I wish I knew, I haven't had any place to point people to. I'm going to start sharing this on YouTube since I already spend a few hours each week talking some friend or user through the latest best practices.

Since you asked, the answer is that I built and use an agent multiplexer called Clor https://clor.com

I have a $200/mo Claude subscription and a $200/mo Codex subscription, and I'm signed in to both. The Docker containers keep each session isolated, so dev servers, browser testing, etc. can work without conflicts.

It includes `/ask-claude` and `/ask-codex` skills that I use very frequently to have the Claude or Codex harness call out to the other one for advice on plans, bug repro, code review, etc.

The agents run in total "yolo" mode, so there are no permission prompts to approve. The risk is mitigated by the Docker containers (which don't necessarily provide a security barrier but do limit accidents).

I was doing this manually in Ghostty tabs for a long time, and it got painful, so I built a much more sophisticated version that I (and my friends/colleagues) could use.

I use a workflow that has different named subagents. [1] Agent profiles can be pinned to models. So you set the model you want on your main thread as the orchestrator. Create an agent for the "planner", "implementer", and "reviewer" and set the model you want for each. Right now I am orchestrating and implementing with Deepseek, planning with Astra, and reviewing with Opus.

I am doing this with the Pi harness right now. To use a Claude monthly plan you need to use the pi-claude-bridge plugin.

If you are using just Claude for example you can use Sonnet as the implementer and Fable/Opus as the planner.

[1] https://github.com/gregwebs/skills-sdlc/

My trick for using Opus is using it exclusively as a subagent managed by Fable.

"Use Opus subagents for this work where possible" is all it takes generally.

In my experience Astra/Sol are both quite good as workhorses, but not at Fable's level. I use them every day very successfully and I'm very picky.

I agree with you on this. Opus feels tedious and it cannot be stopped from doing change-narration comments, but Fable feels like a real collaborator. I am usually pretty happy with the code it writes.

Interesting, in my experience Astra is a marked improvement over both Sol 5.6 and Fable 5.1. Its output feels a lot more natural, and it is just less "dumb." But individual experiences may vary.

What have you noticed about Astra? I haven't used Claude models lately so I can't compare but it seems fine compared to 5.6 Sol

Which Qwen3.8? Qwen3.8-Max? Qwen3.8-Flash-Next? Qwen3.8-27B? They are all different models.

Low IQ: "Just use Claude and Codex"

Midwit: "No, you see, you need a deterministic 12-stage multi-agent orchestration framework with vector embedding semantic routing, and five open weight models with custom harnesses!"

Genius: "Just use Claude and Codex"

> Your ability to continue to perform this work can be withdrawn at any time for any (or no) reason.

Thanks to the fact that there are no widespread stories about this actually occurring in practice, at least not yet, people do not take it as a relevant risk at the moment.

> You have little ability to validate that the work is being performed as expected and isn't being silently nerfed or outright subverted based on competitive considerations, bribes, overactive 'safety', or cost management.

Yes, I agree that this is a real concern that many people might rightfully have. And I am unaware of any way to mitigate this concern while using black box AI models. Because the only thing that their creators can do is to tell their customers: "trust us". But there is no way to objectively verify whether they serve tainted AI model responses or not.

I think that's a bit strong of an assertion. How many people use copilot daily under an enterprise agreement? I don't necessarily disagree in spirit, especially given the questionable data sanitization around the recent Navier-Stokes announcement, but most companies disclose huge amounts of data regularly to hopefully-trusted third parties. I think the internet -- and a good share of the world's commerce -- would grind to a halt if we suddenly stopped. Setting up, securing, and maintaining local models for even a small user base is non-trivial and there is way more demand than supply for that skillset right now.

Anyway here's how Cloudflare orchestrates AI reviews at scale: https://blog.cloudflare.com/ai-code-review/

AI token machine go brrr!