Imagine software that could fix itself. A fascinating new research paper explores the potential of Large Language Models (LLMs) to act as real-time error handlers, creating a pathway to adaptive self-healing software systems. Traditionally, unhandled runtime errors can abruptly halt program execution, causing data loss or system crashes. While developers try to anticipate and prevent these issues, some errors inevitably slip through the cracks. Current self-healing techniques rely on predefined rules, struggling with the diversity of real-world errors. This is where LLMs step in. The research proposes using LLMs as 'virtual developers' constantly on call. When an unexpected error occurs, the LLM analyzes the error message and program state to generate a code snippet that dynamically corrects the issue, allowing the program to continue running. A framework called 'Healer' was developed to test this idea. The results? Without any specific training, GPT-4 helped programs recover from a remarkable 72.8% of runtime errors. The study explored different LLMs and error types, finding varying levels of success. Notably, fine-tuning the LLMs significantly boosted their performance, with fine-tuned GPT-3.5 rivaling the effectiveness of GPT-4. While this approach introduces a negligible performance overhead during normal execution and an acceptable delay (under 4 seconds) for error handling itself, the potential benefits are enormous. Imagine software that can adapt and recover from unforeseen errors, ensuring continuous operation and reducing the need for costly manual intervention. While challenges remain, such as ensuring the trustworthiness and optimizing the operational costs of using LLMs for error handling, this research points to a future where self-healing software becomes a reality, ushering in a new era of reliable and resilient systems.