It doesn’t strike me as AI. The writing is reasonably information-dense and specific, logically coherent, a bit emotional. Rarely overconfident or vague. If it is AI then there was a lot more human effort put into refining it than most AI writing I’ve read.

As long as IPv4x support was just something you got via software update rather than a whole separate configuration you had to set up, the vast majority of servers probably would have supported IPv4x by the time addresses got scarce.

However, if it did become a problem, it might be solvable with something like CGNAT.

This article has a whole lot of "it's not X, it's Y"…

In reality this isn't much of a change. For decades it's been a given that mainstream CPUs have vector instructions. RISC-V was the odd man out in _not_ mandating vector instructions. Even so, most CPU code doesn't use them.

And this is unlikely to change anytime soon. Yes, ML workloads are becoming much more popular, but CPUs are still not parallel enough to do a good job at them. Only occasionally is it a good idea to try anyway.

Edit: Note that there is something novel about the approach that RISC-V and ARM are now following, namely being vector-length agnostic, but this is unlikely to have much impact on how much CPU code is vectorized in the first place. It improves scalability a little, but also gives compilers a little harder of a job. It is not something that's going to fundamentally transform the extent to which CPU code uses vector instructions.

That would not be a good approach on Macs where most users are using reduced/laptop keyboards that have no Insert key.

In this respect, Apple got pretty lucky. Most users were not using reduced keyboards in 1987 when they originally decided to add the Control key separate from Command. Plus, Mac OS didn't even have a native terminal at the time; I assume there were terminal emulators for networking/serial use but I can't imagine that was top-of-mind for Apple either.

Regardless, Cmd-C is definitely a more convenient shortcut than Control-Insert, even if you do have the keys for the latter.

Supposedly the frontier LLMs are multimodal and trained on images as well, though I don't know how much that helps for tasks that don't use the native image input/output support.

Whatever the cause, LLMs have gotten significantly better over time at generating SVGs of pelicans riding bicycles:

https://simonwillison.net/tags/pelican-riding-a-bicycle/

But they're still not very good.