#441 It's Michaels All the Way Down

WEBVTT
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Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds.
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This is episode 441, recorded July 21st, 2025, and I am Brian Okken.
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Who am I? I am Michael Kennedy. Hello, everyone.
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This episode is sponsored by DigitalOcean. Thank you, DigitalOcean.
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Hear about them later in the show.
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If you'd like to give us some topics for the show or just ask us questions or connect,
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The links to Mastodon and Bluesky are in the show notes and on our webpage, pythonbytes.fm.
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While you're there, you may as well go ahead and sign up for our newsletter,
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or we'll send you links for the show afterwards.
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And I apologize, because last week was a little late, because I've been sick, but I am bitter now.
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And if you'd like to join the show, we do post it live, usually Tuesday mornings.
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Monday, no, Monday mornings, sorry, Monday mornings at 10.
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It used to be Tuesday.
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But just go ahead and go over to pythonbytes.fm/live
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and it'll tell you when the next one is.
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And then even if we change it, you can see there.
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Yeah, so let's kick it off.
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What do you got for us, Michael?
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Let's do some follow-up for our first item here.
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So remember, last week I talked about RQ Lite,
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the lightweight user-friendly distributed relational database built on SQLite.
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And that was pretty cool.
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You could build fault-tolerant relational databases in clusters,
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even potentially geo-distributed using SQLite.
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Well, as always, our wonderful listeners are like,
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that's cool.
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Did you know about?
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Right?
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Yeah.
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I love that though.
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Yeah.
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I do too.
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Yeah.
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Thank you.
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So two people wrote in and said,
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did you know about? And one was Michael Booth. And Michael Booth said, you should check out
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Terso, the next evolution of SQLite. So Terso is interesting in that it is kind of like SQLite,
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but rewritten in Rust with the idea that it can be a distributed database type of thing. Also,
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adding some of these AI features, as in vector search and other things that you need to be able
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to support working with AI.
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So pretty cool.
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If you look through the website,
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it's like their proper tercio.tech,
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which it's cool, but I don't know.
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It kind of leaves it a little bit unsure
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like what exactly it is.
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But like, what is this relation to SQLite, right?
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So it's, go over to their GitHub,
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it's a little bit clearer, right?
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So it has SQLite compatibility.
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It is not based on SQLite like RQ Lite is,
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but it has language biodeans for Python
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and other, maybe some of these other languages
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people have heard of like Java and JavaScript and so on.
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But it has asyncio support, which is really cool.
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And you might say, well, it's SQLite.
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Can it really have asyncio support?
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Yes, at least on Linux.
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It uses IO Uring to make that happen,
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but it runs on all the server or all the platforms.
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So you can like develop on macOS,
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but then you get better perf on Linux, for example.
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It also, they're working on adding a begin concurrent operation,
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like a SQL statement or query statement,
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and indexing for vector search,
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like probably pretty much required,
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and better alter support, right?
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So basically think of it as a more modern take,
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not just because it's rewritten in Rust,
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which seems to be like the thing you do,
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but as part of that,
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taking on a lot of ideas like concurrency others. Okay. Big question. Is it ready for production
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use? No, no. But these folks also worked on this thing called LibSQL. So it says that the
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Tarot database is the next evolution of SQLite and Rust. They had previously worked on something
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called LibSQL, which is an attempt to evolve SQLite in a similar direction, but through a fork
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rather than a rewrite. And they're like, we think this new database thing is a better way to go.
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So if what I talked about with RQ Lite inspires you, this also might.
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They also have some kind of what you might expect as open core,
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but we'll do the infrastructure side of things for you.
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So they've got this thing called Terso Cloud,
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which is a production or a paid sort of thing, right?
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So serverless access or sync or whatever you want, right?
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Replication, sync, vector search, all that kind of stuff.
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Basically managed.
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All right, so that's thing one.
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the other one comes to it.
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This is like all,
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it's all Michael.
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Michael's all the way down.
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So we had Michael Booth recommend that one.
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Got me,
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Michael presenting it.
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We got Mike Fiedler talking about the next one here.
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And Mike Fiedler says,
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have you heard,
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well,
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listen to the most recent Python bytes and RQ Lite.
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It's cool.
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Hadn't heard of that.
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Something related that might be interesting is lightstream.io.
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Okay.
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Now lightstream.io is more SQLite than before.
