AI
AI Agent for Valentines Cards
Hacker NewsAI Agent for Valentines Cards
I made an AI agent that brainstorms valentines day cards This is the workflow I ended up with: - LLM call to brainstorm ideas - second LLM call to rate ideas from 1 to 10 - create cards for each idea, sorted by rating I also added the ability for users to iterate on the cards via a chat interface (similar to how you can iterate on UIs generated by tools like bolt or v0). https://valentines.assistant-ui.com/ Repo: https://github.com/Yonom/tech-bro-valentine I built it with AI SDK, NextJS and assistant-ui. I spent most of my time on prompt engineering and a little bit on making the UI look nice.
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3points
Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user · Missing: mac, agents, macos77%77% 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.
AppSumoStrong fit for a featured deal · Strong signals: interface, users · Missing: plus, platform, intuitive52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released37%37% 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, revenue16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency0%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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