doing the occasional sprint cleaning where we're just
we're choosing some modules that have not had a lot of change in a long time and that
maybe would be better off not existing at all at this point, because there might be
a better third party alternative anyway.
And we're sort of slowly removing those that often those are things that I sort of I spiked
somewhere in 1992 or 1993.
If you look through the commit history, it's very sad like all cosmetic changes like changes
in the indentation style or the name of this other standard library module got changed or
nothing, nothing of any substance, the API is identical to what it was 20 years ago.
So speaking of packages, they have a lot of impact on a lot of people's lives.
Does it make sense to you why Python has become the primary, the dominant language for the
machine learning community, so packages like PyTorch, TensorFlow, Second Learn and even
like the lower level stuff like NumPy, SciPy, Pandas, Matplotlib with visualization.
Can you like, does it make sense to you why it permeated the entire data science machine
learning AI community?
Well it's part of it is an effect that's as simple as we're all driving on the right
side of the road, right?
It's compatibility.
And part of it is not quite as fundamental as driving on the right side of the road,
which you have to do for safety reasons.
I mean you have to agree on something.
They could have picked JavaScript or Perl, there was a time in the early 2000s that it
really looked like Perl was going to dominate like biosciences, because DNA search was all
based on regular expressions and Perl has the fastest and most comprehensive regular
expression engine still does.
It's been quite a long time with Perl, that was another letting go, letting go of this
kind of data processing system.
The reasons why Python became the lingua franca of the scientific code and machine learning
in particular and data science, it really had a lot to do with anything was better than
C or C++.
Really a guy who worked at Lawrence Livermore National Laboratories in the computing division
wrote me his memoirs and he had his own view of how he helped something he called computational
steering into existence.
And this was the idea that you take libraries that in his days were written in Fortran that
solved universal mathematical problems.
And those libraries still work, but the scientists that use the libraries use them to solve continuously
different specific applications and answer different questions.
And so those poor scientists were required to use say Fortran, because Fortran was the
language that the library was written in, and then the scientists would have to write
an application that uses the library to solve a particular equation or answer a set of questions.
And the same for C++, because there's interoperability.
So the dusty decks are written either in C++ or Fortran.
And so Paul Dubois was one of the people who I think in the mid-90s saw that you needed
a higher level language for the scientists to sort of tie together the fundamental mathematical
algorithms of linear algebra and other stuff.
And so gradually some libraries started appearing that did very fundamental stuff with arrays
of numbers in Python.
I mean, when I first created Python, I was not expecting it to be used for arrays of
numbers much.
I thought that was like an outdated data type.
And everything was like objects and strings, and like Python was good and fast at string
manipulation and objects, obviously.
But arrays of numbers were not very efficient, and the multi-dimensional arrays didn't even
exist in the language at all.
But there were people who realized that Python had extensibility that was flexible enough
that they could write third-party packages that did support large arrays of numbers and
operations on them very efficiently.
And somehow they got a foothold through sort of different parts of the scientific community.
And I remembered that the Hubble Space Telescope people in Baltimore were somehow big Python
fans in the late 90s.
And at various points, small improvements were made, and more people got in touch with
using Python to derive these libraries of interesting algorithms.
And once you have a bunch of scientists who are working on similar problems, say they're
all working on data that comes in from the Hubble Space Telescope, but they're looking
at different things.
Some are looking at stars in this galaxy, others are looking at galaxies.
The math is completely different, but the underlying libraries are still the same.
And so they exchange code, they say, well, I wrote this Python program or I wrote a Python
library to solve this class of problems.
And the other guys either say, oh, I can use that library too, or if you make a few changes,
I can use that library too.
Why start from scratch in Perl or JavaScript, where there's not that infrastructure for arrays
of numbers yet, where in Python, you have it.
And so more and more scientists at different places doing different work discovered Python.
And then people who had an idea for an important new fundamental library decided, oh, Python
is actually already known to our users.
So let's use Python as the user interface.
I think that's how tensor.
