WEBVTT 00:00:00.000 --> 00:00:04.600 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:05.040 --> 00:00:09.700 This is episode 477, recorded April 20th, 2026. 00:00:10.020 --> 00:00:11.020 I am Brian Okken. 00:00:11.280 --> 00:00:12.140 And I'm Michael Kennedy. 00:00:12.440 --> 00:00:17.540 And this episode is sponsored by us and you guys. 00:00:18.240 --> 00:00:21.520 So there's a bunch of courses over at Talk Python Training. 00:00:21.920 --> 00:00:27.200 There's pytest courses over at PythonByte, wait, Pythontest.com, my own site. 00:00:27.320 --> 00:00:28.280 I forgot the name of it. 00:00:28.280 --> 00:00:35.920 But thanks to everybody for the Patreon supporters and a lot of people encouraging me and grabbing copies of LeanTDD. 00:00:36.460 --> 00:00:38.700 And I've gotten some good feedback. 00:00:38.840 --> 00:00:40.880 So I'll plug that later in the show as well. 00:00:41.180 --> 00:00:43.580 Yeah, if you'd like to reach out, send us topics. 00:00:43.700 --> 00:00:47.440 One of the topics I'm covering is from somebody that sent it in. 00:00:47.700 --> 00:00:49.000 And we really appreciate that. 00:00:49.000 --> 00:00:56.520 So get a hold of us through Bluesky, Mastodon, or through email or the contact form on pythonbytes.fm. 00:00:56.520 --> 00:00:59.520 And all of that info is at pythonbytes.fm. 00:00:59.620 --> 00:01:07.080 If you are listening and you're thinking, hey, I'd like to watch this live sometime, you can just head on over to pythonbytes.fm. 00:01:07.080 --> 00:01:09.120 Or just look around. 00:01:09.220 --> 00:01:13.920 You can find links to watch us live on YouTube or watch the past episodes. 00:01:14.620 --> 00:01:21.940 And finally, please join the newsletter because we send out links and information and background information. 00:01:22.340 --> 00:01:27.220 And some people have mentioned to us before that some topics are a little over their head. 00:01:27.220 --> 00:01:28.500 But we don't want that. 00:01:28.680 --> 00:01:33.600 So we send you some background information so that you can understand every topic we talk about. 00:01:33.720 --> 00:01:37.860 And with that, I'll take a rest and it'll be your turn. 00:01:38.240 --> 00:01:38.500 You know what? 00:01:38.520 --> 00:01:40.200 I need to take a rest too, honestly, Brian. 00:01:40.340 --> 00:01:47.160 I've had a long weekend, big party for my wife and had a bunch of our friends in. 00:01:47.220 --> 00:01:49.280 We rented this party van bus thing. 00:01:49.360 --> 00:01:50.720 I drove around a bunch of wineries. 00:01:51.080 --> 00:01:52.240 And I just need to rest. 00:01:52.240 --> 00:01:55.720 But, you know, the Django rest type? 00:01:55.860 --> 00:01:56.260 I don't know. 00:01:56.340 --> 00:01:57.460 I need legit rest. 00:01:57.640 --> 00:01:58.660 I need legit rest. 00:01:58.860 --> 00:02:00.860 But I'm going to tell you about Django rest. 00:02:00.940 --> 00:02:07.120 In fact, modern rest, which is a framework for Django that is type-based. 00:02:07.400 --> 00:02:13.720 So all the classes use runtime type information to do all of their magic, right? 00:02:13.740 --> 00:02:16.280 Not just autocomplete or blending. 00:02:16.580 --> 00:02:18.840 And it has true async support. 00:02:18.840 --> 00:02:23.860 So think Django Ninja-like, but with a different take, okay? 00:02:24.120 --> 00:02:25.760 So this is a pretty cool project here. 00:02:26.080 --> 00:02:30.840 And, you know, I just looked at it and thought, you know, this looks like something that's really fun. 00:02:31.240 --> 00:02:40.880 It actually, one of the differences than, say, Django Ninja is it supports multiple model foundations, I guess. 00:02:41.220 --> 00:02:44.560 So Pydantic, which is the first one listed here, which is great. 00:02:44.560 --> 00:02:47.400 But also msgspec and Attrs. 00:02:47.640 --> 00:02:48.360 Remember Atters? 00:02:48.520 --> 00:02:49.620 Like Attrs is still a thing. 00:02:49.760 --> 00:02:49.960 Yeah. 00:02:50.200 --> 00:02:53.540 From Hennick and, you know, kind of data class style. 00:02:53.880 --> 00:03:02.300 And one of the things that's interesting here is it says if you use msgspec, it allows you 5 to 15 times faster APIs than the alternative. 00:03:02.620 --> 00:03:07.060 msgspec is all about ultra-compact exchange on the wire type of thing. 00:03:07.500 --> 00:03:07.600 Okay. 00:03:07.600 --> 00:03:12.980 Also has true support for ASGI, ASGI, async applications. 00:03:13.300 --> 00:03:16.580 But one of the things that's interesting is it's just good old Django. 00:03:16.840 --> 00:03:18.320 Like nothing too new. 00:03:19.020 --> 00:03:20.900 Nothing that you wouldn't expect. 00:03:21.040 --> 00:03:24.620 So if you're doing Django, it feels just like, yeah, that totally fits in. 00:03:24.820 --> 00:03:31.280 There's a getting started page here, which is a little zoomed for all of us, that we can go down and sort of go through. 00:03:31.440 --> 00:03:32.020 It's pretty interesting. 00:03:32.020 --> 00:03:41.880 One of the things that's interesting also supports PyPy, P-Y-P-Y, not the mispronunciation of PyPy, but literally PyPy, which I think is interesting. 00:03:42.000 --> 00:03:43.860 And Django 4.2 or above. 00:03:44.540 --> 00:03:46.480 Hat tip to an upcoming topic. 00:03:46.800 --> 00:03:50.640 The default recommended way to install it is uv, then Poetry, and then Pip. 00:03:50.740 --> 00:03:51.460 So that's pretty cool. 00:03:51.460 --> 00:04:08.040 So you've got to do things like when you install it or you set it up, you'd say, I want Django modern rest as a package bracket Pydantic or bracket adders or bracket msgspec so that you get your various dependencies installed that you're going to need. 00:04:08.100 --> 00:04:09.020 Or, you know, just whatever. 00:04:09.200 --> 00:04:10.860 Just put Pydantic as a dependency as well. 00:04:10.900 --> 00:04:11.520 Then you're good to go. 00:04:11.720 --> 00:04:12.560 Also interesting. 00:04:12.760 --> 00:04:18.680 Remember I talked about LLMs.txt and how I added that to Talk Python so people can get better. 00:04:19.040 --> 00:04:20.960 LLMs understand better how to work with Talk Python. 00:04:20.960 --> 00:04:23.740 They also understand how better to work with my courses. 00:04:24.120 --> 00:04:25.100 So this does this as well. 00:04:25.200 --> 00:04:32.020 It has explicitly a LLMs.txt and a LLMs-full.txt. 00:04:32.180 --> 00:04:37.000 So if you just say, hey, Claude or whatever I'm working with, I'm going to start this project. 00:04:37.320 --> 00:04:40.180 And it's using Django modern rest, a pretty new framework. 00:04:40.280 --> 00:04:41.160 You might not know it. 00:04:41.420 --> 00:04:48.700 So you can actually just drop that URL and say, please read this before you begin this project and make a note that this is a resource for you. 00:04:48.700 --> 00:04:51.020 It also has support for Context 7. 00:04:51.220 --> 00:04:52.240 Are you familiar with Context 7? 00:04:52.580 --> 00:04:52.780 No. 00:04:53.040 --> 00:04:57.620 So Context 7 is, I honestly don't really know what to make of Context 7. 00:04:57.720 --> 00:04:59.960 I thought I understood it, but I kind of don't necessarily. 00:05:00.180 --> 00:05:06.160 But what it does is Context 7 is a website where you can enter different libraries into. 00:05:06.160 --> 00:05:13.660 And then they parse it and turn it into something that AIs can use to understand that library better. 00:05:13.820 --> 00:05:18.520 I'm not sure how great it works, but you come in here and it has different skills, for example. 00:05:18.660 --> 00:05:23.920 Like it has a Django modern rest from Django rest framework skill. 00:05:23.920 --> 00:05:37.240 So you could give it this skill and say, hey, use this agent that understands both of these frameworks because I want to upgrade from Django rest framework DRF, which has some of the craziness that we talked about last week. 00:05:37.300 --> 00:05:37.560 Remember? 00:05:37.920 --> 00:05:39.380 Or two weeks ago, but last episode. 00:05:39.640 --> 00:05:41.860 Anyway, this is a pretty cool project. 00:05:42.580 --> 00:05:43.020 Interesting. 00:05:43.580 --> 00:05:44.300 Yeah, yeah, yeah. 00:05:44.640 --> 00:05:47.080 It's got stuff for, hey, for mine. 00:05:47.280 --> 00:05:48.480 Let's see what it says about it. 00:05:48.500 --> 00:05:48.800 I don't know. 00:05:48.860 --> 00:05:49.940 Actually, it just takes me to it. 00:05:49.940 --> 00:05:54.100 But, you know, it's got, you can submit your own library to this, by the way. 00:05:54.160 --> 00:05:55.860 So that's, I think, how this got here. 00:05:55.900 --> 00:05:58.140 I think I may have submitted this and so on. 00:05:58.140 --> 00:06:01.820 But, yeah, anyway, you can say, hey, I want AIs to understand my library better. 00:06:01.960 --> 00:06:04.440 And this also has a MCP, which you can install. 