BreezePDF – Free, in-browser PDF editor

Hacker News

BreezePDF lets you edit, sign, merge, compress, redact, OCR, fill forms, extract tables, and use 30+ more PDF tools — all in the browser, no sign-up. Files never leave your computer. I built it because when people search Google for common PDF tasks, many of the tools they find upload documents to a server. I wanted an option that keeps files local instead. I posted an earlier version on HN last spring: https://news.ycombinator.com/item?id=43880962 At the time it only supported a small set of features. Over the last 10 months I rebuilt large parts of it and expanded it to nearly 40 tools, including several ideas that came from comments in that earlier thread. There is also now a desktop app for macOS, Windows, and Linux, plus a CLI/SDK for developers.

Visit product View on Hacker NewsMar 29, 2026

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Actual performance

97points

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Launch Intel predictions

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

79%79% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

TrustMRRLess likely to generate early MRR · Strong signals: month, google · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% 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

15%15% 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.

Correct prediction on native model

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