We built a CLI app for electronic PDF signature

Hacker News

As developers, Philipp and me (Thomas) were frustrated with the lack of options for easily signing PDFs via CLI. Most solutions were either proprietary, had no CLI interface or had restrictive licenses, making it difficult for us to use them in our projects. That's why we created open-pdf-sign - a simple, easy-to-use tool for signing PDF from the commandline. We designed with developers in mind, and released it under the Apache-2.0-license. We also added a configurator that configures your Nginx server. It will automatically install and configure your Nginx so all your PDFs will be signed with your SSL certificate on the fly. You can even use your existing Let’s Encrypt certificates. We believe that open-pdf-sign will be a valuable tool for any developer looking to quickly and easily sign PDFs. Give it a try and let us know what you think!

Visit product View on Hacker NewsDec 5, 2022

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

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

48%48% 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

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

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

33%33% 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

11%11% 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.

Incorrect prediction on native model

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