Earn cashback for reducing food waste – Cherripick – YC W20 Hopeful

Hacker News

Hey everyone - we just concluded YC Startup School '19 and would like to get your feedback on if we are on our way to building something people want. "Cherripick rewards shoppers who purchase food close to its expiration date. It functions like iBotta - just snap your receipt, item expiration date, and earn up to 30% cashback." We're launched (closed/alpha) to Google Play, so if you want to give it a go, request access at our website https://www.cherripick.com Thanks for your time, and cheers to a Cherripicking future! Get in touch: Marco Ignacio Co-Founder marco@cherripick.com

Visit product View on Hacker NewsSep 27, 2019

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Actual performance

6points

2comments

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

73%73% 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.

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

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

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

47%47% 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 · Strong signals: google, way · Missing: mobile apps, ios, personal

41%41% 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

38%38% 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 · Strong signals: reward · 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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