What Python's asyncio primitives get wrong about shared state - Inngest Blog

We tried Event, Condition, and Queue. Each one gets closer but still breaks under real concurrency. Here's the observable pattern that finally works.

Anthropic Economic Index report: Economic primitives

This report introduces new metrics of AI usage to provide a rich portrait of interactions with Claude in November 2025, just prior to the release of Opus 4.5.

Replicate is joining Cloudflare – Replicate blog

Deep Learning with JAX

Accelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library.</b> The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. In Deep Learning with JAX</i> you will learn how to: Use JAX for numerical calculations</li> Build differentiable models with JAX primitives</li> Run distributed and parallelized computations with JAX</li> Use high-level neural network libraries such as Flax</li> Leverage libraries and modules from the JAX ecosystem</li> </ul> Deep Learning with JAX</i> is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX’s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You’ll learn how to use JAX’s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment.