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Microsoft says it will let anyone access Xbox Cloud Gaming in November, without the need to subscribe to Xbox Game Pass. The change to a pay-as-you-go model is part of some sweeping changes to Xbox Cloud Gaming today, and is designed for people who don't use cloud access enough to warrant a monthly subscription. "Players […]

Microsoft is announcing some major changes to Xbox Cloud Gaming today, less than a year after opening up cloud gaming to its Xbox Game Pass Premium and Essential tiers. While all Game Pass plans have included unlimited cloud gaming over the past year, from November this will be limited to just 15 hours on Ultimate, […]

TL;DR — On a MSI Stealth A16 AI+ (Ryzen AI 9 365, XDNA2 NPU) running Arch, I got OpenAI's whisper-large-v3-turbo transcribing on the NPU — not the CPU, not the GPU — at RTF ≈ 0.18 (a 30 s clip in ~5.2 s) for roughly a tenth of the energy the same job costs on the CPU, plus an LLM answering on the same NPU through an OpenAI-compatible API. The whole path is local and offline. This is the write-up of the driver stack, the one real gotcha (memlock), and the runtime that made it a 20-minute job instead of a weekend. Why this is worth writing down AMD's "Ryzen AI" NPU (the XDNA / XDNA2 block in Phoenix / Hawk Point / Strix Point laptops) is marketed almost entirely around Windows: the Ryzen AI SDK, the ONNX Runtime VitisAI execution provider, Lemonade, and the demos all assume you're on Windows with the official stack. On Linux the picture in early 2026 is better than most people think — the NPU driver has been in the mainline kernel as amdxdna since 6.14 — but the "load a real model and run it" story still isn't well documented. Here's what actually worked, end to end. The hardware Part Detail Laptop MSI Stealth A16 AI+ A3HVGG APU AMD Ryzen AI 9 365 (Strix Point) NPU XDNA2, 8 columns, exposed as /dev/accel/accel0 NPU firmware 1.1.2.64 Kernel 7.1.9-arch1 ( amdxdna in-tree) OS Omarchy (Arch Linux) AMD quotes the Strix Point NPU at up to 50 TOPS, INT8 . 1. The driver stack Three pieces have to be in place before any runtime can touch the NPU: amdxdna — the kernel driver. In-tree from Linux 6.14; it's what creates /dev/accel/accel0 . Check it's bound: $ ls /dev/accel/ accel0 $ dmesg | grep -i amdxdna XRT (Xilinx/AMD Runtime) + the xrt-plugin-amdxdna shim. XRT is the userspace API; the plugin teaches it about the XDNA device. On Arch both are in extra : $ sudo pacman -S xrt xrt-plugin-amdxdna $ xrt-smi examine ... XRT Version : 2.21.75 NPU Firmware Version : 1.1.2.64 Device(s) Present |BDF |Name | |----------------|--------------| |[0000:66:00.1] |RyzenAI-npu4 | You want a D

Moving a Next.js app off Vercel and onto a plain Ubuntu VPS usually starts with a painful realization: either your serverless functions are timing out on background jobs, or your client just handed you a strict "you must host this on our infrastructure" requirement. Deploying the app itself is easy. What trips people up (and what cost me hours of debugging and locking myself out of my own server) is everything around the app. Here are the 12 things that actually break when you leave the serverless ecosystem, in the order you'll hit them. 1. Next.js needs a process manager, not just npm start Running npm start in a terminal dies the moment you disconnect. You need something that keeps the process alive, restarts it on crash, and survives a reboot. PM2 is the simplest option for a single-server Node deploy. npm install -g pm2 // ecosystem.config.js module . exports = { apps : [{ name : " my-app " , script : " node_modules/.bin/next " , args : " start " , cwd : " /var/www/my-app " , instances : 1 , exec_mode : " fork " , autorestart : true , max_memory_restart : " 512M " , env : { NODE_ENV : " production " , PORT : 3000 }, }], }; cd /var/www/my-app && pm2 start ecosystem.config.js pm2 save pm2 startup systemd -u YOUR_USER --hp /home/YOUR_USER That last line is the one people forget - without it, PM2's process list doesn't survive a server reboot. 2. Nginx needs to proxy to the port, not serve the files Next.js is not a static site (unless you've explicitly exported it as one). Nginx's job is to forward requests to the Node process, not serve files from disk: upstream nextjs_upstream { server 127.0.0.1 : 3000 ; keepalive 64 ; } server { listen 80 ; server_name example.com www.example.com ; location / { proxy_pass http://nextjs_upstream ; proxy_set_header Host $host ; proxy_set_header X-Real-IP $remote_addr ; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for ; proxy_set_header X-Forwarded-Proto $scheme ; # WebSocket support - required for HMR and any realtime f

Zoox is expanding to this critical ride-hailing destination a few weeks after it started charging for robotaxi rides.

Xbox controllers continue to ship reliant on AA batteries, but you don't have to keep burning through disposables.

