If the report of Aamir Khollam from Interesting Engineering were true, then the tiny robotic insects may soon become lifesaving tools in disaster zones. The report further stated that MITT researchers have unveiled an aerial microrobot that flies with unprecedented speed and agility, mirroring the gymnastic motion of real insects.

In the future, these miniature flying machines could navigate collapsed buildings after earthquakes and help locate survivors in places larger robots cannot reach.

The breakthrough marks a significant shift in micro-robotics, where flight stability and speed have historically lagged far behind nature’s engineering.

Earlier versions of insect-scale robots could only fly slowly and along predictable paths. The new robot changes that dynamic entirely.

Roughly the size of a microcassette and lighter than a paperclip, the machine uses soft artificial muscles that power its large flapping wings at high frequency.

The updated hardware enables tight turns, rapid acceleration, and aerial tricks that resemble insect maneuverability.

But hardware alone wasn’t enough. The robot needed a smarter and faster "brain."

That came in the form of a new AI-based controller that interprets the robot’s position and environment, then decides how it should move in real time.

Previous control systems required manual tuning by engineers, which limited performance and didn’t scale for complex movement.

Kevin Chen, associate professor in MIT’s Department of Electrical Engineering and Computer Science, explains the goal clearly – "We want to be able to use these robots in scenarios that more traditional quad copter robots would have trouble flying into, but that insects could navigate."

He adds, "Now, with our bioinspired control framework, the flight performance of our robot is comparable to insects in terms of speed, acceleration, and the pitching angle. This is quite an exciting step toward that future goal."

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It was on 3 December 1992 that a simple message that read "Merry Christmas" was sent and it marked a historic shift in global communication.

Neil Papworth, then a 22-year-old engineer, sent the message from his computer to the Orbitel 901 phone of Vodafone director Richard Jarvis. He was working on Vodafone UK’s Short Message Service Centre as part of the now-defunct Sema Group Telecoms. At the time, he saw it as routine work rather than a milestone.

"It didn’t feel momentous at all," he later said in an interview with CBC in 2017. "For me it was just getting my job done on the day and ensuring that our software that we’d been developing for a good year was working OK."

The idea for SMS started years before the first message. In 1984, Finnish engineer Matti Makkonen proposed the concept at a conference in Copenhagen.

A year later, Friedhelm Hillebrand at Deutsche Telekom suggested a 160-character limit after studying everyday written messages.

The European Telecommunications Standards Institute began developing formal standards by 1991, and the first message followed a year later in the United Kingdom.

At the time, mobile technology was shifting from analog to digital with GSM networks. Phones did not have keyboards, so Papworth had to send the message through his computer. Jarvis received it while attending a Christmas party.

Shortly after, Papworth got a confirmation call from the event that proved the test worked.

Early SMS relied on 7-bit encoding and routing through SMS centres that stored and forwarded messages when phones were out of range.

The first text message in history was sent 33 years ago today.

It read, “Merry Christmas.” pic.twitter.com/Ak9M8uEeBL

— Pop Base (@PopBase) December 3, 2025

Nokia helped push SMS from experiment to mainstream. In 1994, the company released a handset that allowed users to both send and receive messages.

Adoption grew slowly at first because phones were costly and carriers focused on voice services.

By the late 1990s, texting surged especially among younger users.

T9 predictive typing made input easier, and prepaid plans made texting cheaper. Network providers later enabled cross-network messaging, accelerating mass usage.

By February 2001, users in the United Kingdom sent around one billion texts every month. Charges reached 10 pence per message, generating major revenue.

By 2010, the International Telecommunications Union reported trillions of messages sent yearly, turning SMS into a global cultural habit that shaped abbreviations like LOL and BRB.

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When tech company Xpeng unveiled its Next Gen Iron humanoid recently, the robot glided across the stage with movement so fluid that the crowd froze. Many of those in the crowd thought they saw an actor in a suit. Clips spread online within hours, and people everywhere claimed the same thing: it looked too human to be a machine.

The reaction spread fast, so Xpeng's CEO He Xiaopeng returned to the stage one day later with a plan to settle the argument. He cut into Iron's leg to show its internal machinery. It felt theatrical but also necessary to end the rumor that a human controlled the robot from inside.

The demonstration showed Iron was a real machine with complex systems beneath its flexible skin.

