Requirement5 – a digital card collection game

Hacker News

I used to collect Pokemon cards when I was a youth. I grew up and my Pokemon cards collected dust (and I learnt to be a software engineer). Then one day I stumbled across https://poke-holo.simey.me/ , which is an incredible piece of virtual Pokemon card work. Fuelled by this HTML, JS and CSS wizardry, I decided I'd build my own virtual collecting card game - which is call Requirement5. You discover cards, and collect our in game currency as you do so. If you want to save a card or create your own card, it costs currency. If you create a card and others save it, you get 70% of the currency back. A few friends and I have seeded it with our own creations (helped by the fact we have a CLI, so coding agents can do their bit). Our lore is about an interplanetary need for earth to express imagination: https://requirement5.com/about Discover cards: https://requirement5.com/ Create cards: https://requirement5.com/create Source: https://github.com/vilt9/requirement5

Visit product View on Hacker NewsSep 14, 2026

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Actual performance

2points

Did not reach leaderboard

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

63%63% 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.

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 · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: agents, agent, coding · Missing: mac, macos, cursor

27%27% predicted probability of success on Product Hunt, 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 · Missing: web3, chat, crypto

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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