GreenQuest AI: Turning Screen Time into Green Time | Open-Source AI Challenge

Yashi Yadav — Fri, 09 Oct 2026 11:14:38 +0000

🌿 GreenQuest AI — Touch Grass, Not Screens

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

What I Built

GreenQuest AI is an AI-powered outdoor activity planner that encourages people to spend less time scrolling and more time exploring the real world. 🌱

In a world where we spend hours looking at screens, GreenQuest AI turns the simple idea of “touching grass” into a fun, personalized experience.

Users can choose their mood, available time, and preferred outdoor activity. Based on their choices, the application generates a personalized outdoor mission to help them take a break from their screens and reconnect with nature.

✨ Key Features

🌿 AI-Powered Missions: Generates personalized outdoor challenges based on the user's preferences.
😊 Mood-Based Recommendations: Suggests activities that match how the user feels.
⏱️ Flexible Time Options: Provides activities based on the time users have available.
🎯 Gamified Experience: Earn points and track completed missions.
📊 Progress Tracking: Keeps track of completed missions and outdoor minutes.
🔄 Fallback Missions: Provides suggested activities even when the local AI model is unavailable.
📱 Responsive Design: A clean, nature-inspired interface designed for different screen sizes.

GreenQuest AI is designed for students, busy individuals, and anyone who wants to build healthier digital habits while enjoying outdoor activities.

The goal is simple: Less scrolling. More living. 🌎

Demo

🌐 Live Website:
https://yashishanvi95-lang.github.io/-GreenQuest-AI-Touch-Grass-Not-Screens/

Try choosing your mood, selecting how much time you have, and generating your own outdoor mission!

Code

💻 GitHub Repository:
https://github.com/yashishanvi95-lang/-GreenQuest-AI-Touch-Grass-Not-Screens

The project is built using HTML, CSS, and JavaScript, with an AI integration designed to work with a locally running model through Ollama.

Feel free to explore the code, suggest improvements, and contribute to making outdoor activities more engaging!

How I Built It

I built GreenQuest AI using HTML, CSS, JavaScript, and open-weight AI running locally through Ollama.

🛠️ Tech Stack

HTML5: Structures the website and its interactive sections.
CSS3: Creates the responsive, nature-inspired user interface.
JavaScript: Handles user input, AI requests, mission generation, and progress tracking.
Ollama: Runs the AI model locally.
Qwen2.5 3B: The open-weight language model used to generate personalized outdoor missions.
LocalStorage: Saves progress, points, and completed missions in the browser.

🤖 How the AI Works

The user selects their mood, available time, and preferred activity.
JavaScript sends the selected preferences to the locally running Ollama API.
The Qwen2.5 3B model generates a personalized outdoor mission.
The website displays the suggested mission for the user to complete outside.
After completing a mission, users can track their progress and earn points.

I also included fallback missions so that the core experience can remain useful when the local AI service is unavailable.

One of the main challenges was connecting a frontend website to a locally running AI model. This helped me understand how frontend applications can communicate with AI services and how local inference can be incorporated into a practical project.

Why Does Open Innovation Matter?

Open innovation made GreenQuest AI possible as an accessible learning project without depending on a paid, proprietary AI API.

By using an open-weight model through Ollama, I could explore local AI inference, experiment with prompts, and build a personalized experience while learning how AI integration works in a real application.

It also offers several advantages:

Accessibility: Experimenting with AI without requiring a paid API for every request.
Privacy potential: User preferences can be processed locally rather than automatically being sent to a third-party AI provider, depending on the application's configuration.
Flexibility: The model can be changed or customized as the project evolves.
Learning by building: Developers can understand AI integration beyond simply calling a hosted API.
Community collaboration: Other developers can explore the project, suggest new outdoor missions, and contribute features.

Open innovation is not just about making technology available. It is about giving people the freedom to experiment, learn, and build solutions to everyday problems.

With GreenQuest AI, I wanted to use AI to encourage people to spend more time away from technology itself. 🌿

Note: Qwen2.5 3B is an open-weight model. Its usage and redistribution are subject to the applicable model licence.

My Agent Session

This section is optional. If I share a DevRelay session demonstrating the development process, I will add the session link here.

Prize Categories

Please verify the challenge's official partner categories and select only those that apply to your project. I am leaving this section unclaimed rather than listing unverified categories.

🌱 Final Thought

Technology should improve our lives, not consume all our time.

GreenQuest AI is my small step toward combining artificial intelligence with healthier digital habits and a stronger connection with the world outside our screens.

Touch grass. Complete a quest. Make real-world memories. 🌍💚

Hacktoberfest #OpenSourceAI #AI #Ollama #Qwen #WebDevelopment #GreenQuestAI

DEV Community: Yashi Yadav