# Project State & Progress

**Last Updated:** 2026-01-27
**Current Status:** Phase 3 Plan 3 complete - proactive scaling with hybrid monitoring implemented

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## Current Position

| Aspect | Value |
|--------|-------|
| **Milestone** | v1.0 Core (Phases 1-5) |
| **Current Phase** | 03: Resource Management |
| **Current Plan** | 4 of 4 in current phase |
| **Overall Progress** | 3/15 phases complete |
| **Progress Bar** | ███████░░░░ 30% |
| **Model Profile** | Budget (haiku priority) |

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## 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

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## 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

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## 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.

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## 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.

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## 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