I've written a little about some my findings and workflow in detail here: https://github.com/sutt/agro/blob/master/docs/case-studies/a...
Richard Stallman is rolling in his grave with this.
But in all seriousness, nice work, I think this _is_ where the industry is going, hopefully we don't have to rely on using proprietary models forever though.
You can set up a FOSS toolchain to do similar work, it’s just something I haven’t spent the time on. I probably should.
The downside is I don’t have as much of a grasp on what’s actually happening in my project, while with hand-written projects I’d know every detail.
Not a gotcha I'm just extremely skeptical that AI is at a point to have the level of responsibility you're describing and have it turn into good code long term
- there's a devlog showing all the prompts and accepted outputs: https://github.com/sutt/agro/blob/master/docs/dev-summary-v1...
- and you can look at the ai-generated tests (as is being discussed above) and see they aren't very well thought out for the behavior, but are syntactically impressive: https://github.com/sutt/agro/tree/master/tests
- check out the case-studies in the docs if you're interested in more ideas.
The most effective way I’ve found to use CC so far is this workflow:
Have a detailed and also compressed spec in an md file. It can be called anything, because you’re going to reference it explicitly in every prompt. (CC usually forgets about CLAUDE.md ime)
Start with the user story, and ask it to write a high-level staged implementation plan with atomic steps. Review this plan and have CC rewrite as necessary. (Another md file results.)
Then, based on this file, ask it to write a detailed implementation plan, also with atomic stages. Then review it together and ask if it’s ready to implement.
Then tell Claude to go ahead and implement it on a branch.
Remember the automated tests and functional testing.
Then merge.