Cook Master

AI recipe generator for everyday cooking

Cook Master is a full-stack cooking product that turns a simple ingredient prompt into a structured recipe workflow: guest recipe generation, a registered-user dashboard, favorites and meal planning, credit-based billing, and AI-generated recipe images.

Project type

AI recipe generation platform + cooking dashboard

Role

Full-stack developer

Year

2026

Scope

Product UX, landing page, auth, dashboard, AI recipe generation, meal planning, credits system, PayPal payments, Supabase schema and RLS, edge functions, image generation, PDF export, testing setup

Cook Master shown across desktop, tablet, and phone in a kitchen setting, with the landing page, recipe library, and recipe list on screen

Generated recipe card for Honey-Glazed Apple and Pear Nut Bites, with photo, macros, and ingredient list, shown on a tablet while the dish is prepared

Recipe list on a phone propped up in the kitchen, showing the Spinach and Feta Omelette recipe while it's being cooked

Weekly meal planner board on a desktop monitor, with breakfast, snack, lunch, dessert, and dinner slots per day

Summary

Built for

Home cooks who want fast meal ideas from ingredients they already have, plus a more structured personal recipe space once they create an account.

The problem

Most recipe apps are either static content libraries or generic AI demos. They don't handle the full product loop: guest conversion, usage limits, saved recipes, favorites, meal planning, credits, and a personal dashboard that makes AI output feel like an actual product.

Approach

Cook Master was built as a layered cooking product. The public side gives guests an immediate "try it now" recipe-generation experience. The authenticated side expands that into a recipe workspace with saved content, meal-planner utilities, personalization, credits, and billing.

Showcase story

Cook Master starts with a simple promise: tell the app what ingredients you have, and it generates a usable recipe in seconds. The interesting part is everything around that core moment — guests can try the product once without registering, then the app converts them into full users with credits, personalization, favorites, and a richer dashboard experience.

The problem

A recipe generator alone isn't enough to feel production-ready. A real product needs a fast public entry point, guest-to-user conversion logic, personal recipe history, favorites, a way to plan meals beyond one-off generations, and billing and credit control for AI usage.

What I built

Public landing page with hero CTA, recipe form, and generated recipe result card
Guest mode with one free recipe generation before an account is required
Full dashboard with recipe generation, my recipes, favorites, meal planner, and settings
Personalized recipe generation using ingredients, cuisine, time, difficulty, and servings
Background AI image generation for each recipe after text generation succeeds
Favorites and a saved-recipe cookbook flow
Meal planner supporting manual recipe assignment plus AI-generated full-day meal plans
Credit system with daily bonus logic, wallet balance, and PayPal-based credit purchases
Recipe and cookbook PDF export

Design decisions

The product is split into two very different modes on purpose. The landing page is conversion-first: warm food imagery, a fast form, and a low-friction guest tryout. The dashboard is utility-first: denser navigation, credit visibility, and saved-recipe organization for returning users. The AI output is also treated like product data, not just a chat response. Recipes are structured, stored, rendered as cards, turned into images, and reused again in favorites, meal planning, and PDF export.

Under the hood

The frontend is a React application styled with Tailwind CSS and shadcn/ui. Supabase provides auth, Postgres, storage, RLS-backed data access, and edge functions. OpenAI handles recipe generation and recipe image generation, and PayPal is used for credit purchases and post-payment capture.

Why it matters

This project signals:

Building an actual AI product instead of a single AI demo screen
Connecting frontend UX with billing, persistence, and backend automation
Designing both acquisition UX and retained-user workflows
Handling structured AI output as reusable product data

Final positioning line

A full-stack AI cooking platform with guest onboarding, structured recipe generation, dashboard workflows, meal planning, and credit billing behind the scenes.

Under the hood

Cook Master combines a conversion-focused public experience with an authenticated cooking workspace. Supabase owns the application backbone — auth, database, RLS, and edge functions — while OpenAI powers recipe, meal-plan, and image-generation logic, and PayPal handles credit top-ups.

React

Public site and dashboard interface

Supabase

Auth, database, RLS, storage, and edge functions

OpenAI

Recipe generation, meal planning, and recipe image generation

Tailwind CSS + shadcn/ui

Design system and UI components

PayPal

Credit purchases and payment capture

TanStack Query

Client-side data fetching and state