TECH

RTX Spark laptop hands on review: The OmniBook Ultra 16 is HP's new flagship

HP has leaned into its revived “Omni” branding with two new systems to amplify the company’s mobile and desktop computing AI chops. The new HP OmniBook Ultra laptop aims to claim (however briefly) the “world’s fastest AI PC” title with a custom-design AMD Ryzen AI 300-series processor that’s faster even than AMD’s own claims for its latest silicon just a month prior. 

Meanwhile, the HP OmniStudio X all-in-one desktop comes in two sizes and combines current-generation Intel Core Ultra processing with the option for Nvidia GeForce RTX graphics. I had the chance to play with both at HP’s debut event in New York City and learn more about them.

HP’s second such laptop to bear the revived OmniBook name, the OmniBook Ultra is a performance-focused notebook that aims to reach on-trend techies with work to get done. It has high-end competition like Apple's MacBook Pro squarely in its sights, and HP has cooked up a few "firsts" to draw a bead on it.

But first, this 14-inch pseudo-workstation's recycled-metal frame, at 3.48 pounds, feels quite dense when picking it up. That isn’t necessarily bad—just don’t expect a feathery experience. Regardless, it won’t strain your backpack too heavily, weighing just as much as a 14-inch MacBook Pro with the M3 Pro chip. 

Opening the lid reveals a 2.2K (2,240-by-1,400) IPS touch screen with 100% SRGB color coverage that can shine at up to 300 nits. However, at least on paper, the MacBook Pro display still comfortably outshines this one with finer resolution, higher brightness, and broader color coverage.

The OmniBook Ultra has a starting price of $3,199 in the US—$200 more than the smaller OmniBook X, which features the same 18-core RTX Spark N1X chipset. Meanwhile, HP charges $3,619 for the 20-core Nvidia variant. In contrast, the 20-core variant can be purchased in the UK for £3,199 (~$4,235), tax included.

Alternatively, the new OmniBook Ultra can be configured with 64 GB of RAM for $4,199. At the time of writing, HP charges an extra $1,110 for 128 GB of RAM and $850 for a 2 TB PCIe Gen 5 SSD to replace the standard 1 TB drive. Consequently, a fully maxed-out OmniBook Ultra costs $6,159.

Furthermore, all SKUs include the same 99 Wh battery. HP also equips every OmniBook Ultra variant with the same 16-inch OLED display. With a resolution of 2,880 x 1,800 pixels, this screen offers a 120 Hz refresh rate, just like its 14-inch counterpart. Likewise, according to the specifications, the display reaches a peak brightness of 500 nits for SDR content and 1,100 nits for HDR content.

It is the beginning of a new era for the PC market: A new competitor is entering the historically Intel and AMD dominated field of PC processors. In this case, it is a very well known player in Nvidia, the most important manufacturer of GPUs for computers.

The launch of Nvidia's new RTX Spark platform of ARM-based Socs is close, and Nvidia has managed to get all the big PC manufacturers on board. One of them is HP, the second biggest PC manufacturer if you go by sales numbers. The American tech company is launching two devices with RTX Spark, and both are under the OmniBook brand: The OmniBook X 14 and the OmniBook Ultra 16.

Ahead of the launch, we had a chance to take a look at the bigger of the two, the new HP OmniBook Ultra 16. Unfortunately, we could only look at a turned-off sample, as Nvidia does not want any benchmarks ahead of the official release, but the physical quality of the laptop is interesting to us all the same.

HP OmniBook Ultra 16: An OmniBook that could be a ZBook...When compared with the new colorful entry-level OmniBook 5, the OmniBook Ultra 16 looks far more business-like. It is clad in an all-black aluminum chassis, with the aluminum being painted with a smooth finish as well as polished sides. The overall quality of the device is very high, thanks to the CNC unibody construction chosen, but so is the weight. With 2.2 kg, this is not a device many people will want to lug around every day, and it feels substantial in when held.

A contributing factor here is the glass front of the screen. It also makes the screen highly glossy, which is the most distinguishing factor marking this as a consumer device. In other ways, the OmniBook Ultra 16 also could be sold as a workstation HP ZBook model with no issues. Take the hinge design, for example: The hinges on the top of the base allow for big fan exhausts on the back and thus a better airflow. A negative of this design: The screen opening angle is fairly limited, to around 135 degree.

A look at the bottom of the system reveals a rather large air intake for the two fans. While we could not open up the laptop, HP says that the new OmniBook Ultra has a total of two fans and three heatpipes, enabling the 20 core version of the Nvidia RTX Spark used here to consume up to 70 W. The power is supplied by a 140 W charger or a 99 Wh battery.

