# Touch Grass Planner: a local Gemma model that plans your walk, then turns the screen dark

> Source: <https://dev.to/sahan20030814/touch-grass-planner-a-local-gemma-model-that-plans-your-walk-then-turns-the-screen-dark-5f86>
> Published: 2026-10-11 08:34:46+00:00

*This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass](https://dev.to/challenges/hacktoberfest-week1-2026-10-05)*

Touch Grass Planner is a web app whose whole job is to get closed quickly.

You say what you feel like doing (walk, hike, run, birdwatching, gardening) and how many minutes you have. A Gemma model running on your own machine writes a short plan. The app draws a loop sized to your time, and gives you five things to notice on the way. Then you press **Start** and the page turns into a dark countdown with your route on it. You leave. When you get back you press **I'm back**, and it keeps your streak.

It is for anyone who opens the laptop "for a minute" and would rather be outside. I built it in Sri Lanka, so it also has a garden planner with a tropical mode (Maha and Yala seasons) and a frost mode for colder places.

What it does:

*Planner page: live weather, a plan from Gemma 3 4B running locally, a notice checklist, and a triangle loop on the map.*

Live: [https://touch-grass-planner-oi06.onrender.com/](https://touch-grass-planner-oi06.onrender.com/)

One honest note: the hosted copy runs on Render's free tier, which cannot run a language model. There, the app shows a clearly labelled template plan instead of Gemma. Everything else (garden, loop, map, timer, notice list) works. The first load can take up to a minute while the free instance wakes up. The real experience is the local version, where Gemma writes the plan.

*The dark Start screen: a countdown with the loop map and legs. Press "I'm back" to log the outing.*

A local-first outdoor companion. An open-weight model on your own machine plans the outing, then the screen goes dark and you go outside.

Built for the **Hacktoberfest 2026 Open-Source AI Challenge, Week 1** (theme: *Touch Grass*).

**Live demo:** [https://touch-grass-planner-oi06.onrender.com/](https://touch-grass-planner-oi06.onrender.com/) (the hosted copy cannot run the model, so it shows a clearly labelled template plan; the first load can take up to a minute on Render's free tier)

| Feature | What you get | Needs internet? | 
|---|---|---|
| **Outing plan** | A short, specific plan written by Gemma (via Ollama) from your activity, time, conditions, garden jobs, route, and your climate and season | No | 
| **Loop route** | A triangle loop sized to your time, computed on-device from compass bearings, drawn as a sketch and on a map, with a Google Maps walking link. With internet it checks OpenStreetMap tiles and rotates or shortens the loop to keep it out of |  | 

`render.yaml` blueprint for Render.
The main design decision was to let the model write words and nothing else. A 4B model should not be trusted with facts, so everything that has to be right is ordinary code: the crop calendars, the loop geometry and the journal. The model only gets a short, factual prompt: your activity, minutes, weather, garden jobs, the loop, and the climate and season (for example "Tropical Sri Lanka, Maha season, no autumn or winter"). If the model is not running, the app says so on screen and uses a plain template. It never pretends.

One extra feature came from a problem I hit. The loop is a straight-line triangle, and on my first test it ran into the sea, because I live near the coast. So the app now reads the colour of the OpenStreetMap tile under points along the loop. If it sees water, it rotates the loop, or shortens it, until it stays on land. This needs internet, and it only avoids water, not fences or private land.

To run it yourself:

```
ollama pull gemma3:4b
python server.py
```

Then open [http://localhost:8000](http://localhost:8000). Set `TG_MODEL` to use a different model.

The app is about leaving the laptop, so it should work where you go, and that includes places with no signal. With open weights the planning step runs on my machine. The plan, garden list, timer, journal and notice list all work with the wifi off. A closed API cannot promise that.

It also keeps my data with me. Where I walk, when, and what I noticed is saved in a local file, not on a server I do not control.

The weights are ordinary files. When the model download kept failing on my connection, there were other ways to get it instead of depending on one service. I can swap the model with one setting (`TG_MODEL`), and the app costs nothing to run because there are no API keys or per-call fees.

There is a trade-off. A 4B model is less polished than a big closed one. My first plan for a walk in Sri Lanka was called an "autumn walk", because my prompt only told it the month. I fixed that by passing the climate and season in the prompt and stripping season words that do not apply. That is why the facts live in code and the model only writes the words.

I have tested this on my laptop, not on a long walk. Here is what I found. The timer, journal, notice checklist and loop all work end to end. My first loop ran into the sea, because a laptop's location is a guess from the internet connection, not GPS. That is why the app now checks the map for water and lets you click the map to set your start. Gemma also called a Sri Lanka walk an "autumn walk" at first, which is why the prompt now carries the climate and season.

I did not field-test the bird identification feature on a trail. Next, I want to take it on a real walk and see how the plan and the loop hold up.

I built this with Claude as a coding assistant, and I tested and ran it myself.

Known limits: the loop is a rough straight-line shape (use the maps link for real streets), a laptop's location can be kilometres off (you can click the map to set your start), and a small model sometimes gets details wrong.