I

I built a leaderboard for coffee lovers all around the world

Hacker News

I built a leaderboard for coffee lovers all around the world

I drink coffee every morning. One morning, an idea occurred to me: what country drinks coffee the most? Sure, you can read about that somewhere online, but that data is static. I wanted to create a global leaderboard where people can track their daily coffee intake. The idea is simple: you drink coffee, track it, and the score (how many mL you drank) gets added to the leaderboard. The more you drink, the more your country climbs up the leaderboard.

Visit product View on Hacker NewsMar 6, 2024

Actual performance

3points

2comments

Did not reach leaderboard

Launch Intel predictions

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60%60% 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.

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

50%50% 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

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

Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released

43%43% 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 HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

33%33% predicted probability of success on Product Hunt, 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

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

Correct prediction on native model

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