Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.
🌱 What I Built
TerraQuest AI — Turn Your Free Time Into an Outdoor Adventure
TerraQuest AI is an AI-powered outdoor companion designed to help people disconnect from their screens and reconnect with nature.
Instead of spending free time scrolling through social media, users can generate personalized outdoor adventures based on their available time, preferred activities, interests, and exploration style.
Whether someone wants a peaceful nature walk, an energetic hike, a wildlife discovery mission, or a photography adventure, TerraQuest AI helps them find an activity and gives them a structured plan to get started.
The application combines AI-generated outdoor missions with semantic search to recommend relevant recreational routes from California State Parks data.
✨ Key Features
- Personalized Outdoor Missions: Generate activities for walking, hiking, running, cycling, nature exploration, and relaxation.
- Interest-Based Adventures: Choose from nature, fitness, wildlife, photography, and adventure.
- Flexible Time Planning: Get an outdoor mission that fits your available free time.
- AI-Generated Activity Plans: Receive a warm-up, main activity, and cooldown tailored to your preferences.
- Semantic Trail Discovery: Find relevant route records using text embeddings and vector similarity instead of relying only on exact keyword matches.
- Location-Aware Search: Optionally use manually entered coordinates to support location-based route discovery.
- Digital Detox Philosophy: Get your mission, put your phone away, and enjoy the real world.
TerraQuest AI is built for anyone who wants to make their free time more meaningful by exploring the world beyond their screen.
🎥 Demo
The project includes screenshots of the working Streamlit application.
1. TerraQuest AI Interface
View the application interface
2. Personalized Outdoor Missions
3. Trail Recommendations
The application currently runs locally and has not yet been publicly deployed.
To install and run it, follow the setup instructions in the project README.
💻 Source Code
🌿 GitHub Repository: TerraQuest AI — Source Code
📸 Project Screenshots: View the screenshots
The repository contains the Streamlit application, AI mission-generation logic, semantic retrieval components, database integration, and installation instructions.
Note: The repository and screenshot links currently point to the original project. Update the repository name, screenshots, and documentation if you create a separate TerraQuest AI version.
🛠️ How I Built It
TerraQuest AI combines open-weight language models, semantic search, vector databases, and an interactive Python interface.
Technology Stack
- Python: Application logic and time allocation.
- Streamlit: Interactive web interface.
- Ollama + Gemma 3 4B: Local AI-powered mission generation.
- Sentence Transformers: Text embedding generation.
- BAAI/bge-base-en-v1.5: Semantic search embeddings.
- Tiger Cloud PostgreSQL: Storage for route information and vector embeddings.
- pgvector: Similarity-based retrieval of relevant routes.
- psycopg and python-dotenv: Database connectivity and environment configuration.
⚙️ How It Works
- User Preferences: The user chooses an activity, interests, mission style, and available time.
- Search Query Creation: The application builds a semantic query from the selected preferences.
- Embedding Generation: The BGE model converts the query into a numerical vector representation.
- Route Retrieval: PostgreSQL and pgvector search for relevant route records using vector similarity.
- AI Mission Generation: Gemma 3 4B generates activity instructions organized into a warm-up, main activity, and cooldown.
- Time Allocation: Python manages the activity's time distribution and assembles the final mission.
- Mission Display: Streamlit presents the generated plan and relevant route recommendations.
I also included fallback instructions so that the application can still provide a useful outdoor activity plan when AI generation fails or its response cannot be parsed correctly.
The route dataset comes from California State Parks recreational routes. These records represent route segments rather than necessarily complete trails, so individual segment lengths should not be interpreted as the total distance of an entire trail.
🌍 Why Open Innovation Matters
Open innovation makes it possible to experiment with AI systems, understand their underlying components, and build applications without depending entirely on proprietary hosted AI services.
With Gemma 3 4B running through Ollama, TerraQuest AI can generate outdoor missions locally. This makes experimentation with prompts and mission-generation logic more accessible.
The BGE embedding model and pgvector provide another important capability: finding relevant recreational routes based on semantic similarity rather than relying exclusively on exact keyword matches.
Together, these technologies demonstrate how open models, accessible infrastructure, and open-source tools can be combined to build a practical AI-powered application.
TerraQuest AI is not completely offline because route retrieval depends on its configured PostgreSQL database service. Its recommendations are also limited by the available California route dataset. Users should verify trail accessibility, weather, current conditions, and safety information before heading outdoors.
🚀 What Makes TerraQuest AI Different?
Many AI applications encourage users to spend more time interacting with technology. TerraQuest AI takes a different approach.
It uses AI to help people plan an activity, discover a place to explore, and then step away from their devices.
The technology is meant to support the outdoor experience—not replace it.
The long-term vision is to make discovering outdoor activities easier, encourage people to explore their surroundings, and turn small amounts of free time into memorable real-world experiences.
🤖 My Agent Session
I have not recorded a DevRelay agent session for this project yet.
🏆 Prize Categories
- Best Use of Gemma: Gemma 3 4B generates personalized outdoor mission instructions locally through Ollama.
- Best Use of Tiger Data: Tiger Cloud PostgreSQL and pgvector power semantic retrieval across route records and their embeddings.
🌿 The Final Goal
Technology should not always demand more of our attention. Sometimes, it should help us put our phones away.
TerraQuest AI is my attempt to use open-source AI to encourage more outdoor exploration, healthier digital habits, and meaningful experiences beyond the screen.
Find your adventure. Step outside. Explore more. Touch grass. 🌱
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