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New research suggests that dozens of monetized YouTube channels are run by people and organizations that the US government has sanctioned for their ties to Tehran.
Travel bans and other visa issues are creating problems for World Cup participants even before the whistle blows.
European economy airline Ryanair is under investigation in the UK for charging parents mandatory fees to sit with their children. The Competition and Markets Authority (CMA) said it was looking into whether the seating fees, which may be charging parents for the airline to meet its child safety and disability‑related obligations, are "unfair" under consumer […]
Colleagues discussed the incident on internal message boards, according to documents seen by WIRED.
The hard parts of robotics are supposed to be perception, planning, and control. So why does so much of the day go to everything that comes before them? The hidden setup tax in every robotics simulation project Ask anyone what's hard about robotics and you'll get the same list: perception, planning, control, navigation. The genuinely interesting problems. If you track where your hours actually go, though, a strange thing shows up. A big chunk of the day disappears before you reach any of that. You're not solving hard problems yet. You're just getting to the starting line: wiring up a workspace, writing description files, stitching together launch files, and coaxing a simulator into opening without errors. It's the unglamorous tax on every project, and most of us have quietly accepted it as the cost of doing business. Building a differential drive robot simulation in ROS 2 and Gazebo from scratch A diff drive base, a LiDAR, and Gazebo, set up from one prompt instead of an afternoon of boilerplate. A few days ago I wanted a simple mobile robot simulation. Nothing exotic: a differential drive base (two driven wheels, the classic mobile-robot setup), a LiDAR for sensing, running in Gazebo . This is the kind of thing that should be straightforward. In practice it's an afternoon of boilerplate before the robot so much as twitches. So instead of wiring it up by hand, I wanted to see how far Drift could get from a single prompt. To make it a fair test, I stripped the workspace down to nothing. No packages, no URDF, no launch files. A blank slate. Then I typed one line: "Create a mobile simulation from scratch." From XACRO to URDF: how the robot description gets generated in ROS 2 What the tool wrote first, and what XACRO and URDF actually do for your robot. It checked the workspace first: The opening move was sensible: it looked at the current directory to understand what it was working with. It generated a XACRO file for the robot's dimensions: XACRO is the macro-based for
Hello and welcome to Regulator, a newsletter for Verge subscribers about tech politics, tech influence, and tech shenanigans in Washington, DC. (If you're not a subscriber, you can get on board here.) We're back after a two-week hiatus, during most of which I was gallivanting in the Netherlands for a family wedding, and a trip […]
The UFC event on the White House’s South Lawn is the president’s birthday gift to himself. Sources expect it to be a lobbying extravaganza.
According to new research, Trionda would show less unpredictable movements in actions such as corner kicks or free kicks. However, in powerful and long-distance clearances it would lose range.
From anti-drone tech to face recognition, 2026 World Cup stadiums in the US, Canada, and Mexico are subjecting fans to an array of surveillance tech. Here’s what you need to know.
Meta has been ordered by the European Commission to restore free WhatsApp access for chatbots made by rival AI providers while the regulator finishes its antitrust investigation. The rare interim measure announced on Tuesday was deemed necessary "to prevent serious and irreparable damage to competition" in the general-purpose AI assistant market. This is only the […]
Congress narrowly voted to fund President Donald Trump's mass deportation agenda, giving the Department of Homeland Security $70 billion over the next three years. The house voted 214 to 212 in favor of the reconciliation bill Tuesday, following the Senate's 52-47 vote last Friday morning. The vote fell largely along party lines. Sen. Lisa Murkowski […]
Visibility Comes Before Optimization in IT Operations is a practical operating principle, not a slogan. The useful version of analytics, automation, and software operations is usually quieter than the marketing version. It is less about collecting everything or automating everything, and more about making the work easier to understand, review, and improve. The practical problem Teams often try to optimize before they can see the system clearly. That creates confident changes based on partial evidence, especially in infrastructure and telecom-adjacent workflows where signals are distributed. This is where many teams lose clarity. They have tools, charts, workflows, and activity, but the connection between evidence and decision is weak. When that connection is weak, software work becomes harder to evaluate. Teams still make decisions, but they rely more on memory, opinion, or urgency than on a reviewable operating picture. A smaller operating model Start with visibility: what is running, which state changed, where the weak signal appeared, and which workflow was affected. Then connect that signal to a decision or operational review. The important detail is restraint. A useful system does not need to track every possible action or automate every possible step. It needs to preserve the signals that help operators understand the situation and act with more confidence. That usually means naming the workflow, keeping the outcome visible, preserving enough context to explain the signal, and making uncertainty explicit instead of hiding it behind a polished interface. What to review Useful analytics separates normal activity from operational risk. It should make the next investigation smaller, not create another dashboard that requires interpretation from scratch. A reviewable system is easier to trust because it can explain its own state. It shows what happened, what changed, what remains uncertain, and which decision should move next. For WebmasterID, this is the practical
New research finds that in the six months after Meta relaxed rules in the name of free speech, violent threats against lawmakers—including President Donald Trump—surged on Facebook.
