cross-posted from: https://piefed.social/c/[email protected]/p/2509642/linus-torvalds-posts-his-first-ai-assisted-patch-to-the-linux-kernel-synthetic-test-clo
cross-posted from: https://piefed.social/c/[email protected]/p/2509642/linus-torvalds-posts-his-first-ai-assisted-patch-to-the-linux-kernel-synthetic-test-clo
You can also have multiple models review the result, seek consensus, and then review the results of that report. I find that’s the best way to find legitimate issues, prior manual review. Huge time saver because the multitude of models can converge while you make yourself a cup of coffee, and you can find things that need to be fixed before you ask someone to spend their precious time in reviewing a pull request.
You don’t throw more oil on oil fires.
Dont get me wrong, you can’t use AI to speed things up. But if you’re facing the problems I’m describing, then you need a targeted and explicit recovery effort — not just more bots.
Remember part of the problem is the increased cost of every subsequent change.
Humans will have this little voice in the back of their head, always screaming at them about how the architecture of an app should be adjusted to better account for a feature. We’re constantly reorganizing our apps, or backlogging features because we know that it should come with a reorganization.
By contrast, an agent just does as it’s told. When you start using agents in a manner that outpaces your capacity to review and understand the breadth of its work, you’re necessarily ignorant of what changes should and shouldn’t necessarily be occurring. It creates the conditions for the problem which I’m referring to. Also, AI is really good at moving fast — so it created the preconditions of the conditions for the problem which I am referring to. It’s turtles all the way down.