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Claude's new voice model will let you reschedule your meeting or draft an email

Until now, voice mode has only been available on Claude Haiku, Anthropic's faster but less powerful model. Now the company is making its Opus and Sonnet models available in voice mode, and extending its reach into apps like Gmail, Slack, and Canva. When Anthropic launched voice mode earlier this year, it was primarily focused on […]

~7 min read · Tutorial I look at a lot of MUI admin templates. material-kit-react from the minimals people is one I keep going back to. Clean, typed, and the folder structure makes sense. Repo: minimal-ui-kit/material-kit-react . But every time I just want to see it, or change one color to check something, it's the same ritual. git clone , npm install , wait, npm run dev , wait more, tab over to localhost. Few minutes gone, a few hundred MB of node_modules on disk. All that to look at a dashboard. So this time I skipped the build. Opened the folder in CrossUI Studio , rendered src/main.tsx directly. No install, no Vite, no localhost. Below is what I did, including the bits that made me stop and think. One honest note first. This does not replace your dev server. You still need the real thing for tests, prod builds, actual feature work. It's good for the look-and-tweak loop. Evaluating a template, recoloring something, showing a client. The stuff where booting the whole toolchain costs more than the task itself. Quick note : local folder support requires a Pro account. To test it out, use the code in the original blog for a free upgrade. No credit card required, available while it lasts. Your browser does not support video. Watch on YouTube . 1. Clone to local disk (don't open it straight from GitHub) Studio can mount a GitHub repo directly. For a small repo that's the nicest path. For this one I cloned to disk first: git clone https://github.com/minimal-ui-kit/material-kit-react The reason is boring. src/ alone is ~130 files, ~245 in the whole project, spread over sections/ , components/ , layouts/ , theme/ , routes/ . Opening a project means the tool has to pull the files it touches. Over the GitHub API, on demand, that's a lot of small requests. It works, just not snappy, and you can hit the rate limit if you poke around. A local folder is only the filesystem, so it's instant. For a template this size, local wins. No npm install here. I only cloned the source. The

The clip features the David Bowie track “Five Years,” which includes lyrics such as “Earth was really dying (dying).”

The Series A was led by Battery Ventures, bringing AegisAI total funding to $49 million.

Let's say you need to implement a feature that returns a different package based on the user-provided coupon code. So you start with a model: public record Package { public int Id { get ; set ; } public string Name { get ; set ; } public double Price { get ; set ; } } And you write a function that returns a different package based on the coupon code: private static Package GetPackageFromCoupon ( string coupon ) { if ( coupon == "ABC" ) { return new Package { Id = 1 , Name = "PS5 Controller" , Price = 50.00 }; } if ( coupon == "EBC" ) { return new Package { Id = 2 , Name = "Iphone X" , Price = 200.00 }; } if ( coupon == "DDD" ) { return new Package { Id = 3 , Name = "X7 Mouse" , Price = 20.00 }; } return new Package { Id = 1000 , Name = "Soda" , Price = 1.00 }; } And invoke it from your main method: internal class Program { static void Main ( string [] args ) { var package = GetPackageFromCoupon ( "ABC" ); Console . WriteLine ( package ); Console . ReadLine (); } } Quick test Provide expected parameters and inspect the results. "ABC" => Package { Id = 1 , Name = PS5 Controller , Price = 50 } "EBC" => Package { Id = 2 , Name = Iphone X , Price = 200 } "DDD" => Package { Id = 3 , Name = X7 Mouse , Price = 20 } Works as expected. Also, if you enter something that doesn't exist: "a" => Package { Id = 1000 , Name = Soda , Price = 1 } The Problem What if you need to add more coupon codes and return different variations of the Package object? Well, it's gonna get pretty messy very soon. Quick solution - Dictionary Rather than writing every possible variation in the if block, create a dictionary where the key is the coupon code and the value is the Package: private static readonly Dictionary < string , Package > _packages = new () { [ "ABC" ] = new Package { Id = 1 , Name = "PS5 Controller" , Price = 50.00 }, [ "EBC" ] = new Package { Id = 2 , Name = "Iphone X" , Price = 200.00 }, [ "DDD" ] = new Package { Id = 3 , Name = "X7 Mouse" , Price = 20.00 }, }; The next step is to

Patreon is laying off 20 percent of its workers, or around 93 employees, as reported earlier by 404 Media. In a memo to employees, Patreon CEO Jack Conte writes that the company isn't making these changes "because we believe AI replaces humans," but says AI has "fundamentally transformed the tech industry, including how we work, […]

If you build anything on top of hosted AI APIs, latency isn't a detail you get to ignore — it's a feature. A sluggish time-to-first-token is the difference between an assistant that feels alive and one that feels broken. Yet when I went looking for an honest answer to a simple question — how fast is provider X from where my users actually are? — I couldn't find one. The numbers people quote tend to come from a single machine in a single region (usually somewhere in the US), from vendor-reported status pages, or from a benchmark that got run once and never refreshed. There was no independent, measured , regional view. So I built one: LLM Latency Tracker , a provider-neutral tracker of latency and uptime for AI inference APIs. How it works The core idea is boring on purpose: actually measure, don't scrape. A small Python prober — standard library only, no API key needed for the edge probes — opens real connections to each provider's endpoint and times every phase of the handshake: DNS resolution → TCP connect → TLS negotiation → time-to-first-byte (TTFB) . That's the edge view: how long the network path itself takes before a single byte comes back. Separately, where possible, it measures inference time-to-first-token (TTFT) — the thing your users actually feel, i.e. how long after you hit "send" the model starts streaming. Those two numbers answer different questions, and keeping them apart matters. Edge latency is about the network and the front door; TTFT is about the model and the queue behind it. The probes run from four regions — Europe (Germany), US Central, Asia (Tokyo), and South America (São Paulo) — because "fast" is meaningless without "from where." Results land in a SQLite time-series; a static-site generator turns that into the pages you see, hosted on Cloudflare Pages, and the whole thing is self-updating on a schedule. There's no always-on backend to rot or page me at 3am. It currently covers ~45 providers — the usual Western labs (OpenAI, Anthropic, Go

MSG’s sprawling surveillance system can monitor guests down to the second. Its owners made an exception for the pop star’s rehearsal dinner.