[stillsut]

I've actually written my own a homebrew framework like this which is a.) cli-coder agnostic and b.) leans heavily on git worktrees [0].

The secret weapon to this approach is asking for 2-4 solutions to your prompt running in parallel. This helps avoid the most time consuming aspect of ai-coding: reviewing a large commit, and ultimately finding the approach to the ai took is hopeless or requires major revision.

By generating multiple solutions, you can cutdown investing fully into the first solution and use clever ways to select from all the 2-4 candidate solutions and usually apply a small tweak at the end. Anyone else doing something like this?

[0]: https://github.com/sutt/agro

[thethimble]

There is a related idea called "alloying" where the 2-4 candidate solutions are pursued in parallel with different models, yielding better results vs any single model. Very interesting ideas.

https://xbow.com/blog/alloy-agents

[stillsut]

Exactly what I was looking for, thanks.

I've been doing something similiar: aider+gpt-5, claude-code+sonnet, gemini-cli+2.5-pro. I want to coder-cli next.

A main problem with this approach is summarizing the different approaches before drilling down into reviewing the best approach.

Looking at a `git diff --stat` across all the model outputs can give you a good measure of if there was an existing common pattern for your requested implementation. If only one of the models adds code to a module that the others do not, it's usually a good jumping off point to exploring the differing assumptions each of the agents built towards.

[michaelbarton]

This reminds me of an an approach in mcmc where you run mutiple chains at different temperatures and then share the results between them (replica exchange MCMC sampling) the goal being not to get stuck in one “solution”