# EVE - Personal Desktop Assistant
## Comprehensive Project Plan

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## 1. Project Overview

### Vision
A sophisticated desktop assistant with AI capabilities, multimodal interaction (voice & visual), and gaming integration. The assistant features a customizable avatar and supports both local and cloud-based AI models.

### Core Value Propositions
- **Multimodal Interaction**: Voice-to-text and text-to-voice communication
- **Visual Presence**: Interactive avatar (Live2D or Adaptive PNG)
- **Flexibility**: Support for both local and remote LLM models
- **Context Awareness**: Screen and audio monitoring capabilities
- **Gaming Integration**: Specialized features for gaming assistance

---

## 2. Technical Architecture

### 2.1 System Components

#### Frontend Layer
- **UI Framework**: Electron or Tauri for desktop application
- **Avatar System**: Live2D Cubism SDK or custom PNG sprite system
- **Screen Overlay**: Transparent window with always-on-top capability
- **Settings Panel**: Configuration interface for models, voice, and avatar

#### Backend Layer
- **LLM Integration Module**
  - OpenAI API support (GPT-4, GPT-3.5)
  - Anthropic Claude support
  - Local model support (Ollama, LM Studio, llama.cpp)
  - Model switching and fallback logic
  
- **Speech Processing Module**
  - Speech-to-Text: OpenAI Whisper (local) or cloud services
  - Text-to-Speech: ElevenLabs API integration
  - Audio input/output management
  - Voice activity detection

- **Screen & Audio Capture Module**
  - Screen capture API (platform-specific)
  - Audio stream capture
  - OCR integration for screen text extraction
  - Vision model integration for screen understanding

- **Gaming Support Module**
  - Game state detection
  - In-game overlay support
  - Performance monitoring
  - Game-specific AI assistance

#### Data Layer
- **Configuration Storage**: User preferences, API keys
- **Conversation History**: Local SQLite or JSON storage
- **Cache System**: For avatar assets, model responses
- **Session Management**: Context persistence

---

## 3. Feature Breakdown & Implementation Plan

### Phase 1: Foundation (Weeks 1-3)

#### 3.1 Basic Application Structure
- [ ] Set up project repository and development environment
- [ ] Choose and initialize desktop framework (Electron/Tauri)
- [ ] Create basic window management system
- [ ] Implement settings/configuration system
- [ ] Design and implement UI/UX wireframes

#### 3.2 LLM Integration - Basic
- [ ] Implement API client for OpenAI
- [ ] Add support for basic chat completion
- [ ] Create conversation context management
- [ ] Implement streaming response handling
- [ ] Add error handling and retry logic

#### 3.3 Text Interface
- [ ] Build chat interface UI
- [ ] Implement message history display
- [ ] Add typing indicators
- [ ] Create system for user input handling

### Phase 2: Voice Integration (Weeks 4-6)

#### 3.4 Speech-to-Text (STT)
- [ ] Integrate OpenAI Whisper API or local Whisper
- [ ] Implement microphone input capture
- [ ] Add voice activity detection (VAD)
- [ ] Create push-to-talk and continuous listening modes
- [ ] Handle audio preprocessing (noise reduction)
- [ ] Add language detection support

#### 3.5 Text-to-Speech (TTS)
- [ ] Integrate ElevenLabs API
- [ ] Implement voice selection system
- [ ] Add audio playback queue management
- [ ] Create voice customization options
- [ ] Implement speech rate and pitch controls
- [ ] Add local TTS fallback option

#### 3.6 Voice UI/UX
- [ ] Visual feedback for listening state
- [ ] Waveform visualization
- [ ] Voice command shortcuts
- [ ] Interrupt handling (stop speaking)

### Phase 3: Avatar System (Weeks 7-9)

#### 3.7 Live2D Implementation (Option A)
- [ ] Integrate Live2D Cubism SDK
- [ ] Create avatar model loader
- [ ] Implement parameter animation system
- [ ] Add lip-sync based on TTS phonemes
- [ ] Create emotion/expression system
- [ ] Implement idle animations
- [ ] Add custom model support

#### 3.8 Adaptive PNG Implementation (Option B)
- [ ] Design sprite sheet system
- [ ] Create state machine for avatar states
- [ ] Implement frame-based animations
- [ ] Add expression switching logic
- [ ] Create smooth transitions between states
- [ ] Support for custom sprite sheets

#### 3.9 Avatar Interactions
- [ ] Click/drag avatar positioning
- [ ] Context menu for quick actions
- [ ] Avatar reactions to events
- [ ] Customizable size scaling
- [ ] Transparency controls

### Phase 4: Advanced LLM Features (Weeks 10-11)

#### 3.10 Local Model Support
- [ ] Integrate Ollama client
- [ ] Add LM Studio support
- [ ] Implement llama.cpp integration
- [ ] Create model download/management system
- [ ] Add model performance benchmarking
- [ ] Implement model switching UI

#### 3.11 Advanced AI Features
- [ ] Function/tool calling support
- [ ] Memory/context management system
- [ ] Personality customization
- [ ] Custom system prompts
- [ ] Multi-turn conversation optimization
- [ ] RAG (Retrieval Augmented Generation) support

