Wes McKinney explores the arithmetic shortcomings of large language models (LLMs) like Anthropic's Claude Code. He shares his experiences using these coding agents, highlighting how they can improve productivity but often struggle with basic calculations and reliability. Testing various models, he finds that local models perform better than many API options in handling arithmetic tasks.

The article discusses the author's preference for faster AI models over smarter ones when coding. It highlights how speed aids productivity, especially for simple coding tasks, while slower models can disrupt focus and workflow. The author emphasizes using AI for quick, mechanical edits rather than complex decisions.