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You just keep using SQLite like the one built into Python.
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but you launch a little daemon that watches it and constantly syncs it with S3 or Azure Blob
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Storage or something like that. When you do that, what do you get? You get something pretty cool.
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It just constantly streams changes to AWS S3, Azure Blob Storage, Google Cloud Storage, etc.
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And that means you basically get failover. So imagine you're like, I would love to just have
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a SQLite database that I can run on a simple little app.
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But what if that server goes down?
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What if that server fails?
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Then all my data is lost from the last time I backed it up because my alternative was to
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run some kind of cluster of Postgres or Mongo or whatever, right?
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But then you're into lots of DevOps and managing those things, or you're paying for managed
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databases, which can be relatively expensive to small servers like DigitalOcean has.
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And what you can do is you can just keep on using regular SQLite,
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but then use this Lightstream,
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which has no real performance differences in terms,
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like these other two things are like trying to change how SQLite works
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or re-implement the compatible API.
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This one just says, we promise to hook into some,
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I put a link in the show notes.
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There's a nice write-up about this that they did.
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It's sort of like, why is this?
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And called Lightstream revamped.
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And it also just talks about like, why did they build this?
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How does it work?
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So it says it takes over the wall checkpointing process to continuously stream updates to
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the database.
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So basically as commits are committed to the SQLite file, it just keeps pushing that to
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blob storage.
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Cool, right?
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So if that RQ Lite thing was interesting, but not quite what you're looking for, maybe one
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of these two things is.
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Yeah.
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Interesting.
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Pennies a day.
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I like that.
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Dirt cheap.
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Yeah.
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Dirt cheap.
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only costs pennies a day because it's just blob storage, right?
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So however you do that.
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And the Lightstream, I don't know that they,
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I don't see pricing.
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How about that?
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So this is just pure open source,
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whereas the Terso was like,
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you can do the open source thing yourself
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or we can do our cloud thing, right?
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I don't know which of those makes you happier,
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but they're not the same in that regard.
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Or they are the same with a wrapper, pay wrapper.
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Anyway.
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Yeah.
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Okay, cool.
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Awesome.
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Over to you.
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I want to talk about project status a little bit.
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So I do want to talk about this new PEP.
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So PEP 792 project status markers in the simple index.
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So project status, what is this about?
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So this is a new PEP that was, well, it's not right, really that.
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It is pretty new.
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So it actually updates in February of this year, and it's now accepted.
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So I'm going to talk about this a little bit,
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but I want to talk about project status and what this means.
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So if I take a look at a couple of my silly projects,
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I've got pytestCheck, which is a project I maintain,
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and I actually intend people to be able to use it so it's maintained.
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And so I have added a Trove classifier
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to say the status of the project is production stable.
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I think you can use it in production, but it's, you know, yeah.
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But this is optional.
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Maintainers don't have to put this in.
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For instance, I also have another one called pytestCrayons
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i just did for a uh a talk that i gave and um i didn't put the um the status in there so that's
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an it's an optional thing so what we have we have in the current situation there is the trove
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classifiers that you can optionally add to um uh to to your project and you if you like for instance
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start taking it down you can say that it's inactive it's not but i don't think very many people do
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that often inactive just means somebody left but it's still they haven't updated a new one that's
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like please close the door on your way out usually they just walk out the door so and then there's
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also there's actually three kinds of status so there's that the trove classifiers but then there's
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also uh indices uh can mark uh a district can mark distributions and releases as yanked so you can
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like a version you can yank.
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So it's not there anymore.
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So then it's still like got an entry there,
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but it's been a yanked, whether it's true or false.
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You know what, I've started seeing that a lot lately, actually.
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Have you?
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At least uv pip warnings.
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So I'll say, you know, update the dependencies of this project,
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basically uv pip compile, and then I'll install the requirements.
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And it'll say, this thing has been yanked because of,
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and you don't have some reason, like broken metadata or whatever.
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I'm like, well, I don't know what I'm supposed to do about this
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because something needed it.
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Yeah.
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Okay.
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I've not seen anything fail.
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I haven't seen the apps no longer run because something got that status,
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but I've seen warnings about this now.
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And then there's another type of status that PyPI itself
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can have a status for an entire project.
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And the entire project can be quarantined
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if the administrators of PyPI think that the project has been,
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like, it's got, like, malware in it or something,
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so they can quarantine it.
