Commit Graph

263 Commits

Author SHA1 Message Date
admin 841d1c09fc Merge pull request #6 from metacube2/claude/macos-audio-vu-meter-j8fVB
Add USB auto-probing to detect VU meter hardware
2025-12-14 11:46:57 +01:00
Claude f5e266b22b Add USB auto-probing to detect VU meter hardware
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
2025-12-14 10:41:27 +00:00
admin 3dd5a1891f Merge pull request #5 from metacube2/claude/macos-audio-vu-meter-j8fVB
Add physical VU meter hardware support (4 dials)
2025-12-14 11:27:59 +01:00
Claude 52fa522d6d Add physical VU meter hardware support (4 dials)
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...
2025-12-14 10:15:20 +00:00
admin e4e08037c3 Merge pull request #4 from metacube2/claude/macos-audio-vu-meter-j8fVB
Create macOS app for audio level monitoring
2025-12-14 11:05:17 +01:00
Claude 2ad21cad58 Add macOS Audio VU Meter app with system monitoring
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.
2025-12-14 10:03:56 +00:00
admin dd1d45d3e0 Merge pull request #3 from metacube2/claude/twelve-tone-synthesizer-01BgdmRVwhTdP8FRvAbntAqo
Build twelve-tone synthesizer with reverb in PHP
2025-12-13 17:29:21 +01:00
Claude a93e940b71 Add Twelve-Tone Synthesizer - Dodekaphonie nach Schönberg
Complete web-based synthesizer implementing Arnold Schönberg's
twelve-tone technique (Dodekaphonie) with:

- PHP backend for tone row generation and matrix calculation
- JavaScript Web Audio API for real-time sound synthesis
- Four row transformations: Original, Retrograde, Inversion, RI
- Convolver-based reverb effect with adjustable wet/dry mix
- Real-time audio visualization (waveform and spectrum)
- Interactive controls for tempo, octave, attack, release
- Multiple waveform options (sine, triangle, square, sawtooth)
- Full 12x12 twelve-tone matrix display
- Automatic continuous playback with random transformations
2025-12-13 16:26:02 +00:00
admin b50ef8bc00 Merge pull request #2 from metacube2/claude/paperless-finance-tool-01Te1nvY5VTkoZ9VFsZ16Jyk
Create Paperless finance reporting tool
2025-12-07 11:10:52 +01:00
Claude d2dd837f26 Add Paperless Finance Report Tool - Complete implementation
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)
2025-12-07 10:09:10 +00:00
admin 3134418e6a Merge pull request #1 from metacube2/claude/github-sync-website-017YXsy55JgZ3uUCZx13NfZG
GitHub Sync Website with Apache Server
2025-12-06 10:55:26 +01:00
Claude 45b15c7fd5 Add GitHub Sync - Automated repository synchronization tool
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.
2025-12-06 09:53:32 +00:00
Claude 1456995462 Add complete Mail Fine-Tuning Web-App for macOS Apple Silicon
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.
2025-12-03 07:35:35 +00:00