This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.
What I Built
🤖🌿 What if AI stopped giving you more things to read and started giving you reasons to explore the real world?
That's the idea behind OutsideQuest — an open-source AI project powered by Qwen 3.5 4B running locally through Ollama. It turns ordinary outdoor moments into curiosity-driven observation missions.
We often use AI to write, code, summarize, and automate tasks. With OutsideQuest, I wanted to explore a different possibility: what if AI encouraged us to spend less time staring at screens and more time noticing the world around us?
OutsideQuest generates outdoor missions that encourage users to observe, question, and investigate their surroundings.
Some of its mission categories include:
- 🔊 Sound Detective: Pay attention to sounds around you and investigate their possible sources.
- 🌗 Light & Shadow: Observe how sunlight, shadows, and lighting change your surroundings.
- 🌱 Urban Nature Mystery: Explore small details of nature around you.
- 🏙️ Human Impact: Notice how human activities affect the environment.
- 🔎 Micro-Discovery: Investigate an ordinary detail more carefully.
The app also includes a local discovery journal that saves observations in discoveries.json. Its follow-up investigation feature helps users turn observations into hypotheses and consider what they could investigate next.
It's designed for curious people, students, and anyone who wants a simple reason to explore their surroundings.
The goal isn't to make AI do all the thinking. It's to use AI to encourage people to observe, think, and discover things for themselves.
Demo
OutsideQuest currently runs locally on my machine using Ollama and Qwen 3.5 4B. I haven't deployed a public hosted version yet.
OutsideQuest Dashboard
Discovery Journal
To run the application, follow the setup instructions in the GitHub repository.
Code
🌿 GitHub Repository: https://github.com/pratyushmishra9920-ship-it/outsidequest-ai
The repository contains the project source code, README, and instructions for running the application locally.
Feel free to explore the code and share suggestions or ideas for improving the project.
How I Built It
I wanted to experiment with an open-weight model that could run locally rather than making the entire experience dependent on a hosted AI API.
Here's the technology stack:
- Python: Application logic and data handling.
- Streamlit: Interactive web interface.
- Ollama: Local model runtime.
- Qwen 3.5 4B: The model used to generate outdoor missions.
- JSON: Local storage for the discovery journal.
How the application works
- The user selects an outdoor mission category.
- The application sends a prompt to the locally running model through Ollama.
- The model generates an observation mission.
- The user explores their surroundings and records an observation.
- The follow-up investigation flow helps the user develop a hypothesis and consider a possible next experiment.
- The observation is saved in the local discovery journal.
One of my main design decisions was to build around an actual real-world activity instead of creating another chatbot interface.
I also wanted the missions to be accessible. They shouldn't require expensive equipment or assume that everyone has access to a particular park, garden, or outdoor location.
Why Does Open Innovation Matter?
Open innovation gives developers the freedom to experiment, learn from existing tools, and explore ideas beyond conventional use cases.
For OutsideQuest, using an open-weight model locally allowed me to experiment with AI-powered mission generation without making a hosted model API the centre of the application.
Local inference also means prompts can be processed by the model on the user's machine rather than being sent to a remote model provider. The model must be installed locally, and the application currently runs on the user's computer.
But the idea goes beyond the technology itself.
AI is often used to help us work faster, automate tasks, and spend more time interacting with digital tools. I wanted to explore another possibility: Can AI help us reconnect with the physical world instead?
OutsideQuest uses AI to suggest what to investigate, but the actual experience happens outside the screen. The user still has to observe their surroundings, think critically, and decide what to investigate.
Open tools made it possible for me to experiment with this idea using local inference and build an experience that doesn't look like a traditional AI chatbot.
Sometimes, the best AI-generated response is a reason to put your phone away and go outside.
My Agent Session
The project uses Ollama with Qwen 3.5 4B for local inference.
I don't have a separate, verified DevRelay agent-session link to share, so I've included the GitHub repository as the main reference for the project.
Prize Categories
Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass 🌿
What's Next?
OutsideQuest is a starting point, and I have several ideas for improving it:
- Making missions more varied and responsive to users' observations.
- Improving the follow-up investigation flow.
- Adding better ways to revisit and reflect on previous discoveries.
- Making the application easier to set up on different machines.
For now, the core experience is deliberately simple:
Get a mission. Step outside. Pay attention. Discover something. 🌿
I'd love to hear your thoughts.
If you could build an AI tool that encouraged people to spend more time in the real world, what would it do?
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