HN reader with "Page Down" for mobile and other QoL tweaks

Hacker News

This replaces my previous HN client, which I've been using over 10 years. - Major innovation: [Page Down] for mobile. I wish all sites had this: just tap the gray region to precisely scroll that item to the top. - Defaults to all front page items in (hckr news[1]) reverse chronological order. But also supports the official HN "top" ranking (as well as most of the other "lists"[2]) - Interesting stories highlighted: point/comment icons turn orange after passing 50. The number also turns orange after passing 100. - Extra info shown (when possible.) Good examples here: https://hn.leftium.com/hckrnews/2025.10.20 ~ Last week a front page submission got 107 points before being killed. Besides the vote count, we can still see the domain. ~ The deltas after the time are how much time the story took before reaching the front page. Large times indicate the story was probably "re-upped," so they are highlighted. (Some human decided this item was worth inserting into the front page) More details + source code: https://github.com/Leftium/hn Currently individual item rendering is not implemented; I simply defer to to the previous version, but I plan some major innovations when rendering items, too. Your questions/comments welcome! [1]: https://hckrnews.com/ [2]: https://news.ycombinator.com/lists

Visit product View on Hacker NewsOct 27, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, using, code · Missing: mac, agents, macos

26%26% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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