Kerstman, a secret santa app built using rails

Hacker News

This is now the second secret santa matching app I've built to fill a problem I've had with many groups over the years. Loads of secret santas I've organised or been involved with have had couples who you don't really want to end up matched. As a result I've built a web app that allows you to specify a partner who you don't want that person to be giving to. There's a few bugs wherein it won't successfully match everyone but I've made sure it won't bomb out or send an email to some people before everyone has a giftee and gifter. I'm mainly looking for feedback before I take it outside of testing at work and with friends so hopefully someone finds it of use. https://github.com/RyanMacG/kerstman/tree/master

Visit product View on Hacker NewsDec 8, 2014

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, email, using · Missing: agents, macos, agent

87%87% 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.

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released

55%55% 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 · Missing: plus, platform, intuitive

44%44% 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

44%44% 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, revenue

19%19% 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.

Incorrect prediction on native model

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