标签：#AR

If you've been in tech over the last year, you've probably noticed that almost every conversation eventually ends up talking about AI. LLMs, AI Agents, RAG, MCP, prompt engineering...there's something new every week. Like many of you, I've been spending time learning these technologies, experimenting with different tools, and trying to understand where everything is heading. But while learning AI, one question kept coming back to me. Whre does AI actually fit within Enterprise Architecture? Most conversations start with the model. I think they should start with the enterprise. Looking Back Over the last two decades, I've worked through several technology shifts. Physical infrastructure → Virtualization Virtualization → Cloud Cloud → Platform Engineering Automation → Everything Every transition introduced new tools, new platforms, and new buzzwords. But something interesting never changed. Successful enterprise systems still depended on the same fundamentals: Business goals Enterprise Architecture Reliable platforms Security Data Governance Operations Technology changed. Engineering principles didn't. That's one of the reasons I don't see AI as something completely separate. AI Is Just Another Enterprise Capability Today, AI is often treated like its own universe. Dedicated AI teams. AI platforms. AI roadmaps. AI strategies. That all makes sense. But I think there's a risk if we start treating AI as something that sits outside Enterprise Architecture. AI doesn't work in isolation. It needs good data. It needs infrastructure. It needs platforms. It needs security. It needs governance. It needs integration with business applications. And, most importantly, it needs to solve a real business problem. From an architect's point of view, AI isn't an island. It's another enterprise capability. Just like databases, APIs, messaging platforms, Kubernetes, and cloud services became part of our enterprise landscape, AI is becoming another capability that needs to be architected—n

Angular apps can now run any agent, with the streaming, tool calls, and shared state already handled. Today we're releasing Angular support for CopilotKit , an open source client that brings any AG-UI agent into your Angular app. It's built with Angular's own patterns, standalone components, dependency injection and signals. You get the building blocks for agent-native apps in Angular: pre-built chat components or a fully headless setup, generative UI, shared state, human-in-the-loop, multimodal attachments, threads and more. Use the CLI to scaffold a full starter Angular app with a Google ADK agent. npx copilotkit@latest init --framework adk-angular Let's see how to set everything up, then go through each of the pieces and give your agent the context. Quickstart docs are on docs.copilotkit.ai/angular . Rainer Hahnekamp (Angular GDE, NgRx core) and Murat Sari helped build the integration and are now taking on its ongoing maintenance. How everything fits together Everything runs on Agent-User Interaction Protocol (AG-UI) , the open protocol that connects agents to user-facing apps. It streams an agent's entire lifecycle as events, the messages, the tool calls, the state changes, which is what keeps your Angular app and the agent in sync. That matters because the agent becomes a choice you can change. The runtime can point at a BuiltInAgent , LangGraph, Google ADK, Mastra, Pydantic AI, Claude Agents SDK or any framework that speaks AG-UI and your Angular code doesn't change. Here's the architecture. ┌──────────────────────────┐ ┌──────────────────────────┐ │ ANGULAR APP │ │ COPILOT RUNTIME (Node) │ │ │ │ │ │ provideCopilotKit() │ ─────► │ holds your model keys │ │ <copilot-chat /> │ AG-UI │ connects to your agent │ │ tools · context · state │ ◄───── │ streams events back │ └──────────────────────────┘ └──────────────┬───────────┘ │ ▼ ┌───────────────────────────┐ │ YOUR AGENT + MODEL │ │ LangGraph · ADK · Mastra │ │ OpenAI or a local model │ └─────────────────────────

When engineers talk about visual regression or website change monitoring, pixel-level diffing algorithms (like pixelmatch or Euclidean RGB distance) are usually the default solution. However, in real-world web environments, pixel-by-pixel comparisons fundamentally fail under normal user interactions and dynamic rendering conditions: Elastic Layout Shifts: A single 20px dynamic banner inserted at the top of a page pushes every subsequent DOM element down, causing 100% of the downstream pixels to fail a pixelmatch test, even if the content itself hasn't changed. Sub-Pixel Anti-Aliasing Jitter: Operating systems (macOS vs. Linux vs. Windows) render font glyphs with subtle sub-pixel anti-aliasing variations, creating thousands of false-positive pixel deltas. Semantic vs. Cosmetic Changes: Changing a single word in a paragraph should trigger a localized alert, but a minor color gradient shift in a hero image shouldn't trigger an emergency notification. At PageWatch.tech , we solved this by combining classical Structural Similarity (SSIM) , ORB Feature Alignment , and Siamese Neural Networks (SNN) for latent-space semantic comparison. In this article, I will dive into the mathematics, neural network architectures, and TypeScript implementation of our computer vision diff pipeline. 🧮 1. Beyond Pixel Comparison: Structural Similarity Index (SSIM) Unlike raw Mean Squared Error (MSE), SSIM measures visual change based on human perception across three dimensions: Luminance , Contrast , and Structure . Mathematically, the SSIM between two image windows $x$ and $y$ is defined as: $$\text{SSIM}(x, y) = \frac{(2\mu_x\mu_y + C_1)(2\sigma_{xy} + C_2)}{(\mu_x^2 + \mu_y^2 + C_1)(\sigma_x^2 + \sigma_y^2 + C_2)}$$ Where: $\mu_x, \mu_y$ are the local pixel mean intensities. $\sigma_x^2, \sigma_y^2$ are the local variances. $\sigma_{xy}$ is the covariance between $x$ and $y$. $C_1, C_2$ are stabilization constants. TypeScript Implementation of SSIM Window Sliding Below is a snippet of how

If there's a place in the universe without GPUs, Nvidia is sending them there.

