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Review your favorite coffee beans and discover new ones

Hacker News

Review your favorite coffee beans and discover new ones

Hello! Coffeebeans.fyi lets you review your favorite coffee beans and discover new ones. It's my solo project in the hope of sharing my favorite coffee beans with you and discovering other great coffee beans from you. As a coffee lover in Seattle, I've been trying to taste different coffee beans that have deep aroma, nutty flavor, and smooth aftertaste with low acidity (I am not a coffee expert, by the way), but I've only found a few ones: beans from Monorail Espresso, Espresso Vivace, and Stumptown Roasters (Trust me they are great). I am eager to know what your favorites are and try some of them! I'd LOVE to hear your feedback since I will probably reflect it in the next product development. Also, if you like some of the beans on the website and want to let your friends know about them, you can just click the "Share" button on each bean page :) Thank you and don't forget to have a nice cup of coffee today! Eric

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
88%88% 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 NewsMay not resonate with HN audience · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
41%41% 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
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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.

Correct prediction on native model

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