It's a rare chance to save on the outdoor-coded Canadian company's understated and stylish performance clothing. Now that Amazon Prime Day is over, it's time to start gearing up for Fourth of July sales. Most large retailers pivoted their summer-sale timing to compete head-on with Amazon's accelerated schedule, but you can still snag great deals this July 4th, particularly in active and outdoorsy categories. REI has the hottest sale of the weekend as far as the WIRED Reviews team is concerned, but there are notable midsummer sales on other sites we shop, like Backcountry, Home Depot, and Lululemon . Also, make sure you don't sleep on Duer.

Samsung's Excellent OLED Monitors Are Up to 36 Percent Off for Prime Day Samsung makes some of the very best OLED gaming monitors, and they've never been this affordable. Samsung makes some of the very best OLED monitors out there, and some of its top gaming monitors are getting some solid discounts for Prime Day. The strongest deal on offer is on the Odyssey G6 (G61SH). This 27-inch monitor is one of the company's latest displays, offering a 240-Hz refresh rate at a 2560 x 1440 resolution. It's been sold below its retail price off and on over the past few months, but this is the lowest it's ever dropped to.

I just took a quick break from Prime Day to do a 15-minute workout on the Hydrow Arc rowing machine . Hydrow is the Peloton of rowing machines. The Arc is the top-of-the-line offering from Hydrow, which makes touchscreen-enabled machines that provide real-time stats and gentle coaching via video, and it is sadly not for sale on Amazon or during Amazon's annual event. However, two other machines from Hydrow are included, among them the successor to the original, which we gave an 8/10 review during the late pandemic home-workout boom . The Wave is 11 percent off, while the Origin is discounted by almost 25 percent.

Pump.Fun's Bounties Platform Is a Black Hole of Circular Grifting The crypto platform claims you can "pay anyone to do anything," from quitting a job on camera to getting a memecoin-themed tattoo. But it mostly seems like people trying to scam each other. Would you run into a crowded university lecture hall, fart into a megaphone, and bellow "fartcoin" at the top of your lungs? If so--and should you have the means to document this stunt on video, preferably capturing the audience's reaction--you may claim a reward of approximately $1,000 . The money, of course, will be dispensed in fartcoin, a meme cryptocurrency trading at a little over 10 cents at time of publication, with a total market capitalization hovering around $130 million. Such is the promise of Pump.Fun GO, a new feature on Pump.Fun, one of the fastest-growing crypto businesses of the past few years.

Graph neural networks (GNNs) are frequently used for knowledge graph completion. Their black-box nature has motivated work that uses sound logical rules to explain predictions and characterise their expressivity. However, despite the prevalence of GNNs that use mean as an aggregation function, explainability and expressivity results are lacking for them. We consider GNNs with mean aggregation and non-negative weights (MAGNNs), proving the precise class of monotonic rules that can be sound for them, as well as providing a restricted fragment of first-order logic to explain any MAGNN prediction. Our experiments show that restricting mean-aggregation GNNs to have non-negative weights yields comparable or improved performance on standard inductive benchmarks, that sound rules are obtained in practice, that insightful explanations can be generated in practice, and that the sound rules can expose issues in the trained models.

The meteoric rise of Artificial Intelligence (AI), with its rapidly expanding market capitalization, presents both transformative opportunities and critical challenges. Chief among these is the urgent need for a new, unified paradigm for trustworthy evaluation, as current benchmarks increasingly reveal critical vulnerabilities. Issues like data contamination and selective reporting by model developers fuel hype, while inadequate data quality control can lead to biased evaluations that, even if unintentionally, may favor specific approaches. As a flood of participants enters the AI space, this "Wild West" of assessment makes distinguishing genuine progress from exaggerated claims exceptionally difficult. Such ambiguity blurs scientific signals and erodes public confidence, much as unchecked claims would destabilize financial markets reliant on credible oversight from agencies like Moody's. In high-stakes human examinations (e.g., SAT, GRE), substantial effort is devoted to ensuring fairness and credibility; why settle for less in evaluating AI, especially given its profound societal impact? This position paper argues that a laissezfaire approach is untenable. For true and sustainable AI advancement, we call for a paradigm shift to a unified, live, and quality-controlled benchmarking framework--robust by construction rather than reliant on courtesy or goodwill.

We Asked the Author to Explain How He Used AI. A book about how AI shapes perceptions of reality came under fire for using AI-generated quotes. Its problems go beyond that. Earlier this month, WIRED published an excerpt from Steve Rosenbaum's buzzy new book,, which looks at how artificial intelligence warps people's sense of reality. Shortly thereafter, The New York Times reported that the book contained over a half-dozen made-up or misattributed quotes.

An autonomous robot from the company Honor ran a half marathon in 50:26, beating the human record by 7 minutes. A humanoid robot from the Honor remote-controlled team crosses the finish line during the E-Town Humanoid Robot Half Marathon in Beijing on April 19, 2026. Over the weekend in China, a humanoid robot shattered world half-marathon record--the human record--by seven minutes. The star performer was a robot developed by the Chinese company Honor (the smartphone maker), which finished the 13.1-mile race in 50 minutes, 26 seconds. The human record, set by Ugandan Olympic medalist Jacob Kiplimo, is 57 minutes, 20 seconds.