Meals You Love – AI-powered meal planning and grocery shopping

Hacker News

Meals You Love is a meal planning app that creates weekly meal plans tailored to your tastes and dietary preferences. It integrates with Kroger and Instacart's APIs so you can add your meal plan groceries directly to your cart. You can also import your own recipes to include alongside AI suggestions. I originally built this to help my wife with meal planning and grocery shopping. We were always struggling to decide what to make and inevitably forgot ingredients. Most meal planners felt too rigid or generic, and few handled the grocery side well (or at all). We've also used meal kits like Home Chef in the past but they end up being quite expensive and produce a comical amount of packaging waste, plus you still wind up needing to purchase groceries anyway. In all honesty, I also wanted an excuse to try building something "real" using AI and to see if it could be used in an actually useful manner. Would love feedback from anyone interested in food, meal planning, or product design! Tech stack: - Cloud Run - Firestore - Vertex AI / Gemini https://mealsyoulove.com

Visit product View on Hacker NewsOct 27, 2025

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

87%87% 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: gemini, using, apis · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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: plus · Missing: platform, intuitive, reviews

30%30% 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

14%14% 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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