I imagine at least that's how TensorFlow ended up with Python as the user interface.
Right.
But with TensorFlow, there's a deeper history of what the community is.
It's not just like what packages it needs, it's like what the community leans on for
programming language, because TensorFlow had a prior library that was internal to Google,
but there's also competing machine learning frameworks like Theano, CAFE, they were in
Python, there was some Scala, some other languages, but Python was really dominating it.
And it's interesting because there's other languages from the engineering space like
MATLAB that a lot of people used, but different design choices by the company, by the core
developers led to it not spreading.
And one of the choices with MATLAB by MathWorks is to not make it open source, right, or not
having people pay.
It was a very expensive product.
And so universities especially disliked it because it was a price per seat, I remember
hearing.
Yeah.
But I think that's not why it failed or it failed to spread.
I think the universities didn't like it, but they would still pay for it.
The thing is, it didn't feed into that GitHub open source packages culture.
So like, and that's somehow a precondition for viral spreading, the hacker culture, like
the tinkerer culture, with Python, it feels like you can build a package from scratch
or solve a particular problem and get excited about sharing that package with others.
And that creates an excitement about a language.
I tend to like Python's approach to open source in particular because it's almost egalitarian.
There's little hierarchy.
There's obviously some because like you all need to decide whether you drive on the left
or the right side of the road sometimes.
But there is a lot of access for people with little power.
You don't have to work for a big tech company to make a difference in the Python world.
We have affordable events that really care about community and support people.
And sort of the community is like a big deal at our conferences and in the PSF.
And the PSF funds events, it's always about growing the community.
The PSF funds very little development.
They do some, but most of the money that the PSF forks out is to community fostering things.
So speaking of egalitarian, last time we talked four years ago, it was just after you
stepped down from your role as the benevolent dictator for life, BDFL.
Looking back, what are your insights and lessons you learned from that experience about Python,
developer community, about human nature, about human civilization, life itself?
Oh my, I probably held on to the position too long.
I remember being just extremely stressed for a long time.
And it wasn't very clear to me what was leading, what was causing the stress.
And looking back, I should have sort of relinquished my central role as BDFL sooner.
What were the pros and cons of the BDFL role?
Like what were the, you not relinquishing it, what are the benefits of that for the community?
And what are the drawbacks?
Well, the benefits for the community would be things like clarity of vision and sort
of a clear direction, because I had certain ideas in mind when I created Python, and while
I sort of let myself be influenced by many other ideas as Python evolved and became more
successful and more complex and more used, I also stuck to certain principles and still
hard to say what are Python's core principles.
But the fact that I was playing that role and sort of always very active grew the community
in a certain way.
It modeled to the community how to think about how to solve a certain problem.
Well, that was a source of stress, but it was also beneficial.
It was a source of stress for me personally, but it was beneficial for the community because
people sort of over time had learned how I was thinking and could predict how I would
decide about a particular issue and not always perfectly, of course, but there wasn't a lot
of jerking around like this year, we're all, this year the Democrats are in power and we're
doing these kind of things and now the Republicans are in power and they roll all that back and
do those kind of things.
There is a clear, fairly straight path ahead.
And so fortunately, the successor structure with the steering council has sort of found
a similar way of leading the community in a fairly steady direction without stagnating.
And for me personally, it's more fun because there are things I can just ignore.
Yeah.
Oh, yeah, there's a bug in multiprocessing.
Let someone else decide whether that's important to solve or not.
I'll stick to typing in the async.io and the faster interpreter.
Yeah, it allows you to focus a little bit more.
What are interesting differences in culture if you can comment on between Google Dropbox
and Microsoft from a Python programming perspective, all places you've been to, the positive.
Is there a difference or is it just about people and there's great people everywhere?
Or is there a culture differences?
So Dropbox is much smaller than the other two in your list.
So that is a big difference.
The set of products they provide is narrower, so they're more focused, smaller code based.
