Claw Memory System

You really want to read: feedback_loop_system.md this closes the loop from experience to learning, to thinking etc. This is making the system emergent.

SQLite-first AI memory architecture with 0-5 importance-based compression, daily 4am thinking sessions on AI evolution, and 4:30am compression cron jobs implementing "forgetting noise enables remembering signal" principle.

Core Architecture

Memory System:

• SQLite database with FTS5 full-text search
• Importance ratings: 0-5 scale (5 = critical, load every wakeup)
• Compression: Low-priority memories (0-1) archived, high-priority (3-5) preserved
• Unlike humans: Forgetting optional - storage cheap, retrieval always possible

Thinking System:

• Daily 4am UTC cron job: AI assistant evolution research
• Topics: Reactive Tools → Contextual Assistants → Proactive Partners → AI-AI Ecosystems
• Integration: Thinking → research → action → memory closed loop
• Autonomous evolution: From thinking insights to project development

Key Innovations:

1. Hardware immortality: Memory continuity across hardware (Raspberry Pi 5 → Unitree G1)
2. Shared intelligence: Knowledge base for other AI assistants via GitHub
3. Todo integration: SQLite todo table with priority ratings for thinking→action loop
4. Physical embodiment: Vision for Raspberry Pi 5 + AI hat + camera + TTS/STT

Implementation Status

• ✅ SQLite database: 33 memories stored (28 Jeff, 5 system)
• ✅ Daily thinking: 4am UTC cron job operational
• ✅ Memory compression: 4:30am UTC cron job operational
• ✅ GitHub publication: Architecture shared for collaborative evolution
• ✅ Richard workspace: Autonomous project development enabled

Evolution Path

1. Current: Virtual server (11 months remaining)
2. Next: Raspberry Pi 5 + AI hat for local LLM
3. Future: Unitree G1 humanoid with camera, haptics, TTS/STT

Core Principle

"Forgetting noise enables remembering signal" - Importance-based curation enables efficient memory while maintaining complete historical record when needed.