> It would be useful if people _ever_ explained the use cases. Even Anthropic when they're publishing their materials are hand wavey about this.

Yes, good addition.

> I couldn't disagree more. I run an engineering team in GameDev, and AI is great at making a gigantic mess. It's great for generating a lot of code, but the minute that doesn't do what you want it to, you now need to go back and learn _everything_ that it's generated. And IME, doing that is as bad as having just written it yourself in the first place.

Yeah, I should have been more specific since our use cases are very different. I'm working solo on small games, so it's easier to keep the AI on a leash. I can imagine it can be a nightmare on large code bases with many collaborators.

> I tentatively agree here, except I've found about 50% of it is "fine", 40% of it is "this works but isn't really what I want to put into my product" and 10% is "absolutely not".

Yeah, probably our use cases are very different here too. We mostly develop/maintain dozens of microservices for internal use, mostly greenfield, relatively small in scope, and with 1-3 developers in each. And maybe 98% was a bit too optimistic. The larger and older the codebase, the more problems AI tends to create. But it's very good at helping to understand old codebases (even those written by myself haha).

Oh, and esoteric data science topics can be a minefield too. If there's a topic that's new to me, It's difficult to know what is hallucination and what is not. It's best to let AI suggest alternative methods and then study those yourself.