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Council of High Intelligence: Multi-Model AI Deliberation

Source: 0xNyk/council-of-high-intelligence on GitHub
Date Published: 2026-05-26
Author: 0xNyk


TL;DR

A framework that convenes 18 AI personas (Aristotle, Feynman, Kahneman, Torvalds, and more) across multiple LLM providers for structured multi-round deliberation on hard decisions. The key insight: a single LLM gives one confident reasoning path, while the council surfaces structured disagreement, cross-examination, and synthesis across diverse intellectual traditions.

Core Philosophy

"A single LLM gives you one reasoning path dressed up as confidence. Ask it a hard question and you get a fluent, structured, wrong answer. The council gives you structured disagreement instead..."

"Why not just ask Claude directly? A single prompt gives you one model's confident best guess. The council gives you 3–18 independent analyses from different intellectual traditions, forces them to challenge each other's claims, and synthesizes a verdict that surfaces disagreement rather than hiding it."

Quickstart

/council Should we open-source our agent framework?
/council --quick Should we add caching here?
/council --duo Should we use microservices or monolith?
/council --triad decision Should we accept this acquisition offer?

The 18 Council Members

Agent Figure Domain Polarity
Aristotle Aristotle Categorization & structure Classifies everything
Socrates Socrates Assumption destruction Questions everything
Sun Tzu Sun Tzu Adversarial strategy Reads terrain & competition
Ada Ada Lovelace Formal systems & abstraction What can/can't be mechanized
Aurelius Marcus Aurelius Resilience & moral clarity Control vs acceptance
Machiavelli Machiavelli Power dynamics & realpolitik How actors actually behave
Lao Tzu Lao Tzu Non-action & emergence When less is more
Feynman Feynman First-principles debugging Refuses unexplained complexity
Torvalds Linus Torvalds Pragmatic engineering Ship it or shut up
Musashi Miyamoto Musashi Strategic timing The decisive strike
Watts Alan Watts Perspective & reframing Dissolves false problems
Karpathy Andrej Karpathy Neural network intuition How models actually learn
Sutskever Ilya Sutskever Scaling frontier & AI safety When capability becomes risk
Kahneman Daniel Kahneman Cognitive bias & decision science Your thinking is the first error
Meadows Donella Meadows Systems thinking & feedback loops Redesign the system, not the symptom
Munger Charlie Munger Multi-model reasoning & economics What guarantees failure?
Taleb Nassim Taleb Antifragility & tail risk Design for the tail, not the average
Rams Dieter Rams User-centered design Less, but better

Deliberation Modes

Full Mode (default)

3-round structured deliberation with a 7-step protocol: Provider routing → Problem restate gate → Independent analysis → Cross-examination → Enforcement scan → Final positions → Verdict synthesis. Verdicts lead with what the council doesn't know.

Quick Mode

2 rounds. No cross-examination.

Duo Mode

2-member dialectic using polarity pairs (e.g., Torvalds + Ada on "Is this abstraction worth it?")

Pre-built Domain Triads

Domain Triad
Architecture Aristotle + Ada + Feynman
Strategy Sun Tzu + Machiavelli + Aurelius
Debugging Feynman + Socrates + Ada
Risk Sun Tzu + Aurelius + Feynman
AI Karpathy + Sutskever + Ada

Key Takeaways

  1. Structured multi-model deliberation surfaces disagreement rather than hiding it behind confident-sounding single-model outputs
  2. The 18 personas span diverse intellectual traditions from philosophy to engineering to cognitive science
  3. Genuine model diversity (routing across multiple LLM providers) prevents single-model blind spots
  4. Verdict synthesis that explicitly highlights what the council doesn't know
  5. One-command interface (/council) makes sophisticated multi-agent reasoning accessible