ElementBlur – Free Chrome extension to blur elements/text/regions

Hacker News

I hacked together a small Chrome extension called ElementBlur that lets you blur stuff on a webpage in 3 ways: - Select text → blur only the highlighted text - Click element → blur entire DOM element - Draw region → free-form blur with a rectangle I was inspired by a few Product Hunt tools Blur It, Blurweb, but all of them are paid or require a license. So I thought: why not just build an open source version? With some help from Claude 4, I got it working. This is super handy for screenshots, screen recordings, or sharing sensitive info without exposing everything. Would love feedback! Any bugs or edge cases I missed? Features you’d like (hotkeys, persistent settings, export)?

Visit product View on Hacker NewsSep 20, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

92%92% 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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released

46%46% 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: way · Missing: mobile apps, ios, personal

41%41% 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

32%32% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto

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

Correct prediction on native model

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