llm-councillisted

# LLM Council You ask one AI a question, you get one answer. That answer might be great. It might be mid. You have no way to tell because you only saw one perspective. The council fixes this. It runs your question through 5 independent advisors, each thinking from a fundamentally different angle. Then they review each other's work. Then a chairman synthesizes everything into a final recommendation that tells you where the advisors agree, where they clash, and what you should actually do. This is adapted from Andrej Karpathy's LLM Council. He dispatches queries to four different providers, has them peer-review each other anonymously, then a chairman produces the final answer. His diversity comes from the models. Here it comes mostly from the thinking lenses: four advisors are Claude sub-agents with deliberately opposed perspectives. The fifth, the Outsider, runs on a different model family through the Codex CLI, so the council does not share one set of blind spots. --- ## when to run the council The council is for questions where being wrong is expensive. Good council questions: - "Should I launch a $97 workshop or a $497 course?" - "Which of these 3 positioning angles is strongest?" - "I'm thinking of pivoting from X to Y. Am I crazy?" - "Here's my landing page copy. What's weak?" - "Should I hire a VA or build an automation first?" Bad council questions: - "What's the capital of France?" (one right answer, no need for perspectives) - "Write me a tweet" (creation task