TinyCompressor – Free, Privacy-First Image/Video/PDF Compression Tool

Hacker News

I've built TinyCompressor, a modern web-based compression and conversion tool that processes everything locally in your browser. No uploads, no server storage, completely free. What it does: - Image Compression: PNG, JPEG, WebP, AVIF, GIF, BMP – reduce file sizes by up to 90% while maintaining quality - Format Conversion: Convert between formats including HEIC→JPG, PNG↔JPEG, WebP↔AVIF, and more - PDF Compression: Reduce PDF sizes by up to 60% with image compression and metadata removal - Video Compression: Compress MP4, AVI, MOV, WebM using FFmpeg.wasm (up to 85% reduction) Key features: 100% Client-Side Processing – All compression happens in your browser using WebAssembly. Your files never leave your device. Completely Free – No registration, no limits, no hidden costs. Forever. Fast – Optimized algorithms powered by Squoosh and FFmpeg.wasm for sub-second processing Privacy-First – Zero server-side storage. We literally can't see your files. Multilingual – Available in 11 languages (EN, ZH, JA, KO, AR, DE, ES, FR, IT, PT, RU) Mobile-Optimized – Responsive design that works great on all devices Tech stack: Next.js 14, TypeScript, Squoosh (Google's WebAssembly compressor), FFmpeg.wasm, Tailwind CSS Why I built this: I was frustrated with existing compression tools that required uploads (privacy concerns), had file size limits, or didn't support modern formats like AVIF/HEIC. So I built TinyCompressor to solve all of these – completely client-side, free forever, modern UI, and support for all major formats. Technical highlights: Web Workers for non-blocking compression, Progressive Web App capabilities, performance optimizations, WCAG compliant accessibility, SEO optimized with dynamic sitemap generation. Try it out: Visit https://tinycompressor.com and drag & drop your files. No signup required. I'd love to hear your feedback, especially on performance across different devices/browsers, any bugs or edge cases, and feature requests.

Visit product View on Hacker NewsNov 28, 2025

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Did not reach leaderboard

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

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

26%26% 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

13%13% 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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