This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
The most useful number in a vegetable garden is the last spring frost date. Get it wrong by a week and one cold night turns your tomato seedlings to mush.
Every tool that gives you that number has the same flaw. It lives on a server. You look it up on your phone, standing in the one corner of the allotment that gets a bar of signal, hoping the page loads before the rain starts.
So I built FrostWise: it predicts your last frost and tells you what to plant, and it does the whole thing on your laptop with the Wi-Fi off. Two open-weight models, no API key, no account, nothing leaves the machine.
I built it for a specific person: my friend who keeps a plot at a community garden on a hill where the signal drops to nothing. Every spring she texts me "is it safe to plant yet" because she cannot load a frost-date site from the plot itself. FrostWise is the answer she can keep on her own phone and open with no bars.
FrostWise answers one question honestly: when does the cold let go where you are, and what should you do about it.
You pick a location (or type your own climate numbers), choose a crop, and it returns three things: A date. Your predicted last spring frost, with an honest range, not a fake single-day promise. The advice. Plain language for your exact crop. Wait until after the frost for tender things like tomato and basil. Sow a few weeks early for hardy things like peas and kale. A reason to trust it. The number comes from a model you can inspect and retrain, and the range tells you the risk.
It gets people off the screen in the most literal way. The screen is the ten seconds you spend before you walk outside and put a seed in the ground. The whole point is to make that part short.
Who it is for: anyone with a patch of soil and a spotty signal. Community plots, hillside allotments, a herb bed at a trail-head cabin. The places where a cloud API is useless are exactly where people garden.
That hosted link is a static site (S3 plus CloudFront, near zero cost) that serves real, pre-computed TabPFN-v2 and Gemma results for the preset cities, so you can click through actual model output. The live models themselves run on your own machine. For custom climate input, fully offline, clone the repo and run python run.py.
Here is the full flow across three very different climates: Miami (effectively frost-free), Chicago (a real May frost), and Fargo (frost that lingers into summer). Same code, three honest answers.
Here is what one answer looks like: a predicted last frost, the honest range around it, and planting advice written locally by Gemma for the exact crop.
The frost date animates in, then a local Gemma model writes the planting advice live. Nothing in that video calls out to the internet.
Clone it, python run.py, and it is on localhost:8077. The first forecast caches the model weights once; after that you can pull the network cable and it still works.
TabPFN is a tabular foundation model from Prior Labs. It is the strange and wonderful part. Instead of training a model on my frost data, I hand it the data as context and it predicts in a single forward pass. No training loop, no hyperparameter search, no GPU.
I use the v2 weights on purpose. The default TabPFN-3.5 weights are non-commercial and want a browser login. The v2 weights are the Prior Labs License (Apache-2.0 plus attribution), so the whole stack stays genuinely open and runs headless. For an "open innovation" project that distinction is the point, not a footnote.
The features are the things that actually drive frost timing: latitude, elevation, the February and March mean temperatures, the coldest winter night, and an ENSO index for that winter. The target is the last-frost day-of-year. On a plain 8-core CPU it predicts in about 3 seconds, and I read the 10 to 90 percent quantiles for the range.
For a Chicago-like input it lands on May 20, range May 15 to 26. For Fargo it says June 19. Those match the real climate norms, which told me the model was learning the physics and not memorizing noise.