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8 Commits

Author SHA1 Message Date
Anto01
ea437d360e docs: Major documentation overhaul - restructure folders, update tagline, add Getting Started guide 
- Restructure docs/ folder (remove numeric prefixes):
  - 04_USER_GUIDES -> guides/
  - 05_API_REFERENCE -> api/
  - 06_PHYSICS -> physics/
  - 07_DEVELOPMENT -> development/
  - 08_ARCHIVE -> archive/
  - 09_DIAGRAMS -> diagrams/

- Replace tagline 'Talk, don't click' with 'LLM-driven optimization framework' in 9 files

- Create comprehensive docs/GETTING_STARTED.md:
  - Prerequisites and quick setup
  - Project structure overview
  - First study tutorial (Claude or manual)
  - Dashboard usage guide
  - Neural acceleration introduction

- Rewrite docs/00_INDEX.md with correct paths and modern structure

- Archive obsolete files:
  - 01_PROTOCOLS.md -> archive/historical/01_PROTOCOLS_legacy.md
  - 03_GETTING_STARTED.md -> archive/historical/
  - ATOMIZER_PODCAST_BRIEFING.md -> archive/marketing/

- Update timestamps to 2026-01-20 across all key files

- Update .gitignore to exclude docs/generated/

- Version bump: ATOMIZER_CONTEXT v1.8 -> v2.0
 2026-01-20 10:03:45 -05:00
Anto01
b1ffc64407 feat: Implement SAT v3 achieving WS=205.58 (new campaign record) 
Self-Aware Turbo v3 optimization validated on M1 Mirror flat back:
- Best WS: 205.58 (12% better than previous best 218.26)
- 100% feasibility rate, 100% unique designs
- Uses 556 training samples from V5-V8 campaign data

Key innovations in V9:
- Adaptive exploration schedule (15% → 8% → 3%)
- Mass threshold at 118 kg (optimal sweet spot)
- 70% exploitation near best design
- Seeded with best known design from V7
- Ensemble surrogate with R²=0.99

Updated documentation:
- SYS_16: SAT protocol updated to v3.0 VALIDATED
- Cheatsheet: Added SAT v3 as recommended method
- Context: Updated protocol overview

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-31 16:06:33 -05:00
Anto01
773f8ff8af feat: Implement ACE Context Engineering framework (SYS_17) 
Complete implementation of Agentic Context Engineering (ACE) framework:

Core modules (optimization_engine/context/):
- playbook.py: AtomizerPlaybook with helpful/harmful scoring
- reflector.py: AtomizerReflector for insight extraction
- session_state.py: Context isolation (exposed/isolated state)
- feedback_loop.py: Automated learning from trial results
- compaction.py: Long-session context management
- cache_monitor.py: KV-cache optimization tracking
- runner_integration.py: OptimizationRunner integration

Dashboard integration:
- context.py: 12 REST API endpoints for playbook management

Tests:
- test_context_engineering.py: 44 unit tests
- test_context_integration.py: 16 integration tests

Documentation:
- CONTEXT_ENGINEERING_REPORT.md: Comprehensive implementation report
- CONTEXT_ENGINEERING_API.md: Complete API reference
- SYS_17_CONTEXT_ENGINEERING.md: System protocol
- Updated cheatsheet with SYS_17 quick reference
- Enhanced bootstrap (00_BOOTSTRAP_V2.md)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-29 20:21:20 -05:00
Anto01
cf454f6e40 feat: Add TrialManager and DashboardDB for unified trial management 
- Add TrialManager (trial_manager.py) for consistent trial_NNNN naming
- Add DashboardDB (dashboard_db.py) for Optuna-compatible database schema
- Update CLAUDE.md with trial management documentation
- Update ATOMIZER_CONTEXT.md with v1.8 trial system
- Update cheatsheet v2.2 with new utilities
- Update SYS_14 protocol to v2.3 with TrialManager integration
- Add LAC learnings for trial management patterns
- Add archive/README.md for deprecated code policy

Key principles:
- Trial numbers NEVER reset (monotonic)
- Folders NEVER get overwritten
- Database always synced with filesystem
- Surrogate predictions are NOT trials (only FEA results)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-28 12:20:19 -05:00
Anto01
f13563d7ab feat: Major update - Physics docs, Zernike OPD, insights, NX journals, tools 
Documentation:
- Add docs/06_PHYSICS/ with Zernike fundamentals and OPD method docs
- Add docs/guides/CMA-ES_EXPLAINED.md optimization guide
- Update CLAUDE.md and ATOMIZER_CONTEXT.md with current architecture
- Update OP_01_CREATE_STUDY protocol

Planning:
- Add DYNAMIC_RESPONSE plans for random vibration/PSD support
- Add OPTIMIZATION_ENGINE_MIGRATION_PLAN for code reorganization

Insights System:
- Update design_space, modal_analysis, stress_field, thermal_field insights
- Improve error handling and data validation

