China's state-owned space company recovered its first orbital rocket booster after launch.

Affected users will have to configure their lights and settings all over again.

Netflix has shows and movies. And video games. And live sports. And podcasts. And also, apparently, YouTube videos? For a company that used to seem like the next big thing in TV, it all feels a little frenetic, and maybe a tad desperate. For a company that sees sleep as its primary competitor, it might […]

The launch of free content would allow Disney+ to better compete with free services like YouTube and Tubi, which are capturing a growing share of consumers’ viewing time.

You just have to know where to look on the Steam page.

The Earth may not be that massive, but it still distorts space-time.

Dumb Co sells flip phones that sync to your smartphone, bridging the infinite connectivity of the iPhone and the unrealistic limitations of an early 2000s relic.

How migrating Copilot code review to shared Unix-style code exploration tools reduced review cost by reshaping agent workflows around pull request evidence. The post Better tools made Copilot code review worse. Here’s how we actually improved it. appeared first on The GitHub Blog .

If you were to look up into the night sky, what would you see? Countless points of light, scattered in every direction. Most of what you're looking at are stars. But some of those points are whole galaxies—vast collections of stars, spread across incomprehensible distances, compressed by that distance into a single pinprick of light. And what you can see with the naked eye is only a small fraction of what's actually out there. I want to use this as a way to offer you a way of thinking about how large language models work. Just an analogy, not literally what's happening inside the mathematics—that's not my forte. My hope is that it captures something true about the mechanics, and more importantly, it gives you a mental model you can actually use when you're working with these systems. About two years ago, I was wrestling with finding a way of explaining what an LLM does. My first analogy was that of a dictionary. The naive view was that a dictionary uses words to define other words, and an LLM holds a matrix of words with weights that describe their relationships to each other. So the parallel seemed natural: both systems work through relational structure. However, a dictionary gives you denotation—the surface-level meaning. It's a lookup tool for individual words, not a model of language itself. And critically, you have to already understand language before a dictionary is useful to you at all. The analogy didn't capture what was actually happening in the weight relationships—the distributional semantics, the contextual patterns that let an LLM generate coherent text. Ok, so back to galaxies, when you look up at the night sky, you're not seeing distance—you're seeing direction. That galaxy over there, the one that looks like a point of light, could be millions of light-years away, but what matters for our analogy isn't how far it is. It's which way you're looking. And when you point yourself in that direction and venture toward it, you discover it's not a point at a