40% Fewer Vulnerabilities? Testing GPT-5 and Claude 3.5 for Security скачать в хорошем качестве

40% Fewer Vulnerabilities? Testing GPT-5 and Claude 3.5 for Security

Does a better LLM always mean better code? In this interview from NeurIPS 2025, Jordan Schneider from ‪@ChinaTalkMedia‬ sits down with Joseph Tyler from Sonar to discuss their groundbreaking research: "Assessing the Quality and Security of AI-Generated Code: A Quantitative Analysis." They dive into a surprising correlation: as models become more "functionally correct," they often neglect maintainability—like defining generic exception classes that pass unit tests but create long-term technical debt. What we cover: Benchmarking Llama 3.2, Claude 4, GPT-4o, and GPT-5. The "Generic Exception" trap in AI coding. How Sonar’s new "Sonar Sweep" and fine-tuned GPT-OSS for Java are reducing bugs by 40%. The future of static code analysis in an AI-first world. These interviews were brought to you by SAIL Media, in partnership with ‪@lambda-ai‬.