### Phase 5: Screen & Audio Awareness (Weeks 12-14)

#### 3.12 Screen Capture
- [ ] Implement platform-specific screen capture (Windows/Linux/Mac)
- [ ] Add screenshot capability
- [ ] Create region selection tool
- [ ] Implement OCR for text extraction (Tesseract)
- [ ] Add vision model integration (GPT-4V, LLaVA)
- [ ] Periodic screen monitoring option

#### 3.13 Audio Monitoring
- [ ] Implement system audio capture
- [ ] Add application-specific audio isolation
- [ ] Create audio transcription pipeline
- [ ] Implement audio event detection
- [ ] Add privacy controls and toggles

#### 3.14 Context Integration
- [ ] Feed screen context to LLM
- [ ] Audio context integration
- [ ] Clipboard monitoring (optional)
- [ ] Active window detection
- [ ] Smart context summarization

### Phase 6: Gaming Support (Weeks 15-16)

#### 3.15 Game Detection
- [ ] Process detection for popular games
- [ ] Game profile system
- [ ] Performance impact monitoring
- [ ] Gaming mode toggle

#### 3.16 In-Game Features
- [ ] Overlay rendering in games
- [ ] Hotkey system for in-game activation
- [ ] Game-specific AI prompts/personalities
- [ ] Strategy suggestions based on game state
- [ ] Voice command integration for games

#### 3.17 Gaming Assistant Features
- [ ] Build/loadout suggestions (MOBAs, RPGs)
- [ ] Real-time tips and strategies
- [ ] Wiki/guide lookup integration
- [ ] Teammate communication assistance
- [ ] Performance tracking and analysis

### Phase 7: Polish & Optimization (Weeks 17-18)

#### 3.18 Performance Optimization
- [ ] Resource usage profiling
- [ ] Memory leak detection and fixes
- [ ] Startup time optimization
- [ ] Model loading optimization
- [ ] Audio latency reduction

#### 3.19 User Experience
- [ ] Keyboard shortcuts system
- [ ] Quick settings panel
- [ ] Notification system
- [ ] Tutorial/onboarding flow
- [ ] Accessibility features

#### 3.20 Quality Assurance
- [ ] Cross-platform testing (Windows, Linux, Mac)
- [ ] Error handling improvements
- [ ] Logging and debugging tools
- [ ] User feedback collection system
- [ ] Beta testing program

---

## 4. Technology Stack Recommendations

### Frontend
- **Framework**: Tauri (Rust + Web) or Electron (Node.js + Web)
- **UI Library**: React + TypeScript
- **Styling**: TailwindCSS + shadcn/ui
- **State Management**: Zustand or Redux Toolkit
- **Avatar**: Live2D Cubism Web SDK or custom canvas/WebGL

### Backend/Integration
- **Language**: TypeScript/Node.js or Rust
- **LLM APIs**: 
  - OpenAI SDK
  - Anthropic SDK
  - Ollama client
- **Speech**: 
  - ElevenLabs SDK
  - OpenAI Whisper
- **Screen Capture**: 
  - `screenshots` (Rust)
  - `node-screenshot` or native APIs
- **OCR**: Tesseract.js or native Tesseract
- **Audio**: Web Audio API, portaudio, or similar

### Data & Storage
- **Database**: SQLite (better-sqlite3 or rusqlite)
- **Config**: JSON or TOML files
- **Cache**: File system or in-memory

### Development Tools
- **Build**: Vite or Webpack
- **Testing**: Vitest/Jest + Playwright
- **Linting**: ESLint + Prettier
- **Version Control**: Git + GitHub

---

## 5. Security & Privacy Considerations

### API Key Management
- [ ] Secure storage of API keys (OS keychain integration)
- [ ] Environment variable support
- [ ] Key validation on startup

### Data Privacy
- [ ] Local-first data storage
- [ ] Optional cloud sync with encryption
- [ ] Clear data deletion options
- [ ] Screen/audio capture consent mechanisms
- [ ] Privacy mode for sensitive information

### Network Security
- [ ] HTTPS for all API calls
- [ ] Certificate pinning considerations
- [ ] Rate limiting to prevent abuse
- [ ] Proxy support

---

## 6. User Configuration Options

### General Settings
- Theme (light/dark/custom)
- Language preferences
- Startup behavior
- Hotkeys and shortcuts

### AI Model Settings
- Model selection (GPT-4, Claude, local models)
- Temperature and creativity controls
- System prompt customization
- Context length limits
- Response streaming preferences

### Voice Settings
- STT engine selection
- TTS voice selection (ElevenLabs voices)
- Voice speed and pitch
- Audio input/output device selection
- VAD sensitivity

### Avatar Settings
- Model selection
- Size and position
- Transparency
- Animation speed
- Expression preferences

### Screen & Audio Settings
- Enable/disable screen monitoring
- Screenshot frequency
- Audio capture toggle
- OCR language settings
- Privacy filters

