Python Bytes - #413 python-build-standalone finds a home

Starting point is 00:00:00 Hello and welcome to Python Bytes where we deliver Python news and headlines directly to your earbuds. This is episode 413 recorded December 9, 2024. And I'm Brian Ocken. And I'm Michael Kennedy. This episode is sponsored by us. So check out the links in our show notes, but also check out TalkPythonTraining and PythonTest.com. There's courses over there. And of course, thank you to Patreon supporters. And we have links if you want to get a hold of us, you can reach us on Blue Sky or Mastodon. The links are in the show notes. And also, if you'd like to to if you're listening to the show, thank you. And please share it with a friend. Also, if you'd like to participate in the

Starting point is 00:00:40 discussion while we're recording, you can head on over to pythonbytes.fm slash live and see when we're recording next. But usually it's Monday at 10 a.m. Pacific time. Sometimes it shifts though. And during the holiday season, who knows what we might do. But so far we're sticking with that. And if you'd like to get the links in your email inbox,

Starting point is 00:01:00 go ahead and go and sign up for the newsletter at pythonbytes.fm. And we will send you all of the links in the show notes right in your inbox. So, Michael, let's kick it off. Let's kick it off. I want to talk about a little bit of jitter. Maybe I've had too much coffee this morning or something. I don't know. What do you think?

Starting point is 00:01:18 Maybe. Jitter is a thing from the folks at Pydantic. And the idea here is they need really fast JSON parsing as the foundation of Pydantic, right? Basically Pydantic is about how do I exchange, validate and transform JSON data with Python classes, right? Into Python classes and types. Yeah. So you want that to be fast. The folks over at Pydantic created this thing called Jitter, G-I-T-E-R, and it is a fast, iterable JSON parser. Now, if the Pydantic usage does not catch your attention, OpenAI is also using Jitter, which is pretty interesting.

Starting point is 00:01:58 Ask Chat Ship PT about it. So the reason that they were interested in it is they want to be able to work with i believe pydantic as well but they want to work with uh responses coming out of llms and anyone who's used llms until maybe very recently knows that they kind of like spit out the answers in a little progressive way right and so with this you can parse parts of data as it comes down, which is pretty cool. So there's some examples of partial in here you can go look for somewhere, I think maybe on the docs website or something like that. But you can give it a partially formed string and

Starting point is 00:02:36 it'll come up with perfectly good answers for it. So that's pretty neat. And that's one of its features. The other is that it's faster than what I think the default Rust JSON parser is, even for non-iterable, just straight parse it, which is, that's pretty impressive. Okay. And then there's also, this is why we are talking about it, there's Python parse, which parses JSON strings into a Python object. So you can go and run that as well, which is pretty cool. Jitter example. Yeah. Anyway. Yeah. So you can go and parse it into different pieces using basically if you need a really fast JSON

Starting point is 00:03:13 parser with Python, you can use Python parse and it'll parse into a structure, right? Yeah. So awesome. I thought people might be interested in both an iterable, iterating JSON parser, and back to this one, iterating JSON parser, and also a really fast one. Plus, it's being built by the folks at Pydantic, Sam Colvin and team. And yeah, excellent. Nice work. Oh, yeah. I think I've got several uses for this. This is cool. Yeah, cool. cool yeah cool yeah i recently had uh samuel colvin on um with david seddon to talk about building rust extensions or integrating rust with python and things like that and talk python and he talked about this is one of the things they're building which like oh okay this is pretty

Starting point is 00:03:56 interesting yeah definitely well i'm going to talk about python pre-builds a little bit this this is big news brian I'm glad you're covering it. So Python build standalone is a project that we've talked about on the show, but mostly we talked about it in association with UV. Because if you use UV sync or UV install Python or UV virtual environment or UVVNV and then install and use Python there,

Starting point is 00:04:24 if it can't find it on your system, the Python on your system, it's going to pull it from Python build standalone, which is a separate project, not part of UV. So we've discussed that. But the big news right now is that Python build standalone is now part of Astral or under the Astral umbrella, which is huge. So yeah, there's a we're going to link to a an article from Charlie Marsh, head of Astral saying a new home for Python build standalone.

