An AI-powered tarot card reader that gives surprisingly deep readings

Hacker News

I've always been fascinated by the structure of tarot and the psychology behind why people find it meaningful. As an experiment, I built https://www.randomtarotcard.org/ai-tarot-reading to see if modern LLMs (I'm using a fine-tuned OpenAI model) could generate thoughtful, personalized tarot readings. The tech stack is simple but effective: Next.js frontend, a serverless function to handle the AI API call, and a prompt engineering system that provides the AI with rich context on each card's symbolism (upright and reversed) before it generates the interpretation for the user's specific question. It's completely free—this is just a fun side project. I'd love the HN community's feedback on a few things: The quality of the interpretations. Do they feel generic or surprisingly insightful? The user experience. Is it clear and engaging? The ethical considerations of an "AI mystic." It's a strange new world. You can try it here: https://www.randomtarotcard.org/ai-tarot-reading The code is not open source, but I'm happy to answer any technical questions about the implementation!

Visit product View on Hacker NewsAug 20, 2025

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

86%86% 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

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

39%39% 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

30%30% 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

9%9% 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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