What I find they do badly in code is building abstraction and breaking the problem down in a way a human does. They don't seem to really build simplifying components unless you expressly tell them too, they are more than happy to just nest 10 deep or embed a 20 line called back function and use lots of shortened names. I find I can improve the code just by organising better and choosing some abstractions that make sense to the humans and then ironically you save tokens too because the AI will reuse it, but it wont create most of it itself. Its bad for domain work unless you are explicit about it.

> This presumes an LLM has something to say. The reason the text doesnt make much sense, is because it doesnt. It isnt "on topic" because it has no topic to be on. Sentences go on, are joined irrelevantly, and so on because theyre sampled from a distribution of "possibly relevant sentences". They aren't being addressed to a reader (whose mind is being modelled), on a topic the writer wishes to express.

While I don't like LLM voice either, I'd strongly disagree with this. LLM writing can be very tight these days -- sentences do not "go on" and are not "joined irrelevantly". I'm not sure when you last used an LLM, but it must have been a very long time ago; this hasn't been the case since late 2024 for frontier models, in my experience.

Obviously there's no underlying consciousness behind it all, but the verisimilitude is good enough for stimulating conversations on the recent research literature in my field, which I appreciate. For me, at least, the communication level is absolutely fine.

(YMMV, of course, and perhaps my bar for this is lower than yours.)