This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
🌿 What I Built
TerraQuest AI — Stop Scrolling. Start Exploring.
What if artificial intelligence could encourage us to spend LESS time using technology?
We usually associate AI with chatbots, productivity tools, and digital experiences.
But I wanted to explore a different possibility:
What if AI could inspire people to close their laptops, put away their phones, and discover the world around them?
That idea inspired me to build TerraQuest AI.
TerraQuest AI is an AI-powered outdoor adventure generator that creates personalized real-world missions using an open-weight language model.
🌍 The Problem
Digital entertainment is everywhere.
People spend hours scrolling through social media, watching videos, and interacting with screens.
But sometimes, the biggest challenge to going outside isn't finding time.
It's deciding what to do.
TerraQuest AI addresses this problem by turning outdoor exploration into a personalized adventure.
💡 The Solution
TerraQuest AI generates creative outdoor missions based on four user preferences:
- 📍 Location
- ⏱️ Available time
- 🌱 Interests
- ⚡ Difficulty level
For example, a user interested in nature might receive missions such as:
- Find three different types of leaves.
- Observe a bird and describe its behavior.
- Discover an interesting natural pattern.
- Spend five minutes enjoying the outdoors.
The application also includes:
📵 Touch Grass Mode
Once an adventure is generated, users are encouraged to put their devices away and complete their missions in the real world.
🏆 Adventure Achievements
Users can mark adventures as completed and unlock achievement badges.
🧠 Personalized AI Adventures
Instead of displaying predefined activities, TerraQuest AI generates missions using a locally running language model.
The goal is simple:
Spend less time scrolling. Spend more time exploring.
🎬 Demo
🌿 TerraQuest AI in Action
The application follows a simple workflow:
- Open TerraQuest AI.
- Enter your city.
- Select your available time.
- Choose your interests and difficulty level.
- Click "Generate My Adventure".
- Receive personalized outdoor missions.
- Activate Touch Grass Mode.
- Complete your adventure and unlock achievements.
📸 Application Screenshots
Homepage — Create Your Adventure
AI-Generated Outdoor Adventure
Touch Grass Mode
Adventure Achievements
💻 Running the Demo
TerraQuest AI currently runs locally using Streamlit and Ollama.
To try the application, follow the setup instructions in the GitHub repository linked below.
💻 Code
GitHub Repository
https://github.com/sandeepji11/terraquest-ai
The project includes the Python application and instructions for running it locally.
🛠️ Technology Stack
| Technology | Purpose |
|---|---|
| Python | Core application logic |
| Streamlit | Interactive web interface |
| Ollama | Local AI model execution |
| Qwen2.5 1.5B | Personalized adventure generation |
| GitHub | Open-source code hosting |
🧠 How I Built It
Step 1: Building the Interface
I used Python and Streamlit to create a simple, interactive web application.
The interface allows users to enter their location, select their available time, and choose the type of outdoor adventure they prefer.
I designed the application with a green-and-white theme inspired by nature.
Step 2: Integrating Open-Weight AI
The core of TerraQuest AI is the Qwen2.5 1.5B language model.
I used Ollama to run the model locally.
When users request an adventure, the application creates a prompt containing their preferences.
This prompt is sent to Ollama through its local API.
The model then generates a personalized outdoor mission.
The response is displayed directly inside the Streamlit application.
Step 3: Creating Touch Grass Mode
The goal of the challenge is to encourage people to spend less time looking at screens.
To support this goal, I created Touch Grass Mode.
After generating an adventure, users can start their mission and put their devices away.
The application encourages real-world participation rather than continuous digital engagement.
Step 4: Adding Achievements
I implemented a simple achievement system using Streamlit session state.
Users can track completed adventures and unlock badges.
The available achievements include:
- 🌱 Nature Explorer
- 🌿 Nature Enthusiast
- 🏆 Outdoor Champion
The current version stores progress temporarily during the application session.
Step 5: Testing
I tested the application by generating personalized outdoor missions with different input preferences.
The AI successfully generated adventure suggestions through the locally running language model.
🚧 Challenges
One challenge was integrating the locally running AI model with the Streamlit interface.
Another was designing the application around minimal screen time.
Rather than creating an experience that requires constant interaction, I focused on generating short missions that encourage users to step away from their devices.
🌱 Why Does Open Innovation Matter?
Open innovation is at the heart of TerraQuest AI.
I wanted to build an AI-powered application without depending entirely on proprietary cloud-based AI services.
Using an open-weight model made that possible.
🔒 Privacy
TerraQuest AI processes adventure-generation prompts locally through Ollama.
This means users do not need to send their prompts to a hosted language-model API.
💻 Local AI Inference
The core adventure-generation feature runs on a local machine.
Once the required model and dependencies are installed, it does not require an internet connection for AI inference.
🔄 Flexibility
Open-weight models allow developers to experiment with different model configurations.
TerraQuest AI can potentially support alternative models in future versions.
🌍 Accessibility
Open-weight AI gives developers opportunities to build intelligent applications without paying for every inference request.
💚 Why It Matters for This Project
TerraQuest AI demonstrates that AI doesn't always need to keep people engaged with technology.
Sometimes, AI can be most useful when it encourages people to disconnect.
The best AI experience might be the one that inspires you to close your screen.
🤖 My Agent Session
I used AI assistance during development to help plan the application, write Python code, troubleshoot errors, and improve the Streamlit interface.
The project uses Ollama and Qwen2.5 1.5B for its core adventure-generation functionality.
I did not record a DevReplay agent session for this version.
🏆 Prize Categories
No optional partner-technology prize category is being claimed for this submission.
🔮 Future Improvements
I would love to expand TerraQuest AI with:
- 🗺️ Nearby park and trail discovery
- 🌿 AI-assisted plant identification
- 📵 Offline adventure cards
- 🏅 Persistent achievements
- 🌎 Community outdoor challenges
These improvements could help transform TerraQuest AI into a more complete outdoor exploration companion.
🌍 Final Thoughts
Building TerraQuest AI helped me explore an interesting question:
Can artificial intelligence encourage people to disconnect from technology?
I believe it can.
AI doesn't always need to maximize the amount of time people spend using applications.
Sometimes, its greatest contribution is helping people discover meaningful experiences beyond their screens.
🌿 TerraQuest AI — Stop Scrolling. Start Exploring.
Thanks for reading!
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