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It feels bad enough when an open letter signed by leading economists warns that AI might steal your job. The fact it may soon be better than you at making dinner? Insult to injury. But that’s exactly what the company 1X promised when it showed off a pair of new, impressively dexterous (and, to some,…
In a new disclosure, OpenAI says its agent used exposed logins to gain access to at least four “publicly available services” in its unhinged quest to solve a test.
Spur Intelligence has raised a $200 million round from Insight Partners for its tech that can identify legit human traffic from bots.
Process mismatch In tools built for Scrum, a task is an input: something you file, size, and work on. In Shape Up, a task is an output — something discovered while building work that was already shaped and bet on. That's the core mismatch, and it plays out differently depending on the tool. Jira Jira does exactly what it was built to do. Its shape is Scrum's shape: a backlog, estimates, sprints. Teams bring Shape Up in anyway and try to make it fit the tool's shape. A scope becomes an epic. A task becomes a ticket. The pitch — Shape Up's document for a problem, its appetite, and a proposed solution — has no equivalent object in Jira, so it ends up living in a Confluence doc, disconnected from the work it's supposed to govern. The substitutions are each small and reasonable on their own: An estimate field is there, so it gets filled in — and the velocity report looks broken without it. Losing bets need somewhere to go, so they land in the backlog. They aren't dead, they're waiting — and now someone has to groom them. Appetite ("how much is this worth") quietly reverts to estimate ("how long will this take"). Before long, the team is running Scrum, with a backlog-refinement meeting back on the calendar. The tool's requirements pull the ceremonies back in. Linear Linear is fast and well made. It even has cycles. The mismatch here isn't a quality problem — it's an inheritance problem. Linear carries the same assumptions as Scrum, just executed better. When a cycle ends with work unfinished, Linear rolls it forward automatically into the next one. It's meant as a convenience feature. It's also the inverse of Shape Up's circuit breaker. Shape Up's bet is that the deadline is real. The whole mechanism depends on a hard stop forcing a decision — cut the scope and ship what's done, while there's still time to make that call. A tool that quietly carries unfinished work forward removes the one moment the method needs. Every six weeks, it says: the deadline was just a suggestio
Runlayer is suing Rippling after Rippling evaluated the startup's MCP gateway product and then opted to build one itself.
Ozlo’s first major update to its sleep earbuds introduces longer battery life, improved connectivity, enhanced audio, and new sleep features as the startup continues the product line once abandoned by Bose.
For people who want the power of a phone that transforms into a miniature tablet, the Galaxy Z Fold 8 offers a better way to watch shows and movies.
If you've got an older iPhone hanging out in the junk drawer, grab it out and bring it back to life with these tips.
For years, we've heard rumors of a new home hub device from Apple, meant to take on smart home devices like the Google Nest Hub Mini and Amazon's Echo Show. Besides "HomePod with a screen," details have been sparse. But now, a new report from Bloomberg's Mark Gurman sheds some light on the alleged device, […]
Apple's new program gives you lease options for iPhone, iPad, Apple Watch and Mac.
Your mom never gets you a thoughtless gift, so you shouldn’t get her one, either. Here’s every cool gift WIRED writers would give their mothers.
Grafana Labs has expanded the capabilities of Grafana Assistant, enabling its AI-powered observability assistant to query and correlate data across more than 30 different data sources through natural language. By Craig Risi
Ben Greene discusses how software engineers can adapt and thrive in an era of rapid AI code automation. Drawing on his startup experience, he explains key mindsets like starting simple, maintaining code comprehension, attacking hard problems first, and focusing on customer impact. He shares why human empathy, agency, and practical problem-solving remain irreplaceable when code is automated. By Ben Greene
In 2023, the Times sued OpenAI and Microsoft for copyright infringement. They’ve since spent more than $20 million on the case, and publisher A.G. Sulzberger has no plans to stop fighting it.
Anthropic and OpenAI employees are expected to give generously after their companies go public. “It’s going to be a wild ride,” says one nonprofit leader.
Lee, an engineer at Samsung’s semiconductor division, clocks out when his shift ends. He used to work longer hours, going the extra mile to excel at his projects. But lately, he’s been coming straight home to work on his job application for the chipmaker’s South Korean rival SK Hynix, sharing tips with his coworkers on…
Researchers tested top image editing models on Hugging Face and found they could easily create explicit deepfakes—and 1,000 image editing prompts show how people use the software.
Cursor says India is now its third-largest market globally and plans to expand local hiring and enterprise sales.
A few years ago, the hardest part of building a browser test framework was getting started. You had to choose a runner, configure browsers, create page objects, wire up reporting, add retries, manage secrets, connect it to CI, and convince someone else on the team to learn how the whole thing worked. Today, you can open an AI assistant and ask it to generate most of that before lunch. That sounds like a dramatic improvement. In some ways, it is. But it also moves the bottleneck. The question is no longer, “Can we create a framework?” The question is, “Can we operate what was created?” That distinction matters more than it appears. Generation cost is not ownership cost A generated framework feels cheap because the first version arrives quickly. The code compiles, a few tests pass, and the pull request looks more complete than anything you could have written in an afternoon. Then reality starts applying pressure. The application changes. Authentication behaves differently in staging. A shared helper starts hiding failures. Parallel workers collide over test data. Someone upgrades a dependency and three reporters stop agreeing with one another. The initial generation was fast. The ownership cost was merely deferred. This is the central problem described in what actually breaks when Claude generates a large Playwright framework . Large generated systems often fail in the seams: fixtures, abstractions, environment assumptions, test data, and conventions that were never explicitly agreed upon. The code may be readable line by line while the system remains difficult to reason about as a whole. That is a dangerous form of complexity because it looks productive. More code can hide less understanding Teams sometimes evaluate AI-generated automation by counting output: number of test files; number of scenarios; number of passing checks; number of prompts completed; number of lines added. Those numbers are easy to produce and easy to report. They are also weak proxies for confi
The outgoing executive director of the Office of Homeland Security Statistics is one of very few federal officials to speak out against the Trump administration’s immigration crackdown.