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## Stack Analysis - Llama 3.1 8B Instruct (128K context, 4-bit quantized) - Discord.py 2.6.4+ async-native framework - Ollama for local inference, ChromaDB for semantic memory - Whisper Large V3 + Kokoro 82M (privacy-first speech) - VRoid avatar + Discord screen share integration ## Architecture - 6-phase modular build: Foundation → Personality → Perception → Autonomy → Self-Mod → Polish - Personality-first design; memory and consistency foundational - All perception async (separate thread, never blocks responses) - Self-modification sandboxed with mandatory user approval ## Critical Path Phase 1: Core LLM + Discord integration + SQLite memory Phase 2: Vector DB + personality versioning + consistency audits Phase 3: Perception layer (webcam/screen, isolated thread) Phase 4: Autonomy + relationship deepening + inside jokes Phase 5: Self-modification capability (gamified, gated) Phase 6: Production hardening + monitoring + scaling ## Key Pitfalls to Avoid 1. Personality drift (weekly consistency audits required) 2. Tsundere breaking (formalize denial rules; scale with relationship) 3. Memory bloat (hierarchical memory with archival) 4. Latency creep (async/await throughout; perception isolated) 5. Runaway self-modification (approval gates + rollback non-negotiable) ## Confidence HIGH. Stack proven, architecture coherent, dependencies clear. Ready for detailed requirements and Phase 1 planning. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>