VMPrint is now a PDF generator with embeddable preview. 1.7MB ONLY

Hacker News

You generate a PDF and need to preview it on your web page. You realize you can't. Not without a giant PDF.js. Or perhaps you made a nice preview on Canvas, then you realize you can't do anything useful with it—it won't give you a PDF that looks the same. I've bundled the entire VMPrint engine—a DTP-grade layout virtual machine—into a compact, 1.7MB standalone NPM package you can embed even in a static HTML page. It gives you accurate document preview and PDF/SVG output that look exactly the same. Enjoy!

Visit product View on Hacker NewsMar 24, 2026

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Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac · Missing: agents, macos, agent

46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

38%38% 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

14%14% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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