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MealPractice – Meal planning with GPT 3.5 generated recipes

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MealPractice – Meal planning with GPT 3.5 generated recipes

Hi, I'm Ben Bjurstrom and I've been hacking away on a meal planning site called MealPractice for the last couple years. The main goals for the site are: 1. Enable users to create weekly meal plans, either from individual recipes or from a collection of recipes saved as a menu. 2. Allow users to publish the meals they're actually cooking, helping them identify their core meals and enabling other users with similar tastes or nutritional goals to discover new recipes. With the rise of generative AI I thought it would be fun to allow users to create new recipes based off a few preselections. To my surprise the suggestions it comes up with are quite good. I've personally been using them to cook meals for my family for the last couple months. Would love your feedback on the recipe generation process, the quality of the recipes, and the site overall. Here to answer any questions. Thanks!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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: user, new, using · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month, users · Missing: mobile apps, ios, entrepreneurs
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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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