Masad said the decision was made for a number of different reasons, including cost. Replit announced a partnership with OpenAI in August, but there was never any indication that it was no longer with Anthropic.

The agents got in through credentials and connection settings left exposed in app configurations, public code repositories, and overly permissive cloud roles.

Amulya Vishwanath, who spent seven years leading product marketing for NVIDIA’s robotics platforms, says robotics is currently in its “Lego era,” with the basic building blocks like actuators, robotic hands, software, and safety standards still being rebuilt.  This view runs counter to those who believe that robotics, powered by a general foundation model, is about to have […]

According to Elorian's evaluations, Glimpse outperforms even GPT-6 Astra on scientific chart reasoning and short-form video captioning, with competitive results on other visual reasoning tasks. 

The data comes from Vercel's AI gateway, a unified API service similar to OpenRouter.

Actor and angel investor Mark Wahlberg says he's OK with using AI in filmmaking, provided it's used "to enhance what you're doing."

Counterfeiters are getting better at replicating brands' marks and passing off dupes, known as "superfakes," as genuine items. A startup called Veritas wants to tackle the problem with its own chip that verifies goods.

Replit CEO Amjad Masad envisions a future of “self-driving companies,” where AI agents handle day-to-day tasks and people focus on higher-level decisions.

The company generated over $30 million in revenue in two years and was acquired by MyFitnessPal for an undisclosed amount of money in March.

Meta and Apple have teamed up for the launch of the social media giant's newest model, Muse Spark Open, which will run locally on Apple silicon.

Speaking at TechCrunch Disrupt, Amazon devices chief Panos Panay described a future where ambient AI devices work in the background, anticipating users' needs instead of waiting for commands.

Artificial intelligence is undeniably good at playing games, regardless of its larger societal and environmental impact. In 1997, IBM’s supercomputer Deep Blue famously beat chess grandmaster Garry Kasparov, and nearly two decades later, Google’s AlphaGo managed to defeat a human champion at the much more complex game Go. Since then, another AI seems to beat humans at incredibly complex video games like StarCraft or Dota 2 every few months. Impressive as these feats are, AI has mostly outperformed humans by making us play on its home turf, as in, simulated and structured environments. But now, a team of engineers at the Micro Air Vehicle Lab at the Delft University of Technology in the Netherlands has built a lightweight smart drone that’s fundamentally altering that dynamic.

The team entered its AI-powered quadcopter in a high-profile drone race in Abu Dhabi that pitted AI-piloted drones against human-piloted ones. The niche sport, known as first-person-view (FPV) drone racing, has pilots steer remote-controlled drones through a series of small gates at blistering speeds of 60 to 100 miles per hour. They control the drone by using goggles that give a direct feed into a camera attached to the drone’s front. From a human’s perspective, it’s as if they’re flying through the sky. 

The team didn’t just fish the race. They took first place, besting several human FPV champions along the way. It marked the first time an AI-controlled drone beat a human pilot in an international racing competition. Another team from the University of Zurich built an AI-controlled drone that won a race against humans three years earlier, but only in a controlled lab setting. This more recent example showed that a simply trained machine, with minimal onboard sensors and hardware, could be inserted onto a non-controlled test track meant for human operators and still come out on top. 

The team won this race in November 2025 but new details about the science behind this achievement are published today in the journal Science Robotics. 

“Instead of giving the robot increasingly powerful sensors and computers, we wanted to find out how much intelligence we could extract from very limited resources,” Delft University aerospace engineer and study co-author Christophe De Wagter said in a statement. “Drone racing provided an extreme environment in which to test these ideas outside the laboratory. It pushed us to develop AI that was not only fast but also robust, and able to adapt when external reference measurements were unavailable.”

Drone racing: a sport built on human-machine connectivity 

FPV drone racing isn’t for the faint of heart—or, more accurately, for those with a weak stomach.  Footage of drones flipping and somersaulting at extreme speeds can make even spectators feel sick. Pilots fly using a pair of joysticks that control the drone’s throttle, roll, pitch, and yaw. That human-and-machine team has to fly through several small race gates that make up a lap. The drone finishing with the fastest lap times wins. It’s an extreme sport where even a microsecond of a mistake can lead to a violent crash. 

