GeniusPlants – AI-Powered Gardening Assistant

Hacker News

I built GeniusPlants, an AI-powered gardening assistant that uses LLMs to solve practical gardening challenges. The app provides personalized plant recommendations based on your garden conditions, generates optimal garden layouts, and creates customized care plans. Technical stack: - Next.js (App Router) with TypeScript and Tailwind CSS - OpenAI GPT-4o-mini for plant recommendations and care plans - Prisma ORM with PostgreSQL for data management - Zustand for state management - Auth.js for authentication Instead of maintaining a traditional plant database, I implemented a pure AI approach with structured prompts and response parsing. The system includes a block-based garden space visualization that calculates optimal plant placement and uses a natural language interface for adding plants (e.g., "I want to grow 3 tomatoes, some basil, and carrots"). The app handles interesting problems like: - Garden space management using a block-based system (1 block = 1 sq ft) - AI-driven plant positioning within garden layouts - Intelligent care plan generation that adapts to automated watering systems - Plant age tracking for growth-stage-appropriate care instructions I'd love feedback from the HN community, especially on the AI implementations, UX design choices, and handling garden space constraints.

Visit product View on Hacker NewsMay 20, 2025

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

84%84% 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: visual, openai, using · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% 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

28%28% 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: arr, growth · Missing: mrr, revenue, profit

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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