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Template2PDF Templates to PDF API
Hacker NewsTemplate2PDF Templates to PDF API
Hey all, same idea 10 years later :) https://news.ycombinator.com/item?id=7892813 Life got in the way and I abandoned the project. Then I needed it for my own use, so now back, with a slightly different approach: Normal google docs as templates. API even simpler. Just finished building a zapier integration - hope I can publish it soon. Hope to get some feedback and perhaps even some people to test it. As always, thanks for your time :) Sven
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: google, new · Missing: mac, agents, macos41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive26%26% 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, revenue14%14% 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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