00:06:04.740 --> 00:06:06.400 Actually, I'm not a super huge fan of it. 00:06:06.420 --> 00:06:07.760 I got other things that I do for this. 00:06:07.820 --> 00:06:15.320 But, anyway, it's interesting that they explicitly went to that effort to help you get started, both converting and just working with. 00:06:15.500 --> 00:06:16.600 All right, so let's look at this showcase. 00:06:16.600 --> 00:06:21.360 Like, notice here, oh, actually, I gave them some short shrift here. 00:06:21.460 --> 00:06:21.800 Look at this. 00:06:22.220 --> 00:06:23.860 They do message spec, which is cool. 00:06:23.940 --> 00:06:24.820 So you can do msgspec. 00:06:25.000 --> 00:06:27.900 But they also do Pydantic, Atchers, Data Classes. 00:06:28.100 --> 00:06:29.160 We're going to be coming back to that. 00:06:29.440 --> 00:06:30.100 Typed Dict. 00:06:30.440 --> 00:06:31.840 Now, that I did not see coming. 00:06:32.100 --> 00:06:32.660 In Typed Dict. 00:06:32.820 --> 00:06:36.540 And straight named tuples as one of your foundations, if you like. 00:06:36.680 --> 00:06:37.380 How interesting is this? 00:06:37.860 --> 00:06:38.040 Okay. 00:06:38.500 --> 00:06:39.300 All right, so let's go down. 00:06:39.340 --> 00:06:41.820 If you scroll down, I didn't want to do the, I'm going to do message. 00:06:41.960 --> 00:06:43.120 I'll do Pydantic, whatever. 00:06:43.120 --> 00:06:44.760 So we can go down a little bit further. 00:06:44.760 --> 00:06:46.440 And there's a full example here. 00:06:46.700 --> 00:06:49.940 And it just shows you, like, a one file Django thing. 00:06:50.020 --> 00:06:53.260 So it shows you how to set up your Django app, your templates, et cetera, et cetera. 00:06:53.420 --> 00:07:00.860 And then you just can create, you create these models for data exchange in your web app, which I think is pretty interesting. 00:07:00.860 --> 00:07:05.840 Because a lot of people think of the model exchange to be, like, basically database classes. 00:07:06.060 --> 00:07:07.420 But that's not really what you want. 00:07:07.480 --> 00:07:09.080 You want, like, what does this form submit? 00:07:09.120 --> 00:07:13.280 Or what does this API receive as data, regardless of how we store the database, right? 00:07:13.800 --> 00:07:14.000 Yeah. 00:07:14.000 --> 00:07:21.700 So as user create model, which just has an email, or a user response model, which just has a UUID, which is the ID of the created user, right? 00:07:21.920 --> 00:07:23.480 So then you can create an endpoint. 00:07:23.760 --> 00:07:26.040 And you say this thing accepts a post. 00:07:26.500 --> 00:07:33.960 It is, you derive the class from controller of Pydantic serializer or controller of actors serializer. 00:07:33.960 --> 00:07:40.340 And then for your signature of your function, you say, hey, I want body of user create model. 00:07:40.420 --> 00:07:43.820 And then it automatically parses and validates that Pydantic style. 00:07:44.060 --> 00:07:44.920 And then you just use it. 00:07:44.980 --> 00:07:49.120 So you'll never get to your code if your Pydantic model doesn't validate, parse, all those things. 00:07:49.200 --> 00:07:50.560 So you don't have to check that kind of stuff. 00:07:50.720 --> 00:07:51.080 Pretty neat. 00:07:51.340 --> 00:07:51.980 Yeah, that is cool. 00:07:52.240 --> 00:07:52.380 Yeah. 00:07:52.400 --> 00:07:53.580 So there's a lot more to this. 00:07:54.160 --> 00:07:57.400 People can dig into it and explore it. 00:07:57.400 --> 00:07:59.200 But it looks like a pretty strong contender. 00:07:59.200 --> 00:08:03.860 If you bump over to GitHub, it's got about 1,000 stars, 100 forks. 00:08:04.020 --> 00:08:04.620 Pretty active. 00:08:04.700 --> 00:08:05.460 How old does it look? 00:08:05.700 --> 00:08:06.100 A month? 00:08:07.000 --> 00:08:07.680 No, six months. 00:08:07.960 --> 00:08:09.260 It was created six months ago. 00:08:09.380 --> 00:08:11.260 So I think that's still, that's pretty good growth. 00:08:11.520 --> 00:08:13.180 1,000 stars in six months. 00:08:13.440 --> 00:08:15.100 I mean, it's not open claw, but that's. 00:08:15.520 --> 00:08:18.600 It looks like there's a lot of active development going on still right now. 00:08:18.800 --> 00:08:20.740 Yeah, let's, yeah, a commit an hour ago. 00:08:20.800 --> 00:08:21.600 Let's look at the commits. 00:08:22.060 --> 00:08:22.920 What's going on here? 00:08:23.100 --> 00:08:26.460 Four hours, five hours, seven hours, 11 hours. 00:08:26.460 --> 00:08:29.100 Yeah, that's just, those are the active commits of today. 00:08:29.200 --> 00:08:30.380 That's pretty solid, honestly. 00:08:30.580 --> 00:08:31.080 Really good. 00:08:31.340 --> 00:08:31.460 Yeah. 00:08:31.740 --> 00:08:37.400 So anyway, I throw it out there as another Django area that people can pay attention to. 00:08:37.560 --> 00:08:38.560 Another Django framework. 00:08:38.820 --> 00:08:45.900 It's very similar to FastAPI and very similar to Django Ninja, but it seems like it's a little more flexible in the way you work with it. 00:08:46.440 --> 00:08:46.880 Yeah. 00:08:47.220 --> 00:08:51.440 Interesting comment here from SendPos. 00:08:51.820 --> 00:08:54.500 Django Ninja, Django Bolt, Django Modern REST. 00:08:54.500 --> 00:08:56.780 I guess people get back to Django and enjoy it. 00:08:57.000 --> 00:08:57.560 Nice to see. 00:08:57.560 --> 00:09:07.600 And I think that has, I think there's a lot to be said for using LLMs and AIs because Django's been around for a while, so they know how to deal with it. 00:09:08.620 --> 00:09:08.820 Yeah. 00:09:09.780 --> 00:09:12.480 Django is very well understood by AI. 00:09:12.700 --> 00:09:15.640 So that's actually, that's actually a huge bonus in my mind. 00:09:15.820 --> 00:09:16.040 Yeah. 00:09:16.280 --> 00:09:16.460 Yeah. 00:09:16.620 --> 00:09:18.380 Well, should we shift gears? 00:09:19.020 --> 00:09:19.480 What's new? 00:09:19.820 --> 00:09:21.580 Well, what's new is Python. 00:09:22.180 --> 00:09:22.160 So. 00:09:22.460 --> 00:09:23.640 I think it's been on for 30 years. 00:09:23.640 --> 00:09:24.360 What are you talking about? 00:09:25.400 --> 00:09:28.420 Well, so Python, I'm, I'm looking forward. 00:09:28.860 --> 00:09:31.440 So there's a Hugo von Caminad. 00:09:32.540 --> 00:09:33.240 I'm sorry. 00:09:33.360 --> 00:09:36.060 I always mispronounce your name, but we love Hugo. 00:09:36.060 --> 00:09:49.340 So, so 315, three, there's 315, alpha eight is out plus a release of 314, 314, 4, and also 313, 13 are out. 00:09:49.580 --> 00:09:53.420 There's a post about that, but I, I'm looking forward to 315. 00:09:53.700 --> 00:09:58.400 So when does 315, we're, we're looking at the status of the versions. 00:09:58.400 --> 00:10:04.260 we get, we still have like six months to go before we can really solidify the start using it. 00:10:04.340 --> 00:10:08.980 But, but I think that I'm excited to get started sooner anyway. 00:10:08.980 --> 00:10:12.280 So we've got what, a beta has come out in May. 00:10:12.620 --> 00:10:15.640 CR is in September and the final is planned for October. 00:10:15.760 --> 00:10:17.180 But, look at it. 00:10:17.180 --> 00:10:23.500 What, what, what's already in there all already in the alpha, we've got, explicit lazy imports. 00:10:23.500 --> 00:10:27.740 and that we would, we've been talking about that on the show. 00:10:28.100 --> 00:10:30.420 and that's, that's already there. 00:10:30.500 --> 00:10:32.160 Frozen dict built-in type. 00:10:32.300 --> 00:10:34.600 anyway, what do I have up here? 00:10:34.640 --> 00:10:38.100 I've got, yeah, the frozen, frozen dict built-in type. 00:10:38.200 --> 00:10:45.740 This is pretty cool to be able to, to by default do a, like a dictionary that can be used as that's hashable. 00:10:45.740 --> 00:10:52.560 You assign it at instantiation time and, or when you define it and, and you can't change it after that. 00:10:52.560 --> 00:10:54.020 so it's hashable. 00:10:54.160 --> 00:10:55.380 So that's, that's pretty cool. 00:10:55.640 --> 00:10:55.760 Yeah. 00:10:55.780 --> 00:11:05.580 And one thing I'd like to add to this frozen dict that I think is super interesting, we've had other frozen types like set, I think, and list, but in this Python T, the free threaded 00:11:05.580 --> 00:11:13.280 Python world, one of the things that can really unlock concurrency is not having to worry about locking on different objects. 00:11:13.400 --> 00:11:19.460 If you work with frozen dicts, it's read only, and you can just have all the threads ram on it all at once, right? 00:11:19.520 --> 00:11:21.760 You don't have to worry about locks once it's created. 00:11:21.760 --> 00:11:27.280 So people just consider adopting immutable data types in general when possible. 00:11:27.280 --> 00:11:34.380 Like if you're creating a dict, but you're not going to change it, frozen dict seems like something cool to put in place that like adds a little more security. 00:11:34.380 --> 00:11:37.900 So like if you don't expect it to change, like you can set it up now. 00:11:37.900 --> 00:11:38.980 So it cannot change. 