Imagine effortlessly converting code between languages like Python, Java, or C++. While Large Language Models (LLMs) show promise for automated code translation, they often stumble on tricky syntax and semantics. Researchers have discovered a surprisingly effective technique to improve LLM-based code translation: using intermediate "bridge" languages. This innovative method, dubbed InterTrans, takes a source code snippet and, instead of directly translating it to the target language, strategically routes it through a sequence of intermediate translations. Think of it like finding the optimal flight path – sometimes, taking connecting flights is faster than a direct route. For example, to translate Python to Java, InterTrans might first convert the Python to C++ and then C++ to Java. This process leverages the LLM’s existing multilingual knowledge without needing extra training data. Tested with various LLMs like Code Llama, Magicoder, and StarCoder2, InterTrans dramatically improved translation accuracy by 18.3% to a whopping 43.3% compared to direct translation. The research revealed that the choice of intermediate language significantly impacts the final result. For instance, translating C++ to Java through Rust proves particularly effective. This 'bridge language' approach helps smooth over tricky syntax differences and ensures the LLM understands the code's meaning more accurately. While InterTrans shows remarkable promise, it does come with computational costs. Exploring multiple translation paths requires significant resources. However, the payoff in improved translation accuracy and saved developer time could outweigh the costs, especially for complex projects. Future research aims to streamline this process by predicting the most effective translation paths, making this clever trick even more powerful.