The podcast features the creators of Terminal-Bench, a new benchmark designed to evaluate large language model agents by testing their ability to execute tasks using code and terminal commands within a containerized environment. The conversation explores the origins and design of the benchmark, which grew out of the earlier Swebench framework but was abstracted to cover any problem solvable via a terminal, including non-coding tasks like DNA sequence assembly. The creators discuss the benchmark's increasing adoption by major labs like Anthropic, the challenges of evaluating agents versus the underlying models, and their future roadmap, which includes hosting the framework in the cloud and expanding the evaluation beyond simple accuracy to include cost and economic value. The discussion emphasizes the belief that terminal-based interaction is currently the most effective way for these models to control computer systems compared to graphical user interfaces.