FetchPDF – Custom PDF Designer and API

Hacker News

Hi, FetchPDF is a template designer and API to generate PDFs from custom templates. (It's also possible to generate from your own HTML or URLs) The template designer can be shared or embedded, bringing PDF customisation and rendering to your own application with a few simple API calls. Placeholders can be added to templates and replaced with your data when generating via the API. Background: I was working for a platform that delivered certificates and invoices on behalf of other organisations, but used a single template for everyone. Needless to say, organisations wanted control over the designs and information on their PDF outputs. There was a bit of hacking to get by, but no scalable solution. I built FetchPDF to solve this problem. My current focus is on getting it out there and growing! The main technologies I used are ASP.NET and Mithril.js https://fetchpdf.com

Visit product View on Hacker NewsOct 31, 2018

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4points

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Did not reach leaderboard

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

72%72% 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.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

43%43% 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: platform, calls · Missing: plus, intuitive, reviews

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

34%34% 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

16%16% 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.

Correct prediction on native model

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