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Notes -
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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