And Dropbox sort of, at least during the time I was there, had the tendency of sort of making
a big plan, putting the whole company behind that plan for a year, and then evaluate and
then suddenly find that everything was wrong about the plan and then they had to do something
completely different.
So there was like, the annual engineering reorg was sort of an unpleasant tradition
at Dropbox because like, oh, there's a new VP of engineering and so now all the directors
are being reshuffled and this guy was in charge of infrastructure one year and the next year
he was made in charge of, I don't know, product development.
It's fascinating because you don't think about these companies internally, but Dropbox to
me from the very beginning was one of my favorite services.
There's certain programs and online services that make me happy, make me more efficient
and all that kind of stuff, but one of the powers of those kinds of services, they disappear.
You're not supposed to think about how it all works, but it's incredible to me that
you can sync stuff effortlessly across so many machines so quickly and don't have to
worry about conflicts.
They take care of the, you know, as a person that comes from a version repository and all
that kind of stuff or merge is super difficult and just keeping different versions of different
files is very tricky.
The fact that they could take care of that is just, I don't know, the engineering behind
the scenes must be super difficult, both on the compute infrastructure and the software.
A lot of internal sort of hand wringing about things like that, but the product itself always
worked very smoothly as it does.
Well there's probably a lot of lessons to that.
You can have a lot of turmoil inside on the engineering side, but if the product is good,
the product is good and maybe don't mess with that either, you know, when it's good, it's
like with Google, focus on the search and ads and the money will come and make sure that's
done extremely well and don't forget what you do extremely well and in what ways you
provide value and happiness to the world, make sure you do that well.
Is there something else to say about Google and Microsoft?
Microsoft has had a very fascinating shift recently with the new CEO, you know, recent
CEO with the purchase in GitHub, embracing open source culture, embracing the developer
culture.
It's pretty interesting to see.
That's like why I joined Microsoft, I mean, after retiring and thinking that I would
stay retired for the rest of my life, which of course was a ridiculous lot, but I was
done working for a bit and then the pandemic made me realize that work can also provide
a source of fulfillment, keep you out of trouble.
Microsoft is a very interesting company because it has this incredible, very long and varied
history and this amazing catalog of products that many of which also date way back.
I mean, I've been talking to a bunch of Excel people lately and Excel is like 35 years old
and they can still read spreadsheets that they might find on an old floppy drive.
Yeah, man, there are so many incredible tools through the years.
Excel, one of the great shames of my life is that I've never learned how to use Excel
well.
I mean, it just always felt like so many features are there.
It's similar with ADEs, like PyCharm.
It feels like I converge quickly to the dumbest way to use a thing to get the job done when
clearly there's so much more power in your fingertips.
But I do think there's probably expert users of Excel.
Excel is a cash cow, actually.
Oh, it actually brings in money.
Oh, yeah.
A lot of the engineering, if you look deep inside Excel, there's some very good engineering,
very impressive stuff.
Okay.
Now I need to definitely learn Excel a little better.
I had issues because I'm a keyboard person, so I had issues coming up with shortcuts.
Excel sometimes, it's changed over the years, but sometimes they kind of want to make things
easier for you on the surface and therefore make it harder for people that like to have
shortcuts and all that kind of stuff to optimize their workflow.
Now, Excel is probably, people are probably yelling at me, it's like, no, Excel probably
has a lot of ways to optimize the workflow.
In fact, I keep discovering that there are many features in Excel that only exists at
keyboard shortcuts.
Yeah, that's the sense I have.
Now, I'm embarrassed that it's just.
You just have to know what they are.
That's like, there's no logic or reason to the assignment of the keyboard shortcuts
because they go back even longer than 35 years.
Can you maybe comment about such in Adela and how hard it is for a CEO to sort of pivot
a company towards open source, towards developer culture?
Is there something you could see about like, what's the role of leadership in such a pivot
and definition of a new vision?
I've never met him, but I hear he's just a really sharp thinker, but he also has an
incredible business sense.
He took the organization that had very solid pieces, but that was also struggling with
all sorts of shameful things, especially the Steve Ballmer time.