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And also, a project owner can archive a project
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and say, basically, you can disable new releases.
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It's still around, but nobody can update to it,
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and it's just archived.
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It's there for historical purposes, I guess.
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Anyway, so there's three statuses,
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And it's a little confusing on how to use those as a as a if you had an alternative index, if you were trying to like do dependency resolution or other things.
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So trying to clean that up a bit.
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So there's really this proposals to have one project always has exactly one status and the status will be it'll be active.
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And then there's some semantics around that active or archived or quarantined.
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and deprecated and this makes sense so it's either either it's it's in use it's active or
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it's not um but it's also what does it not mean it's either you know there's various levels of
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why you can't you shouldn't use it um and i think this is um this is a way for good way forward to
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have like to kind of consolidate these now this is this has been accepted this uh this active
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archived, quarantined, deprecated status,
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and it can only be one of those.
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But this has been accepted.
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However, it's not implemented yet.
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So don't go out and look in PyPI
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to try to find this yet.
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So this just was accepted July 8th resolution.
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So we will update you further
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as we hear from people in the Python community
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that this is all implemented.
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Yeah, excellent.
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I think I'm behind it.
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The quarantine one is good.
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I'm very glad I've never seen that warning.
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And the archive one feels to me like a little bit of a supply chain safety type thing.
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Like, I'm not going to mess with this.
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I will never update this.
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But let's not allow something to happen where someone else could either.
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Well, and I'm not sure.
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So I was trying to read this.
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I was trying to understand kind of how it's going to be set.
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So it's probably not going to be something just in the Trove classifier.
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So it probably is going to be something that outside of the data itself.
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So because there's times where it's clear there's nobody updating it anymore
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and you can't get a hold of the maintainer,
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you need to be able to say, this one's dead.
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I think it's too bad that we don't have a better way to say,
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but after the fact, if somebody just stops maintaining something,
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hey, does anybody else want to start maintaining this?
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I know there's security problems around that,
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But, you know, it might be good to be able to have some things live on.
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But yeah, and just archiving old stuff.
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It's not good.
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Was it GitHub?
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I think that got into a big uproar because they decided that they're going to archive a bunch of stuff if it hadn't been updated in like years.
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Or was it NPM?
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I don't know.
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One of these types of places was like, if stuff doesn't get touched, people are like, hey, it's not unmaintained.
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It's just done.
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There's nothing to add.
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It's perfect.
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works. Yeah. Exactly. Cool. All right. Well, before we move on, let's talk about our sponsor,
00:13:42.760 --> 00:13:49.460
huh? Yeah. Yeah. So super excited to have DigitalOcean back to support the show. As always,
00:13:50.000 --> 00:13:54.820
this episode is brought to you by DigitalOcean. And DigitalOcean is a comprehensive cloud
00:13:55.200 --> 00:14:00.640
infrastructure that's simple to spin up even for the most complex workloads. And it's way better
00:14:00.920 --> 00:14:05.060
value than most cloud providers. I've looked at the big three and they're not even close.
00:14:05.580 --> 00:14:08.700
in how much value you get per dollar
00:14:08.810 --> 00:14:10.260
and how much easier it is to use.
00:14:10.470 --> 00:14:13.020
So at DigitalOcean, companies can save up to 30%
00:14:13.460 --> 00:14:14.360
off their cloud bill.
00:14:14.710 --> 00:14:18.640
They boast a 99.99% uptime SLA.
00:14:18.820 --> 00:14:22.960
That means they promise to support that level of uptime.
00:14:23.320 --> 00:14:25.380
Our experience, Brian, running Python Bytes
00:14:25.380 --> 00:14:27.500
and other things on DigitalOcean for many years
00:14:27.680 --> 00:14:29.600
was way higher than four nines.
00:14:29.940 --> 00:14:30.880
Really, really reliable.
00:14:31.380 --> 00:14:31.520
Love them.
00:14:31.900 --> 00:14:34.620
They also have industry-leading pricing on bandwidth.
00:14:35.140 --> 00:14:39.460
Also true, like 10 times, 8 times cheaper than AWS and Azure.
00:14:39.600 --> 00:14:39.860
Really good.
00:14:40.660 --> 00:14:44.440
So it's built to be the cloud backbone of businesses, small and large.
