Imagine coding in a language so new, even cutting-edge AI struggles to keep up. That's the challenge with Mojo, a blazing-fast programming language designed for AI development. But what if AI could not only understand Mojo but also help you write it? Enter MojoBench, a groundbreaking project that's bridging the gap between AI and this emerging programming paradigm. Mojo's speed and efficiency have made it a rising star, but its novelty means most AI models, trained on vast amounts of Python and other established languages, are left scratching their heads. They can grasp the logic of a coding problem but stumble over Mojo's unique syntax. This is where MojoBench comes in. Researchers have created a specialized 'corpus' of Mojo code, essentially a textbook for AI, along with 'HumanEval-Mojo,' a set of coding challenges designed to test an AI's Mojo proficiency. They've also developed Mojo-Coder, an AI model specifically trained on this Mojo-rich diet. The results? Mojo-Coder outperforms leading general-purpose AI models on Mojo coding tasks by a significant margin, demonstrating the power of specialized training. Even more impressive, it can understand instructions in multiple natural languages, translating your requests into clean, efficient Mojo code. This opens doors for developers worldwide to harness Mojo's power, regardless of their native language. MojoBench is not just about improving AI's understanding of a single language; it's about developing strategies for adapting AI to any emerging technology. This research highlights the crucial role of targeted training data, even in small amounts, for achieving optimal AI performance. As new languages and platforms emerge, MojoBench provides a blueprint for quickly and effectively bringing AI up to speed. The future of coding might just be AI-assisted, and projects like MojoBench are paving the way.