This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend
A colleague from my office lives alone and has a maid who cooks at home.
The problem was not that food was unavailable. His fridge usually had eggs, paneer, spinach, chicken, onions, tomatoes, and the usual basics. The problem was deciding what to make.
Most evenings ended the same way:
“Paneer bana dete hain.”
“Egg bhurji kar lete hain.”
“Chicken curry?”
After a few weeks, even good food starts feeling repetitive.
So I built Aaj Kya Banega, a local AI kitchen companion that recommends meals based on what is already in the fridge, what you've cooked recently, and how much time you have.
The app lets him:
The core question is simple:
What can I make today without getting bored of eating the same thing again?
A local-first meal planner for the daily question: what should we cook today?
Built for a colleague who lives alone and was tired of deciding what to tell his maid to cook every day. With a full fridge but the same paneer bhaji appearing again and again, he needed more than another recipe website. He needed a simple, private way to decide tomorrow's meal.
Add what is in the fridge, explore recipes that fit, shuffle tomorrow's menu, and keep a lightweight record of what was cooked.
npm install
npm run dev
Open http://localhost:3000.
Aaj Kya Banega is built with:
The app has two layers of recommendation.
First, it uses a regular ingredient-matching system to identify recipes that already fit what is in the fridge. This is quick, predictable, and useful even without generating anything.
Then, Ollama runs Gemma 3 locally to make the recommendations more personal. The model receives a focused kitchen context:
Available ingredients:
Eggs, paneer, spinach, onion, tomatoes, green chillies
Recently cooked:
Egg bhurji, paneer butter masala
Preferences:
Quick Indian home-style dinner for two people.
Avoid repeating recent meals.
Suggest exactly three meal ideas.
Clearly mention what is already available and what needs to be bought.
Keep the cooking instructions simple.
Return structured JSON only.
The model returns structured recipe data that the app can display cleanly:
{
"title": "Palak Paneer with Roti",
"whyItFits": "Uses spinach and paneer already available at home.",
"availableIngredients": ["spinach", "paneer", "onion", "garlic"],
"missingIngredients": ["cream"],
"prepTime": "30 minutes",
"maidInstruction": "Make palak paneer with roti for two people. Keep it mildly spicy."
}
This keeps the AI useful without letting it become a vague chat interface.
For this project, open innovation matters because a kitchen is personal.
A fridge inventory may sound harmless, but it can reveal habits, food preferences, budgets, dietary restrictions, and daily routines. My colleague should not need to send all of that to a cloud API just to decide dinner.
With Ollama and Gemma 3 running locally:
The next feature is a WhatsApp or SMS handoff.
Once a recipe is selected, Aaj Kya Banega should create a short cooking message that can be sent directly to the maid:
Today: Palak Paneer with Roti
For: 2 people
Use: spinach, paneer, onion, garlic
Buy: cream
Please make it mildly spicy.
The current version already supports generating this instruction locally. The next step is connecting it to a messaging provider.
I also plan to add:
Aaj Kya Banega uses Gemma 3 running locally through Ollama to recommend practical meals from ingredients already available at home. The model considers fridge inventory, recently cooked meals, cooking time, and dietary preferences while keeping the recommendation flow private and local.