DEV Community

Shitanshu Jha
Shitanshu Jha

Posted on

WalkQuest AI: Turning Screen Time into Outdoor Adventures 🌿

#devchallenge #hf26challenge

Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass Submission 🌿

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

What if AI didn't try to keep you on your screen, but actually encouraged you to step away from it?

That question inspired WalkQuest AI, a small project built around a simple idea: turn a user's available time, interests, and mood into a short outdoor quest.

🌱 What is WalkQuest?

WalkQuest AI helps people take small breaks from screens and reconnect with the world around them. Instead of scrolling through another feed, users can generate a simple outdoor activity plan for walking, gardening, mindfulness, nature observation, or exploration.

The goal isn't to replace the outdoors with another app. It's to make the time spent using the app short, useful, and purposeful.

✨ Features

  • AI-generated quests: Describe what you feel like doing and how much time you have.
  • Multiple activity categories: Walking, gardening, mindfulness, nature, exploration, and observation.
  • Three-step quests: Break an outdoor break into three manageable activities.
  • Phone Pocket: A reminder to put the phone away and focus on the real world.
  • Quest timer and completion flow: Start an outdoor session, return, and reflect on the experience.
  • Progress tracking: Keep track of completed quests and related activity stats.

🧠 How I built it

WalkQuest uses Gemma 3 1B through Ollama for local AI inference. The application is built with Java and Spring Boot, with a browser-based frontend.

The backend sends a structured prompt to the local model and processes the generated quest into a title, duration, category, difficulty, and three activities.

I also worked on fallback behavior so that Hinglish requests can receive predictable, understandable activities when generated language quality is unreliable.

Tech stack

  • Java and Spring Boot
  • Gemma 3 1B
  • Ollama for local inference
  • HTML, CSS, and JavaScript
  • Maven

🎬 Demo

Watch the WalkQuest AI demo: Upload/embed the attached
Demo Video: https://youtu.be/PcGwOobycqA video here.

The demo shows the quest-generation flow and the app's outdoor-session experience.

🌍 Why open innovation matters

An app designed to help people spend less time on screens should not depend entirely on a remote AI service.

Using an open-weight model with local inference gives developers more control over how AI is run, how prompts are designed, and how the model fits into the application. Once the model has been downloaded, inference can run locally through Ollama, without sending each generation request to a hosted model API.

It also makes experimentation more accessible: developers can inspect the code, change the prompts, improve fallback behavior, and explore other compatible models instead of being locked into one closed service.

Local inference does not automatically make every part of an application offline, but it gives this project a foundation for more private and flexible AI experiences.

🛠️ Run it locally

Repository: https://github.com/Shitanshu686/walkquest

Requirements:

  • Java 17 or later
  • Git
  • Ollama installed
  • The gemma3:1b model downloaded

Clone the repository:

git clone https://github.com/Shitanshu686/walkquest.git
cd walkquest
Enter fullscreen mode Exit fullscreen mode

Download the model and start Ollama:

ollama pull gemma3:1b
ollama serve
Enter fullscreen mode Exit fullscreen mode

In another terminal, launch the application using the repository's run-walkquest.bat script on Windows, or use the Maven wrapper:

./mvnw spring-boot:run
Enter fullscreen mode Exit fullscreen mode

Open the local address and port printed by the application.

🚀 What's next?

I'd like to improve multilingual generation quality, expand the available quest types, and make outdoor progress tracking more useful without adding unnecessary screen time.

WalkQuest is an experiment in building AI that helps people do something beyond interacting with AI itself.

Build with open AI. Step outside. Touch grass. 🌿

Prize categories: Best Use of Gemma

Tags: #devchallenge #hf26challenge #opensource #ai #java

🎬 WalkQuest AI — Live Demo

Watch the demo to see WalkQuest AI in action, including outdoor quest generation and the screen-free activity flow.
Demo Video: https://youtu.be/PcGwOobycqA

▶️ Watch WalkQuest AI Demo on YouTube

The demo showcases how WalkQuest turns a simple request into an outdoor activity experience.

Source Code: https://github.com/Shitanshu686/walkquest

Top comments (0)