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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteAaj Kya Banega is a custom meal-planning app built to answer a familiar household question: what can I cook with what is already in the kitchen, without repeating the same meal? Its creator describes a two-part approach: match recipes to available ingredients, then use a local AI model to suggest more personalized ideas. The project is a single developer’s account, not an independently audited product or a general claim about how households plan meals.
Why the app was built
In a project article published October 4, 2025, developer Kaustubh Jogle recounts building Aaj Kya Banega for a colleague who lived alone and had a maid who cooked at home. The colleague reportedly kept returning to the same meal despite having ingredients available. Jogle presents this as the motivation for the project, not as a broader survey of cooking habits.
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The app aims to make the kitchen inventory and recent meals part of the decision, so the user can get ideas without starting from a blank slate. Its name, translated roughly as “What will be made today?”, captures the daily choice it is designed to address.
How its meal suggestions work
Start with ingredient matching
The first layer finds recipes that fit ingredients the user has marked as available. This offers a more predictable route: a suggestion is tied to the inventory rather than generated from an open-ended prompt. The project description says users can track ingredients, add common items, and enter custom ones.
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Use local AI for more tailored ideas
A second layer can request ideas from a local model, with context such as ingredients on hand, meals cooked recently, preferences, and available cooking time. Jogle names Ollama and Gemma 3 as the local AI components, alongside Next.js, TypeScript, Tailwind CSS, and browser localStorage.
The article illustrates the prompt with a request for three meal ideas, identifying ingredients already available and items to buy, and asking for simple instructions. One example is palak paneer with roti and a 30-minute preparation-time field. That is an illustration in the project article, not a verified recipe, timing guarantee, or performance benchmark.
What the app is described as doing
- Track available ingredients, including common and custom items.
- Rank recipes according to how well they match the ingredients on hand.
- Generate meal ideas locally using kitchen context, including recent cooking history and available time.
- Help avoid recently cooked dishes, save recipes, mark meals as cooked, and produce short cooking instructions.
These capabilities are reported in the developer’s project description; the account does not provide an independent product audit.
What “local” means here—and what it does not prove
Jogle says the app keeps fridge data, preferences, and cooking history in browser localStorage, without requiring an account. The stated rationale is that household routines can be personal and need not be sent to a cloud AI service simply to decide what to make. The author also says the local flow needs neither an API key nor an internet connection and avoids per-request API charges.
Those are the author’s design claims, not the findings of a privacy audit or measured cost comparison. In particular, local storage describes where the app’s saved browser data is kept; it is not, by itself, an independent verification of every data flow in the software. The project article supplies no benchmark comparing local inference with a cloud service.
Current features versus planned work
The project description says short cooking instructions can be generated locally. It lists a WhatsApp or SMS handoff, allergy and dietary preference support, better substitutions, and weekly variety planning as future work. Those items should not be treated as features already shipped.
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Who may find this approach useful
A local-first planner like this is most relevant to someone who wants suggestions grounded in a manually maintained kitchen inventory and recent meals, and who values keeping that context in the browser. Ingredient matching is the straightforward part; local generation adds flexibility, but the project account does not establish how accurate or varied its suggestions are in everyday use. Anyone relying on allergy safeguards should not assume the planned dietary controls are available.
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Kaustubh Jogle’s project article on DEV Community, published October 4, 2025.
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