Packs Club, Trading Card Subscription Service Built in 7 Hours

Hacker News

I'm sharing a recent project that I've launched - Packs Club, a trading card subscription service. For just $10 a month, members receive 10 assorted trading card packs delivered directly to their doorstep. It's a mixture of vintage and modern cards, sports, collectible card games, and other [1]. I think this is an engaging and convenient way to expand a trading card collection, explore new series, and potentially uncover rare finds. The platform is built on Express and Node.js, which is now boring but should be good to go with what we expect for traffic. It's deployed via AWS Elastic Beanstalk. For payments, we've integrated Stripe. The entire site was built and deployed in just 7 hours. This is roughly the amount of time it took to source the cards! If the idea is validated, we will look to build a social platform and marketplace. If you're interested in trading cards, or just want to see what can be accomplished with this tech stack in a short amount of time, check out Packs Club at www.packsclub.net. Looking forward to hearing your thoughts and feedback. [1] Post on what kind of cards you can expect to get: https://packsclub.net/blog/2023/07/23/the-packs-june-2023/

Visit product View on Hacker NewsJul 24, 2023

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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

78%78% 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.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

49%49% 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: ide, io · Missing: https docs, excited, just released

42%42% 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 · Strong signals: platform · Missing: plus, intuitive, reviews

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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 · 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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