He shared how his robotics team stayed awake through the night, seeing viewers accuse them of staging a stunt. After the reveal, Iron walked again in front of the crowd without a human inside. The moment closed the debate and highlighted how far the company has come since its first model in 2024.

The latest Iron uses a humanoid spine with bionic muscles and flexible skin. It moves with 82 degrees of freedom, and its human-sized hands include 22 degrees of freedom supported by a tiny harmonic joint engineered by the company. The robot runs on all solid-state batteries that keep the body light and strong.

Iron also uses Xpeng's second-generation VLA model. Three Turing chips with 2,250 TOPS of power support tasks like conversations, walking and natural interactions. It responds in ways that feel closer to a person than a robot.

Xpeng says future versions will offer different body shapes. That claim hints at customizable designs when these units reach consumers.

Xpeng's long-term vision goes far beyond a single showcase moment. The company plans to place the Next Gen Iron model in real-world environments. Early units will focus on commercial roles such as tour guides, shopping guides and customer service helpers. These placements allow the robots to interact with large crowds, gather feedback and refine their behavior in dynamic public spaces.

This rollout forms part of what Xpeng describes as a gradual path toward mass production. The team aims to reach large-scale manufacturing by the end of 2026. That milestone could introduce hundreds or even thousands of humanoid units into select venues. Businesses may adopt them to manage foot traffic, assist guests or support basic retail tasks.

While the company talks openly about commercial integration, the timeline for home use remains unclear. They have not shared when consumers will be able to buy a version suited for daily household tasks. Engineers still need to address safety, privacy and reliability standards before a humanoid can operate inside private homes.

Even so, this moment signals a clear shift: robots that move and react in a lifelike way are no longer far-off ideas. They are stepping into public spaces where people will see them operate up close. This shift could reshape how we all view service work and personal assistance in the years ahead.

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The International Mathematical Olympiad (IMO), being held annually since 1959, is widely regarded as the world’s most prestigious maths competition. It tests participants with problems that demand deep insight, creativity, and rigorous reasoning, according to Harvard AI researcher Huang Yichen and UCLA computer science professor Yang Lin.

Now, Chinese AI startup DeepSeek has made its Math-V2 model widely available, open-sourcing it on Hugging Face and GitHub under a permissive license that allows developers to adapt and repurpose the system, according to Bojan Stojkovski of Interesting Engineering.

Math-V2 has demonstrated gold-medal-level performance at the IMO, a feat requiring not just correct answers but also transparent reasoning behind them – a standard only about 8 per cent of human participants achieve.

The company says its Math-V2 model achieved gold-level scores on problems from both this year’s International Mathematical Olympiad and the 2024 Chinese Mathematical Olympiad. By open-sourcing the model, DeepSeek aims to lower barriers for researchers and developers eager to experiment with advanced AI capable of reasoning through high-level mathematical challenges, a domain traditionally dominated by proprietary systems, the South China Morning Post reported.

In a Hugging Face post, DeepSeek researchers emphasized that further developing AI’s mathematical capabilities could have a transformative impact on scientific research, from complex simulations to theoretical problem-solving.

They cautioned, however, that many of today’s AI systems have been primarily optimized to perform well on standard maths benchmarks, achieving high scores without necessarily improving the underlying reasoning and problem-solving abilities that drive real innovation.

To strengthen the rigour of its AI’s mathematical reasoning, DeepSeek focused on enabling the model to "self-verify" its answers, even for problems without pre-existing solutions, the researchers explained. This self-checking ability allows the AI to assess the consistency and validity of its reasoning, helping ensure that its conclusions are not only correct when known solutions exist, but also reliable when tackling novel or unsolved mathematical challenges.

DeepSeek’s approach tackles a longstanding limitation in AI development: most systems only show improvement on tasks where solutions can be easily verified. By enabling self-verifiable reasoning, the model can extend its capabilities to more complex, open-ended problems. The researchers noted that, although significant work remains, these results indicate that self-verifying mathematical reasoning is a promising research direction that could pave the way for more advanced and capable AI systems in mathematics and beyond.

After achieving gold at the International Mathematical Olympiad, Google DeepMind made its proprietary model accessible to subscribers of its premium Ultra plan, giving a select group of developers early access to the advanced AI. In contrast, OpenAI’s CEO Sam Altman announced that the company’s experimental model, which also earned a gold medal at the IMO, would remain unavailable to the public for many months, SCMP added.

