I made a site to quick identify any plant and learn how to care for it

Hacker News

My friend was spending €23/month (GPT) to plant ID during her forest walks. After building her a simple solution, she loved it and encouraged me to share it with others - so here it is! This is actually my second HN. Your feedback on my first post was eye-opening (thanks!). I'm bootstrapping this as a student and planning to grow it beyond just plant identification. Working on adding community features and leaderboards - turning it into a cozy corner of the internet for plant parents. Think less "utility app" and more "home for plant enthusiasts". Open for feedback from the HN community - especially from fellow plant lovers! Get 30 free credits to try it out and let me know what you think. For those who ask - this is not just LLM wrapper.

Visit product View on Hacker NewsOct 29, 2024

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Did not reach leaderboard

Launch Intel predictions

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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.

best fitHighest predicted score across all platforms for this description.

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

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

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

43%43% 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

34%34% 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

27%27% 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

15%15% 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

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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