This is a submission for the GitHub Finish-Up-A-Thon Challenge
What I Built
MCOP Framework 2.0 is a deterministic, cryptographically verifiable meta-cognitive optimization protocol — a production-ready layer designed to sit on top of LLM stacks.
Its foundation is a recursive triad:
- NOVA-NEO Encoder — SHA-256 context-to-tensor conversion for byte-identical reproducibility.
- Stigmergy v5 — Merkle-chained pheromone memory with cosine recall and full provenance tracking.
- Holographic Etch — Append-only confidence ledger with eudaimonic (positive-resonance) scoring that rewards trajectories aligned with human flourishing.
v2.4 adds the Proteome — a 150-node sparse interaction graph for chaotic and game-theoretic abstraction discovery, with edge-of-chaos controls (homeostasis, mutationTemperature) and optional CUDA acceleration — plus an LS20 ARC benchmark scaffold, a Universal Adapter Protocol (native OpenAI, Anthropic, Google, Groq, Ollama, and xAI Grok), 96.6% test coverage, Docker reproducibility, and hardened CI/security.
What it means to me: MCOP is my long-term commitment to AI cognition that is auditable, negentropic, and oriented toward genuine human flourishing rather than pure capability scaling. It evolved from early conceptual kernels into a full multi-language implementation — TypeScript core, Python distribution, Next.js interfaces — ready for scrutiny and real use.
Demo
Primary link: https://github.com/Kuonirad/MCOP-Framework-2.0
Try it locally:
bash
git clone https://github.com/Kuonirad/MCOP-Framework-2.0.git
cd MCOP-Framework-2.0
pnpm install
pnpm test # 96.6% coverage
pnpm build && pnpm start
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