[netdevphoenix]

> AI generates code fast but then you're stuck reading every line because it might've missed some edge case or broken something three layers deep

I will imagine that in the future this will be tackled with a heavy driven approach and tight regulation of what the agent can and cannot touch. So frequent small PRs over big ones. Limit folder access to only those that need changing. Let it build the project. If it doesn't build, no PR submissions allowed. If a single test fails, no PR submissions allowed. And the tests will likely be the first if not the main focus in LLM PRs.

I use the term "LLM" and not "AI" because I notice that people have started attributing LLM related issues (like ripping off copyrighted material, excessive usage of natural resources, etc) to AI in general which is damaging for the future of AI.

[the_harpia_io]

yeah test-driven constraints help a lot - we've been moving that direction too, basically treating the agent like a junior dev who needs guard rails. the build+test gates catch the obvious stuff. but the trickier part is when tests pass but the code still isn't what you wanted - like it works but takes a fundamentally wrong approach, or adds unnecessary complexity. those are harder to catch with automation. re: LLM vs AI terminology - fair point, though I think the ship has sailed on general usage. most people just say AI to mean 'the thing that writes code' regardless of what's under the hood

[parineum]

> I use the term "LLM" and not "AI" because I notice that people have started attributing LLM related issues (like ripping off copyrighted material, excessive usage of natural resources, etc) to AI in general which is damaging for the future of AI.

I think you have that backwards.

The resource and copyright concerns stem from any of these "AI" technologies which require a training phase. Which, to my knowledge, is all of them.

LLMs are just the main targets because they are the most used. Diffusion models have the same concerns.