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Notes -
Why say something trivially disproved by common experience?
Maybe they don't feel helpful to you but, objectively, a lot of developers are being helped by them, myself and many of my coworkers included.
Why claim something trivially disproved by common experience?
Personally I'm with @SubstantialFrivolity on this one.
Generating a thousand lines of code in 5 minutes doesn't mean a thing if it's going to take a week or more to validate it.
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I mean, it's a matter of opinion, not fact. But I don't think they are useful, and I think people who are using them for programming are playing with fire. You can't safely use anything they give you without validating it is correct, and if you have to check the code anyways it's not saving you work compared to just writing it.
LLMs are the worst sort of help - the unreliable kind, which you can't trust to actually do its job. Help like that is generally worse than no help at all, because at least you know what to expect and can plan for it when you have no help at all.
One of the main use cases I have is "take this algorithm described in this paper and implement it using numpy" or "render a heatmap" where it's pretty trivial to check that the code reads as doing the same thing as the paper. But it is nice to skip the innumerable finicky "wait was it
numpy.linalg.eigenvalues()ornumpy.linalg.eigvals()" gotchas - LLMs are pretty good at writing idiomatic code. And for the types of things I'm doing, the code is going to be pasted straight into a jupyter notebook, where it will either work or fail in an obvious way.If you're trying to get it to solve a novel problem with poor feedback you're going to have a bad time, but for speeding up the sorts of finicky and annoying tasks where people experienced with the toolchain have just memorized the footguns and don't even notice them anymore but you have to keep retrying because you're not super familiar with the toolchain, LLMs are great.
Also you can ask "are there any obvious bugs or inefficiencies in this code". Usually the answer is garbage but sometimes it catches something real. Again, it's a case where the benefit of the LLM getting it right is noticeable and the downside if it gets it wrong is near zero.
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