Show HN: Real-time system that tracks how news spreads across 200k websites (yandori.io)
I 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/
49 comments
[ 3.1 ms ] story [ 76.3 ms ] threadA 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.
‘masterphai’ is evidence of how effective a good LLM and better prompt can be now at evading detection of AI authorship… but there’s no way this authors comments are written by a sane human.
From the comment history it appears it has tricked quite a few humans to-date. Interesting!
The view showing the flow with a play animation was a nice concept but I couldn’t see much value in it, wondering if you could try to get a more aggregate stats that shows a connection between these different flows, maybe they follow a pattern like ad-based campaigns or publishers who own these domains, which would explain things. Expanding on this idea, could even try and setup different scores and metrics based on major groups and sponsored content versus organic spread.
Ubuntu 24.04, Firefox 145.0.1 (64-bit)
Curious how you sourced the feeds? It seems to have a bias towards Indian/Srilanka/Iran/Indonesia/Turkey etc - i.e. not the traditional western centric reporting. Always interested in trying to get a more balanced news diet so anything you could share around that would be interesting. Most out of the box news tools seem to automatically lean west
FYI layout sometimes breaks like so:
https://i.imgur.com/FXeqB9R.png
Thanks for that bug feedback - ill get fix.
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That isn't even remotely important at all so really unreliable.
I get most of it, but I think especially around the holiday some stuff is getting through... Some black friday deals were actually hitting like news does...
I’ve been curious how much news starts from social media. So many news stories today are “someone said x on twitter”.
I'm not pulling from social media yet.
I hope so. In my experience it's never that simple with dynamic data. Even with predictable data timezones cause issues. You're putting a lot of value in order in your visualizations.
No translation yet.
I think the biggest problem is im relying on published date from the news source itself too much and its wrong sometimes... not super often, but if 1 out of 100 sources get its wrong then it can steal credit for being source article when its not.
- https://newscord.org/latest
- https://www.instagram.com/newscord_org
We have been (low-keep) working on something similar (more from an academic point of view) for the past few years:
This is the introductory article (open access): "Comparison of news commonality and churn in international news outlets with TARO" https://dl.acm.org/doi/abs/10.1145/3603163.3609062
(Allow me a moment of pride for the student leading this project: the paper won the Ted Nelson Award at ACM Hypertext 2023.)
Cool concept though - the source count and "+N" spread metrics give a quick sense of which stories have legs.
For any given clip, short or excerpt, find the most complete, unedited version that it was taken from.
Cool website. As others note if this could tie in deep sources like FB, X, Reddit, etc...it would be almost "chain of evidence" canonical.
A view where websites/sources were associated with geo data (possibly involving a globe or map) would be very cool, too.