TECH

This design software reimagines everyday objects as self-aware devices

As a child, you likely saw a few Disney movies depicting inanimate objects, such as clocks, cups, and toys, as interactive companions to humans — an act of pure magic, seemingly. But scientists at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are now doing something similar: transforming stationary items into self-aware tools that perceive and respond to human motion to complete a task.

Engineers have 3D-printed these dynamic items with built-in sensing, using conductive materials for sensors and traces (sort of like wires) inside. The issue: Both components were typically fabricated using the same material, so they’d emit similar signals. It’s sort of like hearing two radio stations at the same time, a mixup that’s hard to interpret. This confuses, say, a glove that relays instructions to a robotic hand about how to handle a specific object. The device couldn’t filter out the noise or verify the angle of a user’s motions, leading a humanoid student to make errors.

Researchers at CSAIL and Tianjin University (TJU) may have found a way to 3D print more precise interactive items. With their “X-Hinges” software, you can customize a design, and the tool will automatically embed two different materials: a filament for the sensors designed to send the strongest signal possible, and another for the wire-like elements that don’t interfere. Your interactions won’t get lost in translation, meaning you can fabricate objects that respond in real time, such as small robots, household decor, object-detection systems, and toys.

With X-Hinges, you can put together an item by selecting and connecting different shapes, then choosing how certain parts move. Your item won’t be limited to moving in just one way like previous approaches; instead, you can select up to three “axes”: compressing, up and down, or to the sides. If you built a robotic hand, for example, you’d likely want its fingertips to move vertically and laterally. CSAIL’s tool would then embed sensors where the object bends (in this case, its joints) to capture motion data from both angles. That way, the device can understand and respond to your interactions more dynamically once it’s 3D-printed.

“It’s kind of like adding a conscious element to everyday objects,” says Jiaji Li, MIT electrical engineering and computer science (EECS) postdoc and CSAIL researcher, who is a senior author on a paper introducing the work. “What you get is objects that better understand how they’re being used and have more ways to respond. In the same way an AI agent is programmed to make certain decisions based on our instructions, these creations have built-in sensing to adaptively react to human touch.”

Jiaji Li and lead author Xiang Chang, a Tianjin University PhD student and visiting researcher at CSAIL, used X-Hinges to create many clever devices. For example, they made a glove that can teach a robot how to grasp blocks. It adjusts to the size of a user’s hands, then records how the joints in their fingers bend via sensors before rapidly relaying that information to a humanoid hand to mimic it. The glove could be useful in factories or homes, where a robot often needs to learn new interactions quickly.

The researchers also produced a small, origami-style lamp using X-Hinges. It senses when a human pinches it closed and adjusts its brightness accordingly. When you turn on this adaptive, portable light source (sort of like a reading light), it looks like a piece of folded-up paper that sprouts into a lamp.

X-Hinges can even assemble devices that identify which objects have been placed on it. You could put a banana on top, for instance, and it’ll recognize the fruit almost immediately based on the tactile data it’s recorded using its sensors. This advanced physical awareness could be integrated into detection systems in places such as airports and grocery stores.

A shark tale...Behind each of the scientists’ creations is an easy-to-use interface that helps you design interactive objects from scratch. Users can choose how each part of their design looks and moves, just as the researchers did when making a video game controller that resembles a cartoon shark.

X-Hinges gives you four shapes to choose from: a custom option, cylinder, rectangle, and thin outline. To recreate the unique look of a shark’s tail, the researchers designed their own shape. They then chose how the toy bent, opting for its tail to move up and down, sort of like a joystick. Finally, the program embedded the sensors, and the scientists 3D-printed their device.

The researchers say that X-Hinges’ diverse motions are a novel feature compared to past attempts to 3D-print responsive everyday objects. “Most methods only allow for one axis of motion, often due to signal interference. X-Hinges enables your 3D-printed item to move from three angles,” says Chang. “Ultimately, you get a design with the intended movement you want. It also records motion data in each of those areas at the same time, making the device responsive to different kinds of interactions.”

The tool could help realize the untapped potential of the seemingly mundane. Objects we use every day for a specific purpose, like a water bottle for drinking or a lamp for lighting, could become interactive gadgets. Much like how a chatbot uses a sort of digital intelligence to answer your prompts, an object could leverage a kind of physical intelligence to execute a physical task when users interact with it.

For now, though, X-Hinges faces a calibration bottleneck. Each time an item is 3D-printed, its layout of electrical elements varies slightly, so two devices that appear identical from the outside may not make the same sensor readings. This is crucial if you’re building a deformable fitness glove, for instance, for two separate users. To fix this, Chang and Li built a computer vision system that can recalibrate outputs to align, ensuring data is accurate across two of the same items, though they hope to develop a built-in signal recalibration mechanism in the future.

That ambition traces back to the throughline in Jiaji Li's own research: letting actuation and sensing “grow” directly inside a 3D-printed object, instead of adding them on afterward. He previously built printed tendon-driven mechanisms that gave objects the ability to move; X-Hinges is how he's now building on the ability to sense. The next step, he says, is combining the two in a single print — it's actuation and sensing born together in the same fabrication process, opening the door to new physical platforms for embodied intelligence and AI agents.

