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.

The actor-director discussed his least favorite currency and read a series of mean tweets--about us!--at our inaugural WIRED@Night event. Ben McKenzie had a question: "When did WIRED die?" Specifically, the actor-director wanted to know when did WIRED "'DIE,' all caps." McKenzie wasn't asking for himself; he was engaging in the time-honored celebrity tradition of reading mean tweets . McKenzie, who famously played Ryan on before becoming a leading voice of crypto skepticism, was sharing the stage with WIRED senior correspondent Andy Greenberg for the first of what will hopefully be a series of smaller events that we are calling WIRED@Night. On April 16, about 100 people gathered at event partner Ace Hotel Brooklyn to sip drinks from Aplos, Faccia Brutto, The Sorting Table, and Manojo and ponder the future of cryptocurrency.

Nvidia's new AI upscaling gaming technology struck gamers as uncanny and off-putting. Developers don't seem to like it, either, but it could be "the default" in a few years. Nvidia announced a new version of its DLSS AI upscaling technology for its graphics cards earlier this week at its GPU Technology Conference (GTC), which it calls the Super Bowl of AI . But unlike previous versions of DLSS that used AI to improve frame rates in video games, DLSS 5 has a much more ambitious calling: using generative AI to make character faces in games look more realistic and detailed. The demonstration received sharp blowback on social media, with many finding the effect off-putting, reacting with outright disgust, and calling it yet another example of AI slop .

A more exciting MacBook Pro is waiting in the wings, but the M5 Max shows the continued success of Apple Silicon. The M5 Max is a monster performer. Gaming is surprisingly smooth, and on-device AI speeds up. The display, keyboard, ports, and speakers remain top-of-class. The MacBook Pro is in its awkward era.