ive spent the past few months designing a framework for orchestrating multiple large language models in parallel — not to choose the “best,” but to let them argue, mix their outputs, and preserve dissent structurally.
It’s called Maestro heres the whitepaper https://github.com/d3fq0n1/maestro-orchestrator (Narrative version here: https://defqon1.substack.com/p/maestro-a-framework-for-coher...)
Core ideas:
Prompts are dispatched to multiple LLMs (e.g., GPT-4, Claude, open-source models)
The system compares their outputs and synthesizes them
It never resolves into a single voice — it ends with a 66% rule: 2 votes for a primary output, 1 dissent preserved
Human critics and analog verifiers can be triggered for physical-world confirmation (when claims demand grounding)
The feedback loop learns not only from right/wrong outputs, but from what kind of disagreements lead to deeper truth
Maestro isn’t a product or API — it’s a proposal for an open, civic layer of synthetic intelligence. It’s designed for epistemic integrity and resistance to centralized control.
Would love thoughts, critiques, or collaborators.
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