Chang and Li wrote the paper with MIT associate professor of EECS and CSAIL principal investigator Stefanie Mueller and recent TJU graduate Haiyang Yan. Their work was supported, in part, by a postdoctoral research fellowship from Zhejiang University and the MIT-GIST Program.

 

MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL)

DIGITAL LIFE

Mathematics and AI

The creator of ChatGPT announced on Tuesday that one of its AI models had solved a century-old mathematical equation in less than four days.

That announcement was overshadowed by public statements from Australian mathematician Tristan Buckmaster, who questioned the similarities between his own work—predating OpenAI's project—and that of the AI ​​model.

Artificial intelligence "offers the potential to strengthen and accelerate the study and understanding of mathematics," renowned researchers wrote in an open letter on Friday.

"The mathematics profession will have to adapt to these changes in various ways," they explained.

For mathematicians, it is "human decisions controlling this technology" that will determine, "to a large extent," whether it proves beneficial to the field or has a destructive effect.

The 25 medalists therefore call on leading AI companies to "urgently address" these questions.

Since last year, several of these companies have reported breakthroughs in the world of mathematics, but the solution to the Navier-Stokes equation, announced on Tuesday, is the most notable.

It is one of the seven "Millennium Prize Problems" selected in 2000 by the research-supporting Clay Mathematics Institute, each carrying a $1 million prize.

Over the past few months, the mathematical capabilities of large language models (LLMs) have improved dramatically, to the point where they can solve major unsolved problems in many fields of mathematics. However, the push by AI companies to solve mathematical problems as a benchmark is detrimental to the field of mathematics and to the mathematical community. The goals of AI companies and those of the mathematical community are severely misaligned. We view these as part of broader alignment issues affecting other scientific and creative professions, as well as society as a whole.

Mathematical research focuses on understanding the basic structures of shapes, numbers, and natural phenomena. Over the course of generations, it has built up a vast body of sophisticated ideas, methods, abstractions, and other tools for understanding the mathematical landscape. In turn, modern technologies and sciences are based on mathematical tools.

Famous problems have often served as landmarks and beacons against which one can gauge a deeper understanding of this field. Solving one of these problems has been a sure sign of new insights and interesting methods, which would then be studied by a community of mathematicians through a long and arduous process of talks, discussions, and simplifications. At the end of this process, one will ideally find a textbook presentation of the results suitable for any graduate or even undergraduate student to study. Some of these mathematical ideas continue their journey even further, becoming—decades or centuries later—tools that are understood and used by the general public.

The mathematical community functions, in many ways, as a miniature version of humanity. It consists of individuals who use a wide variety of different approaches, united by core values. The most precious resources of our profession are students and ideas, and we nurture them with great care. We feel responsible for helping them reach their full potential, until they can stand on their own in the mathematical world. For students, we often suggest problems with the primary goal of developing skills that will position them well for advances in research and beyond. We disseminate our ideas through talks, private discussions, and carefully crafted write-ups, connecting them to the previous ideas of others. These processes invariably take time and are based on human interaction.

In recent months, AI’s success in solving major mathematical problems has made headlines even outside mathematical circles. But problem-solving is only a tool and a means to an end—the primary goal of conceptual understanding and insight. Forgetting this in the world of AI may cause the tool to work against that primary goal. Indeed, the mass production of “true/false” statements at an ever-faster pace could destroy fertile ground instead of breathing life into new ideas.

Often these solutions are announced in a hurry, leaving no time for a proper write-up, the identification of new methods and ideas, and the citation of relevant prior work by others. As in all creative professions, this raises serious questions regarding attribution and plagiarism. Moreover, without the willing mathematicians who must oversee their development and integration into the mathematical canon, AI-conceived ideas would never fully come to life, and the crucial human chain of transmission among mathematicians would be lost.

We are witnessing a general threat to intellectual work, with a misalignment between the outcome of AI use and its original purpose. In many fields and activities, years of training have traditionally served not only to produce a final answer or product, but also to develop understanding and the ability to formulate new questions and ideas. However, building on a vast body of previous human work, AI systems are becoming increasingly capable of producing the results of such work directly, and these goals are no longer aligned. The challenges the mathematical community now faces are similar to those facing other scientific and creative professions, and point to challenges that all of humanity may face: how to ensure that, as AI changes the way work is done, we do not lose sight of what that work was meant to achieve in the first place.

AI offers the potential to enhance and accelerate genuine mathematical study and understanding. The field of mathematics will need to adapt to these changes in several ways. However, whether these changes ultimately benefit the field or have a destructive effect will largely depend on the decisions made by the people in control of this new technology.

Mathematicians rebel against AI...Here is the statement, signed by Terry Tao among many other math notables, most of you probably have read it by now.  I do not accept the most cynical interpretations of this proclamation.  Some of you for instance may recall that I made and indeed stressed a similar point in the last chapter of my recent “generative book” on marginalism.  In some near future, perhaps fewer economists will carry around marginalist insights and modes of thought in their heads, since you can just get the right answer by pressing the proverbial button on the AI.