00:14:44.620 --> 00:14:51.200
And now they have GPU-powered virtual machines, plus storage, database, networking capabilities, all-in-one platform.
00:14:51.440 --> 00:14:55.720
So AI developers can confidently create apps that their users will love.
00:14:56.020 --> 00:15:02.860
And devs have access to a complete set of infrastructure tools they need, both training and inference, so they can build anything they dream up.
00:15:03.140 --> 00:15:05.840
DigitalOcean provides full-service cloud infrastructure that's simple to use,
00:15:06.260 --> 00:15:09.880
reliable no matter the use case, and scalable for any business.
00:15:10.180 --> 00:15:13.140
So did I say good value? $4 a month for a server.
00:15:13.800 --> 00:15:16.400
That's yours. Your Linux server, USSH, too. Amazing.
00:15:16.840 --> 00:15:19.780
And GPUs, servers for under $1 per hour.
00:15:20.280 --> 00:15:25.560
So easy to spin up infrastructure to simplify even the most intense business demands.
00:15:25.760 --> 00:15:26.340
That's DigitalOcean.
00:15:26.780 --> 00:15:35.220
If you use the DO4bytes code, then you can get up to $200 in free credit to get started.
00:15:36.200 --> 00:15:36.680
DO4bytes.
00:15:36.960 --> 00:15:39.400
Just click the link in your podcast player show notes.
00:15:39.700 --> 00:15:41.320
DigitalOcean is the cloud that's got you covered.
00:15:41.660 --> 00:15:43.200
So like I said, please use our link.
00:15:43.240 --> 00:15:47.780
You'll find the podcast player show notes, a clickable chapter URL while I'm speaking right now.
00:15:48.240 --> 00:15:51.560
And at the top of the page at pythonbytes.fm for the episode.
00:15:51.900 --> 00:15:54.960
Thank you to DigitalOcean for supporting Python Bytes.
00:15:55.220 --> 00:15:55.500
Indeed, indeed.
00:15:55.860 --> 00:15:57.360
Now, over to you.
00:15:57.640 --> 00:16:02.620
Well, so I want to talk about testing a little bit.
00:16:03.020 --> 00:16:07.400
So Hugo van Kaminade,
00:16:08.980 --> 00:16:10.520
love you, Hugo, sorry,
00:16:11.080 --> 00:16:13.700
wrote a post called Run Coverage on Tests.
00:16:13.720 --> 00:16:15.820
And this is something that I've taught everybody to do
00:16:16.840 --> 00:16:21.540
or taught everybody I can get hold of their ears to do this
00:16:21.920 --> 00:16:22.700
because it's important.
00:16:23.060 --> 00:16:25.180
But I'm glad that Hugo wrote a post about it.
00:16:25.260 --> 00:16:26.620
And this was just going to be an extra.
00:16:27.180 --> 00:16:30.240
However, I was blown away by a few things here.
00:16:30.580 --> 00:16:33.600
And I'm like, oh, we got to make this highlighted a little bit more.
00:16:34.180 --> 00:16:37.280
So the classic reason why you should run coverage on your test
00:16:37.560 --> 00:16:41.700
is because of the copy-paste-modify problem with pytest.
00:16:41.980 --> 00:16:45.580
Because pytest really kind of, the name of the test
00:16:45.680 --> 00:16:47.400
is just sort of shows up in the reporting.
00:16:47.700 --> 00:16:52.980
So it's very easy to copy my, you take an old test.
00:16:53.400 --> 00:16:55.720
And since you're not calling the test function yourself,
00:16:57.120 --> 00:16:59.280
it's easy to copy, paste, modify,
00:16:59.760 --> 00:17:01.120
and forget to change the name.
00:17:01.320 --> 00:17:02.260
And if you do that,
00:17:02.960 --> 00:17:05.319
the second test just hides the name,
00:17:05.459 --> 00:17:06.620
it hides the first one.
00:17:08.020 --> 00:17:09.380
And that is the classic reason
00:17:09.400 --> 00:17:11.260
why I tell people to run coverage
00:17:11.540 --> 00:17:12.540
so that you don't do this.
00:17:12.880 --> 00:17:14.959
And it's hard to figure out otherwise.
