PDFx – a 2D canvas for multiple PDFs, stored inside a single PDF

Hacker News

I was asked to provide 17 PDF files for my mortgage application. I got tired of opening and closing files, and started looking for ways to process multiple PDFs at once. I came across Adobe's portfolio spec, but that only renders correctly in Acrobat, every other PDF viewer only displays the cover page of each document. So I asked something simpler: what if I could scroll horizontally to see the pages of one document and then vertically to see more documents? I achieved that via an embedded JSON manifest as a standard PDF file attachment. This enabled bundling multiple PDFs into a single, backwards-compatible PDF. When opened with a regular viewer (Chrome, Acrobat, Preview), it reads as a long, continuous document. When imported into the react-based app I built (PDFx), it's rendered in a 2D canvas, where each document is displayed as a separate entity. I would highly appreciate your feedback, please let me know if any other open spec exists that I might have missed.

Visit product View on Hacker NewsJun 29, 2026

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

78%78% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: single, open · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

57%57% 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 · Strong signals: way, para · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% 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

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.

Incorrect prediction on native model

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