Analytics engineering fundamentals: the role between data engineering and analysis, ELT, the transformation layer, testing, and the semantic layer.

Are We There Yet Development names the AI-era habit of demanding software at the speed of thought, and the toll it takes.

Learn HTTP status codes with five mental models: a mnemonic for all five classes (1xx-5xx), the codes you’ll meet most, and how to read them with curl.

Why I run Mac OS, Linux, and Windows side by side, what jumping between operating systems taught me, and how to start exploring the OS wildlife yourself.

Build a working knowledge graph in one pure-Python file. Store facts as triples, then query and traverse them, with no database and no libraries.

A knowledge graph stores data as entities and relationships, not rows. Learn what it is, how it works, and when it’s useful.

Put an established software ontology to work in daily workflows, design sessions, and database schemas; keep it fresh and make it usable by the whole team.

Create a software ontology with Domain-Driven Design: capture the ubiquitous language, draw bounded contexts, and model aggregates into a shared map.

A software ontology is a shared, explicit model of a domain. Understand what it is in Domain-Driven Design terms, why it exists, and how it works.

How AI is impacting software engineering: rising load on every dev ecosystem node. Software ecology explains why, and which principles to keep.