00:17:16.020 --> 00:17:17.100
But a couple of cool things
00:17:17.420 --> 00:17:19.319
that I learned from this post
00:17:19.579 --> 00:17:22.939
is that Ruff's rule F811,
00:17:23.240 --> 00:17:32.680
f811 catches that apparently so there is if you run ruff on your test code also with at least f811
00:17:32.940 --> 00:17:40.280
turned on you'll it it checks for variables which the function name is officially a variable
00:17:41.260 --> 00:17:49.200
that are defined and and redefined or otherwise shadowed and unused so if you used a variable
00:17:49.180 --> 00:17:55.780
didn't use it and redefined it um that's just that's similar to defining a test twice so um i
00:17:55.880 --> 00:18:01.020
didn't know that so i will make sure that i've got rough running on my test code also so that's cool
00:18:01.300 --> 00:18:06.260
also a tip to say hey if you're really just copy paste modifying a test maybe think about py test
00:18:06.440 --> 00:18:11.060
parameterization which i agree um you might want to just parameterize the test but this is also
00:18:11.240 --> 00:18:17.180
pretty common it's so common to copy paste the second example is what the one that i really was
00:18:17.200 --> 00:18:21.460
like oh my gosh we have to cover this because i would have never i wouldn't have caught this so
00:18:21.480 --> 00:18:28.820
his second example is this weird okay so it's like a he's got an image some image stuff that he's
00:18:29.000 --> 00:18:35.420
testing and it's like i don't know it's i guess i'm just it's a little bit of a complicated thing
00:18:35.740 --> 00:18:40.480
you've got some colors and images and you're trying to test in the in the end you're asserting
00:18:40.740 --> 00:18:46.480
whether or not images are similar of a reloaded image versus an expected image and i don't really
00:18:46.420 --> 00:18:52.760
understand what's going on here but um this isn't terrible but one of the things in here is that um
00:18:53.160 --> 00:18:59.040
when he ran coverage it said that uh these two lines at the bottom are not being run at all and
00:18:59.180 --> 00:19:06.200
he's got a a some images a set of images that he's iterating over and apparently there's nothing in
00:19:06.200 --> 00:19:15.020
them so what's going on um and the thing that's going on apparently is uh this his his images are
00:19:15.040 --> 00:19:22.060
a generator is what's going on so he's got some images that are being uh set up as a generator so
00:19:22.160 --> 00:19:28.900
like a uh you know for i in a generator comprehension yeah generator comprehension
00:19:29.250 --> 00:19:36.200
and then he passes that to a function that consumes the generator um and then uh and then when he tries
00:19:36.250 --> 00:19:42.220
to use it as the as in the assert section there's nothing there anymore and i would have never like
00:19:42.420 --> 00:19:47.780
obviously this is this is highlighted because i use generated way more than in generator
00:19:48.120 --> 00:19:53.220
comprehensions more than i used to um because they're cool um and they're efficient and if
00:19:53.280 --> 00:19:58.840
you've got huge things they use up less memory so i'm using them more and more and i probably am
00:19:58.920 --> 00:20:05.280
using in my test i better make sure that i'm not like mucking up with things like this um so uh
00:20:05.810 --> 00:20:11.419
pretty cool reason to use um uh use coverage on your test code to make sure that you don't do this
00:20:11.740 --> 00:20:19.820
Oh, yeah. The fix, by the way, of doing this is just instead of using a generator comprehension, just use a list and pass it to a desk.
00:20:19.890 --> 00:20:20.960
Square brackets, not parentheses.
00:20:21.590 --> 00:20:21.700
Yeah.
00:20:22.180 --> 00:20:22.680
Problem solved.
00:20:23.880 --> 00:20:25.580
It's subtle, though. I agree. Very subtle.
00:20:25.820 --> 00:20:30.660
Oh, yeah. That's the difference, really? Yeah. A parentheses for those brackets, and then it works.
00:20:31.740 --> 00:20:32.160
Well, yeah.
00:20:32.450 --> 00:20:34.220
Okay. Anyway, thanks, Hugo.
00:20:34.640 --> 00:20:35.400
Yeah. Thanks, Hugo.
00:20:35.880 --> 00:20:43.900
All right, let's talk about something simpler than all that SQLite distributed async business.
00:20:44.340 --> 00:20:45.060
Here's a problem, Brian.
00:20:45.490 --> 00:20:52.780
You have a program, probably in our case, often a Python program, and you want to give it to somebody and let them run it.