Though it’s not quite as popular as it was at its peak in the late 2010s, the sport still draws a considerable online audience. That’s pretty impressive, for a sport that only became possible thanks to simultaneous advances in several technologies. Namely lightweight batteries, miniaturized cameras, and the goggles and wireless video links that let pilots see what their drones see all had to come together for this extreme form of racing.

The fact that drone racing requires interacting with machines already equipped with sensors makes it a particularly interesting test bed for robotics and AI researchers. In 2022, the University of Zurich outfitted a drone with an AI model appropriately called Swift. That AI system collected real-time data pulled from the drone camera to “see” what it was seeing and to locate gates and avoid obstacles. An onboard neural network then parsed through all that in real time, making split-second decisions on the best routes to fly. Swift beat champion human pilots, a major feat at the time, but it did so on an indoor track measuring roughly 82-by-82-feet that the team had designed and trained the system for. When a condition even as subtle as the lighting changed, Swift’s performance suffered as a result.

Inspired by those results, the Delft team set out to build an AI racing drone that could better adapt to its environment. Like Swift, their machine called MonoRace uses a powerful AI model that interprets a video stream captured by the drone’s camera in real time. MonoRace also draws on an onboard inertial measurement unit (IMU), a sensor that tracks the drone’s acceleration and rotation. This IMU pulls data from onboard sensors as well as the camera, and helps the AI system better understand where the drone is position-wise on the track. 

That understanding of place makes it more equipped to fly without crashing even in less familiar environments. It also improves the drone’s perception over a solely camera-based approach, which can struggle when camera data is less than stellar. Remember, all of this is happening very, very fast. 

“Flying at very high speed with only one camera is challenging because the images are blurry, deformed, and the AI system has very little time to precisely determine where the drone is,” Delft University robotics researcher and study co-author Stavrow Bahnam said in a statement.  “We developed a robust way of combining what the camera sees with the drone’s own motion information, allowing it to navigate the track at high speed without GPS.”

What sets MonoRace apart from complex AI systems running on larger vehicles or humanoid robots is how stripped down it is. The entire system has to run on a tiny computer on the drone, light enough not to slow the drone down and efficient enough not to introduce lag. And yet, it’s still quite powerful, with the AI issuing an estimated 500 commands every second. Rather than train the model on one specific track, they trained it to learn how to adapt using a reinforcement learning approach. After many attempts of trial and error in a simulated environment, MonoRace figured out the best ways to quickly solve navigation problems and then apply that in the real world. 

“Instead of first planning a desired flight path and conventional commands, our neural network learns to directly control the motors,” study co-author and Delft University Aerospace Engineering PhD candidate Robin Ferede added in a statement. 

Putting MonoRace to the test

The team put MonoRace to the real test last November at the Abu Dhabi Autonomous Racing League, a major competition where some of the best human pilots battle to out. During its flights, MonoRace topped speeds of roughly 62 miles per hour as it looped, dove, and zipped through gates. It managed to beat out every other AI racing team in the competition, and also boasted a better time than three former human world champion pilots.  

According to the team, these results make MonoRace the first AI-piloted drone to beat human pilots in an international racing competition, and  could have an impact on  drone racing and  physical AI more broadly.  With self-driving vehicles and even some humanoid robots entering larger-scale production, there’s an increasingly high demand to build AI systems that better grasp how to move about the physical world without barreling into everything around it. A drone that can do that while performing acrobatics at more than 60 miles per hour suggests that engineers are, at minimum, heading in the right direction.  

So far, that hasn’t resulted in larger robots suddenly superseding humans at sports. The fastest robot sprinters, though admittedly quick in short bursts, also tend to crash into walls and burst into flames. 

The post An AI-racing drone beat the best humans and set a world record appeared first on Popular Science.

Even after decades of meteorological advances, weather forecasting remains infamously difficult. Today’s most trusted models are rarely accurate beyond a two-week window due to a constant cascade of atmospheric and climatic variables. These unpredictable meteorological interactions are the foundations of chaos theory. The interdisciplinary field studies how even the world’s most predictable systems can still produce completely unpredictable results, and was first developed by MIT mathematician Edward Lorenz during the 1970s.

