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The $10 coffee that tanked my credit score

Hacker News

The $10 coffee that tanked my credit score

A $10 latte cost me 50 credit points last year. Not because I couldn't afford it - because it pushed my Chase card from 29% to 31% utilization. That's when I learned: Your balance won't hurt your credit score. Your utilization will. The problem isn't the rule (everyone knows 30%), it's tracking it. With multiple cards, changing balances, and daily spending, you're basically guessing which card is "safe." So I built Cretit - daily traffic lights for every credit card: Green = safe to use Yellow = be careful Red = will hurt your score Hope you guys like this one more than HumanAlarm

Visit product View on Hacker NewsSep 22, 2025

Actual performance

1points

6comments

Did not reach leaderboard

Launch Intel predictions

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best fitHighest predicted score across all platforms for this description.

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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

35%35% 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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

27%27% 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

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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