App in Review

Hacker News

App in Review

I'm one of the creators of App in Review. My buddy and other creator, Mubs (@mubashariqbal), approves this message. We recently submitted our first app into the Apple app store and began the indeterminate waiting process. There is no visibility into Apple's review process, which is fine because all we want to know is where in the line are we? Even a rough guess would be fine. It was during this waiting process that we had the idea for App In Review. A little site where iOS developers can share the apps they've submitted and how long they've been in the Apple review process. With enough people simply posting when they submitted we might be able to get a better idea of just where our apps are in the queue, and how long it will be until they get reviewed. When it comes down to it, we're all impatient. Would love your feedback, and if you're an iOS developer we would love for you to add your apps! There's nothing more in it for us, this a tool for the community, by the community. Happy coding! http://appinreview.com/

Visit product View on Hacker NewsMar 26, 2012

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Actual performance

17points

6comments

Made the leaderboard

Launch Intel predictions

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

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

50%50% 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, apps · Missing: mobile apps, personal, entrepreneurs

42%42% 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

35%35% 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

19%19% 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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