I love the interview at the end of the video. The kubectl-inspired CLI, and the feedback for improvements from Claude, as well as the alerts/segmentation feedback.

You could take those, make the tools better, and repeat the experience, and I'd love to see how much better the run would go.

I keep thinking about that when it comes to things like this - the Pokemon thing as well. The quality of the tooling around the AI is only going to become more and more impactful as time goes on. The more you can deterministically figure out on behalf of the AI to provide it with accurate ways of seeing and doing things, the better.

Ditto for humans, of course, that's the great thing about optimizing for AI. It's really just "if a human was using this, what would they need"? Think about it: The whole thing with the paths not being properly connected, a human would have to sit down and really think about it, draw/sketch the layout to visualize and understand what coordinates to do things in. And if you couldn't do that, you too would probably struggle for a while. But if the tool provided you with enough context to understand that a path wasn't connected properly and why, you'd be fine.

I see this sentiment of using AI to improve itself a lot but it never seems to work well in practice. At best you end up with a very verbose context that covers all the random edge cases encountered during tasks.

For this to work the way people expect you’d need to somehow feed this info back into fine tuning rather than just appending to context. Otherwise the model never actually “learns”, you’re just applying heavy handed fudge factors to existing weights through context.