Imagi announced a $4.5 million seed round, with investors including Brighteye Ventures, Day One Capital, and artist Will.i.am.

Etched, founded by three Harvard dropouts, has created new chips and memory components that speed up inference on any AI model -- no GPUs required, it says.

Gemini had over 750 million monthly users in February.

It's a sign of how hard we're braced for the RAMpocalypse that Samsung adding just $100 to its prices seems reasonable.

Volunteer at TechCrunch Disrupt 2026 and get a behind-the-scenes look at how one of the world's biggest startup conferences gets built.

Ford plans to embed Apple Maps directly into its Universal Electric Vehicle Platform, which will start with a $30,000 midsize pickup truck set for release in 2027. In an announcement on Thursday, Ford says it will use Apple Maps to offer "turn-by-turn directions using natural language, real-time traffic and incident information, intuitive search featuring detailed […]

Ultrahuman's revamped app now offers better advice even when your phone has no connection.

Letting a stranger contact a car owner without giving them the number There is a small, very common problem in Indian cities that turns out to be a surprisingly good systems design exercise. A car is parked badly. It is blocking a gate, a driveway, another car. Someone needs the owner to move it, right now. The traditional fix is a phone number written on a sticker on the windscreen. That works. It also means a stranger — any stranger, forever — has the owner's personal number. Why the sticker is worse than it looks Once a number is visible on a windscreen, a few things follow: It gets scraped. Numbers on vehicles end up in marketing lists. It cannot be revoked. Change your number and every sticker you own is now wrong. It has no context. A 2am call could be a genuine emergency or someone who saw the number three months ago. It is a safety issue for some owners in a way it is not for others. A number attached to a vehicle, at a known parking spot, at predictable times, is more information than most people realise they are publishing. So the requirement is oddly specific: a stranger must be able to reach the owner, immediately, without ever learning how to reach them again. That constraint is what makes this interesting. The naive version, and why it fails First instinct: put a QR code on the vehicle that opens a page with a "call owner" button, and put the number behind the button. This solves nothing. The number is still in the page source. Anyone who wants it can get it, and now you have added a scan step for the honest majority while stopping none of the dishonest minority. Second instinct: put a contact form behind the QR. The stranger types a message, the owner gets a notification. Better on privacy, useless in practice. The person needs the car moved in the next ninety seconds. They are not filling in a form and waiting for an email. If the fast path is not there, they go back to writing an angry note. The real requirement is synchronous contact with asynchron

Before I hand this over, I want to tell you where I'm actually standing right now. I'm looking at the biggest mountain yet: building our hospital's own ERP and EMR — the systems that run the business side and the medical records side of the whole place. That's usually the job of specialized companies, and it's tangled up with outside regulators too. Nobody knows how long it'll take. I'm assuming at least three years. Honestly, it feels overwhelming. The size of the fear hasn't changed. My grip on it has. Here's the strange part. That overwhelmed feeling isn't unfamiliar at all. A year ago, staring at a blank screen and not knowing what code even was, I felt exactly this lost. What's different isn't the size of the fear. It's that I now know what to do with it. Get curious, look it up. Look it up, a direction shows up. See a direction, find a method, and just start. Fail, and you've learned something. Succeed, and you move to the next thing. That loop is the only thing that ever carried me anywhere , and it's the same loop I'm running at the foot of this much bigger mountain. Right now the wall isn't technical The thing I don't know isn't code. It's other departments. I moved from the treatment room to the planning office not long ago, and I still don't really know how the other departments do their work. You can't automate what you don't understand. So before anything else, I'm doing the one thing that actually comes first: listening. Hearing what people in the field actually need, what's grinding on them, and figuring out the shape of a fix together, one conversation at a time. I've noticed something before, more than once: problems that look completely different from the outside — counting a stack of forms, tracking down misplaced supplies, sorting a pile of ID cards — usually reduce to the exact same shape underneath. Make the pile of things findable. I suspect the same pattern is waiting inside every other department too. I just haven't looked yet. That's how I

Deck out your room and make class a cinch with these WIRED-tested favorites.

Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to…

Cordless, handheld, robot, and traditional—we tested them all to find the vacuum that’s fantastic for fur.

New paper: “ Encryption and Globalization 15 Years Later: End-to-End Encryption and the Third Round of the ‘Going Dark’ Debate “: Abstract : This Article updates and expands on 2012 research on encryption and globalization, analyzing what the authors call “Round 3” of the Going Dark Debate: the current controversies over end-to-end encryption (E2EE). Governments around the world have proposed, and in some cases enacted, laws limiting E2EE for law enforcement and national security purposes. This Article explains the underlying technologies and market developments for a law and policy audience to assess those proposals critically. The Article proceeds in three parts tracking three rounds of the Going Dark Debate. Round 1 covers the Crypto Wars of the 1990s, when U.S. export controls on strong encryption ultimately fell in 1999. Round 2 covers the period roughly 2010 to 2015, when encryption-in-transit became widespread but lawful access remained available through cloud providers, giving rise to what the authors called a “golden age of surveillance” rather than a period of going dark. Round 3 addresses the current debate over E2EE, where no entity between sender and recipient can read the plaintext...

The slim and convenient magnetic wallets you’ll actually want stuck to the back of your phone.

Gas prices are up, and US car dealerships are full of hybrids. Customers are going for it.

Here are the best delivery services and meal kits in 2026, including Martha Stewart’s meal service, a GLP-1–supportive plan, and a budget-friendly option.