# Chess Teaching Assistant Application Technical Plan

## Architecture:
- Native MacOS application using SwiftUI for the UI
- Core components:
  * Screenshot Capture Module
  * Board Position Analysis Engine
  * Visual Overlay System
  * Stockfish Integration
  * Machine Learning Pipeline

## Implementation Details:
### Screenshot Capture:
- Use VisionKit and native Screen Capture API
- Implement keyboard shortcut using Swift Concurrency
- Screen Recording permission handling
- Multi-monitor support with DPI awareness

### Board Position Analysis:
- Apple Vision framework + Create ML for computer vision
- CoreML 5+ models for piece recognition
- Apple Neural Engine optimization
- Convert detected positions to FEN notation
- Handle various chess.com board themes and piece sets
- Real-time position validation

### Stockfish Integration:
- Use Stockfish ARM64 binary for MacOS Silicon
- Implement in-memory engine with async/await pattern
- Configure appropriate depth and time limits
- Multiple analysis lines support
- Position evaluation caching

### Visual Overlay System:
- Metal-accelerated rendering for optimal performance
- NSWindow with transparent background
- Hardware-accelerated move visualization
- Support multiple visualization types:
  * Suggested moves (arrows)
  * Attack patterns (highlighted squares)
  * Defensive possibilities (colored areas)
  * Threat visualization
  * Piece mobility indicators
- Accessibility features integration

## Modern Architecture:
- Clean Architecture with domain-driven design
- Dependency injection for modularity
- Swift Concurrency for async operations
- Observation framework for state management
- App Sandbox compliance
- Privacy manifests implementation

## Development Plan:
Phase 1: Core Infrastructure
- Set up project with modern architecture
- Implement screenshot capture with permissions
- Basic board detection using Vision framework

Phase 2: Analysis Engine
- Stockfish integration with async/await
- Position analysis pipeline
- ML model training and integration

Phase 3: Visual System
- Metal-based overlay system
- Move visualization components
- Real-time rendering optimization

Phase 4: Performance & Security
- Performance benchmarking
- Security audit and sandbox compliance
- Privacy features implementation

Phase 5: Polish & Distribution
- TestFlight integration
- UI/UX refinement
- Documentation and deployment

## Technical Stack:
- Swift & SwiftUI for application framework
- Vision framework & Create ML for computer vision
- Metal for GPU-accelerated graphics
- CoreML for machine learning
- Stockfish for chess analysis
- Swift Concurrency for async operations
- XCTest for testing infrastructure

## Monitoring & Maintenance:
- Analytics integration for performance monitoring
- Crash reporting system
- Automated testing pipeline
- Regular security audits
- User feedback collection system