This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
Good Ground is a garden planner that turns your frost dates, sunlight, and chosen crops into a short list of gardening tasks for the week.
It’s for anyone who wants a little help getting started in the garden—and a reason to put their phone down and get outside. No gardening account or location sharing required.
Demo
Good Ground runs locally on your computer.
Demo video: [Add a link to your demo video]
To try it, install Ollama, download the model with ollama pull qwen2.5:1.5b, then run python server.py from the project folder and open http://127.0.0.1:8000.
Code
How I Built It
Good Ground uses the open-weight Qwen2.5 1.5B model with Ollama for local inference. A small Python server sends the garden details to Ollama running on the same computer, then returns a structured list of suggested tasks to the browser.
After the one-time model download, the planner can work offline. It doesn’t need a Hugging Face token, a hosted inference API, or a per-request payment. The model and prompt can also be changed to suit a different garden or setup.
Why Does Open Innovation Matter?
Open weights and a local inference runner let this project make garden plans without sending someone’s frost dates or notes to a hosted AI provider. There are no per-request inference fees, and the prompt and model are replaceable.
The trade-off is that users need to download the model and have enough local resources to run it. In return, they get more control over their data and can keep using the planner without an internet connection after setup—something a hosted API alone wouldn’t provide.
My Agent Session
Not included.
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