00:11:39.220 --> 00:11:39.560 Yeah. 00:11:39.560 --> 00:11:45.660 And even things that generally we think of changing, we, you can use data flows like algorithmic stuff to do. 00:11:45.660 --> 00:11:46.840 That's functional. 00:11:46.840 --> 00:11:55.300 Like, like you said, it's a functional model for some part of your system that can be easily async asynced because it's all, it's all immutable types. 00:11:55.300 --> 00:11:56.780 So yeah, pretty cool. 00:11:57.660 --> 00:12:02.080 We got unpacking of comprehensions better. 00:12:02.460 --> 00:12:03.420 So that's kind of fun. 00:12:03.700 --> 00:12:06.340 Star and star star are working better for that stuff. 00:12:07.200 --> 00:12:07.800 Let's see. 00:12:07.840 --> 00:12:09.620 What did I have here that I wanted to talk about? 00:12:09.800 --> 00:12:10.340 I don't remember. 00:12:10.660 --> 00:12:10.840 Anyway. 00:12:11.960 --> 00:12:12.440 Yeah. 00:12:12.720 --> 00:12:13.900 Lots of great stuff. 00:12:14.140 --> 00:12:14.460 Let's see. 00:12:14.540 --> 00:12:15.720 Annotated type forms. 00:12:16.180 --> 00:12:18.720 Oh, Python now uses UTF-8 as default encoding. 00:12:19.020 --> 00:12:21.960 Can't wait for that because I'm tired of typing UTF-8. 00:12:21.960 --> 00:12:25.180 Did you shout out lazy imports? 00:12:25.940 --> 00:12:26.200 Yeah. 00:12:26.400 --> 00:12:27.900 Well, explicit lazy imports. 00:12:28.080 --> 00:12:37.300 That's, that's probably what I'm most excited about is, is being able to just say, just say lazy import Jason or lazy import whatever. 00:12:37.540 --> 00:12:41.680 And it doesn't actually get imported until somebody actually uses it at runtime. 00:12:41.940 --> 00:12:49.080 That's going to make, that's just such a clean interface and it's going to make everything, a lot of stuff so much faster. 00:12:49.080 --> 00:12:58.520 In my world, it's the testing stuff because so much you, because pytest imports everything to start with and, but it doesn't need anything. 00:12:58.520 --> 00:13:01.880 Like the tests only need this stuff when they're, the tests are actually running. 00:13:02.080 --> 00:13:06.080 So having test runs will be a lot faster with lazy imports. 00:13:06.560 --> 00:13:09.940 And can I add that that's a little bit of foreshadowing right there? 00:13:10.300 --> 00:13:11.040 Is it? 00:13:11.220 --> 00:13:11.680 It is. 00:13:11.800 --> 00:13:12.120 Carry on. 00:13:12.120 --> 00:13:12.560 Okay. 00:13:12.560 --> 00:13:12.760 Okay. 00:13:13.020 --> 00:13:20.320 No, I just, just a lot, a lot of exciting stuff going on in, in 3.15 that I'm, I, I'm art. 00:13:20.600 --> 00:13:23.120 Oh, I'm already excited to play with it. 00:13:23.120 --> 00:13:31.120 And, and it used to be, I think like, I don't even remember how far ago it was where it was sort of hard to grab an alpha release. 00:13:31.120 --> 00:13:39.040 But now with uv, I just said uv self update and uv Python install 3.15 and bam, I had the alpha. 00:13:39.840 --> 00:13:41.120 So it's pretty great. 00:13:41.120 --> 00:13:41.220 That's awesome. 00:13:41.400 --> 00:13:41.560 Yeah. 00:13:41.600 --> 00:13:47.520 You can even just say uv, V, E, and V, and just say, you know, dash that, like give it a version of the Python. 00:13:47.760 --> 00:13:50.560 And if you don't have it, it'll just go and say, okay, we're getting 3.15. 00:13:50.820 --> 00:13:51.160 Yeah. 00:13:51.360 --> 00:13:51.900 Pretty cool. 00:13:52.180 --> 00:13:52.340 Yeah. 00:13:53.240 --> 00:13:57.240 Anyway, that's all I wanted to say, but just, I'm excited about 3.15. 00:13:57.480 --> 00:13:57.680 Cool. 00:13:57.780 --> 00:14:00.140 I am excited about 3.15 as well. 00:14:00.480 --> 00:14:13.200 And that is because I just, I'm excited about a lot of the things there, but I'm extra excited about this, this lazy, this PEP 810 lazy imports, because I've discovered that it can make a mega 00:14:13.200 --> 00:14:17.860 difference and it lets you write a lot of clean code, a lot of cleaner code than you would right now. 00:14:17.860 --> 00:14:24.960 So I wrote an article that was sort of a, a guide of some project I did a couple of weeks ago. 00:14:25.180 --> 00:14:28.980 And I would have talked about it last week, but we skipped last week because I was at a conference. 00:14:29.200 --> 00:14:36.760 So we're talking about this week and the title of the article is cutting Python web app memory by over 31%. 00:14:36.760 --> 00:14:44.240 And that's across the entire server, running Python bytes, running talk Python, Talk Python Training, all those things. 00:14:44.240 --> 00:14:50.640 So I just sat down and said, you know, it's kind of ridiculous how much memory these apps use, like Talk Python Training alone. 00:14:50.960 --> 00:14:55.020 I'm just going to focus on that, but like apply to this, to most Python web apps or APIs. 00:14:55.180 --> 00:14:59.360 It alone was using one point, almost 1.3 gigs to run. 00:14:59.460 --> 00:15:01.140 Like that seems a little ridiculous. 00:15:01.560 --> 00:15:03.480 And there's a separate little search daemon process. 00:15:03.480 --> 00:15:05.860 And it was using 700 megs. 00:15:06.020 --> 00:15:06.740 Just chilling. 00:15:06.920 --> 00:15:08.380 Why do you need so much memory? 00:15:08.720 --> 00:15:09.560 Bad Python app. 00:15:09.740 --> 00:15:10.860 Who wrote you is what I want to know. 00:15:10.860 --> 00:15:17.020 So I set about the process of going like, at least let me understand what, where this memory is going. 00:15:17.360 --> 00:15:20.420 And if there's any way that I could do something to make it better. 00:15:20.520 --> 00:15:20.920 Okay. 00:15:21.240 --> 00:15:23.240 It's not like we were running out of memory, right? 00:15:23.260 --> 00:15:30.460 I have a 16 gig server running in the cloud and I think it was using nine or 10 gigs. 00:15:30.520 --> 00:15:34.200 So there were six gigs left, but at the same time, it's what if I want to run other apps? 00:15:34.200 --> 00:15:43.580 Like I want to self host something that would maybe power the web apps or, you know, be like a CRM or some other thing that I just want to run and not have to set up other infrastructure. 00:15:43.800 --> 00:15:45.580 It'd be great if there's like, oh, there's so much RAM. 00:15:45.640 --> 00:15:46.400 It doesn't even matter. 00:15:46.640 --> 00:15:47.360 You know what I mean? 00:15:47.640 --> 00:15:54.000 And RAM by far is by far more critical and scarce than CPU. 00:15:54.400 --> 00:15:58.820 Not, you know, put this RAM crisis aside, this stuff that AI is triggering. 00:15:58.820 --> 00:16:04.960 Just straight, you get 16 gigs RAM, you get eight CPU for most people, most workloads. 00:16:05.240 --> 00:16:08.820 The CPU is pretty chill and the RAM is a lot higher. 00:16:08.940 --> 00:16:12.420 You got to get a lot of traffic before CPU becomes the problem, right? 00:16:12.660 --> 00:16:14.200 So thinking about the RAM, I think is important. 00:16:14.480 --> 00:16:17.760 So I started working on this and said, well, what can I do? 00:16:17.820 --> 00:16:22.160 And the starting point was 100, 1,280 megabytes. 00:16:22.380 --> 00:16:33.240 And the little search daemon thing that I told you about that once an hour, once every day, I can't remember the schedule, I think it's a few times a day, it'll pull all the content of Talk Python training and turn it into a search engine. 00:16:33.360 --> 00:16:38.100 So like you go over here and you're like, hey, I'm interested in, you know, taking some class. 00:16:38.360 --> 00:16:43.680 And in your class, you can say, oh, I'm not logged in, but you could just go over and search and say, well, do you talk about pytest? 00:16:43.740 --> 00:16:44.140 Let's see. 00:16:44.460 --> 00:16:45.780 Well, why, yes, we do. 00:16:46.280 --> 00:16:51.260 And it has like all these really nice, deep understanding of like the hierarchy of stuff. 00:16:51.340 --> 00:16:52.820 It's not just like a regular search, right? 00:16:52.880 --> 00:17:00.780 Like it's something I put together, but it's still, this is a ridiculous amount, 700 megs for something that just like reads from the database and writes to the database and otherwise is doing nothing. 00:17:01.020 --> 00:17:02.880 So I'm like, well, how can I do this better? 00:17:03.080 --> 00:17:05.840 The first thing I did, there's five things I'm going to talk about. 00:17:06.080 --> 00:17:14.760 Number one is I was running two to three worker processes to scale out web requests because everything was still based on Pyramid. 00:17:15.020 --> 00:17:15.860 It's synchronous. 00:17:16.060 --> 00:17:16.660 It's WSGI. 00:17:17.000 --> 00:17:23.000 I'm going to have fewer worker processes than I really want to have better concurrency in the one worker process that's there, right? 00:17:23.000 --> 00:17:26.680 I don't want like one slow request to be just like, well, that's it. 00:17:27.180 --> 00:17:29.000 The site's not responding, right? 00:17:29.040 --> 00:17:29.600 Because of the kill. 00:17:29.600 --> 00:17:38.240 I decided the first thing to do was to rewrite everything in Quart and it could be other languages, anything that's async. 