I find this future disturbing, and not altogether pleasant for me personally, given how much personal status I have wrapped up in particular modes of economic thought.  Yet I also know the Bastiat distinction between the seen and the unseen, and I expect the benefits to economic science from AI will be enormous, even if current practitioners cannot foresee most of those benefits today.

I do very much differ with at least one part of the mathematicians’ proclamation.  They write: “…whether these changes ultimately benefit the field or have a destructive effect will in large part be determined by the decisions of the humans in control of this new technology.”  There is no actual argument for that proposition, and I would sooner expect that the main “action variable” is how well the mathematicians adapt to the new reality.  For instance there is nothing stopping the mathematics community from awarding status, pay, and promotions to people who “fill in the important blanks in math understanding,” even if an AI already has proven or disproven the underlying theorems.  If that kind of work is so important, we still can do it and reward it professionally.  In the meantime, I expect the funding for mathematics, and the interest in the topic, to rise considerably, at least in the medium term.  All of a sudden, math matters much more than it used to, all the more so if P vs. NP happens to go the wrong way, or if the distribution of the primes turns out to be a little too predictable.

The mathematicians may not in every way enjoy being the subordinates or handmaidens of the AIs, but that is a change in status they simply will have to get used to, just as I realize AIs someday will end up as better column and blog writers than I am.  I do not look to the companies — which I fully expect to “act like companies” — to somehow manage, moderate, or assuage that pending trend.  It really is up to me to parlay my current intellectual portfolio into new, more AI-compatible intellectual and yes also marketing approaches.  I’ve been given plenty of “legs up” along the way already, as is true for the Fields Medal winners as well, and it is up to me to figure out how to contribute in the future.

Might someone not invent/discover/prompt a way to use AIs to produce, articulate, and teach “more mathematical understanding” along the way?  I get that solving famous dramatic math problems is the current commercial priority of the major AI companies.  But as the AI space grows, these other paths hardly seem unlikely to me, and in fact the human mathematicians are the ones who can do the most to lead the way along those dimensions.

In this regard the current manifestation of complaints seems oddly early.  “I didn’t like the first week or two of your intellectual revolution” is an accurate, and perhaps better reframed way of putting it.  At which point perhaps a bit of patience is needed before anything else?  These days, we all have more mathematical resources at our disposal, and so a bit of celebration is in order as well.

mundophone

 

TECH

How randomness tames vast network problems

Networks are everywhere. They connect computers across the internet, carry electricity through power grids and represent transport routes for moving people and goods. Yet many problems that appear to have nothing to do with networks can also be represented and solved as networks. Examples include matching passengers with drivers in a ride-hailing app or distributing computing tasks across servers.

The research group of theoretical computer scientist Rasmus Kyng develops new computational methods for solving equations that describe very large networks. The challenge lies in the scale of these networks.

Once a network contains millions or even billions of connections, solving the equations becomes particularly demanding. Methods that work well on small networks often require so much time and memory that they become impractical at this scale.

Kyng's work combines two activities: developing exceptionally fast algorithms, the mathematical procedures that guide a computation, and turning these algorithms into practical software.

Recently, Kyng, his former student Yuan Gao and his one-time doctoral supervisor Daniel Spielman presented a new solver for Laplacian equations in the SIAM Journal on Scientific Computing. A solver is a computer program that automatically solves mathematical equations.

The prototype of the new solver already proved both reliable and substantially faster than existing software across a wide range of test cases. "The results show that methods developed in theoretical computer science can become practical tools for solving very large network problems," says Kyng.

A two-stage research process...The story of Laplacian solvers provides a particularly clear example of how a theoretical proof can eventually become software for supercomputers. Kyng's research typically proceeds in two stages: mathematical theory and practical implementation.

The first stage centers on fundamental mathematical questions such as this: Can one prove that a problem can be solved by an algorithm whose computational cost grows only moderately as the problem becomes larger?

This question matters because the computational cost of many algorithms rises sharply as problems grow. If a network becomes 10 times larger, one would ideally want the required computation to increase by roughly the same factor. In practice, however, the computational effort often increases much more rapidly than the size of the problem.

A major focus of Kyng's research is the development of algorithms with nearly linear running times. In such algorithms, the computational effort grows almost in step with the size of the problem. In recognition of his scientific work on highly efficient algorithms, Kyng received the 2025 ETH Zurich Latsis Prize.

Alongside the goal of making computational efforts grow as close as possible to the size of the problem itself, another central question guides his work: What information can a computation ignore without losing accuracy?

The mathematical question and its proof are only the beginning. A second stage focuses on turning theoretical insights into practical software that works reliably on real computers. This stage is often challenging: It requires retaining the core mechanisms that create the speed-up, shedding the rest of the theoretical machinery and making those mechanisms work on a real computer.

But is it worth spending years proving that an algorithm is efficient if it initially exists only on paper? "The history of Laplacian solvers provides a compelling answer," says Kyng. "It shows how theoretical insights push the boundaries of what is possible and how they gradually evolve into usable software."

The long path from proof to supercomputer...Back in 2004, computer scientists Daniel Spielman and Shang-Hua Teng proved mathematically that Laplacian equations could be solved in nearly linear time. The result marked a major breakthrough. Yet their approach relied on highly complex mathematical constructions and was not well suited for practical use.

