About

Deciding what to cook accounts for a measurable portion of daily decision fatigue. Studies in behavioral economics estimate adults make over 200 food-related decisions per day. This generator queries TheMealDB database containing over 300 meals across 27 cuisine categories. Each result returns the full recipe: name, origin area, ingredient list with precise measures, step-by-step instructions, and an embedded preparation video. The API endpoint random.php uses uniform random selection across the entire dataset. Note: results depend on TheMealDB availability and may occasionally return incomplete entries for lesser-documented regional dishes.

Formulas

The random meal selection follows a uniform discrete distribution across the full database. The probability of any single meal being selected on a given query:

P(meal_i) = 1N

where N = total meals in database (≈ 300). The ingredient extraction algorithm iterates indices i = 1 to 20, filtering entries where strIngredient_i is neither NULL nor an empty string. YouTube video ID is extracted via pattern matching: /[?&]v=([a-zA-Z0-9_-]{11})/ applied to the strYoutube field.

Reference Data

Cuisine Area	Example Dish	Typical Protein	Avg. Ingredients	Complexity
British	Beef Wellington	Beef	12	High
American	Banana Pancakes	Eggs	8	Low
French	Crème Brûlée	Dairy	6	Medium
Italian	Lasagne	Beef/Pork	14	Medium
Mexican	Tacos	Chicken/Beef	10	Low
Chinese	Kung Pao Chicken	Chicken	13	Medium
Japanese	Teriyaki Chicken	Chicken	9	Low
Indian	Chicken Tikka Masala	Chicken	16	High
Thai	Pad Thai	Shrimp/Tofu	14	Medium
Moroccan	Lamb Tagine	Lamb	15	High
Greek	Moussaka	Lamb/Beef	12	High
Spanish	Paella	Seafood	15	High
Turkish	Kumpir	Mixed	10	Low
Vietnamese	Pho	Beef	12	Medium
Canadian	Poutine	Cheese	5	Low
Jamaican	Jerk Chicken	Chicken	11	Medium
Egyptian	Koshari	Lentils	10	Medium
Polish	Pierogi	Pork/Cheese	9	Medium
Malaysian	Nasi Lemak	Chicken/Fish	13	Medium
Croatian	Štrukli	Cheese	7	Medium

Frequently Asked Questions

The API uses server-side random selection from the full database. With approximately 300 meals, the probability of consecutive duplicates is roughly 1/300 (≈ 0.33%). The generator does not implement client-side deduplication by design, as true randomness permits repeats. If you receive a duplicate, simply generate again.

TheMealDB is a community-maintained database. Some entries use informal measurements ('a pinch', 'to taste') rather than standardized metric or imperial units. Approximately 5-10% of meals may have incomplete measurement data. The generator displays exactly what the API provides without modification to preserve recipe accuracy.

This generator uses the random endpoint which does not support filtering parameters. TheMealDB categorizes meals by type (e.g., 'Vegetarian') but the random API returns from the full pool. The category and tags displayed with each meal can help you quickly identify suitability before reading the full recipe.

The generator implements error handling with automatic retry logic. If the network request fails, a toast notification appears with the specific error. The last successfully loaded meal is cached in localStorage and will be displayed on page reload even without an internet connection.

Not all meals in the database have an associated YouTube tutorial. Approximately 15-20% of entries have a null or empty strYoutube field. When no video URL exists, the video section is hidden rather than displaying a broken embed. The recipe text instructions remain fully available regardless.