标签：#tech

Nothing is shaking up the branding of its cheapest phones, following up last year's Phone 3A Lite with a new Phone 4B. Combining design elements of the 4A and 4A Pro, it follows Nothing's previous cheaper handsets in skipping the US market. The company already uses the "A" branding to market its cheaper tier of […]

Nothing announced a new pair of budget-friendly wireless earbuds, but not as part of the company's affordability-focused sub-brand, CMF. The new Nothing Ear 3A are available starting today through the company's online store for $99, which is the same price as Nothing's original Ear (a). That's good news in a time when price increases are […]

Imagine you are planning a surprise birthday party. You need invitations, food, decorations, and a cake. You call different places to get these things. You tell each one exactly what you need. "I need 20 red balloons." "I need a chocolate cake for 10 people." This is how many computer programs talk to each other. They use something called an API (Application Programming Interface). An API is like a menu. You pick what you want. You get exactly that. It works well for simple tasks. But what if your party plans change? What if you decide on a theme mid-conversation? Traditional APIs can feel a bit rigid then. They don't always remember your past requests. They don't understand the bigger picture. Now, imagine talking to a super-smart party planner. You start by saying, "I'm planning a party." The planner asks, "For how many people?" You say, "About 20." Then you mention, "It's for a birthday." The planner instantly suggests a cake size. It recommends decorations based on your earlier answers. This smart planner remembers everything you said. It understands your overall goal. It uses something like MCP (Model Context Protocol). MCP is a new way for computers to talk. It's like having a real conversation. It's much smarter than a simple menu order. You will soon understand why this difference is a game-changer. Traditional APIs: The Fixed Menu Approach Let's start with what you might already know. Many apps you use every day rely on APIs. An API is like a waiter in a restaurant. You look at the menu. You tell the waiter your exact order. "I want a cheeseburger with fries." The waiter takes your order to the kitchen. The kitchen prepares only that specific meal. Then the waiter brings it back to you. This is how most apps work together. One app sends a very specific request. It asks for a certain piece of information or to perform a specific action. The other app performs that task. It sends back a very specific response. Think of ordering from an online store. You click

Drone intruders that possibly flew from Russian ships showed Europe isn’t ready.

If your Prime Day purchases included a new desk, TV stand, bookshelf, or other furniture you still haven’t assembled, Hoto’s PixelDrive cordless screwdriver can help speed up the process. It’s currently on sale for $59.99 ($20 off) at Amazon, matching its best price to date. From tightening loose screws on furniture to repairing electronics, the […]

Every beginner developer hits the same issue at some point. You learn a few basics, finish a tutorial, and then you have no idea what to build next. That gap can feel bigger than learning the code itself, because now the question is not “How do I write this?” but “What should I build at all?” This article is for that moment. I want to make it simple, practical, and useful, because project ideas do not need to be too advanced to be valuable. A good project is one that teaches you something, keeps you going, and gives you enough confidence to build the next one. Why project ideas are important There’s a common thing that I have noticed in most of the beginners, that is, watching too many tutorials. Tutorials are helpful, but actual learning starts when you try to build something on your own. That is when you start facing real decisions, small bugs, unclear logic, and the feeling of connecting different parts into one working product. That is one of the reasons why project ideas matter so much. The right idea gives you direction, but it also gives you energy. When the project feels too huge, you get stuck. When it feels too small or boring, you stop caring. The sweet spot is a project that feels possible and still a little exciting. This matters even more today. Tools like ChatGPT or Copilot can help you write code faster, but that doesn't solve the real problem beginners have. Writing the code was never the hard part for long but knowing what to build is. Start with problems you already know The easiest project ideas often come from your own life. Think about small things you do every day that feel annoying, repetitive, or messy. A simple to-do list, habit tracker, note saver, expense log, study planner, or meal planner can all become strong beginner projects if you build them well. This works because the problem is already familiar to you. You do not have to invent a fake use case or force a complicated feature list. You already know what the app should do, what feel

Microsoft cut around 4,800 roles, or 2.1% of its global workforce, on Monday — the latest in a series of layoffs that’s stoking fears of AI replacing jobs. The layoffs will hit Xbox and commercial sales the hardest.