Starting point is 00:04:55 There's also a that just says we'll be taking over suit will be taking stewardship of this project from Gregory's orc. Cool last name anyway uh the foundational project for building installing portable python distributions and uh there's a link to gregory's announcement also um and the uh discussion around that like the python build standalone powers uv powers rye um also uh pipx and hatch and more and it's got like 70 70 million downloads so far oh wow um pretty big uh project and definitely instrumental to going forward with python or with python packaging and using python so astral is really like trying to make make uv with along with this python build standalone project um uh the new way to install python and

Starting point is 00:05:48 for me it is i'm i'm using it all every day now so 100 same for me um so a pretty short article talking about this but it is kind of interesting that talking it talks about uh what the project is at first and talks about the future of Python, standalone Python distributions. Also what they have in mind for the project that looks like they want to keep the project up to date with Python releases, of course. And then upstream changes to the CPython build system possibly.

Starting point is 00:06:17 And then remove some of the, third is remove some of the project's existing limitations. Well, the existing ones. It ships some MUSL-based Python builds. They're incompatible with Python extension modules. I don't know what that means, but... I don't know what MUSL is, so I'm going to move on from that. Okay.

Starting point is 00:06:36 And then improve the project's Python build and release process. Just, you know, good stewardship for this project, and I'm really happy about that. I, along with this, I was interested to read a thread from Charlie Marsh about what said Python builds downloads exploded in popularity with over 70 million downloads all time. I'm going to put this link to this thread from on blue sky into the show notes also, because it's interesting to, it's an interesting discussion. And I, I learned something through here that I didn't understand, didn't know before it said that the Python,

Starting point is 00:07:14 I didn't know this, that the python.org download the download from python.org. It actually downloads an installer that builds Python from source on your machine. For Linux. For Linux. Okay. It says for Linux. Okay. So for Linux, downloads an installer that builds python from source on your machine for linux for linux okay um it says for linux okay so for linux yeah because the mac os and the the windows ones install way too fast the building python from source is like a 10 minute deal if you run the if it runs the tests and stuff so okay yeah so i because, I didn't think I was doing that on, on, yeah. Anyway, you didn't get the air VC VARs bat.

Starting point is 00:07:46 Couldn't be found. I didn't see that for a while. So, yeah, I guess a bigger deal for people that are not running windows or Mac, but that's really like all the servers and stuff. So, yeah.

Starting point is 00:07:58 Well, I think the other thing that's really non-obvious here is like, what is this build standalone anyway? Why don't we just download the installer and just run it or just take the result of the installer and clunk it out into your machine or something? So my understanding is the non-standalone one depends on other foundational things in the system, especially in Linux, but also in other places. If you want to be able to just copy it over, you can't do that. And so one of the things that they're talking about, one of the four points of the direction

Starting point is 00:08:29 that they're trying to go that Charlie laid out was trying to upstream some of these changes back into CPython itself. I think it might be number one of the feature. Yeah, upstream, no, number two. Upstream the changes to the CPython build system because they have to patch Python in order to make this actually build,

Starting point is 00:08:48 which is why it's a pain in the butt to maintain. And then how many combinatorial variations of that do you get for different platforms and stuff, right? Yeah. And so trying to say, look, we've done these things to make it build more simply with fewer dependencies. Let's maybe make that part of Python.

Starting point is 00:09:03 I don't know about you, but I have not seen a single problem with UV Python, you know, Python build standalone Python compared to system Python. It's not like, oh, well, the certificates don't validate or this thing doesn't work or it doesn't have SSL or some weird thing like a dependency might be missing. It seems fine to me.

Starting point is 00:09:20 And actually I'd be more, if I'm like running a server or something i'd be more worried about installing it separately and building it on each of the machines i'm installing it on than i would having like you know one install that goes everywhere um because you know yeah yeah anyway yeah and i can tell you that pythonbytes.fm is powered by python 313.1 based uh derived from or gotten from this this method here yeah yeah anyway um big news that actually probably doesn't mean much to individual users other than i think that we can try we we had a little bit of a concern about whether or not you know this this one project it's it was sitting heavily on one

Starting point is 00:10:06 person one developers to maintain and i'm glad that it's uh astral helping out with this now too yeah i i agree and if you read greg's announcement um there transferring python build standalone stewardship to astral that he talks about how the astral folks actually for a while have been core contributors to the project and they've been working from the outside to help keep this thing going because they realize how important it is to this feature right yeah and uh i know also i read i don't know if it was in this or somewhere else but i essentially read that the project was i mean astral was really working on it for several months anyway um yeah this is this is mostly a um is mostly an official announcement is all.