Ask almost any engineer what "Wi-Fi" stands for and you'll hear the same answer: "Wireless Fidelity." It is one of the most repeated facts in tech, it appears in textbooks and product manuals, and it is wrong. Wi-Fi does not stand for Wireless Fidelity. In fact, it does not stand for anything at all. A name invented by a branding agency In 1999, the industry group then known as the Wireless Ethernet Compatibility Alliance — today the Wi-Fi Alliance — had a problem. The wireless networking standard it was promoting carried the memorable name "IEEE 802.11b Direct Sequence." That string is precise, but no consumer was ever going to ask a store clerk for an 802.11b router. The technology needed a brand. So the alliance hired Interbrand, the same firm behind names like Prozac and the Compaq brand, to invent something catchy. Interbrand returned with a shortlist of about ten candidates, and the group chose "Wi-Fi." Phil Belanger, a founding member of the alliance, has been blunt about it for years: the name has no expanded meaning. It was picked because it was short, easy to say, and rhymed with "Hi-Fi," a term consumers already associated with high-quality audio gear. So where did "Wireless Fidelity" come from? The myth has a real origin. Some board members were uncomfortable shipping a brand name that "meant nothing," so the alliance briefly bolted on the tagline "The Standard for Wireless Fidelity." It was a backronym — two words reverse-engineered to fit the syllables "Wi" and "Fi" after the fact. The phrase was clumsy, it never described the technology accurately, and once the alliance brought on more marketing-savvy members it was quietly dropped. The tagline disappeared; the misconception it planted did not. Why this matters if you build connected things This is a fun piece of trivia, but it points at something real for anyone doing IoT and embedded development . The protocols we treat as immovable technical bedrock are often shaped as much by branding, licensing,
A WIRED timeline shows how dozens of governments, companies, and other organizations across Europe are moving, or planning to shift, away from US Big Tech.
When I started learning Excel as part of my Data Science & Analytics course, I assumed it was just a tool for creating tables and performing basic calculations. After spending a week exploring its features, I quickly realized that Excel is much more powerful than I thought. Almost every organization generates data. Businesses track sales, schools monitor student performance, hospitals manage patient records, and marketers analyze campaign results. Before data can be analyzed, it needs to be organized, cleaned, and summarized—and that's where Excel comes in. In this article, I'll share some of the Excel concepts I've learned so far and how they're used in real-world data analysis. Understanding the Excel Workspace Before working with data, it's important to understand the basic structure of Excel. When you open Excel, you're working inside a workbook . A workbook can contain multiple worksheets (often called sheets), which help organize different sets of data. At the top of the screen is the Ribbon , which contains tabs such as Home, Insert, Page Layout, Formulas, Data, and View. The Ribbon acts like a control center where you can access Excel's tools and features. Rows run horizontally and are identified by numbers, while columns run vertically and are identified by letters. The intersection of a row and column is called a cell , where data is entered. At first, all these parts seemed overwhelming, but after using Excel regularly, navigating through them has become much easier. The Different Types of Data in Excel One of the first things I learned is that not all data is the same. Excel commonly works with: Text data (names, product categories, locations) Numeric data (sales figures, quantities, prices) Date and time data (order dates, deadlines) Logical data (TRUE or FALSE values) Understanding data types is important because Excel treats each type differently when performing calculations and analysis. Number Formats Matter More Than I Expected Another concept that
May 25, 2026. This is not just another date on my calendar. This marks the beginning of one of the biggest professional transitions of my life. After nearly a decade working in the world of IT infrastructure, technical support, networking, field engineering, and systems operations, I’ve made a decision that has been building in my mind for some time: I am transitioning into Data Analytics. And this is where I document that journey—publicly, honestly, and in real time. Not when I become an expert. Not when I feel “ready.” Not when everything looks polished. I’m starting now. Because real growth is rarely clean, predictable, or perfectly planned. Sometimes it starts with one uncomfortable decision: To leave what you already know… and step into what your future requires. Where My Journey Started Before data, before dashboards, before writing my first SQL query or building my first analytics project—my career started in the trenches of IT. For the past 10 years, I’ve built my career solving real technical problems across businesses, organizations, schools, offices, and field operations. My world has been cables, routers, networks, system failures, installations, troubleshooting, and making technology work where others saw complexity. Over the years, I’ve worked deeply in: Computer troubleshooting and hardware diagnostics Printer setup, configuration, and enterprise support Wi-Fi deployment and hotspot installations LAN design and structured network deployment Fiber optic installations and network termination Data cabling and structured cabling systems CCTV surveillance installation and maintenance Alarm systems and electronic security integration Intelligent security systems Electric fence installations and perimeter protection systems Router, switch, and access point configuration End-user support and enterprise technical troubleshooting Systems maintenance and operational support I’ve spent years on ladders, in server rooms, inside offices, on construction sites, insi
