Yakread – An RSS reader powered by machine learning
This is a web-based reading app I've been working on since August. The main differentiator is that Yakread uses machine learning to rank the articles in your feed: as you click on articles from a particular RSS/newsletter subscription, other articles from that subscription will tend to be ranked higher in the future (via a bandit algorithm). Yakread also uses ML to recommend articles that other users have read, so your feed will have articles in it even before you sign up and add your own subscriptions. For the recommendations, I'm using the collaborative filtering implementation from…
In plain words
Yakread is a web-based RSS reader that uses machine learning to personalize the reading experience. It ranks articles based on user click behavior through a bandit algorithm, so frequently-clicked feeds appear higher in the feed over time. The app also employs collaborative filtering to recommend articles that other users have engaged with, populating feeds with suggestions even before users add their own subscriptions. This combination of personalized ranking and community-based recommendations helps users discover relevant content automatically.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
This is a web-based reading app I've been working on since August. The main differentiator is that Yakread uses machine learning to rank the articles in your feed: as you click on articles from a particular RSS/newsletter subscription, other articles from that subscription will tend to be ranked higher in the future (via a bandit algorithm). Yakread also uses ML to recommend articles that other users have read, so your feed will have articles in it even before you sign up and add your own subscriptions. For the recommendations, I'm using the collaborative filtering implementation from Spark MLlib[1]. I model RSS feeds instead of individual articles: when you click an article, that counts as a "point" for that article's RSS feed; at recommendation time, the algorithm first selects an RSS feed to recommend, and then it picks one of the popular/recent articles from that feed. To counter popularity bias, I have a pre-ranking step that probabilistically filters out RSS feeds that have already been recommended a lot. I manually approve all RSS feeds before they're eligible to be recommended. In addition to scrolling through the algorithmic feed, you can read articles chronologically on the subscriptions page, which I sometimes prefer when I have a larger chunk of reading time. There's also a daily digest email that lists new articles from your subscriptions; skimming that is part of my morning routine. I find the whole system gives me a nice balance between algorithmic filtering and manual control. This is the culmination of the past four years I've spent as a full-time bootstrapped founder; Yakread both scratches a personal itch and attempts to fix various deficiencies that my previous businesses have had. In a nutshell, I've come to believe that "discovery is a feature, not a product," which is why Yakread is a full reading app instead of a standalone recommender system like my previous products.[2] From a business perspective, the recommendation algorithm is primarily intended to help onboard new users quickly/easily. More ideologically, I think RSS is ready for a comeback :). [1] https://spark.apache.org/docs/latest/mllib-collaborative-fil... -- I'm using the implicit feedback setting. [2] Show HN for Yakread's immediate predecessor, The Sample: https://news.ycombinator.com/item?id=27664020. The Sample does bring in $1k or so per month, but long-term retention is too low for me to grow it sustainably.
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