The article by David Loker discusses the improvements introduced by GPT-5 Codex in addressing the drawbacks of GPT-5 for AI code reviews. While GPT-5 was recognized for its generational leap in reasoning, it was found to produce a higher volume of comments, leading to a decreased signal-to-noise ratio (SNR), with users finding the reviews too pedantic. GPT-5 Codex, however, enhances the SNR by delivering fewer but more precise comments, improving overall review precision by about 35% compared to GPT-5, and reducing comment volume by 32%. These improvements make the review process faster and more focused, without sacrificing bug-finding capabilities. Codex is particularly adept at handling concurrency issues, lock ordering, and subtle API traps, providing actionable suggestions often directly translatable into patches. Additional enhancements include severity tagging and optimized filtering, which prioritize critical issues and reduce unnecessary commentary, thereby enhancing user experience. The Codex model also benefits from reduced latency, allowing for quicker feedback loops. Despite these advancements, there are ongoing efforts to address coverage gaps and refine the refactor suggestion process to ensure that important bugs are consistently identified and prioritized efficiently.