Yanhuo Shijian Turns Meal Planning Into a Structured, Adjustable Workflow
Most AI cooking tools dump a block of text and call it a meal plan. Yanhuo Shijian treats the output as mutable, structured data — a menu, a shopping list, a recipe library — that can be partially edited, exported, and reused, which is closer to how people actually cook across a week.
Yanhuo Shijian (Hearth & Mealtime) tackles the full meal-decision chain: deciding what to eat, generating a menu, adjusting dishes, viewing recipes, and organizing a shopping list. Users set parameters like headcount, days, meals per day, dietary restrictions, budget, and available cooking time. The system returns a structured menu where any single dish can be swapped with a reason — no need to scrap the whole plan. A separate “What to Eat Today” mode solves a single meal from ingredients already on hand, and a “What’s Nearby” page searches real restaurant locations when cooking isn’t an option.
Menus feed into an auto-generated, categorized grocery list that can be marked off, exported as CSV or image, and printed. Dishes can be saved to a personal recipe library, and feedback like “liked” or “don’t want again” informs future generations. The homepage functions as a workbench rather than a landing page, surfacing the current menu and quick actions.
Privacy is handled by keeping user-supplied API keys in the browser’s local storage; they are never written to the Supabase database. The project is a free public-welfare demo, with source code available on GitHub.
Treating a meal plan as mutable structured data — not a static text block — is the core design decision that separates this from most AI recipe generators. Partial edits, categorized exports, and feedback loops make the output actionable across multiple days.
The inclusion of a restaurant-search fallback acknowledges that home cooking isn’t always the right answer; a meal-planning tool that ignores takeout and dining out is planning for an idealized week that rarely happens.
Storing API keys exclusively in the browser and never on the server is a practical privacy stance for a demo, but it also means the tool’s AI features break silently if the user clears local storage or switches devices — a trade-off worth documenting upfront.