Nature Bingo — Touch Grass 🌿

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.

What I Built

What if a bingo game could give you a reason to put your phone down and notice the world around you?

That’s the idea behind Nature Bingo — Touch Grass, a 45-minute outdoor exploration game designed to make spending time in nature more engaging, mindful, and interactive.

The app presents a 5×5 bingo board containing 25 nature-observation challenges across five categories: Flora, Fauna, Senses, Earth, and Sky. Instead of completing ordinary tasks on a screen, players are encouraged to step outside, observe their surroundings, and record what they discover.

Challenges include identifying a leaf edge, observing an insect at work, listening to birdsong, noticing contrasting natural textures, and watching cloud formations. Depending on the challenge, players can record evidence using photos, audio, GPS, timed observation, or an honour-based completion option.

The goal is simple: complete five challenges in a horizontal, vertical, or diagonal line to win.

I also wanted the experience to feel like an outdoor exploration journal rather than another generic productivity app. The botanical visual style, earthy colours, and nature-inspired details are designed to make the experience inviting while keeping the focus on real-world exploration.

The principle behind it: use technology to encourage people to spend more time experiencing the world beyond their screens.

Demo

🌿 Try the live application:

Try Nature Bingo

🎥 Watch the project walkthrough:

The video demonstrates the application, its bingo board, and the features shown in the walkthrough.

Screenshots

Nature Bingo Landing Page

Nature Bingo Game Play

Nature Bingo Evidence Collection

Nature Bingo Session Completion

Me collecting evidence!!

And confirming completion!!

Code

💻 GitHub repository:

View the source code on GitHub

The project is open source, and the repository contains the implementation.

How I Built It

I built Nature Bingo using a React and Vite frontend with a Node.js and Express backend.

The frontend handles the bingo board and player interactions, while the backend provides API endpoints for challenge delivery and related functionality. The application also includes challenge evidence options and logic for tracking completed cells and identifying bingo lines.

Tech stack

• Frontend: React, Vite, CSS
• Backend: Node.js, Express
• AI experimentation: Gemma 4 (open-weight model) through Ollama
• Deployment: Vercel for the frontend and Render for the backend

Exploring open-weight AI

One of the learning goals for this project was to explore how open-weight AI models could fit into a practical application.

I experimented with running Gemma locally through Ollama and explored using the model to generate nature-based challenges. This introduced me to the practical side of local inference, including model availability, hardware constraints, response time, and integration with an application backend.

The deployed application uses curated fallback challenges when the local model is unavailable on the hosting server. This keeps the core game playable without depending on a running local model in production.

This distinction was important to me: experimenting with AI is valuable, but the application should still provide a usable experience when the model is unavailable.

Why Does Open Innovation Matter?

For me, this project was an opportunity to move beyond simply calling a hosted AI API and explore what working with an open-weight model actually involves.

Open innovation makes it possible to experiment with models such as Gemma, explore local inference through tools such as Ollama, and learn how AI systems can be integrated into applications without relying exclusively on a closed, hosted model.

It also exposes the engineering trade-offs that can be easy to overlook when using an API: hardware requirements, inference latency, deployment constraints, and reliable fallback behaviour.

Nature Bingo taught me that incorporating AI is not just about generating an impressive response. It is about deciding where AI adds value, understanding its limitations, and designing the surrounding system so that the experience remains useful.

The project is still an exploration, but that is part of what made building it worthwhile: learning by experimenting, testing, and making the application work under real constraints.

Ultimately, I wanted to build something that connects technology with an offline experience—and use open-source tools to learn along the way.

Prize Categories

I’m submitting Nature Bingo for the Best Use of Gemma category, based on my experiments with the open-weight Gemma model through Ollama to explore nature-challenge generation.

The deployed version currently uses curated fallback challenges when the local model is unavailable in production.

Thanks for checking out Nature Bingo! 🌱

devchallenge #hf26challenge