This was intended to be a learning-focused Zig implementation of a Windows 11 theme switching application. However, I've shifted focus to some other approaches, so this may not end up going anywhere.
Dark Watcher is more than just a theme switching utility—it's a comprehensive learning journey into systems programming with Zig, designed to demonstrate modern development practices with AI assistance while building a genuinely useful application.
- 🔧 Learning First: Every architectural decision optimizes for educational value and skill development
- 🤖 AI-Collaborative: Structured for effective partnership with AI coding assistants
- 🚀 Architecture Driven: Comprehensive planning and design before implementation
- 🌍 Cross-Platform Vision: Built for 65-70% code sharing across Windows, macOS, and Linux
- 📖 Open Learning: Documenting the entire learning process for the community
We're currently in the foundational design phase, with a complete architectural blueprint ready for implementation.
✅ Completed:
- Comprehensive project architecture design
- Cross-platform abstraction layer specification
- Complete module breakdown and responsibility mapping
- Build system configuration and dependency management
- AI collaboration workflow optimization
- 24-week implementation roadmap with learning milestones
🚧 Next Phase: Core Implementation (Weeks 1-8)
- Development environment setup and toolchain configuration
- Platform abstraction layer implementation
- Windows registry operations and theme switching
- Global hotkey system with Win32 integration
- Configuration management with YAML support
- Background service architecture
- Weeks 1-4: Memory management, error handling, C interop
- Weeks 5-8: Advanced patterns, comptime programming, interfaces
- Weeks 9-16: Performance optimization, testing, service architecture
- Weeks 17-24: Cross-platform development, release engineering
- Windows APIs: Registry manipulation, global hotkeys, service integration
- Cross-Platform Design: Platform abstraction, conditional compilation
- Service Architecture: Background services, IPC, system integration
- Performance Engineering: Memory management, resource optimization
- Structured collaboration sessions with clear learning objectives
- Implementation guided by AI with comprehensive code review
- Alternative approach exploration and best practice validation
- Documentation enhanced through AI partnership
┌─────────────────────────────────────────────┐
│ Application Core │
│ (65-70% shared across all platforms) │
├─────────────────────────────────────────────┤
│ Platform Interface Layer │
├─────────────┬─────────────┬─────────────────┤
│ Windows │ macOS │ Linux │
│ Implementation│ Implementation│ Implementation │
│ │ (Future) │ (Future) │
└─────────────┴─────────────┴─────────────────┘
- 🎛️ Theme Manager: Central orchestration of theme switching operations
- ⚙️ Configuration System: YAML-based configuration with live updates
- ⌨️ Hotkey Manager: Cross-platform global hotkey registration and handling
- 🔧 Service Manager: Background service lifecycle and coordination
- 💬 IPC Server: External API for programmatic theme control
- 📊 State Management: Persistent application state with recovery
📖 Comprehensive Architecture Documentation
Dark Watcher embraces AI-assisted development as a learning accelerator and collaboration enhancement tool, not a replacement for understanding.
