标签：#AR

There are over a dozen cases around the country where police officers are using the Flock surveillance camera system to obsessively and illegally stalk people . Alternate link .

Sitting at a desk for hours? Upgrade your WFH setup and work in style with these comfy WIRED-tested seats.

Sitting at a desk for hours? Upgrade your WFH setup and work in style with these comfy WIRED-tested seats.

Sitting at a desk for hours? Upgrade your WFH setup and work in style with these comfy WIRED-tested seats.

Sellers on TikTok used pricey items to lure bidders to “Surprise Sets” livestreams, but most of the auctions yielded only cheap prizes. Gambling experts are concerned about the potential for harm.

Amazfit’s trail running smartwatch delivers solid off-road performance at a bargain price compared to its competitors.

We Built Korexbase: A Lead Generation Platform for Finding Business Leads by City and Niche Building software is exciting. Building software that solves a real problem is even better. Over the past few months, we've been working on Korexbase , a lead generation platform designed to help businesses discover targeted leads faster. The Problem Many agencies, sales teams, freelancers, and startups spend hours manually searching for potential customers. The process usually looks something like this: Search for businesses online Collect contact information Copy everything into spreadsheets Repeat the process every day It's slow, repetitive, and difficult to scale. We wanted to simplify that workflow. The Idea Korexbase allows users to search for business leads by: City Industry Business category Instead of manually collecting data, users can generate leads and manage them through a clean dashboard. The goal isn't to replace sales. The goal is to help businesses spend less time searching and more time closing deals. Building the Platform A major focus during development was creating a dashboard that feels simple and easy to navigate. Some areas we focused heavily on included: Responsive layouts User-friendly navigation Clear data presentation Fast loading interfaces Consistent design patterns Challenges Like most projects, we faced a number of challenges: Designing for Simplicity One of the biggest lessons was that adding more features doesn't automatically create a better product. We spent a lot of time simplifying interfaces and removing unnecessary complexity. Creating a Better Dashboard Experience Presenting lead generation data in a way that is useful without overwhelming users required multiple design iterations. We focused on: Better spacing Better visual hierarchy Cleaner cards and tables Improved responsiveness Product Positioning An interesting challenge was refining the product's positioning. As development progressed, we learned more about what users actually w

Originally published on rikuq.com . Republished here for Dev.to's readers. I dropped my $100/month Claude Max subscription and migrated entirely back to Antigravity. If you want the verdict upfront: Claude Desktop is still the best tool for beginners who need the AI to guess their intent from clumsy prompts. But if you have solid documentation discipline and cost efficiency is a serious factor for your SaaS, Antigravity is now the clear winner. I'm a Chartered Accountant by trade with zero formal coding experience. I’ve shipped three production AI SaaS— Prism , Citare , and BatchWise —relying entirely on AI tools. I started with VSCode, moved to Antigravity (when it was just an IDE), and eventually landed on the Claude Desktop App. Claude was incredible; it operated in the background, handled my stack, and I didn't need to know what was happening under the hood. But the bills started stacking up. When my Claude usage consistently hit $100 a month, efficiency became a priority. I fired up the new version of Antigravity and found the recent updates had completely transformed it. It is no longer just an IDE—it is a full agentic desktop experience that mirrors what made Claude so good. TL;DR — The 2026 Reality Feature Claude Desktop App Antigravity (New Update) Best for Beginners, unlimited budgets, "pure performance" Experienced AI directors, cost-conscious solo founders Pricing $100+/mo (Claude Max) $20/mo (Gemini Advanced) Agentic Workflow Exceptional. The benchmark. Identical. Background execution, zero friction. Context Handling Better at anticipating intent from messy prompts Huge total memory, but requires tighter prompting MCP Support Native Native (handles them just as well) Verdict Keep it if cost doesn't matter Switch to it if efficiency is the goal The Catalyst for Switching My path to Antigravity wasn't a calculated feature comparison. It was pure economics combined with a pleasant surprise. I had previously dropped Antigravity when it was just an IDE. When

