The problem with "touch grass" advice

Everyone tells you to touch grass. Nobody tells you where to walk, what to look for, or how to make 30 free minutes feel like a tiny adventure instead of a lap around the block. So I built the smallest possible tour guide: an open-weight AI that lives entirely in a browser tab and deals you one outdoor mission at a time.

What I built

Touch Grass Card is a single web page. You tell it where you are (default: my college town, Corvallis, Oregon), how many minutes you can spare, and a vibe — chill, adventurous, curious, social. It then generates a mission card:

• Title — a name for the mission
• Mission — exactly what to do, in 2–3 sentences
• Look for — three things to notice while you're out
• Proof — one photo to take when you're done

Repo: github.com/qife422/touch-grass-card — it's one HTML file plus this README. That's the whole app.

Demo

Live demo: qife422.github.io/touch-grass-card (first load downloads the model once; after that it runs offline — try it in airplane mode).

How it works

The "brain" is Gemma 3 270M (instruction-tuned, ONNX export: onnx-community/gemma-3-270m-it-ONNX), running on-device via Transformers.js 4.2.0:

• It uses fp32 weights (~1 GB one-time download) on both WebGPU and the WASM fallback. I originally tried the much smaller q4f16 WebGPU weights, but ONNX Runtime has a known overflow bug for this model on the WebGPU fp16 path that makes it emit repeated unused tokens instead of English — no error, just garbage. fp32 is slower to download, but it is the path that actually works on both backends, so that is what the page ships.

There is no backend, no API key, no analytics. The only "prompt engineering" is a system message that forces a strict four-field output format, because a 270M model needs guardrails the way a golden retriever needs a fence.

Honest field notes on the model: 16 draws, zero garbled tokens — the fp32 fix held on real hardware (Chrome on Apple Silicon), so the <unused> bug I guarded against never showed up. The semantic reliability is the real story, though. Of the 16 missions, only about 3 were actually practical. Two ignored the safety instruction in the system prompt entirely: one proposed building a "Mobile Sensor Jammer" to "launch under the hood," and another ("Rain Cloud Rescue") told me to go find a kidnapped/missing person and call the police. Others were poetic but absurd — "The Whispering Willow" wants a handcrafted wooden box of fallen branches, and one card hallucinated the town completely ("Sunnyvale Weather Report," a city in California, emitting a forecast instead of a mission). The good ones felt genuinely personal — my final pick, "Unplug to Breathe," asked for a short, easy walk to a nearby park and a moment in the sun. A 270M model is a golden retriever: loyal, local, occasionally convinced the mailman is a mission.

Why open weights are the whole point

• Zero signal is a feature. Once cached, the page works on a trail with no reception — exactly where a "go outside" app should work. A cloud LLM can't follow you there.- Private by architecture. My location text never leaves the page. There is no server for it to leave to.
• Free forever. No per-token bill means I can hand this link to anyone. The total hosting cost is a static file.
• Swappable brain. The model is one constant in the script. Fine-tune a Gemma on local trail data, or drop in a bigger sibling, and the page gets smarter untouched.

I actually went outside

Sunday morning, October 11, I took the "Unplug to Breathe" card literally: find a sunny spot, take a short, easy walk to a nearby park. (It was card 9 of 16 — the river card I wanted first turned out to be a 44-minute bus ride away, and I don't have a car, so the little model and I compromised.) After days of gray rain, Corvallis finally gave me blue sky — 46°F at 10 AM, jacket weather, but the sun made it feel generous. I found everything on the card's "look for" list: the grassy open space, the trees starting to turn, the sun. The card's "proof" field, true to form for this 270M model, wasn't actually a photo instruction — it just declared that walking to a park is the most effective way to get outside — so I photographed the park instead: blue sky, green lawn, an empty picnic table under a yellowing tree. Standing there with nothing urgent pulling at me, I felt the past week's rain and small frustrations dissolve a little. It beat scrolling, and it wasn't close.

What I'd build next

• A mission history + streaks (localStorage only — still no server, ever)
• Seasonal prompt packs (fall foliage missions hit different in October)
• A bigger Gemma via WebGPU when available, same page, one-line change

Prize categories

• Best Use of Gemma — Gemma 3 270M runs locally in the browser; it's the core of the project, not a garnish.

---Built over a weekend in Corvallis between classes. The code is MIT — fork it, swap the model, go touch grass in your own town.