00:17:38.300 --> 00:17:45.480 I could have rewritten in FastAPI, but I really like the Flask model and Quart is the true async version of Flask, right? 00:17:45.800 --> 00:17:47.500 So that's what I did. 00:17:47.500 --> 00:17:54.220 And that let me turn it, turn the other worker process off, which right there just cuts your memory straight in half, right? 00:17:54.240 --> 00:17:56.560 Because they both use the same amount of memory and there's two of them. 00:17:56.780 --> 00:17:58.320 And you know, people think like, oh yeah, whatever. 00:17:58.440 --> 00:18:00.480 Like Michael, your site's just a, it's just a blog. 00:18:00.560 --> 00:18:01.620 Like dude, what are you talking about? 00:18:01.640 --> 00:18:05.240 Like you seem to think like there's a lot going on here, but I work on a real app. 00:18:05.380 --> 00:18:06.580 So it doesn't apply to me. 00:18:06.800 --> 00:18:11.840 I ran my little tallyman thing against Talk Python training, not the, any of the podcasts of just the courses. 00:18:12.680 --> 00:18:16.040 178,000 lines of Python, 300,000 lines total. 00:18:16.040 --> 00:18:19.320 Like that's enough to like spend some time figuring out what's going on, right? 00:18:19.340 --> 00:18:23.380 That's complicated enough for most apps I imagine, at least to be somewhat representative. 00:18:23.820 --> 00:18:26.740 But you're like running courses and podcasts and stuff. 00:18:26.820 --> 00:18:28.440 This is more complicated than just a blog. 00:18:28.660 --> 00:18:29.280 Yeah, that's true. 00:18:29.380 --> 00:18:29.880 That's true. 00:18:30.060 --> 00:18:30.240 Yeah. 00:18:30.300 --> 00:18:31.260 But tell Reddit that. 00:18:31.560 --> 00:18:43.540 So then the next thing, the number two was I'm going to rewrite this in that the raw plus DC design pattern that I talked about just using straight queries, not ORMs or ODMs. 00:18:43.640 --> 00:18:46.020 Although I have something interesting to say my extra about that, but still. 00:18:46.040 --> 00:18:50.680 So just straight queries and then mapping data classes, or it could be pydantic or adders, right? 00:18:50.720 --> 00:18:51.340 It doesn't really matter. 00:18:51.440 --> 00:18:53.420 Pick a model, a simple data model. 00:18:53.640 --> 00:18:55.600 And that actually made a pretty big difference. 00:18:55.600 --> 00:19:04.300 That dropped 200 megs, 100 megs per worker off just switching away from an ORM to just raw queries and data classes. 00:19:04.840 --> 00:19:05.540 Makes sense. 00:19:05.860 --> 00:19:08.660 And it almost doubled the request per second, which is wild. 00:19:08.660 --> 00:19:09.060 Okay. 00:19:09.160 --> 00:19:17.740 So then number three, well, once I had done the court thing, I was able to just tell Granny and like, I want one worker or not two or three. 00:19:18.040 --> 00:19:19.180 For a while it was three or four. 00:19:19.260 --> 00:19:20.780 And I've been like dialing it back. 00:19:20.860 --> 00:19:21.520 It's just got faster. 00:19:21.860 --> 00:19:23.640 So that saved 500 megs there. 00:19:23.720 --> 00:19:27.720 So now we're down to, yeah, we're down to 530 megs for this process. 00:19:27.720 --> 00:19:36.020 And then for the search thing, what was happening is it would load up a bunch of imports, PEP 5, 8, 8, 10, getting exciting here. 00:19:36.060 --> 00:19:49.100 It would load up a bunch of imports and then it would run a bunch of code that would like get all, I don't know exactly where the memory was going, but a lot of stuff that it would work with by doing, interact with the database and so on would get cached or just left in memory. 00:19:49.100 --> 00:19:53.440 So it was using 700, 708 megs of memory. 00:19:53.720 --> 00:20:05.620 So I said, well, what if like really the main core loop of the app is just start, look at a timer after a certain amount of time, run a really complicated set of queries, and then 00:20:05.620 --> 00:20:11.740 write a bunch of structured data indexed back into a certain structure so I can query ultra fast, right? 00:20:11.960 --> 00:20:20.460 That, that main part doesn't need, it was importing like all the library, like all of Talk Python training, which would pull in everything that Talk Python training's main dunder. 00:20:20.560 --> 00:20:25.280 And it would pull in, which would pull in all the libraries that every, you know, it would cascade into this mega import. 00:20:25.600 --> 00:20:38.120 So I said, well, what if you just had the loop and then in a separate file, you ran, you would start a process that ran that separate file that did the indexing and then stopped and like just finished, like, cause it didn't need a response. 00:20:38.180 --> 00:20:39.600 It was like, okay, I'm done indexing. 00:20:39.660 --> 00:20:44.360 And that temporary sub process would be the thing that did all the imports. 00:20:44.360 --> 00:20:46.080 And when it shuts down, those imports go away. 00:20:46.080 --> 00:20:49.200 So that took it from 708 megs to 22. 00:20:49.540 --> 00:20:50.180 That's great. 00:20:50.420 --> 00:20:51.540 That's insane, right? 00:20:51.840 --> 00:20:54.240 And all I had to do is change where, sorry, go ahead. 00:20:54.260 --> 00:20:58.220 The sub process is still getting like 700 megs or whatever, but, but it goes away. 00:20:58.380 --> 00:21:01.360 But for like 30 seconds to a minute, not constantly. 00:21:01.620 --> 00:21:01.840 Right. 00:21:02.060 --> 00:21:07.680 And these spikes are, I mean, they're fine, but it's like, you know, not everything is spiking in memory at the same time, just like they don't in CPU. 00:21:07.860 --> 00:21:11.540 It's just like, it's not a fixed cost, which is pretty interesting. 00:21:11.540 --> 00:21:22.900 You know, the work was just like, I'm just going to do the index, move the indexing function to a new file, move the imports that it needs to a new file and just do a sub process to call it instead of calling it directly done. 00:21:23.160 --> 00:21:23.240 Yeah. 00:21:23.460 --> 00:21:27.240 And it made, I don't know what that division is, but like 20 times, 30 times better. 00:21:27.440 --> 00:21:28.420 You know, it's incredible. 00:21:28.420 --> 00:21:32.940 And the last thing, this one I think is going to surprise people. 00:21:33.040 --> 00:21:37.220 And this is the one that really hits the point home for lazy imports. 00:21:37.380 --> 00:21:47.340 If you type the words import Boto3, because you're doing something with S3 or something similar, your working memory goes up by 25 megs per process, per worker. 00:21:47.480 --> 00:21:51.820 If you type the words import matplotlib, your working memory goes up by 17 megs. 00:21:51.900 --> 00:21:54.640 If you type import pandas, your memory goes up by 44 megs. 00:21:54.640 --> 00:21:58.080 Those three imports right there are almost a hundred megs of memory usage. 00:21:58.080 --> 00:21:58.560 Yeah. 00:21:58.680 --> 00:21:59.500 It's a lot. 00:21:59.760 --> 00:22:00.840 So are they needed? 00:22:00.920 --> 00:22:02.620 If you're doing core data science, they are. 00:22:02.940 --> 00:22:08.120 But for me, there's like an admin section where I can go and view some reports. 00:22:08.280 --> 00:22:16.820 If I view the reports, I run, I use these libraries, but if I don't view the reports, which I really don't look at them hardly ever, just like maybe once a month, like, Hey, I wonder what that looks like. 00:22:17.100 --> 00:22:17.820 Let me go hit it. 00:22:17.920 --> 00:22:20.540 It pulls all that stuff in and then it generates the report. 00:22:20.540 --> 00:22:24.260 But the worker process recycles a couple of times a day. 00:22:24.720 --> 00:22:24.740 Yeah. 00:22:24.740 --> 00:22:32.500 So even if I view the report five hours later, that stuff's unloaded again and I get a new version and it's not another month until I load up that a hundred megs. 00:22:32.600 --> 00:22:44.260 So I went from like 500 megs to 450 just by saying, well, instead of importing the top of the file, let's import in the function that generates the actual picture, you know, the report that I need from this. 00:22:44.300 --> 00:22:46.460 And boom, a hundred megs less memory usage. 00:22:46.460 --> 00:22:50.460 And if that had the word lazy in front of it, I wouldn't have to rewrite my code. 00:22:50.580 --> 00:22:53.120 It would have effectively the same behavior. 00:22:53.260 --> 00:22:57.940 It wouldn't import until I actually run the function, but I could do PEP 8 magic and put it at the top. 00:22:58.040 --> 00:22:58.680 What do you think of that? 00:22:58.880 --> 00:22:59.500 That's pretty cool. 00:22:59.560 --> 00:23:05.220 So now I'm thinking that like lazy imports, it imports when it needed, but it doesn't, it doesn't ever unimport. 00:23:05.220 --> 00:23:14.720 And I'm wondering if like a future Python will add like, you know, some, something to the lazy import that like caches stuff out of memory. 