Recent advances in large language model-powered coding agents have enabled the rapid modernization of legacy educational software and the accelerated development of new interactive mathematical visualizations. A mathematician who originally developed a suite of Java-based applets in 1999 for teaching complex analysis and linear algebra has successfully migrated dozens of these tools to contemporary JavaScript environments, demonstrating the practical efficiency of AI-assisted software engineering. The original applets, designed to visualize geometric and algebraic structures such as honeycombs and Besicovitch sets, were rendered obsolete when modern web browsers phased support for legacy Java implementations. Rather than manually rewriting the deprecated code, the developer recently employed an AI coding agent to port the entire collection to JavaScript. The migration process was completed in a matter of hours, yielding functional applications with upgraded graphical rendering, including the addition of colorization to previously monochrome visualizations. Testing revealed a high degree of accuracy in the AI-generated code. Out of approximately two dozen applets, only a single minor interface bug was identified, related to drag-event handling outside a defined boundary. More notably, the AI agent automatically detected and resolved two pre-existing logic errors in the original 1999 source code, resulting in a net improvement in overall code quality. Given that these tools function as secondary educational aids rather than mission-critical systems, the developer concluded that the residual risk of AI-generated imperfections remains well within acceptable bounds for academic use. Capitalizing on the streamlined workflow, the developer subsequently commissioned the AI agent to construct two entirely new visualization tools. The first implements a long-held conceptual framework for visualizing special relativity within Minkowski space, a project previously abandoned due to Java implementation complexity. The second provides an interactive graphical model for the Gilbreath conjecture, designed to accompany recent academic publications. Both applications were developed through iterative conversational programming within a few hours, with the code now deployed for public testing and feedback. This initiative highlights a shifting paradigm in technical communication and educational software development. By leveraging LLM-driven development environments, researchers can rapidly prototype complex mathematical visualizations without requiring extensive low-level programming expertise. The developer intends to institutionalize this approach, integrating AI-generated interactive supplements into future academic papers. As machine-assisted coding continues to lower barriers to software implementation, this workflow offers a replicable model for academic developers seeking to enhance scholarly output with modern, interactive digital assets while maintaining rigorous quality control standards.