Me

MewtruCard – AI-generated animated SVG cards for Valentine's Day

Hacker News

MewtruCard – AI-generated animated SVG cards for Valentine's Day

After being laid off last year, I started exploring AI-powered greeting cards. My first attempt was a simple Birthday AI Chrome extension that just generated text messages - it wasn't engaging enough and I shelved it. Then I discovered birthday.mewtru.com (credit where credit is due - this inspired both the name and concept). I realized that static messages weren't enough - what if we could create dynamic, animated SVG cards powered by LLMs? Tech Stack: - Next.js App Router + TypeScript - LLM for card generation - SVG animations - Server-side rendering for performance The cards are programmatically generated and animated, making each one unique. While it's perfect for Valentine's Day (running ad-free today!), you can create cards for anyone you care about. Try it: https://mewtrucard.com This is a solo project born from a layoff, inspired by another indie hacker, and built with modern web technologies. I'd love to hear your feedback and suggestions. P.S. Usually there are ads to cover the LLM costs, but they're turned off for Valentine's Day. Consider it my gift to the community.

Visit product View on Hacker NewsFeb 14, 2025

Actual performance

1points

Did not reach leaderboard

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92%92% 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 NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

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

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

26%26% 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

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.

Incorrect prediction on native model

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