Use AI for reviewing code especially when the diff is huge (simianwords.bearblog.dev)

In a recent discussion on code review practices, stakeholders are encouraged to leverage AI tools, particularly for substantial code changes (10,000 lines or more). The speaker emphasizes that traditional methods of line-by-line code review are inefficient and often unnecessary, particularly given that large language models (LLMs) have advanced to effectively identify critical vulnerabilities. Instead of nitpicking minor details, reviewers should focus on providing unique insights and contextual knowledge that LLMs and authors may lack, such as recent architectural discussions or adherence to specific coding principles that maintain the integrity of the codebase. This shift towards integrating AI in code reviews is significant for the AI/ML community, as it showcases the growing role of AI in enhancing productivity and accuracy in software development processes. By combining AI's strengths in recognizing patterns and potential flaws with the reviewer’s contextual expertise, teams can streamline their workflows and ensure more effective code assessments. However, it's important to note that this approach may not be suitable for all scenarios, especially in sensitive domains like embedded systems where every line of code is critical.