The practical discipline of building, evaluating, and operating systems that use large language models, spanning knowledge architecture, agent control flow, inference optimization, and the human and organizational costs of getting it wrong.

A broad discipline covering architecture, tooling, testing, and craft decisions that determine how software is built, maintained, and extended — a theme connecting sources on agent reliability, CSS platform primitives, component design, shell scripting, and more.

The startup ecosystem is shaped as much by failure as by success: dead companies leave behind viable ideas and cautionary patterns, while systemic forces like AI infrastructure costs can erode the conditions that let new ventures survive.