# What's for Dinner/Aaj Kya Banega? A 20B model trained to solve my Mom's kitchen crisis without the chatty fluff > Source: > Published: 2026-10-04 19:09:46+00:00 *This is a submission for the [Hacktoberfest Weekend Challenge: Build for a Friend](https://dev.to/challenges/hacktoberfest-weekend-2026-10-01)* The most dangerous question in any Indian household: ``` "Aaj khane mein kya banau?" (What's for dinner?) ``` Obviously, most days I'll say, "Make whatever you want." And there's a good chance either Mom gets frustrated with that answer, or we're having the same dish for the fourth time that week. So I built **What's for Dinner?** for my Mom — and honestly, for the survival of the whole family (especially me). It's an AI-powered meal-planning assistant that takes **what's actually sitting in the fridge**, using the names we naturally use at home — *palak*, *paneer*, *bache hue chawal*, etc. — and suggests at most **3 Indian home-cooked dishes** that can actually be made. It also remembers what the family has eaten recently, so nobody has to suffer through *Aloo Gobi* three days in a row. But the most important feature isn't the recipes. **It's knowing when to stop talking.** **Stop asking. Start cooking.** *The demo is running on Render's free tier, so the first request may take a little longer while the service wakes up.* The entire project is open source: A full-stack application that suggests meals based on your available pantry items. It leverages the **Tinker API** to sample LLM-generated suggestions and uses the **Backboard API** to maintain a history of your recently selected meals, ensuring you don't get the same suggestion twice! `main.py``/api/suggest` for meal suggestions and `/api/select` to save your choice.`frontend/`` train/``train.jsonl`).` render.yaml` & `build.sh` The React frontend and FastAPI backend are deployed together as a single Web Service on Render, making the deployment simple and helping me make the most of the Hacktoberfest credits. I wanted to build something **fast, lightweight, culturally aware, and actually useful in a real Indian kitchen.** I fine-tuned the **20B open-weight `gpt-oss-20b` model** using the Tinker Cookbook. I trained an adapter called **`mom-chef-v1`** on a synthetic dataset containing: The goal wasn't just to teach the model about Indian food. I wanted to teach it **how to respond**. The adapter is hosted on Tinker's cloud GPUs, so my application doesn't need to carry around ~43 GB of model weights. And the best part: **The entire fine-tuning run cost me just $0.16.** I integrated **Backboard** as the family's memory layer. It keeps track of recent meals and provides that context to the model before it makes a suggestion. This means the AI isn't only looking at what's in the fridge. It also knows what we've already eaten. So if someone says: ``` "Aloo ki sabzi toh kal hi bani thi." ``` The system can treat that as a real constraint instead of suggesting the same thing again. The React application is compiled into static assets and served directly through the FastAPI backend. Everything is deployed as a **single Web Service on Render**. The deployment configuration is included in the repository in `render.yaml`. This project started because I tried using generic AI models for my Mom. They understood Hinglish and local ingredients, but they completely failed to understand *how* she wanted the answer. A typical interaction looked like this: **Mom:** "Aaj kya banau? Aloo hai, paneer hai aur palak bhi hai." **AI:** "Aloo ki sabzi sounds wonderful!" **Mom:** "Aloo ki sabzi toh kal hi bani thi, koi nahi khayega." **AI:** "Yes, you're absolutely right! Since you've already had aloo yesterday, let's explore some other delicious alternatives..." Mom doesn't want a conversation. She doesn't want validation. She wants the AI to eliminate the bad option and give her a practical answer immediately. So, I designed *What's for Dinner?* around **atomic, decisive responses**. Instead of a chatty assistant, it just gives the options: ``` Palak Paneer Paneer Paratha Kadhai Paneer ``` No explanations. Just the answer. This is where **open innovation** changed the game. Instead of fighting a closed model's inherently chatty nature with massive, brittle system prompts, I fine-tuned an open-weight model on household constraints and the exact short, decisive answers my Mom expects. The goal wasn't just to build an AI that knows Indian food. **The goal was to build an AI that knows when to stop talking.** And thanks to open weights, I was able to build it for exactly **$0.16**. I'm submitting What's for Dinner? for: **Best Use of Tinker** — Fine-tuned gpt-oss-20b into mom-chef-v1 for concise, culturally-aware Indian meal suggestions. Fine-tuning cost: $0.16. **Best Use of Render** — Deployed the complete React + FastAPI application as a single Web Service on Render. **Backboard** — Used Backboard as the family's memory layer to track recent meals and preferences.