I used DeepSeek to build a CLI setup for Stripe

Hacker News

So I was thinking of a way to test out the DeepSeek chat and see how good it is, and then I stumbled on this gold from Theo. It's his recommendation on how to set up Stripe. If you've set up Stripe a lot or payment, you'd know how annoying it can be, so his recommendations are pretty good. Anyway, I decided to build a CLI setup for the Stripe recommendations using DeepSeek. Here's Theo's recommendation: https://github.com/t3dotgg/stripe-recommendations Here's the CLI by DeepSeek: https://github.com/dantelex/stripe-x Note: I didn't write any of this code. I'd love to know what you guys think about how it did.

Visit product View on Hacker NewsJan 28, 2025

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: stripe, using, code · Missing: mac, agents, macos

74%74% 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 · Strong signals: ide, 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.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · 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

32%32% 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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