If AI is influencing decisions, semantics decide whether you can trust it. This report argues the semantic layer is now essential AI infrastructure, explaining how shared definitions, governance, and open standards are becoming prerequisites for reliable, explainable enterprise AI. Download free.
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Faster queries. Smaller indexes. Fewer rules for humans to remember. This post shows how to use PostgreSQL’s lesser-known features to outsmart full table scans and bloated B-trees, without changing your data model.
Haki Benita

Matrix multiplication is easy until you lose track of the shapes. This post introduces a simple trapezoid sketching trick that makes PCA, SVD, and neural network layers easier to reason about on paper or a whiteboard. Surprisingly helpful intuition for familiar linear algebra.
Max Watson

This is a nice collection of interactive visualizations and demos that make gradients, embeddings, and model behavior intuitive. Helpful for building ML intuition without heavy formalism.
Miriam Posner

Tools like Claude Code may be built for programmers, but non-engineers can do a lot too. This post shows what happens when AI can run autonomously for long stretches, manage its own context, and use tools effectively. It’s not just faster coding, it’s a real shift in how work gets done.
One Useful Thing | Ethan Mollick

This tutorial shows how to predict where customers go, not just who they are. Using the R package spopt, it applies the Huff model to turn spatial interaction theory into practical market-share analysis.
Kyle Walker

Reliable data starts earlier than feature engineering. When schemas and APIs change unexpectedly, analysts and data scientists feel it downstream. Buf focuses on the schema layer that structures and validates data across the entire stack and is sharing a free O’Reilly Media book connecting ingestion, data quality, and observability patterns to real-world reliability.
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Ever seen a 3-D population chart from 1880? This post reimagines Luigi Perozzo’s stereogram with modern tools, showing how to read its layers and the demographic insights embedded in the design. A visual tour of data history and chart craft.
Chartography | RJ Andrews

Here's a deep, practical guide for turning analysis outputs into clear, publication-ready tables in R. It focuses on the how and why of table design: structure, formatting, hierarchy, footnotes, and nanoplots, with reproducible examples you can actually reuse.
Richard Iannone

Take two large random matrices. Linearly interpolate between them in hundreds of steps. At each step, compute the eigenvalues and plot them in the complex plane. The result isn’t noise. It’s structure.
Simone Conradi