Photos Backup Anywhere' – My First Mac App for Photo Backups

Hacker News

Hey HN, I've just rolled out my very first app for Mac, 'Photos Backup Anywhere'. It's been a mix of fun (learning Swift!) and frustrating debugging (buggy Apple libraries), and now I'd like to share it with you all. 'Photos Backup Anywhere' allows Mac users to continuously sync and back up their entire photo library, including iCloud Photos, to any chosen destination (be it a local drive, network mount, NAS, etc.). Photos (including Live Photos) and videos maintain their original quality. *Try the Preview Version*: - Available for 90 days. - Requires the free Apple TestFlight app. I genuinely value feedback. Whether you share it through TestFlight or right here, it'd mean a lot. If you're keen on a promo code for a free download of the released version when it's available, please leave me a way to contact you privately in your feedback. (There's a limited batch of these codes.) http://www.ibeni.net All feedback, critiques, and suggestions are appreciated. Thanks for giving it a whirl!

Visit product View on Hacker NewsSep 21, 2023

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · 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: mac, apple, user · Missing: agents, macos, agent

71%71% 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, including · 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, users, way · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

36%36% 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

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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