LLM coding agents struggle with code manipulation, lacking the ability to effectively copy-paste, which creates an awkward coding experience. Additionally, their problem-solving methods are flawed due to a tendency to make assumptions rather than ask clarifying questions, limiting their effectiveness compared to human developers. These limitations highlight that LLMs are more akin to inexperienced interns than replacements for skilled programmers.

The article presents a proposal for integrating inline instructions for large language models (LLMs) directly within HTML documents. This approach aims to enhance the interaction and usability of LLMs by allowing users to specify instructions alongside content, potentially improving the context and relevance of generated responses. The discussion includes the technical implications and potential benefits of such an implementation.

The article discusses the design principles for creating effective live assistance systems powered by large language models (LLMs). It emphasizes the importance of user interaction and adaptability to enhance the overall experience while providing accurate and timely assistance. The author suggests strategies for optimizing LLM performance in real-time applications.