PurePlates – A Recipe Scraping iOS App

Hacker News

Hey Everyone! This is my first post on Hacker News, and I wanted to share my project with you! From a young age, I dreamed of becoming a chef, but life took me down the path of software engineering instead. Nevertheless, my passion for creating delicious meals has always remained close to my heart. This journey led me to seek out recipes to enhance my cooking skills, but I often found myself frustrated by the websites I encountered—ads everywhere, walls of text, and recipes that were difficult to follow. That’s where PurePlates comes in. PurePlates allows users to share a recipe through their mobile browser or simply copy and paste the URL directly into the app. In an instant, you receive a scraped recipe that includes instructions, ingredients, and any available nutrition information. You can easily start cooking and follow along step by step, making the process much more enjoyable. Plus, if you find a recipe you love, you can add it to your favorites for quick access later! As this is just an MVP, there’s still plenty of room for improvement, and I’m actively working on enhancing the extraction rate for recipes. Thanks for reading, and keep hacking! Connor

Visit product View on Hacker NewsSep 30, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · 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: user, new · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released

56%56% 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: plus, users · Missing: platform, intuitive, reviews

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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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