Cooky – AI recipe parser that turns messy web recipes into clean JSON

Hacker News

I got frustrated with recipe websites that force you through 3,000 words about someone's grandmother before showing the ingredients. So I built Cooky - an AI-powered recipe parser and organizer. What it does: - Paste any recipe URL or text → AI extracts structured recipe data - Automatically detects timers in instructions ("bake for 25 minutes" → countdown button) - Scale servings up/down with automatic ingredient recalculation - Track your progress with checkable ingredients and steps Tech stack: - Frontend: Expo (React Native) with TypeScript for iOS/web - Backend: Supabase (PostgreSQL with JSONB for flexible schema) - AI: OpenAI GPT-4o-mini for parsing - Deployed on Vercel The interesting technical bit: I went with a "dumb scraper, smart parser" approach. Instead of trying to handle every recipe site's HTML structure, I just fetch the raw HTML and let GPT-4o-mini extract the recipe. It handles weird formats, handwritten recipes, even recipes pasted from emails surprisingly well. Web app: https://cooky-app-ivory.vercel.app/ iOS version coming soon (currently in TestFlight) Would love feedback on the UX and what features you'd want in a recipe app!

Visit product View on Hacker NewsOct 20, 2025

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

82%82% 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: email, openai, open · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

41%41% 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: soon · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% 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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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