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

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

Note: Google Cloud credits are provided for this project. In this blog post, I want to demonstrate how I use Antigravity CLI to build an image analysis demo using Angular, Firebase Hybrid & On-device Inference Web SDK, and Gemini models. Users upload an image and use a Gemini model to analyze it to generate a few alternative texts, tags, recommendations, and CSS tips to enhance the image quality. When the demo is running on Chrome 148+, the Hybrid & On-device SDK leverages the Prompt API of the on-device Gemini Nano model to perform the image-to-text tasks, and the token usage is 0. When other browsers such as Safari or Firefox executes the same tasks on the demo, the SDK falls back to Cloud AI (Gemini 3.5 Flash model), and the token usage is greater than 0. Next, I will describe how I installed the skills in my Angular project, and registered the Stitch MCP server in the Antigravity CLI to develop the infrastructure, services, and UI design of my demo. 1. Skills I installed grill-with-docs , angular , and firebase skills in my project for the following reasons: grill-with-docs: Conduct a rigid Q&A session to generate a specification for a feature, refactor or a critical fix. AI is responsible for performing a thorough analysis and putting in more effort to generate code to achieve the task. Angular: Provide the best practices of Modern Angular architecture, such as using signals and signal forms. Firebase: Provide the skill for Firebase AI Logic, Firebase Remote, etc. Resources Firebase Hybrid & On-device Image Analysis App Firebase Hybrid & On-device Inference Chrome Built-in Prompt API Stitch Stitch MCP Server grill-with-docs Angular skill Firebase skill

I run ezyZip , a browser-based archive tool, so "which format should I use?" is a question I field constantly. The honest answer is usually "it depends," which satisfies nobody. So I stopped hand-waving and measured it. We benchmarked 42 archive and compression formats, spanning four decades, from 1984's Unix compress through today's Zstandard, Brotli, and context-mixing paq8px. Everything ran against the same realistic 55 MB corpus, every archive was round-trip verified byte for byte, and the whole thing reproduces from a single command. Here's what came out of it, and what I'd actually reach for. The setup Most compression benchmarks measure raw codecs on standardized corpora like Silesia. That's the right call for algorithm research and the wrong call for answering "what should I zip my folder with?" I wanted end-user formats, real CLI tools, container overhead and all, on data that looks like an actual folder. So the corpus is deliberately mixed: about 11 MB of text, 15 MB of office documents, 16 MB of images, and 13 MB of video, all public domain so it can be committed and redistributed. That mix matters. Office documents ( .docx , .xlsx , .pptx ) are themselves ZIP containers, so they stress how a tool handles already-compressed data. The JPEG and H.264 media is near-incompressible and sets an honest lower bound. The plain text and uncompressed images are where formats actually separate. Two rules kept it fair and practical: Only two levels per tool: its default, and its one "maximum compression" dial. No method tuning, no dictionary sizes, no thread-count games. That's what a normal person can reach. Everything is round-trip verified. Each archive gets extracted, and every file is hashed with SHA-256 against the original manifest. Exit codes are not trusted. That last rule earned its keep immediately. The verification gotcha On the image category, a 1985-era ARC build produced an archive that its own extractor happily unpacked, while printing a CRC warning an

A recent study tracked hundreds of soccer fans until their favorite team reached the final of a tournament. Their stress levels skyrocketed, and their heart rates jumped too.

Hey there, My name is Clem, I've been a solo indie dev for a couple years now, exploring frontier tech like XR and agentic workflows in the context of creative / interactive work. I've been building creation tools for a while and some common design challenge is to figure out the right level of abstraction for your tool. You can always make it super advanced and complex with low level concepts (shader composition, actual code etc.) but then you get something with a high complexity / learning curv

Take on sand, snow, mud, gravel—this is the ebike for all your off-roading adventures.