TrailMate AI: Turn Your Free Time into an Outdoor Adventure with Local AI

Sahil desai — Sat, 10 Oct 2026 14:06:30 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

I built TrailMate AI, a local AI-powered outdoor adventure planner that turns your free time into an outdoor mission.

The idea is simple: instead of spending your free time scrolling through your phone, use AI to help you get outside and explore the world around you.

Users enter their available time, preferred activity, intensity level, budget, and location. TrailMate generates a personalized adventure plan with a mission, route idea, packing checklist, outdoor challenge, and safety tips.

Whether you have 30 minutes for a quick walk or a few hours for a more challenging adventure, TrailMate helps you make the most of your time outdoors.

The goal is to make the screen the shortest part of the experience.

Demo

Video demo: Watch TrailMate AI in action

The demo shows TrailMate AI running locally, from entering adventure preferences to generating an outdoor plan using an open-weight AI model.

Code

GitHub repository: sahil1399m/trailmate-ai

The repository contains the frontend, FastAPI backend, and code for running the local AI inference pipeline.

How I Built It

TrailMate AI is built around a locally running, open-weight language model rather than a proprietary hosted AI API.

Tech stack:

AI model: SmolLM2 1.7B Instruct, quantized to Q4_K_M
Local inference: llama.cpp
Backend: Python and FastAPI
Frontend: React, Vite, and Tailwind CSS
Data validation: Pydantic

The application follows a straightforward architecture:

The user enters their preferences in the React frontend.
The frontend sends a request to the FastAPI backend.
The backend constructs a prompt using the user's available time, activity, intensity, budget, and location.
llama.cpp runs SmolLM2 locally to generate the adventure plan.
The backend validates and structures the response before returning it to the frontend.

I chose a relatively small, quantized model so the project could run on a regular laptop without a dedicated GPU. The model is downloaded separately rather than committed to the GitHub repository.

An important part of building TrailMate was making the output reliable. I added structured response validation, normalization of malformed text, and safeguards against inventing specific parks or trails.

Why Does Open Innovation Matter?

Open innovation made it possible to build an AI application that runs locally without depending on a paid, proprietary model API.

Using an open-weight model and llama.cpp gives developers the freedom to experiment with inference, prompting, and application architecture on their own hardware. It also reduces dependence on external AI services and helps keep users' preferences and location information within the local application workflow.

For a project designed to encourage people to spend less time on screens, I wanted the AI itself to be a lightweight tool rather than another service that requires constant cloud connectivity.

TrailMate AI demonstrates how accessible open AI models can be turned into practical applications that encourage real-world experiences.

My Agent Session

I built TrailMate AI individually, using Antigravity as my AI coding assistant throughout development. I used it to help implement the FastAPI backend, integrate SmolLM2 through llama.cpp for local inference, and build the React frontend.

Prize Categories

TrailMate AI uses SmolLM2 1.7B Instruct, an open-weight language model, with local inference through llama.cpp.

I built this project individually for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.

DEV Community: Sahil desai

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