🌿 TrailBuddy AI — Plan Your Adventure, Then Put Your Phone Away
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.
What I Built
🌱 TrailBuddy AI is a local-first AI outdoor adventure companion that helps people spend less time staring at screens and more time exploring the real world.
The idea behind this project is simple: technology should help us experience the world, not keep us glued to our screens.
TrailBuddy AI lets users create personalized outdoor micro-adventures based on their available time, environment, preferred activity, and mood. Instead of endlessly scrolling to decide what to do, users can generate a plan, head outside, complete their missions, and enjoy some screen-free time.
🌳 Key Features
- 🧭 Personalized Adventure Planner: Choose from 10, 20, 30, or 60 minutes and select an environment, activity, and mood.
- 🎯 AI-Generated Nature Missions: Uses the Gemma 2 model to generate achievable outdoor challenges tailored to your preferences.
- 📵 Go Outside Mode: Includes a countdown and a reminder to leave the screen behind and enjoy the adventure.
- ✅ Mission Tracking: Check off your completed missions after returning from your outing.
- 📋 Local Adventure History: Saves completed adventures in your browser using
localStorage, without requiring an account or cloud database. - 🛡️ Safety-Focused Prompts: Encourages responsible outdoor exploration, avoiding dangerous activities, trespassing, approaching wildlife, and touching unknown plants.
TrailBuddy AI is built for students, nature lovers, beginners, and anyone who wants to turn a little free time into a meaningful outdoor experience.
The goal isn't to keep users engaged with the app. It's to help them plan something worthwhile, put their phones away, and actually do it.
Demo
🌿 GitHub Repository: https://github.com/Maheshwar247/Trailbuddy-AI
TrailBuddy AI is designed to run locally on your computer. Follow the setup instructions in the repository to install the dependencies, configure Ollama, and start the application.
The repository also contains screenshots of the application interface and its adventure-planning experience.
Code
💻 Explore the source code: https://github.com/Maheshwar247/Trailbuddy-AI
The project is open source under the MIT License.
Feel free to explore the code, try it locally, suggest improvements, or contribute ideas that make outdoor adventures more accessible and enjoyable.
How I Built It
I built TrailBuddy AI using a simple frontend-and-backend architecture with local AI inference.
🛠️ Tech Stack
- Python + Flask: Backend and communication with the local AI model.
- TypeScript: Application logic, compiled into browser-compatible JavaScript.
- HTML5 + CSS3: Responsive, nature-inspired user interface.
- Ollama: Runs the AI model locally on the user's computer.
-
Gemma 2 (
gemma2:9b): The open-weight language model used to generate personalized outdoor missions. -
Browser
localStorage: Stores completed adventures locally.
⚙️ How It Works
- The user selects their available time, environment, activity, and mood.
- The frontend sends the adventure preferences to the Flask backend.
- The backend communicates with the locally running Gemma 2 model through Ollama.
- The generated missions are presented in the interface for the user to follow.
- The user heads outside, completes the adventure, and can mark missions as completed when they return.
One of my main goals was to make AI a meaningful part of the application rather than simply adding an AI button. The model helps create the adventure itself, while the rest of the app encourages users to spend less time interacting with their devices.
Why Does Open Innovation Matter?
Open innovation matters because developers should have the freedom to understand, experiment with, and adapt the tools they build with.
For TrailBuddy AI, using an open-weight model through Ollama makes local inference possible without depending on a paid, hosted AI API.
This brings several advantages:
- Privacy and control: With the default local setup, adventure preferences are processed on the user's own machine rather than sent to a hosted AI inference service.
- Accessibility: Users can experiment without needing a paid AI API key.
- Flexibility: The model can be changed through configuration to try other compatible models supported by Ollama.
- Learning: Building with an open-weight model helped me explore how an AI model can connect to a real application through a backend.
- Community collaboration: Other developers can improve the prompts, interface, accessibility, and safety features.
For me, this project demonstrates how open-source AI can be used to build something practical while keeping the user in control.
The most important part is that the technology supports an offline activity instead of encouraging endless screen time.
My Agent Session
I don't have a DevRelay session link to include in this submission yet. If I record and publish one, I'll add it here.
Prize Categories
My primary focus is the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.
TrailBuddy AI combines an open-weight AI model, local inference, and a practical goal: encouraging people to step away from their screens and spend more time outdoors.
🌿 Plan the adventure. Put your phone away. Go explore.
I'd love to hear your feedback and suggestions for making TrailBuddy AI better!
Top comments (0)