Following this theoretical breakthrough, Kyng and Sushant Sachdeva introduced a much simpler approach in 2016, called Approximate Cholesky. The latest work by Gao, Kyng and Spielman now makes this approach work well in practice.

To build the software prototype, the researchers relaxed some of the very strict theoretical requirements of the 2016 method. The result was a significant increase in speed. In experiments, the software ran about five times faster while remaining reliable across a broad range of Laplacian equations, including problems on which existing software fails.

The work is not finished, however. In the open-source project apxchol, Kyng and his doctoral student Yves Baumann are developing the method into robust software for scientific computing on supercomputers.

Left: When a node is removed, many new connections appear between neighbouring nodes. This makes computations increasingly demanding. Right: Approximate Cholesky considers only a small random sample of these connections. As a result, computations can be significantly accelerated without materially reducing the quality of the results. Credit: Conceptual illustration based on work by Kyng, Sachdeva and collaborators

Pushing beyond existing limits...Why are computations involving Laplacian equations so demanding in the first place?

The researchers build on a technique known as Cholesky elimination. It simplifies a network step by step by removing individual nodes. However, every time a node is removed, new connections appear between the remaining nodes. As a result, the computations become increasingly demanding, requiring ever more computation time and memory.

For small networks, this additional effort is usually manageable. For very large networks, however, it can grow so much that the computations become difficult to carry out in practice.

The Approximate Cholesky method developed by Kyng follows a different strategy. Instead of taking all newly created connections into account, it considers only a small, carefully selected random sample of them. These few connections are sufficient to obtain almost the same results as the full network. As a consequence, computations become much faster without significantly reducing accuracy.

Any remaining errors can then be corrected iteratively. The solution is repeatedly checked and refined until the desired level of accuracy is reached.

The new method is therefore faster than previous approaches while remaining highly reliable across many different test cases. The solver was even able to handle problems on which existing programs failed.

Ultimately, the work demonstrates how a mathematical idea can become a practical tool, capable of solving network problems that were once beyond reach.

The strategic use of randomness allows for solving complex problems in massive networks by drastically reducing computational effort without sacrificing result accuracy. Recently, computer scientists led by Rasmus Kyng at ETH Zurich transformed theoretical concepts into practical software capable of analyzing large networks in near-linear time.

The challenge of traditional networks:

Data explosion: Conventional methods lose speed and require massive amounts of memory when handling immense networks.

Connection buildup: When a network node is removed during standard mathematical calculations (based on so-called Laplacian equations), countless new connections emerge between neighboring nodes, making the process heavy and slow.

The solution: Approximate Cholesky and randomness:

Smart sampling: Instead of recording and processing every newly generated connection, Kyng's algorithm selects only a small, carefully designed random sample of these connections.

Maintaining accuracy: This fraction of connections is statistically sufficient to yield a result virtually identical to what would be generated by analyzing the entire network.

Iterative correction: Any discrepancies or residual errors are repeatedly refined and corrected until the desired level of precision is achieved.

Practical impact and speed:

Five times faster: In real-world experiments, the software prototype ran about five times faster than traditional competitors.

Solving failures: The new system demonstrated high reliability, successfully solving complex problems where previous software simply crashed or failed.

Open source: The breakthrough is being integrated into the open-source software *apxchol*, aiming to accelerate large-scale scientific simulations on supercomputers.

 

Provided by ETH Zurich

TECH

3D-printed houses are no longer science fiction—but they don't work quite the way many people imagine

Imagine arriving at an empty lot in the morning and finding, two days later, the walls of a 120-square-meter house practically finished. It sounds like an exaggeration, but large concrete 3D printers are already capable of doing part of this work. The technology is gaining ground in Argentina, too, promising to cut construction times and waste, and even reduce some costs. However, there is one important difference between what we picture as a "printed house" and what these machines actually deliver.

Forget the small desktop 3D printer that produces plastic objects.

In construction, the equipment can form massive structures set up around the site where the building will rise. A print head moves, following the exact coordinates defined by the digital design.

Instead of plastic, it deposits a special cement-based mixture.

The material flows out continuously, building up layer upon layer. Gradually, walls, partitions, and spaces for doors and windows take shape.

The equipment used by a company that introduced this technology on a large scale to the Argentine market, for instance, measures approximately 11 by 11 meters and stands about seven meters tall. It operates by connecting to a central mixing unit and a pump that transports the concrete to the print head.

The advantage lies precisely in the automation.

Since the machine follows a digital file, it deposits material only where it is needed, reducing the cutting, waste, and measurement errors common in conventional processes.

A 120-square-meter house can reach the "grey shell" stage in 48 hours... This is where the figures really grab attention.

According to estimates released by companies working with this technology, the structure of a home measuring approximately 120 square meters can reach the "grey shell" stage—the basic structural framework—in about 48 hours.

Furthermore, those behind the technology estimate a reduction of nearly 35% in construction time compared to conventional systems. But there is one essential word in this promise: structure.

The machine doesn't start working on Monday and deliver a furnished home ready for residents by Wednesday.

