Albums Backup – Offline tool to back up iPhone photos via USB

Hacker News

Hi everyone , Albums Backup is a simple offline tool I built to back up iPhone and iPad photos and videos to PC or Mac via USB — without needing iCloud, without compression, and keeping Live Photos, GIFs, and 4K videos at full quality. At first, I built it for someone I love. She had thousands of photos and was tired of expensive cloud subscriptions. I just wanted to make a simple, fast, and private way to save memories. Today, I made it available for others who might have the same need. - No account needed - No cloud storage involved - Supports original Live Photos, videos, and RAW files - Works on both Windows and Mac Would love to hear your thoughts! Website: https://albumsbackup.framer.website/

Visit product View on Hacker NewsApr 28, 2025

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

91%91% 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: mac · Missing: agents, macos, agent

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

66%66% predicted probability of success on AppSumo, 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.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

13%13% 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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