In February 2026 Mitchell Hashimoto gave a name to a habit, and within six months OpenAI, Anthropic, Thoughtworks and Red Hat had all published on it. The practices underneath the name are decades older than the name — which is the most interesting thing about it.
Picking a Claude model is really two decisions — which model and how much effort — and the more interesting question is who should make them: a config file, a skill, a cheap router model, or the most capable model delegating downward.
Every "always allow" click quietly trades a little control for a little convenience — and after a few months of daily agent use, those clicks add up to a security posture nobody designed. Here's how to audit what your coding agent can already do without asking, which grants deserve scrutiny, and how to put deliberate guardrails back.
Claude Code skills have no requires: field, no imports, no dependency resolver — and yet you can build a real graph of skills that depend on each other. This post lays out the mental model, the three composition mechanisms that fall out of it, and a refactor of this blog's own skill folder as a case study in what happens when you skip them.
Every agent is, at heart, a loop: a model calling tools until the job is done. The interesting engineering question of 2026 is what happens when you wire many of those loops together into a graph — and when you shouldn't.
Every post on this blog needs two images that do completely different jobs: a social card that has to earn a click in a feed, and an inline hero that opens the article. This post is about how the blog makes both — a typographic default that costs nothing, an opt-in artificial-intelligence (AI) cover that costs about four cents, and the little Model Context Protocol (MCP) server that wires an image model straight into Claude Code. The cover above was made by that exact pipeline.
"Self-maintaining" is a seductive phrase, so let's be honest about it up front. You're not going to walk away for a month and come back to a repo that grew new features on its own. What you can build is a loop where Claude does most of the mechanical work — turning issues into pull requests (PRs) — and a separate Claude pass reviews that work before it ever reaches you. You stay the final gate, but you review a clean, already-critiqued PR instead of a blank diff.
Claude Code can be customized along a spectrum: at one end, plain-language instructions you hope the model follows; at the other, hooks and settings the harness enforces whether the model cooperates or not. This post maps that full surface and shows how to actually wire it up on a real project.
This article explores the growing significance of linguistic precision and thorough understanding of foundational knowledge in the context of AI and software development, highlighting the historical importance of clear communication in the field and the relevance of Agile methodologies in the AI era. It also outlines the skills needed to effectively work with AI and navigate the evolving landscape of web development and other tech-related fields.