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Aodhan Collins
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docs/planning/PROJECT_PLAN.md
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docs/planning/PROJECT_PLAN.md
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# EVE - Personal Desktop Assistant
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## Comprehensive Project Plan
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---
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## 1. Project Overview
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### Vision
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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.
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### Core Value Propositions
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- **Multimodal Interaction**: Voice-to-text and text-to-voice communication
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- **Visual Presence**: Interactive avatar (Live2D or Adaptive PNG)
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- **Flexibility**: Support for both local and remote LLM models
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- **Context Awareness**: Screen and audio monitoring capabilities
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- **Gaming Integration**: Specialized features for gaming assistance
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---
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## 2. Technical Architecture
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### 2.1 System Components
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#### Frontend Layer
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- **UI Framework**: Electron or Tauri for desktop application
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- **Avatar System**: Live2D Cubism SDK or custom PNG sprite system
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- **Screen Overlay**: Transparent window with always-on-top capability
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- **Settings Panel**: Configuration interface for models, voice, and avatar
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#### Backend Layer
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- **LLM Integration Module**
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- OpenAI API support (GPT-4, GPT-3.5)
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- Anthropic Claude support
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- Local model support (Ollama, LM Studio, llama.cpp)
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- Model switching and fallback logic
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- **Speech Processing Module**
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- Speech-to-Text: OpenAI Whisper (local) or cloud services
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- Text-to-Speech: ElevenLabs API integration
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- Audio input/output management
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- Voice activity detection
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- **Screen & Audio Capture Module**
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- Screen capture API (platform-specific)
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- Audio stream capture
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- OCR integration for screen text extraction
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- Vision model integration for screen understanding
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- **Gaming Support Module**
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- Game state detection
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- In-game overlay support
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- Performance monitoring
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- Game-specific AI assistance
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#### Data Layer
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- **Configuration Storage**: User preferences, API keys
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- **Conversation History**: Local SQLite or JSON storage
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- **Cache System**: For avatar assets, model responses
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- **Session Management**: Context persistence
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---
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## 3. Feature Breakdown & Implementation Plan
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### Phase 1: Foundation (Weeks 1-3)
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#### 3.1 Basic Application Structure
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- [ ] Set up project repository and development environment
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- [ ] Choose and initialize desktop framework (Electron/Tauri)
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- [ ] Create basic window management system
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- [ ] Implement settings/configuration system
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- [ ] Design and implement UI/UX wireframes
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#### 3.2 LLM Integration - Basic
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- [ ] Implement API client for OpenAI
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- [ ] Add support for basic chat completion
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- [ ] Create conversation context management
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- [ ] Implement streaming response handling
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- [ ] Add error handling and retry logic
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#### 3.3 Text Interface
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- [ ] Build chat interface UI
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- [ ] Implement message history display
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- [ ] Add typing indicators
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- [ ] Create system for user input handling
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### Phase 2: Voice Integration (Weeks 4-6)
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#### 3.4 Speech-to-Text (STT)
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- [ ] Integrate OpenAI Whisper API or local Whisper
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- [ ] Implement microphone input capture
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- [ ] Add voice activity detection (VAD)
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- [ ] Create push-to-talk and continuous listening modes
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- [ ] Handle audio preprocessing (noise reduction)
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- [ ] Add language detection support
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#### 3.5 Text-to-Speech (TTS)
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- [ ] Integrate ElevenLabs API
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- [ ] Implement voice selection system
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- [ ] Add audio playback queue management
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- [ ] Create voice customization options
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- [ ] Implement speech rate and pitch controls
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- [ ] Add local TTS fallback option
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#### 3.6 Voice UI/UX
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- [ ] Visual feedback for listening state
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- [ ] Waveform visualization
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- [ ] Voice command shortcuts
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- [ ] Interrupt handling (stop speaking)
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### Phase 3: Avatar System (Weeks 7-9)
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#### 3.7 Live2D Implementation (Option A)
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- [ ] Integrate Live2D Cubism SDK
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- [ ] Create avatar model loader
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- [ ] Implement parameter animation system
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- [ ] Add lip-sync based on TTS phonemes
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- [ ] Create emotion/expression system
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- [ ] Implement idle animations
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- [ ] Add custom model support
