📰 Briefs - AI-Powered News Reader

A sophisticated, intelligent news aggregator that fetches, deduplicates, and presents tech news from multiple RSS feeds in a clean, interactive interface.

🚀 Features

📰 Multi-Source News Aggregation

• 7 Premium Tech Sources: TechCrunch, VentureBeat, The Verge, Hacker News, Wired, Ars Technica, MIT Technology Review
• Smart Deduplication: AI-powered similarity detection to eliminate duplicate articles
• Real-time Fetching: Fresh content with progress indicators

🎯 Intelligent Content Processing

• Smart Summaries: Auto-generated article summaries with highlighted key metrics
• Quantifiable Highlighting: Automatically bold numbers, percentages, and financial data
• Timestamp Parsing: Robust handling of various RSS date formats
• Domain Extraction: Clean source attribution

💾 Personalized Experience

• Like System: Save interesting articles for later reading
• Discard Functionality: Remove unwanted articles permanently
• Read Tracking: Mark articles as read with detailed analysis
• Persistent Storage: TinyDB-based local storage for user preferences

🔍 Advanced Search & Organization

• Real-time Search: Filter articles by title, summary, or source
• Multiple Sort Options:
  • 🕒 Most Recent
  • 🔤 Alphabetical
  • 📰 By Source
• Expandable Articles: Click to view detailed summaries without scrolling

🎨 Modern UI/UX

• Responsive Design: Clean, modern interface with smooth animations
• Interactive Elements: Hover effects and visual feedback
• Keyboard Shortcuts: Quick navigation and actions
• Progress Indicators: Visual feedback during content fetching

📋 Table of Contents

• Installation
• Usage
• Features in Detail
• Architecture
• Configuration
• API Reference
• Contributing
• License

🛠️ Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Quick Start

1. Clone the repository

   git clone <repository-url>
   cd briefs

2. Create a virtual environment

   python -m venv env
   source env/bin/activate  # On Windows: env\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Run the application

   streamlit run app.py

5. Open your browser Navigate to http://localhost:8501

🎮 Usage

Getting Started

1. First Launch: The app automatically fetches the latest articles from all sources
2. Browse Articles: Scroll through the main article list in the left column
3. Expand Details: Click any article tile to view detailed summary and actions
4. Like Articles: Use the ❤️ button to save articles to your liked collection
5. Search & Filter: Use the search bar in the sidebar to find specific content

Keyboard Shortcuts

Shortcut	Action
r	Refresh articles
s	Focus search bar
Esc	Clear selection

Article Actions

• ❤️ Like: Save article to your collection
• ✅ Mark as Read: Add detailed analysis and mark as read
• 🗑️ Discard: Remove article from view permanently
• 🔄 Refresh: Fetch latest articles
• 📄 Collapse: Hide expanded article details

🔧 Features in Detail

RSS Feed Integration

The app integrates with 7 premium tech news sources:

Source	URL	Color
TechCrunch	techcrunch.com/feed/	#ff6b6b
VentureBeat	venturebeat.com/feed/	#4ecdc4
The Verge	theverge.com/rss/index.xml	#45b7d1
Hacker News	hnrss.org/frontpage	#ffa726
Wired	wired.com/feed/rss	#ab47bc
Ars Technica	feeds.arstechnica.com/arstechnica/index	#26a69a
MIT Technology Review	technologyreview.com/feed/	#42a5f5

Content Processing Pipeline

1. Fetch: Retrieve articles from RSS feeds with progress tracking
2. Parse: Extract title, summary, link, and timestamp
3. Clean: Remove HTML tags and normalize text
4. Deduplicate: Group similar articles using sequence matching
5. Enhance: Highlight quantifiable data and format timestamps
6. Store: Save to local database with user interactions

Data Persistence

• TinyDB: Lightweight JSON-based database
• Session State: Streamlit session management for UI state
• User Preferences: Liked articles, discarded items, read status

🏗️ Architecture

Code Structure

briefs/
├── app.py                 # Main application file
├── requirements.txt       # Python dependencies
├── liked_articles.json    # Database file (auto-generated)
├── README.md             # This file
└── LICENSE               # License information

Module Organization

# Session State Initialization
initialize_session_state()

# Database Operations
initialize_database()
is_liked(), like_article(), discard_article()

# Content Processing
fetch_articles_with_progress()
deduplicate_articles()
summarize_entry()

# UI Components
render_sidebar()
render_article_list()
render_expanded_article()

Key Components

• Session Management: Streamlit session state for UI persistence
• Database Layer: TinyDB for user data storage
• RSS Parser: Feedparser for RSS feed processing
• Content Processing: BeautifulSoup for HTML cleaning
• UI Framework: Streamlit for web interface

⚙️ Configuration

RSS Feeds

Add or modify RSS feeds in the RSS_FEEDS configuration:

RSS_FEEDS = [
    {
        "name": "Your Source",
        "url": "https://yoursource.com/feed/",
        "color": "#yourcolor"
    }
]

Sort Options

Customize sorting options in SORT_OPTIONS:

SORT_OPTIONS = {
    "recent": "🕒 Most Recent",
    "alphabetical": "🔤 Alphabetical",
    "source": "📰 By Source"
}

Styling

Modify CSS styles in the load_custom_styles() function for custom theming.

📚 API Reference

Core Functions

initialize_session_state()

Initialize all Streamlit session state variables.

fetch_articles_with_progress() -> List[Dict]

Fetch articles from RSS feeds with progress indicator.

deduplicate_articles(articles: List[Dict]) -> List[List[Dict]]

Group similar articles together using similarity matching.

like_article(article: Dict)

Save article to user's liked collection.

discard_article(article_id: str)

Mark article as discarded and hide from view.

Database Operations

is_liked(article_id: str) -> bool

Check if article is in user's liked collection.

is_discarded(article_id: str) -> bool

Check if article has been discarded.

get_article_stats() -> tuple

Get statistics about liked articles and sources.

UI Components

render_sidebar()

Render the sidebar with controls, search, and statistics.

render_article_list()

Render the main article list with sorting and filtering.

render_expanded_article(article: Dict, is_liked_article: bool = False)

Render detailed article view with actions.

🤝 Contributing

We welcome contributions! Here's how you can help:

Development Setup

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes
4. Add tests if applicable
5. Commit your changes: git commit -m 'Add amazing feature'
6. Push to the branch: git push origin feature/amazing-feature
7. Open a Pull Request

Code Style

• Follow PEP 8 guidelines
• Add docstrings to all functions
• Use type hints where appropriate
• Keep functions focused and single-purpose

Feature Ideas

• Add more RSS sources
• Implement article sharing
• Add export functionality
• Create mobile-responsive design
• Add article recommendations
• Implement user accounts

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Streamlit for the amazing web framework
• Feedparser for robust RSS parsing
• BeautifulSoup for HTML processing
• TinyDB for lightweight data storage
• All RSS feed providers for their excellent content

📞 Support

If you encounter any issues or have questions:

1. Check the Issues page
2. Create a new issue with detailed information
3. Include your Python version and error messages

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