Ho

House on Fire, Who Would You Save?

Hacker News

House on Fire, Who Would You Save?

Hi HackerNews, I got fairly bored last week and decided to use my spare time to create a social experiment where theoretically there's a house on fire and you have to save one of your friends. I created it pretty quickly so the code may be a bit heavy but it works pretty well (I'm sure one of you will be able to break it though!). Hopefully the homepage is self explanatory... If you login via FB (I'm adding Twitter shortly) you will be able to play the House on Fire game--it basically loads two of your Facebook friends and you have to choose who you would save. It's pretty fun and I hope a few people enjoy it (if any). Feedback is welcomed too. Thanks!

Visit product View on Hacker NewsJul 30, 2013

Actual performance

2points

2comments

Did not reach leaderboard

Launch Intel predictions

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

58%58% 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, code · Missing: mac, agents, macos

56%56% 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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% 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

21%21% 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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