I built a minimalist AI news aggregator while teaching myself NLP. It's a single static HTML page that updates every 4-6 hours with clustered AI news stories - no accounts, no cookies, no JS frameworks.

Technical approach: - Python scraper for AI/tech sources - Custom NLP pipeline with BERT embeddings to filter for AI-specific content - Hierarchical clustering to group related stories - ChatGPT API for generating cluster titles and short summaries - Served as a static HTML via Cloudflare Pages - Lightweight analytics with GoatCounter and Umami (understanding these two frameworks to choose one over the other) - Experimental JSON-based search (considering proper search if this scales)

The project started when I realized I was wasting hours daily checking multiple sources as a PM trying to track AI developments. Built this over 3 months between work commitments.

Interesting challenges: - Finding the right threshold for story similarity (still tuning this) - Balancing comprehensive coverage with noise filtering - Keeping the page lightweight while maintaining content density

Would appreciate feedback on clustering accuracy, false positive/negative rates, and overall UX.

Link: https://currentai.news