We’ve got another special episode of Decoder today, recorded at the Cannes Lions advertising festival in the South of France. I’m talking with Ali Berman and Raina Penchansky, who run the Creators division at United Talent Agency. UTA is an enormous talent agency. Half the people you’ve ever heard speak or perform or who show […]

Microsoft is laying off 4,800 employees today, and more than 30 percent of the job losses are in the company's Xbox division. The significant gaming cuts will affect nearly every part of Xbox and also involve four game studios being spun off to be run independently from Microsoft. Today's layoffs, which are being described as […]

A year after cutting around 9,100 employees, Microsoft is making further layoffs today as it begins its new financial year. The software maker is laying off around 4,800 employees today, approximately 2.1 percent of its workforce. Most of the employees affected by today's cuts are in Microsoft's commercial sales business or the company's Xbox division. […]

Banking software cannot afford to be casual about anything. Every transaction needs to be verified, logged, protected from tampering, and traceable if something goes wrong. This is exactly the kind of environment where NestJS quietly shines, since its architecture was built around structure and discipline from the start, not added on as an afterthought. Financial institutions and fintech companies increasingly choose NestJS for banking applications, investment platforms, and trading systems, largely because it gives teams a consistent, testable structure for handling something as sensitive as money moving between accounts. Here is what that actually looks like underneath. Why structure matters more in banking than almost anywhere else In most applications, a messy folder structure or inconsistent error handling is annoying. In a banking application, it is a liability. If five different developers write five different ways of validating a transaction, you end up with five different ways something could slip through unnoticed. NestJS solves this by enforcing a consistent pattern across the entire application, modules, controllers, providers, all following the same shape no matter who wrote them. A new developer joining a banking backend built with NestJS already knows where to look for validation logic, where authorization happens, and where a transaction actually gets processed, because the framework itself dictates that structure. Guards, the first line of defense Every request that touches a bank account should be verified before it does anything else. NestJS handles this through guards, which run before a request ever reaches your actual business logic. @ Injectable () export class TransactionAuthGuard implements CanActivate { canActivate ( context : ExecutionContext ): boolean { const request = context . switchToHttp (). getRequest (); const user = request . user ; if ( ! user || ! user . isVerified ) { throw new UnauthorizedException ( ' Account verification req

Plenty of SEO audits end with a single tool report. You run Lighthouse, screenshot Search Console coverage, save a "12 issues found" panel, and call it done. The trouble is that most audits finished that way silently revert within three months. Someone publishes a new post, refactors a component, swaps a font, and the issue quietly comes back. Nobody notices. Over the last five days I actually audited my four-language blog (ko/ja/en/zh, 298 posts per language). Five items, all fixed. But what I really want to talk about isn't what I fixed. It's that the five fixes mattered less than the build gates that keep them from ever returning. An audit should be a loop, not an event. Why a one-report audit always comes back Most technical SEO issues aren't "the code is wrong." They're "an invariant was never enforced anywhere." Take a clear rule: a published post must not link internally to a draft. Obvious enough. But if a human has to remember that every time, then the moment a recommendation generator pulls in one draft slug, a 404 is born. The report catches that 404 and shows it to you, but it does nothing to prevent the next one. So I ran the audit as a three-step loop. Measure. Fix the biggest item first. Then turn that item into a checker and nail it to the build . Skip the third step and the first two become a chore you repeat every six months. Once a gate is in place, the same class of problem makes npm run build fail. A pipeline enforces the rule, not human memory. This isn't a new invention. It's the same logic by which tests prevent bug regressions, applied to the content and markup layer. It's just oddly rare in SEO, where most teams leave "SEO checks" as a quarterly manual task. The five items I actually ran over five days Measurement first. Each item got a before/after in numbers, not a vibe that "things feel better" but reproducible figures. (The raw log of all five lives on the improvement history page too.) Date Item Before After Gate 07-02 relatedPosts int

