I’ve gotten 0 production usable python out of any LLM. Small script to do something trivial, sure. Anything I’m going to have to maintain or debug in the future, not even close. I think there is a _lot_ of terrible python code out there training LLMs, so being a more popular language is not helpful. This era is making transparent how low standards really are.
> I’ve gotten 0 production usable python out of any LLM
Fascinating, I wonder how you use it because once I decompose code to modules and function signatures, Claude[0] is pretty good at implementing Python functions. I'd say it one-shots 60% of the times, I have to tweak the prompt or adjust the proposed diffs 30%, and the remaining 10% is unusable code that I end up writing by hand. Other things Claude is even better at: writing tests, simple refactors within a module, authoring first-draft docstrings, adding context-appropriate type hints.
0. Local LLMs like Gemma3, Qwen-coder seem to be in the same ballpark in terms of capabilities, it's just that they are much slower on my hardware. Except for the 30b Qwen3 MoE that was released a day ago, that one is freakin' fast.
I agree - you have to treat them like juniors and provide the same context you would someone who is still learning. You can’t assume it’s correct but where it doesn’t matter it is a productivity improvement. The vast majority of the code I write doesn’t even go into production so it’s fantastic for my usage.
Different experience here. Production code in banking and finance for backend data analysis and reporting. Sure the code isn't perfect, but doesn't need to be. It's saving >50% effort and the analysis results and reporting are of at least as good a standard as human developed alternatives.
