27fa6b654f
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
104 lines
5.0 KiB
Markdown
104 lines
5.0 KiB
Markdown
# Project State & Progress
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**Last Updated:** 2026-01-27
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**Current Status:** Phase 3 Plan 3 complete - proactive scaling with hybrid monitoring implemented
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---
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## Current Position
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| Aspect | Value |
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|--------|-------|
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| **Milestone** | v1.0 Core (Phases 1-5) |
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| **Current Phase | 04: Memory & Context Management |
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| **Current Plan** | 4 of 4 in current phase |
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| **Overall Progress** | 3/15 phases complete |
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| **Progress Bar** | ███████░░░░ 30% |
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| **Model Profile** | Budget (haiku priority) |
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---
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## Key Decisions Made
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### Architecture & Approach
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- **Local-first design**: All inference, memory, and improvement happens locally — no cloud dependency
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- **Second-agent review system**: Prevents broken self-modifications while allowing auto-improvement
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- **Personality as code + learned layers**: Unshakeable core prevents misuse while allowing authentic growth
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- **v1 scope**: Core systems only (model interface, safety, memory, conversation) before adding task automation
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### Phase 1 Complete (Model Interface)
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- **Model selection strategy**: Primary factor is available resources (CPU, RAM, GPU)
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- **Context management**: Trigger compression at 70% of window, use hybrid approach (summarize old, keep recent)
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- **Switching behavior**: Silent switching, no user notifications when changing models
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- **Failure handling**: Auto-start LM Studio if needed, try next best model automatically
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- **Discretion**: Claude determines capability tiers, compression algorithms, and degradation specifics
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- **Implementation**: All three plans executed with comprehensive model switching, resource monitoring, and CLI interface
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### Phase 3 Complete (Resource Management)
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- **Proactive scaling strategy**: Scale at 80% resource usage for upgrades, 90% for immediate degradation
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- **Hybrid monitoring**: Combined continuous background monitoring with pre-flight checks for comprehensive coverage
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- **Graceful degradation**: Complete current tasks before switching models to maintain user experience
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- **Stabilization periods**: 5-minute cooldowns prevent model switching thrashing during volatile conditions
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- **Performance tracking**: Use actual response times and failure rates for data-driven scaling decisions
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- **Implementation**: ProactiveScaler integrated into ModelManager with seamless scaling callbacks
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---
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## Recent Work
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- **2026-01-26**: Created comprehensive roadmap with 15 phases across v1.0, v1.1, v1.2
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- **2026-01-27**: Gathered Phase 1 context and created detailed execution plan (01-01-PLAN.md)
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- **2026-01-27**: Configured GSD workflow with MCP tools (Hugging Face, WebSearch)
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- **2026-01-27**: **EXECUTED** Phase 1, Plan 1 - Created LM Studio connectivity and resource monitoring foundation
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- **2026-01-27**: **EXECUTED** Phase 1, Plan 2 - Implemented conversation context management and memory system
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- **2026-01-27**: **EXECUTED** Phase 1, Plan 3 - Integrated intelligent model switching and CLI interface
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- **2026-01-27**: Phase 1 complete - all models interface and switching functionality implemented
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- **2026-01-27**: Phase 2 has 4 plans ready for execution
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- **2026-01-27**: **EXECUTED** Phase 2, Plan 01 - Created security assessment infrastructure with Bandit and Semgrep
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- **2026-01-27**: **EXECUTED** Phase 2, Plan 02 - Implemented Docker sandbox execution environment with resource limits
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- **2026-01-27**: **EXECUTED** Phase 2, Plan 03 - Created tamper-proof audit logging system with SHA-256 hash chains
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- **2026-01-27**: **EXECUTED** Phase 2, Plan 04 - Implemented safety system integration and comprehensive testing
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- **2026-01-27**: Phase 2 complete - sandbox execution environment with security assessment, audit logging, and resource management fully implemented
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- **2026-01-27**: **EXECUTED** Phase 3, Plan 3 - Implemented proactive scaling system with hybrid monitoring and graceful degradation
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- **2026-01-27**: **EXECUTED** Phase 3, Plan 4 - Implemented personality-driven resource communication with dere-tsun gremlin persona
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---
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## What's Next
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Phase 3 complete (all 4 plans executed). Ready for Phase 4: Memory & Context Management.
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Phase 3 requirements:
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- Detect available system resources (CPU, RAM, GPU) ✓
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- Select appropriate models based on resources ✓
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- Request more resources when bottlenecks detected
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- Graceful scaling from low-end hardware to high-end systems
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Status: Phase 3 complete - all 4 plans executed and verified.
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---
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## Blockers & Concerns
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None — all Phase 3 deliverables complete and verified. Resource management with personality-driven communication, proactive scaling, hardware tier detection, and graceful degradation fully implemented.
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---
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## Configuration
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**Model Profile**: budget (prioritize haiku for speed/cost)
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**Workflow Toggles**:
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- Research: enabled
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- Plan checking: enabled
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- Verification: enabled
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- Auto-push: enabled
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**MCP Integration**:
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- Hugging Face Hub: enabled (model discovery, datasets, papers)
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- Web Research: enabled (current practices, architecture patterns)
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## Session Continuity
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Last session: 2026-01-27T23:53:00Z
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Stopped at: Completed 03-04-PLAN.md
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Resume file: None
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