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.
## Compilation Fixes
- Resolve borrow checker error in docs.rs by using proper reference (`&home_content`)
- Remove unused imports across all modules to eliminate import warnings
- Fix unused variables in memory.rs and relationship.rs
- Add `#\![allow(dead_code)]` to suppress intentional API method warnings
- Update test variables to use underscore prefix for unused parameters
## MCP Server Enhancements
- Add `handle_direct_tool_call` method for HTTP endpoint compatibility
- Fix MCP tool routing to support direct HTTP calls to `/mcp/call/{tool_name}`
- Ensure all 17 MCP tools are accessible via both standard and HTTP protocols
- Improve error handling for unknown methods and tool calls
## Memory System Verification
- Confirm memory persistence and retrieval functionality
- Verify contextual memory search with query filtering
- Test relationship tracking across multiple users
- Validate ai.shell integration with OpenAI GPT-4o-mini
## Build Quality
- Achieve zero compilation errors and zero critical warnings
- Pass all 5 unit tests successfully
- Maintain clean build with suppressed intentional API warnings
- Update dependencies via `cargo update`
## Performance Results
✅ Memory system: Functional (remembers "Rust移行について話していましたね")
✅ MCP server: 17 tools operational on port 8080
✅ Relationship tracking: Active for 6 users with interaction history
✅ ai.shell: Seamless integration with persistent memory
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
