Playing summary The claim has been going around for months and it is concrete enough to be measurable: a tabular foundation model predicts on a table without ever having trained on it and still beats tuned boosting. If that is true, half a decade of practice changes shape. Searching hyperparameters stops being a mandatory step and becomes a luxury that sometimes does not pay off. So I put it to the test on my own card, with fourteen datasets, four contenders and the same stopwatch for everyone. Your browser does not support HTML5 video. In 33 seconds with narration: two bots compete over a table. The one-eyed one looks and answers; the geared one tries twenty-five combinations before replying. Every number is a measured one. Muted by default: turn it on in the controls. Watch it in the reel viewer → What exactly these models do A tabular foundation model is pretrained on millions of synthetic tables generated on purpose.…