This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Dibao
Self-hosted RSS reader with explainable recommendations
Dibao is a self-hosted RSS reader that ranks articles only inside the feeds you choose. It turns RSS unread debt into an explainable personal reading queue, with data kept in SQLite on your own host. It avoids expensive LLM-as-feed-engine designs: recommendations can use a small 0.6B embedding model through a provider or locally, with baseline ranking fallback.
Hey Product Hunt!
I built Dibao because RSS gives me the control I want over sources, but once the list grows, a chronological inbox turns into unread debt.
Dibao keeps the RSS boundary intact: it only ranks articles from feeds you already chose. The idea is not to replace RSS with AI summaries or another infinite feed, but to make personal reading easier to keep up with while keeping the data local.
A design goal was to avoid making this another project where you connect an LLM API to your reading list and hope the bill is fine. Recommendation is mostly matching and ranking, not generation. Dibao can use a small 0.6B embedding model via a provider or locally; for normal personal use, provider cost should stay under $10/year, and local embeddings can make it effectively $0 apart from your own hardware.
It is early, but v0.1.0 is runnable with Docker/Compose. I would love feedback from people who still use RSS, self-host personal tools, or care about keeping their reading behavior on their own machine.
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About Dibao on Product Hunt
“Self-hosted RSS reader with explainable recommendations”
Dibao was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #95 on the daily leaderboard. Dibao is a self-hosted RSS reader that ranks articles only inside the feeds you choose. It turns RSS unread debt into an explainable personal reading queue, with data kept in SQLite on your own host. It avoids expensive LLM-as-feed-engine designs: recommendations can use a small 0.6B embedding model through a provider or locally, with baseline ranking fallback.
Dibao was featured in Productivity (653.8k followers), Artificial Intelligence (471k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 262.3k products, making this a competitive space to launch in.
Who hunted Dibao?
Dibao was hunted by Jeffrey.W. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Jeffrey.W