Dead simple interface for bulk filling PDF Forms

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Around a year ago my mom asked me to if I would be able to fill in a PDF formular with existing data from an excel sheet. After a few hours and some ugly python code later i was able to get the job done. From time to time I went back to the code and build a simple webapp around it. My goal was to have a dead simple interface for it and not build another copycat of the online pdf services that can make everything with your PDF from a simple merge to a building houses of cards with it if you know how to use the bloated thing. Now I am wondering if I should simply open source the project or keep going and providing a dead simple service for it. The current hosted example on https://bulk-pdf.com provides a basic version without any advanced features that you can just use without any signup. -> Upload a PDF, select your fields, upload excel sheet, download it - done! (Hint: Nothing except the PDF template without the data provided by the excel will be stored permanently, at least for now) Happy to hear any thoughts!

Visit product View on Hacker NewsMar 13, 2021

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

83%83% 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, open source, existing · Missing: https docs, excited, just released

66%66% 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 · Strong signals: host, interface · Missing: plus, platform, intuitive

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

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

19%19% 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

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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