Articles

A foundation model that reads tables directly, with no shared keys, labels, or preprocessing.

A tabular foundation model is pre-trained on many tables and predicts, imputes, and detects anomalies on a new table without per-dataset training.

What scale adds to a tabular foundation model: one checkpoint for many tasks, more than 1.5 billion parameters, and the trade-offs of serving it.

What real-time AI is, how Apache Kafka and Apache Flink feed it, and how online and continual learning keep models current.

How retrieval-augmented generation works, why naive RAG fails, and how advanced, graph, and agentic RAG fix it, with evaluation.

How AI, machine learning, and deep learning relate, which learning paradigms apply to tables, and when to choose trees or a foundation model.

Sector, column profile, cross-table map, imputation, predictions, and a held-out block.

How a model can align columns and match rows across sources, and why the result should be a proposal.

What a held-out report should contain, and how to read one without being fooled by a headline number.

Two ways to use a table model: a one-shot call that stores nothing, and a persistent endpoint that holds state.

Why immutable data snapshots make reports, upgrades, and refreshes trustworthy.