Tabular data has long been the last stronghold of gradient boosting, so a major vendor pushing pretrained table models behind one PyTorch interface is a quietly significant move. In our view, the real story is in-context learning reaching full relational databases — if that works even half as well as advertised, feature-engineering pipelines could shrink dramatically. We'd still stay cautious: benchmarks on curated datasets rarely survive contact with messy enterprise schemas, and inference cost on large tables remains an open question. For practitioners, this is worth a hands-on test before betting a production pipeline on it — and the tutorial lowers the barrier to exactly that.