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Aaj Kya Banega is a custom meal-planning app built to answer a familiar question: what can I cook with the ingredients I already have without repeating the same dishes? Its creator describes a two-part approach: match pantry ingredients to existing recipes, then use a local AI model for more tailored suggestions. The project is a personal build, not an independently tested commercial product.
Why build a menu planner around the kitchen inventory?
In a project article published on DEV Community on October 4, 2025, developer Kaustubh Jogle described making Aaj Kya Banega for a colleague who lives alone and has a maid who cooks at home. The colleague reportedly had ingredients available but kept facing the same meal decision, summed up as: “What can I make today without getting bored of eating the same thing again?” That is the author’s account of one person’s situation, not a broader survey or statistic. Read Jogle’s project description.
The app is designed to make the available food, rather than a generic recipe search, the starting point. Users can record ingredients, choose recipes based on ingredient overlap, account for recent meals, and ask for locally generated ideas. The aim is to make deciding what to cook less repetitive while avoiding the assumption that every meal requires a fresh grocery run.
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Ingredient matching provides a predictable starting point
The first layer checks the ingredients a user has against recipes and ranks the recipes by how well they match. The project description says users can add common ingredients or create custom entries. This direct matching gives the user a clear connection between pantry contents and suggested dishes, rather than relying solely on a generated answer.
#1 Best Overall
Local AI adds context to suggestions
A second layer can generate meal ideas using context such as ingredients on hand, recent meals, preferences, and available cooking time. The author names Ollama and Gemma 3 as the local AI components, with Next.js, TypeScript, and Tailwind CSS in the application stack. The described flow can also produce short cooking instructions.
Jogle’s article illustrates the prompt with a request for three ideas, a distinction between ingredients already available and items to buy, and simple directions. One example is palak paneer with roti, accompanied by a 30-minute prep-time field. That is an illustrative output, not evidence that the app has verified the recipe, timing, ingredient quantities, or nutritional suitability.
What is local, and what the project does not establish
According to the project description, fridge inventory, preferences, and cooking history are stored in browser localStorage, without an account. The author’s rationale is that these details can reveal household routines and need not be sent to a cloud API just to choose dinner. The article also says the local flow requires neither an API key nor an internet connection and avoids per-request API charges.
Those are the builder’s design claims, not the results of an independent privacy audit, security review, reliability test, or cost comparison. Local browser storage means the described data is kept in that browser’s storage; it does not by itself establish how the app handles backups, browser profiles, shared devices, or every possible data flow. The article does not report those details or compare model quality or speed against a cloud service.
Current capabilities and planned work
The article describes ingredient tracking, recipe matching, locally generated ideas and short instructions, saving recipes, marking meals as cooked, and avoiding dishes cooked recently. It also identifies future ideas rather than shipped features. The distinction matters if someone is considering the project for allergy-sensitive or longer-term meal planning.
- Described as available: ingredient inventory, recipe ranking by ingredient match, local meal suggestions, saved recipes, cooked-meal tracking, and recent-meal avoidance.
- Described as future work: WhatsApp or SMS handoff, allergy and dietary preference controls, improved substitutions, and weekly variety planning.
The source does not establish that the planned allergy controls or substitution improvements are present, so the project description should not be treated as confirming allergy-safe recommendations. Generated ideas also remain suggestions to check against the ingredients and needs of the people eating.
Rank #4
Who this approach may suit
A local-first planner like this is most relevant to someone who wants to keep a simple kitchen inventory, values control over where household meal data goes, and is comfortable with recommendations that may need human review. Ingredient matching can help when the priority is using what is available; the AI layer is intended to help add variety and context.
The Tool Desk
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