Aggregate recipes and make shopping lists, is it useful though?

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I built a small app where you can pull recipes from your favourite websites and generate an interactive shopping to then buy the ingredients to make them. You can give it a try for free, no payment details required. This idea will hardly set the world alight but it scratched an itch for me and wanted to blow the cobwebs off of shipping a product, so I thought I would throw something together quickly and would love any feedback on it if possible! This idea came about from - my lack of cooking skills requiring me to reference recipes I’ve already made before - having to go on a deep dive through WhatsApp to find the recipes that had been sent to me - writing the aggregated shopping list for multiple recipes took way too long - alot of recipe websites not getting to the cooking steps quick enough I have had issues getting the AI model to scrape the websites correctly. I had to add the ability of users to copy & paste steps and ingredients into the form to improve accuracy. If anyone has any tips on improving the accuracy of models (using Open AI Assistants), that would be great! Thanks for your time if you check it out, it is very much appreciated! I'm also on X if you want to discuss there! https://x.com/jaackevans_

Visit product View on Hacker NewsMay 28, 2024

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

86%86% 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

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% 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 · Missing: https docs, excited, just released

40%40% 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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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.

Correct prediction on native model

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