The most useful thing my home server taught me was not how to install another Docker container. It was how quickly a problem stops belonging to one tidy layer. A service can be running while DNS is wrong. Plex can work while the machine doing the transcoding cannot reach the storage. A reverse proxy can be configured correctly while the network around it is a mess. When it is your own server and you actually want to use it, those boundaries become your problem. That is very different from the way many application-focused software-engineering jobs feel. You can spend years building applications without having to join Linux, storage, DNS, HTTPS and networking together yourself. The experiments that kept getting turned off Around the start of 2020, I got a Raspberry Pi and repeatedly installed Raspbian or Debian on it. I would add Sonarr, Radarr, maybe Prowlarr, a torrent client and Plex. Sometimes Pi-hole joined them. There was no reverse proxy and I was not putting my own domains behind it. It was primitive, and I learnt something each time, but it never stuck, right? I would decide to play with it and eventually turn it off again. The Pi proved that I could run these services. It did not give me infrastructure I depended on. That changed in summer 2024. I had an old i5 desktop lying around, knew it worked and could connect drives to it easily. Why the hell not? I installed OpenMediaVault and spent the next two or three months building the setup out. Docker-managed services were joined by Traefik as a reverse proxy, Tailscale , proper DNS and network sharing. The useful result was a repeatable path for a new service. I could put it behind HTTPS and decide whether it should be public or only reachable inside my network. The machine was no longer an experiment waiting to be unplugged. A second machine made the lessons real I also bought a separate OptiPlex with 4 GB of RAM and installed Debian. Its main job was Plex Pass transcoding, reading media over the network from

The World Meteorological Organization is warning that the climate phenomenon that has the Pacific running a fever will last until at least February, with dire consequences for weather worldwide.

1. How this project started? I'm going to be honest, it's been ages since I have written something without the use of AI to fix my writing. English is not my first language so please bear with me! With the rapid rise of AI, I felt that I have been losing passion for what I used to love at some point: learning . Nowadays, we can quickly solve most of our problems with the use of AI, often times, not even reviewing if it correct or not. That's why I decided to take some time daily to learn something new without or minimal use of AI. The first step is deciding, what should I try to learn first? Well, it was quiet easy to find out what. If you are into Linux, you have probably heard of Omarchy at this point. Like it or not, there's no deny that it's getting more popular among developers. So, why not try to build a decent looking Arch workspace? Before continuing, I would like to mention that this is not a guide. There are lot of resources online that teaches you how to install Archlinux and other packages. 2. Why Archlinux? Archlinux has -or had- the reputation of being difficult to get started with. Most of us are used to booting into a nice-looking, functional operating system. Although I have some Linux knowledge, I wanted to have a better understanding of what it takes to have a decent workspace. 3. Installing Archlinux The first step is actually installing Arch on my device. The device I'm going to use is my trusty built PC that I currently use exclusively for gaming. There are a few things to consider before jumping into installing Arch: My PC has an Nvidia RTX 5050 and AMD Ryzen 5 CPU. Need dual boot to switch between Windows 11 and Archlinux. I don't want to change the BIOS options repeatively. With this in mind, I quickly created a bootable USB using RUFUS . 3.1. Booting the USB If you are a Windows 11 user and have dual boot, you may know that Windows requires Windows Secure Boot. In order to boot another operation system, you'll need to change your Secure Boo

"Requirements are being adjusted to reflect near-term mission needs."

Amazon is trying to combat impersonation scams with a new feature that allows you to use its AI assistant to determine whether an email, text message, or phone call actually came from the company. With the update, you can ask Alexa for Shopping about a message you received, and it will use AI to compare […]

He reported from space that only "fat" Japanese tree frogs enjoyed weightlessness.

The electric-auto maker will officially debut its steering-wheel-free robotaxi on Thursday. But building the vehicle is the easy part.

Our favorite Sony headphones are on sale, with discounts of up to 50 percent on top models.

X is investigating a wave of unsolicited password reset emails that it believes may be tied to the rollout of its new payments service.

Waymo argued that fully autonomous vehicles aren't possible without using a mix of sensors and warned that pure end-to-end AI systems aren't safe enough.

A look into whether Dolby Atmos for Headphones - with its free trial on both PC and Xbox - can unlock spatial audio on a normal pair of stereo headphones.

Amazon is adding a new Alexa-powered feature called “Update Me When” that can send personalized alerts about product launches, tours, books, shows, and other events that could trigger a purchase.

Newlight has raised a $9 million seed round and completed an 8,500-nautical-mile test run from Singapore to Ghana.

In this Expo & React Native tutorial, you’ll learn how to run a large language model (LLM) directly on a user’s device: no server, no API key needed. We’ll start from scratch with a simple chat exchange, and progressively introduce more advanced features: multimodal input, speech-to-text, text-to-speech, voice activity detection, tool calling and RAG. Each concept is explained before the code, so you can follow along whether you're new to on-device AI. Why run AI On-Device? Most AI features rely on a cloud API: you send a request to a remote server, it runs the model, and sends a response back. That works well, but it comes with tradeoffs. Running the model directly on the device avoids all of them: Works offline — no internet connection required Privacy by design — user data never leaves the device Low latency — no network round-trip No cloud costs — inference is free The tradeoff is raw capability: on-device models are smaller and less powerful than frontier cloud models. But for many use cases like summarization, chatbots, or local search, they're more than good enough. About NobodyWho We'll use the NobodyWho library throughout this tutorial. It wraps llama.cpp in Rust and exposes a clean React Native API for running locally any model in .gguf format. Install it with npm install react-native-nobodywho or npx expo install react-native-nobodywho for Expo. Loading a Model NobodyWho can download a GGUF model for you directly from Hugging Face, cache it, and reuse it on every subsequent launch. That means you don't need to bundle anything into your app or manage downloads yourself: import { Chat } from " react-native-nobodywho " ; const chat = await Chat . fromPath ({ modelPath : " huggingface:NobodyWho/Qwen_Qwen3-0.6B-GGUF/Qwen_Qwen3-0.6B-Q4_K_M.gguf " , }); The first time this runs, the model is downloaded to the app’s cache directory. Every call after that loads the model directly. modelPath accepts a few different forms: Form Example Notes HuggingFace reference hf