Record every day of 2013 with iOS App

Hacker News

Example of a montage can be found at http://momentmontage.me I started developing this in January when I saw a video of a girl who recorded a video everyday of 2011 and I wanted to make one myself, but thought the editing would become too tedious and annoying. So, I made an app that completely automates the process, you record a clip every single day (or more than one per day, this was a popular feature request which you can see in the example URL), put some music from your iTunes library and you can upload it to Facebook straight from the app, or to your camera roll so you can do anything you want to the video. Hope you guys like it, it was ten months in the making.

Visit product View on Hacker NewsDec 27, 2012

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

88%88% 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.

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

82%82% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% 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 · Missing: plus, platform, intuitive

39%39% 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

21%21% 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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