Build Wide, Ship Narrow

That was hard to understand. So the idea if I'm not mistaken is to iterate without constraint/move fast and break things (build wide).

Demo the work, keep changing. Don't worry about the amount of changes, don't worry about nice commits. You are free to explore and produce as much changes as required.

Then use AI agents to clean up the history and produce a nice change set that you can ship in a more traditional way, small clean commits.

That is an interesting idea I guess. AI agents can be used to clean up git histories, not only produce large amounts of code.

Somehow, I think heavy LLM users don't really care about a clean git history / changesets? May be if the project or existing workflow you are bound to requires it you will do the extra steps but really I don't see it being done much.

Are you really going to review these small changes? So far the example I saw from agents are terrible. Full of drive-by, unrelated changes here and there. Mostly harmless or OK changes, but not focused.