Open-pdf-sign, a CLI for digital PDF signing

Hacker News

Link: https://openpdfsign.org/ Hey HN, we want to introduce you to open-pdf-sign - the open-source solution for automatically signing PDFs from your CLI. As developers, Thomas and I (Philipp) were frustrated with the lack of options for easily signing PDFs. Most solutions were either proprietary or had restrictive licenses, making it difficult for us to use them in our projects. That's why we decided to create open-pdf-sign - a simple, easy-to-use tool for signing PDF from your CLI. We designed it to be user-friendly for developers, with a permissive license allowing easy integration into any project. 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’sEncrypt certificates. We believe that open-pdf-sign will be a valuable tool for any developer looking to quickly sign PDFs on their webserver or with their project. Give it a try and let us know what you think! Thomas & Philipp

Visit product View on Hacker NewsDec 4, 2022

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

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

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

44%44% 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 · Strong signals: friendly · Missing: plus, platform, intuitive

38%38% 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

8%8% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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