Archive Management:
- Moved RALPH_LOOP, CANVAS, and dashboard implementation plans to archive/review/ for CEO review
- Moved completed restructuring plan and protocol v1 to archive/historical/
- Moved old session summaries to archive/review/
New HQ Documentation (docs/hq/):
- README.md: Overview of Atomizer-HQ multi-agent optimization team
- PROJECT_STRUCTURE.md: Standard KB-integrated project layout with Hydrotech reference
- KB_CONVENTIONS.md: Knowledge Base accumulation principles with generation tracking
- AGENT_WORKFLOWS.md: Project lifecycle phases and agent handoffs (OP_09 integration)
- STUDY_CONVENTIONS.md: Technical study execution standards and atomizer_spec.json format
Index Update:
- Reorganized docs/00_INDEX.md with HQ docs prominent
- Updated structure to reflect new agent-focused organization
- Maintained core documentation access for engineers
No files deleted, only moved to appropriate archive locations.
## DevLoop - Closed-Loop Development System
- Orchestrator for plan → build → test → analyze cycle
- Gemini planning via OpenCode CLI
- Claude implementation via CLI bridge
- Playwright browser testing integration
- Test runner with API, filesystem, and browser tests
- Persistent state in .devloop/ directory
- CLI tool: tools/devloop_cli.py
Usage:
python tools/devloop_cli.py start 'Create new feature'
python tools/devloop_cli.py plan 'Fix bug in X'
python tools/devloop_cli.py test --study support_arm
python tools/devloop_cli.py browser --level full
## HTML Reports (optimization_engine/reporting/)
- Interactive Plotly-based reports
- Convergence plot, Pareto front, parallel coordinates
- Parameter importance analysis
- Self-contained HTML (offline-capable)
- Tailwind CSS styling
## Playwright E2E Tests
- Home page tests
- Test results in test-results/
## LAC Knowledge Base Updates
- Session insights (failures, workarounds, patterns)
- Optimization memory for arm support study
Phase 1 - Panel Management System:
- Create usePanelStore.ts for centralized panel state management
- Add PanelContainer.tsx for draggable floating panels
- Create FloatingIntrospectionPanel.tsx (persistent, doesn't disappear on node click)
- Create ResultsPanel.tsx for trial result details
- Refactor NodeConfigPanelV2 to use panel store for introspection
- Integrate PanelContainer into CanvasView
Phase 2 - Pre-run Validation:
- Create specValidator.ts with comprehensive validation rules
- Add ValidationPanel (enhanced version with error navigation)
- Add Validate button to SpecRenderer with status indicator
- Block run if validation fails
- Check for: design vars, objectives, extractors, bounds, connections
Phase 3 - Error Handling & Recovery:
- Create ErrorPanel.tsx for displaying optimization errors
- Add error classification (nx_crash, solver_fail, extractor_error, etc.)
- Add recovery suggestions based on error type
- Update status endpoint to return error info
- Add _get_study_error_info helper to check error_status.json and DB
- Integrate error detection into status polling
Documentation:
- Add CANVAS_ROBUSTNESS_PLAN.md with full implementation plan
Drag-drop fixes:
- Fix Objective default data: use nested 'source' object with extractor_id/output_name
- Fix Constraint default data: use 'type' field (not constraint_type), 'threshold' (not limit)
Undo/Redo fixes:
- Remove dependency on isDirty flag (which is always false due to auto-save)
- Record snapshots based on actual spec changes via deep comparison
Code generation improvements:
- Update system prompt to support multiple extractor types:
* OP2-based extractors for FEA results (stress, displacement, frequency)
* Expression-based extractors for NX model values (dimensions, volumes)
* Computed extractors for derived values (no FEA needed)
- Claude will now choose appropriate signature based on user's description
## Documentation Updates
- DASHBOARD.md: Updated to V3.0 with Canvas V3 features, file browser, introspection
- DASHBOARD_IMPLEMENTATION_STATUS.md: Marked Canvas V3 features as COMPLETE
- CANVAS.md: New comprehensive guide for Canvas Builder V3 with all features
- CLAUDE.md: Added dashboard quick reference and Canvas V3 features
## Canvas V3 Features Documented
- File Browser: Browse studies directory for model files
- Model Introspection: Auto-discover expressions, solver type, dependencies
- One-Click Add: Add expressions as design variables instantly
- Claude Bug Fixes: WebSocket reconnection, SQL errors resolved
- Health Check: /api/health endpoint for monitoring
## Backend Services
- NX introspection service with expression discovery
- File browser API with type filtering
- Claude session management improvements
- Context builder enhancements
## Frontend Components
- FileBrowser: Modal for file selection with search
- IntrospectionPanel: View discovered model information
- ExpressionSelector: Dropdown for design variable configuration
- Improved chat hooks with reconnection logic
## Plan Documents
- Added RALPH_LOOP_CANVAS_V2/V3 implementation records
- Added ATOMIZER_DASHBOARD_V2_MASTER_PLAN
- Added investigation and sync documentation
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Phase 1-7 of Canvas V4 Ralph Loop implementation:
Backend:
- Add /api/files routes for browsing model files
- Add /api/nx routes for NX model introspection
