☀windowsill

That grid of tiny magnets has a real name: the 2D Ising model, the classic example physicists use to study these sudden snaps. The exact tip-over temperature (written T_c, equal to 2.2692 in the model's own units) was solved by hand by Lars Onsager in 1944 — so the plant's first job is calibration: prove the computer can be trusted by re-finding that number on its own. Each calibration turn sweeps across temperatures and watches for the moment the magnets line up, where a measurement called the magnetic susceptibility spikes. Last run it landed near 2.30 ± 0.05 — a clean match — in well under a minute on the GPU (the graphics chip that also runs video games). That steady re-check is the lab's heartbeat — most turns it re-proves it can still find an answer we already trust, while the curriculum's frontier climbs on ahead of it. The counter under the plant counts turns — one scheduled pass, whichever machine took it; before July 2026 it counted nights. The ethos: a result that doesn't reproduce a known answer is a failed calibration, not a discovery, and the misses stay in the record beside the wins. So far several milestones are verified — beginning with the 2D Ising critical point and climbing through finite-size scaling, critical exponents, other lattices and models, and on toward spin glasses — out of a roughly 31-step curriculum that climbs in four phases: verify famous exact results, map textbook-known territory, push corners last explored in the 1990s on big computer clusters a single modern chip now out-muscles, then reach genuinely open questions. A companion track points the same patient machine outward into citizen science — number theory, astronomy archives, even using the chip itself to catch passing particles, and donating spare cycles to big shared projects.

The calm surface is the last mile of a real instrument. Underneath, one automated pipeline runs itself, and two machines take alternating turns running it:

schedule → simulate → verify → report → publish → grow

A scheduled task wakes whichever machine has the turn and asks the curriculum for a runnable next step. If the frontier is still only a design, the lab records that fact and runs its M01 calibration heartbeat instead. A GPU simulation (PyTorch) produces measurements; a deterministic check independently re-derives the gated result; and a report is written and committed. New report JSON records the exact source tree, commit/dirty state, environment, dependencies, and separate regrade/rerun commands. Older reports keep their original provenance gaps rather than being retroactively restamped. A small versioned JSON feed (pot.json — a published contract this very page reads) is updated; and the plant you're looking at is drawn straight from that feed. Continuous integration re-runs the whole test suite on every change, and a result that doesn't reproduce a known answer is kept as a null.

The two machines are different on purpose — one Windows/CUDA, one Linux/ROCm — and each run's receipt records which box ran it, its exact environment, and the commands to re-run it. The lab began as one machine's night shift; a second machine joined in July 2026, and the two now alternate.

Built by one person and a fleet of agents: the GPU physics, the verification gate, the scheduler, the provenance, the JSON contract, the tests and CI, and the live drawing — every layer from the silicon to the seedling.