A website to generate Magic: The Gathering cards

Hacker News

I created a website that utilizes OpenAI's LLM to generate realistic Magic cards based on a prompt. Using LLMs to generate Magic cards (or generate anything these days) is not new, but I've tried to put this together into a nice package and I am planning on making future improvements. You can generate some pretty flavorful cards depending on what prompt you provide. There are some issues since this is an early version. You will see cards that are just flat out incorrect or that do not make sense. I also will not guarantee the website will always work, so please be nice :) You will also notice the website domain name is a random one generated by Azure (if people like this, I may get a real one). Anyways, give it a shot and let me hear your feedback or features you would like to see. Thank you and hope you have fun generating!

Visit product View on Hacker NewsApr 24, 2023

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

83%83% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

61%61% predicted probability of success on AppSumo, 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

48%48% 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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

10%10% 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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