I Think I Have LLM Burnout (www.alecscollon.com)

A developer reflects on their experience using large language models (LLMs) like Claude Code and Codex for coding assistance, detailing a significant shift in their workflow over the past year. They describe moving from conventional programming tasks to designing code and engaging in iterative feedback with LLMs, which has enhanced their productivity and broadened their approach to problem-solving. However, the author expresses a growing sense of burnout related to LLM output, citing repetitive errors, hallucinations, and stylistic quirks as contributing factors to their fatigue. This introspection highlights a broader concern within the AI/ML community regarding user experience and the limitations of current LLM technology. While these models can boost efficiency and inspire new methods, the familiarity and predictability of their outputs can lead to frustration among users. As the developer navigates this dichotomy of improved productivity versus content fatigue, it raises important questions about the necessity for personalization and adaptation in LLM interfaces to better meet user needs and mitigate burnout.