The keyboard of the OmniBook looks familiar to the EliteBook X we recently tested, but it is different, with a lower key travel. But HP has changed the design, given the key-dish a slightly concave form to make it more comfortable. For the mouse, HP puts in a huge haptic touchpad, which we unfortunately could not test due to the device being switched off. As for ports, HP includes Thunderbolt as well as USB A and HDMI, as well as a microSD slot - too bad that it could not squeeze in a full-size SD card reader. At least there are four speakers, and HP managed to build a physical webcam shutter, too. That is another feature that is also very common in HP's business series.

Your AI.Running locally. Built on the NVIDIA CUDA™ platform, with up to 1 petaflop of FP4 AI performance and with 64 GB of unified memory, it lets you run larger models locally, accelerate demanding workflows, and keep your data private.

Keeps its cool when the work gets intensive. Larger heat pipes and dual ultra-thin fans efficiently dissipate heat to help sustain peak performance through demanding creative projects, AI development, and agentic workflows. So you can stay focused on creating what's next—not managing your machine's limits.

See every detail come to life.Great ideas deserve exceptional fidelity. The HP OmniBook Ultra 16 brings your work to life with a stunning 16-inch2 3K OLED6 display with VESA DisplayHDR™ True Black 1000, delivering vibrant color, remarkable contrast, and exceptional clarity so every image, design, and frame looks exactly as you intended.

Big sound. Exceptional clarity. Quad speakers powered by smart amplifiers delivering louder, fuller sound with deeper bass and exceptional clarity—even at higher volumes—so every soundtrack, voice, and creative detail sounds exactly as it was meant to be.

mundophone

 

TECH

Rowan engineers develop new way to monitor 3D printed concrete during construction

A Rowan University team of engineers has developed a new way to measure how 3D printed concrete deforms during printing and curing, revealing how lower layers respond to the weight and pressure of newly deposited material. The approach could help researchers and manufacturers better understand how printed concrete behaves during construction and what factors affect the quality of finished structures. 

Around the world, concrete additive manufacturing is increasingly used to speed construction. Rather than pouring concrete into forms, builders can use industrial-scale 3D printers to extrude the paste-like material layer by layer to create walls and foundations.  

Researchers have looked for ways to monitor what happens to concrete during the earliest stages of printing, when newly deposited layers are still deforming, curing and bearing the weight of additional material. Understanding that process could advance research aimed at improving the quality, durability and efficiency of 3D printed concrete construction. 

Led by Islam Mantawy, Ph.D., and Behrad Koohbor, Ph.D., researchers in the Henry M. Rowan College of Engineering developed and tested a new method for reconstructing how concrete changes throughout the printing process. That’s important because multiple factors, from temperature and humidity to the material’s chemical composition, can affect how a printed structure behaves as it cures. Understanding those factors and their precise effects could help engineers identify potential problems earlier and improve future concrete printing processes.     

“The idea is we need to avoid any premature failure,” said Mantawy, an assistant professor in the Department of Civil & Environmental Engineering and head of the Additive and Robotic Construction Laboratory. “If you're building an 8-foot-tall wall and the bottom layer starts to crack and collapse, the entire structure can fail before you reach the final layer. What was supposed to be a faster, more efficient construction method becomes slower and less efficient because you have to start over from scratch.”

They started with a long-established imaging technique called digital image correlation (DIC) that measures how materials and structures deform under force. Using a reference image of an existing object, researchers use cameras to take thousands of images as the material and structures respond to external stimuli such as mechanical forces, temperature change and humidity. 

In concrete printing, however, there is no reference image because the structure is being created as the process unfolds.

The team used DIC “in reverse time,” starting with images of the completed structure and working backward through thousands of images captured during printing and curing. With those images, the team was able to construct a mathematical approach to validate the reverse-time DIC method and for explaining what happens as concrete deforms, shrinks and cures, even during the first moments after a layer is printed.  

Published in the journal Experimental Mechanics, the paper demonstrates that images of a fully cured 3D printed structure can be analyzed in reverse order, using the natural surface roughness of the concrete as a tracking pattern, to reveal how the structure deformed throughout printing and curing. 

Beyond concrete printing, the researchers say DIC “in reverse time” could be useful in other additive manufacturing processes where conventional quality control methods are limited because a structure does not yet exist when production begins.

“We are literally writing the history of the process, but we start from the end and go back,” said Koohbor, an associate professor in the Department of Mechanical Engineering and affiliated faculty with Rowan’s Advanced Materials & Manufacturing Institute.  

The researchers see the work as an early step toward smarter, more autonomous forms of additive manufacturing.

