Fluid: Towards Transparent, Self-Explanatory Research Outputs

Fluid: Towards Transparent, Self-Explanatory Research Outputs

Explore a 19-minute conference talk introducing Fluid, a "transparent" programming language that incorporates bidirectional dynamic dependency analysis into its runtime. Learn how Fluid tracks dependencies as outputs like charts and tables are computed from data, automatically enriching rendered outputs with interactive elements. Discover how this innovative approach allows readers to explore the relationship between inputs and outputs, promoting transparent and self-explanatory research outputs. Gain insights from speakers Joe Bond, Cristina David, Minh Nguyen, and Roly Perera as they present this cutting-edge development in programming language design at the ACM SIGPLAN conference.