PPT Compressor – Compress PowerPoint files in the browser

Hacker News

Hi HN I built a simple online PowerPoint compressor that runs entirely in the browser: https://www.pptcompressor.online/ No server uploads. No login. No installation. It works by unpacking `.pptx` files, recompressing embedded images with Canvas/WebP, and zipping them back — all using JavaScript and Web APIs. Great for reducing file sizes before sending slides by email or uploading to platforms with size limits. Built with: - JSZip - Canvas API - Web Workers - Pure HTML/CSS/JS (no backend) I’d love feedback on speed, compatibility, and ideas for new features (e.g. batch mode or PDF support). If you're working on browser-native tools too, happy to connect! Thanks

Visit product View on Hacker NewsApr 27, 2025

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

79%79% 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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% 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

27%27% 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

18%18% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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