The post discusses the emerging concept of semantic unit testing, which aims to analyze test code without executing it, primarily using large language models (LLMs). While some commenters appreciate the potential benefits, expressing that it can aid local workflows by providing feedback on code correctness, others raise valid concerns regarding the unpredictability and effectiveness of such an approach. Commenters highlight that reliance on LLMs might introduce nondeterminism into the testing process, similar to issues faced with flaky tests. The efficacy of this method remains questionable, particularly regarding its integration into existing coding practices and its performance with dynamically versus statically typed languages. There is enthusiasm for innovative uses of LLMs, but skepticism about overpromising their utility in serious testing scenarios is prevalent. Overall, while this technique is an interesting development in the software testing landscape, it is not yet a comprehensive solution for traditional testing challenges, and further analysis of its reliability and false positive/negative rates on diverse codebases is necessary to validate its effectiveness.