u/pplonski

I've seen TabPFN-3's recent results, and there is a lot of buzz about foundation models for tabular data (TabICL, TabPFN). The performance that those models achieve is really amazing. What makes me a little suspicious about them? They can analyze small datasets only, so a few MB of data, and you need to have a large GPU machine and download a few GB of model to predict on a few MB of data. That doesn't sound rational ... I really miss the old school approach of running a single decision tree or a linear model on the data.

What do you think about it? Do you think feature engineering + classic ML can achieve performance comparable to that of foundation models? Maybe with better explainability?

I'm looking for a way to define todo list for my agents, mostly coding agents, so they will follow the list and do the job. Have you heard of such approach? If yes could you please share any links, resources?

For example i would like to define Todo list:

1. Fix issue #19
2. Check internet mentions for new python package.
3. Draft a article proposal and sen me by email.
4. Work in issue #21.