The article discusses a technique for optimizing Rust code by using agents to iteratively suggest improvements. The author, Ben Eadie, proposes a system called 'agentic iteration' that utilizes a meta-optimizer to analyze and optimize code. This approach involves creating a 'meta-agent' that observes the code's execution and generates suggestions for improvement. The agent can then be trained on the code's performance metrics to refine its suggestions. The author presents a simple example of how this approach can be applied to a Rust program, demonstrating a 2x speedup with minimal code changes.

Researchers from Sitefire, a company that helps marketing teams get recommended by AI search, trained a model to identify AI-generated web content from its structure alone. The model was trained on 2,250 blog posts from 268 B2B company websites and was able to distinguish between AI-generated and human-written posts with 98% accuracy. The model found that AI-generated content often follows a 'tidy, self-announcing' structure, where the main point is repeated in the title, intro, and conclusion. The model was also able to identify which AI model wrote a post with 79% accuracy.