LearnPrompt/ai-news-radar

What it solves

AI News Radar solves the problem of information overload in the fast-moving AI field. Instead of manually checking dozens of sources, it automatically filters, aggregates, and prioritizes AI news, separating high-value stories from noise.

How it works

The project operates as a lightweight, automated pipeline. It gathers content from various sources (RSS, GitHub feeds, etc.), uses AI to score relevance, merges reports about the same event into a single story, and publishes the results to a static website. It functions without requiring API keys or servers for core features, though advanced users can add keys for enhanced capabilities like AI-generated summaries and persona-based commentary.

Who it’s for

• General readers: People who want a clean, organized daily summary of AI updates without the clutter.
• Content creators: Those who need to track trends, verify sources, and find high-quality topics for their own work.
• Developers: Those who want to fork the project to create a personalized news radar using their own sources (OPML/RSS) or integrate it into agentic workflows.

Highlights

• Single-layer interface: Features a clean, mobile-first design with tabs for different categories (Models, Products, Papers, etc.) and a toggle between "Featured" and "All" content.
• Story aggregation: Automatically collapses multiple reports of the same event into a single card to reduce repetitive reading.
• Persona-based insights: Provides optional, multi-perspective commentary (e.g., Pragmatic, Cynic, Paper-Police) to help evaluate news from different angles.
• Zero-dependency core: The core pipeline runs on GitHub Actions and publishes to GitHub Pages, requiring no ongoing server costs or API keys.
• Agent-ready: Includes a "Bo Le" (Talent Scout) skill that helps agents maintain sources, manage scraping logic, and deploy the radar.