Ever feel like some code is crystal clear while others struggle to decipher the same lines? You're not alone. Code readability, a cornerstone of software maintenance, is subjective and depends heavily on individual developer experience and preferences. New research explores how Large Language Models (LLMs), like the tech behind ChatGPT, can be personalized to evaluate code readability based on individual needs. Traditionally, LLMs offer a general assessment, but this new approach uses a technique called collaborative filtering—similar to how recommendation systems suggest products you might like—to calibrate the LLM's judgment to match your specific understanding of readable code. The research found that personalized LLM evaluations were significantly more accurate, leading to a noticeable improvement in predicting readability scores. This could revolutionize how we maintain and collaborate on software projects, ensuring everyone is on the same page when it comes to understanding code. Imagine a future where AI helps tailor code reviews and automatically flags potentially confusing sections based on *your* team's unique preferences. This personalized approach to code readability evaluation is just the beginning, promising more efficient and harmonious software development in the years to come.