3 ms·
ML is basically the one use case for Python anymore. And that even shrinks by the day
by mountainriver 4mo ago
ML is basically the one use case for Python anymore.
And that even shrinks by the day
- sideway 4mo agoHow so?
- mountainriver 4mo agoWith 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 4mo agoBecause the English to code translation step is fallible
- hocuspocus 4mo agoWhich is precisely a reason for not using Python, despite LLMs being good at it.
- nijave 4mo agoWhy is that?
- wolvesechoes 4mo ago> 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 4mo agoImplementing whole features is? What are we talking about?
- hocuspocus 4mo agoLLMs 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.