Tech-updates (Personal Tech News Aggregator)
An intelligent news aggregator that uses AI to scrape, categorize, and present personalized tech news from multiple sources. Features advanced content management and real-time updates.
Project Overview
Tech-updates is a sophisticated news aggregation platform that combines web scraping, artificial intelligence, and modern web development to deliver personalized tech news. The system automatically collects articles from multiple sources including Medium, Y Combinator, and Crunchbase, then uses Azure OpenAI for intelligent categorization and content analysis. The backend is built with Flask and PostgreSQL, providing a robust REST API for data management. The frontend uses React with Vite for optimal performance and user experience. The system integrates Qdrant vector database for efficient content storage and retrieval, enabling fast search and recommendation features. Key features include automated content scraping, AI-powered categorization, personalized news feeds, and a responsive web interface. The project demonstrates advanced full-stack development skills with AI integration, database design, and modern web technologies.
Key Features
AI-Powered Categorization
Azure OpenAI integration for intelligent article categorization and analysis
Vector Database
Qdrant vector database for efficient content storage and similarity search
Multi-Source Scraping
Automated web scraping from Medium, Y Combinator, and Crunchbase
Real-time Updates
Flask REST API with PostgreSQL for real-time content management
Technical Implementation
- React.js with Vite for fast frontend development
- Flask REST API for backend services
- PostgreSQL database for data persistence
- Azure OpenAI for intelligent content categorization
- Qdrant vector database for similarity search
- Web scraping from multiple tech news sources
- Real-time content updates and notifications
- Responsive design for all device types
- Content filtering and personalization
- Search and recommendation algorithms
Challenges Faced
- Implementing reliable web scraping across multiple sources
- Integrating Azure OpenAI for content categorization
- Managing vector database operations efficiently
- Handling real-time content updates and synchronization
- Optimizing search performance with large datasets
Key Learnings
- AI integration in web applications
- Vector database design and optimization
- Web scraping techniques and best practices
- Real-time data management and synchronization
- Full-stack development with AI components