"What if Opus wrote the code, and GPT 5~ reviewed it?" I started evaluating this question, and started to get higher quality results and better control of complexity.

I could also trust this process to a greater degree than my previous process of trying to drive Opus, look at the code myself, try and drive Opus again, etc. Codex was catching bugs I would not catch with the same amount of time, including bugs in hard math, etc -- so I started having a great degree of trust in its reasoning capabilities.

I've codified this workflow into a plugin which I've started developing recently: https://github.com/evil-mind-evil-sword/idle

It's a Claude Code plugin -- it combines the "don't let Claude stop until condition" (Stop hook) with a few CLI tools to induce (what the article calls) review gates: Claude will work indefinitely until the reviewer is satisfied.

In this case, the reviewer is a fresh Opus subagent which can invoke and discuss with Codex and Gemini.

One perspective I have which relates to this article is that the thing one wants to optimize for is minimizing the error per unit of work. If you have a dynamic programming style orchestration pattern for agents, you want the thing that solves the small unit of work (a task) to have as low error as possible, or else I suspect the error compounds quickly with these stochastic systems.

I'm trying this stuff for fairly advanced work (in a PhD), so I'm dogfooding ideas (like the ones presented in this article) in complex settings. I think there is still a lot of room to learn here.

It's cool to see others thinking the same thing!