Cairn

Nolan Lawson challenges the dominant narrative that AI coding tools exist to produce output as fast as possible. He argues LLMs are flexible enough to do the opposite: improve code quality through a deliberate, methodical review workflow that often takes *longer* than before. This is a different philosophy than the "10x productivity slop-cannon" approach — and he finds it more satisfying.

Johanna Larsson argues that LLM-generated content violates a fundamental implicit contract between writers and readers. The problem isn't primarily about quality or factual accuracy — it's about the *effort gap*. Historically, writing required more intellectual effort than reading; readers could reasonably trust that authors understood their subject. LLMs collapse that gap, letting authors produce volume at near-zero effort while readers still pay the full cost of attention.

A format specification for describing a visual identity to coding agents. DESIGN.md gives agents a persistent, structured understanding of a design system.

Easy self-hosting for Docker-based web apps

Rails 8+ app template

Using Claude and Claude Code to digest research, spike a landing page, and then apply design-driven prompting to build a product.

A better way to do the whole find-or-create-and-increment dance.

Fixture-level performance without giving up FactoryBot-style test setup.