00:20:52.990 --> 00:20:53.860
How do you do that?
00:20:54.400 --> 00:20:58.620
Still, to this day, there's no amazing options to do that.
00:20:58.660 --> 00:21:07.860
I know we have Py2.exe and Py2.app, but those things are, while awesome, they're not consistently
00:21:08.420 --> 00:21:10.100
reliable and all sorts of things.
00:21:10.300 --> 00:21:13.180
So this is a little bit more dev-focused than that.
00:21:13.180 --> 00:21:17.020
But if you have Docker installed on the machine, right?
00:21:17.140 --> 00:21:24.300
So if Docker is like Docker Desktop or OrbStack or whatever, and I guess even in the upcoming
00:21:24.320 --> 00:21:30.040
macOS. We're supposed to have a built-in Docker equivalent built straight into macOS,
00:21:30.240 --> 00:21:34.060
which should be interesting. Anyway, if you have any of those, there's this project called Docker
00:21:34.420 --> 00:21:39.000
to EXE. Too executable. I believe it works on all the platforms. I don't think it's just
00:21:39.460 --> 00:21:44.400
Windows EXE, even though, you know, yeah, it absolutely works on Windows, Linux, and Darwin,
00:21:44.780 --> 00:21:50.940
aka macOS. So just too executable, right? And the idea is that if you have a Docker image
00:21:51.260 --> 00:21:54.380
with an entry point or something like that that will start an app running,
00:21:55.080 --> 00:22:02.140
you can simply build that as an executable, either a.exe on Windows, a.app.
00:22:02.350 --> 00:22:04.280
You have to rename it, I think, more or less.
00:22:04.560 --> 00:22:05.940
They're just executable binaries, right?
00:22:06.380 --> 00:22:10.340
So you can take that Docker image and build it into an executable,
00:22:10.470 --> 00:22:11.540
which is pretty awesome.
00:22:12.160 --> 00:22:15.720
So you could, you know, the example they give is kind of, it's a bad,
00:22:15.920 --> 00:22:18.840
I'm going to call it as a full-on bad example because it says,
00:22:18.920 --> 00:22:21.360
here is a bare Linux image.
00:22:21.860 --> 00:22:23.340
You can distribute that as a binary.
00:22:23.420 --> 00:22:24.380
It's like, okay, great.
00:22:25.500 --> 00:22:26.880
That's not exactly the use case.
00:22:27.300 --> 00:22:29.120
You know, the use case is I have something
00:22:29.540 --> 00:22:31.780
I want to give to someone and run it, right?
00:22:32.080 --> 00:22:33.560
So as a Docker thing,
00:22:33.620 --> 00:22:35.180
so maybe a better example would be like,
00:22:35.380 --> 00:22:36.820
here's copying over my source code,
00:22:37.140 --> 00:22:38.280
building a Docker image,
00:22:38.480 --> 00:22:40.300
and then turning that base Docker image
00:22:40.400 --> 00:22:42.620
into an executable that I can distribute, right?
00:22:42.860 --> 00:22:44.160
But this thing would do that.
00:22:44.300 --> 00:22:46.040
It's just the example doesn't show that.
00:22:46.340 --> 00:22:47.980
So I think that is pretty neat.
00:22:48.280 --> 00:22:57.180
And if you want yet another way to distribute a more durable tool to somebody, if they have Docker, then you're good to go.
00:22:57.540 --> 00:22:58.200
That's pretty cool.
00:22:58.580 --> 00:23:00.420
Well, do they have to have Docker?
00:23:00.840 --> 00:23:01.040
Yes.
00:23:01.360 --> 00:23:01.560
Okay.
00:23:01.920 --> 00:23:11.240
Because basically somehow this thing just bundles up the Docker image and then just executes it using Docker in the same way that you would just say Docker run, etc.
00:23:11.800 --> 00:23:32.420
The other thing that's not clear to me is if there's a way to pass through mappings, for example, map port 8000 to 2722, whatever, you know what I mean? Or map this volume to this folder. Probably you can in the exit, like when you run the executable, can you pass that kind of stuff to it?
00:23:32.480 --> 00:23:35.240
Also, again, the reason I don't like this example here,
00:23:35.470 --> 00:23:36.940
boy, they just like bundle up Linux.
00:23:37.500 --> 00:23:41.060
In the Docker file itself, you can say expose this port
00:23:41.300 --> 00:23:44.240
and do other type of things like that in the Docker file.
