This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
The screen must be the shortest part of the experience. 30 seconds on your phone → 45 minutes on the trail.
LeafPace is a zero-backend progressive web app that gets runners, walkers, and hikers off the screen and into the wild — in under 30 seconds:
Who it's for: anyone who wants their run or hike to happen outside the algorithm — and whose home address shouldn't be uploaded to a fitness social network to do it.
Privacy by architecture: there is no backend. Your GPS coordinates never leave your device — every network call LeafPace makes (OSM Overpass, Nominatim, OSRM/Valhalla) is a location-independent public API query, and the AI runs 100% on-device.
🔗 Live app: https://big0boy.github.io/LeafPace/ (GitHub Pages, zero hosting cost)
Try it on your phone: open the link, tap 🍃 Synthesize Trail, then 📱 Lock Phone & Touch Grass.
📦 Repo: https://github.com/Big0boy/LeafPace — every dependency is open source, every data source is an open commons.
Stack: React 18 + TypeScript + Vite + Tailwind, Leaflet, @mlc-ai/web-llm (lazy-loaded), SunCalc, Vitest, plus a dependency-free CDP smoke-test harness (scripts/smoke.mjs) that drives the real app in headless Chromium — fake GPS lock, route synthesis, GPX download and XML validation included.
The part I'm proudest of is the green-space lookup, because public open infrastructure is flaky and the app must never fail — it races three Overpass mirrors in parallel, hedges to Nominatim (a completely separate OSM service) if none answers in 2.5 s, retries, and only then falls back to synthetic parks:
// First usable answer wins; a broken mirror can never stall route generation.
const winner = await firstSuccess<GreenSpaceLookup>([overpassWave, nominatimWave]);
if (winner) {
console.info(`${winner.source === 'overpass' ? 'Overpass' : 'Nominatim'} green-space lookup succeeded (${winner.spaces.length} results)`);
return winner.spaces;
}
The AI core is open-weight models running natively on the phone's GPU via WebLLM (WebGPU):
SmolLM2-360M-Instruct (quantized, ~250 MB cached in the browser) — swappable to Qwen2.5-0.5B/1.5B-Instruct by changing one constant in src/services/webgpu-llm.ts.
Around the model sits the open geospatial pipeline: Overpass API + Nominatim (green-space discovery), OSRM foot + Valhalla (pedestrian snapping), SunCalc (solar geometry), and a hand-rolled GPX 1.1 serializer. All of it is a static PWA with a service worker — deployable anywhere, free forever.
Testing: 29 vitest unit tests (loop geometry, polyline decoding, GPX, mirror-race fallbacks) plus the headless-browser smoke test, both run in CI on every push.
Because both halves of LeafPace only exist because of open infrastructure, and each has a closed-API twin that would be worse for the user:
Entering every category this touches — please verify final category names against the challenge page:
AI was used to create this project. The on-device briefing model (SmolLM2-360M-Instruct) is an open-weight model distributed by its creators — no closed or proprietary LLM API was used at any point, since the app's premise is that location data must never leave the device.