It primarily accelerates the construction of walls and specific structural components.

After that, electricians, plumbers, carpenters, and other professionals are still required.

Electrical and plumbing systems, windows, doors, flooring, wall coverings, painting, finishing touches, and—depending on the design—the roof itself still rely on traditional methods.

Therefore, 3D printing does not eliminate conventional construction.

It seeks to transform one of the most time-consuming stages of the process.

Rapid construction grabs attention, but there is another important promise hidden within the layers of concrete:

Less material waste.

In conventional construction, cuts, offcuts, and waste are part of the process. A printer works differently: it deposits a controlled amount of material exactly along the path dictated by the design.

European researchers indicate that certain additive construction systems can save up to 50% on material in some applications, as walls do not necessarily need to be completely filled with concrete. Internal voids can also be utilized for insulation or utility installations.

This freedom offers another unexpected advantage.

Curved shapes and complex geometries no longer have to be a construction nightmare.

In certain projects, the geometry itself allows for less material usage while maintaining the necessary structural strength.

It is an interesting shift: for decades, building unusual shapes usually meant increased costs and complexity. With 3D printing, some of these shapes can simply be designed on a computer and reproduced by the machine.

The dream of printing houses also faces very traditional limitations...Despite its futuristic appearance, the technology remains subject to the same basic realities as any construction project.

The site must be suitable. The structure must comply with local regulations. The concrete must provide sufficient strength, and the design must take into account climate, soil conditions, logistics, and seismic factors where necessary.

There is also the initial cost of the machines themselves and the need for professionals capable of operating them. This helps explain why 3D printing tends to be particularly attractive for repetitive or large-scale projects.

Imagine a development with dozens of similar houses.

Once the digital design is prepared and the equipment is set up, the machine can repeat the same movements countless times with high precision.

It is precisely in this type of scenario that automation, waste reduction, and speed can begin to yield greater economic advantages.

The construction industry may be entering its automation phase... 3D-printed houses already exist in various parts of the world.

In the United States, ICON participated in a Texas development featuring over 100 homes built using 3D printing. In Europe, residential projects are also experimenting with the technology, while various initiatives explore its application in infrastructure.

This does not mean that bricklayers will disappear or that entire neighborhoods will be printed overnight.

The transformation will likely be far less cinematic.

Machines will take over certain repetitive steps, while professionals will remain responsible for countless other construction tasks.

But a significant shift is underway.

For centuries, building a wall meant manually placing units or materials, one after another.

Now, a machine can receive a digital file, move a print head across the site, and transform those coordinates into a physical structure.

The house of the future might not emerge fully formed from a printer.

But an increasingly large part of it could begin exactly that way.

What are 3D Printed Houses? A 3D-printed house is exactly what it sounds like: a home whose walls and structural components are built layer by layer using a robotic 3D printer and a custom concrete or mortar mix. Instead of laying bricks or assembling timber frames, these machines extrude material along precise paths to shape walls, curves, and foundations.

Most 3D-printed homes still use traditional roofing, insulation, doors, and windows, the printing is mostly for the structure.

Gantry-based systems...These are large, fixed-frame setups where the print head moves along rails (like a giant 3D printer). They’re often used for printing simple, box-like homes quickly.

✅ Pros: Stable, efficient for mass production, good for identical units.

❌ Cons: Limited in design freedom, harder to scale or move to new sites.

How are 3D Printed Houses Built?...‍‍Building a 3D-printed house might seem like pressing “print” and walking away, but the actual process involves multiple phases—and a lot of human coordination.‍

1. Design & Preparation...Everything starts with a digital model. Architects or engineers design the structure in 3D modeling software, often using parametric design tools. This model is then converted into a printable path using slicing software, which translates the design into robotic movement and material extrusion instructions.

At Vertico, this step is where the innovation lives—parametric design lets us go beyond basic rectangles and explore curves, overhangs, and biomimicry-inspired structures.

2. Site setup & foundation...The printing usually begins once a traditional concrete foundation is poured. Leveling is key—any unevenness in the base can throw off the precision of the robotic print.

3. 3D Printing the Walls...A robotic system—either gantry-based or arm-based—extrudes a custom concrete mix in horizontal layers. The system follows the toolpaths created in the design stage, printing everything from straight walls to rounded corners or even custom textures.

Some systems include rebar or reinforcement, while others rely on wall geometry.

Printing takes hours to days, depending on the complexity and scale.

4. Post-processing & integration...This is where reality sets in. After the printing is done, there’s still a long to-do list:

Windows & doors need to be fitted

Insulation is installed manually

Plumbing and electrical work is added conventionally

Roofing is almost always traditional

mundophone

 

TECH

HP quietly releases new Intel Wildcat Lake-powered Mac mini rival

The OmniDesk will be available in up to four configurations, starting with the Intel Core 3 304 processor. This is a 1+4 core CPU, featuring one Performance Cougar Cove core and four Darkmont LP-E cores. Users will be given options to choose between the Core 3 304 or the Core 5 320, which is a 2+4 core CPU from the same Core Series 3 family. In both cases, the system will be ultra-power-efficient while offering satisfactory computing capabilities.

