SnazzyPDF – Convert Any JSON Data to Beautifully Formatted PDFs

Hacker News

Hi HN, I'm a freelance developer, and I built SnazzyPDF in my spare time to solve a problem I encountered frequently: converting structured data into PDF documents. The existing solutions I found either required manually creating a template or forced you to transform your data into a specific structure. I wanted a tool where you could simply input arbitrary JSON data and get a decent-looking document out. That's why I built SnazzyPDF. * Works with any JSON: No need to restructure your data. SnazzyPDF handles (almost) any JSON data, including deeply nested arrays and objects. * Use REST API or drag & drop web interface. * Zero configuration: Convert your JSON to a finished PDF in seconds. Start simple and incrementally add customization. * Save time: Automate PDF generation for reports, logs, or other structured data. No more manual formatting or custom export workflows. * Free tier for testing or light use, paid plans that scale for larger needs. I'd love to hear feedback from the HN community. Check it out, and let me know what you think! I'm happy to answer any questions. Thanks!

Visit product View on Hacker NewsSep 12, 2024

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

85%85% 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, existing, io · Missing: https docs, excited, just released

52%52% 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

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% 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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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