Here’s a draft you can paste into DEV. Replace the Demo placeholder with a real video or deployed link before publishing. I’ve kept the outdoor-testing claims honest.
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
I built TouchGrass, a local-first outdoor activity planner. It asks how much time you have, what kind of activity you’d like, your energy level, where you’re going, and any interests or limitations. It then creates a timed outdoor mission.
The idea is to make planning a short step, not another reason to stay on your phone. Once the mission is ready, you can switch to a simple phone-free view, read the steps at a glance, and put your phone away.
Demo
[https://youtu.be/0tacnZu845k]
The demo should show the preferences form, a mission generated by the local model, and the phone-free view.
Code
How I Built It
TouchGrass uses a small vanilla HTML, CSS, and JavaScript frontend with a FastAPI backend. The backend sends the user’s preferences to a locally running Ollama instance using the qwen2.5-coder:3b model.
The model returns structured JSON for the mission: preparation, timed steps, safety notes, and a completion message. The backend validates the response, including checking that the step durations add up to the selected time. If the model returns an invalid response, the app retries once and then reports an error instead of presenting it as a valid plan.
The app doesn’t require an account or save mission preferences. Model generation runs through the configured Ollama server; downloading the model requires internet, but inference can run locally once it’s installed.
Why Does Open Innovation Matter?
Running the model locally made it possible to build the core feature without relying on a proprietary cloud AI API. The user’s preferences can stay on their own machine when Ollama is configured locally, and the model can be changed through configuration rather than tied to one hosted service.
That comes with trade-offs: users need to install Ollama and download a model, and local models can return generic or unsuitable suggestions. TouchGrass validates the mission structure and includes safety reminders, but those safeguards can’t guarantee that an activity is right for every person or situation.
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