Just Pick One-Restaurant Finder

Suhani — Mon, 05 Oct 2026 14:49:04 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

🍽️ Just Pick One.

An AI restaurant decision-maker for people who say “anything is fine” and then reject every option.

There is a very specific kind of group-chat problem:

“Where should we eat?”

Someone says “anything.”

You suggest pizza.

“I don't feel like pizza.”

You suggest burgers.

“Something healthier?”

You suggest a restaurant.

“Too expensive.”

At this point, the group has spent 25 minutes deciding where to eat and somehow nobody is eating. 😭

So I built Just Pick One for a friend who regularly gets stuck in exactly this situation.

What I Built

Just Pick One is a restaurant recommendation tool that takes the things people actually care about when choosing a place:

💰 Budget
🥗 Vegetarian-friendly options
🍜 Cuisine / food preference
📍 Distance / location
😋 What you're actually craving

Instead of dumping a giant list of restaurants on the user, the goal is to help narrow the decision down to options that actually fit.

Less scrolling. Less arguing. More eating.

🎥 Demo

Video Demo: https://drive.google.com/file/d/1CtKR_HJyUgUFbJMqcJNFNX-i5ir-CKag/view?usp=sharing

The video shows the complete flow from entering preferences to getting restaurant recommendations.

How I Built It

The project uses open-source/open-weight AI as the decision-making layer, combined with openly available restaurant/location data.

The basic flow is:

User preferences → Restaurant candidates → AI reasoning → Recommendations

The AI isn't just there to generate a fancy paragraph about food.

It helps reason about the user's constraints and rank the available options based on what they actually asked for.

I also kept the project intentionally lightweight rather than building a giant stack of APIs, databases and authentication just to answer the question:

“Bro, where are we eating?”

🤖 Why Open Innovation?

I wanted the AI component to be based on an open-weight model rather than making the entire project dependent on one closed AI provider.

That gives the project more flexibility:

Models can be swapped.
The system can potentially be run locally.
Open models can be adapted or fine-tuned.
There is more control over how the AI layer is used.
The project isn't locked into a single proprietary model.

For a recommendation tool like this, that flexibility matters because the AI is being used as a reasoning component rather than simply as a chatbot.

🧑‍🤝‍🧑 Built for a Friend

The whole idea came from a very real problem.

My friend doesn't need 100 restaurant recommendations.

They need someone to finally say:

“We're going here. Stop searching.”

So that's what I tried to build.

A tiny AI-powered decision-maker for one of the most difficult problems known to humanity:

getting a group of friends to agree on food. 🍕

And honestly, if this saves even one group chat from 47 messages of “you decide”, I'll consider it a success.

What I Learned

The biggest lesson from this build was that a useful AI project doesn't necessarily need to be huge.

The interesting part was figuring out how to use an open-weight model for a specific decision-making problem, instead of just making another general-purpose chatbot.

Sometimes the best product idea is hiding inside the most annoying sentence in your group chat:

“Where do you guys wanna eat?”

DEV Community: Suhani