### Gaming Settings
- Game profiles
- Performance mode
- Overlay opacity
- In-game hotkeys

---

## 7. Potential Challenges & Mitigations

### Challenge 1: Audio Latency
- **Issue**: Delay in STT → LLM → TTS pipeline
- **Mitigation**: 
  - Use streaming APIs where available
  - Optimize audio processing pipeline
  - Local models for faster response
  - Predictive loading of common responses

### Challenge 2: Resource Usage
- **Issue**: High CPU/memory usage from multiple subsystems
- **Mitigation**:
  - Lazy loading of features
  - Efficient caching strategies
  - Option to disable resource-intensive features
  - Performance monitoring and alerts

### Challenge 3: Screen Capture Performance
- **Issue**: Screen capture can be resource-intensive
- **Mitigation**:
  - Configurable capture rate
  - Region-based capture instead of full screen
  - On-demand capture vs. continuous monitoring
  - Hardware acceleration where available

### Challenge 4: Cross-Platform Compatibility
- **Issue**: Different APIs for screen/audio capture per OS
- **Mitigation**:
  - Abstract platform-specific code behind interfaces
  - Use cross-platform libraries where possible
  - Platform-specific builds if necessary
  - Thorough testing on all target platforms

### Challenge 5: API Costs
- **Issue**: Cloud API usage can be expensive (ElevenLabs, GPT-4)
- **Mitigation**:
  - Usage monitoring and caps
  - Local model alternatives
  - Caching of common responses
  - User cost awareness features

---

## 8. Future Enhancements (Post-MVP)

### Advanced Features
- Multi-language support for UI and conversations
- Plugin/extension system
- Cloud synchronization of settings and history
- Mobile companion app
- Browser extension integration
- Automation and scripting capabilities

### AI Enhancements
- Fine-tuned models for specific use cases
- Multi-agent conversations
- Long-term memory system
- Learning from user interactions
- Personality development over time

### Integration Expansions
- Calendar and task management integration
- Email and messaging app integration
- Development tool integration (IDE, terminal)
- Smart home device control
- Music streaming service integration

### Community Features
- Sharing custom avatars
- Prompt template marketplace
- Community-created game profiles
- User-generated content for personalities

---

## 9. Success Metrics

### Performance Metrics
- Response time (STT → LLM → TTS) < 3 seconds
- Application startup time < 5 seconds
- Memory usage < 500MB idle, < 1GB active
- CPU usage < 5% idle, < 20% active

### Quality Metrics
- Speech recognition accuracy > 95%
- User satisfaction rating > 4.5/5
- Crash rate < 0.1% of sessions
- API success rate > 99%

### Adoption Metrics
- Active daily users
- Average session duration
- Feature usage statistics
- User retention rate

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## 10. Development Timeline Summary

**Total Estimated Duration: 18 weeks (4.5 months)**

- **Phase 1**: Foundation (3 weeks)
- **Phase 2**: Voice Integration (3 weeks)
- **Phase 3**: Avatar System (3 weeks)
- **Phase 4**: Advanced LLM (2 weeks)
- **Phase 5**: Screen & Audio Awareness (3 weeks)
- **Phase 6**: Gaming Support (2 weeks)
- **Phase 7**: Polish & Optimization (2 weeks)

### Milestones
- **Week 3**: Basic text-based assistant functional
- **Week 6**: Full voice interaction working
- **Week 9**: Avatar integrated and animated
- **Week 11**: Local model support complete
- **Week 14**: Screen/audio awareness functional
- **Week 16**: Gaming features complete
- **Week 18**: Production-ready release

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## 11. Getting Started

### Immediate Next Steps
1. **Environment Setup**
   - Choose desktop framework (Tauri vs Electron)
   - Set up project repository
   - Initialize package management
   - Configure build tools

2. **Proof of Concept**
   - Create minimal window application
   - Test OpenAI API integration
   - Verify ElevenLabs API access
   - Test screen capture on target OS

3. **Architecture Documentation**
   - Create detailed technical architecture diagram
   - Define API contracts between modules
   - Document data flow
   - Set up development workflow

4. **Development Workflow**
   - Set up CI/CD pipeline
   - Configure testing framework
   - Establish code review process
   - Create development, staging, and production branches

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## 12. Resources & Dependencies

### Required API Keys/Accounts
- OpenAI API key (for GPT models and Whisper)
- ElevenLabs API key (for TTS)
- Anthropic API key (optional, for Claude)

### Optional Services
- Ollama (for local models)
- LM Studio (alternative local model runner)
- Tesseract (for OCR)

### Hardware Recommendations
- **Minimum**: 8GB RAM, quad-core CPU, 10GB storage
- **Recommended**: 16GB RAM, 8-core CPU, SSD, 20GB storage
- **For Local Models**: 32GB RAM, GPU with 8GB+ VRAM

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## Notes
- This plan is flexible and should be adjusted based on user feedback and technical discoveries
- Consider creating MVPs for each phase to validate approach
- Regular user testing is recommended throughout development
- Budget sufficient time for debugging and unexpected challenges
- Consider open-source vs. proprietary licensing early on