As far as RAM and storage options are concerned, the base model starts with 8 GB of system memory, bringing LPDDR5X-8533 to the desk. The next configuration upgrades it to 16 GB of LPDDR5X memory. For storage, there will be dual M.2 slots, supporting fast NVMe SSDs with up to 16 TB of total storage capacity. The base model will feature a 256 GB Gen 3 NVMe SSD, and users can choose up to 2 TB of storage, but that will add a significant amount of cost to the system.

Out of the box, the OmniDesk will feature WiFi 6 and Bluetooth 5.4, but users can also choose the WiFi 6E + Bluetooth 5.3 configuration. Coming to the pricing, the system starts at $799, which isn't very cheap given the base configuration. However, due to the rise in component costs, Wildcat Lake systems have started soaring in prices significantly.

This is a refresh to the Core Ultra Series 3-equipped mini PC that the company introduced back in CES 2026, but given that this one is powered by the non-Ultra processors, it resides in the lower performance tier.

Specifically, the base configuration of the Wildcat Lake OmniDesk Mini has the Intel Core 3 304, the lower-end processor available within the lineup. The top-end option, on the other hand, features the Core 5 320, a slightly more capable CPU.

So, while the form factor may make this HP mini PC rival the Mac mini, coming in at 5.12x5.12x1.89 inches, the two reside in different performance tiers. Speaking of which, the company highlights that while the OmniDesk Mini has a small footprint, it comes with a good number of ports, including:

1x USB 3.2 Gen 2 Type-C

2x USB 3.2 Gen 2 Type-C with DP 2.1

2x USB 3.2 Gen 2 Type-A

1x LAN

1x HDMI

1x 3.5mm audio

The base configuration is available with 8GB of RAM, while the top-end option has 16GB of memory. Although the RAM of this OmniDesk Mini isn't upgradeable, there are two M.2 slots, each of which should be able to hold an 8TB SSD. For wireless connectivity, it's possible to equip the mini PC with up to a WiFi 6E wireless card, which would also bring Bluetooth 5.3.

Create with confidence and run Agentic AI with the HP OmniDesk Mini—an ultra-compact desktop powered by Intel® Core™ Ultra processors with built-in AI capabilities. It is built for demanding workflows, featuring advanced multitasking, multi-display support, and room for expansion.

The HP OmniDesk features the square form factor common to mini PCs, measuring 5.12 x 5.12 x 1.89 inches, but sports a rounded case designed to look like a single, seamless unit. Most of the chassis sits elevated on a narrow stand that conceals the power cable connection.

Connectivity options include a total of three USB-C 3.2 ports—two of which support DisplayPort 2.1 video output, an unusual configuration for a mini PC with Intel Wildcat Lake processors. A standard HDMI port brings the total video output count to three, and the compact computer also offers two USB-A 3.2 ports.

The new HP OmniDesk mini PC is already available on the official website and offers significant configuration flexibility.

The entry-level model comes with an Intel Core 3 304 processor, 8GB of LPDDR5X-8533 memory, and a 256GB M.2 Gen 3 SSD, with a suggested price of US$800.

Alternatively, buyers can opt for the Intel Core 5 320, with RAM configurations of 8GB, 12GB, or 16GB. The top-tier option is priced at US$1,010.

HP also offers additional customization options for the OmniDesk, such as larger SSDs or a more advanced modem. These price points are higher than what is typically found in Wildcat Lake mini PCs, but the company appears to be targeting users looking for a premium device that is still geared toward everyday use.

mundophone

TECH

3D-printed prosthesis mimicking bone and tendon shows promising results in rabbit trials

Utilization of three-dimensional (3D) printing processes in regenerative medicine has advanced our ability to develop therapies to repair bone and cartilage using personalized bioactive scaffolds. This approach has tremendous potential in regenerating highly complex tissues involving the regeneration of cartilage and subchondral bone layers that are difficult to regenerate due to the unique biological characteristics of these tissues. 

Work that has involved calcium phosphate bio-ceramic materials has demonstrated that 3D printing allows for the creation of scaffolds that have tailored biodegradation rates integrated into their structure, property that is needed for successful regeneration of bone tissue. Additionally, scaffolds that have been constructed as composites with bioactive ions have increased the bio functionality of the scaffolds to add to the osteogenic and chondrogenic effect of the scaffolds. 

Polysaccharide hydrogels can effectively replicate elements of the extracellular matrix and create a suitably functioning microenvironment for tissue regeneration to significantly improve clinical outcomes in the osteochondral and cartilage repair clinical situations. Personalized scaffolds not only provide scaffolds with the structural components required for good integration, but also allow for the biological process involved in healing. This demonstrates how advances in technology can fill gaps in the area of current surgical treatment options.

Researchers have developed a 3D-printed titanium prosthesis that mimics various body structures to address a key challenge in reconstructions involving significant bone loss: enabling a single implant to integrate with both bone and tendon.

The study was published on September 11 in the scientific journal *Biomaterials Research*. The technology is currently in the preclinical stage, with researchers having conducted laboratory cell experiments and trials on rabbits.

