94 lines
2.8 KiB
Text
94 lines
2.8 KiB
Text
# Chess Teaching Assistant Application Technical Plan
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## Architecture:
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- Native MacOS application using SwiftUI for the UI
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- Core components:
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* Screenshot Capture Module
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* Board Position Analysis Engine
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* Visual Overlay System
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* Stockfish Integration
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* Machine Learning Pipeline
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## Implementation Details:
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### Screenshot Capture:
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- Use VisionKit and native Screen Capture API
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- Implement keyboard shortcut using Swift Concurrency
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- Screen Recording permission handling
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- Multi-monitor support with DPI awareness
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### Board Position Analysis:
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- Apple Vision framework + Create ML for computer vision
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- CoreML 5+ models for piece recognition
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- Apple Neural Engine optimization
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- Convert detected positions to FEN notation
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- Handle various chess.com board themes and piece sets
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- Real-time position validation
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### Stockfish Integration:
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- Use Stockfish ARM64 binary for MacOS Silicon
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- Implement in-memory engine with async/await pattern
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- Configure appropriate depth and time limits
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- Multiple analysis lines support
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- Position evaluation caching
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### Visual Overlay System:
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- Metal-accelerated rendering for optimal performance
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- NSWindow with transparent background
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- Hardware-accelerated move visualization
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- Support multiple visualization types:
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* Suggested moves (arrows)
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* Attack patterns (highlighted squares)
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* Defensive possibilities (colored areas)
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* Threat visualization
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* Piece mobility indicators
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- Accessibility features integration
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## Modern Architecture:
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- Clean Architecture with domain-driven design
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- Dependency injection for modularity
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- Swift Concurrency for async operations
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- Observation framework for state management
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- App Sandbox compliance
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- Privacy manifests implementation
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## Development Plan:
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Phase 1: Core Infrastructure
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- Set up project with modern architecture
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- Implement screenshot capture with permissions
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- Basic board detection using Vision framework
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Phase 2: Analysis Engine
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- Stockfish integration with async/await
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- Position analysis pipeline
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- ML model training and integration
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Phase 3: Visual System
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- Metal-based overlay system
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- Move visualization components
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- Real-time rendering optimization
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Phase 4: Performance & Security
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- Performance benchmarking
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- Security audit and sandbox compliance
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- Privacy features implementation
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Phase 5: Polish & Distribution
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- TestFlight integration
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- UI/UX refinement
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- Documentation and deployment
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## Technical Stack:
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- Swift & SwiftUI for application framework
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- Vision framework & Create ML for computer vision
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- Metal for GPU-accelerated graphics
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- CoreML for machine learning
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- Stockfish for chess analysis
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- Swift Concurrency for async operations
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- XCTest for testing infrastructure
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## Monitoring & Maintenance:
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- Analytics integration for performance monitoring
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- Crash reporting system
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- Automated testing pipeline
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- Regular security audits
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- User feedback collection system
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