The text discusses the evaluation of a new AI model, Claude Sonnet 3.7 Thinking, for bug detection in code. The author created a dataset of 210 programs with small, difficult-to-catch bugs and used this dataset to compare the performance of Claude 3.7 Thinking with its non-thinking counterpart. The results showed that the thinking version of the model outperformed the non-thinking model in detecting bugs, particularly in languages where pattern matching was less effective, such as Ruby and Rust. The author attributes this improvement to the reasoning capabilities of the thinking model, which allowed it to think through possible issues logically. The study highlights the potential benefits of AI code reviewers in software verification and suggests that future improvements will be significant as foundational model capabilities continue to advance.