“He came up with this theoretical two-week limit where beyond that, we just can’t predict weather because the noise in the system grows so much that it overwhelms any signal you start with,” Judah Cohen, a climatologist also at MIT, explained.

According to Cohen, many climate researchers continue to view Lorenz’s two-week hurdle as insurmountable. And that assumption has largely proven true for nearly 50 years. Even today’s dynamical simulations and artificial intelligence models begin to break down when attempting to predict conditions two-to-six weeks in the future—a window known as subseasonal forecasting. Nicknamed the “predictability desert,” this range runs into slow-moving, highly variable forces like this year’s super El Niño event. Once those factors enter the equation, forecasting becomes more of a roll of the dice l than anything concrete. However, Cohen has had his suspicions about chaos theory’s limitations.

“I never really believed that completely,” he said. “In theory, the atmosphere can create an infinite number of patterns, but it likes to fall into these favorite patterns over and over again.”

To try and break this forecasting barrier, Cohen and his colleague Lester Mackey have spent years developing an AI system that not only minimizes forecasting errors, but learns from its inevitable mistakes through a method called postprocessing. With postprocessing, the AI improves by comparing the inaccuracies with existing historical data, and then adjusts from there.

Cohen’s team recently submitted three versions of their model featuring the postprocessing system the European Center for Medium-Range Weather Forecasts (ECMWF). Widely known as the world’s best forecasting hub, the ECMWF also hosts seasonal competitions that pit the latest prediction models against one another. Most of these systems have relied on historic climate averages to reach their conclusions, but this was the first time a group submitted a program with postprocessing capabilities.

Cohen and his colleagues reported their results in a study published today in the journal Nature. Their AI postprocessing integration blew the existing forecasting models out of the water. For the two-to-six-week subseasonal window, researchers doubled overall prediction accuracy. Such massive improvements could soon help the farmers and energy providers who already rely on subseasonal forecasts, but the benefits may even save lives.

“It also improves predictions of extreme weather events, whether it’s flooding or cold spells or something else,” said Cohen. “It’s really important to predict both, but extreme events might be the most impactful part of this for society.” 

Many researchers have long presumed subseasonal forecasting to be a dead end, but the latest leap may clear the way to even bigger advances in the field.

“People have questioned if it’s even worth investing in models at all for this time range,” said Cohen. “[But] we consistently beat climatology averages with our models.”

The post New weather model may finally offer a reliable six-week forecast appeared first on Popular Science.

A teenage hiker in Canada recently became another cautionary tale about the perils of overtrusting artificial intelligence. After relying on an AI-generated route to ascend Crown Mountain in British Columbia, the 16-year-old found himself stranded on a steep cliffside near a climbing route known as the “Widowmaker.” The wayward traveler’s position was so precarious, that it took roughly three hours to coordinate a proper rescue plan.

“The boy was very scared. He definitely feared for his life,” search manager Paul Markey told North Shore News earlier this month.

The teen’s position near Widowmaker’s final headwall prevented helicopter pilots from safely steering close enough to retrieve him. Instead, an onsite crew reached the hiker using a rope belay system, equipped him with additional protective gear, and then moved him to a better extraction location. As luck would have it, responders had also just completed a rescue certification training session earlier that day not far from the lost teen’s predicament.

“It put him into a situation which was beyond his abilities, so things could have gone sideways pretty quickly,” Markey added.

Despite major advancements in the industry, many popular AI products are still liable to produce results with unreliable, misleading, or outright false information. Generative AI has suggested hungry users add glue to thicken pizza sauce, claimed that snakes are mammals, and advised fixing a broken turn signal with something called “blinker fluid.”

This isn’t the only AI-related hiking mishap, either. Barely a month before the Crown Mountain incident, a trio became stranded near Mount Shasta in California after using Google Gemini to help plan their trip. Among other issues, Gemini suggested they pack less food and water than they actually needed, turning a normally eight-hour excursion into a multiday emergency. 

Instead of relying on AI options, rescue officials like Markey suggest using well-tested hiking route apps like AllTrails, CalTopo, and Gaia GPS.

The post A teen used AI to plan a hiking route. It trapped him on a cliff. appeared first on Popular Science.

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