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Google's selfie videos can be used for account access, AI Avatars, and age verification.

Bill would let Homeland Security chief decide when an AI should be shut down.

Claude's voice mode hasn't been the most reliable, but this update should help.

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

I'm a solo dev, and for the past few months I've been building Pixanima — a pixel-art and animation editor that runs entirely in the browser, with an optional AI assistant baked in. It just launched, and I wanted to share the parts that were technically interesting: making a general image model output clean pixel art, an atomic credit system, AI inbetweening for animation, and why the whole business model falls out of one architectural fact. What it is Draw pixel art with layers, groups and effects, animate it on a frame timeline with onion-skin, and export to GIF / sprite sheets / PNG — all client-side, no install. On top of that, an AI assistant turns a text prompt into sprites, seamless tiles and palettes, re-poses characters, and generates in-between animation frames. The frontend is Vue 3 driving an HTML canvas; the backend is Laravel 12 (PHP 8.4) . Here's what I learned. Everything runs in the browser — and that decided the business model The entire editor is client-side. Projects live in IndexedDB ; nothing is uploaded. That's great for privacy and speed, but it has a consequence a lot of people miss: you cannot meaningfully gate a client-side feature. If drawing, layers and export all run in the user's browser, any "pro" paywall around them is both unenforceable and, honestly, hostile to a price-sensitive hobbyist community. So I flipped it: the editor is 100% free, forever . The only paid thing is AI — because AI is the only part with a real marginal cost, and it requires a backend (which conveniently also protects the code that costs money to run). More on that below. Making a general model output clean pixel art The naive approach — prompt a diffusion model with "pixel art, 16 colors" — gives you pixel-art-ish mush: anti-aliased edges, hundreds of colors, no real grid. Useless as an actual sprite. The fix is a post-processing pipeline. The model just produces raw input; the "pixel art" is made deterministically afterward with PHP's GD: 1. Generate a norma

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

Last month I set out to build a new product, and after a full week I had shipped exactly zero features. Not because I was slow. Not because the work was hard. Because before anyone could write a single line of business logic, my team had to re-decide a dozen things our company should have settled years ago . I've been a full-stack developer for almost five years — long enough to have worn most of the hats: WordPress developer, QA, frontend, backend, solution architect, founding engineer. I've built more than twenty web applications and a handful of mobile ones. Some of them I'm genuinely proud of: systems that poll PLCs every five seconds to watch over industrial equipment, a cybersecurity dashboard that mapped attacks across the world in real time using tree-based graphs, an OTT platform that streamed live events — including FIFA — to millions of concurrent viewers. Today I work on enterprise supply-chain finance software. So when I tell you the hardest part of that new product had nothing to do with code, I know how it sounds. Let me explain. The week that disappeared The experiment was ambitious on purpose. I wanted to build the new application — eventually a microfrontend inside a larger enterprise platform — but I didn't want to write most of it myself. I wanted Claude Code to implement while I acted as the architect: review, test, challenge, refine, repeat. That part worked. The AI wasn't the bottleneck. The bottleneck was everything that came before the first feature. Should we use React? Vite? Keep Create React App because the parent app still runs it — or migrate both? How does the parent consume the child, and does local development still work? Does authentication still work? Does routing? Do we adopt TypeScript when the existing app doesn't, knowing that splits one product into two standards? The app also had to feel native to the existing product — same spacing, typography, colors, interactions — except the company had a component library, not a design s

You have a row in Postgres. You want that same row in MongoDB — not tonight in a batch job, but the instant it changes. And next month you'll want it in Redis too, and maybe POSTed to some webhook. Today that's a Kafka cluster, a Debezium connector, a sink connector, a schema registry, and a weekend. For a job that is, at its heart, one sentence: a source → one or more destinations → kept in sync. Syncle is an open-source tool that does exactly that sentence, and nothing you didn't ask for. Connect your databases, draw a bridge from a source to one or more destinations, and the moment a row changes in the source it's written to every destination you linked. Any engine to any engine — PostgreSQL · MySQL/MariaDB · SQLite · MongoDB · Redis — plus HTTP endpoints when you need them. This post is a tour of what it does and, for the curious, how it's built. The core idea: a bridge A bridge reads rows from a source and writes each one to its destinations . A destination is either: another database — the headline feature. Postgres → MongoDB, MySQL → SQLite, MongoDB → Redis. One bridge can fan out to several databases at once, and bridges can chain (A → B → C). an HTTP endpoint — POST/PUT/PATCH each row to a URL with a payload you design, for feeding a service instead of a database. The interesting part isn't that it copies data — plenty of things copy data. It's the guarantees around how . No duplicates, ever Every database write is an idempotent upsert , keyed by columns you choose. So replays, retries, and at-least-once redeliveries never double-write. Under the hood each engine does it with its own native atomic operation: Engine Upsert PostgreSQL / SQLite INSERT ... ON CONFLICT MySQL INSERT ... ON DUPLICATE KEY UPDATE MongoDB updateOne(filter, ..., { upsert: true }) Inserts, updates, and deletes all propagate — a delete routes to a keyed delete on each target. Missing table? It builds it If the destination table or collection doesn't exist, Syncle creates it from the sou