00:23:44.670 --> 00:23:46.740
So you can bake in, like if you're saying,
00:23:47.160 --> 00:23:50.880
my thing is a little Flask app that runs using maybe SQLite for the database.
00:23:51.900 --> 00:23:54.540
It exposes this port so that you can then, you know,
00:23:54.580 --> 00:23:57.460
it could print out a thing like click here to like talk to the server
00:23:57.940 --> 00:24:00.360
and launches a URL, the web browser to like a local URL.
00:24:00.760 --> 00:24:04.860
So you can do that with, I'm pretty sure, with expose and other stuff in the Docker file.
00:24:05.240 --> 00:24:09.740
But yeah, anyway, you want to ship a Docker image as just an executable binary?
00:24:10.030 --> 00:24:10.600
Check this thing out.
00:24:10.880 --> 00:24:11.140
Cool.
00:24:11.830 --> 00:24:12.080
Very neat.
00:24:12.420 --> 00:24:12.800
All right.
00:24:12.980 --> 00:24:14.760
I know that you have extras and I don't.
00:24:15.120 --> 00:24:15.300
Okay.
00:24:15.680 --> 00:24:18.020
I've got some testing code news.
00:24:18.200 --> 00:24:19.800
So testing code has sort of been on pause.
00:24:19.970 --> 00:24:20.800
When was the last time?
00:24:20.880 --> 00:24:21.580
May 7th.
00:24:22.960 --> 00:24:23.940
It's July now.
00:24:24.440 --> 00:24:26.800
It's been on pause for a while because of life.
00:24:26.980 --> 00:24:29.060
But life has got a little bit of room in it now.
00:24:29.160 --> 00:24:31.120
So I'm going to do a little bit of testing code.
00:24:31.370 --> 00:24:32.200
So there is a,
00:24:32.620 --> 00:24:36.800
there's three episodes in the queue and hopefully more to come.
00:24:37.080 --> 00:24:37.520
So at least,
00:24:38.120 --> 00:24:42.120
and so this week there will be an episode and I'm pretty excited because the,
00:24:42.250 --> 00:24:46.100
this week's episode is covering pytest Django with Adam Johnson.
00:24:46.280 --> 00:24:48.220
And a lot of people have asked me about pytest Django.
00:24:48.700 --> 00:24:52.000
And since I am not a Django expert,
00:24:52.440 --> 00:24:52.580
I,
00:24:53.220 --> 00:24:54.100
and Adam is,
00:24:54.520 --> 00:24:56.160
Adam was willing to talk with me about it.
00:24:56.320 --> 00:24:56.960
And it's a,
00:24:57.340 --> 00:24:58.540
it's a really great discussion.
00:24:58.900 --> 00:25:00.680
So I'll be excited to get that out.
00:25:01.500 --> 00:25:02.880
I'll be excited to get that out this week.
00:25:03.130 --> 00:25:05.260
And then also I went ahead.
00:25:05.550 --> 00:25:11.240
So I'm using a hosting provider I'm using for this is Transistor FM.
00:25:11.490 --> 00:25:16.420
And Transistor has the ability to push things to YouTube, but it's a little bit wacky.
00:25:16.620 --> 00:25:18.760
So I have went ahead and turned that on.
00:25:19.260 --> 00:25:22.480
And so everything now is for testing code.
00:25:22.590 --> 00:25:23.580
Well, not everything yet.
00:25:24.340 --> 00:25:27.140
207 episodes are now up on YouTube.
00:25:27.840 --> 00:25:31.660
The catch is they sort of show up in random order.
00:25:32.780 --> 00:25:37.300
They push, I think, 80 episodes a day or something like that.
00:25:37.960 --> 00:25:38.840
They do a lot.
00:25:39.000 --> 00:25:42.820
So by the time you probably hear this,
00:25:42.900 --> 00:25:44.540
if you don't hear it on the first day,
00:25:45.360 --> 00:25:46.660
they'll probably be all up.
00:25:46.880 --> 00:25:49.780
But anyway, they're there, but they're just in weird order.
00:25:50.000 --> 00:25:52.140
That's a lot of content to be pushing up there.
00:25:52.300 --> 00:25:55.200
I suspect, though, after it sort of bulk uploads,
00:25:55.400 --> 00:25:58.500
Once it does, as you start publishing, it'll go in the right order.