00:23:15.000 --> 00:23:15.900 Right, right, right. 00:23:15.900 --> 00:23:22.320 Like the runtime could see nobody is caring about it being imported anymore and no one has set a value on it. 00:23:22.380 --> 00:23:24.460 So maybe it could just go away a hundred percent. 00:23:24.460 --> 00:23:35.560 But that means, I mean, people I think often think of lazy import as something that's a speed, a speed up like, oh, it's faster because you don't have to do all the imports until you use them. 00:23:35.740 --> 00:23:36.680 I'm sure that's true. 00:23:36.760 --> 00:23:37.760 I don't have numbers around it. 00:23:37.900 --> 00:23:44.120 But what's really interesting is there are some imports that are mega in amount, how much they actually increase your working memory. 00:23:44.560 --> 00:23:48.240 If they're lazy and you don't use them very often, they will not run very often. 00:23:48.240 --> 00:23:50.580 And I think it'll actually make a pretty big difference. 00:23:50.580 --> 00:23:55.420 So, you know, like you just write code, like just do import inside the function instead of at the top. 00:23:55.520 --> 00:23:57.240 Like your lenders will go, you shouldn't do this. 00:23:57.340 --> 00:23:58.340 Like you leave me alone. 00:23:58.420 --> 00:23:59.000 I'm doing this. 00:23:59.060 --> 00:24:00.640 This is, this is really good for me. 00:24:01.400 --> 00:24:07.580 So the final thing, I just moved a bunch of caches to disk caches instead of memory caches, which is good. 00:24:07.880 --> 00:24:19.660 And so I put a little picture in there, but it, it saved a ton of memory across 3.2 gigs, less memory used on the server by applying that to Python bytes, talk Python and Talk Python Training. 00:24:19.660 --> 00:24:22.940 There's a bunch of other apps, but they, oh, and the search thingy. 00:24:23.220 --> 00:24:25.760 But yeah, those were the four big ones. 00:24:26.040 --> 00:24:26.140 Cool. 00:24:26.180 --> 00:24:26.620 Pretty cool, huh? 00:24:26.820 --> 00:24:26.960 Yeah. 00:24:27.040 --> 00:24:27.780 Yeah, it's very cool. 00:24:28.060 --> 00:24:35.820 So a little bit long there, but I thought that people might appreciate some kind of a roadmap and how you can do that yourself. 00:24:35.940 --> 00:24:44.920 Like, so the total savings was 190, 1,988 megabytes to 472 megabytes used across the apps. 00:24:44.920 --> 00:24:49.180 That is a lot of difference and you can get a way, you can run more apps. 00:24:49.380 --> 00:24:54.940 You can scale out more and get way better performance because now you can like run more workers if you really needed to or whatever. 00:24:55.020 --> 00:24:57.980 I think there's a lot of, a lot of benefits there. 00:24:58.480 --> 00:24:59.380 So cool. 00:24:59.460 --> 00:25:01.860 A lot of excitement out in the audience. 00:25:01.860 --> 00:25:03.720 They're talking about this topic. 00:25:03.800 --> 00:25:04.340 I think it's cool. 00:25:04.600 --> 00:25:05.120 All right. 00:25:05.220 --> 00:25:07.660 Well, one of the things that I get excited about is testing. 00:25:07.660 --> 00:25:09.440 You don't say. 00:25:09.700 --> 00:25:11.520 Pick that up about me a little bit. 00:25:12.160 --> 00:25:17.260 So I want to talk about trike right now, like as in a tricycle. 00:25:17.520 --> 00:25:20.300 So this is a new, a new project. 00:25:21.060 --> 00:25:22.320 And how new? 00:25:22.540 --> 00:25:29.200 So it's got four stars, but it, I mean, it just went up like last month or something. 00:25:29.720 --> 00:25:30.620 Very recent. 00:25:30.620 --> 00:25:37.140 So taking a look at this, this was submitted by the person that created it, Justin Chapman. 00:25:37.260 --> 00:25:41.940 But, but I'm, you know, I like the idea of like thinking outside the box. 00:25:41.940 --> 00:25:43.140 So for testing. 00:25:43.280 --> 00:25:55.020 So this is trike is a rust based Python test runner with a just style API, which is, so I'm not, I'm not familiar with just, but that's what said, said JavaScript just. 00:25:55.180 --> 00:25:55.960 I don't remember. 00:25:56.300 --> 00:25:57.500 Anyway, maybe. 00:25:57.500 --> 00:25:58.100 I don't know. 00:25:58.100 --> 00:25:58.800 I think so. 00:25:58.800 --> 00:25:59.080 Yeah. 00:25:59.460 --> 00:26:04.940 So yeah, you, you can tell how Python focused I am most of the time. 00:26:05.680 --> 00:26:07.840 But so what is it going to look like? 00:26:07.880 --> 00:26:09.620 It's let's, let's zoom in a little bit. 00:26:10.940 --> 00:26:11.800 Getting started. 00:26:12.020 --> 00:26:16.140 So the, it, it looks like it looks way different than by tests. 00:26:17.140 --> 00:26:24.160 So we've got a, what a, let's say we've got a normal function that's add, for example, and we want to test that. 00:26:24.160 --> 00:26:27.960 We'd say like with describe and add. 00:26:27.960 --> 00:26:32.580 So with describe and then some comment with this, like your test name, I guess. 00:26:32.580 --> 00:26:35.580 And then decorators of tests. 00:26:35.660 --> 00:26:39.000 And then another, I think that's just a description, but it's saying. 00:26:39.000 --> 00:26:39.300 Yeah. 00:26:39.300 --> 00:26:42.120 First I thought it was a string that was being parsed to make it run. 00:26:42.120 --> 00:26:42.280 Yeah. 00:26:42.280 --> 00:26:43.060 Whereas this one plus one. 00:26:43.120 --> 00:26:44.840 I think that's just the message that comes out. 00:26:44.840 --> 00:26:45.020 Yeah. 00:26:45.020 --> 00:26:46.140 It's just the test case. 00:26:46.140 --> 00:26:51.140 And so this is a very basic, we're going to get more from Justin to describe this. 00:26:51.220 --> 00:26:53.480 I've like reached out to him and said, this is really interesting. 00:26:53.600 --> 00:26:54.560 I'd like to know more. 00:26:54.660 --> 00:26:55.760 So I'm going to do more research. 00:26:55.880 --> 00:27:00.620 I haven't really played with this yet, but, but I, but I, I'm intrigued by it. 00:27:01.360 --> 00:27:03.160 I kind of like pytest. 00:27:04.140 --> 00:27:07.360 pytest is, has, uses just assert. 00:27:07.360 --> 00:27:14.960 And for async for, for things that soft asserts, I guess I use the, my pytest plugin called check, pytest check. 00:27:15.260 --> 00:27:16.440 But this is different. 00:27:16.540 --> 00:27:21.400 All by default, all of these are soft asserts. 00:27:21.400 --> 00:27:22.660 So it doesn't stop the test. 00:27:22.720 --> 00:27:24.640 You can, you can expect a lot of things. 00:27:24.940 --> 00:27:26.160 It uses the expect keyword. 00:27:26.360 --> 00:27:27.460 So what do we got here? 00:27:27.500 --> 00:27:33.420 We've got a watch mode so that watches, watches to see if, if you have new things to, to test. 00:27:33.420 --> 00:27:37.540 Native async support, fast test discovery in source testing. 00:27:37.940 --> 00:27:43.880 So you can, the ability to just put tests right in the source code, instead of having to have a separate test. 00:27:44.000 --> 00:27:47.620 You can do that with pytest, of course, but mostly people don't. 00:27:47.760 --> 00:27:49.440 Doc test support, kind of like pytest. 00:27:49.600 --> 00:27:52.520 Client server mode, which is, that's, this is an interesting one. 00:27:52.620 --> 00:27:57.760 So the client server, they're all interesting, but this idea that you can have a server running. 00:27:58.060 --> 00:27:59.060 So why would you do that? 00:27:59.120 --> 00:28:06.440 So one of the things like I was just talking about for memory wise, if you run pytest, it has to import everything, imports a lot of stuff, and then you're running tests. 00:28:06.700 --> 00:28:11.700 The server is just doing that so that it's, it's, while you're doing watching, it's, it's done that. 00:28:11.760 --> 00:28:16.840 It's got a warm cache of everything so that, that the individual tests can go faster. 00:28:17.240 --> 00:28:22.040 So you get client server mode, you know, pretty assertion of diagnostics. 00:28:22.160 --> 00:28:25.360 Of course, we would expect no less from a new test framework. 00:28:25.360 --> 00:28:31.300 So does it basically parse all, do the test discovery once and then just rerun it? 00:28:31.500 --> 00:28:33.660 Unless, is that, is that why that, then? 00:28:34.040 --> 00:28:35.120 I think so. 00:28:35.240 --> 00:28:38.440 So it's doing, like, for instance, it's doing a changed mode. 00:28:38.780 --> 00:28:43.760 Oh, like there's, I think it's for picking up new tests. 00:28:43.760 --> 00:28:52.020 So as you're like, if you're doing test and development and you're modifying a test and modifying code, it'll pull in the stuff that changed into the, into the server. 00:28:52.020 --> 00:28:59.560 But it can probably skip discovery or limit discovery, like the change file or something like that. 00:28:59.620 --> 00:28:59.780 Yeah. 00:28:59.880 --> 00:29:00.360 That's interesting. 00:29:00.400 --> 00:29:00.780 Well, right. 00:29:00.860 --> 00:29:09.200 And it's using, I think it's using Git information to find out which, which, which elements have been modified and are, yeah, why not? 00:29:09.320 --> 00:29:10.760 Most people are using Git anyway. 