Tarotread.ai – Your Go-To Digital Tarot Deck with AI Reader

Hacker News

Just launched tarotread.ai and I'm kinda stoked to see what you all think. It's a digital tarot reading experience that tries not to lose the vibe of a real tarot session. Wanted to blend some AI magic with the traditional tarot card feel. Why I Made This: Always been intrigued by tarot cards and tech. Thought, why not mix them? The goal was to keep the personal touch and make it a bit modern with AI. What It Does: You get to play around with digital tarot cards, and the AI throws back interpretations that are meant to be deep and thoughtful. Tried to keep it as real as it gets, minus the physical cards. I Need Your Brain: Got it up and running, but I know it can be better. Here’s where you come in. Hit me up with: What feels right, what doesn’t? Any cool feature ideas? General thoughts or a pat on the back? Super keen to hear your feedback. It’ll help a ton to shape this up better. Check it out and let me know your thoughts. Cheers!

Visit product View on Hacker NewsMar 31, 2024

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

82%82% 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.

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

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, 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

30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

17%17% 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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