The cases for and against Markdown

A roundup of perspectives on Markdown's growing role in AI/LLM workflows, presenting both arguments for its adoption and criticism against it. Proponents highlight token efficiency, semantic structure for LLMs, clean data input, standardization, and plain-text operational benefits. Critics argue Markdown is overly complex, inconsistently used, and even vulnerable to ReDoS attacks. The post links to several articles debating whether Markdown deserves its status as a de facto standard for AI context and documentation.