00:29:10.920 --> 00:29:12.860 So I think that's how it's using it anyway. 00:29:13.140 --> 00:29:13.720 Oh, interesting. 00:29:14.000 --> 00:29:21.800 I commented that I just sent him information about pytestCheck and, and he added that to the, Oh yeah. 00:29:21.920 --> 00:29:23.680 Soft decisions like pytestCheck. 00:29:23.840 --> 00:29:24.160 Pretty cool. 00:29:24.660 --> 00:29:32.700 And I'll throw out there that I also, I'm plus one for them on the fluent API instead of the raw assert. 00:29:32.900 --> 00:29:37.120 I really like the expect this to equals dot. 00:29:37.120 --> 00:29:49.820 And like, you kind of like put it together as kind of an English like sentence where, you know, you can say like in list and give it an item and a list or something like, rather than, you know, just doing the exact rule. 00:29:50.100 --> 00:29:50.320 I don't know. 00:29:50.320 --> 00:29:51.600 I like that fluent API. 00:29:51.600 --> 00:29:51.940 Do you? 00:29:52.160 --> 00:29:52.360 Okay. 00:29:52.440 --> 00:29:52.640 Yes. 00:29:52.880 --> 00:29:53.920 Cause that's not something. 00:29:54.020 --> 00:29:54.260 I don't know. 00:29:54.480 --> 00:29:55.280 No, it's not weird. 00:29:55.400 --> 00:30:05.020 It shows up a few times in a, I've seen it in a couple of different test frameworks and it's, and even some, there's a, there's a, there's an extension for unit tests to be able to do this. 00:30:05.080 --> 00:30:10.380 And then I think I've seen pytest extensions to do this, but I, it's not something I am. 00:30:10.380 --> 00:30:16.920 I, I could get used to it, but, it reads in English fairly well. 00:30:17.160 --> 00:30:18.140 So yeah, that's why I like it. 00:30:18.180 --> 00:30:25.560 It's more writing, but the truth is if your editor is not auto completing when you type two, you're probably doing it wrong, right? 00:30:25.560 --> 00:30:29.440 You're not writing all those words, those characters you're, you're selecting them from a short list. 00:30:29.680 --> 00:30:29.860 Yeah. 00:30:29.920 --> 00:30:37.680 And for me, I like the readability rather than looking in a search statement and go, what is it really trying to get out with this like combination of something that resolves to a Boolean? 00:30:37.680 --> 00:30:44.580 You can say like, you know, expect this value, like to be in the list or to not be in, you know, like something like that. 00:30:44.620 --> 00:30:44.800 Right. 00:30:45.060 --> 00:30:45.500 Or, yeah. 00:30:45.820 --> 00:30:46.140 I don't know. 00:30:46.160 --> 00:30:47.740 I like the readability of it. 00:30:47.740 --> 00:30:53.840 I, I would just, just notice also this is made by Zensical, tie into previous conversations. 00:30:54.060 --> 00:30:55.180 At least the website is. 00:30:55.420 --> 00:31:02.660 So, anyway, I think that I'm, I think there's some interesting, interesting work here. 00:31:02.780 --> 00:31:04.300 I like the async support. 00:31:04.300 --> 00:31:08.380 I definitely want to play with this because I think that's, it's an interesting idea. 00:31:08.680 --> 00:31:12.100 so anyway, cool, cool. 00:31:12.220 --> 00:31:12.640 Well done. 00:31:12.740 --> 00:31:13.400 It's very new. 00:31:13.520 --> 00:31:15.620 We'll have to, we'll have to see where it goes. 00:31:15.620 --> 00:31:22.860 and I sent him some, I haven't, I'm sorry, Justin, I haven't, haven't read your reply yet. 00:31:22.920 --> 00:31:26.080 He's, he already responded to me with some questions. 00:31:26.080 --> 00:31:27.360 I sent some questions yesterday. 00:31:27.560 --> 00:31:29.880 kind of like I was curious about startup. 00:31:29.980 --> 00:31:33.560 Apparently there is a setup, setup, tear down sort of fixture. 00:31:33.560 --> 00:31:35.560 Like there is a fixture feature. 00:31:35.620 --> 00:31:36.760 I just haven't figured it out yet. 00:31:36.760 --> 00:31:38.300 So it's somewhere in the documentation. 00:31:38.600 --> 00:31:44.800 so anyway, exciting things and I'll keep, keep an eye on this, keep, in, on this space. 00:31:44.800 --> 00:31:50.420 So indeed, let me throw out a meta topic before we get to our extras, Brian, sort of inspired by this, but more broad. 00:31:50.760 --> 00:31:57.700 I know people are hesitant to adopt new frameworks, like a new testing framework or a new web framework or a new database thing or something. 00:31:57.820 --> 00:32:12.200 But with the agentic AI stuff that we have these days, if you pick one and you're like, Oh, it turns out it's no longer updated or I don't like it anymore or whatever, you know, it's so much easier to just go make that back into one of these or, or convert it onto the next thing. 00:32:12.200 --> 00:32:17.880 Instead of having some huge, like, Oh no, now we've got to take, you know, two weeks and we're all rewriting the tests. 00:32:17.880 --> 00:32:20.860 Like you probably could get two from pie dust pretty quickly. 00:32:21.200 --> 00:32:21.480 Yeah. 00:32:21.840 --> 00:32:22.040 Yeah. 00:32:22.100 --> 00:32:23.340 We probably could. 00:32:23.400 --> 00:32:30.780 And also, yeah, that, that, the questionable thing that the scary thing is like, like, this is pretty new. 00:32:30.780 --> 00:32:31.960 Is it going to stick around? 00:32:32.480 --> 00:32:35.660 Like, that, that, that's the big one. 00:32:35.780 --> 00:32:38.540 A lot of people have excitement around something. 00:32:38.700 --> 00:32:44.880 I was a little bit, so I took a look at this is under the Jay chap. 00:32:44.980 --> 00:32:48.280 and he's the current CTO of a new V. 00:32:48.460 --> 00:32:53.240 So he's got a, he's probably using this at work, with his, on his day job. 00:32:53.240 --> 00:32:54.820 So this is a good thing. 00:32:54.820 --> 00:32:59.520 he's contributed to ty and uv, which is kind of cool. 00:32:59.520 --> 00:33:00.360 So that's cool. 00:33:00.640 --> 00:33:09.700 There's some hints that maybe this will stick around and, maybe in, especially if he's using it, on a regular basis, he's probably using it and supporting it himself. 00:33:09.700 --> 00:33:11.840 So, a little bit more. 00:33:11.940 --> 00:33:12.120 Yeah. 00:33:12.140 --> 00:33:14.040 So I do check these, these sort of things out. 00:33:14.080 --> 00:33:21.840 I've seen a lot of new projects that are, probably assisted by you, AI to get created. 00:33:22.000 --> 00:33:24.220 It might just be a fun toy for somebody. 00:33:24.220 --> 00:33:30.100 And that's not something I really want to cover in this, on this podcast, but something that looks like maybe they're serious about it. 00:33:30.280 --> 00:33:30.500 Yeah. 00:33:30.560 --> 00:33:31.320 We'll cover it. 00:33:31.400 --> 00:33:31.980 It doesn't matter. 00:33:32.300 --> 00:33:32.980 So cool. 00:33:33.120 --> 00:33:33.320 Yeah. 00:33:33.320 --> 00:33:34.480 It looks very, very neat. 00:33:34.960 --> 00:33:35.560 All right. 00:33:35.800 --> 00:33:37.540 I've got a handful of extras. 00:33:37.760 --> 00:33:40.380 You want to hit your extras next or go ahead. 00:33:40.660 --> 00:33:40.840 Go ahead. 00:33:40.980 --> 00:33:41.220 Okay. 00:33:41.220 --> 00:33:42.280 let's see. 00:33:42.280 --> 00:33:49.820 I, I saw this came up in a couple of newsletters and I was intrigued by an article called why aren't we uv yet? 00:33:50.060 --> 00:33:59.180 talking about it did, did some analysis of different, not sure how they got them, but like some top Python projects, stack over. 00:33:59.320 --> 00:34:02.360 I don't know, that, you know, uv is popular. 00:34:02.500 --> 00:34:04.020 Why isn't it being used more? 00:34:04.020 --> 00:34:18.140 The interesting thing, I just, I'm not, I will, we'll link to it of course, but, I think one of the reasons why this, this gap looks so big is that a lot of people with requirements.txt are, are you using, are still using UV? 00:34:18.500 --> 00:34:29.880 we, I have a lot of projects at work that, are requirements.txt based and everybody I know, I'd like that our instructions are to use uv, that you just because it doesn't, 00:34:29.880 --> 00:34:34.860 we're not publishing, uv lock file doesn't mean that we're not using uv. 00:34:35.140 --> 00:34:38.400 So every single one of my projects, no, that's not true. 00:34:38.480 --> 00:34:44.440 Almost every one single one of our, my projects has a requirements.txt and no uv lock and it's all uv. 00:34:44.700 --> 00:34:44.980 Yeah. 00:34:45.320 --> 00:34:50.720 So I don't know if the assumptions of this study are correct is all I'm saying. 00:34:50.960 --> 00:34:51.180 Yeah. 00:34:51.180 --> 00:34:51.880 That's a really good point. 00:34:51.880 --> 00:34:56.740 I very much prefer requirements.txt with pinned stuff and like then the uv lock. 00:34:56.820 --> 00:34:57.800 I don't know why I just do. 00:34:58.400 --> 00:34:59.480 Well, I don't know why. 00:34:59.480 --> 00:35:06.740 Well, right now I, I'm, I'm leaving it open for the develop, the developer to choose, if they want to use uv or not. 00:35:06.880 --> 00:35:07.060 Yeah. 00:35:07.280 --> 00:35:12.180 we probably get to the point where we're not making that a choice, but, anyway. 