Reading - AI-Powered Reading System

Reading is an intelligent reading system that collects, curates, and organizes tech articles into weekly reading collections. Features AI-powered content processing, rich markdown editing, and a clean web interface for focused reading experiences.
🌐 Demo: reading.qijun.io · 📋 RSS Sources

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🌟 Features

📰 Smart Article Collection

• RSS integration & web scraping from multiple sources
• AI-powered content classification & tagging
• Article notes and skip functionality for better organization

📖 Weekly Reading Collections

• Create curated article collections with custom titles and descriptions
• Rich markdown editor with AI assistant for content creation
• Draft/publish workflow with cover images
• Public reading interface for published collections

🤖 AI Processing

• Automatic article summarization
• Content filtering by interest and relevance
• Smart categorization and quality assessment
• AI-powered image generation for collection covers

🛠️ Tech Stack

• Frontend: Next.js 15, TypeScript, Tailwind CSS, Shadcn/ui
• Backend: Python 3.8+, SQLite, RSS parser, LLM APIs
• Database: SQLite with migration system using yoyo-migrations

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🚀 Quick Start

Prerequisites

• Node.js 18+ and pnpm
• Python 3.8+
• SQLite

Installation

git clone https://github.com/yourusername/reading.git
cd reading
pnpm install
cd packages/tasks && pip install -r requirements.txt

Configuration

1. Copy environment file: cp .env.example .env
2. Configure API keys and database settings in .env
3. Initialize database: yoyo apply -d sqlite:///data/reading.db packages/tasks/migrations/

Development

Start both services:

# Terminal 1 - Web frontend
cd packages/web && pnpm dev

# Terminal 2 - Article scraping (optional)
cd packages/tasks && python scraper.py

Visit http://localhost:3000 to access the application. Admin interface: http://localhost:3000/admin

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🔐 Authentication & Security

Admin Access Setup

The application uses password-based authentication for admin features:

1. Generate password hash:

   cd packages/web
   node generate-hash.js your-admin-password

2. Add to .env.local:

   ADMIN_PASSWORD_HASH_ENCODED=<generated-hash>

3. Access admin via: http://localhost:3000/auth?token=your-access-token

Access Levels

• Public: Browse published collections and articles (read-only)
• Authenticated: Full CRUD access to collections, articles, and admin features

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🐳 Docker Deployment

./scripts/deploy.sh

• Web: Next.js frontend
• Scraper: scheduled article collection
• SQLite with volume persistence

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💾 Database & Data Management

Database Migrations

# Apply all pending migrations
yoyo apply -d sqlite:///data/reading.db packages/tasks/migrations/

# List migration status
yoyo list -d sqlite:///data/reading.db packages/tasks/migrations/

Data Management Scripts

./scripts/data-manager.sh backup   # Backup database
./scripts/data-manager.sh restore  # Restore from backup
./scripts/data-manager.sh export   # Export SQL dump

Article Quality Management

# Run migrations first for new installations
yoyo apply -d sqlite:///data/reading.db packages/tasks/migrations/

# Preview articles to be removed
./scripts/clean-database.sh --dry-run

# Clean specific source
./scripts/clean-database.sh --source "Hacker News"

# Test on limited articles
./scripts/clean-database.sh --limit 50 --dry-run

# Check processing status
./scripts/clean-database.sh --status

# Execute cleanup (after preview)
./scripts/clean-database.sh --confirm

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🛠️ Development

Project Structure

packages/
├── web/                 # Next.js frontend
│   ├── src/app/        # App router pages
│   ├── src/components/ # Reusable UI components
│   ├── src/services/   # API service layers
│   └── src/lib/        # Database and utilities
└── tasks/              # Python backend
    ├── migrations/     # Database schema migrations
    ├── scraper.py     # RSS feed scraper
    ├── article_filter.py # AI-powered filtering
    └── llm_processing.py # LLM integration

Development Commands

Web Frontend (Next.js)

cd packages/web
pnpm dev          # Start development server (localhost:3000)
pnpm build        # Build for production
pnpm start        # Start production server
pnpm lint         # Run ESLint

Python Backend

cd packages/tasks
pip install -r requirements.txt  # Install dependencies
python scraper.py                # Run article scraper
make lint                        # Run code quality checks
make format                      # Format code with black and isort

Environment Configuration

• LLM_API_ENDPOINT and LLM_API_KEY for AI processing
• DASHSCOPE_API_KEY for AI image generation (optional)
• ADMIN_PASSWORD_HASH_ENCODED for authentication
• Database path: ../../data/reading.db (relative to project root)

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📄 Licensed under MIT.
✨ Issues & Feature Requests