A
A free HackerNews reader App for iOS
Hacker NewsA free HackerNews reader App for iOS
I create a free HackerNews reader App for iPhone/iPod touch. I have worked really hard 2 months. You can vote / reply the news. I appreciate any feedback, any reviews to me. Thanks very very much. You can download it from: http://itunes.apple.com/us/app/hackernews+-free/id490595576?mt=8 You can download it from: http://itunes.apple.com/us/app/hackernews+-free/id490595576?mt=8
Share cardActual performance
16points
9comments
Made the leaderboard
Launch Intel predictions
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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting79%79% 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: io · Missing: https docs, excited, just released57%57% 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 · Strong signals: ios, month · Missing: mobile apps, personal, entrepreneurs46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: apple, new · Missing: mac, agents, macos38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive37%37% 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, revenue24%24% 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, crypto5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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