Starting point is 00:10:46 But yeah. One final parting thought, Brian, is right there where you are. It says, this is in Greg's announcement, as I wrote in my shifting open source priorities in March, this is an interesting challenge that people can run into with projects that are run by one person, right? Yeah.

Starting point is 00:11:02 The guy had a kid, wanted to spend more time with the kid, was feeling worn out by the projects and decided, well, and also talks about how he really just cares way more about Rust than he does about Python these days, which is fine. You're not married for life to a technology.

Starting point is 00:11:20 Go where your heart takes you. But that's a challenge for projects that are run by one person. I think it's worth reading this thing as well, just for people to get a sense of, you know, when open source projects take off, but it's not necessarily a good fit. Yeah. Yeah. But thanks to Gregory for creating this and keeping it going. He's also known for the PyOxidizer project, which came close, didn't quite get us to a single binary of our Python apps. Interesting. Okay. Yeah, it's really cool that he made sure that this was in good hands

Starting point is 00:11:52 before shifting it over. Yeah, absolutely. Absolutely. All right. All right. On to the next. On to the next thing. So I talked about, there's a theme here, I talked about the jitters from having too much coffee. Well, let's talk about Mocha. Maybe if we put some hot chocolate and some sugar in with it, it'll be better. No, probably not. This project, this project is by Del Rio and it's called Mocha Pie. So Mocha let's like work our way inside out. So Mocha is a high performance concurrent caching library for Rust.

Starting point is 00:12:24 Not a concurrent caching server like Redis. Think SQLite, but for caching, right? SQLite's written in C, not Rust, but it's an in-process sort of deal, which is pretty neat. And this itself is inspired by Caffeine for Java, right? This is kind of like turtles all the way down, like ports all the way down. So it provides a caching implementation on top of dictionaries. They support full concurrency of retrievals in the high expected concurrencies for updates.

Starting point is 00:12:53 All right. So thread safe, highly concurrent in-memory cache implementation sync and async can be bounded by the maximum number of entries, the total weighted size, size aware eviction, like kicking large things out versus small things. You can have cache controlled by least frequently used, by last recently used, like I want to kick out things that are over two minutes, but if you've got room based on something, that's fine. You can give them a time to live, a time to idle, right? Idle is a really cool interesting one, like when was this last accessed?

Starting point is 00:13:26 So you've got something that's old but is used all the time in your app, and then something that's somewhat new but it kind of hasn't got used that much. It would be better to kick out that new one rather than the old one, right? Mm-hmm, yeah. Okay, so that's all just straight Mocha. Mocha Pie is a Python binding for this. Here we go again. Rust library for Python. They're probably getting VC money from this. I'm telling you. Okay. No, just joking.

Starting point is 00:13:51 Sort of. So for the MochaPy thing, it has a synchronous cache, which supports basically thread-safe memory. It just like wraps the thing. So time to live, time to idle, size of concurrency, all these things that you can imagine. And so there's a couple interesting ways. You can just say cache.set some value, or you can say cache.get some value. That's one way to use it. Another one is you can use it as, this is actually pretty amazing. You can use it as an LRU cache function decorator alternative. Oh, wow. Right? So one of the things you can do that's really easy to speed up python code with not writing much code you have to maintain much as you just put a

Starting point is 00:14:30 decorator functools.lr ucache onto it and it'll look at the hash value of all the inbound parameters and say if you pass me the same parameters you're getting the same output right yeah it just does that just straight in python memory but this would be backed by this high performance concurrent rust internal library it's still in process right yeah you can see yeah go ahead sorry what you with the time to live and time to you know idle yeah especially that's that's cool yeah this is pretty cool and there's so much talk about the thing supporting a the mocha itself the rust version supporting asynchronous behavior right i'm like okay if um if it has all these asynchronous capabilities what's the story with

Starting point is 00:15:13 python and it's async and await right yeah so i filed an issue which i don't really like to do but that's how you ask questions apparently and then you close it so i said hey cool project since it says thread safe highly concurrent in-memory implementation, what's the Python async story? And so they responded, this will work if you put the decorator on there. So remember how I was complaining that it's sort of weird that the func tools and iter tools don't support async? This func tool-like thing supports async and sync functions as well, right?

Starting point is 00:15:48 So they just have an implementation in the center that says, is it a coroutine? Do this. Else, do that. So you can use the caching decorator, like we talked about, like the LRU cache thing already on async functions and sync functions. So that's fine. And then I said, well, what about cache get and set?