At the same time, such moves highlight differing strategies among leading AI firms, with some opting for controlled access to protect intellectual property and ensure responsible use, while others focus on gradually broadening availability to researchers and developers.

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Can you imagine a robot that doesn’t just follow commands but actually plans its actions, adjusts its movements on the go, and learns from feedback—much like a human would? This question may sound like a far-fetched idea, but researchers at NYU Tandon School of Engineering have achieved this with their new algorithm, BrainBody-LLM.

According to Rupendra Brahambhatt of Interesting Engineering, one of the main challenges in robotics has been creating systems that can flexibly perform complex tasks in unpredictable environments.

Traditional robot programming or existing LLM-based planners often struggle because they may produce plans that aren’t fully grounded in what the robot can actually do.

BrainBody-LLM addresses this challenge by using large language models (LLMs)—the same kind of AI behind ChatGPT to plan and refine robot actions. This could make future machines smarter and more adaptable.

The BrainBody-LLM algorithm mimics how the human brain and body communicate during movement. It has two main components: the first is the Brain LLM that handles high-level planning, breaking complex tasks into smaller, manageable steps.

The Body LLM then translates these steps into specific commands for the robot’s actuators, enabling precise movement.

A key feature of BrainBody-LLM is its closed-loop feedback system. The robot continuously monitors its actions and the environment, sending error signals back to the LLMs so the system can adjust and correct mistakes in real time.

"The primary advantage of BrainBody-LLM lies in its closed-loop architecture, which facilitates dynamic interaction between the LLM components, enabling robust handling of complex and challenging tasks," Vineet Bhat, first study author and a PhD candidate at NYU Tandon, said.

To test their approach, the researchers first ran simulations on VirtualHome, where a virtual robot performed household chores.

They then tested it on a real robotic arm, the Franka Research 3. BrainBody-LLM showed clear improvements over previous methods, increasing task completion rates by up to 17 percent in simulations.

On the physical robot, the system completed most of the tasks it was tested on, demonstrating the algorithm’s ability to handle real-world complexities.

BrainBody-LLM could transform how robots are used in homes, hospitals, factories, and in various other settings where machines are required to perform complex tasks with human-like adaptability.

The method could also inspire future AI systems that combine more abilities, such as 3D vision, depth sensing, and joint control, helping robots move in ways that feel even more natural and precise.

However, it’s still not ready for full-scale deployment. So far, the system has only been tested with a small set of commands and in controlled environments, which means it may struggle in open-ended or fast-changing real-world situations.

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After they released the new iPhone models during the fall of every year for time immemorial, recent rumors suggest that tech giant Apple is planning to change its launch schedule by adding new revenue growth opportunities for the company.

The latest report to highlight that comes from Bloomberg's Mark Gurman. In his Power On newsletter, the journalist says Apple's next fall product release will only consist of the iPhone 18 Pro, iPhone 18 Pro Max, and iPhone Fold.

Apparently, the company is set to shift the standard iPhone 18's release to early 2027, alongside the iPhone 18e and possibly the second generation of the iPhone Air.

According to Gurman, this move to release new iPhone models every six months could help Apple get steadier revenue throughout the year, reduce strain on employees and manufacturing partners, and prevent the different models from cannibalizing each other's sales. It's also going to be easier for customers to choose their preferred model when Apple releases future iPhones.

Over the past few weeks, there have been rumors suggesting that Apple is uncertain about launching a second generation of the iPhone Air. While some reports suggested the company will scrap the second-gen model. The Information says Apple will launch the iPhone Air 2 in spring 2027 with a dual-lens camera system. On the other hand, Bloomberg's Mark Gurman says Apple isn't preparing a major design change to the Air, but is instead focusing on the new A20 Pro chip based on the 2nm manufacturing process to extend the battery life.

Not only that, but Gurman notes that Apple didn't promote the iPhone Air as the "iPhone 17 Air" because it doesn't want to tie its release schedule to the main line, which means the company could release it every 15 or 18 months, or even less frequently.

Still, the journalist says the iPhone Air was more of an experiment in preparation for the iPhone Fold, which is expected to be like two iPhone Airs stacked together. The company is also expected to unveil the all-new iPhone 20 in 2027 to celebrate the iPhone's 20th anniversary.

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