Launch Intel
HN

HN Browser, yet another Hacker News reader on iPhone

Hacker News

HN Browser, yet another Hacker News reader on iPhone

I thought building something that I would use while learning iOS SDK was a good idea so I built HN Browser. https://itunes.apple.com/us/app/hn-browser/id578535095?ls=1&mt=8 HN Browser is a simple app which lets you to read articles and comments on Hacker News. (no voting and writing comment yet) For the logo, I reached out to Joel Glovier(http://joelglovier.com) for his previous work on Hacker News logo(http://news.ycombinator.com/item?id=4731140) and he kindly granted the use of his work. It was really fun experience to collaborate and build a mobile app using iOS SDK. (although approval process was painful) I would really appreciate if you share any thought.

Visit productView on Hacker NewsNov 30, 2012
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released
71%71% 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: apple, new, using · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: collaborate · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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