[maccard]

> It would be useful if people always explained their exact use cases when making claims one way or another. I understand it's not feasible for many use cases to reveal exact details,

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

> Gamedev is a good example of where AI shines, since the risks of fucking things up are not so big

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.

> he main problem is that there's so much stuff to review, and maybe 98% of it is fine. It's then very boring to try to spot the 2%. I don't have good solutions for that yet, and idk if anyone does.

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".

[jimberlage]

AI is great at reading, so give it things to read. Claude will try to throw big markdown docs in your repo, but you don't have to do that. Make it keep docs by the code.

I include a line in AGENTS.md that says "We *always* add docstrings to methods, classes, structs, and namespaces - there should be 100% coverage of docstrings."

It will make better choices about what functions to make or remove if you force it to justify why the function exists.

And when you have to go back and understand, it becomes easier.

[maccard]

It’s clear at this point that agents don’t actually follow agents.md

They try to but they don’t. As an example, we use perforce in games. I regularly have to stop an agent and remind it to use p4 edit rather than blasting through the read only flag, despite the first paragraph of Claude.md being “this is a project using perforce. Batch call p4 edit on all files before modifying, do not manually remove the read only flag”

[hokapo]

> 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.