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I made a tool so devs never have to deal with PDF generation again
Hacker NewsI made a tool so devs never have to deal with PDF generation again
Hi HN, After years of frustration with PDF generation, I decided to tackle this problem and create a SaaS product. My goal was to combine: -Mailgun for transactional templates -Tailwind CSS for fast styling -Google Fonts to allow font family updates in one click -VS Code for a great development experience -Chromium for pixel-perfect PDF rendering Because as developers, our value lies in building great products, not generating PDFs… Kevin
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting85%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: ide, io · Missing: https docs, excited, just released59%59% 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 HuntOn track for Day 1 leaderboard · Strong signals: google, code · Missing: mac, agents, macos56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue15%15% 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, crypto0%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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