Show HN: Cognition-wheel – parallel LLM fusion with bias masking and judging

GitHub: https://github.com/Hormold/cognition-wheel | npx mcp-cognition-wheel

While building agent flows, I kept hitting a gap: sequential-thinking helps models reason step-by-step — but what about cross-checking?

cognition-wheel runs 3 top-tier models (Claude-4, Gemini 2.5, OpenAI o3) in parallel, anonymizes them (Alpha / Beta / Gamma), and uses a random judge model to fuse the answers into one take.

The pain: • Models contradict each other — which one do you trust?

• Brand bias skews output

• One model fails? You’re stuck

The fix:

• 3 LLMs think in parallel

• Names masked to avoid bias

• Random judge model synthesizes a final answer

How it fits:

1. Call sequential-thinking for step-by-step reasoning

2. Pipe the result into cognition-wheel for fast consensus check → deep logic + fast cross-validation

Built this because I wanted multi-model reasoning without orchestration hell. Would love feedback, weird edge cases, or ideas on smarter judge selection.