Hello from PhotoVaultOne, a Google Photos backup and recovery service

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PhotoVaultOne is a Software-as-a-Service solution for taking daily backups of your Google Photos library contents. You can restore deleted content directly back to your library, or download it locally. Before I built PhotoVaultOne, I had wired together synchronization to my local NAS to keep an offline copy of my content. While I managed to set it up, it wasn't something I would recommend to a non-tech friend. Then, a friend of mine had a weird incident where they lost months of content from their library - we still dont know why, and with no way to recover. PhotoVaultOne is my side project to help with this problem. Why? - There is no backup in Google Cloud. Deleted content cannot be recovered (excluding trash, yes) - Alternative methods, such as synchronization to a local device, or taking manual Google Takeout's are difficult for many users to setup or remember to do. - I could not find an existing SaaS solution in this space - so took it as an opportunity to practice building one. Its fun to build. :) Unfortunately I cannot offer the service long term without charging for it, there are AWS infrastructure expenses that need to be covered. There is a free 3 month trial though, with no commitment needed, and pricing of the plans is as aggressive as I deared to launch with.

Visit product View on Hacker NewsFeb 7, 2023

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

85%85% 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: google, user · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · 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.

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

50%50% 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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% 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.

Incorrect prediction on native model

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