# Active Context

## Current Status
- Screen capture module successfully implemented
- Window detection and capture working correctly
- SwiftUI interface with capture controls functioning
- Error handling system properly managing states
- Resource cleanup implemented
- Metal resource management optimized
- Automatic board detection and capture implemented
- Visual capture status indicator added
- Continuous board monitoring system implemented

## Recent Changes
1. Implemented Continuous Board Monitoring:
   - Added separate monitoring and capture tasks:
     * Monitor constantly checks for chess boards (0.5s interval)
     * Capture processes frames when active (0.1s interval)
   - Auto-capture behavior:
     * Starts monitoring when app launches
     * Automatically starts capture when board appears
     * Stops capture (but keeps monitoring) when board disappears
     * Resumes capture when new board is detected
   - Fixed image conversion pipeline:
     * Proper NSImage → CGImage → CIImage conversion
     * Efficient resource management
     * Clean error handling

2. Enhanced Status Indication:
   - Visual status indicator shows capture state:
     * Green: Actively capturing board
     * Yellow: Waiting for board
     * Gray: Not capturing
   - Clear error messages for different states
   - Automatic status updates based on board detection

3. Resource Management Optimization:
   - Implemented shared CIContext pattern:
     * Prevents command queue exhaustion
     * Reduces Metal resource usage
     * Enables long-running captures
   - Proper cleanup on task completion
   - Efficient resource utilization

4. Improved Window Detection:
   - Using SCShareableContent for window access
   - Precise window identification:
     * Exact bundle ID matching (com.chess.iphone)
     * Window visibility verification (isOnScreen)
     * Size validation (width > 100 && height > 100)

5. Enhanced Capture System:
   - Continuous capture implementation:
     * Single persistent capture stream
     * Smooth frame processing (no flickering)
     * Efficient resource usage
   - Proper frame dimensions from window
   - Clean task cancellation handling
   - Main thread safety for UI updates

6. Error Handling:
   - Improved error resilience:
     * Continues monitoring even if capture stops
     * Only stops on critical errors
     * Shows error state without interrupting monitoring
   - Clear error states
   - Proper async/await usage
   - Task cancellation management
   - Thread-safe state updates

## Current Focus
1. Board Detection:
   - Implement chess board recognition
   - Handle different board themes
   - Process captured frames efficiently

## Next Steps
1. Implement board detection:
   - Pattern recognition for chess pieces
   - Board coordinate mapping
   - Position validation
2. Add position analysis
3. Create move detection system
4. Implement visual overlay
5. Integrate Stockfish engine

## Known Issues
- Need to handle different chess.com themes
- Need to implement piece recognition
- Position analysis pending implementation