FoliageStride AI — Scenic Autumn Run & Park Route Explorer

A developer built FoliageStride AI, an open-source browser-based running route generator that uses Google's Gemma 2 (gemma2:2b) model through Ollama to synthesize scenic autumn routes from distance, terrain and pacing preferences. The app runs the model locally for offline-first use, falls back to a deterministic route-generation engine when the model is unavailable, and uses the Web Audio API to play cadence chimes at kilometer milestones so runners avoid checking their phones. The project was pair-programmed with Google Antigravity and its code is published on GitHub.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass https://dev.to/challenges/hacktoberfest-week1-2026-10-05 FoliageStride AI is a scenic autumn run & park route builder built to solve a modern fitness irony: runners going outdoors just to stare at GPS screens, pace charts, and turn-by-turn prompts every 30 seconds. Instead of keeping runners glued to their devices, FoliageStride AI uses Google Gemma 2 to synthesize autumn running routes based on distance 2 km, 5 km, 8 km , terrain preference Maple & Oak canopies, riverbank reflections, historic park perimeters, or hilltop ridges , and pacing style. In under 30 seconds: The application runs in modern browsers and includes a fallback route-generation engine when the local AI model is unavailable. The complete open-source code is available on GitHub: GitHub Repository: https://github.com/ahanghosh77/foliagestride-ai https://github.com/ahanghosh77/foliagestride-ai The repository contains: index.html — Main application interface with distance selection, route visualization, elevation profile, visual waypoints, stopwatch, and Stride Journal. style.css — Responsive autumn-themed UI with glassmorphism styling. app.js — Gemma 2 integration through Ollama, fallback route generation, Web Audio API cadence chimes, and localStorage-based journal tracking. FoliageStride AI is designed with an offline-first architecture. The core AI component uses Google Gemma 2 gemma2:2b through Ollama. The model receives the runner's preferences, including: It then generates structured route information and memorable physical landmarks that the runner can remember before starting the run. The application uses the browser's native Web Audio API with OscillatorNode and GainNode to generate cadence chimes at kilometer milestones. This means the runner does not need to continuously look at the phone during the run. The application uses SVG-based graphics to display the route and elevation profile, together with visual markers representing foliage and important landmarks. If the local Gemma 2 model is unavailable, the application uses a deterministic fallback route-generation engine. This allows the application to remain usable even without a running local AI model. The Stride Journal uses browser localStorage to keep track of completed sessions and outdoor streaks without requiring a cloud database. Open innovation and open-weight models like Google Gemma 2 are especially useful for outdoor and fitness applications. Outdoor runners can lose cellular reception inside parks, ravines, mountain trails, and nature reserves. A local AI model reduces dependence on a continuously available cloud API. Running applications can involve sensitive information such as routes, workout frequency, and frequently visited locations. FoliageStride AI is designed around local processing and local journal storage rather than sending this information to a third-party fitness server. Running an open-weight model locally avoids paying for every AI request through a hosted API. This makes experimentation more accessible to students, developers, and open-source communities. This project was built through pair-programming with Google Antigravity . The agent helped with the responsive UI layout, autumn visual design, SVG course elevation profile, structured JSON schema for Google Gemma 2 prompting, hands-free Web Audio API split-chime synthesizer, and overall project structure. The final project was refined around the core idea of helping runners touch grass instead of constantly checking a screen . FoliageStride AI uses Google Gemma 2 gemma2:2b as the local route-synthesis engine, converting a runner's distance, scenery preference, and pacing style into structured scenic route information with memorable physical landmarks. FoliageStride AI turns an ordinary run into a more screen-free outdoor experience. The idea is simple: Plan with AI → Remember the landmarks → Pocket the phone → Run → Touch Grass. landmarks.