Just published a comprehensive guide on implementing intelligent related posts that go way beyond simple tag matching. Instead of just comparing tags or dates, the system actually understands the meaning of your content using BGE-Large embeddings via Ollama.

*Key features:*

- Semantic similarity using local AI (no external APIs)

- Smart caching with 77% compression (36MB → 8.1MB)

- Works in production without Ollama (uses cached embeddings)

- Combines semantic + tag + temporal similarity for better results

- Full TypeScript implementation with Astro integration

The system finds connections like "Neural Networks" → "Deep Learning Fundamentals" even without shared tags. Perfect for technical blogs where content relationships are more nuanced than simple categorization.

https://reckoning.dev/posts/semantic-related-posts-ollama-as...

*What's covered:* Complete implementation with all algorithms, caching strategies, deployment approaches, and performance optimizations. Code examples for everything from content preprocessing to similarity calculations.

Has anyone else experimented with embeddings in their Astro sites? Would love to hear about other AI integrations in the static site worl