Starting point is 00:16:05 And Delirio says, probably doesn't make sense to do it. It takes 230 nanoseconds. So you can do 4.4 million calls a second. And set is 1.3 million sets per second for a cache size of 10,000 that's fully occupied on a simply M1 Mac. So you know what? Probably not. But there might be some ways to expand this in the future.

Starting point is 00:16:30 I don't know. But yeah, I would say probably not. Probably not needed, because you're going to probably add more overhead just to juggle the async stuff, right? Yeah. And also, if the supported method is through the decorator and whatever you need,

Starting point is 00:16:44 you could just put your code in a function to do. Yeah. I mean, if that were Redis, you would absolutely want an async version because you're talking to another server. Yeah. And there's that latency in the network and all. But yeah, if you can do 4 million a second, then probably, I doubt you can do 4 million of weights a second, but it's much lower.

Starting point is 00:17:00 So the cache get and set really are just that where you, the benefit of those is, is probably just for, because we want a really fast caching system or something. Yeah, yeah, exactly. And you, there's plenty of times where you say in this situation, I want to get this out of the cache and then keep it for a while. Like if I had a user who logged in and I want to just hold their user account with all their details and I've used their ID as the key and their actual user object as the object that goes in that's fine but you wouldn't you wouldn't use that as a cache decorator because typically you might see that coming out of a database something like that and then if you pass the same user in it's like it's similar but it's a different database object instead right you can run into real weird situations where they're equivalent but they're not equivalent

Starting point is 00:17:43 you know and then you end up not using cache. So anyway, I think that might be where you would do it. But anyway, I think this is pretty cool. People can check it out. Yeah. And it's,

Starting point is 00:17:52 it is not, I don't believe it is like super popular here and, you know, a hundred stars, it kind of shine a light on it. But if you go over to the Mocha thing, you know, it's got a thousand 700 stars and this is kind of just a Python UI on top

Starting point is 00:18:04 or API on top of it. Yeah. But it's, it's pretty 1,700 stars. And this is kind of just a Python UI on top or API on top of it. Yeah, but it's pretty recent. I mean, it's a few weeks old, looks like. It's just a baby. Just a baby. It's okay to have 100 stars. It's pretty good. Yeah, it's pretty good, actually. It looks cool. So, now you know.

Starting point is 00:18:20 Alright, I want to shift back to UV. I'm kind of in a UV mood. I'm missing the sun, apparently. But there's an article from the SAS Pegasus blog about when I learned about UV sync, and started using that and all the different ways to use UV, it's a pretty powerful tool. So it's not really one thing, it's designed to be a lot. So, so I appreciate, you know, articles like this, but also, I really like this one. So it starts out with pretty much who is, which is with a funny meme of a whole bunch of different commands to install Python and update it and install, create a virtual environment

Starting point is 00:19:15 and sync your requirements. And all of that is just done with UV sync now. You can do it all in one, which is pretty sweet. So- I don't use UV sync. I use UV, V, E, and VV dash dash Python 3.13 or something. But, you know, same. Yeah, I'm using both,

Starting point is 00:19:30 depending on whether or not I have a project set up already. So it talks about what is UV, why use it, and we're just going to assume that you already know if you listen to this podcast, because it's really fast. But a lot of discussion of different workflows talks about um installing uh adopting uv into your existing workflows doing uh install and but i'm gonna pop down to the end uh adopting uv into your workflow there's this cool cheat sheet this is pretty much what the entire article talks about the different parts is um you can use uh uv uh uv

Starting point is 00:20:05 python install to install python you can use a virtual environment or venv to create virtual environments it's really fast um and then install packages with uv pip install but then also uh you can build your dependencies like we would have used pip compile you can use uv pip compile um but um it's all in one place to all these different commands. And these really are the commands. The commands listed in this article are really the way I use UV as well. So that's why I appreciated it. And then a discussion about how to adopt this into your workflow and what that means to get, you know, talking about, I mean, some of this, a lot of people might not have used

Starting point is 00:20:45 sort of lock files before, but using lock files with UV or it's so easy that, you know, why not? And pinning your dependencies, just some good workflow. It's good Python project practices anyway. So why not? Yeah. Yeah. That's great. And there's even a few more that you could throw in for the tool like the equivalent c table there yeah you know there's you there's you for um installing cli tools you could say pip x yeah and just create a virtual environment install the things and make that in the path and all those sorts of things versus uv tool install or uv run right those kind of things as well tool install or UV run, right? Those kinds of things as well. So, yeah. Yeah. It's missing that, which is, I'll feed it back to Corey.

