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HACKOBAR_
One feed for AI signal. No noise.
There's no single place to check what's happening in AI. GitHub trending, arXiv, HuggingFace, HN, newsletters. You check them all and still feel behind. HACKOBAR_ pulls from all of them into one swipeable feed. Scores by engagement and cross-platform signal so research isn't buried by social noise. Hype-free summaries. Updates every 30 minutes. No infinite scroll, no algorithm keeping you hooked. Just the signal, then you're done.
Hey PH,
Built this for myself. The problem isn't that there's too much AI news, it's that it's scattered everywhere with no good way to get the signal without the noise.
HACKOBAR_ pulls from HN, arXiv, GitHub, HuggingFace, Reddit, lab blogs, newsletters and Twitter into one feed. Scoring weighs engagement, recency and cross-platform presence, so a paper that shows up on arXiv, gets posted to HN and picked up in a newsletter ranks higher than something that just went viral on one platform. Source authority weights stop social volume from drowning out research. The LLM rates each story's significance independently so a quiet but important paper isn't buried by a loud but shallow one.
Summaries are hype-free by design. No "groundbreaking", no "revolutionary".
Still early. Would love to know what sources you think are missing and whether the scoring feels right.
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Rahul
About HACKOBAR_ on Product Hunt
“One feed for AI signal. No noise.”
HACKOBAR_ was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #159 on the daily leaderboard. There's no single place to check what's happening in AI. GitHub trending, arXiv, HuggingFace, HN, newsletters. You check them all and still feel behind. HACKOBAR_ pulls from all of them into one swipeable feed. Scores by engagement and cross-platform signal so research isn't buried by social noise. Hype-free summaries. Updates every 30 minutes. No infinite scroll, no algorithm keeping you hooked. Just the signal, then you're done.
On the analytics side, HACKOBAR_ competes within News, Software Engineering and Artificial Intelligence — topics that collectively have 548.6k followers on Product Hunt. The dashboard above tracks how HACKOBAR_ performed against the three products that launched closest to it on the same day.
Who hunted HACKOBAR_?
HACKOBAR_ was hunted by Rahul Janagouda. 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.
For a complete overview of HACKOBAR_ including community comment highlights and product details, visit the product overview.
Rahul Janagouda