00:35:12.420 --> 00:35:15.740 I find that requirements.txt diffs a little nicer. 00:35:15.880 --> 00:35:19.280 It's like, it's easier to read and especially get diffs to look and go, oh yeah, okay. 00:35:19.280 --> 00:35:20.300 This is what changed. 00:35:20.600 --> 00:35:28.760 Whereas the uv lock has got like so much, especially with the hashes and so on that it's like, ah, you know, there's so much noise in the uv lock versus the requirements.txt. 00:35:28.760 --> 00:35:29.560 So yeah. 00:35:29.720 --> 00:35:32.420 But I, I'm, I, mine are pretty noisy though. 00:35:32.420 --> 00:35:41.000 Cause I'm using like, requirements.in and, like using uv to publish a more detailed requirements.txt. 00:35:41.220 --> 00:35:41.340 Yeah. 00:35:41.340 --> 00:35:43.500 That's, I do the same thing, but I don't include the hashes. 00:35:43.580 --> 00:35:44.040 Maybe I should. 00:35:44.180 --> 00:35:44.600 I don't know. 00:35:44.600 --> 00:35:51.120 I think according to listening when I was listening to Python bytes recently and we are supposed to save ashes there. 00:35:51.580 --> 00:35:51.980 Okay. 00:35:52.240 --> 00:35:53.460 what else? 00:35:53.560 --> 00:35:57.240 the PyCon us, talk schedule is up. 00:35:57.500 --> 00:35:59.760 if you're going, you can check it out. 00:35:59.760 --> 00:36:10.240 One of the things that noticed that I knew, I knew as a, as a submitter, I did notice this, but there's an entire AI, channel, like what are they track an AI track? 00:36:10.240 --> 00:36:13.440 And I don't, I don't know how I feel about that, but yeah, whatever. 00:36:13.560 --> 00:36:14.540 I think I'm excited about it. 00:36:14.540 --> 00:36:14.860 Honestly. 00:36:15.160 --> 00:36:15.420 Oh yeah. 00:36:15.620 --> 00:36:15.820 Okay. 00:36:15.860 --> 00:36:16.060 Yeah. 00:36:16.060 --> 00:36:16.540 I think so. 00:36:16.580 --> 00:36:20.280 I won't be there, but everybody that does, you're going to be there. 00:36:20.440 --> 00:36:27.180 I think you will be there in spirit and I will hand out Python bytes stickers and they will be carrying some of you with them. 00:36:27.820 --> 00:36:28.220 Cool. 00:36:28.760 --> 00:36:29.720 let's see. 00:36:29.920 --> 00:36:37.540 Oh, I wasn't going to cover this, but now that I've already have it up, Justin Jackson, has a, what has technology done to us? 00:36:37.540 --> 00:36:38.680 blog post. 00:36:38.800 --> 00:36:40.420 I was just reading about that recently. 00:36:40.620 --> 00:36:43.380 Oh, that's it for my, Oh, I have one other extra. 00:36:43.540 --> 00:36:44.000 Here it is. 00:36:44.000 --> 00:36:48.400 the lean TDD book, I hinted at that earlier. 00:36:48.560 --> 00:36:54.900 I, in a couple of days ago, put out version 0.6.1. 00:36:55.600 --> 00:36:57.160 I'm still on, on track. 00:36:57.280 --> 00:37:00.640 So I, this is really close to what I want to read. 00:37:00.640 --> 00:37:04.100 So I'm, I, when I, I'm taking a trip business trip. 00:37:04.100 --> 00:37:10.940 And then when I get back from the business trip, I'm going to start recording the audio book for this, but I've got new, new cover art with the, little rocket. 00:37:10.940 --> 00:37:16.360 I like rockets and, I'm pretty excited about the state of this right now. 00:37:16.400 --> 00:37:17.520 I'm happy with the flow. 00:37:17.760 --> 00:37:21.440 the first, the first iteration of it, I didn't enjoy reading it. 00:37:21.440 --> 00:37:24.560 And why would I want somebody to buy a book that I'm not enjoying reading it? 00:37:24.620 --> 00:37:26.600 But now I'm like reading it all the time. 00:37:26.600 --> 00:37:28.340 I don't want to read other people's books anymore. 00:37:28.340 --> 00:37:31.000 I'm, I'm liking my, anyway, enough about me. 00:37:31.480 --> 00:37:33.520 that is my extras so far. 00:37:33.760 --> 00:37:34.200 Awesome. 00:37:34.260 --> 00:37:34.640 Awesome. 00:37:34.880 --> 00:37:36.240 Congrats on making progress on the book. 00:37:36.240 --> 00:37:42.800 So I was going to cover that three 14 four Python three 14 four is out, but you kind of already talked about that before. 00:37:42.920 --> 00:37:45.500 Well, we just zoomed by 14 four though. 00:37:45.640 --> 00:37:46.340 So I'm glad. 00:37:46.520 --> 00:37:46.740 Yeah. 00:37:47.000 --> 00:37:49.400 There's some nice stuff out here and I'm not going to go into detail. 00:37:49.520 --> 00:37:54.620 It's just an extra and all those kinds of things, but there are fixes CVE such and such. 00:37:54.880 --> 00:37:57.480 There's two, two security vulnerabilities address. 00:37:57.540 --> 00:37:58.140 Actually three. 00:37:58.260 --> 00:37:58.380 Sorry. 00:37:58.400 --> 00:37:58.800 I missed one. 00:37:58.800 --> 00:38:06.580 So at least, there's at least three security CVE things fixed and just three 14 four. 00:38:06.740 --> 00:38:10.760 And there's a couple of other security issues as well that don't seem to have CVEs. 00:38:10.760 --> 00:38:12.100 So that's alone. 00:38:12.200 --> 00:38:12.780 It's probably worthwhile. 00:38:12.780 --> 00:38:18.060 So I will instead tie back to your, why aren't we uv are, why aren't we uv yet? 00:38:18.060 --> 00:38:29.280 And just point out that if you just type uv Python upgrade, it will do now an in place upgrade of three 14 three or two or one into three 14 four. 00:38:29.680 --> 00:38:33.160 And so your virtual environments and all that stuff just should pick that up at the bank. 00:38:33.260 --> 00:38:36.620 If not, at least a minimum of you can now recreate the virtual environment with it. 00:38:36.920 --> 00:38:37.020 Yeah. 00:38:37.080 --> 00:38:43.320 Well, not only that, any of them that you have installed, if you've got all the, all the versions of Python, you've got to install. 00:38:43.460 --> 00:38:46.400 Well, if you say uv Python upgraded upgrades all of them. 00:38:46.600 --> 00:38:47.760 So yeah, it's excellent. 00:38:47.760 --> 00:38:50.920 And in true uv style, it does it in parallel because it should. 00:38:51.440 --> 00:38:51.520 Yeah. 00:38:51.520 --> 00:38:56.420 Actually, after you mentioned that, I just went over and clicked and did it and it's done already. 00:38:56.680 --> 00:38:57.980 So yeah, that's awesome. 00:38:58.380 --> 00:38:58.540 All right. 00:38:58.540 --> 00:38:59.560 One more release. 00:38:59.620 --> 00:39:00.580 I just want to give a shout out to you. 00:39:00.580 --> 00:39:04.420 I've been kind of like bagging on Beanie a little bit, although I'm a huge fan of Beanie. 00:39:04.420 --> 00:39:05.120 Bagging on Beanie. 00:39:05.380 --> 00:39:06.220 Beanie bags. 00:39:06.420 --> 00:39:09.560 Just saying, like, remember, I've been talking about my raw DC pattern. 00:39:09.720 --> 00:39:11.640 Like there were two reasons I was moving to it. 00:39:11.640 --> 00:39:24.760 One, because I think AIs are much, much better understanding raw query syntax instead of ORMs or ODMs wrapped around something, which wrapped around classes, which, and then results somehow sometimes into raw queries, like that kind of thing. 00:39:25.000 --> 00:39:25.140 Yeah. 00:39:25.280 --> 00:39:31.680 So I've been talking a lot about that and so on, but also that it was some of the libraries I was using were no longer updated. 00:39:31.920 --> 00:39:34.200 And I'm like, ah, that's such a, such a hassle. 00:39:34.200 --> 00:39:37.800 Like you looked at the releases for Beanie and you waited for that to come out. 00:39:37.840 --> 00:39:44.020 You'd see that like seven months ago, it was like, oh yeah, we fixed a couple of things. 00:39:44.160 --> 00:39:51.200 And then a while ago, we made some changes that introduced like some weird breaking changes, but like we kind of fixed the breaking changes later. 00:39:51.300 --> 00:39:53.380 You know, it was not really getting a lot of love. 00:39:53.500 --> 00:40:00.280 So there's actually a major release to Beanie that has like a ton of fixes and a ton of contributors, a ton of stuff. 00:40:00.380 --> 00:40:02.700 So if you're using Beanie 2.1.0 is out. 00:40:02.760 --> 00:40:04.080 You should definitely check that out. 00:40:04.480 --> 00:40:04.700 Nice. 00:40:04.980 --> 00:40:05.140 Yeah. 00:40:05.760 --> 00:40:07.600 I feel like we got a joke maybe. 00:40:07.820 --> 00:40:08.380 What do you think? 00:40:08.660 --> 00:40:09.720 You want to take this one? 00:40:09.940 --> 00:40:10.720 Yeah, I'll take this one. 00:40:10.820 --> 00:40:25.100 But on the topic of updates, I am, that's one of the things with agents that I didn't realize that I was going to enjoy is that I think I want to write it, write this up for next week or the next time we record. 00:40:25.100 --> 00:40:33.500 But the maintaining an open source project is easier now with, when you can offload some work to an agent. 00:40:34.300 --> 00:40:37.000 I'm actually a better maintainer now than I was before. 