pocket_assistant/documentation/project_outline.md

Pocket Assistant

A personal assistant application with modular extentions and AI integration

Overview

The Pocket Assistant is a personal assistant application that will provide a suite of tools and extentions to help users with their daily tasks. It is designed to be modular, with different extentions that can be added to the application to provide additional functionality as required. The application also has AI integration, allowing users to interact with the application using natural language.

The primary goals of Pocket Assistant are:

• Simplify daily tasks: Consolidate common productivity and organizational tools into one application
• Personalization: Allow users to customize their experience through modular extensions
• Privacy-focused: Keep user data local and secure by default, with optional cloud features
• AI-enhanced productivity: Leverage artificial intelligence to augment user capabilities
• Cross-platform accessibility: Provide a consistent experience across different devices and operating systems

This document will focus on a MVP (Minimum Viable Product) that will provide the core functionality of the application.

Architecture

The Pocket Assistant will follow a modular architecture to support its extensibility goals:

• Core Application: Central system that manages extensions and provides common services
• Extension API: Well-documented API for third-party extension development
• Data Layer: Local database with optional cloud synchronization
• Service Layer: Handles communication between components and external services
• UI Layer: Web-based interface that adapts to different extensions

For the technical stack:

• Backend: Node.js or Python FastAPI for the service layer with REST/GraphQL endpoints
• Frontend: React or Vue.js for the web interface with responsive design
• Database: SQLite for local storage with option to sync to cloud database (PostgreSQL)
• Extension Framework: Standardized API for extensions to integrate with the core application

Platform

For initial development, the application will be built for Windows, macOS, and Linux. Android and iOS support will be added later.

Desktop Implementations:

• Windows: Compatible with Windows 10 and newer, utilizing system notifications and features
• macOS: Native integration with macOS notifications and services where appropriate
• Linux: Support for major distributions (Ubuntu, Fedora, Debian), with consideration for different desktop environments

Cross-platform Strategy:

• Web technologies (Electron) for consistent UI across platforms
• Platform-specific adapters for native OS integration
• Shared core logic and data models
• Responsive design to accommodate different screen sizes and resolutions

Future Mobile Support:

• Native applications for Android and iOS
• Synchronized data and settings with desktop versions
• Mobile-optimized UI with touch-first interaction design
• Integration with mobile-specific features (location services, notifications, etc.)

Extentions

Modularity will be a core feature of the application, allowing users to add or remove extentions as required. We will implement features in this priority order:

1. AI Chat Extension (Phase 1):

• Natural language interaction with personality selector
• Customizable AI personas (professional, creative, technical, etc.)
• Context-aware conversations with memory
• Integration with other extensions
• Support for multiple AI providers

2. User Goal Planner Extension (Phase 2):

• Short/medium/long-term goal setting and tracking
• Progress visualization and milestone management
• Actionable steps generation
• Goal categorization and prioritization
• Smart recommendations based on user patterns

3. Reminders & Calendar Extension (Phase 3):

• Time-based and location-based alerts
• Recurring event support
• Priority levels and categorization
• Potential integration with Goal Planner in a unified calendar interface
• Visual timeline view of upcoming events and goals

4. Core Productivity Suite (Phase 4):

• Notes with rich text/markdown
• To-Do lists with subtasks
• File organization system

AI Integration

For initial development we will provide integration with Claude and LM Studio.

Claude Integration:

• API connection to Anthropic's Claude models
• Authentication and API key management
• Context window management for effective conversations
• Support for different Claude model versions based on user needs
• Streaming responses for better user experience

LM Studio Integration:

• Local model running capabilities for privacy-conscious users
• Model selection and management interface
• Performance optimization for different hardware configurations
• Parameter adjustment for advanced users
• Option to use custom models

Core AI Capabilities:

• Natural language understanding and generation
• Context-aware conversations with history
• Task completion through extension integration
• Information retrieval and summarization
• Content generation (writing assistance, creative tasks)

AI Privacy and Control:

• Transparent data usage policies
• User control over conversation storage
• Options for local-only AI processing
• Clear disclosure of capabilities and limitations
• Ability to delete stored conversations and data

Tools

Development of the Pocket Assistant will utilize:

• Development Environment: VS Code, Git for version control
• Build System: Webpack for frontend, Electron for desktop application packaging
• CI/CD: GitHub Actions for automated testing and deployment
• Documentation: Markdown for developer docs and user guides
• Package Management: npm/yarn for JavaScript dependencies, pip for Python components
• Containerization: Docker for consistent development and deployment environments

Security

Security will be a priority for the Pocket Assistant:

• Data Encryption: End-to-end encryption for sensitive user data
• Authentication: Local authentication for offline use, OAuth integration for cloud services
• API Keys: Secure storage for third-party service API keys
• Permissions System: Granular permissions for extension access to user data
• Regular Security Audits: Scheduled code reviews and security testing
• Privacy-First Design: Minimal data collection, local processing when possible
• Compliance: GDPR and other relevant privacy regulations compliance

Testing

A comprehensive testing strategy will be implemented:

• Unit Testing: Jest for JavaScript components, pytest for Python
• Integration Testing: Test extension interoperability and API contracts
• UI Testing: Selenium or Cypress for frontend testing
• User Testing: Beta program for early adopters
• Performance Testing: Resource usage optimization and benchmarking
• Security Testing: Regular penetration testing and vulnerability scanning
• Accessibility Testing: Ensure the application is usable by people with disabilities

Deployment

The deployment strategy will include:

• Release Channels: Alpha, beta, and stable release channels
• Distribution: Packaged for Windows (MSI), macOS (DMG), and Linux (AppImage/deb/rpm)
• Auto-Updates: Automatic update mechanism with user notifications
• Installation Process: Simple one-click installation with minimal configuration
• Dependencies: Bundled dependencies to minimize installation issues
• Resource Requirements: Clear documentation of minimum system requirements

Maintenance

Long-term maintenance plan includes:

• Version Control: Semantic versioning (MAJOR.MINOR.PATCH)
• Issue Tracking: GitHub Issues for bugs and feature requests
• Documentation: Regular updates to user guides and API documentation
• Backward Compatibility: Extensions continue working with new versions
• Performance Optimization: Regular profiling and optimization
• Community Support: Forum for user questions and extension development
• Regular Updates: Security patches and feature enhancements

Future Development

Plans for future development include:

• Mobile Apps
• Cloud Sync
• Marketplace
• Workflow Automation
• Voice Interface

Conclusion

The Pocket Assistant aims to be a versatile, extensible personal assistant that adapts to users' needs. By focusing on modularity, security, and user experience, the application will provide value while respecting user privacy and preferences. The initial MVP will establish core functionality, with a clear roadmap for future enhancements and community involvement.

User Interface

For PC version, we will use a web interface.