Starting point is 00:21:32 So one of the things, reasons why this, this came up on my radar is I'm working on a project that uses SAS Pegasus. So I'm in touch with Corey a lot. Yeah. But like the UV, the tool thing, instead, I'm not using PipEx anymore. The UV tool install is like super cool. Yeah, it's super cool. It is.

Starting point is 00:21:56 I've also started using Docker for certain things as well. So it's kind of similar. But like, for example, Glances, which is a Python-based server UI visualization tool, you can just say Docker run Glances versus installing Glances, and you just leave the machine a little more untouched. Yeah, one of the interesting things about this article

Starting point is 00:22:15 was the point of view. Because at the start, Corey talks about how he's not usually somebody to jump on like multi-tool fads like uh pipenv or pyenv for installing um uh for doing virtual environments better and big project wides and and i i like hatch but i'm not really a uh using hatch for my entire workflow sort of person i was using it just for a packager. So I'm in the same boat of like, I didn't really need an all-in-one tool,

Starting point is 00:22:50 but this one changed my mind and I really like this all-in-one tool. So yeah, I'm still not bought into the project management side, but I love using UV for the stuff. Yeah, yeah. Anyway, what do we got next? We have a quick bit of follow-up here

Starting point is 00:23:04 that I just did some searching. So over on pipx, so one of the things that you say, like you could use pipx, or there is an open issue on pipx that says integrate UV in some way, right? Because pipx is really just a wrapper around create virtual environment, pip install package, pip install dash u package, right?

Starting point is 00:23:23 And so if they just change the internals to say uv pip install, then pipx would all of a sudden become super awesome. This recommendation is unfortunately over half a year old, but it does have 21 upvotes. So you know what? Yeah, who knows? That's there. Yeah, okay.

Starting point is 00:23:40 Yeah, okay. But that's not what I want to cover next. Come on, computer, respawn. There we go. I think that's it for our items, right next. Come on, computer, respawn. There we go. I think that's it for our items, right? We're on to extras. Yeah. Let's have extras now. Yeah. Let's extra it up. Extra.

Starting point is 00:23:54 So registered for PyCon, I did. Oh, cool. Yeah. Registration came out two days ago. I don't know. Whenever I posted some message on Blue Sky and Mastodon saying, I registered. How about you? Whenever that was, that's when the announcement came out. So I think a day and a half ago or something like that. So there's early bird pricing and all details on there. If you want to go and check it out, it's normally 450 bucks for individuals, but you could save $50 if you register before January, which is pretty cool. There's a bunch of stuff. It has all the detailed timeline, which is always interesting. You know, like if I want to make sure I attend the Pied Ladies auction, when do I need to do that?

Starting point is 00:24:29 When is the main thing? When is the job fair, et cetera? So most importantly, main conferences, May 16 to May 18, 2025. So there it is. And congruent with current times mask policy. Hooray. Optional and encouraged, but not required. Yeah.

Starting point is 00:24:44 How about that? Yeah. Cool. Okay. I've got a few but not required. Yeah. How about that? Yeah. Cool. Okay. I've got a few more real quick ones here. I recommend, you know what? It's something I came across just thinking like, why don't I support more open source projects? Looking at my dependencies and stuff that I'm working on. Like how much, you know, if everybody who used Flask put $1 towards it per month, everybody used it in an important way where it's not just like oh i did a tutorial with flask but like no i have a project that is important to me and i use flask if everyone put one dollar towards it it would transform that project if everyone who used g unicorn put one dollar towards it that would transform it right so i decided you know

Starting point is 00:25:20 i'm gonna just go to some projects and find the one that I use most. And yeah, I found four that had sponsorships that were available. I was going to support UV and Pydantic as well, but they, for some reason, they do like corporate sponsorships or I tried to do individuals and it didn't work. And then some other ones like Beanie don't currently have sponsorships, but, you know, are really important for the database layer and stuff. But just think about, you know, put a couple of dollars towards some of these projects it it'll make zero difference to you if you have a job as a software developer and in the aggregate it'll make a big difference to the health of the ecosystem yeah uh it's interesting to think about like that like just you know a couple less coffees a month and um yeah one you probably cover like three or four projects. Yeah. Yeah. Anyway,

Starting point is 00:26:07 I want to encourage people to do that, but you know, can't obviously don't, but I don't think it's a big deal. Uh, come here. Computer. Very slow for some reason.

