I

I made a gender reveal app

Hacker News

I made a gender reveal app

When we were getting ready to reveal our baby’s gender, we dreamed of having a simple link from our midwife that could instantly tell us if we were having a boy or a girl. After searching the internet for a free and easy solution, we came up empty-handed. So, I decided to create one myself. Introducing GenderGram! We used it for our own reveal, and it worked perfectly—we couldn’t have been happier. What’s more, we’ve been able to share the same link with our family and friends to let them in on the exciting news. I’d love to hear your thoughts—check it out!

Visit product View on Hacker NewsJan 16, 2025

Actual performance

2points

1comments

Did not reach leaderboard

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

76%76% 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 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.

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

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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, revenue

16%16% 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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