A Modern HN Client (iOS)

Hacker News

There are many like it but this one is mine: https://itunes.apple.com/us/app/pivit/id705715915?mt=8 Seriously, though, this Hacker News reader is better than what's out there, or I wouldn't have spent time on it. How? Most importantly, it utilizes the background refresh APIs, so content is always up to date. Pull to refresh is there, but you probably won't need it. The more addicted you are, the more often it updates. So if you check every few hours, you'll always get the current front page. Complementing this, it helps you see not only what you've read but what you've decided not to. I kept wasting precious milliseconds re-scanning titles that I wasn't interested in. Pivit fixes that by showing a little orange indicator to the left of an article if it's new to you. Stuff you've already read is dimmed. It also uses a new text rendering engine (Text Kit) to respects system font settings, for the senior devs out there... If that's not enough, well, it's the only client sporting a modern iOS7-native design, with beautiful typography. It does not currently offer posting or account mumbo jumbo, but I'll add that if it gets any traction. Try it out today and let me know what you think. I built it mostly for me, but also for you. Free today, a buck tomorrow.

Visit product View on Hacker NewsSep 27, 2013

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Actual performance

9points

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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

85%85% 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, apis · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, 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

54%54% 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, way · Missing: mobile apps, personal, entrepreneurs

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

36%36% 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

23%23% 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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