What I Built

Touch Soil is a garden planner that tells you what to plant this week, and it runs entirely in your browser. You pick your climate zone, optionally mention what you already grow, and an open-weight Gemma model writes a weekly plan: what to plant now, the one job worth doing, and the thing you should skip.

No account. No server. Nothing you type ever leaves your device, because there is no "elsewhere" to send it to. After the first model download, it works with the internet unplugged, which is exactly where a garden usually is.

I built it for this week's theme, Touch Grass: the whole point is that the screen part is short. You open the page, read a plan that takes one minute, and go put your hands in the soil.

Code

Faris0520 / touch-soil

An offline garden planner with Gemma running in your browser

Touch Soil

An offline garden planner: you pick your climate zone, and an open-weight Gemma model running entirely in your browser (via WebLLM) writes a "what to plant this week" plan. No server, no account, and your garden data never leaves your device.

Why open

Runs with no internet after the first model download: WebGPU inference in the browser, so it works at the garden, on a trail, or anywhere the signal doesn't.
Data stays local: the zone you pick and what you're growing are never sent anywhere; there is no backend at all.
Everything is swappable: the climate data is a small JSON-style table anyone can fork and correct (frost-date datasets are US-centric, so this one includes tropical wet/dry seasons too), and the model is one string away from being any WebLLM-supported open-weight model.

How it works

Engine picker. Two ways to run the same open-weight…

View on GitHub

The interesting files:

src/data/climates.ts: the climate table. Nine zones, from USDA frost zones to tropical monsoon seasons.

src/lib/planner.ts: the prompt, the JSON schema, and the deterministic offline guide.

src/lib/llm.ts: model selection and the WebLLM engine setup.

How I Built It

The stack is deliberately boring: Vite, React, TypeScript, plain CSS. All the novelty is in how the AI piece works:

You choose where the model runs. A picker offers two engines for the same open-weight Gemma: downloaded into the browser (WebLLM), or an Ollama server on your own machine. The Ollama path only detects models you have already pulled; it never downloads anything by itself. This also gives browsers without WebGPU a real AI option.

Heavy actions ask first. Before the first browser download (about 1 GB) and before sending your garden details to the local Ollama server, a confirmation panel explains exactly what is about to happen.

The model runs in the browser. WebLLM runs open-weight models on your GPU through WebGPU. The app picks the smallest Gemma available in WebLLM's prebuilt list (Gemma 3 1B, quantized) and caches it after the first download.

The model cannot break the format. Small models love to ignore instructions, so instead of trusting prompts I constrain decoding: WebLLM gets a JSON schema via response_format, Ollama gets the same schema via its format parameter. The model can only emit { plants: [{ name, why }], task, skip }, and the UI renders that structure itself. My first free-form version produced headings like "Plant now (3)" and a greeting, the constrained version cannot.

The climate facts are plain data. Frost-date planners assume USDA zones, which is unhelpful if your garden has a wet season instead of a winter. Touch Soil bundles a small table: four temperate bands, tropical lowland and highland (monsoon wet/dry seasons), arid, and Mediterranean. It is intentionally small and honest about being approximate, and it is a JSON file you can fork and correct for your region.

There is a no-AI fallback. If neither engine can run, a rule-based guide written from the same dataset takes over. The app is never empty.

One war story from the build: Gemma 3's prebuilt WebLLM record ships both a context_window_size override and a sliding_window_size base config, and the engine refuses to start with both active. The fix (the engine tells you this, to its credit) is switching the record to sliding-window mode with attention_sink_size: 0.

Why Does Open Innovation Matter?

For this project, the open pieces are not a detail, they are the whole argument:

It works where the garden is. No signal, no problem: once cached, the in-browser model runs offline, and the Ollama engine only ever talks to your own machine. A closed API would make the app useless exactly when and where it should be most useful.

Your garden stays yours. What you grow, your zone, your questions: none of it reaches a cloud. The Ollama engine even shows a confirmation naming the exact server your details will go to, and the WebLLM engine shows one before its one-time download.

You can swap the brain. The model is one string in src/lib/llm.ts. Want a bigger Gemma, a different open-weight model WebLLM supports, or another local Ollama model? Change a filter or type a name. Want better climate data for your region? Edit a JSON table and open a pull request.

It costs nothing to run. No API bill per plan. The compute is the device you already own.

DEV Community

Touch Soil: an offline garden planner with Gemma running in your browser