Show HN: Real-time system that tracks how news spreads across 200k websites
yandori.ioI built a system that monitors ~200,000 news RSS feeds in near real-time and clusters related articles to show how stories spread across the web.
It uses Snowflake’s Arctic model for embeddings and HNSW for fast similarity search. Each “story cluster” shows who published first, how fast it propagated, and how the narrative evolved as more outlets picked it up.
Would love feedback on the architecture, scaling approach, and any ways to make the clusters more accurate or useful.
Live demo: https://yandori.io/news-flow/
Interesting project - it’s rare to see news-flow tracking done in real time at this scale. One thing you may want to stress-test is how stable the clustering remains when stories evolve semantically over a few hours. Embeddings tend to drift as outlets rewrite or localize a piece, and HNSW can sometimes over-merge when the centroid shifts.
A trick that helped in a similar system I built was doing a second-pass “temporal coherence” check: if two articles are close in embedding space but far apart in publish time or share no common entities, keep them in adjacent clusters rather than forcing a merge. It reduced false positives significantly.
Also curious how you handle deduping syndicated content - AP/Reuters can dominate the embedding space unless you weight publisher identity or canonical URLs.
Overall, really nice work. The propagation timeline is especially useful.