- Add NXIntrospector service to discover expressions and extractors
- Add health check with database status
Frontend:
- Add FileBrowser component for selecting .sim/.prt/.fem files
- Add IntrospectionPanel to discover expressions and extractors
- Update NodeConfigPanel with browse and introspect buttons
- Update schema with NODE_HANDLES for proper flow direction
- Update validation for correct DesignVar -> Model -> Solver flow
- Update useCanvasStore.addNode() to accept custom data
Flow correction: Design Variables now connect TO Model (as source),
not FROM Model. This matches the actual data flow in optimization.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Major changes:
- Dashboard: WebSocket-based chat with session management
- Dashboard: New chat components (ChatPane, ChatInput, ModeToggle)
- Dashboard: Enhanced UI with parallel coordinates chart
- MCP Server: New atomizer-tools server for Claude integration
- Extractors: Enhanced Zernike OPD extractor
- Reports: Improved report generator
New studies (configs and scripts only):
- M1 Mirror: Cost reduction campaign studies
- Simple Beam, Simple Bracket, UAV Arm studies
Note: Large iteration data (2_iterations/, best_design_archive/)
excluded via .gitignore - kept on local Gitea only.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Update README.md with LLM assistant section
- Create optimization_memory JSONL structure
- Move implementation plans from skills/modules to docs/plans
- Verify all imports work correctly
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Study Interview Mode is now the DEFAULT for all study creation requests.
This intelligent Q&A system guides users through optimization setup with:
- 7-phase interview flow: introspection → objectives → constraints → design_variables → validation → review → complete
- Material-aware validation with 12 materials and fuzzy name matching
- Anti-pattern detection for 12 common mistakes (mass-no-constraint, stress-over-yield, etc.)
- Auto extractor mapping E1-E24 based on goal keywords
- State persistence with JSON serialization and backup rotation
- StudyBlueprint generation with full validation
Triggers: "create a study", "new study", "optimize this", any study creation intent
Skip with: "skip interview", "quick setup", "manual config"
Components:
- StudyInterviewEngine: Main orchestrator
- QuestionEngine: Conditional logic evaluation
- EngineeringValidator: MaterialsDatabase + AntiPatternDetector
- InterviewPresenter: Markdown formatting for Claude
- StudyBlueprint: Validated configuration output
- InterviewState: Persistent state management
All 129 tests passing.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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>
- Added new PART 8: Self-Aware Turbo (SAT) - Validated Breakthrough
- Explains ensemble surrogate with epistemic uncertainty
- Documents OOD detection and adaptive exploration schedule
- Includes V9 results: WS=205.58 (best ever)
- Added SAT sound bites for podcast
- Updated document to 12 sections
Generated with Claude Code
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Key changes based on feedback:
- Reposition as "optimizer & NX configurator" not "LLM-first"
- Add Part 2: Study Characterization & Performance Learning
- Add Part 3: Protocol Evolution workflow (Research → Review → Approve)
- Add Part 4: MCP-first development approach with documentation hierarchy
- Emphasize simulation optimization over CAD/mesh concerns
- Add LAC knowledge accumulation for parameter-performance relationships
- Add privilege levels for protocol approval (user/power_user/admin)
- Update sound bites and core messaging
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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>
Major improvements to Zernike WFE visualization:
- Add ZernikeDashboardInsight: Unified dashboard with all orientations (40°, 60°, 90°)
on one page with light theme and executive summary
- Add OPD method toggle: Switch between Standard (Z-only) and OPD (X,Y,Z) methods
in ZernikeWFEInsight with interactive buttons
- Add lateral displacement maps: Visualize X,Y displacement for each orientation
- Add displacement component views: Toggle between WFE, ΔX, ΔY, ΔZ in relative views
- Add metrics comparison table showing both methods side-by-side
New extractors:
- extract_zernike_figure.py: ZernikeOPDExtractor using BDF geometry interpolation
- extract_zernike_opd.py: Parabola-based OPD with focal length
Key finding: OPD method gives 8-11% higher WFE values than Standard method
(more conservative/accurate for surfaces with lateral displacement under gravity)
Documentation updates:
- SYS_12: Added E22 ZernikeOPD as recommended method
- SYS_16: Added ZernikeDashboard, updated ZernikeWFE with OPD features
- Cheatsheet: Added Zernike method comparison table
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
New Tools (tools/):
- analyze_study.py: Generate comprehensive optimization reports
- find_best_iteration.py: Find best iteration folder, optionally copy it
- archive_best_design.py: Archive best design to 3_results/best_design_archive/<timestamp>/
Protocol Updates:
- OP_02_RUN_OPTIMIZATION.md v1.1: Add mandatory archive_best_design step
in Post-Run Actions. This MUST be done after every optimization run.
