I Made Computer Science Themed Collectible Cards

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Hi all, I made a Computer Science themed Collectible Cards deck. There are 36 cards in it ranging from simple data structures such as linked list and stack to more advanced ones such as bloom filter and merkle tree. I'm a software engineer with a knack for drawing. A few years back I thought about making playing cards based on Computer Science concepts but I did not make it very far. When I revisited this idea again I decided to start with an MVP: Data Structure Collectible Cards. Each card would depict a data structure, state its space/running time complexities, and provide a brief description of said data structure (so in a way it's like Pokemon collectible/playing cards :)) I have a long roadmap that covers many Computer Science/Software Engineering concepts for both collectibles and playing cards. My hope is these decks could be something that spurs people's interest in Computer Science, or something that academics/engineers will feel proud displaying on their shelves (long shot, I know). I'm in the process of figuring out fulfillment and kickstarter campaign. Once I have a gauge of people's interest from email sign-ups, I will put in a bulk order. Feel free to navigate around my site; I've put in all my heart into designing it and I hope you will like it :)

Visit product View on Hacker NewsJul 28, 2022

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

69%69% 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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

59%59% 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: way · Missing: mobile apps, ios, personal

43%43% 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

12%12% 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.

Incorrect prediction on native model

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