Usually, when you see a feel-good story about finding a lost dog, you don’t immediately react with fear and revulsion. But that was indeed the case in response to a Super Bowl commercial from Amazon-owned security camera company Ring. There’s now a group offering to dole out a $10,000 bounty to wrest back control of the user data Ring controls.

The ad showed off a new feature from Ring called Search Party. It uses a network of Ring cameras to scour a neighborhood for signs of lost dogs. But as the details of a leaked internal Ring email reported by 404 Media revealed, the service could eventually be used to find other animals and people as well.

The commercial was met largely with widespread criticism across social media and the tech press, which called out Search Party for essentially being a thinly-veiled neighborhood surveillance dragnet. People are even publicly destroying their Ring cameras. In response, Ring immediately canceled its partnership with the controversial AI surveillance company Flock. Ring CEO Jamie Siminoff has been on something of an apology tour since the Super Bowl commercial aired. (A Ring spokesperson acknowledged our request for comment and says the company will provide one shortly; we’ll update this story when we hear back.)

The Fulu Foundation, a group founded by repair advocate and YouTuber Louis Rossmann, pays out bounties to people who can remove user-hostile features on connected devices. The nonprofit saw this pushback as a moment of opportunity for people to take back control of their devices.

“It’s been an interesting moment for people to grasp exactly the trade-off that they have had to accept when they installed these security doorbell cameras,” says Fulu cofounder Kevin O’Reilly. “People who install security cameras are looking for more security, not less. At the end of the day, control is at the heart of security. If we don’t control our data, we don’t control our devices.”

Fulu’s latest bounty is for Ring’s video doorbell cameras, meant to encourage hackers and tinkerers to disable software features that require the devices to send data to Amazon. The reward is a potential payout of $10,000 or more.

To score the bounty, the winner will have to adhere to a few requirements designed to make sure the hardware itself stays in working order. After modifications, the device must be able to work with a local PC or server, and be capable of halting data sent to Amazon servers or requiring a connection to other Amazon hardware. All of this must be done without disabling on-device hardware features like motion detecting and color night vision. The job also has to be accomplishable with “readily available and inexpensive tooling” and “instructions that a moderately technical user could carry out” in less than an hour.

“This needs to be a weekend project,” O’Reilly says, “where someone who was creeped out by a commercial and wants to take back control can take care of it, get it done, and be able to sleep soundly at night knowing that they’re the only ones who can see their footage.”

The first person to accomplish all of that with a Ring camera—and prove they can do it—gets the money. The reward starts at $10,000, but will likely grow as donors contribute more money (it’s already sitting closer to $11,000 as of publication). On top of that, Fulu will award up to an additional $10,000 to match donations for the winner.

When the Executive Branch soft-launched in Washington, DC, last spring, the private club’s initial buzz centered on its starry roster of backers and founding members. The president’s eldest son, Donald Trump Jr., is one of the club’s several co-owners, according to previous reporting. Founding members reportedly include Trump administration AI czar David Sacks and his All-In podcast cohost Chamath Palihapitiya, as well as crypto bigwigs Tyler and Cameron Winklevoss.

“We wanted to create something new, hipper, and Trump-aligned,” Sacks said at the time. Proximity to Trumpworld didn’t come cheap; though the club headquarters is located in a basement space behind a shopping complex, fees to join are reportedly as high as $500,000.

The initial wave of press for the MAGA hot spot identified Trump Jr. and his business associates Omeed Malik, Chris Buskirk, and Zach and Alex Witkoff as the club’s co-owners. A Mother Jones report later revealed the involvement of David Sacks’ frequent business associate Glenn Gilmore, a San Francisco Bay Area real estate developer who is given a variety of titles on official documents, including co-owner, managing member, director, and president.

But according to corporate filings reviewed by WIRED, there’s another key figure whose involvement has not been previously reported and whose connection to its more famous founders remains unclear: Sean LoJacono, a former Metropolitan Police Department cop in Washington, DC, who gained local notoriety for his role in a stop and frisk that resulted in a lawsuit.

According to the legal complaint, in 2017, after questioning a man named M.B. Cottingham for a suspected open-container-law violation, LoJacono conducted a body search. A recording of the incident went viral on YouTube, sparking intense debate over aggressive policing tactics. “He stuck his finger in my crack,” Cottingham says in the video. “Stop fingering me, though, bro.” The next year, the American Civil Liberties Union of the District of Columbia sued LoJacono on behalf of Cottingham, alleging that LoJacono had “jammed his fingers between Mr. Cottingham’s buttocks and grabbed his genitals.” Cottingham agreed to settle his lawsuit with LoJacono and was paid an undisclosed amount by the District of Columbia (which admitted no wrongdoing) in 2018.

The MPD announced its intention to dismiss LoJacono following an internal affairs investigation, which concluded that the Cottingham search was not a fireable offense but that another search he had conducted the same day was. By early 2019, LoJacono had appealed his dismissal, arguing in well-publicized hearings that he had conducted searches according to how he had been taught by fellow officers in the field. Initially, the dismissal was upheld. However, the police union’s collective bargaining agreement enabled LoJacono to further appeal to a third-party arbitrator, which in November 2023 ruled in LoJacono’s favor.

Instead of returning to the police force, though, LoJacono has gone down a different path. A LinkedIn account featuring LoJacono’s name, likeness, and employment history lists his profession as “Director of Security and Facilities Management” at an unnamed private club in Washington, DC, from June 2025 to the present. Official incorporation paperwork for the Executive Branch Limited Liability Company filed to the Government of the District of Columbia’s corporations division in March 2025, shortly before the club launched, lists LoJacono as the “beneficial owner” of the business. The address listed on the paperwork matches the Executive Branch’s location. Donald Trump Jr. and other reported owners are not listed on the paperwork; Gilmore is listed on this document as the company’s “organizer.”