We’ve all been there: it’s 7:00 PM, you’re exhausted after a long sprint, and you open a food delivery app. Your brain screams "Double Cheeseburger," but your body is still recovering from that mid-afternoon sugar spike. What if your phone was smart enough to say, "Hey, your blood sugar is currently 160 mg/dL and rising—maybe skip the extra fries?" In this tutorial, we are building a Chief Health Officer (CHO) Agent . This isn't just a simple chatbot; it’s a sophisticated AI Agent using LangGraph to bridge the gap between real-time medical data (CGM) and real-world actions (Food Delivery APIs). By leveraging automation , function calling , and state machines , we’ll create a system that actively protects your metabolic health. The Architecture: How the CHO Agent Thinks To build a reliable agent, we need a "stateful" workflow. We aren't just sending a prompt to an LLM; we are creating a loop that monitors glucose levels, analyzes food options, and interacts with the browser. graph TD A[Start: Hunger Trigger] --> B{Fetch CGM Data} B -->|Sugar High/Unstable| C[Constraint: Low GI Only] B -->|Sugar Stable| D[Constraint: Balanced Meal] C --> E[Scrape Delivery App Menu] D --> E E --> F[Agent: Analyze Ingredients & GI Index] F --> G[Selenium: Mark/Filter Non-Compliant Items] G --> H[End: Safe Ordering] subgraph "The LangGraph Loop" C D E F end Prerequisites Before we dive into the code, ensure you have the following: LangGraph & LangChain : For the agent's cognitive architecture. Dexcom API Credentials : To fetch real-time Continuous Glucose Monitor (CGM) data. Selenium : For interacting with food delivery web interfaces (Meituan/Ele.me). OpenAI API Key : Specifically for GPT-4o’s reasoning and function-calling capabilities. Step 1: Defining the Agent State In LangGraph, everything revolves around the State . Our CHO agent needs to track the current glucose level, the user's health constraints, and the list of available food items. from typing import TypedDict , List , Anno

Baking sourdough bread is inherently old-fashioned, relying on natural fermentation and wild yeast instead of the simple, predictable commercial stuff. So it might sound anathema to bring a gadget into the mix. The trick to the Sourdough Sidekick - backed and branded by King Arthur flour - is that it promises to automate the boring […]

Before Keurig, the coffee in your office was almost certainly terrible. Old, burned, made by someone who would rather poorly eyeball than properly measure. Just altogether gross. After Keurig? You could make your own coffee, a cup at a time, exactly when you needed it. The single-cup brewer was an elegant solution to an extremely […]

When I first started testing Vizio's 65-inch Mini LED Quantum TV, I thought the big story was that Vizio was back and that it had a quantum-dot TV for under $398 - the cheapest on the market. Vizio's been pretty quiet since it was acquired by Walmart in 2024, so putting out a TV with […]

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on the bleak state of the video game industry, follow Andrew Webster. The Stepback arrives in our subscribers' inboxes on Sunday at 8AM ET. Opt in for The Stepback here. How it started Microsoft closed out Summer […]

The 2026 World Cup has $3.89 billion bet on it across Polymarket. That's not retail money — that's whales. I built WhaleTrack to track exactly what those big wallets are doing. Here's the stack: Backend: Node.js server fetching live data via Bullpen CLI Frontend: Vanilla JS, real-time updates Data: Polymarket CLOB API via Bullpen Analytics: Google Analytics for traffic tracking The hardest part wasn't the code — it was getting users. Pure SEO and content distribution (Reddit, Twitter, IH). The site is live at whaletrack.app — would love feedback from devs on the UX and performance. Happy to open source parts of it if there's interest.

The Matic is our favorite robot vacuum by a pretty comfortable margin. If you’ve been thinking about buying one, you may want to plan on doing it sooner than later. The company will raise its price by $250 on September 9th, going from $1,245 to $1,495. Matic told The Verge that the new price reflects […]

Ever wondered why sometimes the simplest things throw a wrench in our beautifully crafted code? I recently had a realization that hit me like a ton of bricks: the bottleneck could literally be the air in the room. It sounds absurd, right? But let me take you on a little journey through my recent experiences that led me to this conclusion. The Setup: A Frustrating Week Just a few weeks ago, I was knee-deep in a project using Python and TensorFlow to build an AI model for image classification. I was feeling pretty confident, you know? I had my dataset prepped and cleaned, my model architecture designed, and I was ready to train. But then, out of nowhere, my training took an eternity. I was kicking myself for not optimizing my code, but something just felt off. I started checking everything from my training loop to the data pipeline. I even considered that maybe I had some rogue semicolons in my Python code—classic mistake, right? But no, everything seemed fine. Then, in a moment of clarity, I realized my laptop was struggling to keep up. The fan was roaring like it was auditioning for a heavy metal band. It hit me that maybe, just maybe, the problem was my environment—specifically, the air conditioning. Environment: The Unsung Hero I’ve learned that environment can have a huge impact—like, why didn’t I think of this sooner? I had been training my model in my home office, where the temperature was rising faster than my enthusiasm for debugging. I decided to take things to the next level and moved my setup to a cooler room. And guess what? My training speed improved significantly. It turned out that my laptop was throttling itself to prevent overheating. This was my "aha moment." It was a reminder that sometimes the bottlenecks in tech aren’t just about code or hardware; they’re about the conditions we create for them. The Code: Finding Efficiency Once I had a handle on my environment, I dove back into my code. I had learned the hard way that performance optimization is