The concept involves creating distinct "environments" within a single prosthesis. In the region designed to contact bone, scientists printed a porous structure inspired by trabecular bone—a mesh-like tissue found inside bones. Meanwhile, in the area intended for tendon attachment, they created ordered microstructures designed to replicate that tissue's organization.

This issue arises primarily with large bone defects and complex joint reconstructions. In such cases, bone loss can also eliminate the natural attachment points for tendons and ligaments. Consequently, a prosthesis must fulfill two biologically distinct functions: integrating with the bone to remain stable while simultaneously providing a suitable surface for soft tissue attachment.

In laboratory experiments, each architecture offered different advantages. The tendon-inspired structure promoted the alignment and differentiation of tendon-derived stem cells. Conversely, the trabecular bone-like structure favored the differentiation of bone marrow stem cells into bone tissue and their mineralization.

The researchers also tested the implants on 36 rabbits, divided into three groups. One group received a structure inspired solely by tendon, another a trabecular structure, and the third a prosthesis combining both architectures in distinct regions. In the animal subjects, the patellar tendon was detached from its attachment point on the tibia and connected to the upper part of the prosthesis, while the lower part of the implant was inserted into the bone.

The results highlighted the differences between the structures. In mechanical tests conducted 12 weeks after implantation, the model combining both architectures required the highest maximum force to separate the tendon from the prosthesis among the three groups. According to the authors, this indicates that dividing the prosthesis into specialized regions improved the mechanical stability of the implant-tendon interface.

The authors note the concept's potential for complex joint reconstructions and large bone defects; however, the results presented so far are experimental and do not demonstrate efficacy or safety in humans.

A novel 3D-printed biocomposite graft designed to mimic the transition between bone and tendon has shown highly promising results in rabbit trials, marking a major leap forward for orthopedic regenerative medicine.

The interface where a pliable tendon meets rigid bone—known as the tendon-to-bone insertion (TBI) or enthesis—is notoriously difficult to heal. Because the two tissues have vastly different mechanical properties, standard surgical repairs frequently fail due to concentrated mechanical stress

Recent advancements in bio-3D printing address this challenge by creating graded, multi-layered scaffolds that smoothly bridge the gap between hard and soft tissues

Why this architecture matters...Historically, engineered implants focused on either bone or tendon individually. This new wave of biomimetic design uses specialized techniques like core-shell and multi-nozzle bioprinting to replicate a seamless structural gradient.

The Bone Region: Printed with mechanically reinforced, porous structures (often utilizing polymers like PCL blended with bioactive ceramics) to allow bone cells to attach and mineralize.

The Transition Zone (TBI): Features a gradient porosity and material blend that mimics natural fibrocartilage, diffusing mechanical load

The Tendon Region: Composed of aligned, flexible hydrogels or synthetic fibers optimized for soft-tissue elongation and cell alignment

Key insights from the rabbit trials...When tested in animal models, such as rabbit rotator cuff tears or ACL reconstructions, these multi-layered biomimetic grafts demonstrated remarkable advantages:

Accelerated Tissue Integration: The gradient transition zone significantly promoted the growth of high-quality fibrocartilage, allowing the bone and tendon sections to fuse naturally

Enhanced Mechanical Performance: The bioprinted constructs withstood physiological loads far better than traditional, non-graded synthetic options, minimizing the risk of re-tearing at the insertion site

Biological Activation: By embedding the scaffolds with specialized stem cells or chemical factors, the implants accelerated blood vessel formation (angiogenesis) and new extracellular matrix growth

mundophone

TECH

High gas prices reignite risk of energy crisis in Europe

Europe's energy crisis has returned to the spotlight after the UN warned of the risk of a new surge in costs this coming winter, at a time when gas prices remain high and European reserves are below levels seen in recent years.

Simon Stiell, Executive Secretary of the UN Framework Convention on Climate Change (UNFCCC), told the European Parliament that a new winter energy crisis threatens the region due to its reliance on fossil fuels. In a speech published by the UNFCCC, he described volatile oil and gas imports as an economic vulnerability for Europe.

However, the warning does not mean the European Union is facing an imminent gas shortage. The European Commission believes the energy system is better prepared than in 2021 and 2022, thanks to supplier diversification, increased liquefied natural gas (LNG) import capacity, and reduced consumption.

European gas prices hit highest level since 2023...European natural gas prices briefly climbed past €80 per megawatt-hour on Wednesday afternoon, reaching a level not seen since January 2023. The Amsterdam Stock Exchange (AEX) closed with a significant loss in Amsterdam due to the prices, with investors concerned about the risk of inflation.

The conflict between the United States and Iran has pushed up prices for natural gas and liquefied natural gas as well as oil. Ongoing fighting has left the Strait of Hormuz, a crucial energy shipping route, almost entirely blocked.

In the meantime, conflicts between Saudi Arabia and Houthi rebels in Yemen have also escalated, which has led to more difficulties for world trade. The suspicion is that the Houthis want control over Bab al-Mandeb, a maritime strait between Yemen and the Horn of Africa that offers access to the Red Sea.

The energy bills are likely going to be higher for many people. Netherlands Authority for Consumers and Markets (ACM) has said that gas prices under standard energy contracts rose by more than 6 percent in August compared with July. Prices were more than 11 percent higher than they had been a year earlier.

