I've also had decent experiences with Rust recently. I haven't done enough Haskell programming in the AI era to really say.

But it could be that different programming languages are a bit like different human languages for these models: when they have more than some threshold of training data, they can express their general problem solving skills in any of them? And then it's down to how much the compiler and linters can yell at them.

For Rust, I regularly tell them to make `clippy::pedantic` happy (and tell me explicitly when they think that the best way to do that is via an explicit ignore annotation in the code to disable a certain warning for a specific line).

Pedantic clippy is usually too.. pedantic for humans, but it seems to work reasonably well with the agents. You can also add clippy::cargo which ain't included in clippy::pedantic.

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solomonb 4 hours ago | parent [-]

> But it could be that different programming languages are a bit like different human languages for these models: when they have more than some threshold of training data, they can express their general problem solving skills in any of them? And then it's down to how much the compiler and linters can yell at them.

I think this is exactly right.

	▲	jaggederest 2 hours ago \| parent [-]
		Exactly my opinion - I think the more you lock down the "search space" by providing strong and opinionated tooling, the more LLMs perform well. I think of it as starting something like a simulated annealing trying to get a correct solution, versus the same simulated annealing run while using heuristics and bounds to narrow the solution space