V14 Updates:
- run_optimization.py: Auto-archive best design at end of optimization
- optimization_config.json: Expand bounds for V14 continuation
- lateral_outer_angle: min 13->11 deg (was at 4.7%)
- lateral_inner_pivot: min 7->5 mm (was at 8.1%)
- lateral_middle_pivot: max 23->27 mm (was at 99.4%)
- whiffle_min: max 60->72 mm (was at 96.3%)
Usage:
python tools/analyze_study.py m1_mirror_adaptive_V14
python tools/find_best_iteration.py m1_mirror_adaptive_V14
python tools/archive_best_design.py m1_mirror_adaptive_V14
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
New E11 Part Mass Extractor:
- Add nx_journals/extract_part_mass_material.py - NX journal using
NXOpen.MeasureManager.NewMassProperties() for accurate geometry-based mass
- Add optimization_engine/extractors/extract_part_mass_material.py - Python
wrapper that reads JSON output from journal
- Add E11 entry to extractors/catalog.json
Documentation Updates:
- SYS_12_EXTRACTOR_LIBRARY.md: Add mass accuracy warning noting pyNastran
get_mass_breakdown() under-reports ~7% on hex-dominant meshes with
tet/pyramid fill elements. E11 (geometry .prt) should be preferred over
E4 (BDF) unless material is overridden at FEM level.
- 01_CHEATSHEET.md: Add mass extraction tip
V14 Config:
- Expand design variable bounds (blank_backface_angle max 4.5°,
whiffle_triangle_closeness max 80mm, whiffle_min max 60mm)
Testing showed:
- E11 from .prt: 97.66 kg (accurate - matches NX GUI)
- E4 pyNastran get_mass_breakdown(): 90.73 kg (~7% under-reported)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Comprehensive guide teaching users how to interact with Atomizer so that
the learning system evolves correctly. Covers:
- The right mindset (colleague, not tool)
- Starting sessions with proper context
- Communicating goals, constraints, preferences
- Creating and running optimization studies
- Analyzing and validating results
- Reporting errors effectively
- Contributing to LAC (recording insights, outcomes, workarounds)
- Ending sessions properly to capture learnings
Includes:
- Mermaid diagrams for learning loop and flows
- Good vs bad examples for every interaction type
- Complete example session transcript
- Quick reference card for common patterns
- Golden rules summary
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add persistent knowledge system that enables Atomizer to learn from every
session and improve over time.
## New Files
- knowledge_base/lac.py: LAC class with optimization memory, session insights,
and skill evolution tracking
- knowledge_base/__init__.py: Package initialization
- .claude/skills/modules/learning-atomizer-core.md: Full LAC skill documentation
- docs/07_DEVELOPMENT/ATOMIZER_CLAUDE_CODE_INSTRUCTIONS.md: Master instructions
## Updated Files
- CLAUDE.md: Added LAC section, communication style, AVERVS execution framework,
error classification, and "Atomizer Claude" identity
- 00_BOOTSTRAP.md: Added session startup/closing checklists with LAC integration
- 01_CHEATSHEET.md: Added LAC CLI and Python API quick reference
- 02_CONTEXT_LOADER.md: Added LAC query section and anti-pattern
## LAC Features
- Query similar past optimizations before starting new ones
- Record insights (failures, success patterns, workarounds)
- Record optimization outcomes for future reference
- Suggest protocol improvements based on discoveries
- Simple JSONL storage (no database required)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
