I'm the author of what follows; it's open source (AGPL), there's no paid tier and nothing to sell; sharing the architecture and one insightful result of the LLMs reaching consensus.

**The lineage**

This is built on the shoulders of `karpathy/llm-council` (https://github.com/karpathy/llm-council): one question fans out to several LLMs, they review each other's answers, and a final answer is synthesized. His pipeline is one fixed pass (answer → rank → chairman) behind a local server. I kept the council idea and changed the shape: my follow-up runs fully in the browser as a static bundle (zero backend; the "server" is your browser tab), bring-your-own-keys: keys sit in localStorage, and requests go from the browser straight to the providers you pick (Anthropic, OpenAI, Google, Groq, OpenRouter, local Ollama).

**The central difference: Consensus mode**

Instead of ranking first drafts, the models debate over rounds:

1. Every participant answers independently.
   
2. A Mediator model reads the answers (anonymized as "Model A/B/C”) judges whether they've converged, and if not, distills the actual points of disagreement to seed the next round.
   
3. Every participant re-answers, seeing its own prior position plus its peers' arguments. Labels stay stable within a turn but reshuffle across turns, so models can't learn which brand is which.
   
4. Repeat until convergence or a round cap (3 by default, configurable). At the cap, the Mediator reports points of agreement *and* remaining conflicts, no forced harmony.

The Mediator's verdict is a structured output (`convergent` \+ divergence points + a per-model "held/shifted" digest), so the UI can show exactly who moved and on what argument.

**One of the debate examples that was fun to observe**

In one recorded demo (*“pick the best third language for an 8-year-old who already speaks English and Spanish”*), Claude Fable was the 1-vs-2 minority arguing French, while GPT-5.5 and Gemini both picked Mandarin. In round two, both majority models switched to French: each named the specific arguments that moved it (expected value = payoff × probability of actually reaching fluency; the importance and challenge of being immersed in the native-speaker environment; machine translation eroding the transactional value of "hard" languages). GPT literally opens its re-answer with "What changed my mind…". The demo can be seen without any API key.

**Context:**

* Repo: [https://github.com/trekhleb/yesbrainer\](https://github.com/trekhleb/yesbrainer)
* The recorded debate above, no key needed: [https://yesbrainer.ai/council/9123476a-4bc0-4214-8d1b-c76613808eb9\](https://yesbrainer.ai/council/9123476a-4bc0-4214-8d1b-c76613808eb9)

I'm the author of what follows; it's open source (AGPL), there's no paid tier and nothing to sell; sharing the architecture and one insightful result of the LLMs reaching consensus.

The lineage

This is built on the shoulders of karpathy/llm-council (https://github.com/karpathy/llm-council): one question fans out to several LLMs, they review each other's answers, and a final answer is synthesized. His pipeline is one fixed pass (answer → rank → chairman) behind a local server. I kept the council idea and changed the shape: my follow-up runs fully in the browser as a static bundle (zero backend; the "server" is your browser tab), bring-your-own-keys: keys sit in localStorage, and requests go from the browser straight to the providers you pick (Anthropic, OpenAI, Google, Groq, OpenRouter, local Ollama).

The central difference: Consensus mode

Instead of ranking first drafts, the models debate over rounds:

1. Every participant answers independently.
2. A Mediator model reads the answers (anonymized as "Model A/B/C”) judges whether they've converged, and if not, distills the actual points of disagreement to seed the next round.
3. Every participant re-answers, seeing its own prior position plus its peers' arguments. Labels stay stable within a turn but reshuffle across turns, so models can't learn which brand is which.
4. Repeat until convergence or a round cap (3 by default, configurable). At the cap, the Mediator reports points of agreement and remaining conflicts, no forced harmony.

The Mediator's verdict is a structured output (convergent + divergence points + a per-model "held/shifted" digest), so the UI can show exactly who moved and on what argument.

One of the debate examples that was fun to observe

In one recorded demo (“pick the best third language for an 8-year-old who already speaks English and Spanish”), Claude Fable was the 1-vs-2 minority arguing French, while GPT-5.5 and Gemini both picked Mandarin. In round two, both majority models switched to French: each named the specific arguments that moved it (expected value = payoff × probability of actually reaching fluency; the importance and challenge of being immersed in the native-speaker environment; machine translation eroding the transactional value of "hard" languages). GPT literally opens its re-answer with "What changed my mind…". The demo can be seen without any API key.

Context:

• Repo: https://github.com/trekhleb/yesbrainer
• The recorded debate above, no key needed: https://yesbrainer.ai/council/9123476a-4bc0-4214-8d1b-c76613808eb9

I made a thin Docker wrapper (claude-pod) recently for personal use — it gives Claude access to only the current project folder, nothing else on my machine: https://github.com/trekhleb/claude-pod

Honestly, I wasn't using it that often — just the friction of running Claude via PORTS="8000 5173" claude-pod instead of just claude. Pasting/dragging images in isn't great yet either.

But the "Claude Fable is relentlessly proactive" post gave me another nudge toward containerising it: https://simonwillison.net/2026/Jun/11/fable-is-relentlessly-proactive/

Under the hood, it's four tiny files: the container drops all Linux capabilities, runs as your host user, is ephemeral (--rm), forces any published ports to 127.0.0.1, and has opt-in NET=none / memory / CPU caps for running untrusted code.

Not a 100% defence from the potential glitches — the project folder itself is still exposed — but it narrows the worst case from "my whole machine" to "one folder I can restore from git". Good enough in many cases.