Kudos – a new checkout assistant by Google and Honey alum

Hacker News

Hi HackerNews! I'm Brian and I work in Growth at Kudos. After spending several years helping build Google Pay and Affirm, our co-founders came to the realization that card issuers make credit cards unnecessarily complicated and existing wallet products focused more on merchants than building a rewarding checkout experience for consumers. So over the past year, they brought on a team — many from Google Pay and PayPal Honey, and built Kudos. Our mission is to make every purchase as delightful as possible by streamlining the checkout process and taking the guesswork out of credit card rewards and benefits. With Kudos, you can: - Always use the best card at checkout on 2M+ sites - Double your credit card rewards by 2X - Breeze through checkout with autofill We actually launched on Product Hunt today, so your support means the world to us: → https://www.producthunt.com/posts/kudos-checkout-assistant As we're a small team, we'd love your feedback or suggestions on improvement!

Visit product View on Hacker NewsMar 16, 2023

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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

82%82% 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.

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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: growth · 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 · Strong signals: reward · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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