Touch Grass Bingo - offline nature bingo checked by an AI that lives on your phone

Prateek singh — Fri, 09 Oct 2026 15:48:53 +0000

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

What I Built

Touch Grass Bingo is a nature bingo card that you play outside. You open it, get a seasonal card (a red leaf, an acorn, moss, a feather, a mushroom…), and put your phone away. When you find something, you take one photo. An open-weight vision model running on your phone checks it and marks the square.

The center square is never free. It is "Touch grass": a photo of your hand on real grass.

Who it is for:

Families who want a reason to make a walk last longer.
Run clubs and hiking groups. Everyone types the same card code (like FERN-42) and gets the same card. First to bingo wins. There are no accounts and no server.
Anyone doomscrolling who needs a nudge. If you photograph your monitor, the model detects a screen and tells you that you cannot touch grass through a monitor.

The screen is the shortest part of the game: one glance at the card, then the phone goes in the pocket until you find something.

Demo

Play it: https://prateekiiitg56.github.io/touch-grass-bingo/

Open it on your phone. Tap Download photo checker once on Wi-Fi (about 150 MB). After that, it works in airplane mode.

Play the same card as a friend: https://prateekiiitg56.github.io/touch-grass-bingo/?code=FERN-42

Code

Prateekiiitg56 / touch-grass-bingo

Offline nature bingo. Snap what you find outside; open-weight CLIP checks it on your phone. No signal, no uploads.

Touch Grass Bingo

Offline nature bingo for walks, hikes and run clubs. The app deals you a seasonal bingo card (a red leaf, an acorn, moss, a feather…). You go outside and take a photo only when you find a square. An open-weight AI model running on your phone checks the photo. No signal is needed and no photo leaves the device.

Open-weight model: CLIP ViT-B/32 (ONNX, 8-bit quantized)
Open-source runtime: Transformers.js 3.7.6 on ONNX Runtime Web (WASM)
No backend: static files only. Host on GitHub Pages, Netlify or any static host.

How it works

Field kit. On Wi-Fi, tap Download field kit. The model (about 150 MB) goes into the browser cache. A service worker caches the app shell and runtime. From then on the app works in airplane mode.
Deal a card. A card code (for example FERN-42) seeds a deterministic shuffle of the season's item…

View on GitHub

How I Built It

Model: CLIP ViT-B/32, open weights, ONNX export, 8-bit quantized (about 150 MB).
Runtime: Transformers.js on ONNX Runtime Web (WASM). Inference runs in the browser tab.
App: plain HTML, CSS and JavaScript modules. No framework, no build step, no backend. Hosted free on GitHub Pages.
Design: styled like a topographic map. Grid references (A1, B2…) run along the edge of the card, so a group can shout "got B2". A finished line is drawn as an orange trail-blaze route.
Offline: a service worker caches the app shell and runtime. Transformers.js caches the model weights in the browser. After a one-time download on Wi-Fi, the app works in airplane mode.

The core trick is zero-shot classification. Each bingo square is only a text prompt, for example "a photo of an acorn". When you take a photo, CLIP scores it against every item in the season's pool plus a set of distractors (a screen, an indoor room, a selfie, a document, a city street). On a laptop, one photo takes about 100 ms:

If the square you tapped wins, you found it.
If a different unfound square on your card wins clearly, the app marks that one as a bonus ("Not running water, but it is a mushroom").
If a seasonal item that is not on your card wins, you get "Good find, but it is not on this card."
If a distractor wins, the find does not count, and you get a hint.

My first version scored photos only against the squares on the card. In testing, a red maple leaf was marked as the "yellow leaf" square with 95% confidence, because it was the closest option. Scoring against the full season pool fixed this: the same photo now comes back as "red leaf, not on this card."

Group play uses a seeded shuffle: the card code, season and size feed a hash, which seeds a small PRNG that shuffles the season's item pool. Every phone with the same code deals the same card, with no network.

Why Does Open Innovation Matter?

It works where the grass is. Trails, parks and forests often have no signal. A closed vision API needs a network round trip for every photo. Open weights let the model live on the phone, so the game works in airplane mode.
Photos stay private. Families take photos of kids' hands, backyards and favorite spots. With an on-device model, no photo is uploaded to a server that the player does not control.
It costs nothing to run. No API key, no per-image bill, no rate limit. I can host it on GitHub Pages for free, and a whole run club can play without anyone paying.
It is easy to change. Because CLIP is zero-shot and open, a new square is one line of text. A teacher can make a "schoolyard" card, a birding club can make a "local birds" card, and anyone can swap in a different open model (for example SigLIP) by changing one constant.

Prize Categories

Best Use of Gemma
Using Gemma, Google's open-weight model, in building my project

DEV Community: Prateek singh