Hacker News Reader App

Hacker News

Hi everyone big HN fan here ... so big, that I always wanted a better UI on mobile (and desktop). So since none of the existing apps satisfied my taste, I setout to do the only reasonable thing: program an app myself. It's made in flutter + serverpod (so all Dart) and therefore comes in iOS, Android, Web (future Linux, Mac, Windows) Yes, it has all the boring parts: list feeds, read and comment articles, ... More exciting parts: - swipe list items for faster actions (fav, upvote) - customize feed (pure list, preview image, newspaper style) - Customize click actions (open URL, open comment) - highlight OP comments. - collapsable comment threads. The every exciting parts: - Offline reading / caching - Notifications about high-score/interesting/most-commented items. and - AI summary, summaries the article as a bulletin Wanna try out the app (as PWA) before downloading ==> https://app.hn-reader.com All other info here: https://www.hn-reader.com And yes, I'll be in the comments for questions

Visit product View on Hacker NewsMay 22, 2024

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Actual performance

2points

9comments

Did not reach leaderboard

Launch Intel predictions

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

72%72% 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 NewsStrong engagement from HN community · Strong signals: exist, existing, hacker news · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, apps, new · Missing: agents, macos, agent

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, way · Missing: mobile apps, personal, entrepreneurs

47%47% 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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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