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#### 3.8 Adaptive PNG Implementation (Option B)
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- [ ] Design sprite sheet system
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- [ ] Create state machine for avatar states
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- [ ] Implement frame-based animations
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- [ ] Add expression switching logic
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- [ ] Create smooth transitions between states
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- [ ] Support for custom sprite sheets
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#### 3.9 Avatar Interactions
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- [ ] Click/drag avatar positioning
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- [ ] Context menu for quick actions
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- [ ] Avatar reactions to events
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- [ ] Customizable size scaling
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- [ ] Transparency controls
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### Phase 4: Advanced LLM Features (Weeks 10-11)
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#### 3.10 Local Model Support
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- [ ] Integrate Ollama client
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- [ ] Add LM Studio support
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- [ ] Implement llama.cpp integration
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- [ ] Create model download/management system
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- [ ] Add model performance benchmarking
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- [ ] Implement model switching UI
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#### 3.11 Advanced AI Features
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- [ ] Function/tool calling support
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- [ ] Memory/context management system
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- [ ] Personality customization
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- [ ] Custom system prompts
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- [ ] Multi-turn conversation optimization
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- [ ] RAG (Retrieval Augmented Generation) support
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### Phase 5: Screen & Audio Awareness (Weeks 12-14)
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#### 3.12 Screen Capture
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- [ ] Implement platform-specific screen capture (Windows/Linux/Mac)
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- [ ] Add screenshot capability
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- [ ] Create region selection tool
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- [ ] Implement OCR for text extraction (Tesseract)
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- [ ] Add vision model integration (GPT-4V, LLaVA)
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- [ ] Periodic screen monitoring option
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#### 3.13 Audio Monitoring
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- [ ] Implement system audio capture
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- [ ] Add application-specific audio isolation
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- [ ] Create audio transcription pipeline
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- [ ] Implement audio event detection
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- [ ] Add privacy controls and toggles
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#### 3.14 Context Integration
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- [ ] Feed screen context to LLM
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- [ ] Audio context integration
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- [ ] Clipboard monitoring (optional)
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- [ ] Active window detection
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- [ ] Smart context summarization
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### Phase 6: Gaming Support (Weeks 15-16)
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#### 3.15 Game Detection
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- [ ] Process detection for popular games
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- [ ] Game profile system
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- [ ] Performance impact monitoring
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- [ ] Gaming mode toggle
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#### 3.16 In-Game Features
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- [ ] Overlay rendering in games
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- [ ] Hotkey system for in-game activation
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- [ ] Game-specific AI prompts/personalities
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- [ ] Strategy suggestions based on game state
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- [ ] Voice command integration for games
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#### 3.17 Gaming Assistant Features
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- [ ] Build/loadout suggestions (MOBAs, RPGs)
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- [ ] Real-time tips and strategies
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- [ ] Wiki/guide lookup integration
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- [ ] Teammate communication assistance
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- [ ] Performance tracking and analysis
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### Phase 7: Polish & Optimization (Weeks 17-18)
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#### 3.18 Performance Optimization
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- [ ] Resource usage profiling
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- [ ] Memory leak detection and fixes
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- [ ] Startup time optimization
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- [ ] Model loading optimization
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- [ ] Audio latency reduction
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#### 3.19 User Experience
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- [ ] Keyboard shortcuts system
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- [ ] Quick settings panel
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- [ ] Notification system
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- [ ] Tutorial/onboarding flow
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- [ ] Accessibility features
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#### 3.20 Quality Assurance
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- [ ] Cross-platform testing (Windows, Linux, Mac)
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- [ ] Error handling improvements
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- [ ] Logging and debugging tools
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- [ ] User feedback collection system
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- [ ] Beta testing program
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---
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## 4. Technology Stack Recommendations
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### Frontend
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- **Framework**: Tauri (Rust + Web) or Electron (Node.js + Web)
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- **UI Library**: React + TypeScript
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- **Styling**: TailwindCSS + shadcn/ui
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- **State Management**: Zustand or Redux Toolkit
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- **Avatar**: Live2D Cubism Web SDK or custom canvas/WebGL
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### Backend/Integration
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- **Language**: TypeScript/Node.js or Rust
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- **LLM APIs**:
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- OpenAI SDK
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- Anthropic SDK
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- Ollama client
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- **Speech**:
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- ElevenLabs SDK
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- OpenAI Whisper
