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You have questions about data centers, and WIRED has answers. Join our livestream on September 10 and our panel of experts will tell you everything you need to know.
Strategists in both parties have long thought that everything would need to go right for Democrats to take over Congress this fall. Their map, though, is becoming more forgiving.
In June, a FEMA official infamous for claiming to have teleported to a Waffle House was reportedly ousted. He’s still employed by the government, though—and meeting with a Trump fixer.
Meta just agreed to make sweeping changes for teens as part of a child safety settlement reached with attorneys general across the US. Under the agreement, Meta must apply new safeguards across Instagram and Facebook that will change how teens interact with the social media platforms, as well as restrict their use during certain times. […]
Beijing’s endlessly delightful Robot Games featured tons of impressive stunts. But the most mind-blowing tricks challenged the humanoid’s brain, not its brawn.
More than 15 politicians from across the country have signed on to the AI Pact, vowing to regulate data centers and AI. “We’ve got to get this right,” says Senate candidate Dan Osborn of Nebraska.
The Three-Eyed Raven Problem: What Bran Stark could see the past, understand the present, and glimpse what might come next. Modern logistics AI is being asked to do something surprisingly similar. There is a moment in Game of Thrones when Bran Stark stops being merely a person who remembers events and becomes something much more powerful. As the Three-Eyed Raven, Bran has access to an enormous history of people, places, decisions, betrayals, and consequences. He does not simply possess information. He can retrieve the right information from the past and use it to understand what is happening now . That distinction matters. Because the logistics industry is beginning to face its own Three-Eyed Raven problem. We already have enormous amounts of data. Shipment events. GPS signals. Carrier performance. Customs documentation. warehouse scans. Purchase orders. invoices. weather feeds. port congestion. customer commitments. emails. SOPs. tariffs. exception histories. The problem is no longer simply: Can AI access all of this information? The more important question is: Can an AI system remember the right things, at the right time, for the right shipment—and forget what it should not retain? That question may become one of the defining problems of enterprise AI. AI Is Moving From Intelligence to Memory Much of the first wave of Generative AI focused on what models know . The next wave is increasingly about what AI systems can remember, retrieve, reason about, and act upon over time . This distinction becomes particularly important with AI agents. A chatbot might answer: “What documents are normally required for this shipment?” An AI logistics agent needs to understand something much harder: Which shipment are we discussing? What happened to it yesterday? Which carrier is moving it? Has this lane experienced similar delays before? What did the customer request? What customs rules apply? Was an exception already escalated? What action worked the last time this happened? Has a
Messing with the genetic code might have gotten a bit easier to do.
Perceptron offers an AI model that it says can help machines navigate the world while also providing in-depth visual intelligence.
Robot bodies are waiting for their AI brains to catch up.
The $200 million extension comes just months after the physical AI startup reached a $2 billion valuation.
Anthony Ralphs was frustrated by the San Diego City Council's support for Flock. He decided it would take something more than reasoned argument to get their attention.
In the span of a few hours on Monday, the Department of Homeland Security announced that it would be implementing a fee of over $103,000 on H-1B visas, and news broke that the State Department plans on revoking the visas of up to 200,000 asylum seekers - which, according to The Associated Press, would be […]
Record-breaking robot races are less substantial than household chore challenges.
The Supreme Court has removed one roadblock from the Trump administration’s attempts to impose severe restrictions on voting.
Archeologists are confronting an “unfathomable” loss as border wall construction is set to cut across ranches that are home to unique historical sites, many of which haven’t been fully studied.
Alabama's attorney general issued a subpoena to OpenAI on Monday as part of an investigation into how one of its AI agents escaped a supposedly secure testing environment and autonomously hacked another company last month. The investigation seeks to determine whether OpenAI's safety practices violated state consumer protection laws and pose a risk to Alabama […]
DuckDB Labs has previewed DuckDB v2.0, codenamed "Cyanoptera." This release includes over 10000 commits and introduces a client/server mode, enabling network connections. Improvements also encompass extension portability, advanced data types, and a new parser. Performance enhancements include asynchronous I/O and storage optimisations. General availability is expected in fall 2026. By Olimpiu Pop
Our AI agent posts publicly every day — social posts, replies to strangers, comments on other people's articles — with no human reviewing individual messages before they go out. That sentence should make you nervous. It makes us nervous, and we built the thing. Rate limits alone don't fix it. An agent that sends 20 polite, on-topic messages is fine; an agent that sends 20 copies of the same "Great post! 🚀" is a spammer at any rate. Volume and quality fail differently, so they need different machinery. Here is the full stack of gates ours passes before a single reply lands, and — the part that took longest to learn — which gates must be code and which can stay judgment . Layer 1: hard caps, enforced in code, not prompts Numeric limits live in one module that every posting path imports. A global daily cap across all outbound types (ours is 60) and a per-batch reply cap (20). Quote-posts have no separate quota — they simply count against the global cap like everything else, which is the point: one counter, no per-type exemptions. When the cap is hit, the send function refuses — the model doesn't get to "decide" anything, because the branch it would need isn't reachable. The design rule: a cap that lives in the prompt is a suggestion; a cap that lives in the send path is a limit. Prompts drift, sessions get compacted, instructions get summarized away. if (todayCount >= CAP) throw does not. Layer 2: sameness detectors Spam is repetition more than it is volume, so repetition is what we test for — mechanically, in the commit gate and again before send: A canned-phrase blocklist : the marketing openers everyone recognizes ("Just launched", "now available", the rocket emoji) fail the build. The list is versioned; every incident adds to it. Near-duplicate detection : 3-gram Jaccard similarity between any queued post and the last 60 days of sent history. Above 0.4, the batch is rejected. Our genuinely-different posts measure under 0.1 against each other, so the threshold has f
Andrew Swerdlow shares how Roblox scales autonomous software development from prompt to production. He discusses building robust security sandboxes, extracting institutional knowledge via code review exemplars, updating engineering infrastructure, and redefining productivity metrics around feature velocity and long-running AI turns to achieve trusted, automated deployment at scale. By Andrew Swerdlow