My

My Saturday night project: Supercoinflip

Hacker News

My Saturday night project: Supercoinflip

When I have a hard time deciding which restaurant to go to w/ friends or my girlfriend, I usually fire up coffee and do something like: choices = ['pho', 'gyros', 'pizza', 'burritos'] console.log choices[Math.floor(Math.random()*choices.length)] Supercoinflip is meant to save me some typing (although I went back and forth on the styles enough that its payback time is probably in the 10s of years). It is written in coffee/sass, compiled into a single static html file, and then run on heroku w/ rack. http://supercoinflip.com

Visit product View on Hacker NewsFeb 19, 2012

Actual performance

1points

3comments

Did not reach leaderboard

Launch Intel predictions

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

59%59% 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 released

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

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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

32%32% 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

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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