Brent crude climbed past $100 a barrel as tensions between Iran and the United States flared up again.The AEX index dropped by 1.3 percent to 1101,89 points. The MidKap dropped 0.9 percent to 1098,01 points. The stock exchanges in Frankfurt, Paris, and London dropped by 1.9 percent.

Gas prices climbed as well. A megawatt-hour of natural gas briefly traded above €80 on the Dutch gas exchange, which serves as a benchmark for European gas prices. It was the first time prices had reached that level since January 2023.

The rise in oil prices came after the U.S. hit Iranian tankers and retaliatory Iranian attacks on US warships and oil tankers in the Persian Gulf.

Key points:

-The European benchmark gas price reached €75/MWh in early September.

-European reserves stood at around 66% of capacity—the lowest level for this time of year in 15 years.

-The European Commission does not currently identify an immediate risk of gas shortages.

-The main danger may lie in the prices Europe pays to secure supplies for the winter.

-The conflict in the Middle East continues to affect international energy flows. European natural gas benchmark prices hit €75/MWh in the first week of September—more than double the figure recorded a year earlier and the highest level since late 2022.

According to a Reuters analysis of the European gas market, European storage facilities were at nearly 66% capacity—about 12 percentage points below the previous year's level and the lowest figure for this time of year in 15 years.

The same analysis indicates that reserves might reach only 70% to 75% before winter, down from 83% in 2025. Germany, which holds Europe's largest storage capacity, showed levels near 54%, while reserves in the Netherlands hovered around 48%.

Lower reserves force European countries to rely more heavily on imports during the cold months. This need does not necessarily imply a gas shortage, but it increases exposure to international market fluctuations.

The Middle East has once again altered the energy market...The current pressure stems from a different source than the 2022 energy crisis, when Russia's invasion of Ukraine and reduced Russian gas deliveries triggered a historic surge in European prices.

In 2026, the primary source of disruption is the Middle East. Restrictions on energy flows in the region have affected oil and LNG exports and once again exposed Europe's dependence on international markets.

The International Energy Agency is monitoring the current crisis through a mechanism dedicated to government responses, compiling measures such as conservation, fuel switching, and supply reinforcement adopted to address market disruptions. The agency also coordinated the release of emergency oil reserves by member countries.

In the LNG market, the disruption is particularly significant for Europe. The drop in Gulf exports was partially offset by increased production in other markets, including the United States and Canada.

This additional supply helps explain why prices remain well below the peaks seen during the 2022 crisis, when European gas prices briefly exceeded €300/MWh.

Despite lower storage levels and price volatility, the European Commission’s assessment regarding the physical security of supply is less alarmist.

Despite lower storage levels and price volatility, the European Commission’s assessment regarding the physical security of supply is less alarmist.

On September 3, the European Union’s Gas Coordination Group concluded that there is no immediate risk to the security of gas supplies.

Brussels considers the current situation to be substantially different from that of 2021 and 2022. The EU has increased its LNG import capacity, diversified suppliers, and reduced gas demand, allowing the system to operate with lower storage levels.

However, the Commission acknowledges that international instability continues to drive volatility in energy markets.

This distinction is important. The most immediate risk appears to be not the inability to obtain gas, but the cost required to secure that supply.

If prices remain high throughout the winter, households and businesses could face renewed pressure on their energy bills. European industry—particularly energy-intensive sectors—also faces greater exposure to costs that exceed those of competitors in other regions.

Energy prices also pose a risk to inflation and monetary policy. European Central Bank officials have acknowledged that further interest rate hikes might be necessary if inflationary pressures linked to energy—among other factors—persist.

A prolonged period of high gas and electricity prices could further exacerbate the competitiveness issues the European Union is attempting to address through investments in industry, artificial intelligence, defense, and energy infrastructure.

Simon Stiell framed reliance on fossil fuels precisely as both an economic and a security issue. According to the UN official, renewable energy already accounts for roughly half of Europe's electricity generation, and solar power helped avoid over €30 billion in gas imports during the first six months of the current conflict in the Middle East.

Stiell presented this figure during an address to the European Parliament; it should therefore be viewed as an estimate cited by the UN official.

Extreme heat adds to Europe's costs...The United Nations' warning extends beyond the fuel market.

Stiell also cited an estimate suggesting that this summer's extreme heatwaves could result in economic losses of nearly €180 billion for the European Union in 2026—equivalent to approximately 1% of European GDP.

This figure comes from an analysis published by Triodos Bank, which identifies lower labor productivity, agricultural losses, and disruptions in the energy and transport sectors as the primary channels of economic impact.

This is an economic estimate rather than a definitive measurement of losses. The full impact depends on the duration and intensity of the weather events, as well as indirect effects on various sectors of the economy. Europe thus enters the pre-winter period in a different situation than during the 2022 crisis: it has greater import capacity and a more diverse range of suppliers, yet it faces higher gas prices, lower reserves, and an international market vulnerable to geopolitical disruptions.

Supply may well be secured. The key question will be the cost of ensuring that supply during the coldest months.

mundophone