Cognitive Twin V9 — Evolution³

# Cognitive Twin V9 — Evolution³ ### Stage 1: Explore → Stage 2: Compound → Stage 3: Master Plan **Generated:** 2026-02-18 **Method:** Evolution³ — three-stage recursive evoflow **Core Question:** How should we train, deploy, and integrate the Cognitive Twin V9 into our multi-machine architecture (Mac1 gateway + Mac4 local compute + Together AI cloud + 3 Claude Max accounts) to maximize autonomy, minimize cost, and keep the model evergreen with our rapidly evolving ecosystem? **Context Inherited:** - Current dataset: 77,708 records (V5+V6+V7+V8 combined, CTv3.1 JSONL) - V9 expansion potential: ~2,635 new records from 8 sources - 141 skills, 32 CLAUDE.md files, 23 pulse plans, 30K+ Kimi memory turns - Mac4: M4 Mac Mini 16GB, Ollama (Llama 3.2:3B, MiniMax M2.5), macOS 26.3 - Mac1: M4 MacBook Air 16GB, Clawdbot gateway, daily driver - Together AI: Serverless LoRA on Qwen3-235B ($0.20/$0.60/MTk) or Llama 4 Maverick - 3 Claude Max accounts (free frontier inference, rate-limited) - Graph Kernel + RAG++ + Cortex + Dream Weaver operational - Twin Swarm DEP (Feb 14): Alpha/Beta/Gamma lanes designed but never deployed - Previous blockers: Together AI billing limit, Vast.ai not rented **Concept:** Fine-tune Qwen3-235B-A22B on Together AI/Vast.ai, then quantize the merged adapter into GGUF Q4_K_M and run it locally on Mac4 via Ollama. The MoE architecture means only 22B params are active per inference — theoretically fits in 16GB RAM with aggressive quantization. **Why it works:** - Qwen3-235B has best-in-class coding performance among open models - MoE with 22B active params is comparable to running a 22B dense model - Q4_K_M quantization of 22B active params needs ~12-14GB VRAM/RAM - Mac4's M4 chip has unified memory — no CPU-GPU transfer overhead - Once deployed, inference is completely free and private - 262K context window survives quantization