Every day, startups rent expensive GPUs to power AI applications. The problem is that most of those GPUs spend a surprising amount of time doing nothing. Imagine renting an apartment and only using one room while paying for the entire building. That's effectively what many AI teams do with GPU infrastructure. The Hidden Cost of GPU Rentals When you rent a GPU, you're usually paying for uptime. Whether your application is processing requests or sitting idle at 3 AM, the bill keeps running. For many early-stage products: Traffic is inconsistent Usage spikes are unpredictable Most requests arrive in short bursts As a result, GPU utilization can be far lower than expected. The Utilization Problem A startup might rent a GPU for an entire month. But how much of that compute is actually being used? During development: Developers test occasionally Demos happen a few times a day Customer requests arrive sporadically The GPU remains available 24/7, but actual inference workloads often occupy only a small fraction of that time. Yet the infrastructure bill reflects full-time usage. Why This Matters For startups, infrastructure costs directly affect runway. Every dollar spent on idle compute is a dollar that cannot be spent on: Product development Customer acquisition Hiring Experiments Reducing wasted infrastructure spend can significantly improve efficiency. A Different Model Instead of paying for GPU uptime, what if developers only paid when inference actually occurred? For example: Pay per token generated Pay per image generated Pay per second of video generated This approach aligns cost with actual usage rather than reserved capacity. The Future of AI Infrastructure As AI adoption grows, efficiency becomes increasingly important. The next generation of AI infrastructure may look less like traditional server rentals and more like utilities: Use what you need. Pay for what you use. Nothing more. What has your experience been with GPU utilization and AI infrastructure costs? I

How many times have you seen a picture of a plane with red dots posted on the internet without context? There's a famous story about a statistician named Abraham Wald and a bunch of WWII bombers. The military looked at the planes coming back from combat, mapped where they were riddled with bullet holes, and decided to add armor there. Wald, being the kind of person who ruins meetings by being right, pointed out the obvious thing nobody wanted to hear: The planes they were looking at came back . The ones hit in the spots with no bullet holes, the engine, the cockpit, were at the bottom of the English Channel, not available for the survey. Reinforce the parts that aren't shot up. That's where the dead planes got hit. I think about this story a lot, mostly while reading those blog posts titled "X Habits That Made Me a 10x Engineer." The entire industry is a returning-plane survey Here is the uncomfortable thing about software engineering wisdom: almost all of it is collected from the planes that came back. Successful companies write blog posts. Successful founders do podcast tours. Successful engineers give conference talks with titles like "Scaling to 100 Million Users with Three People and a Dream." The companies that did the exact same things and died do not have a booth at the conference. They are not on the panel. They are in the channel, with the engines. And yet we keep doing the survey. We stare at the bullet holes on the survivors and go, "Ah, this is where we add armor." "Netflix uses microservices, so we should too" You have eleven users. Three of them are your co-founders, and one is your mom. Netflix runs a globe-spanning streaming empire on hundreds of microservices because they have hundreds of teams, billions in revenue, and problems you will be lucky to have in a decade. You have a Postgres database that is doing just fine, thank you, and a monolith that boots in four seconds. So naturally, you spend the next eight months splitting your perfectly funct

A Silicon Valley software maker and an ecommerce company reveal to WIRED how they are navigating the emerging challenge of “tokenomics.”

Every engineering team hits this conversation at some point. Someone proposes GraphQL. Someone else says REST is fine. A third person mentions gRPC and half the room goes quiet. The debate usually ends with the most senior person in the room picking what they're most familiar with. That's not a strategy — that's habit. Here's an objective breakdown of all three, when each one wins, and how to actually make the decision for your specific use case. The Core Mental Model Before comparing them, understand what each one is optimizing for: REST optimizes for simplicity and broad compatibility GraphQL optimizes for flexibility and precise data fetching gRPC optimizes for performance and strongly-typed contracts None of them is universally better. Each one is a tradeoff. The right answer depends entirely on who is consuming your API and what they need from it. REST — The Default That Still Wins Most of the Time REST (Representational State Transfer) is not a protocol. It's an architectural style built on HTTP — verbs, URLs, and status codes most developers already understand. Where REST genuinely wins: Public APIs. If external developers are consuming your API, REST is the only reasonable default. The tooling, documentation patterns, and developer familiarity are unmatched. Stripe, Twilio, GitHub — all REST. Simple CRUD services. If your resource model is straightforward, REST maps cleanly to it. No overhead, no learning curve, no ceremony. Browser-native requests. REST over HTTP works directly in the browser without any special client. Fetch it, done. Where REST struggles: Over-fetching and under-fetching. A single REST endpoint returns a fixed shape. Mobile clients that need 3 fields get 40. Separate data needs often require multiple round trips. Versioning overhead. As covered in our previous post — every breaking change forces a versioning decision. This compounds quickly on complex APIs. GraphQL — Powerful, But You Need to Earn It GraphQL is a query language for your A

Commodore's Call Back 8020 is a phone “where the customer is not the product."

