DCTP enables efficient transfer of AI-generated code using delta operations
instead of sending complete files for each modification. Features include:
- Parser for DCTP control commands (NEW, DELETE, INSERT_AFTER, REPLACE, RENUMBER)
- Line-numbered code with language-specific comment formats
- Backup/Undo system with session management
- Diff generation for preview functionality
- CustomTkinter GUI with project management, preview, and diff views
A PHP-based family photo album portal featuring:
- Public gallery with year/month filtering and search
- Mobile-responsive design with Tailwind CSS
- Comment system for family members
- Admin interface for album management
- Flat-file JSON database (no MySQL needed)
- CSRF protection and XSS prevention
- Rate limiting and honeypot spam protection
- Enable Mac Catalyst in Xcode project (SUPPORTS_MACCATALYST=YES)
- Set macOS deployment target to 13.0 (Ventura+)
- Add keyboard support for all scenes (Escape, Space, Enter)
- Add macOS menu bar with game commands (Cmd+P pause, Cmd+R restart)
- Configure window size restrictions for macOS
- Update Info.plist with macOS minimum version
Complete implementation of a 2D top-down arcade collector game where players
control a rolling suitcase through an airport, collecting good dogs and green
people while avoiding bad dogs and gray people.
Features:
- Touch & drag controls for suitcase movement
- Automatic scrolling airport floor with tile pattern
- 4 entity types: good dogs (small/big), bad dogs, green/gray humans
- Spawn system with configurable distribution rates
- Collision detection with visual feedback effects
- Score tracking with high score persistence
- 90-second time limit with 10 dogs + 5 humans goal
- 3 lives system with invincibility frames
- Menu, Game, GameOver, and Victory scenes
- German UI text (Created by Ingo K.)
Technical:
- iOS 15+ with SpriteKit framework
- Modular architecture with Nodes, Managers, Scenes
- Physics-based collision detection
- UserDefaults for score persistence
Complete Phase 1 implementation of the Yaesu FT-991A remote control
application with CAT protocol support over USB serial (CP210x).
Features implemented:
- SerialPortManager with auto-detection of CP210x ports
- Full CAT protocol parser and command builder
- RadioState model with all transceiver parameters
- Modern SwiftUI interface with frequency/mode/level controls
- Skeuomorphic front panel view (switchable)
- Debug panel with CAT command console
- QSO log panel with CSV export/import
- Audio routing panel with BlackHole integration
- Settings with connection, UI, keyboard configuration
- Menu bar extra for background operation
- German/English localization
- Logging system for debugging
Supports: Frequency control, VFO A/B, all modes (LSB/USB/CW/FM/AM/
DATA/RTTY/C4FM), level controls, NB/NR/DNF/ATU/Split functions,
S-meter/Power/SWR metering, PTT control via Shift key.
Target: macOS 15.0+ (Sequoia/Tahoe)
Features:
- Auto-probe scans all USB serial ports to find VU meter
- Tests multiple baud rates (115200, 9600, 57600, 38400, 19200)
- Tests all protocols (Raw, Text, JSON, VU-Server)
- Detects response from hardware to confirm connection
- Known USB device detection (CH340, CP210x, FTDI, Arduino, etc.)
- USB Vendor/Product ID display in port selection
- Quick Connect button for instant auto-connection
- Progress bar and status during probing
- Probe results display for debugging
USB detection:
- Reads USB idVendor/idProduct from IOKit registry
- Marks known VU meter devices with star icon
- Auto-selects detected VU meter port
New features:
- SerialManager for USB/Serial communication with hardware
- Support for 4 physical VU meter dials
- Flexible channel mapping: Audio L/R, Peak, Mono, CPU, RAM, Disk, Network
- Multiple protocols: Raw bytes, Text, JSON, VU-Server compatible
- Per-dial configuration: min/max values, inversion, smoothing
- Hardware panel in main view showing dial status
- Hardware settings sheet for configuration
- Auto-detection of USB serial devices
Protocol formats:
- Raw: [0xAA][D1][D2][D3][D4][0x55]
- Text: CH1:val;CH2:val;CH3:val;CH4:val\n
- JSON: {"dials":[d1,d2,d3,d4]}
- VU-Server: #0:val\n#1:val\n...
Features:
- Real-time audio level monitoring via BlackHole virtual audio device
- Classic VU meter display with dB scale (-60 to 0 dB)
- Peak hold indicators with configurable hold time
- System resource monitors: CPU, RAM, Disk, Network
- SwiftUI interface with dark theme
- Multi-device audio input selection
- Settings window for configuration
Built with AVAudioEngine for audio capture and Mach kernel APIs
for system statistics.
A Python CLI tool for generating financial reports from Paperless-ngx:
- Phase 1 (MVP): Config handling, Paperless API client with auth and
pagination, custom fields extraction, tag-based summation, CLI output
- Phase 2 (Grouping): Multiple grouping criteria (tag, correspondent,
category, payment type, month, quarter, year), percentage distribution
- Phase 3 (Reports): HTML reports with Chart.js diagrams (doughnut, bar,
line charts), PDF export via WeasyPrint, JSON and CSV export
- Phase 4 (Comfort): Automatic tag ID resolution, disk caching with
diskcache, colorized logging, comprehensive error handling
Features:
- Flexible date filtering (year, month, date range)
- Period comparison with change analysis
- Swiss franc formatting (CHF with apostrophe separators)
- Interactive HTML reports with sortable tables and document links
- Multiple output formats (CLI, HTML, PDF, JSON, CSV)
Complete implementation of automated GitHub repository synchronization:
- Webhook-based auto-sync from GitHub
- Multi-repository support with branch selection
- Web dashboard for management
- Manual sync and rollback functionality
- Comprehensive logging and monitoring
Located in /gitpusher/ subdirectory as standalone application.
Implemented a full-stack web application for fine-tuning LLMs on email data, optimized for Apple Silicon (M4 Pro with 24GB RAM).
Features:
- Mail import with drag & drop support (.mbox, .eml, .txt)
- Automated mail cleaning and preprocessing
- Interactive labeling interface with keyboard shortcuts
- Training data export to JSONL format
- MLX-based LoRA fine-tuning with live updates
- Model evaluation and comparison interface
- Server-Sent Events for real-time training progress
- Dark theme UI optimized for extended use
Technical Stack:
- Backend: FastAPI with SQLite database
- Frontend: Vanilla HTML/CSS/JavaScript (no external dependencies)
- ML Framework: MLX for Apple Silicon optimization
- Models: Support for Mistral 7B and Llama 3 8B via MLX
Components:
- data_manager.py: SQLite operations for mail storage and labeling
- mail_parser.py: Parser for multiple mail formats with cleaning
- training.py: MLX training wrapper with LoRA support
- inference.py: Model loading and inference for evaluation
- main.py: FastAPI backend with REST API and SSE
- Frontend: Complete UI with all features
Documentation:
- Comprehensive README with installation and usage guide
- Quick-start guide for rapid setup
- Example mails for testing
- Troubleshooting and best practices
Ready for local deployment and fine-tuning workflows.
admin
Claude