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PrivatePDF – minimal PDF editor that runs in the browser

Hacker News

PrivatePDF – minimal PDF editor that runs in the browser

I created this tool to make it quick and easy to sign PDF contracts, without having to deal with installing additional software and without worrying that my PDFs will get uploaded to a third party. It supports the bare minimum for my most common usecases. It is not intended to be a full-featured PDF editor. Features include filling PDF forms, inserting images and text, and rotating PDFs. Check it out by either picking a PDF of your own - https://photown.github.io/private-pdf/ , or with a preloaded sample PDF - https://photown.github.io/private-pdf/?pdf=https://raw.githu... . In terms of tech stack, it's written in TypeScript, bundled with Webpack. For PDF rendering and editing I used PDF.js and pdf-lib respectively.

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Actual performance

14points
2comments
Made the leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created · Missing: reddit linkedin, podcasting, latex
63%63% 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: io · Missing: https docs, excited, just released
56%56% 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 · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · 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
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
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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