Multiple Distill posts present a community discussion and responses tied to the paper “Adversarial Examples Are Not Bugs, They Are Features.” The articles compile six comments from readers and corresponding replies from the original authors. Across the contributions, participants examine what the authors mean by treating adversarial examples as a property of machine learning systems rather than solely as unwanted defects. The discussion focuses on how adversarial examples arise, how they relate to model behavior and underlying assumptions about robustness, and what implications follow for evaluating and interpreting machine learning models. In addition to the community questions and critiques, the authors’ responses address points raised by commenters and clarify aspects of the argument. Overall, the sources do not report a new empirical study; instead, they synthesize perspectives from the community about the interpretation and significance of adversarial examples. The exchange centers on whether adversarial examples should be understood as predictable consequences of current training and decision processes, and how that perspective affects research directions and evaluation of model reliability.