Show HN : HackerNode - An iOS Universal App for reading Hacker News (iOS 4.0+)

Hacker News

Hello Hacker Newsers, I have made an iOS Universal app for Hacker News. You can download it from: http://itunes.apple.com/us/app/hackernode/id473882597 (Clickable link in comments) Please do check it out when you get time.I have worked really hard to make it happen. It works on iPod Touch,iPhone and iPad (armv6,armv7). The iPhone interface is designed like the old Facebook for iPhone and the iPad interface is designed like Twitter for iPad. I will really appreciate any critical feedback or a nice review in the app store in case you like it :) Thanks.

Visit product View on Hacker NewsFeb 22, 2012

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Actual performance

13points

5comments

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

90%90% 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.

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

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs

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

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

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

nativeThis product was originally launched on this platform.

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

41%41% 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

18%18% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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