[mountainriver]

ML is basically the one use case for Python anymore.

And that even shrinks by the day

[sideway]

How so?

[hocuspocus]

LLMs are leveling the developer experience and productivity in a way that makes Python's strengths almost irrelevant, while it's still suffering from bad tooling (even with uv and friends) and poor performance.

AI/ML: interfacing with C++ libraries directly (or in Rust) is now a real option. For everything else, even 5 years ago I wouldn't have used Python, now there are even fewer reasons to do so. As far as I'm concerned the remaining use cases are notebooks and one-shot scripts.

[mountainriver]

With writing code in english now, why have it use a slow weak language?

ML still has a depth of libraries that can't be replicated easily but ML work is decreasing by the day with LLMs.

[nijave]

Because the English to code translation step is fallible

[hocuspocus]

Which is precisely a reason for not using Python, despite LLMs being good at it.

[nijave]

Why is that?

[wolvesechoes]

> With writing code in english now, why have it use a slow weak language?

Because the feedback loop of writing few lines of Python inside Jupyter cell is much shorter than with your currently favorite AI tool. It costs less too.

[mountainriver]

Implementing whole features is? What are we talking about?