One of the big things I think a lot of tooling misses, which Geoffrey touches on is the automated feedback loops built into the tooling. I expect you could probably incorporate generation time and token cost to automatically self tune this over time. Perhaps such things as discovering which prompts and models are best for which tasks automatically instead of manually choosing these things.

You want to go meta-meta? Get ralph to spawn subagents that analyze the process of how feedback and experimentation with techniques works. Perhaps allocate 10% of the time and effort to identifying what's missing that would make the loops more effective (better context, better tooling, better feedback mechanism, better prompts, ...?). Have the tooling help produce actionable ideas for how humans in the loop can effectively help the tooling. Have the tooling produce information and guidelines for how to review the generated code.

I think one of the big things missing in many of the tools currently available is tracking metrics through the entire software development loop. How long does it take to implement a feature. How many mistakes were made? How many errors were caught by tests? How many tokens does it take? And then using this information to automatically self-tune.