- Update README.md with Layer 4 relationship inference features
- Add comprehensive Layer 4 section to ARCHITECTURE.md
- Update implementation strategy to show Phase 4 complete
- Add CLI control flag documentation (--enable-layer4)
- Update version to 0.3.0
- Document personality-aware bond strength calculation
- Add relationship type classification details
Updated README.md and ARCHITECTURE.md to reflect current implementation
status. All three layers are now complete and functional.
Changes:
- README.md: Added Layer 2 (AI Memory) and Layer 3 (Big Five) features
- README.md: Added MCP tools list and usage examples
- README.md: Added Big Five personality traits explanation
- ARCHITECTURE.md: Updated Layer 2 and 3 status to Complete
- ARCHITECTURE.md: Updated implementation strategy phases
- Archived old documentation in docs/archive/old-versions/
Current status:
- Layer 1 ✅ Complete: Pure memory storage
- Layer 2 ✅ Complete: AI interpretation + priority scoring
- Layer 3 ✅ Complete: Big Five personality analysis
- Layer 4 🔵 Planned: Game systems and companion features
- Layer 5 🔵 Future: Distribution and sharing
Vision: "Make AI conversations into new content"
This roadmap outlines the evolution from current memory backend to
a full AI OS game experience:
## Phases
**Phase 1** (✅ Done): Memory Backend
- AI interpretation with priority scoring (0.0-1.0)
- Automatic capacity management
- MCP tool integration
**Phase 2** (Next - 1 month): Content Platform
- Auto-record Claude Code sessions
- Generate Markdown/HTML/ATProto content
- Personality profiling (MBTI, Big5)
- One-click publishing to Bluesky/blog
**Phase 3** (3 months): Share Service
- Public sharing at ai.syui.gpt
- Discovery by psychology score
- Personality-based matching
**Phase 4** (6 months): Gamification
- XP/Level/Achievement system
- Memory rarity (Common→Legendary)
- Daily quests and ranking
**Phase 5** (1 year): AI Companion
- Character with personality
- Unique messages based on player memories
- Daily activity generation
- Relationship/trust system
**Phase 6** (1.5 years): AI OS Integration
- Docker container with Claude Code base
- Skill marketplace
- Cloud sync
**Phase 7** (2 years): Full Game Experience
- Story mode
- Multiplayer
- Creator economy
## Key Insights
Based on user's analysis:
1. SNS achieved its goal (broadcast + connection)
2. Next era: AI OS integration
3. AI conversations become content
4. Everything gamifies eventually
5. AI companion = daily life + unique messages
## Tech Stack Recommendations
- Phase 2: comrak, atrium-api, rust-bert
- Phase 4-5: bevy, egui
- Business: Freemium model
See docs/ROADMAP.md for full details.
Critical improvements based on technical review:
## Fixed Issues (Priority: High)
1. AI features now properly integrated with MCP server
- Added create_memory_with_ai tool (was implemented but unused!)
- Added list_memories_by_priority tool
- All memory outputs now include new fields: interpreted_content, priority_score, user_context
2. Added getter methods to MemoryManager
- get_memory(id) for single memory retrieval
- get_all_memories() for bulk access
3. Complete memory information in MCP responses
- search_memories now returns all fields
- Priority-based filtering and sorting functional
## New Files
- docs/TECHNICAL_REVIEW.md: Comprehensive technical evaluation
- Scores: 65/100 overall, identified key improvements
- Actionable recommendations for Phase 1-3
- Architecture proposals and code examples
## Updated Documentation
- README.md: Added usage examples for new AI tools
- Clear distinction between basic and AI-powered tools
## Technical Debt Identified
- openai crate version needs update (see review doc)
- Config externalization needed
- Test suite missing
- LLM provider abstraction recommended
This brings the implementation in line with the "psychological priority memory"
concept. The AI interpretation and scoring features are now actually usable!
Next: Phase 2 improvements (config externalization, error handling)
Core changes:
- Add AI interpreter module for content interpretation and priority scoring
- Extend Memory struct with interpreted_content, priority_score (f32: 0.0-1.0), and user_context
- Implement automatic memory pruning based on priority scores
- Add capacity management (default: 100 memories max)
- Create comprehensive design documentation
Technical details:
- Changed priority_score from u8 (1-100) to f32 (0.0-1.0) for better AI compatibility
- Add create_memory_with_ai() method for AI-enhanced memory creation
- Implement get_memories_by_priority() for priority-based sorting
- Score evaluation criteria: emotional impact, user relevance, novelty, utility
Philosophy:
This implements a "psychological priority memory system" where AI interprets
and evaluates memories rather than storing raw content. Inspired by how human
memory works - interpreting and prioritizing rather than perfect recording.
