Generating code with AI is cool, but what if that code could be faster and use less memory? Researchers have developed a new framework called EffiLearner that does just that. It's like having a tireless assistant that automatically refines AI-generated code to make it leaner and meaner. EffiLearner works by first generating code using a large language model (LLM). Then, it acts like a meticulous code reviewer, executing the code and profiling its performance. It measures how long each line takes to run and how much memory it consumes. This profile is then fed back to the LLM, which uses it to identify bottlenecks and rewrite the code for better efficiency. The process repeats, with the LLM iteratively refining the code until it reaches peak performance. The results are impressive. In tests, EffiLearner significantly reduced execution time and memory usage across a range of LLMs, both open-source and commercial. For example, it boosted the efficiency of StarCoder2-15B, decreasing its execution time by a whopping 87% and its memory usage by 91%. This kind of self-optimization has huge implications for the future of software development. Imagine AI generating not just working code, but highly optimized code ready for deployment. This could lead to faster applications, reduced energy consumption, and lower costs. While EffiLearner currently focuses on Python, the research team plans to expand its capabilities to other programming languages. This opens up exciting possibilities for optimizing code across diverse platforms and applications. EffiLearner is a big step towards a future where AI not only writes code but also perfects it, paving the way for a new era of efficient and sustainable software.