In 2009 a group of MIT researchers vented frustration over scientific programming languages that were rigid and slow, often forcing a rewrite in another language for performance. They launched the Julia project with the goal of creating a language as easy to learn as Python or MATLAB yet as fast as C. Julia’s just‑in‑time compilation based on data types gives it both flexibility and several‑fold speed gains. The effort grew into the MIT Julia Lab and later the company JuliaHub. In April 2025 JuliaHub released Dyad 3.0, an AI platform that accepts data and design documents, runs physics simulations, compiles code, and can automatically generate complete hardware designs such as aircraft or rocket engines. Dyad acts as a physics compiler, detecting and correcting solutions that violate physical laws, thereby reducing engineering cycles from months to hours. Today Julia has over one million users across thousands of companies and universities, powering applications from atom simulations, semiconductors, neural networks, race cars, airplanes to black‑hole imaging. It has accelerated Moderna’s COVID‑19 vaccine development, Meta’s audio codec for WhatsApp, and aircraft collision‑avoidance systems, often delivering performance dozens of times faster than Python. Julia’s strength lies in its abstractions: scientists can express ideas at a high level while obtaining near‑C performance. As JuliaHub co‑founder Viral Shah puts it, “making programming easy for non‑programmers while delivering great performance” remains the north star. Review