标签：#AR

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Apple has warned about MacBook prices rising, making these Prime Day deals even more worthwhile to consider.

The app would be independent of Meta's other social media offerings, although sources told the NYT that those social sites could direct users to engagement with the app.

Tesla is pushing back on claims that its Full Self-Driving (FSD) system caused a fatal Texas crash, where a speeding Model 3 barreled into a home, killing a 76-year-old woman inside. In a reply on X, Tesla AI head Ashok Elluswamy says the driver "manually overrode self-driving by pressing the accelerator all the way to […]

Algae blooms, peeling paint, and a host of fixes from hydrogen peroxide to nanobubblers have made it hard to diagnose what's wrong with the Reflecting Pool, let alone how to clean up the mess.

There's a moment in every ambitious AI engineering project where you convince yourself that more agents means more power. I hit that moment early in building my Python orchestration framework — and I spent several painful weeks learning exactly why that intuition is wrong. The seductive pitch: decompose complex tasks into specialized sub-agents, run them in parallel, let them coordinate. What actually happened was a reliability nightmare that taught me more about agentic architecture than any framework documentation ever could. The Problem I Actually Built My Python orchestration system was designed to automate complex, multi-step workflows — the kind that require planning, research, code generation, and validation to happen in a coherent sequence. Early on, I structured it as a web of parallel agents: a planner, several workers, a validator, and a synthesizer, all exchanging structured messages. On paper it was elegant. In practice, it had three failure modes I couldn't engineer away: Context drift. Each agent only saw the slice of information it was handed. The worker writing one module couldn't see what the worker writing another module had decided. By the time the synthesizer tried to combine outputs, I had conflicting assumptions baked into the results — variable names that clashed, patterns that contradicted each other, interfaces that didn't align. Cascading partial failures. When one agent produced ambiguous output, every downstream agent amplified the ambiguity. A planner that returned a slightly underspecified task description produced workers that each interpreted it differently. Nothing failed loudly. Everything just drifted, quietly, until the final output was incoherent. Debugging opacity. When something went wrong in a parallel multi-agent system, tracing the failure was miserable. Was it the planner? One of the workers? The message-passing layer? I'd rebuilt the worst parts of distributed systems debugging inside a single Python process. The Architec

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Although it's on the smaller side, this electric vehicle is not very chill.

"The FCC wants to control who is allowed on the show," ABC ad tells viewers.

The next season of Formula E will feature a new race format and three new race locations when it starts in December to go with the new Gen4 electric cars. The schedule released today by the FIA includes the Circuit of the Americas in Austin, the Brands Hatch circuit in Kent, and the Zandvoort circuit […]

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Two men pleaded guilty in the United Kingdom this week to criminal charges stemming from an August 2024 cyberattack that crippled Transport for London, the entity responsible for the public transport network in the Greater London area. The duo were key members of a prolific cybercrime group known as Scattered Spider, and their guilty pleas came on the first day of what was expected to be a six-week trial.

Explore how my day as a senior leader looks now that I use 40 automations to help, and learn more about some of my favorites. The post I automated my job (and it made me a better leader) appeared first on The GitHub Blog .

Beyond the Prototype: Why Teams Need More Than Vibe Coding Over the last year, AI coding tools such as Lovable, Bolt.new, v0, Base44, and others have fundamentally changed how software gets created. A single founder or developer can now go from a rough idea to a working prototype in hours rather than weeks. That kind of acceleration is genuinely exciting, and it has opened software creation to far more people. That democratization is a good thing. Rapid experimentation, faster feedback loops, and lower barriers to entry are changing how products get started. Many successful companies and ideas will emerge because these tools made building more accessible. As I've followed the conversations happening around these tools—through reviews, articles, community discussions, and the experiences being shared by founders and engineering leaders—I've noticed an interesting pattern. The challenge is no longer getting to the first version. The challenge begins after. The Prototype Was Never the Finish Line The prototype works. Stakeholders become excited. Customers show interest. Momentum builds. Then a different set of questions starts to emerge. How do we align everyone on what we're building? How do we evolve an existing application instead of starting over? How do we maintain quality as complexity increases? How do multiple people collaborate without losing context? How do we know whether we're delivering the outcomes we intended? And how do we continuously improve without creating chaos? These aren't failures of AI coding tools. They're simply different problems. Many of today's AI builders are optimized for individual acceleration and rapid exploration. But once a promising idea becomes a product that teams must own, maintain, and evolve together, different requirements naturally emerge. What works for one person experimenting is not always enough for a group of people building something intended to last. Building Software Is More Than Generating Code Software development

