Sick of reading the same story 10 times across different sources?

Built an AI agent that deduplicates news semantically and synthesizes multiple articles into single summaries.

Uses LangGraph reactive pattern + BGE embeddings to understand when articles are actually the same story, then merges them intelligently. Configured via YAML instead of algorithmic guessing.

Live at news.reckoning.dev

Built with LangGraph/Ollama if anyone wants to adapt the pattern

Full post at: https://reckoning.dev/posts/news-agent-reactive-intelligence

I built an AI system that generates comprehensive academic review articles from web research—complete with citations, LaTeX formatting, and PDF compilation. We're talking hundreds of pages synthesizing vast literature into coherent narratives.

The Reality

While tools like Elicit and Consensus are emerging, building a complete system exposed unexpected complexity. The hardest parts weren't AI reasoning, but orchestration for real-world standards:

- Synthesis vs. Summarization: True synthesis requires understanding relationships between ideas, not just gathering information

- Quality Control: Academic standards demand perfect formatting—AI make systematic errors

- Integration: Combining working components into reliable pipelines is surprisingly difficult

Key Insights

Specialized agents work better than monolithic approaches

Multiple validation layers are essential

Personal solutions outperform one-size-fits-all tools

I documented this journey in an 8-part series covering everything from architectural decisions to citation integrity. The goal isn't prescriptive solutions, but illuminating challenges you'll face building systems that meet professional standards.

Whether automating literature reviews or technical documentation, understanding these complexities is crucial.

https://reckoning.dev/series/aireviewwriter

TL;DR: Built AI for publication-quality review articles. AI reasoning was easy—professional standards were hard.

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

Thanks for pointing. Yes it should be lower value to prevent over-fitting.

df_train_set.Income.value_counts() should be df_test_set probably in the part where it's comparing both.