CarbonDash

CarbonDash is a professional macOS system dashboard prototype built with SwiftUI. It provides real-time system monitoring, advanced process analysis, and AI-powered development assistance.

Key Features

• Real-time Monitoring: Interactive dashboards for CPU, Memory, and Network throughput.
• Process Management: High-load process identification and termination.
• Security & Networking: Listening port scanning and per-process network traffic sniffing.
• AI-Powered Assistance: Integrated local Qwen Code AI assistant for development tasks.
• Dev Ecosystem Integration: Automatic discovery of Python virtual environments and Git repositories.

Architecture

CarbonDash is designed with a performance-first approach:

• Asynchronous Execution: All system command calls (ps, lsof, nettop) are offloaded to background threads to ensure a smooth 60 FPS UI.
• Reentrancy Protection: Sophisticated polling logic prevents task stacking and memory leaks.
• State Management: Centralized manager injection preserves state (like AI chat history) across different dashboard tabs.
• Dynamic Configuration: Persistence-backed environment scanning for flexibility across different machine setups.

Requirements

• OS: macOS 13.0 or later.
• Development: Xcode 14+ (SwiftUI + Charts).
• System Utilities:
  • /bin/ps, /usr/sbin/lsof, /usr/bin/nettop, /usr/bin/git, /bin/kill.
• AI Assistant (Optional):
  • node
  • @qwen-code/qwen-code (accessible via cli.js).

Build & Development

This repository is compatible with Swift Package Manager (SPM).

# Clone the repository
git clone https://github.com/huangruiran666/CarbonDash.git
cd CarbonDash

# Build the project
swift build

# Run tests
swift test

Note: For full macOS App UI debugging, it is recommended to open the project in Xcode or import the Sources/ into your Xcode project.

Continuous Integration

CarbonDash uses GitHub Actions for automated building and testing on macOS runners. See .github/workflows/ci.yml for details.

Recent Enhancements

• Performance: Unified system command orchestration with asynchronous patterns.
• Reliability: Improved AI subprocess lifecycle management and error visibility.
• UX: Added runtime dependency readiness indicators and "Stop Generation" support for AI tasks.
• Cleanliness: Integrated comprehensive .gitignore and resource cleanup on application exit.

Known Limitations

• System command parsing may require updates for future macOS releases.
• AI assistant relies on a pre-configured local environment; automatic model downloading is not yet supported.

License

This project is licensed under the MIT License - see the LICENSE file for details.