AI Powered Meal Planning App

Hacker News

Hi HN! I've been teaching myself to code for the last year or so and am pleased to launch my first (small) product into the world. Mealll uses the Anthropic API to generate meal plans for users. It allows them to select their required nutrient requirements, dietary requirements and food preferences. It then creates a customised weekly meal plan and shopping list for them. I've included a simple nutritional requirements calculator for users who might be unsure of their nutritional needs. Once users have created a meal plan, they can generate recipes for any of the meals that they're unsure how to cook. It's been a lot of fun learning to work with the different models available from Anthropic. I've found sonnet to be a great middle ground for the creation of the meal plan outline and ingredients. I'm currently using the haiku model for the generation of recipes. The big dream for this product is to help everyday people who lack the skills and knowledge to eat healthily eat more nutritious home cooked meals. For now just a couple of users would be great! I've got lots of ideas of how to improve the app but wanted to get it in the hands of users and see if this is something that can help them.

Visit product View on Hacker NewsApr 18, 2024

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

93%93% 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: model, user, models · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

66%66% predicted probability of success on TrustMRR, 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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

11%11% 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.

Correct prediction on native model

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