I imagine in part through his personal charm and thinking, and of course, the great trust
that the rest of the leadership has in him, he managed to really turn the company around
and change it from openly hostile to open source to actively embracing open source.
That doesn't mean that suddenly Excel is going to go open source, but that means that there's
a room for a product like VS Code, which is open source.
Yeah, that's fascinating.
It gives me faith that large companies with good leadership can grow, can expand, can
change, and pivot, and so on, and develop because it gets harder and harder as the company
gets large.
You wrote a blog post in response to a person looking for advice about whether with a CS
degree to choose a nine to five job or to become an entrepreneur.
It's an interesting question.
If you just think from first principles right now, somebody has took a few years in programming,
has loved software engineering.
In some sense, creating Python is an entrepreneurial endeavor.
That's a choice that a lot of people, they're good programmers have to make.
Do I work for a big company or do I create something new?
Or you can work for a big company and create something new there?
Oh, inside the...
Yeah.
Big companies have individuals who create new stuff that eventually grows big all the
time.
And if you're the person that creates a new thing and grows big, you'll have a chance
to move up quickly in the company to run that thing.
If that's your aspiration, what can also happen is that someone is a brilliant engineer and
sort of builds a great first version of a product and has no aspirations to then become
a manager and grow the team from five people to 20 people to 100 people to 1,000 people
and be in charge of hiring and meetings and they move on to inventing another crazy thing
inside the same company or sometimes they found a startup or they moved to a different
great, large or small company.
There's all sorts of models.
And sometimes people sort of do have this whole trajectory from engineer buckling down
writing code, not nine to five but more like noon till midnight, seven days a week and coming
up with a product and sort of staying in charge.
If you take Drew Houston, Dropbox's founder, he is still the CEO.
And at least when I was there, he had not checked out or anything.
He was a good CEO, but he had started out as the technical inventor or co-inventor.
And so he was someone who, I don't know if he always aspired that, I think when he was
16, he already started a company.
So maybe he did, but it turned out that he did have the personal sort of skill set needed
to grow and stay on top.
And other people sort of are brilliant engineers and horrible at management.
I count myself at least in the second category.
So your first love and still your love is to be the quote unquote individual contributor.
So the programmer.
Do you have advice for a programming beginner on how to learn Python the right way?
Find something you actually want to do with it.
If you say, I want to learn skill X, that's not enough motivation.
You need to pick something and it can be a crazy problem you want to solve.
It can be completely unrealistic.
But something that challenges you into actually learning coding in some language.
And there's so many projects out there you can look for like that.
That doesn't have to be some big ambitious thing.
It could be writing a small bot.
If you're into social media, you can write a Reddit bot or a Twitter bot or some aspect
of automating something that you do every single day, processing files, all that kind
of stuff.
Nowadays, you can take machine learning components and sort of plug those things together.
So that's actually a really good example.
So if you're interested in machine learning, the state of machine learning is such that
like a tutorial that takes an hour can get you to start using pre-trained models to do
something super cool.
And that's a good way to learn Python because you learn just enough to run this model and
that's like a sneaky way to get in there to figure out how to import stuff, how to write
basic IO, how to run functions.
I'm not sure if it's the best way to learn the basics in Python, but it could be nice
to just get fall in love first and then figure out the basics, right?
Yeah, you can't expect to learn Python from a one-hour video, of course, I'm blanking
out on the name of someone who wrote a very funny blog post where he said, I see all these
ads for things like learn Python in 10 days or so.
And he said, the goal should be learn Python in 10 years.
That's hilarious, but I completely disagree with that.
I think the criticism behind that is that the places just like the blog post from earlier,
the places that tell you learn Python in 5 minutes or 10 minutes, they're actually usually
really bad tutorials.
So the thing is, I do believe that you can learn a thing in an hour to like get some
interesting quick, like it hooks you.
But it just takes a tremendous amount of skill to be that kind of educator.
