Op
Open-Source Website Builder for Gumroad Products
Hacker NewsOpen-Source Website Builder for Gumroad Products
Hi! This is a small project I did to try out Rails. I felt there was no easy way to create one-off websites to promote your products on Gumroad. So created website builder, that syncs product details from your Gumroad account and gives you a customizable sub-domain. There are definitely some rough edges in the product, more of a POC. Built using React and Ruby on Rails.
Share cardActual performance
5points
2comments
Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Strong signals: using, open · Missing: mac, agents, macos90%90% predicted probability of success on Product Hunt, 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 · Missing: https docs, excited, just released67%67% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: builder · Missing: plus, platform, intuitive59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal52%52% predicted probability of success on TrustMRR, 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, crypto3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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