Asahi Linux is a project to get Linux running on M-series macs, which has taken a bunch of difficult reverse engineering, since Apple doesn’t really provide public documentation on how they work.

Running Linux means they can run steam games like a Linux machine. This is a pretty impressive landmark in terms of their OpenGL rendering support.

Seems like a sensible overhaul, hitting the major issues with the fee, but still going ahead with a version of it. Big points for me:

• Not retroactive. Only affecting the next version of Unity, and you can even opt out of updating to skip the fee.
• Data is now reported by the customers. Still not sure how that plan to enforce this, but it’s a hell of a lot better than some arbitrary data collection scheme being baked into the game.
• Free version is excluded. No charging tiny side projects, or students or something, it only affects already paying customers.

Still not sure I love charging per install as a concept, and they’ve already overplayed their hand and burnt many bridges, but at least this implementation isn’t insanely hostile. Guess we’ll see how this plays out from here.

Can’t believe Mario is getting Dark Souls style multiplayer

Having used tailwind a little bit, I have nothing but praise for it. Effortless copy/pasting of components with confidence, really nice look by default, easy tweaking, absolutely no management or planning required to organize your CSS, and it’s all right there, directly on your html, never anywhere you have to hunt for it. Feels very freeing to just… not think about CSS at all.

And the “clutter” really is fine, modern IDEs with good syntax highlighting, plus a tailwind extension to help complete the class names and clean up accidental duplicates or conflicting properties, and you’re good.

Eh, I’d assume the comparison isn’t flattering. I think the point of this article is to argue you don’t need ElasticSearch to implement a competent Full Text Search for most applications. Splitting hairs over a few milliseconds would just distract from that point, when most applications should be prioritizing simplicity and maintainability over such tiny gains in a reasonable dataset.

Might be interesting to try to analyze at exactly what point elasticsearch becomes significantly useful, however. Maybe at the point where it saves a full tenth of a second? Or where it’s returning in half the time? Could be an interesting follow up article.

Eh, the python one will probably perform better, because sum is probably written in native C under the hood.

Great read!

I think a bonus point in favour of composition here is the power of static typing. Introducing advanced features like protocols can bring back some of that safety that this article describes as being exclusive to inheritance.

Overall, I think composition will continue to be the future going forward, and we’ll find more ways to create that kind of compilation-time safety without binding ourselves into too restrictive or complicated models.

Ah, it makes way more sense for students, absolutely. None of your code is proprietary, so that’s not a concern, student pricing makes things easier.

Plus, your tech stacks are much simpler. Usually just… Java, or Python, or something. Not a python webserver using X framework for templating, Y framework for typing, and Z framework for API calls to some undocumented internal API.

Alright, guess I’ll reiterate my usual beats here. AI code assistance is interesting, and I’m not against it. However, every current solution is inadequate, until it does the following:

1. Runs locally, or in an on-prem instance. I’m not taking it up with legal or security if I’m allowed to send our proprietary code off to be analyzed on a foreign server. And I’m not doing it without asking. It just isn’t happening.
2. It has to be free, or paid for by my company. It’s cool, and it might help me work, but paying a subscription fee on something that only benefits me at work is essentially the same as a pay cut. Not interested.
3. It has to analyze the entire repo. In my current tests of ChatGPT, for most cases I’ve spent long enough giving it context that I could’ve just… solved the problem myself. It needs to have that context already.

What a fantastic read! Quite funny throughout, and genuinely insightful.