TouchGrass: an offline garden planner powered by TabPFN (open-weight tabular AI)

Minh Tuấn Lê — Tue, 06 Oct 2026 15:26:29 +0000

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

What I Built

TouchGrass is an offline garden planner. You tell it your USDA hardiness zone and today's date; it tells you which crops will thrive if you sow them this week — and then you close the laptop and go outside.

The entire decision core is TabPFN, Prior Labs' open-weight tabular foundation model, running on my laptop's CPU with the Wi-Fi off. No API keys, no cloud endpoint, no per-request cost.

🌱 TouchGrass — zone 5, week of 2026-04-20
   last spring frost Apr 15 · first fall frost Oct 15
   this week: 25.4 weeks before first fall frost

   ✅ spinach    thrive probability 84%
   ✅ radish     thrive probability 84%
   ✅ lettuce    thrive probability 83%
   ✅ zucchini   thrive probability 83%
   ✅ peas       thrive probability 83%

   Computed locally with TabPFN. Now go outside. 🍂

And in the fall, asked five weeks before first frost, it correctly gets cautious:

🌱 TouchGrass — zone 7, week of 2026-10-06
   this week: 5.0 weeks before first fall frost

   🤔 spinach    thrive probability 49%
   🤔 peas       thrive probability 49%
   ...

Who is it for? Anyone with a garden bed and a terminal — the goal was to make the screen the shortest part of the experience: one command, five crops, go plant.

Demo

uv sync
uv run touchgrass.py --zone 7 --date 2026-10-06

That's the whole UX. The heavier demo is turning off networking first and watching it still answer — that's the point.

Code

linhlban150612 / touchgrass-planner

Repo: https://github.com/linhlban150612/touchgrass-planner (MIT).

How I Built It

Three small pieces:

data/frost_dates.csv — median last-spring / first-fall frost dates per hardiness zone (zones 3–10).
data/plantings.csv — a training set of historical planting outcomes: crop, weeks from last spring frost, weeks before first fall frost, soil temperature, daylight hours → outcome class (thrived / stunted / failed).
TabPFN as the whole model. For each candidate crop I build one probe row for this week's frost window and ask TabPFN to classify the outcome. There is no training loop, no hyperparameter tuning, no gradient descent — a tabular foundation model doing few-shot classification over 55 rows and just working.

clf = TabPFNClassifier(device="cpu",
                       categorical_features_indices=[0])  # crop
clf.fit(X_train, y_train)
proba = clf.predict_proba(X_probe)  # one row per candidate crop

My first run had a fun bug: I forgot to include the crop as a feature, so every crop scored an identical 51% — TabPFN was correctly telling me all my probe rows were the same row. Adding the crop as a categorical feature gave it the signal it needed.

Why Does Open Innovation Matter?

It runs on a laptop with no internet. TabPFN's weights are downloaded once and run locally. A gardener standing in a field with zero signal gets the exact same answer as one on fibre. A closed API returns a DNS error.
It keeps your data off servers you don't control. Your zone, your dates, your planting history never leave the machine.
It costs nothing to run. Every planting plan is $0.00. No key to rotate, no quota to watch, no endpoint to be deprecated under you.
You can swap it. Because the model is a local artifact, swapping TabPFN for another open tabular model is a one-line change — the tool's value lives in the data and the idea, not in a vendor lock-in.

Where the open approach worked better than a closed one: latency and availability. Local inference returns in well under a second on CPU with zero network round-trips, and the failure mode of "airplane mode" simply doesn't exist. For a tool whose explicit goal is to get you away from connectivity, closed inference would be self-defeating.

My Agent Session

The whole project — code, data, debugging the identical-probabilities bug, git history — was built in a single agent session. The commit history in the repo (including the "crop as categorical feature" fix above) is the honest trace of that session.

Prize Categories

Prior Labs — Best Use of TabPFN: TabPFN isn't bolted on; it is the product. A tabular foundation model forecasting planting success from historical data, fully local, inside a CLI.

DEV Community: Minh Tuấn Lê