A year ago, we introduced TabPFN, the first in-context learning method for tabular data. In this talk, I will discuss what happened since. I will start by briefly discussing CAAFE, a system that uses LLMs for automated feature engineering on tabular data and makes effective use of TabPFN's speed. Then, I will situate prior-fitted PFNs in the in-context learning literature, review various applications of PFN, explain TabPFN in some more detail and then discuss our ongoing work on removing TabPFN's remaining limitations.