标签：#t

I'll admit it. I was wrong. Wildly colorful lighting is delightful for your smart home - well, outdoors, at least. Smart lighting is one of my favorite features of the smart home - it combines convenience with ambiance, letting you change the entire look of your room with just a press of a button. But, […]

I’ve been reviewing laptops for over a decade, and this is my advice on how to find the right laptop for you.

What you need to know about the expiration of keys securing your machine's boot sequence.

On 14 April, the Trump administration quietly acknowledged the widespread use of AI to automate government processes. The office of management and budget (OMB) disclosed a staggering 3,611 active or planned use cases for AI across the federal government. The list has ballooned by 70% from the one published in the final year of the Biden administration, and includes many disturbing-seeming plans to hand over sensitive governmental functions to AI. Scanning this list, many readers may find many causes for alarm. It represents a transfer of decision processes from human to machine on a massive scale over matters of individual freedom, public health and well-being, nuclear reactor safety and more...

Aditya Kumarakrishnan explains how to move past the "amnesia phase" of AI. He shares a blueprint for engineering leaders to build modular agent frameworks using CoALA, leverage decades of process science for scalable workflows, and "terraform" legacy environments into robust, event-sourced artifacts capable of handling unpredictable, cross-functional agent demands. By Aditya Kumarakrishnan

This will be the second market to have an Uber robotaxi service outfitted with Lucid EVs equipped with a self-driving system from Nuro.

Pinterest has launched 'Ask Pinterest,' an experimental AI-powered shopping app that lets users seek recommendations and inspiration through a conversational interface.

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Temperatures have climbed up to 45 degrees Fahrenheit above normal, stopping ice from forming in the dead of Antarctic winter.

Infowars’ would-be creative director talks Sandy Hook, comedy’s MAGA turn, and why the future of satire may look more like a streaming startup than a late-night show.

From defections and protests to moments of national pride, the 2026 World Cup arrives amid decades of tension between identity and the state.

It’s like Big Brother without any limits, or broadcast standards. WIRED goes on location—and on camera—with the cult hit.

Hello Dev Community! 👋 It is officially Day 39 of my non-stop run toward full-stack MERN engineering! Yesterday, I mapped out basic HTTP verbs like GET and POST. Today, I advanced into Prashant Sir's (Complete Coding) backend masterclass to tackle one of the most critical core operations: Handling Data Streams and Body Parsing . When a user submits a form or uploads data, the server doesn't receive the file all at once. It arrives as an asynchronous stream of network data chunks. Today, I learned how to collect and decode those packets natively! 🧠 Key Learnings From Node.js Lecture 7 (Streams & Buffers) Node.js is designed to be non-blocking and memory-efficient. Here is the technical breakdown of how it intercepts client payloads: 1. Inbound Streams & Data Chunks I learned that incoming POST data is treated as a Readable Stream . Instead of loading a massive data file into the server memory instantly, Node transmits the payload in tiny pieces called Chunks (hexadecimal binary data). 2. Event Listeners for Requests ( req.on ) Natively, we don't have an instant req.body object. We have to listen to the network events on the incoming request stream: req.on("data", (chunk) => { ... }) : Fires every single time a fresh chunk of binary data arrives at the network interface. We push these raw chunks into a temporary array. req.on("end", () => { ... }) : Fires automatically once the stream concludes and all chunks have arrived safely. javascript if (req.url === "/submit" && req.method === "POST") { let body = []; req.on("data", (chunk) => { body.push(chunk); // Collecting raw binary chunks }); req.on("end", () => { // Concatenating and converting hexadecimal binary buffers into a readable string layout let parsedBody = Buffer.concat(body).toString(); console.log("Received Form Payload:", parsedBody); res.writeHead(200, { "Content-Type": "text/plain" }); res.end("Data received and parsed successfully!"); }); }

A founder told me last year: "We need admin features shipped by Friday. Can we just hardcode the roles for now?" That was 7 months ago. They're still paying for that decision today. This article is the breakdown I wish I had given them before that Friday — the patterns that separate a quick MVP version of RBAC from one that won't bite you back in six months. If you're a startup CTO, technical founder, or senior engineer about to ship admin/dashboard features for the first time, this is for you. The 4 signs your RBAC is a time bomb I've reviewed this exact pattern in 5+ early-stage codebases. Every one of them eventually hit the wall. Look for these signs: 1. Roles hardcoded in the JWT { "sub" : "user_123" , "role" : "admin" } Simple. Until you need to: Add a new role → migration + token refresh strategy Give one user a temporary elevated permission → "we'll just reissue tokens" Audit who had admin last month → good luck 2. Permission checks scattered across 40+ controllers // In every single handler: if user . Role != "admin" { return c . Status ( 403 ) . JSON ( ... ) } By the time you have 30 endpoints, you've made the same mistake 30 times. Refactoring is a full sprint. 3. One admin superuser that can do everything Until a sales rep accidentally deletes a customer record because they were elevated to admin "just for that one task last week." 4. Zero audit trail When the data goes missing, your only investigative tool is git blame on the source code and a desperate grep through CloudWatch logs. If you've nodded at any of these — keep reading. The real problem: policies-as-code Most teams treat permissions like business logic. They live in source files, gated by if statements, deployed with the rest of the code. This is the original sin. Code changes need deploys. Permissions change daily. When marketing onboards a new role next quarter, when a customer success rep needs view-only access to billing, when legal asks "who could have read this customer's data on Tuesda