00:40:37.500 --> 00:40:38.160 Me too. 00:40:38.400 --> 00:40:39.180 There have been some projects. 00:40:39.300 --> 00:40:40.860 I'm like, gosh, that's kind of tricky. 00:40:41.040 --> 00:40:41.380 I don't know. 00:40:41.620 --> 00:40:42.940 It really justifies the effort. 00:40:43.080 --> 00:40:44.360 Some people are asking for features. 00:40:44.480 --> 00:40:47.800 I'm like, yeah, we really should support that new feature. 00:40:48.180 --> 00:40:49.540 Hey, Claude, how hard would it be? 00:40:49.580 --> 00:40:50.280 And that'll sketch it. 00:40:50.320 --> 00:40:52.600 I'm like, okay, yeah, this is totally doable. 00:40:52.600 --> 00:40:58.940 Yeah, I was doing some gardening this weekend and having an agent work for me while I was doing my own thing. 00:40:59.240 --> 00:41:03.080 So anyway, let's have something funny. 00:41:03.520 --> 00:41:05.240 And I was going to bring this up. 00:41:05.340 --> 00:41:13.300 This is, I think, an April Fool's joke from Mother Duck, which is a great for their Duck DB company, I think. 00:41:13.620 --> 00:41:13.720 Right. 00:41:13.840 --> 00:41:16.460 Mother, they're the company behind Duck DB. 00:41:16.620 --> 00:41:19.200 And this is like their commercial offering. 00:41:19.300 --> 00:41:20.980 So you can run Duck DB better, basically. 00:41:20.980 --> 00:41:21.540 Okay. 00:41:21.820 --> 00:41:26.020 Well, they put out Human DB instead of Duck DB, Human DB. 00:41:26.360 --> 00:41:28.960 And they did like Human Feet instead of Duck Feet. 00:41:29.860 --> 00:41:33.600 And it says, blazingly slow, emotionally consistent. 00:41:34.140 --> 00:41:37.240 The world's first human-powered analytical database. 00:41:37.600 --> 00:41:39.880 Why pay for compute when Dave is right there? 00:41:41.260 --> 00:41:51.080 This is just pretty darn fun that you can just, you can do pip install Human DB and import Human DB and do queries. 00:41:51.080 --> 00:41:54.600 And it just, and it just like plays for you. 00:41:54.720 --> 00:41:55.540 And it plays an audio. 00:41:55.980 --> 00:41:57.560 Dave is squinting at this. 00:41:57.820 --> 00:41:58.940 And it's like, yeah. 00:41:59.660 --> 00:42:01.140 Can you actually install it? 00:42:01.440 --> 00:42:02.140 Yeah, I did. 00:42:02.280 --> 00:42:04.640 I did install and ran it. 00:42:04.640 --> 00:42:07.020 And it's pretty funny. 00:42:07.220 --> 00:42:10.000 Dave is, yeah, contacting Dave for this query. 00:42:10.780 --> 00:42:14.300 The website is very complete about how this all works. 00:42:15.200 --> 00:42:18.340 It's got in-brain storage, post-it indexing. 00:42:19.620 --> 00:42:20.880 Suboptimal but colorful. 00:42:21.140 --> 00:42:24.700 Each index is handwritten and stuck to the monitor bezel. 00:42:25.880 --> 00:42:26.040 Yeah. 00:42:26.340 --> 00:42:29.240 Hola processing, online analytical humans. 00:42:29.240 --> 00:42:30.780 Eventually consistent. 00:42:31.020 --> 00:42:31.860 Dave will get back to you. 00:42:32.100 --> 00:42:33.220 He'll get back to you. 00:42:33.600 --> 00:42:35.140 SLA is one business day. 00:42:35.440 --> 00:42:37.240 Or three if it's quarter end. 00:42:37.320 --> 00:42:39.060 Or five if Dave's on PTO. 00:42:39.440 --> 00:42:40.620 We'll circle back. 00:42:41.340 --> 00:42:42.800 SQL or natural language. 00:42:43.580 --> 00:42:43.740 Yeah. 00:42:45.040 --> 00:42:47.340 Dave learned SQL first, then English. 00:42:47.460 --> 00:42:48.340 He understands both. 00:42:48.720 --> 00:42:49.740 Just ask him anything. 00:42:50.160 --> 00:42:51.280 So, yeah. 00:42:51.880 --> 00:42:52.920 So, yeah. 00:42:52.920 --> 00:42:54.620 I did pip install this. 00:42:54.760 --> 00:42:55.560 Played with it. 00:42:55.560 --> 00:42:59.180 And it's pretty funny to watch. 00:42:59.320 --> 00:42:59.900 Oh, you can do. 00:43:00.040 --> 00:43:00.820 There's examples. 00:43:01.460 --> 00:43:04.400 So, select average salary from employee where or whatever. 00:43:05.320 --> 00:43:06.160 It just runs it. 00:43:07.020 --> 00:43:09.920 Borrowing Gary's ledger pad from the query. 00:43:10.220 --> 00:43:11.220 And then you have to wait for it. 00:43:11.580 --> 00:43:17.500 The average engineering salary is somewhere between, somewhere around $87,000, give or take. 00:43:17.800 --> 00:43:20.320 I ran those numbers using Gary's ledger pad. 00:43:20.460 --> 00:43:21.520 But he wants it back. 00:43:21.640 --> 00:43:22.640 So, you know, rounding. 00:43:22.640 --> 00:43:27.860 So, I just had a lot of fun with it. 00:43:27.900 --> 00:43:33.340 I probably spent 20 minutes playing with HumanDB a couple weeks ago. 00:43:33.760 --> 00:43:34.840 Yeah, that's really funny. 00:43:35.400 --> 00:43:35.920 Benchmarks. 00:43:36.140 --> 00:43:37.160 Oh, it's got benchmarks. 00:43:37.800 --> 00:43:43.000 So, DuckDB is 0.003 seconds. 00:43:43.180 --> 00:43:45.040 HumanDB, two to four business hours. 00:43:45.680 --> 00:43:45.980 Nice. 00:43:46.980 --> 00:43:49.220 DuckDB is pennies to run. 00:43:49.220 --> 00:43:52.920 But HumanDB is $49 a month plus snacks. 00:43:53.400 --> 00:43:53.600 Nice. 00:43:53.940 --> 00:43:54.540 The vibes. 00:43:54.860 --> 00:43:56.200 Clinical versus immaculate. 00:43:56.520 --> 00:43:57.180 Gut feeling. 00:43:57.540 --> 00:43:58.540 A built-in gut feeling. 00:43:58.680 --> 00:43:59.120 That's great. 00:43:59.540 --> 00:44:00.520 Remembers your birthday. 00:44:00.700 --> 00:44:01.320 Dave is thoughtful. 00:44:01.720 --> 00:44:03.380 And DuckDB will not remember your birthday. 00:44:03.520 --> 00:44:05.340 But it probably will if you put it in the database. 00:44:05.740 --> 00:44:06.580 Anyway, that's funny. 00:44:06.840 --> 00:44:07.360 Oh, wow. 00:44:07.420 --> 00:44:08.200 They've got pricing. 00:44:08.520 --> 00:44:09.660 They have an enterprise tier. 00:44:10.020 --> 00:44:10.400 Enterprise. 00:44:10.640 --> 00:44:11.240 Let's talk. 00:44:11.620 --> 00:44:12.900 Unlimited human analysts. 00:44:13.100 --> 00:44:14.260 On-call overnight human. 00:44:14.700 --> 00:44:16.340 We'll figure out the SLA. 00:44:16.800 --> 00:44:18.560 Dedicated Slack workspace. 00:44:18.940 --> 00:44:20.280 Quarterly team pizza party. 00:44:21.460 --> 00:44:22.440 This is funny. 00:44:22.600 --> 00:44:23.120 Dave gets equity. 00:44:23.440 --> 00:44:24.320 Dave gets equity. 00:44:25.440 --> 00:44:27.660 The $0 one for the free one. 00:44:27.960 --> 00:44:29.360 Emotional support not guaranteed. 00:44:30.120 --> 00:44:30.960 So, that's funny. 00:44:31.260 --> 00:44:32.900 I wonder, can you just buy it? 00:44:33.120 --> 00:44:33.860 Upgrade to Pro. 00:44:34.720 --> 00:44:35.240 No. 00:44:35.540 --> 00:44:36.440 Because it is a joke. 00:44:36.720 --> 00:44:39.120 But it's funny when people carry it farther. 00:44:39.120 --> 00:44:41.200 And they actually take a credit card or something. 00:44:41.980 --> 00:44:42.920 Yeah, it's pretty funny. 00:44:43.080 --> 00:44:43.580 I love it. 00:44:43.580 --> 00:44:43.800 Anyway. 00:44:44.040 --> 00:44:46.300 I have one really quick thought to close things out. 00:44:46.560 --> 00:44:46.980 Okay. 00:44:47.000 --> 00:44:47.680 That's a little bit practical. 00:44:47.960 --> 00:44:57.000 For those folks out there that are interested in this, why aren't we uv yet study, you could look at the requirements.txt file. 00:44:57.320 --> 00:45:04.400 And if it was generated with a pip compile sort of thing, a uv pip compile, it would say the command is uv pip compile. 00:45:04.460 --> 00:45:05.580 It'll say generate with command. 00:45:05.660 --> 00:45:06.840 And that command will start with uv. 00:45:06.840 --> 00:45:11.080 So, you could parse the requirements.txt files and get greater visibility. 00:45:11.080 --> 00:45:15.120 But only for the ones that pip compile, not install just unpinned versions. 00:45:15.560 --> 00:45:20.240 Well, and also, if that's how they're, if they are generating the requirements.txt with uv. 00:45:21.320 --> 00:45:21.400 So. 00:45:21.680 --> 00:45:22.500 It would raise the numbers. 00:45:22.660 --> 00:45:24.080 I don't know by how much. 00:45:24.340 --> 00:45:24.540 Anyway. 00:45:24.880 --> 00:45:25.060 Yeah. 00:45:25.060 --> 00:45:32.840 Oh, also, so many projects or libraries that are not, they don't have either. 00:45:33.800 --> 00:45:33.840 Like. 00:45:34.080 --> 00:45:34.260 Yeah. 00:45:34.340 --> 00:45:34.560 True. 00:45:34.820 --> 00:45:35.220 That's true. 00:45:35.700 --> 00:45:38.260 Libraries just have their dependencies in piproject.toml. 00:45:38.340 --> 00:45:40.800 So, there's no, there's not uv lock or requirements. 00:45:41.220 --> 00:45:41.440 You know. 00:45:41.900 --> 00:45:42.940 The world's complicated, Brian. 00:45:43.920 --> 00:45:45.200 Do you know what's not complicated? 00:45:45.640 --> 00:45:45.940 What's that? 00:45:45.940 --> 00:45:47.320 We are at the end of the episode. 00:45:47.700 --> 00:45:48.660 So, thanks everybody. 00:45:48.660 --> 00:45:50.460 I'm going to press the goodbye everybody button. 00:45:50.600 --> 00:45:51.140 Goodbye everybody. 00:45:51.380 --> 00:45:51.700 Bye.