This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
🌿 TrailMate AI — Your AI-powered outdoor activity planner
TrailMate AI helps people spend less time staring at screens and more time exploring the outdoors. Instead of endlessly scrolling for things to do, users can generate personalized outdoor activity plans based on their starting location, available time, preferred activity pace, and favorite scenery.
Whether you have just 20 minutes for a short walk or 90 minutes for a nature adventure, TrailMate AI suggests activities, provides a simple itinerary, recommends what to bring, and includes basic outdoor safety reminders.
The project is designed for students, busy professionals, travelers, and anyone who wants to make getting outside easier.
The main idea is simple: use AI to help people leave their screens, not spend more time on them.
Demo
🌐 Live Demo: [Not Ready Yet]
📸 Screenshots:
The demo currently includes a sample-planning mode, so users can explore the experience without configuring an AI model.
Code
💻 GitHub Repository: [https://github.com/antonluckshman/TrailMate-AI]
TrailMate AI is built with React and Vite. The source code includes the user interface, activity-planning logic, sample plans, and optional local AI integration.
Feel free to explore the code, suggest improvements, report issues, or contribute new outdoor activities and planning features!
How I Built It
I built TrailMate AI using React, Vite, and open-source AI models that can run locally.
The application has two planning modes:
- Demo mode: Generates sample outdoor plans immediately, allowing users to try the application without setting up an AI model.
- Local AI mode: Can connect to Ollama to run a supported open-weight language model locally, such as Llama 3.2 or Qwen2.5.
Users choose their starting location, available time, preferred pace, and scenery. The application uses those preferences to generate an outdoor activity plan with suggested activities, a checklist, and safety reminders.
I wanted to make the project accessible to people who don't have access to paid AI APIs. By supporting local inference, TrailMate AI provides a path toward experimenting with AI without requiring a paid model API key.
The current version is an MVP. Its plans are suggestions, not verified hiking routes or live recommendations based on weather, trail conditions, or real-time maps.
Why Does Open Innovation Matter?
Open innovation makes it possible for developers to experiment, learn, and build useful applications without depending entirely on proprietary platforms.
For TrailMate AI, open-weight models and local inference offer several advantages:
- Accessibility: Developers and users can experiment without necessarily paying for a commercial AI API.
- Privacy and control: With a properly configured local setup, prompts can be processed on the user's machine rather than sent to a hosted AI provider.
- Flexibility: Different supported models can be tested and compared for activity planning.
- Community collaboration: Other developers can contribute new activity types, improve prompts, add local outdoor knowledge, and extend the application.
- Learning by building: Open tools make it easier to explore how AI fits into a complete application, from the user interface to model inference.
A closed API could also generate outdoor plans, but open-weight models give developers more flexibility over where inference happens and which model they choose. They also make experimentation more accessible.
I believe open innovation is especially valuable when building small projects: it lowers barriers to experimentation and gives the community a starting point that others can adapt to their own needs and locations.
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