Imagine an AI that not only finds bugs in your code, but fixes them too, all on its own. That’s the promise of ThinkRepair, a new approach to Automated Program Repair (APR) using the power of Large Language Models (LLMs). ThinkRepair represents a significant leap in how we approach debugging. Unlike traditional methods that rely on predefined templates or vast amounts of training data, ThinkRepair acts more like a human developer, reasoning through the code's logic to identify and resolve issues. This is achieved by incorporating a two-phase process: a 'collection' phase and a 'fixing' phase. In the collection phase, the model gathers examples of fixes from a knowledge pool using a 'Chain of Thought' prompting, mimicking how a developer would consider different fixing approaches. Then, during the fixing phase, it selects the best approach from this knowledge pool and applies it to the current problem. If the first fix isn't successful, ThinkRepair iteratively refines the patch based on feedback, similar to how developers utilize test results to guide their debugging. ThinkRepair isn't just a theoretical concept; it's been put to the test. Evaluations on benchmarks like Defects4J show that ThinkRepair dramatically outperforms existing automated repair methods, correcting bugs that stumped traditional approaches. It also shows promising results on real-world projects, highlighting its potential real-world impact. ThinkRepair might not replace human developers entirely, but by automating tedious debugging tasks, it can free up developers to focus on more creative and challenging work. This self-directed, AI-powered repair system holds the potential to significantly improve software quality and reduce development time. It's still early days, but ThinkRepair offers a glimpse into the exciting future of AI-assisted programming.