🎯 AI Collaboration Strategy:
- Session-Based Learning: Structured 2-4 hour development sessions with clear objectives
- Code Review Partnership: AI-assisted code review focusing on Zig idioms and best practices
- Alternative Exploration: AI-guided exploration of different implementation approaches
- Documentation Enhancement: AI-assisted technical writing and explanation
📋 Implementation Templates:
- Context-setting protocols for maximum AI effectiveness
- Module-by-module implementation approach with learning checkpoints
- Quality assurance checklists combining AI validation with personal understanding
- Knowledge transfer patterns for long-term retention
Phase 1: Windows 11 Foundation
- Native Win32 API integration
- Registry-based theme manipulation
- Windows Service architecture
- MSI installer and auto-update system
Phase 2: macOS Integration
- Objective-C interop and Cocoa integration
- macOS defaults system integration
- LaunchAgent service architecture
- PKG installer with code signing
Phase 3: Linux Desktop Support
- GNOME, KDE, and XFCE theme system integration
- systemd service integration
- DEB/RPM packaging with distribution support
- Desktop environment auto-detection
- 65-70% code reuse across all platforms
- Consistent user experience and feature parity
- Unified configuration and state management
- Cross-platform build and release automation
- Zig: Primary implementation language for performance and safety
- YAML: Human-readable configuration with schema validation
- Win32 API: Native Windows integration for optimal performance
- Zig Build System: Native build configuration with cross-compilation
- AI Coding Assistants: Claude, GPT-4, GitHub Copilot for collaborative development
- VS Code: Primary development environment with Zig language server
- Platform Abstraction Layer: Custom Zig interfaces for cross-platform compatibility
- Configuration Management: YAML parsing with validation and live updates
- Logging System: Structured logging with multiple output targets
Note: Dark Watcher is currently in the design phase. Implementation will begin with the core Windows functionality.
-
📚 Study the Architecture: Review
Zig-MVP-Project-Structure.mdfor comprehensive design details -
🛠️ Setup Development Environment:
# Install Zig (when implementation begins) # Download from https://ziglang.org/download/ # Clone repository git clone https://github.com/username/dark-watcher.git cd dark-watcher
-
🎯 Follow the Learning Journey: Implementation will be documented week-by-week with learning objectives and AI collaboration insights
- Design Phase: Review architecture documentation and provide feedback
- Implementation Phase: Follow coding standards and AI collaboration guidelines
- Testing Phase: Multi-platform testing and validation
- Official Zig Documentation
- Zig Guide - Community learning resource
- Zig Standard Library - API reference
- Win32 API Documentation - Windows development
- The Windows Programming Model - Windows concepts
- Project-specific AI collaboration templates and patterns
- Weekly learning reviews and knowledge transfer sessions
- Implementation documentation with AI partnership insights
We welcome feedback on the architectural design and learning approach:
- 📖 Architecture Review: Examine
Zig-MVP-Project-Structure.mdand suggest improvements - 🎓 Learning Path Feedback: Suggest additional learning objectives or resources
- 🤖 AI Collaboration: Share experiences with AI-assisted development workflows
- Implementation: Module-by-module development following architectural guidelines
- Testing: Cross-platform testing and validation
- Documentation: Learning guides and technical documentation
- Platform Support: macOS and Linux platform implementations
| Week | Focus | Learning Objectives |
|---|---|---|
| 1-2 | Project setup, error handling, logging | Zig toolchain mastery, memory management |
| 3-4 | Platform abstraction, registry operations | Comptime programming, Win32 APIs |
| 5-6 | Theme management, configuration system | Business logic architecture, YAML integration |
| 7-8 | Hotkey system, service integration | Win32 message handling, service lifecycle |
- IPC server and external API development
- Windows Service integration and auto-start
- Advanced state management and persistence
- Performance optimization and comprehensive testing
- macOS platform implementation and integration
- Linux desktop environment support
- Cross-platform build automation and release engineering
- Community documentation and contribution guidelines
📅 Detailed Implementation Roadmap
- 📝 Development Blog: Weekly progress updates with learning insights
- 💬 Discussions: Architecture decisions and learning challenges
- 🎯 AI Collaboration Sharing: Templates and best practices for AI-assisted development
- 📖 Documentation: Comprehensive guides and troubleshooting
- 🐛 Issues: Bug reports and feature requests
- 🔧 Development: Implementation questions and code review
MIT
- Zig Community: For creating an exceptional systems programming language
- AI Development Partners: Claude, GPT-4, and other AI assistants enabling collaborative learning
- Open Source Community: For inspiration and best practices in system utility development
- Learning-First Philosophy: Prioritizing education and skill development alongside practical outcomes
Built with ❤️ using Zig and AI-assisted collaborative development
⭐ Star this repository • 📖 Read the docs • 🤝 Contribute • 📞 Get support