That works well for greenfield code, but if working on compiler optimisations has taught me anything, it's that there is a lot of legacy code out there in the world that would benefit from re-compilation with optimisations - but would be very difficult to re-write for speed (regardless of whether the work is done by a human, AI, or other machine tool). JIT compilation fits very nicely in this niche, as it means that existing Python projects can get (theoretically) better performance without re-writing.

I've had good success with just using mypyc (https://mypyc.readthedocs.io/en/latest/introduction.html) on typed python code.

> on typed python code.

A fairly massive undertaking for existing code, which jit was supposed to be useful for.

You can use static type analysers and runtime type profilers to add type annotations to a legacy codebase, but the interesting bit would be if you're built on top of a large untyped codebase you don't control like, say, Django, how far it can go.

I would imagine that the LLM has a much easier time adding correct type annotations to old Python code than it does translating it to C.

In my experiments with this, maybe, sorta, it'll do some AST stuff then it'll go "dang this shit is dynamic AF" then suggest a static type inference tool, then a runtime type profiler.

Which honestly, is the right approach.

People have been calling Python a "prototyping" language for decades. I'd be interested to see how many Python projects ever actually get rewritten in a more performant language. My guess is, up until the last five years that was almost never done. Even with LLMs to help, is that the best use of your tokens and humans' time? It doesn't add a feature. It could conceivably cut costs after enough time, but only if done correctly.

> My guess is, up until the last five years that was almost never done.

Because having just the hot loop in the performant language is often deemed good enough. And there's tons of Python code out there where the "hot loop" is already automatically in C (the various operations implemented in C by the runtime) or where you can get close enough e.g. with Numpy. Consider that things like Pygame can exist; for a lot of the games people want to make, the only thing that really needs to happen anywhere near "bare-metal" speed is blitting.

Doing it currently, Python to Go. Early indications are a 50-98% increase in speed (e.g. one testcase went from 26hrs to 30mins) for network/CPU heavy workload. Conversion via Qwem, lots of testing needed but compute savings will be worth it.

Did you mean “50-98% decrease in running time”? Just calculating from your 26hrs to 30 mins seems to imply so. In terms of speed that translates to 100% to 5100% increase in speed.

Yes, sorry, typing fast - we knew Go would be faster but not quite on that scale.

Where you using standard optimized libraries (e.g., NumPy for numerical computation) or did you do everything in Python? This kind of increase of speed seems to indicate extremely sloppy Python programming.

Why not go with something like graalpy first? That's much less risk in terms of "we end up slightly changing behavior"

It's the same level of testing for us for either Python2->3 or Go (it's a legacy code base, low automated coverage).

Conversion is now (somewhat) easier, the testing will be the slow part. For the speed improvements, which we also need for a migration from on-prem to cloud, it's a no brainer vs the other options at our disposal (like GraalPy).

> My guess is, up until the last five years that was almost never done.

It was done plenty. Just with extensions to python like numpy and opencv. There is no reason to translate everything versus just the critical pieces.

Agree with the general workflow: prototype with an easy-to-read language then port to lower-level, faster language