Home Cam – turn an old iPhone into an offline hidden security camera

Hacker News

I built Home Cam because I needed a security camera I could trust. Every app I tried either streamed to someone’s server or buried analytics in the code. What it does: • Turns any iPhone (iOS 15+) into a motion-activated camera • Records locally up to 4K 60 fps, stores clips in an AES-256 vault • Works fully offline by default so nothing leaves the device unless you enable iCloud Photos • Discreet modes: fake calculator screen or pure black (coming soon) • Battery Saver dims screen and throttles sensors for longer sessions Why it might interest you: • No signup, no ads, no trackers. Privacy is the core feature. • Built entirely in Swift/SwiftUI. • I’m a solo indie dev & feedback from HN has shaped many of my past projects.

Visit product View on Hacker NewsJun 19, 2025

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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

53%53% 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: 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.

Indie HackersIH features products with proven revenue · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon · 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

22%22% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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