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I made an honest photo cleaner for iOS that puts you first

Hacker News

I made an honest photo cleaner for iOS that puts you first

Hello HN! Driven by the desire to avoid upgrading to the 2TB iCloud tier, I made a photo cleaning app for iOS that helps you systematically review your photos. While there are many similar apps, none of them checked all the boxes for what I wanted: - Clear privacy guarantee that photos never leave your device - Being honest about free limit usage. Some apps let you review photos only to reveal you can delete just a couple per day when you try to empty the trash. Basically taking your time hostage, when you've already gone through hundreds of photos - Context for better decisions. A lot of the apps use swipe-based UIs that only show one photo at a time, making it hard/impossible to see surrounding photos to make a proper decision - An enjoyable experience with features like zooming and sharing. You should be able to have fun and relive your memories, given you are already doing the tedious task of going through hundreds of photos I hope that these factors illustrate why I decided to enter this crowded space with my own take. Thank you for your time, and if you consider giving it a look, I would definitely appreciate any feedback. Tony

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Actual performance

6points
2comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, context · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps · Missing: mobile apps, personal, entrepreneurs
44%44% predicted probability of success on TrustMRR, 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
34%34% 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 · Strong signals: host · Missing: plus, platform, intuitive
32%32% 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
20%20% 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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