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article. Hello friends, System design ** and ** Software design are two important topics for tech interviews and two important skills for Software developers. Without knowing how to design a system, you cannot create new software, and it will also be difficult to learn and understand existing software and systems. That's why big tech companies like FAANG/MAANG pay special attention to System design skills and test candidates thoroughly. Earlier, I have shared system design interview questions like API Gateway vs Load Balancer , Horizontal vs Vertical Scaling , Forward proxy vs reverse proxy , and common System design concepts , and in this article, I am going to share with you the best System design books to learn Software design. Whether you are a beginner or an experienced developer, you can read these books, as you will definitely find valuable stuff. I have read them, and even though I have been doing Software development for more than 15 years, I have learned a lot. System design ** is a complex process, and you need to know a lot of stuff to actually design a system that can withstand the test of time in production. Software architecture is another field where you are expected to learn a lot of things. It's simply impossible to become a software architect by reading a few books, but if you have experience and a hunger to learn, then these books can be a gold mine. These books allow you to learn from other people's experiences. You can read these books to find what challenges they face when they design a real-world system like Spotify, Google, or Amazon, and how they overcome. Each story is a journey in itself, and you will learn a thing or two by reading and then relating with your own experience. I love to read books, and they are my primary source of learning, along with online courses nowadays. In this art

This post is my submission for DEV Education Track: Build Apps with Google AI Studio . What I Built I set out to build the same app as the one mentioned in the Tutorial. Please create an app that generates a unique new Magic the Gathering card, using Imagen for the visuals, and Gemini to create the text descriptions and stats for the card. Apply the "Sophisticated Dark" design theme to the app. Spammed Fix Errors Non-Stop After this other than the Manual Entry option. Demo My Experience You can't trust Gemini Flash even for the Task provided in the Tutorial Standalone at least and well I spammed Fix Errors and they removed the Auto-Fixing of Errors because of idk an infinite loop or something but well the Error Fixing Experience was quite Meh considering I haven't delved into Vue and React in that level yet so I just 'Vibe Coded' and I found out with this experience that Vibe-Coding is UnCool. I think I would do the other course after properly understanding concepts behind it unlike the way I jumped in this One.

A wide day rather than a deep one — four separate threads across a few projects, each with a lesson worth keeping. I'll teach the patterns and keep the specifics generic. The through-line: make the system honest about what it's actually doing — which queries it fires, whether a service is really up, what a tool will do when you call it twice, and in what order a password change should land. The performance thread got big enough that I split it into its own focused post; here's the short version plus the three other threads. Thread 1 — Stop paying for queries you don't use A sustained sweep through an app (and the package behind it) hunting wasted database work. The highlights: Arm an N+1 detector in dev only. A query detector wired in behind an environment check turns invisible lazy-loads into a visible to-do list. Never in production — it's a developer aid, not a runtime guard. Unused eager loads are N+1s in disguise. Index screens love to with(['creator', 'approver']) for columns a redesign later removed. Not a loop, but the same disease: queries you hydrate and throw away. Delete the eager loads with no consumer in the view. Memoize per-request constants. A default-connection resolver and a sidebar unread count were both recomputed on every call. ??= once, reuse for the rest of the request. Collapse a dashboard's stat queries. ~20 count() calls became one grouped query per table, wrapped in a short-lived cache. A dashboard can tolerate being a few seconds stale; trade live-to-the-second for cheap. The meta-lesson: performance at this layer is mostly removal , and you lock it in with a Pest query-count assertion so nobody quietly re-adds an N+1 six months later. Full write-up in the focused post. Thread 2 — Health checks that actually check Here's a trap I keep seeing in "is it up?" tooling: the check verifies the record exists, or that a config row is present, and calls it green. That's not a health check — that's a config check. The service can be configured per

Performance work has a reputation for being glamorous — the heroic "we cut latency by 80%" story. Most days it's not that. Most days it's a janitorial pass: you go looking for the queries you're firing without realizing it, and you quietly delete them. That was today. One sustained sweep across an app and the package that backs it, chasing the same theme everywhere: stop asking the database for things you don't use. Let me walk through the patterns, because they generalize to any Laravel app of a certain age. First, make the invisible visible You can't fix N+1s you can't see. The first move was wiring up an N+1 detector in the local/dev environment only — beyondcode/laravel-query-detector . It hooks into the request lifecycle, watches your Eloquent relationship loads, and screams (in the console, or as an exception if you want it strict) when it spots the classic loop-and-lazy-load pattern. The "dev-only" part matters. You never want a query detector running in production — it adds overhead and it's a developer aid, not a runtime guard. So it goes in behind an environment check, registered only when the app isn't in production: public function register (): void { if ( $this -> app -> environment ( 'local' , 'testing' )) { $this -> app -> register ( \BeyondCode\QueryDetector\QueryDetectorServiceProvider :: class ); } } Think of it like a smoke detector you only arm while you're cooking. It's noisy by design — that's the point. The noise is a to-do list. Eager loads you don't actually use are just N+1s wearing a disguise Here's the counterintuitive one. We're all trained to fix N+1s by adding with() . But the opposite bug is just as common and almost never gets caught: you eager-load a relationship, and then... never touch it in the view. Index screens are the worst offenders. Someone builds a listing, eager-loads creator and approver so the table can show names, then a redesign drops those columns — but the with(['creator', 'approver']) stays. Now every page load hyd

Meta launched a stand accessory that lets you display the company's smart glasses on your desk or nightstand while they're being recharged. The new Charging Stand is listed at $59 on Meta's website - $30 less than the charging case for Meta's non-Ray-Ban glasses - and resembles a metal tube with a groove you can […]

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