There is now a second free WSL management tool out there called WSL Dashboard , published by a developer who goes by owu on GitHub. It is open source, it has more GitHub stars than WSL UI, and it is clearly the product of real effort. I have no problem with any of that. Competition is good for users, and WSL deserves more than one good GUI. What I do have a problem with is this notice, which appears both on wslui.com and in the WSL Dashboard README: "This software is not distributed through the Microsoft Store. Any application listed there under the same name is unauthorized and may be counterfeit. Please do not download it to avoid potential scams." WSL UI is on the Microsoft Store. It has been since January. So that notice is, in plain terms, telling people that the genuine, certified, earlier WSL tool is a counterfeit scam. It isn't. And the entire claim falls apart the moment you look at the dates — all of which are public, timestamped, and independently verifiable. What this post is, and what it isn't This is not a "my project is better than yours" post. I'm not going to pick apart WSL Dashboard's features or pretend it's a bad piece of software. It isn't. This is a correction of one specific, factual claim: that the version of WSL UI on the Microsoft Store is unauthorized or counterfeit. That claim is demonstrably false, and because it's aimed at a tool people trust, it's worth setting straight properly — with evidence, not adjectives. The timeline Every date below comes from a public record. I've linked the sources so you don't have to take my word for any of it. Date Event How to verify 2008-12-11 octasoft.co.uk registered. Octasoft Ltd has existed for well over a decade. Nominet WHOIS for octasoft.co.uk 2025-12-14 The octasoft-ltd/wsl-ui repository is created. GitHub API ( created_at ) 2026-01-10 WSL UI's first public release, v0.2.0. WSL UI releases 2026-01-13 WSL UI v0.14.0 is published to the Microsoft Store. The announcement post 2026-01-18 The owu/wsl-

Hello Dev Community! 👋 It is officially Day 38 of my unbroken streak toward mastering the MERN stack! Yesterday, I learned how to extract query strings from the URL bar. Today, I took a massive step forward into full-stack backend architecture by diving into Prashant Sir's (Complete Coding) roadmap to master HTTP Request Methods . Up until now, our server treated every incoming request the same way. Today, I learned how to make the backend execute completely different actions based on the "intent" (HTTP Verb) of the user! 🧠 Key Learnings From Node.js Lecture 6 (HTTP Verbs) An endpoint is no longer static when you map request methods against it. Here is the technical breakdown of what I locked down today: 1. Cracking req.method I discovered that the incoming Request object holds a crucial property called req.method . This tells the server exactly what action the client wants to perform. 2. The Big Four Core Methods GET: Used when the user simply wants to read or fetch data from the server (e.g., viewing a product page). POST: Used when the user wants to securely send or create new data on the server (e.g., submitting a signup form). PUT/PATCH: Used to update existing data records. DELETE: Used to wipe a specific data entry from the server storage. javascript const http = require("http"); const server = http.createServer((req, res) => { if (req.url === "/api/data") { if (req.method === "GET") { res.writeHead(200, { "Content-Type": "text/plain" }); res.end("Fetching and reading secure database records..."); } else if (req.method === "POST") { res.writeHead(201, { "Content-Type": "text/plain" }); res.end("Securely creating and injecting new data into the server!"); } } else { res.end("Standard Route"); } }); server.listen(8000);

Ask a language model to score a developer portfolio out of 100 and you get a confident number back. Hand it a near-empty page with a name and a broken avatar, and it will often still tell you something like 92. Nice layout. Strong personal branding. The model is being polite, not accurate. That was the first wall I hit building Leon, the reviewer inside getfolio. If the score is not trustworthy, nothing downstream matters: the critique, the suggestions, and the fix button all hang off a number the model invented to sound encouraging. This is the build log of how I stopped letting the model hold the pen. Short version: a deterministic rules engine owns the score, and the language model only owns the words around it. The failure mode: an LLM judge wants to be liked If you have shipped anything with an LLM evaluator you have probably seen this. You hand it a rubric, a JSON schema, even worked examples, and it still drifts upward. Empty inputs get encouraging scores. Weak inputs get the benefit of the doubt. Strong inputs land in the same band as the weak ones, just with longer praise. A few reasons, roughly in order of how much they hurt: Tuning rewards a helpful, encouraging tone. Harsh scoring reads as unhelpful, so the model softens it. The model has no ground truth for what a 70 versus an 85 looks like in your specific domain. It is scoring on vibes. Scoring and explaining are entangled. The model writes the kind explanation first, then picks a number to match the nice things it just said. Run it twice on the same input and you get two different numbers. There is no anchor. For a portfolio reviewer that real recruiters and developers would act on, that was a non-starter. If Leon says 64, an empty page should not be able to reach 64 by accident, and a strong portfolio should not get talked down to it either. The number has to mean something. The fix: rules engine owns the score, model owns the language The architecture splits responsibilities hard. A deterministic e

