Nooshub
RSS Reader with AI to cluster similar content
Nooshub launched on January 27, 2019 with 75 votes, #544 of 2,587 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 7 years on.
What it does
RSS reader that uses AI to cluster similar content and by that detects trending news. It is especially good for the daily dose of news but you can also read blogs or play podcasts. Clustering works for english and german at the moment. P.S. Dark Mode 🦇
A rather minimalist reader with one or two powerful tricks up its sleeve to manage high and low frequency news sources, newsletters and podcasts. The app uses learning algorithms to group similar articles in your feeds. This helps you to not only remove the clutter , it also shows you trending topics and lets you read about a topic from different sources. Models are trained with fresh content on a regular basis. During a day a lot of articles may arrive in your pages. The nutshell 🥜 meta page checks all your pages for trending topics to gives you a quick overview about what is going on. The gems 💎 page does the opposite: it collects unseen articles of low frequency feeds that could…from nooshub.com
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RSS Brain2022 · rssbrain.com · ▲139I wrote an RSS reader called RSS Brain recently. The motivate is the current RSS readers either don't sort articles by priority, which makes it hard to read posts from HackerNews and Reddit, or sort it with some "smart" algorithm which I don't trust. I also like the Google News feature that can show related story from different source, but the sources are not configurable and the algorithm is not transparent either. So with RSS Brain, I implemented these features: * Recommend related articles from the feeds of your choice. It's backed by ElasticSearch and the algorithm is described on RSS…

Real-time system that tracks how news spreads across 200k websitesNov 2025 · yandori.io · ▲256I 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/
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