docs: complete domain research (STACK, FEATURES, ARCHITECTURE, PITFALLS, SUMMARY)
## 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>
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