Claude Code's effort setting controls how much compute the model dedicates to tasks, affecting verification, edge-case testing, and independent judgment. High effort produces more thorough results for hardware, code review, and security work, while low and medium effort enable faster iteration for typical software engineering. The article demonstrates through examples that effort level choice depends on whether users want quick prototypes to iterate on or polished single-pass outputs.
The author explores why people resist reading AI-generated content despite using AI in conversations, arguing that readers seek evidence of human effort, authenticity, and personality behind published work. They identify craftsmanship, genuine imperfection, and individuality as key signals that a real person authored the content, contrasting this with the generic and effortless nature of AI-generated material.