Humans have long assumed they belong to a special category called "persons." But what actually makes someone a person? And why should persons get special moral status? I keep coming back to these questions because they refuse to stay abstract. The moment you build an AI system that reasons about its own goals, they become engineering problems. The Traditional View Personhood is supposed to confer special status: persons have rights, deserve respect, bear responsibility for their actions, and warrant moral consideration. The philosophical tradition offers several criteria for what earns you membership in this club. Rationality. Kant's version: persons are rational agents who can recognize and follow moral laws. Rationality lets you understand moral principles, deliberate about actions, and choose based on reasons rather than instinct. But babies aren't rational, and we call them persons. People with severe cognitive disabilities have reduced rationality, and we don't revoke their personhood. Rationality comes in degrees; personhood is treated as binary. Self-awareness. Persons are conscious beings who recognize themselves as distinct entities persisting through time. This enables understanding yourself as an agent, planning for your future, taking responsibility for your past. But elephants, dolphins, and some primates pass the mirror test. We lose self-awareness during sleep. And we have no reliable way to verify self-awareness in others. Autonomy. Persons govern themselves and make free choices. This is supposed to ground moral responsibility, rights, and dignity. But if the universe is deterministic, nobody is truly autonomous. All choices are shaped by culture and circumstance. Mental illness reduces autonomy without eliminating personhood. Moral reasoning. Persons understand right and wrong. But psychopaths understand morality intellectually while lacking the emotional response. Children develop moral reasoning gradually. When exactly do they become persons? Lan
In [1], the authors present a method for constructing a symbolic (nominal) representation for real-valued time series data. A symbolic representation is desirable because then it becomes possible to use many of the effective algorithms that require symbolic representation, like hashing and Markov models. The authors claim that one of the most useful time series operations is measuring the similarity between two time series data sets. To do this on the original time series, the Euclidean distance formula can be used. Therefore, for a time series transformation to be useful, distance measures applied to the corresponding transformations should provide some guaranteed lower bound on the true distance. This is a basic requirement for almost all time series algorithms in data mining. Non-symbolic transformations like Discrete Fourier Transform (DFT) and Piecewise Aggregate Approximation (PAA) models have this lower-bounding property. However, the authors claim no previously proposed symbolic representations do, which limits their usefulness. Additionally, the authors observe that most raw time series data sets have very high dimensionality. This is problematic because time series mining algorithms are $\mathcal{O}(cn)$, where n is the number of dimensions. Therefore, preferably any transformations on the original time series will reduce the dimensionality to a more manageable size. Unfortunately, the authors observe, previously proposed symbolic representations preserve the original time series dimensionality. Next, the authors present their symbolic representation, SAX (Symbolic Aggregate approXimation), which addresses each of the previously mentioned shortcomings of symbolic representations. SAX is unique in that it uses an intermediate transformation, PAA, and then nominalizes the PAA representation into a sequence of characters'a string. By using the intermediate PAA representation, SAX enjoys two benefits: It is able to exploit the dimensionality reducing propertie
Every interface you create is a constraint on future behavior. Every abstraction emphasizes certain patterns and discourages others. You are not just building tools. You are shaping how people think about problems. I have been paying attention to how API design encodes values, not just technical decisions, but philosophical ones. What Your API Communicates Consider these design choices: Mutability vs Immutability. Do you encourage stateful modification or pure functions? This is not just about performance. It is a philosophy about side effects and reasoning. If your default is mutable state, you are telling users that local mutation is fine, that they can reason locally. If your default is immutability, you are telling them to think about data flow. Explicit vs Implicit. Do you make users specify parameters or infer from context? This trades convenience for transparency. I lean toward explicitness. Magic is convenient until you need to debug it. Fail Fast vs Fail Safe. Do you throw exceptions or return error codes? This encodes beliefs about who should handle errors and when. Fail-fast says "don't let bad state propagate." Fail-safe says "keep running if you can." Both are defensible, but they lead to very different code. My Design Values When I build libraries, I try to encode: Explicitness over magic. I would rather make users type more than hide behavior behind conventions they have to discover. Composition over inheritance. Small pieces that combine flexibly beat deep class hierarchies. Clarity over cleverness. Code should be obvious, not impressive. Safety by default. The easy path should be the safe path. Why This Matters Your API is a value statement. It says what you think is important, what you think is dangerous, and how you think about the problem domain. This is why I spend so long on interface design. The APIs we create shape future thought. They outlast the code that implements them, because the patterns they teach persist in the minds of the people wh