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- **Screen Capture**:
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- `screenshots` (Rust)
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- `node-screenshot` or native APIs
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- **OCR**: Tesseract.js or native Tesseract
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- **Audio**: Web Audio API, portaudio, or similar
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### Data & Storage
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- **Database**: SQLite (better-sqlite3 or rusqlite)
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- **Config**: JSON or TOML files
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- **Cache**: File system or in-memory
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### Development Tools
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- **Build**: Vite or Webpack
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- **Testing**: Vitest/Jest + Playwright
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- **Linting**: ESLint + Prettier
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- **Version Control**: Git + GitHub
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---
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## 5. Security & Privacy Considerations
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### API Key Management
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- [ ] Secure storage of API keys (OS keychain integration)
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- [ ] Environment variable support
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- [ ] Key validation on startup
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### Data Privacy
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- [ ] Local-first data storage
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- [ ] Optional cloud sync with encryption
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- [ ] Clear data deletion options
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- [ ] Screen/audio capture consent mechanisms
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- [ ] Privacy mode for sensitive information
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### Network Security
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- [ ] HTTPS for all API calls
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- [ ] Certificate pinning considerations
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- [ ] Rate limiting to prevent abuse
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- [ ] Proxy support
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---
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## 6. User Configuration Options
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### General Settings
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- Theme (light/dark/custom)
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- Language preferences
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- Startup behavior
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- Hotkeys and shortcuts
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### AI Model Settings
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- Model selection (GPT-4, Claude, local models)
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- Temperature and creativity controls
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- System prompt customization
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- Context length limits
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- Response streaming preferences
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### Voice Settings
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- STT engine selection
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- TTS voice selection (ElevenLabs voices)
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- Voice speed and pitch
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- Audio input/output device selection
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- VAD sensitivity
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### Avatar Settings
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- Model selection
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- Size and position
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- Transparency
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- Animation speed
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- Expression preferences
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### Screen & Audio Settings
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- Enable/disable screen monitoring
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- Screenshot frequency
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- Audio capture toggle
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- OCR language settings
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- Privacy filters
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### Gaming Settings
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- Game profiles
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- Performance mode
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- Overlay opacity
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- In-game hotkeys
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---
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## 7. Potential Challenges & Mitigations
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### Challenge 1: Audio Latency
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- **Issue**: Delay in STT → LLM → TTS pipeline
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- **Mitigation**:
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- Use streaming APIs where available
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- Optimize audio processing pipeline
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- Local models for faster response
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- Predictive loading of common responses
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### Challenge 2: Resource Usage
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- **Issue**: High CPU/memory usage from multiple subsystems
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- **Mitigation**:
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- Lazy loading of features
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- Efficient caching strategies
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- Option to disable resource-intensive features
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- Performance monitoring and alerts
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### Challenge 3: Screen Capture Performance
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- **Issue**: Screen capture can be resource-intensive
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- **Mitigation**:
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- Configurable capture rate
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- Region-based capture instead of full screen
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- On-demand capture vs. continuous monitoring
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- Hardware acceleration where available
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### Challenge 4: Cross-Platform Compatibility
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- **Issue**: Different APIs for screen/audio capture per OS
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- **Mitigation**:
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- Abstract platform-specific code behind interfaces
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- Use cross-platform libraries where possible
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- Platform-specific builds if necessary
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- Thorough testing on all target platforms
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### Challenge 5: API Costs
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- **Issue**: Cloud API usage can be expensive (ElevenLabs, GPT-4)
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- **Mitigation**:
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- Usage monitoring and caps
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- Local model alternatives
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- Caching of common responses
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- User cost awareness features
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---
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## 8. Future Enhancements (Post-MVP)
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### Advanced Features