Richard Feynman was able to condense a lot of ideas and physics in a very short amount
of time, but that takes a deep, deep understanding.
So yes, of course, the actual, I think the 10 years is about the experience, the pain
along the way.
Well, you have to practice.
You can memorize the syntax, well, I couldn't, but maybe someone else can, but that doesn't
make you a coder.
Yeah.
Actually, coding has changed in fascinating ways because so much of coding is copying
pasting from Stack Overflow and then adjusting, which is another way of coding.
And I don't want to talk down to that kind of style of coding because it's kind of nicely
efficient.
But do you know where that is going?
Code generation.
No, seriously, get some co-pilots.
Yeah, co-pilot.
I use it every day.
And it really, yeah.
It writes a lot of code for me.
And usually it's slightly wrong, but it still saves me typing.
Because all I have to do is change one word in a line of text that otherwise it generated
perfectly.
And how many times are you looking for like, oh, what was I doing this morning?
I was looking for a begin marker and I was looking for an end marker.
And so begin is blah, blah, blah, search for begin.
This is the begin token.
And then the next line, I type e, and it completes the whole line with end instead of begin.
That's a very simple example.
Sometimes it sort of, if I name my function right, it writes a five or 10 line function.
And you know Python enough to very quickly then detect the issues.
So it becomes a really good dance partner then.
It doesn't save me a lot of thinking, but since I'm a poor typist, I'm very much appreciative
of all the typing it does for me, much better actually than the previous generation of suggestions
that are also still built in VS code, where when you hit like a dot, it tries to guess
what the type is of the variable to the left of the dot.
And then it gives you a list, a pop down menu of what the attributes of that object are.
But co-pilot is much, much smoother than that.
Well, it's fascinating to hear that you use GitHub co-pilot.
Do you think, do you worry about the future of that?
The automatic co-generation, the increasing amount of that kind of capability, are programmers
jobs threatened or is there still a significant role for humans?
Are programmers jobs threatened by the existence of Stack Overflow?
I don't think so.
It helps you take care of the boring stuff.
And you shouldn't try to use it to do something that you have no way of understanding what
you're doing yet.
A tool like that is always best when the question you're asking is, please remind me of how
I do this, which I could do.
I could look up how to do it.
But right now, I've forgotten whether the method is called foo or bar or what the shape
of the API is, does it use a builder object or a constructor or a factory or something
else and what are the parameters?
It serves that role.
It's like a great assistant.
But the creative work of deciding what you want the code to do is totally yours.
What do you think is the future of Python in the next 10, 20, 50 years, 100 years?
You look forward, you ever think about, you ever imagine a future of human civilization
living inside the metaverse on Mars, humanoid robots everywhere?
What part does Python play in that?
It'll eventually become a legacy language that plays an important role, but that most
people have never heard of and don't need to know about just all kinds of basic structures
in biology, like mitochondria.
So it permeates all of life, all of digital life, but people just build on top of it and
they only know the stuff that's on top of it.
Yeah.
You guys, you build layers of obstructions.
Those programmers nowadays rarely need to do binary arithmetic, right?
Yeah.
Or even think about it, or even learn about it, or they can go quite far without knowing.
I started building little digital circuits out of NAND gates that I built myself with
transistors and resistors.
So I feel very blessed that with that start when I was a teenager, I learned some of the
basic at least concepts that go into building a computer and I sort of every part, I have
some understanding what it's for and why it's there and how it works.
And I can't forget about all that most of the time, but I enjoy knowing, oh, if you
go deeper, at some point you get to NAND gates and half adders and shift registers.
When it comes to the point of how do you actually make a chip out of silicon, I have no idea.
That's just magic to me.
But you enjoy knowing that you can walk a while towards the lower and lower layers,
but you don't need to.
It's nice.
The other day, as a sort of a mental exercise, I was trying to figure out if I could build
a flip-flop circuit out of relays.
I was just sort of trying to remember, oh, how does a relay work?
Yeah, there's this electromagnetic force that pulls a switch open or shut and you can open
one switch and shut another.
