PlantGenieAI – AI app to identify plants and give care advice

Hacker News

Hi HN! I'm a solo builder and a plant lover. I kept struggling to identify my houseplants correctly, and even when I knew their names, I wasn’t always sure how to care for them properly. Watering too much, not enough light… you know the drill. So I built PlantGenieAI, an iOS app that does four things: 1. Instantly identifies plants from a photo (95%+ accuracy) 2. Gives tailored care guides based on species 3. Lets you chat with an AI assistant — ask anything like: “Why are my pothos leaves turning yellow?” “How much sunlight does a fiddle leaf fig need?” 4. Smart reminders for watering, fertilizing, and more I originally built this for myself, but it's now live on the App Store: https://apps.apple.com/us/app/plantgenieai/id6747911595 Would love feedback — on product direction, UX, or growth ideas. Thanks for reading, and happy plant parenting!

Visit product View on Hacker NewsJul 30, 2025

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Did not reach leaderboard

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

79%79% 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.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, way · Missing: mobile apps, personal, entrepreneurs

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

Product HuntUnlikely to reach the leaderboard · Strong signals: apple, apps, new · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% 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

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 · Strong signals: growth · Missing: arr, mrr, revenue

24%24% 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, smart · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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TrustMRR5$23/moArtificial Intelligence

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