[sgarland]

OK, but (I mean this sincerely and in kindness) this isn’t a particularly challenging task. Useful, yes, and the type of thing Python is marvelous at, but not overly difficult.

If you start getting into more esoteric edges of Python, like SharedMemory, ChatGPT quickly falls apart. Or, more relevant to your example, using carriage return to overwrite text for a progress indicator – works great, very simple. Until you try to use it through subprocess. To ChatGPT’s credit, it eventually came up with using pty, which with some massaging, I got to work.

I’m not saying it isn’t useful – far from it. It’s just that on anything modestly complicated, you sometimes have to spend more time fiddling with the prompt than if you just sat down and wrote the code. Another example that comes to mind was implementing a B+tree in pure Python. I know how they work, and wanted to see if ChatGPT could figure it out. You’d think so, right? But no, it kept getting stuck on node splits.

[Karunamon]

Zero offense taken, and I agree completely, it's just that most tasks a person would want to do with Python are probably simple enough to where usage of a language model will save a significant amount of time.

It doesn't mean you can turn your brain off and just mindlessly copy and paste whatever gets output, but for basics and boilerplate it's a massive time save, and I only foresee the capabilities getting better over time. It's just that comments like the one I was replying to come off as childishly throwing one's toys out of the pram because they are imperfect.

[Closi]

Current generation LLM's aren't about doing tasks that humans struggle with - they are about doing tasks that humans find easy quicker than humans can do themselves.

Future generation AI's are highly likely to have more capability though - I wouldn't bet the house on the current state being the future state.