A privacy-first, client-side toolbox (PDF, Imgs, Dev) no server uploads

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Hi HN, I’m Linus. I built this toolbox because I was tired of searching for "PDF Merger" or "Image Compressor" and having to upload sensitive files to random servers filled with ads and tracking. The goal was to build a suite of utilities that runs 100% client-side using WebAssembly and standard Web APIs. Features: - PDF Tools (Merge, Split, Watermark) using pdf-lib/pdf.js - Image Tools (Compress, Resize, EXIF strip) using JSZip and Canvas - Dev Helpers (JSON/SQL formatting, JWT debugging) - 0% Server-side processing - PWA support (works offline) It’s built with Vanilla HTML/JS (no build step required) and hosted via simple FTP. No analytics, no cookies. Source code (GnuGPLv3) is here: https://github.com/immineal/linu-li I’d love to hear your feedback on the implementation or ideas for other client-side tools I should add.

Visit product View on Hacker NewsNov 22, 2025

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3points

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

52%52% 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 · Missing: supports, reddit linkedin, podcasting

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

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: using, code, apis · Missing: mac, agents, macos

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

19%19% predicted probability of success on TrustMRR, 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.

Incorrect prediction on native model

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