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Anthropic just announced Claude Fable 5, a new AI model it said is the most powerful model it has ever made widely available. According to the company, Fable 5 "shows exceptional performance in software engineering, knowledge work, and vision," with its lead over other models growing as tasks become longer and more complex. Fable 5 […]

Founder and CEO RJ Scaringe has called it "maybe the most important thing we've launched to date."

With SpaceX, Anthropic, and OpenAI all eyeing massive public debuts, the tech industry may soon have a new class of corporate overlords — and a new acronym to match. Say goodbye to FAANG and hello to MANGOS.

Visibility Comes Before Optimization in IT Operations is a practical operating principle, not a slogan. The useful version of analytics, automation, and software operations is usually quieter than the marketing version. It is less about collecting everything or automating everything, and more about making the work easier to understand, review, and improve. The practical problem Teams often try to optimize before they can see the system clearly. That creates confident changes based on partial evidence, especially in infrastructure and telecom-adjacent workflows where signals are distributed. This is where many teams lose clarity. They have tools, charts, workflows, and activity, but the connection between evidence and decision is weak. When that connection is weak, software work becomes harder to evaluate. Teams still make decisions, but they rely more on memory, opinion, or urgency than on a reviewable operating picture. A smaller operating model Start with visibility: what is running, which state changed, where the weak signal appeared, and which workflow was affected. Then connect that signal to a decision or operational review. The important detail is restraint. A useful system does not need to track every possible action or automate every possible step. It needs to preserve the signals that help operators understand the situation and act with more confidence. That usually means naming the workflow, keeping the outcome visible, preserving enough context to explain the signal, and making uncertainty explicit instead of hiding it behind a polished interface. What to review Useful analytics separates normal activity from operational risk. It should make the next investigation smaller, not create another dashboard that requires interpretation from scratch. A reviewable system is easier to trust because it can explain its own state. It shows what happened, what changed, what remains uncertain, and which decision should move next. For WebmasterID, this is the practical

US Navy’s Task Force 59 achieved the drone rescue at sea near Strait of Hormuz.

Individual gold atoms move around to form oxidation-proof structures.

Apple kicked off its annual developer conference with bold promises about AI. The company, CEO Tim Cook said, would be "introducing new technologies and innovations that push the limits on what's possible." But its slew of announcements - centered on a brand-new "Siri AI" - had more to do with catching up. After almost entirely […]

MacOS 27 Golden Gate will usher in a bunch of changes to the Mac when it's released later this year, with its biggest new features revolving around Siri AI. But for now, using the first developer beta, Siri AI is only offered through a waitlist. So what's available to try is mostly about how the […]

Rivian's second EV is the sub-$60,000 R2, and it was worth the wait.

With a competitive price, winning design, and better performance than the R1, Rivian could be set to break into the big leagues. Just make sure you get the right model with the right tech.

It's not perfect, but Rivian's latest shows this company is playing for keeps.

Rivian may be all in on robotaxis and autonomy, but it's still got human drivers - and EV buyers - to win over. The pricey R1S SUV and R1T pickup brought Rivian A-list media attention and cult-hit status, but the company faces a critical next step. The 2027 R2 is Rivian's bid for mainstream success, […]

As expected, yesterday's WWDC keynote was mostly about AI. And also as expected, Apple tried to turn its late arrival into its sales pitch: it didn't rush into AI because it was taking its time to do things right. In this case, "right" means "with more privacy than anyone else." It's a good pitch - […]

Netflix engineer Casey Bleifer shares how to achieve rapid, automated code changes across a massive, diverse software fleet. She discusses building an event-driven orchestration platform using composable, Lego-like steps, and explains how Netflix utilizes automated canary validation, compliance checks, and a custom "confidence metric" to eliminate the long tail of manual engineering migrations. By Casey Bleifer

IBM and HashiCorp have announced new LDAP secrets management capabilities in IBM Vault Enterprise 2.0, introducing a redesigned architecture to manage LDAP credentials, support password rotation, and automate the identity lifecycle. By Craig Risi

In 2019, Alex Vindman testified during President Trump’s first impeachment trial–a decision that ended his military career. Now he wants to challenge the president from the halls of Congress.

The outspoken longevity scientist David Sinclair has been predicting that one day, you’ll go to the doctor and get a prescription that will make you 10 years younger. Now MIT Technology Review has learned that he has plans to launch human tests of an oral “reprogramming” drug as part of a $101 million competition organized…

Hey, welcome back. Last time we talked about missing parameterization in test scenarios. Today's mistake is similar in spirit. The test runs. The numbers look great. But what you've built isn't a load test. It's a hammer. ⚠️ The script works. The test is inhuman. Real users don't fire requests like a machine gun. They log in. They pause. They read. They click. They pause again. A typical user journey that takes 60 seconds in real life? Without think time, your script does it in just a few seconds. What this breaks Your throughput numbers are fiction. If users complete journeys 30x faster than reality, your RPS is inflated by 30x. You're not measuring capacity — you're measuring endurance under abuse. You stress the wrong things. Realistic concurrency surfaces real bottlenecks. A firehose of instant requests just overloads your connection pool and calls it a day. Production behaves nothing like your test. Because real users think. Your script didn't. 🛠 The fix Add randomized pauses between steps. Every major tool supports it: JMeter: Gaussian Random Timer, Uniform Random Timer etc. k6: sleep(Math.random() * 5 + 3) Gatling: pause(3.seconds, 8.seconds) Locust: time.sleep(random.uniform(3, 8)) 3–8 seconds between actions is a reasonable starting point. Check your analytics for what real sessions actually look like. Before your next run: Pauses between every major action? Randomized, not fixed? Does the timing feel human? If not — you're not testing load. You're testing collapse. Think time is one piece of the puzzle. But realistic load modeling goes deeper — it's about understanding how real users behave, how to translate that into a load profile, and how to design a test that actually reflects production. That's not something you patch with a timer. It's something you build from the ground up. If you want to understand the full system — from load model design to test execution to results that mean something — that's exactly what Performance Testing Fundamentals course

The startup is one of many entering the RV space, but it's banking on a hybrid power system that can go far beyond campsites.

Donut Lab's solid-state battery claims have been thoroughly debunked by Ryan Inis Hughes on his popular Ziroth YouTube channel. According to Hughes, Donut Lab has engaged in deliberate, calculated deception by claiming to have a solid-state battery ready for mass production. In reality, it's nothing more than a standard lithium-ion design. Hughes' investigation got an […]