Apart from dataclasses (which generally should have slots and/or be frozen), this article gives way too much inapplicable advice, which is not scalable even in a small sized project.
Why so? Just after the forking process Valkey has gone beyond what Redis is capable of due to high volumes of funding and new attention from devs wanting to improve Redis's performance.
Some "smart" folks even downvote this advice. Yeah, I've seen articles on musl's horrible performance back in 2017-2018, and apparently it still holds, yet I get a downvote.
It's hasn't been performant for me all the 3 times I've tried. It kept chugging an entire CPU core. Never had a similar experience with Sway, it's still extremely responsive and light on the system (especially with Vulkan).
Yes, I do agree on most of AI's side effects, but it's already cheap, accurate and reliable enough to be used with a fine-tuned context/pretrained model + tooling (web and local db search in particular) at various monotonous applications without breaking a bank. A human can intervene to guide, add extra context, or even continue working from what an AI model has come up with.
It's not about "good" or "bad", we have to live with it. And as always, those who don't adapt - vanish.
I switched to Monaspace Argon as default, it's a tiny bit more variative compared to JetBrains Mono, which enchances its readability without going full crazy.
Probably they meant that GC introduces a certain level of uncertainty, whereas OS or "systems" programming leans heavily towards manual handling (except when the compiler allows to avoid it, not the runtime).