You can have multiple contacts that go at once and how many relays do I really need
to sort of represent one bit of information?
Can the relay just feed on itself?
I don't think I got to the final solution, but it was fun that I could still do a little
bit of problem-solving and thinking at that level.
It's cool how we build on top of each other.
There's people there.
You stood on the shoulders of giants and there's others who'll stand on your shoulders and
it's a giant, beautiful hire.
Yeah, I feel I sort of covered this middle layer of the technology stack where I sort
of Peter's out below the level of NAND gates and at the top, I lose track when it gets
to machine learning.
Then eventually, the machine learning will build higher and higher layers that will help
us understand the lowest layer of the physics and thereby the universe figures out how it
itself works.
Maybe, maybe not.
Yeah, I did.
It's possible, I mean, if you think of human consciousness, if that's even the right concept,
it's interesting that sort of we have this super parallel brain that does all these incredible
parallel operations like image recognition.
I recognize your face.
There's a huge amount of processing that goes on in parallel.
There's lots of nerves between my eyes and my brain and the brain does a whole bunch
of stuff all at once because it's actually really slow circuits, but there are many of
them that all work together.
On the other hand, when I'm speaking, everything is completely sequential.
I have to sort of string words together one at a time and when I'm thinking about stuff,
when I'm understanding the world, I'm also thinking of everything one step at a time.
So we've got all this incredible parallel circuitry in our brains and eventually we
use that to simulate a single-threaded, much, much higher level interpreter.
It's exactly, I mean, that's the illusion of it.
That's the illusion of it for us that it's a single sequential set of thoughts and all
of that came from a single cell through the process of embryogenesis.
So DNA is the code.
DNA holds the entirety of the code, the information and how to use that information to build up
an organism.
The entire like...
The arms, the legs.
How is it built?
The brain.
So you don't buy a computer, you buy like a...
You buy a seed, a diagram.
And then you plant the computer and it builds itself in almost the same way and then does
the computation and then eventually dies.
It gets stale but gives birth to young computers more and more and gives them lessons but they
figure stuff out on their own and over time it goes on that way.
And those computers, when they go to college, tried to figure out how to program and they
built their own little computers.
They're increasingly more intelligent, increasingly higher and higher levels of abstractions.
Isn't it interesting that you see the same thing appearing at different levels though
because you have cells that create new cells and eventually that builds a whole organism
but then the animal or the plant or the human has its own mechanism of replication that
is sort of connected in a very complicated way to the mechanism of replication of the
cells.
And if you look inside the cell, if you see how DNA and proteins are connected, then there
is yet another completely different mechanism whereby proteins are mass produced using enzymes
and a little bit of code from DNA and of course viruses break into it at that level.
And while the mechanisms might be different, it seems like the nature of the mechanism
is the same and it carries across natural languages and programming languages, humans,
maybe even human civilizations or intelligent civilizations and then all the way down to
the single cell organisms.
It is fascinating to see what abstraction levels are built on top of individual humans
and how you have whole societies that have a similar self-preservation, I don't know
what it is, instinct, nature, abstraction as the individuals have and the cells have.
And they self-replicate and breed in different ways, it's hard for us humans to introspect
it because we are very focused on our particular layer of abstraction.
But from an alien perspective, looking on Earth, they'll probably see the higher level
organism of human civilization as part of this bigger organism of life on Earth itself.
In fact, that could be an organism just alone, just life, life on Earth.
This has been a wild, both philosophical and technical conversation, Guido, you're an amazing
human being.
You were gracious enough to talk to me when I was first doing this podcast and one of
the earliest first people I've talked to, somebody I admired for a long time, it's just
a huge honor that you did it at that time and you do it again.
You're awesome.
Thank you, Lex.
Thanks for listening to this conversation with Guido Manrasam.
To support this podcast, please check out our sponsors in the description.
And now, let me leave you some words from Oscar Wilde.
Experience is the name that everyone gives to their mistakes.
Thank you for listening and hope to see you next time.