App Lock by Lilucat – Hide apps and lock videos and photos

Hacker News

Hey HN! I’m Kevin, co-founder of Lilucat, a small indie team passionate about building privacy-focused and user-friendly products. Today, I’m excited to share one of our most popular projects: App Lock by Lilucat. What is App Lock? App Lock lets you securely lock and hide apps, photos, and videos on your iOS device. It’s designed to enhance privacy, protect sensitive data, and prevent unauthorized access. Here’s what it can do: Lock any app instantly with your PIN or Face ID (password unlock coming soon). Restrict device configurations, like blocking app installations or removals, preventing in-app purchases, and more. Hide apps completely—hidden apps aren’t just removed from the home screen, they’re invisible in the App Library as well. Why use App Lock? Whether you’re safeguarding financial apps, securing personal data, or controlling what kids can access, App Lock provides an extra layer of privacy. It’s especially helpful for those in regions where using certain apps might put them at risk. While iOS 18 introduced Face ID app locking, App Lock offers a more comprehensive solution—on iOS, hidden apps can still be found in a folder in the App Library, whereas with our app, hidden apps remain completely invisible unless unlocked through App Lock. We’ve built this app with privacy at its core, and we’d love your feedback to keep improving! You can find more info on App Lock by Lilucat here: https://lilucat.com/app-lock/ Looking forward to hearing your thoughts and ideas!

Visit product View on Hacker NewsSep 25, 2024

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Actual performance

6points

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

81%81% 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, user, using · Missing: mac, agents, macos

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

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

63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

46%46% 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: friendly, soon · Missing: plus, platform, intuitive

38%38% 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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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