With a retro look and T9 texting, the Commodore Callback 8020 smart flip phone taps into the nostalgic yearning for simpler days. It can run Spotify and Uber, but Instagram is blocked.

At the end of a tense and scoreless first half of a soccer match between the English men’s team and rival Germany, millions of Brits let out a collective sigh and did what they so often do in moments of stress: They made tea. That wave of electric kettles clicking on, however, caused a different…

Respond.io, one of Malaysia startups to watch, uses AI agents to handle high volumes of customer inquiries and charges per convo, not per seat.

Cursor just shipped Composer 2.5 and Composer 2.5 Fast. We benchmarked both across 11 engineering skills, 5 scenarios per skill, averaged across three independent LLM judges. The fast model scored higher, ran 32% quicker, and costs exactly the same. If you are reaching for Composer 2.5 over Composer 2.5 Fast, you are paying the same price for a slower, slightly worse model. Here is the full picture. TL;DR Composer 2.5 Fast scores 92.7% with skill context. Composer 2.5 scores 92.1%. Fast wins. Both are ahead of gpt-5.5, gpt-5.4, and the previous Composer 2. The fast model completes scenarios in 59 seconds on average. The regular model takes 87 seconds. Where They Land in the Benchmark We ran 6 models across 11 skills, scoring each run with three independent judges and averaging the results. Here is where the full leaderboard sits: Model Avg baseline Avg with-skill Lift opus-4-7 80.8% 93.4% +12.6 composer-2.5-fast 79.6% 92.7% +13.1 composer-2.5 79.0% 92.1% +13.1 composer-2 74.2% 89.6% +15.4 gpt-5.5 75.5% 89.4% +13.9 gpt-5.4 74.1% 89.3% +15.2 gpt-5.3 65.5% 83.9% +18.4 gpt-5-codex 68.7% 78.7% +10.0 Composer 2.5 Fast sits 1.3 points behind opus-4-7 and 3.3 points clear of everything else. That is a meaningful gap. The previous Composer 2 sits alongside gpt-5.4 and gpt-5.5 at roughly 89-90%. Cursor has moved its own model up a full competitive tier in a single release. The Fast model seems better. Normally a "fast" variant trades quality for speed. Composer 2.5 Fast does not do that. It scores 0.6 points higher than the regular model while running 28 seconds faster per scenario (59s vs 87s on average across 110 scored runs). The per-skill breakdown shows where the differences accumulate: Skill 2.5 with-skill 2.5-fast with-skill Winner documentation 97% 98% fast fastify 99% 94% 2.5 init 87% 86% 2.5 linting 98% 99% fast node-best-practices 95% 95% tie nodejs-core 98% 98% tie oauth 92% 89% 2.5 octocat 95% 96% fast skill-optimizer 98% 98% tie snipgrapher 93% 93% tie typescrip

description: While most people chase political bets, these traders are dominating daily temperature...

description: Weather markets on Polymarket have become one of the hottest and most consistent...

Agentic QA Pipelines: Why Your Test Scripts Are Already Obsolete You wrote the test. You maintained the test. The app changed. You rewrote the test. If that loop sounds familiar, you're not alone — and in 2026, you're also not competitive. Agentic QA pipelines are replacing script-based test automation not because AI is smarter than your QA engineers, but because describing goals is faster than maintaining instructions. Here's what's actually changing, why it matters, and how forward-thinking teams are shipping without the script debt. The Script Maintenance Tax Is Killing Velocity Traditional test automation follows a simple premise: write explicit instructions, run them, check results. It worked when applications changed slowly and test environments were stable. In 2026, neither is true. AI-generated code ships faster. Features change in days. UI components regenerate. And every change breaks a percentage of your carefully maintained test scripts — creating a maintenance tax that grows proportionally with your automation coverage. Quash's 2026 State of QA Automation Report found that teams spending more than 30% of QA bandwidth on script maintenance are shipping 2.4x slower than teams that have automated that maintenance layer away. The irony: the more test coverage you write, the more you're paying the tax. What Agentic QA Actually Means (Without the Buzzwords) An agentic QA system doesn't follow a script. It follows a goal. Instead of: Click the login button Enter " testuser@example.com " in the email field Enter "password123" in the password field Assert redirect to /dashboard An agentic QA agent receives: Goal: Verify that a registered user can successfully authenticate and access their dashboard. Context: Auth flow supports email/password and OAuth. Dashboard loads user-specific data. The agent then: Explores the auth flow autonomously Generates test scenarios, including edge cases it infers from the UI Executes tests, reads failures, and adapts to UI changes