Python has become a powerful ally for makers looking to automate and streamline 3D printing. From generating complex G-code with libraries like Gscrib to customizing slicing workflows in Cura and ...

Git isn't hard to learn, and when you combine Git and GitHub, you've just made the learning process significantly easier. This two-hour Git and GitHub video tutorial shows you how to get started with ...

We begin by importing the core Python modules that we need for system operations, downloads, timing, and JSON handling. We check whether we are running inside Google Colab, define a reusable section ...

When Nvidia first showed off its Compute Unified Device Architecture (CUDA) parallel computing platform in 2006, it was a multibillion-dollar bet that failed to turn a profit for a decade. Today, it ...

WASHINGTON, DC - APRIL 30: U.S. President Donald Trump (L) listens as Nvidia CEO Jensen Huang speaks in the Cross Hall of the White House during an event on "Investing in America" on April 30, 2025, ...

The CUDA toolkit is now packaged with Rocky Linux, SUSE Linux, and Ubuntu. This will make life easier for AI developers on these Linux distros. It will also speed up AI development and deployments on ...

Apple’s MLX machine learning framework, originally designed for Apple Silicon, is getting a CUDA backend, which is a pretty big deal. Here’s why. The work is being led by developer @zcbenz on GitHub ...

NVIDIA introduces cuda.cccl, bridging the gap for Python developers by providing essential building blocks for CUDA kernel fusion, enhancing performance across GPU architectures. NVIDIA has unveiled a ...

Ian Buck is a lesser-known force behind Nvidia's technological moat and market share. His reputation as a masterful developer changed the course of Nvidia's history — and its future. How Nvidia ...

CUDA enables faster AI processing by allowing simultaneous calculations, giving Nvidia a market lead. Nvidia's CUDA platform is the foundation of many GPU-accelerated applications, attracting ...