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- Multi-language support for UI and conversations
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- Plugin/extension system
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- Cloud synchronization of settings and history
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- Mobile companion app
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- Browser extension integration
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- Automation and scripting capabilities
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### AI Enhancements
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- Fine-tuned models for specific use cases
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- Multi-agent conversations
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- Long-term memory system
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- Learning from user interactions
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- Personality development over time
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### Integration Expansions
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- Calendar and task management integration
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- Email and messaging app integration
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- Development tool integration (IDE, terminal)
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- Smart home device control
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- Music streaming service integration
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### Community Features
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- Sharing custom avatars
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- Prompt template marketplace
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- Community-created game profiles
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- User-generated content for personalities
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---
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## 9. Success Metrics
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### Performance Metrics
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- Response time (STT → LLM → TTS) < 3 seconds
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- Application startup time < 5 seconds
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- Memory usage < 500MB idle, < 1GB active
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- CPU usage < 5% idle, < 20% active
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### Quality Metrics
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- Speech recognition accuracy > 95%
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- User satisfaction rating > 4.5/5
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- Crash rate < 0.1% of sessions
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- API success rate > 99%
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### Adoption Metrics
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- Active daily users
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- Average session duration
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- Feature usage statistics
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- User retention rate
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---
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## 10. Development Timeline Summary
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**Total Estimated Duration: 18 weeks (4.5 months)**
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- **Phase 1**: Foundation (3 weeks)
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- **Phase 2**: Voice Integration (3 weeks)
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- **Phase 3**: Avatar System (3 weeks)
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- **Phase 4**: Advanced LLM (2 weeks)
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- **Phase 5**: Screen & Audio Awareness (3 weeks)
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- **Phase 6**: Gaming Support (2 weeks)
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- **Phase 7**: Polish & Optimization (2 weeks)
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### Milestones
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- **Week 3**: Basic text-based assistant functional
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- **Week 6**: Full voice interaction working
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- **Week 9**: Avatar integrated and animated
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- **Week 11**: Local model support complete
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- **Week 14**: Screen/audio awareness functional
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- **Week 16**: Gaming features complete
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- **Week 18**: Production-ready release
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---
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## 11. Getting Started
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### Immediate Next Steps
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1. **Environment Setup**
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- Choose desktop framework (Tauri vs Electron)
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- Set up project repository
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- Initialize package management
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- Configure build tools
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2. **Proof of Concept**
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- Create minimal window application
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- Test OpenAI API integration
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- Verify ElevenLabs API access
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- Test screen capture on target OS
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3. **Architecture Documentation**
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- Create detailed technical architecture diagram
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- Define API contracts between modules
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- Document data flow
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- Set up development workflow
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4. **Development Workflow**
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- Set up CI/CD pipeline
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- Configure testing framework
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- Establish code review process
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- Create development, staging, and production branches
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---
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## 12. Resources & Dependencies
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### Required API Keys/Accounts
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- OpenAI API key (for GPT models and Whisper)
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- ElevenLabs API key (for TTS)
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- Anthropic API key (optional, for Claude)
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### Optional Services
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- Ollama (for local models)
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- LM Studio (alternative local model runner)
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- Tesseract (for OCR)
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### Hardware Recommendations
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- **Minimum**: 8GB RAM, quad-core CPU, 10GB storage
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- **Recommended**: 16GB RAM, 8-core CPU, SSD, 20GB storage
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- **For Local Models**: 32GB RAM, GPU with 8GB+ VRAM
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---
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## Notes
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- This plan is flexible and should be adjusted based on user feedback and technical discoveries
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- Consider creating MVPs for each phase to validate approach
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- Regular user testing is recommended throughout development
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- Budget sufficient time for debugging and unexpected challenges
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- Consider open-source vs. proprietary licensing early on
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