00:25:58.720 --> 00:26:01.240
Yeah, well, it's because YouTube orders,
00:26:01.580 --> 00:26:06.340
I'm pretty sure YouTube orders in the upload order of when you uploaded it.
00:26:06.560 --> 00:26:08.560
And there's no way to backdate it to say,
00:26:08.880 --> 00:26:10.980
this was actually two years ago or something.
00:26:11.100 --> 00:26:14.400
Yeah, and probably Transistor just batch processes them.
00:26:14.500 --> 00:26:16.780
And the order in which they complete is the order in which they upload.
00:26:17.320 --> 00:26:23.220
Luckily, when I transitioned to Transistor, though,
00:26:23.560 --> 00:26:26.760
They do have a, it just uses your old post dates.
00:26:27.200 --> 00:26:30.080
So even though I transitioned like, I don't know,
00:26:30.160 --> 00:26:31.740
a couple of years ago or something like that,
00:26:32.620 --> 00:26:34.540
I still was able to, you know,
00:26:34.830 --> 00:26:37.700
like some of the early episodes still show up
00:26:37.980 --> 00:26:40.080
as like being happening in 2016,
00:26:40.670 --> 00:26:42.200
even though they got transferred later.
00:26:42.500 --> 00:26:44.080
But anyway, a lot of content.
00:26:44.390 --> 00:26:44.760
Good deal.
00:26:45.100 --> 00:26:46.180
So no extras for you?
00:26:46.580 --> 00:26:48.180
No, I'm not very extra.
00:26:48.590 --> 00:26:49.740
Just been working hard and stuff.
00:26:49.800 --> 00:26:51.020
I'll share, I'll have things to share eventually,
00:26:51.280 --> 00:26:52.640
but right now, no extras.
00:26:53.080 --> 00:26:54.620
I do have a joke I've brought for us.
00:26:54.620 --> 00:26:55.080
What do you think?
00:26:55.460 --> 00:26:55.920
Oh, yeah.
00:26:56.160 --> 00:26:56.440
Let's do a joke.
00:26:56.440 --> 00:26:56.860
Are you here for it?
00:26:57.160 --> 00:26:57.260
Yeah.
00:26:57.340 --> 00:26:57.540
Okay.
00:26:57.920 --> 00:27:01.840
So, Brian, what if programmers were doctors?
00:27:02.360 --> 00:27:03.140
You know, the old joke.
00:27:03.980 --> 00:27:05.180
Hey, doctor, my leg hurts.
00:27:05.900 --> 00:27:06.180
Well, stop.
00:27:07.520 --> 00:27:09.500
My leg hurts when I lift it like this.
00:27:09.500 --> 00:27:11.060
Well, stop lifting it like that, right?
00:27:11.300 --> 00:27:11.440
Yeah.
00:27:11.940 --> 00:27:15.420
There's a fun variation on that joke for programmers,
00:27:16.060 --> 00:27:19.400
and it's basically the doctor equivalent of it works on my machine.
00:27:20.080 --> 00:27:22.420
It says, doctor, my leg hurts.
00:27:22.700 --> 00:27:23.180
That's weird.
00:27:23.270 --> 00:27:24.880
I also have a leg and it doesn't hurt.
00:27:24.950 --> 00:27:26.200
The issue must be on your side.
00:27:29.760 --> 00:27:31.620
Yeah, that's definitely true.
00:27:32.160 --> 00:27:32.240
Yeah.
00:27:34.400 --> 00:27:34.900
I like it.
00:27:35.220 --> 00:27:36.080
That's dumb, but funny.
00:27:36.300 --> 00:27:36.700
I know.
00:27:36.860 --> 00:27:37.800
It's short and sweet.
00:27:39.140 --> 00:27:39.600
I like it.
00:27:39.800 --> 00:27:39.960
Cool.
00:27:40.360 --> 00:27:41.780
Well, thanks for the joke, Michael.
00:27:42.010 --> 00:27:44.440
And thanks, as always, for this wonderful episode.
00:27:45.160 --> 00:27:45.540
You bet.
00:27:46.400 --> 00:27:46.740
Bye, Brian.
00:27:46.920 --> 00:27:47.260
Bye, everyone.
00:27:47.300 --> 00:27:47.400
Bye.