I made a privacy-first minimalist Backblaze

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Creator here. I was looking for something as simple as Backblaze Personal [1] but privacy focused and open source. This is my attempt to build that. Uses PyQt6 [2] for the GUI and Pyinstaller [3] for creating the platform specific binaries. The backup engine under the hood is Restic [4]. The server code is written in Laravel [5]. All the code is on GitHub [6]. I actually really like Backblaze (even use B2 for this offering behind the scenes) so this isn't meant to throw shade their way. Just wanted a private open source alternative. Something like Bitwarden but for backups. [1] https://backblaze.com [2] https://pypi.org/project/PyQt6 [3] https://pyinstaller.readthedocs.io/en/stable [4] https://github.com/restic [5] https://laravel.com [6] https://github.com/blobbackup/blobbackup

Visit product View on Hacker NewsMar 6, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released

63%63% 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: personal, way · Missing: mobile apps, ios, entrepreneurs

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

37%37% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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