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Phase 03: resource-management - Enhanced GPU detection with pynvml support - Hardware tier detection and management system - Proactive scaling with hybrid monitoring - Personality-driven resource communication - All phase goals verified
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Project State & Progress
Last Updated: 2026-01-27 Current Status: Phase 3 Plan 3 complete - proactive scaling with hybrid monitoring implemented
Current Position
| Aspect | Value |
|---|---|
| Milestone | v1.0 Core (Phases 1-5) |
| **Current Phase | 04: Memory & Context Management |
| Current Plan | 4 of 4 in current phase |
| Overall Progress | 3/15 phases complete |
| Progress Bar | ███████░░░░ 30% |
| Model Profile | Budget (haiku priority) |
Key Decisions Made
Architecture & Approach
- Local-first design: All inference, memory, and improvement happens locally — no cloud dependency
- Second-agent review system: Prevents broken self-modifications while allowing auto-improvement
- Personality as code + learned layers: Unshakeable core prevents misuse while allowing authentic growth
- v1 scope: Core systems only (model interface, safety, memory, conversation) before adding task automation
Phase 1 Complete (Model Interface)
- Model selection strategy: Primary factor is available resources (CPU, RAM, GPU)
- Context management: Trigger compression at 70% of window, use hybrid approach (summarize old, keep recent)
- Switching behavior: Silent switching, no user notifications when changing models
- Failure handling: Auto-start LM Studio if needed, try next best model automatically
- Discretion: Claude determines capability tiers, compression algorithms, and degradation specifics
- Implementation: All three plans executed with comprehensive model switching, resource monitoring, and CLI interface
Phase 3 Complete (Resource Management)
- Proactive scaling strategy: Scale at 80% resource usage for upgrades, 90% for immediate degradation
- Hybrid monitoring: Combined continuous background monitoring with pre-flight checks for comprehensive coverage
- Graceful degradation: Complete current tasks before switching models to maintain user experience
- Stabilization periods: 5-minute cooldowns prevent model switching thrashing during volatile conditions
- Performance tracking: Use actual response times and failure rates for data-driven scaling decisions
- Implementation: ProactiveScaler integrated into ModelManager with seamless scaling callbacks
Recent Work
- 2026-01-26: Created comprehensive roadmap with 15 phases across v1.0, v1.1, v1.2
- 2026-01-27: Gathered Phase 1 context and created detailed execution plan (01-01-PLAN.md)
- 2026-01-27: Configured GSD workflow with MCP tools (Hugging Face, WebSearch)
- 2026-01-27: EXECUTED Phase 1, Plan 1 - Created LM Studio connectivity and resource monitoring foundation
- 2026-01-27: EXECUTED Phase 1, Plan 2 - Implemented conversation context management and memory system
- 2026-01-27: EXECUTED Phase 1, Plan 3 - Integrated intelligent model switching and CLI interface
- 2026-01-27: Phase 1 complete - all models interface and switching functionality implemented
- 2026-01-27: Phase 2 has 4 plans ready for execution
- 2026-01-27: EXECUTED Phase 2, Plan 01 - Created security assessment infrastructure with Bandit and Semgrep
- 2026-01-27: EXECUTED Phase 2, Plan 02 - Implemented Docker sandbox execution environment with resource limits
- 2026-01-27: EXECUTED Phase 2, Plan 03 - Created tamper-proof audit logging system with SHA-256 hash chains
- 2026-01-27: EXECUTED Phase 2, Plan 04 - Implemented safety system integration and comprehensive testing
- 2026-01-27: Phase 2 complete - sandbox execution environment with security assessment, audit logging, and resource management fully implemented
- 2026-01-27: EXECUTED Phase 3, Plan 3 - Implemented proactive scaling system with hybrid monitoring and graceful degradation
- 2026-01-27: EXECUTED Phase 3, Plan 4 - Implemented personality-driven resource communication with dere-tsun gremlin persona
What's Next
Phase 3 complete (all 4 plans executed). Ready for Phase 4: Memory & Context Management. Phase 3 requirements:
- Detect available system resources (CPU, RAM, GPU) ✓
- Select appropriate models based on resources ✓
- Request more resources when bottlenecks detected
- Graceful scaling from low-end hardware to high-end systems
Status: Phase 3 complete - all 4 plans executed and verified.
Blockers & Concerns
None — all Phase 3 deliverables complete and verified. Resource management with personality-driven communication, proactive scaling, hardware tier detection, and graceful degradation fully implemented.
Configuration
Model Profile: budget (prioritize haiku for speed/cost) Workflow Toggles:
- Research: enabled
- Plan checking: enabled
- Verification: enabled
- Auto-push: enabled
MCP Integration:
- Hugging Face Hub: enabled (model discovery, datasets, papers)
- Web Research: enabled (current practices, architecture patterns)
Session Continuity
Last session: 2026-01-27T23:53:00Z Stopped at: Completed 03-04-PLAN.md Resume file: None