Starting point is 00:26:15 Don't know why. There we go. All right. Uh, this is the joke. So that I'm skipping the joke for a second. We'll come back to it. There's two things that I wasn't planning on covering, but I'll throw out here really quick.

Starting point is 00:26:22 Uh, yeah, here's my register for PyCon. Also wrote a quick, people said, oh my God, Hetzner, we moved to Hetzner, and they changed this huge thing where they changed their bandwidth and their price, and it's like a no, nothing sort of deal,

Starting point is 00:26:35 like $5 a month more. Anyway, I wrote that up so people can check that out on Mastodon. And then, yeah, that's it for all my items. Okay. And then I just got the joke when you're ready for that. So let's do yours. I don't have much commentary on these.

Starting point is 00:26:48 I just have a few extra things I wanted to point out. Pydantic AI was announced, which Pydantic AI is a Python agent framework designed to make it less painful to build production-grade applications with generative AI. I don't have really any commentary about this other than I didn't see this coming, but interesting. Yeah. I've seen messages or tweets or whatever from people who do machine learning stuff saying, you just need Pydantic. I mean, a lot of this is like, I got a JSON thing here and then I'm going to call some other thing with other JSON and just suggesting, hey, you could probably use Pydantic to make these connections. I bet the Pydantic team noticed that as well. Okay.

Starting point is 00:27:32 A couple of commentaries on maybe society. And anyway, I'll leave it, leave the couple other articles I thought was interesting. Blue Sky announced, I guess this is old, this is from August, but anti-toxicity features on Blue Sky. And I just actually appreciate some of these. I, I already have hit, um, I had a troll come by. Um, and so there's some things where you can, if people, you can, uh, detach a quoted post. If, if somebody quotes you and you

Starting point is 00:27:58 don't want them to, you can detach yourself from that. Um, um, I uh hiding replies i had some a troll you can't like delete replies but i had uh somebody just just idiotic reply to something i said and it was obviously just a bot or a troll so you can you can hide that um and as you know as blue sky grows we'll we'll get trolls also um if they're if they're not affecting you yet they they may in the future so so i do appreciate that there's um there are features around to protect yourself so there's there's that and then um this i don't know what to make of this really but wired fairly mainstream magazine i think uh released the wired guide to protecting yourself from government surveillance wow i'm i just this is a head shaker of like um

Starting point is 00:28:47 i guess we need this i wish we didn't but wow um yeah there's that so yeah you probably say that about some state governments as well right every state's different but yeah yeah depending on your gender and things you know it's touch and go some places. Yeah. Anyway. So that's a little bit of a downer. So maybe we need something funny. We do. I don't want to spend all the time going down that deep rabbit hole.

Starting point is 00:29:13 Instead, let's go infinitely down the rabbit hole. Yes. So check this out, Brian. Somebody who goes by Bits, very personal, on Blue sky posted what the comments seem to indicate is probably a textbook a print this printed by the way a printed textbook on LaTeX okay okay in the index at the back on page 252 there's an entry for infinite loop and it says see page 252 i love it so much it's so simple i love it yeah it's a really good just like a little easter egg in there isn't it yeah i i've i haven't seen it for infinite loop i saw that uh somebody did that for recursion in some yeah they're if you look in the comments

Starting point is 00:29:59 it says that uh carrington and ritchie has the same i guess that's probably c or something the same in under the index for recursion. And it's pretty good. People love it. Yeah. That's funny. And there's somebody that says, for those who can't be bothered,

Starting point is 00:30:14 Google search for recursion. Did you mean recursion? Yeah. Apparently. I kind of feel bad for people that actually really need to know what that means. Good luck. Yeah, good luck with that, huh? So. Wow. All good. I kind of feel bad for people that actually really need to know what that means. Good luck.

Starting point is 00:30:27 Yeah, good luck with that, huh? So, yeah. All good. All good here. We know what recursion and infinite loops are, but we're going to break the loop and get out of here, right? Yeah. Yeah, let's break the loop and say goodbye until next time. So, thanks a lot.

Starting point is 00:30:39 Bye. Bye, all.

Python Bytes - #413 python-build-standalone finds a home

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