PD

PDF Assembler – client-side PDF editing

Hacker News

PDF Assembler – client-side PDF editing

Here's a neat hack I made recently to do basic PDF editing directly in a browser—without having to upload anything to a server. I was initially looking for a way to do simple PDF modification (extracting pages, merging, and adding page numbers). There are some good server-side tools for this (QPDF, PDFTk, PDFBox, iText, Hummus), but for better speed and privacy I really wanted a 100% client-side solution. There are a few good JavaScript PDF libraries for reading and displaying PDFs (pdf.js) and creating PDFs from scratch (jsPDF, PDFKit), but I couldn't find any for editing existing PDFs. So, I did what any self-respecting hacker would do, and rolled my own. :-) Actually, I found out that Mozilla's pdf.js solved half the problem, as it does an excellent job disassembling PDF files. So all I had to do was figure out a way to put them back together again. The result is PDF Assembler, now available on GitHub and NPM. I also put together a demonstration site ( https://www.pdfcircus.com ) which shows some examples of what it can do. I know PDF Assembler still needs some tweaking, but I think the basic idea is sound, and so far I've been pretty happy with how it works. Please take a look and let me know what you think. Thanks! PDF Assembler Links: Demonstration Site - https://www.pdfcircus.com GitHub - https://github.com/DevelopingMagic/pdfassembler NPM - https://www.npmjs.com/package/pdfassembler

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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.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
64%64% 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
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
25%25% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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.

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