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

What I Built

Most modern outdoor and fitness apps do the exact opposite of what they claim: they trap you in infinite feeds, leaderboard notifications, and step badges. You end up looking at glass instead of grass.

TrailPulse was built with a single rule: the screen must be the shortest part of the experience.

It is a lightweight, anti-screen outdoor companion designed for students, developers, and desk-bound workers who want to disconnect without overplanning. In under 30 seconds:

  1. You select your immediate outdoor environment (Park, Trail, Backyard, or Urban Walk) and your available time (10m to 60m).
  2. An open-weight model generates a focused, 3-task sensory micro-mission (e.g., sound identification, tactile ground observation, or canopy awareness).
  3. With a single tap, it reads the mission hands-free through your earbuds and locks into a pure-black "Pocket Disconnect Mode" with an ambient timer so your phone stays in your pocket while you step outside.

Demo

Code

The complete source code, Docker configuration, and documentation are available on GitHub:(https://github.com/AryanMohan818/TrialPulse)

How I Built It

TrailPulse was engineered from the ground up for low-friction execution and zero cloud vendor lock-in:

  • AI Core (Open Weights): Powered by Google's open-weight Gemma 2 (google/gemma-2-2b-it). We prompt Gemma to act as a nature-grounding guide, constraining output strictly to under 80 words with three distinct sensory checkpoints.
  • Offline Fault-Tolerance: Because outdoor trails frequently have spotty or zero cellular reception, TrailPulse features a dual-mode engine. If an API key is absent or connectivity drops off-grid, a local heuristic nature engine takes over instantaneously without error screens.
  • Backend & Serving: Built with FastAPI (Python 3.11) and Uvicorn for asynchronous speed and micro-footprint memory usage.
  • Anti-Screen Frontend: A single, responsive interface styled in dark nature tones with zero heavy front-end frameworks. It integrates the native Web Speech API for hands-free audio briefings.
  • Containerization & Deployment: Packaged into a minimal Docker container (python:3.11-slim) and deployed on Render as a cloud web service.

Why Does Open Innovation Matter?

Open innovation is not just an architectural choice for TrailPulse — it is the fundamental reason the project works:

  1. Trail Resilience & True Off-Grid Mobility: Proprietary closed-source LLMs force an active, high-bandwidth connection to remote corporate servers. When you are on a hiking trail or deep in a park with zero signal, closed APIs fail completely. Open-weight models like Gemma can be packaged directly into local edge containers and laptops to generate missions anywhere on the planet.
  2. Anti-Surveillance & Data Ownership: A user stepping outside to de-stress should not have their geolocation, routines, and mental health logs funneled into proprietary ad-targeting pipelines. Open models keep the pipeline transparent and self-hosted.
  3. Zero Operating Toll: Closed APIs impose metering costs per token. By utilizing open-weight foundations, anyone in the community can host their own instance or community run club planner for zero ongoing API bills.

Prize Categories

  • Featured Category — Best Use of Render ($200): TrailPulse is containerized with Docker and hosted on Render's cloud platform as a fast, accessible AI runtime.
  • Featured Category — Best Use of Gemma ($200): The project relies on Google's Gemma 2 open-weight model as its creative core, turning raw location and time inputs into bite-sized sensory outdoor quests.