The rest of Dyson’s promised 2026 vacuum lineup is here, from the new Dyson V16 Piston Animal to an updated version of the favored Dyson V8 Cyclone.

Researchers have quantified the length and mass of arbuscular mycorrhizal fungal networks globally.

Hidden inside a building in Alabama, the FBI has created its own small town as a dedicated cyber training ground for simulating cyberattacks.

As World Cup action kicks off, we look at the physics of the beautiful game.

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For all the noise that's been made about how generative AI is poised to revolutionize the filmmaking industry, there haven't really been any projects created with the technology that felt like the sort of entertainment people would pay to see. Most AI firms' video models are still only capable of churning out short bursts of […]

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Whether you want to buy cheaper electricity, store solar energy, or guard against outages, EcoFlow’s home battery might be just what you need.

My methodology and data set didn't show any trend, but it demonstrated a more important issue: how wrongly this kind of research can be done and how misinterpreted the conclusions can be. The reason for making this research was an attempt to verify the claim that AI-assisted development increases code duplication. I analyzed 14 well-maintained open-source projects between 2021-2026, excluding new ones developed only with AI. For duplication detection, I compared semantic similarity using https://github.com/rafal-qa/slopo (I'm the author), not exact copies. This data can't prove or deny the claim, no trend is visible. Not only because 14 projects is too little, but also because there is a large variance between projects. The main advantage of this research is that it highlights the pitfalls in the analysis and conclusions and shows how easy it is to create "evidence" to support any claim. submitted by /u/rafal-kochanowski [link] [留言]

RJ Scaringe, the CEO of Rivian Automotive, joined us for a wide-ranging interview about how his company’s new electric SUV fits into the current EV industry, and what comes next.

The default for AI-assisted development is one of two failure modes. Either you're babysitting the agent line by line — approving each diff, re-explaining context it dropped three messages ago — or you've handed it the wheel and you're hoping the PR that lands at the end resembles what you asked for. Son of Anton is neither. It's a delivery orchestrator built on a single claim: there are exactly three moments where a developer's judgment is irreplaceable. The orchestrator owns everything in between. The three gates Every project moves through three human decision points. Nothing important happens without you signing off. Gate 01 — Approve the WHAT ( /soa plan ) A grill-me session forces the AI to surface its assumptions, constraints, and scope decisions back to you before a single ticket exists. You say yes or you refine. It does not proceed until you have. Gate 02 — Approve the HOW ( /soa decompose ) The approved plan becomes a ticket stack — ordered, dependency-aware, sized for review. Architectural judgment stays with you. Ticket authorship goes to the agent. Gate 03 — Approve DONE ( /soa closeout ) An adversarial subagent reviews every ticket before its PR opens. When the phase is complete, you decide whether to accept. Closeout squash-merges the stack onto main. Nothing merges without you. Between the gates, you are not needed That's the whole point. Once you've approved the plan and the tickets, the orchestrator runs the loop:

Want to learn how modern e-commerce applications are built in real-world companies? In this comprehensive project-based series, we'll build a complete online store from scratch using React and industry-standard tools used by professional frontend developers. Here is a link to a playlist freely uploaded in Youtube: Youtube playlist 🎯 What You'll Build ✅ Advanced Product Listing Page ✅ Search, Filtering, Sorting & Pagination ✅ Product Details & Related Products ✅ Shopping Cart with Persistent State ✅ Secure Stripe Payment Integration ✅ Responsive Mobile-First UI ✅ Dynamic Loading & Error States ✅ Reusable Component Architecture ✅ API Integration & Server State Management ✅ Modern Animations & Interactive Effects ✅ Hero Sliders & Product Carousels ✅ Performance Optimization Techniques ✅ Production-Ready Folder Structure 🛠️ Technologies You'll Learn ⚛️ React 🐻 Zustand for Global State Management 🔄 React Query (TanStack Query) for Server State & Caching 📡 Axios for API Communication 💳 Stripe for Payment Processing 🎨 Chakra UI for Accessible UI Components 🧭 React Router for Client-Side Routing 🎠 Swiper.js for Modern Sliders ✨ Tilt Parallax for Interactive Animations 🟨 JavaScript 💡 Why Build This Project? Most tutorials teach you isolated concepts. This series focuses on building a complete real-world application while applying best practices used in professional development teams. You'll learn: Scalable frontend architecture State management patterns Efficient data fetching strategies Payment gateway integration Component reusability Performance optimization Responsive design principles Production-ready coding practices By the end of this series, you'll have a portfolio-worthy project that demonstrates the skills companies expect from modern React developers. Whether you're a beginner building your first serious project or an experienced developer looking to strengthen your React fundamentals, this series will help you gain practical experience by building something real.

Most of the "Cursor vs Claude Code" takes I read are framed wrong. It's not a cage match. They're not competing for the same job — they're good at different jobs, and once that clicked for me, both got more useful. After months of leaning on both for actual day-to-day work (not demos, not toy repos), I've settled into a pretty stable split: Cursor handles about 90% of my coding, and Claude Code handles the 10% that actually moves the needle. Here's where I draw the line, and the rule of thumb that decides it. The 90%: why Cursor owns my day Most coding isn't dramatic. It's small, local, iterative work: tweak this function, rename that, fix the bug in the file I'm already staring at, ask "what does this block do" without breaking focus. That's exactly Cursor's home turf. It lives inside the editor, so I never leave my flow. Inline edits, fast completions, quick questions about the code in front of me — all without context-switching. When the work is local and I want to stay in the loop keystroke by keystroke, an in-editor copilot is the right tool. It keeps me fast and in context, which is most of what a normal coding day actually is. The 10%: where I close the editor and open Claude Code Then there's the other kind of task — the one where I don't want to babysit every edit. Claude Code is terminal-native and agentic. Instead of sitting beside me suggesting the next line, it works more like something I hand a well-described task to and let run across the whole project. That changes what it's good for: Codebase-wide refactors that touch a dozen files at once "Understand this whole repo and do X" type tasks, where the work depends on grasping how everything connects Jobs I want to delegate and step away from , rather than steer line by line The mental model that finally made it stick for me: Cursor is a copilot sitting next to you. Claude Code is more like handing a ticket to a capable teammate and checking the result. Different relationship, different jobs. How I actu