If you're shipping a commercial WordPress plugin, sooner or later you'll need a licensing system. Something that lets paying customers activate the plugin on their site, locks it to that domain, and stops people from sharing the same key across fifty sites. The default answer in the WordPress world is Freemius or EDD Software Licensing. They're great. They're also a revenue share, a third-party dependency, and a black box you don't control. When we built RideCab WP , a commercial WooCommerce taxi booking plugin, we decided to build our own. Here's the architecture, the code, and the gotchas we hit along the way. What a Licensing System Actually Needs to Do Before writing any code, get clear on the requirements. A real plugin licensing system needs to: Generate unique license keys when someone buys Let the customer activate the key on their site Bind that key to one (or N) domains Validate the key periodically so revoked or expired keys stop working Handle deactivation when a customer moves to a new domain Fail gracefully — never lock a paying customer out because your license server hiccuped Optionally: deliver plugin updates only to valid license holders We'll cover 1 through 6 in this post. Update delivery is a separate beast and I'll write it up next. The Architecture The system has two halves that live in two different places. The license server runs on your own infrastructure — for us, it's a WordPress must-use plugin (mu-plugin) on the same WordPress install that powers our marketing site. It: Stores license keys in a custom database table Exposes a small REST API for activate, deactivate, and validate calls Provides an admin dashboard to view, create, and revoke keys The client is a PHP class shipped inside the commercial plugin (RideCab WP, in our case). It: Adds a license settings page to the plugin Calls home on activation Caches the validation result Re-validates quietly in the background Two pieces, talking over HTTPS, with the customer's domain as the b

Originally published on the Sieve blog . Sieve is an open-source (Apache 2.0) context-reduction proxy — I work on it, and I've tried to keep this post about the problem rather than the tool. If you run an LLM agent for real work, the bill is the part nobody warned you about. It starts small, it grows with use, and the worst of it is invisible — most of what you pay for on any given turn is text the model has already seen, or text you never meant to send. There's a temptation to treat this as one problem with one fix. It isn't. An agent's token bill has two distinct causes, and they need two genuinely different kinds of tool. This post is about telling them apart — because once you can, the question stops being "which tool wins" and becomes "which of my two problems am I looking at right now." The bill is mostly things you didn't choose Start with where the tokens actually go, because it's rarely where people assume. When your agent calls a tool, the model doesn't just pay for your request — it pays for the machinery of asking. Anthropic's own pricing documentation spells this out: the tools parameter alone adds hundreds of tokens of schema to every request, the bash tool adds a fixed overhead, and a single web fetch pulls the fetched page straight into your context — "Average web page (10 kB): ~2,500 tokens... Research paper PDF (500 kB): ~125,000 tokens". A tool result you glance at once and never need again can cost more than the entire conversation around it. Now add the part that repeats. On every turn, a typical agent re-sends its system prompt, its full tool catalogue, its persona, and the conversation so far. The variable part of the request — what you actually typed — is often the smallest thing in the payload. The fixed overhead, multiplied across every turn of a long session, is the bill. So the cost has two shapes, and they're not the same shape: Verbose machine output — JSON tool results, logs, search dumps, fetched pages, code listings. Big, one-off, an

If you have spent any time on SY0-701 practice questions, you have hit at least one that looks trivial and then quietly fails you. Authentication factor questions are a favorite for this. The scenario sounds secure, the answer feels obvious, and the obvious answer is wrong. Here is the version that catches people. A login asks for your password, then a PIN, then your mother's maiden name. Three prompts, three steps. Is that multifactor authentication? No. It is single-factor wearing a costume. Factors are categories, not steps The exam wants you thinking about authentication in terms of categories , not how many boxes you fill in. There are three classic factors: Something you know (knowledge): a password, a PIN, a security question, a passphrase. Something you have (possession): a phone running an authenticator app, a hardware token, a smart card, a code texted to a device you are holding. Something you are (inherence): a fingerprint, a face scan, an iris pattern, a voiceprint. Multifactor authentication means pulling from different categories. A password (know) plus a code from your authenticator app (have) is two factors. A password plus a PIN plus a security question is still one factor, because all three are things you know. Stacking more knowledge on top of knowledge never changes the category. That is the entire trick. The question piles on prompts so it feels layered, and the count baits you into answering "three things, must be multifactor." Read for the category, not the quantity. The two factors people forget SY0-701 also expects you to recognize two more that sit just outside the classic three: Somewhere you are (location): access is allowed or blocked based on geolocation or which network you are on. A login permitted only from inside the corporate IP range leans on this. Something you do (behavioral): how you type, your gait, the rhythm of your swipe. This is the fuzziest one, and the exam treats it as a real but supporting signal. You will not see the