NX Journals:
- Add analyze_wfe_zernike.py for Zernike WFE analysis
- Add capture_study_images.py for automated screenshots
- Add extract_expressions.py and introspect_part.py utilities
- Add user_generated_journals/journal_top_view_image_taking.py

Tests & Tools:
- Add comprehensive Zernike OPD test suite
- Add audit_v10 tests for WFE validation
- Add tools for Pareto graphs and mirror data extraction
- Add migrate_studies_to_topics.py utility

Knowledge Base:
- Initialize LAC (Learning Atomizer Core) with failure/success patterns

Dashboard:
- Update Setup.tsx and launch_dashboard.py
- Add restart-dev.bat helper script

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-23 19:47:37 -05:00
Anto01
1612991d0d feat: Add Study Insights module (SYS_16) for physics visualizations 
Introduces a new plugin architecture for study-specific physics
visualizations, separating "optimizer perspective" (Analysis) from
"engineer perspective" (Insights).

New module: optimization_engine/insights/
- base.py: StudyInsight base class, InsightConfig, InsightResult, registry
- zernike_wfe.py: Mirror WFE with 3D surface and Zernike decomposition
- stress_field.py: Von Mises stress contours with safety factors
- modal_analysis.py: Natural frequencies and mode shapes
- thermal_field.py: Temperature distribution visualization
- design_space.py: Parameter-objective landscape exploration

Features:
- 5 insight types: zernike_wfe, stress_field, modal, thermal, design_space
- CLI: python -m optimization_engine.insights generate <study>
- Standalone HTML generation with Plotly
- Enhanced Zernike viz: Turbo colorscale, smooth shading, 0.5x AMP
- Dashboard API fix: Added include_coefficients param to extract_relative()

Documentation:
- docs/protocols/system/SYS_16_STUDY_INSIGHTS.md
- Updated ATOMIZER_CONTEXT.md (v1.7)
- Updated 01_CHEATSHEET.md with insights section

Tools:
- tools/zernike_html_generator.py: Standalone WFE HTML generator
- tools/analyze_wfe.bat: Double-click to analyze OP2 files

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-20 13:46:28 -05:00
Antoine
01a7d7d121 docs: Complete M1 mirror optimization campaign V11-V15 
## M1 Mirror Campaign Summary
- V11-V15 optimization campaign completed (~1,400 FEA evaluations)
- Best design: V14 Trial #725 with Weighted Sum = 121.72
- V15 NSGA-II confirmed V14 TPE found optimal solution
- Campaign improved from WS=129.33 (V11) to WS=121.72 (V14): -5.9%

## Key Results
- 40° tracking: 5.99 nm (target 4.0 nm)
- 60° tracking: 13.10 nm (target 10.0 nm)
- Manufacturing: 26.28 nm (target 20.0 nm)
- Targets not achievable within current design space

## Documentation Added
- V15 STUDY_REPORT.md: Detailed NSGA-II results analysis
- M1_MIRROR_CAMPAIGN_SUMMARY.md: Full V11-V15 campaign overview
- Updated CLAUDE.md, ATOMIZER_CONTEXT.md with NXSolver patterns
- Updated 01_CHEATSHEET.md with --resume guidance
- Updated OP_01_CREATE_STUDY.md with FEARunner template

## Studies Added
- m1_mirror_adaptive_V13: TPE validation (291 trials)
- m1_mirror_adaptive_V14: TPE intensive (785 trials, BEST)
- m1_mirror_adaptive_V15: NSGA-II exploration (126 new FEA)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-16 14:55:23 -05:00
Antoine
0e04457539 feat: Implement Agentic Architecture for robust session workflows 
Phase 1 - Session Bootstrap:
- Add .claude/ATOMIZER_CONTEXT.md as single entry point for new sessions
- Add study state detection and task routing

Phase 2 - Code Deduplication:
- Add optimization_engine/base_runner.py (ConfigDrivenRunner)
- Add optimization_engine/generic_surrogate.py (ConfigDrivenSurrogate)
- Add optimization_engine/study_state.py for study detection
- Add optimization_engine/templates/ with registry and templates
- Studies now require ~50 lines instead of ~300

Phase 3 - Skill Consolidation:
- Add YAML frontmatter metadata to all skills (versioning, dependencies)
- Consolidate create-study.md into core/study-creation-core.md
- Update 00_BOOTSTRAP.md, 01_CHEATSHEET.md, 02_CONTEXT_LOADER.md

Phase 4 - Self-Expanding Knowledge:
- Add optimization_engine/auto_doc.py for auto-generating documentation
- Generate docs/generated/EXTRACTORS.md (27 extractors documented)
- Generate docs/generated/TEMPLATES.md (6 templates)
- Generate docs/generated/EXTRACTOR_CHEATSHEET.md

Phase 5 - Subagent Implementation:
- Add .claude/commands/study-builder.md (create studies)
- Add .claude/commands/nx-expert.md (NX Open API)
- Add .claude/commands/protocol-auditor.md (config validation)
- Add .claude/commands/results-analyzer.md (results analysis)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-07 14:52:25 -05:00