Priviblur – A privacy-focused front end to Tumblr

Hacker News

Hello HN! Priviblur is an alternative frontend to access Tumblr, similar to projects like Invidious or Nitter. You can use it to avoid tracking, or even just to avoid Tumblr's new login wall. The former was the goal but the latter was what really pushed me to actually begin development. Its the newest project in the recentish movement of creating frontends to various proprietary websites. The main "new" thing that this brings is that a modern UI/UX is a central goal of the project along with of course privacy. Though I don't think the UI quite reaches the former goal yet, but it will be eventually. Pretty much everything you'd expect from a Tumblr frontend is already there. Blogs, photosets, etc. I'm still working on stuff like video and audio post support though. Please let me know what you think!

Visit product View on Hacker NewsDec 13, 2023

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3points

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

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

59%59% 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 · 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 · Missing: plus, platform, intuitive

34%34% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · 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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