I built a free and open source pdf redaction tool

Hacker News

Hey everyone, I recently built a free and open-source tool that lets you manually redact PDFs and images right in your browser—no sign-ups, no watermarks. I also added an AI-powered auto-redaction feature to detect and remove sensitive info. Since this part uses external processing (OCR + AI), there are usage limits: you can auto-redact up to 3 single-page PDFs or images for free each day. Higher usage and multi-page support require a paid account. Right now, it works great for removing sensitive details like names, addresses, and emails, but I’d love to get your thoughts: • What redaction features do you wish PDF tools had? • Are there any pain points with existing redaction tools that I should address? Since this is open-source, I’m eager to make it as useful as possible. Any feedback is appreciated!

Visit product View on Hacker NewsFeb 24, 2025

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

66%66% 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

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

58%58% 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 · 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 · Missing: plus, platform, intuitive

30%30% 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 · Strong signals: paid · 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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