LLMs are unusually good at Rust; it's an optimization target. And the constraints provided by "successfully compile with the Rust compiler" make it work well for agent iteration.
reply(I have mixed feelings about that, but empirically it holds true.)
Yes, I found these agents to be better at producing acceptable Rust than at producing acceptable Python code.
replyIn addition to the Rust compiler, you can also tell them to make clippy happy. Both in normal mode or if you are feeling nitpicky, you can also tell them to make clippy::pedantic happy.
Can you give an example where an LLM produced low quality Python code? Python is such a simple language. This seems hard to imagine. Plus, the amount of open source Python that LLMs can be trained upon is enormous.
replyI am looking at an MR a colleague threw at me right now where the code quality produced is atrocious.
replyMultiple redefinitions of enums - except they aren't enum but random lists of strings - that also diverge in different files.
Things consistently typed as `Any` or `dict[str, Any]` even though the functions clearly are expecting specifics, not `Any`
This is the worst MR I've had to review yet in my life. Absolute garbage.
Most people it's good at syntax and the error messages give you a good loop. But the domains rust actually makes sense in tend to be quite punishing on slop both culturally and technically.
replyTo add to this, I find that the delta between the amount of code and pain you get with good and bad abstractions is substantially higher in rust than other languages. It's alright to muddle through in Python or TS, but with Rust bad abstractions are punishing.
replyLLMs are pretty bad at picking abstractions.
It's good that it's punishing when the abstraction are bad: then you notice. With Python or TS, as you say, you get less feedback.
replyThat's true, although inconvenient for production code that needs to be delivered yesterday.
replySadly, agents don't mind generating gigatons of code instead of refactoring the abstractions.
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