The paperwork indicates that LoJacono is considered a beneficial owner of a legal entity associated with the Executive Branch. But what does that mean, exactly?

A new artificial intelligence control system enables soft robotic arms to learn a wide repertoire of motions and tasks once, then adjust to new scenarios on the fly, without needing retraining or sacrificing functionality. 

This breakthrough brings soft robotics closer to human-like adaptability for real-world applications, such as in assistive robotics, rehabilitation robots, and wearable or medical soft robots, by making them more intelligent, versatile, and safe.

The work was led by the Mens, Manus and Machina (M3S) interdisciplinary research group — a play on the Latin MIT motto “mens et manus,” or “mind and hand,” with the addition of “machina” for “machine” — within the Singapore-MIT Alliance for Research and Technology. Co-leading the project are researchers from the National University of Singapore (NUS), alongside collaborators from MIT and Nanyang Technological University in Singapore (NTU Singapore).

Unlike regular robots that move using rigid motors and joints, soft robots are made from flexible materials such as soft rubber and move using special actuators — components that act like artificial muscles to produce physical motion. While their flexibility makes them ideal for delicate or adaptive tasks, controlling soft robots has always been a challenge because their shape changes in unpredictable ways. Real-world environments are often complicated and full of unexpected disturbances, and even small changes in conditions — like a shift in weight, a gust of wind, or a minor hardware fault — can throw off their movements. 

Despite substantial progress in soft robotics, existing approaches often can only achieve one or two of the three capabilities needed for soft robots to operate intelligently in real-world environments: using what they’ve learned from one task to perform a different task, adapting quickly when the situation changes, and guaranteeing that the robot will stay stable and safe while adapting its movements. This lack of adaptability and reliability has been a major barrier to deploying soft robots in real-world applications until now.

In an open-access study titled “A general soft robotic controller inspired by neuronal structural and plastic synapses that adapts to diverse arms, tasks, and perturbations,” published Jan. 6 in Science Advances, the researchers describe how they developed a new AI control system that allows soft robots to adapt across diverse tasks and disturbances. The study takes inspiration from the way the human brain learns and adapts, and was built on extensive research in learning-based robotic control, embodied intelligence, soft robotics, and meta-learning.

The system uses two complementary sets of “synapses” — connections that adjust how the robot moves — working in tandem. The first set, known as “structural synapses”, is trained offline on a variety of foundational movements, such as bending or extending a soft arm smoothly. These form the robot’s built‑in skills and provide a strong, stable foundation. The second set, called “plastic synapses,” continually updates online as the robot operates, fine-tuning the arm’s behavior to respond to what is happening in the moment. A built-in stability measure acts like a safeguard, so even as the robot adjusts during online adaptation, its behavior remains smooth and controlled.

“Soft robots hold immense potential to take on tasks that conventional machines simply cannot, but true adoption requires control systems that are both highly capable and reliably safe. By combining structural learning with real-time adaptiveness, we’ve created a system that can handle the complexity of soft materials in unpredictable environments,” says MIT Professor Daniela Rus, co-lead principal investigator at M3S, director of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-corresponding author of the paper. “It’s a step closer to a future where versatile soft robots can operate safely and intelligently alongside people — in clinics, factories, or everyday lives.”

“This new AI control system is one of the first general soft-robot controllers that can achieve all three key aspects needed for soft robots to be used in society and various industries. It can apply what it learned offline across different tasks, adapt instantly to new conditions, and remain stable throughout — all within one control framework,” says Associate Professor Zhiqiang Tang, first author and co-corresponding author of the paper who was a postdoc at M3S and at NUS when he carried out the research and is now an associate professor at Southeast University in China (SEU China).

The system supports multiple task types, enabling soft robotic arms to execute trajectory tracking, object placement, and whole-body shape regulation within one unified approach. The method also generalizes across different soft-arm platforms, demonstrating cross-platform applicability. 

The system was tested and validated on two physical platforms — a cable-driven soft arm and a shape-memory-alloy–actuated soft arm — and delivered impressive results. It achieved a 44–55 percent reduction in tracking error under heavy disturbances; over 92 percent shape accuracy under payload changes, airflow disturbances, and actuator failures; and stable performance even when up to half of the actuators failed. 

“This work redefines what’s possible in soft robotics. We’ve shifted the paradigm from task-specific tuning and capabilities toward a truly generalizable framework with human-like intelligence. It is a breakthrough that opens the door to scalable, intelligent soft machines capable of operating in real-world environments,” says Professor Cecilia Laschi, co-corresponding author and principal investigator at M3S, Provost’s Chair Professor in the NUS Department of Mechanical Engineering at the College of Design and Engineering, and director of the NUS Advanced Robotics Centre.

This breakthrough opens doors for more robust soft robotic systems to develop manufacturing, logistics, inspection, and medical robotics without the need for constant reprogramming — reducing downtime and costs. In health care, assistive and rehabilitation devices can automatically tailor their movements to a patient’s changing strength or posture, while wearable or medical soft robots can respond more sensitively to individual needs, improving safety and patient outcomes.

The researchers plan to extend this technology to robotic systems or components that can operate at higher speeds and more complex environments, with potential applications in assistive robotics, medical devices, and industrial soft manipulators, as well as integration into real-world autonomous systems.

The research conducted at SMART was supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise program.