“Without knowing what’s happening inside the structure, you won’t be able to achieve self-operating 3D printing,” Mantawy said. “It’s a way to have an eye on the structure as it evolves.”

From conventional construction to digital fabrication...Civil engineering is entering a phase in which construction is no longer defined only by drawings, formwork, manual placement, and conventional sequencing. Digital fabrication and robotic construction introduce a different logic: structures can be designed as data, translated into toolpaths, produced by controlled machines, monitored during fabrication, and verified through a combination of material testing, process control, and structural assessment. 

Three-dimensional concrete printing, often called 3D concrete printing or 3DCP, is one of the most visible examples of this transformation. In many systems, a cementitious material is pumped through a hose and deposited layer by layer by a gantry, robotic arm, or mobile printing platform. The printed object may be a wall, façade element, formwork, shell, bridge component, foundation element, or architectural unit. The attraction is clear: the technology can reduce or eliminate some formwork, allow complex geometries, support material-efficient design, and open new possibilities for automated construction. 

However, 3D concrete printing should not be treated as a simple replacement for conventional casting. It changes the engineering problem. The material must be fluid enough to pump and extrude, but stiff enough to keep its shape and support subsequent layers. The geometry must be printable, not only beautiful. The structure must satisfy load-bearing, durability, reinforcement, connection, fire, tolerance, and inspection requirements. The construction process must therefore be considered as part of the structural system. 

What makes 3D concrete printing different? In conventional reinforced concrete construction, the formwork defines the geometry, reinforcement is placed before casting, and the concrete fills the mould. In 3D concrete printing, the material is deposited directly in its final or near-final geometry. This creates freedom, but also responsibility. Layer geometry, printing speed, extrusion rate, nozzle height, waiting time between layers, ambient temperature, and material thixotropy all influence the final performance of the printed element.

The digital workflow is also different. A printable structure begins as a design model, but it must pass through design-for-additive-manufacturing decisions, toolpath generation, slicing, simulation, robotic control, printing, post-processing, and verification. Recent work on the digital chain in concrete 3D printing emphasizes the need to coordinate parametric design, path planning, numerical simulation, fabrication constraints, and information transfer between platforms. 

For researchers and practitioners, this means that the critical questions are not only “Can we print it?” but also “Can we design it, control it, inspect it, reinforce it, certify it, and maintain it?” A printed element should be judged by the same engineering principles as any other civil infrastructure component, while also accounting for the specific anisotropy, interfaces, geometry, and construction history introduced by layer-wise manufacturing.

The material challenge: printability is a narrow window...The central material challenge in 3D concrete printing is printability. A printable mixture must satisfy several conditions at the same time. It must be pumpable so that it can move through the delivery system without blockage or segregation. It must be extrudable so that the nozzle produces a continuous and stable filament. It must be buildable so that fresh layers can support their own weight and the weight of subsequent layers. It must also provide sufficient interlayer bonding so that the final element behaves as a coherent structural component. 

These requirements can conflict. A highly flowable mixture may be easier to pump but may deform after deposition. A very stiff mixture may keep its shape but may cause pumping problems or weak interfaces. A long delay between layers can reduce interlayer bond, while a short delay can cause excessive deformation if the lower layer has not developed enough stiffness. This is why rheology, thixotropy, setting behavior, admixture compatibility, and early-age mechanical properties are central research topics in printable cementitious materials. 

Environmental conditions matter as well. Temperature, wind, humidity, sun exposure, and moisture loss can affect open time, layer surface condition, bond quality, and dimensional stability. In practice, a printing recipe cannot be separated from the printer, nozzle, hose length, pump, printing speed, layer height, quality-control system, and construction environment.

Structural design, reinforcement, and interfaces...For civil engineering, the largest questions are not only material flow and geometric novelty, but structural reliability. Printed concrete elements can exhibit anisotropic behavior because the interfaces between layers may influence strength, stiffness, fracture behavior, permeability, and durability. The load path must be compatible with the printed geometry and the direction of layers.

Reinforcement remains a key issue. Conventional reinforced concrete depends heavily on steel reinforcement for tensile resistance, crack control, ductility, and robustness. In printed construction, reinforcement may be placed manually, inserted during printing, integrated after printing, combined with printed formwork, or replaced in limited applications by fibers or alternative reinforcement strategies. Each solution has consequences for bond, cover, durability, construction tolerance, and inspection.

Numerical simulation can help engineers understand these problems before printing. FEM-based modelling has been used to simulate fresh-state buildability, layer addition, overhang limits, and print failure modes. These tools are useful because print failure during construction can waste material and time, while validated simulations can help identify safer layer heights, printing speeds, toolpaths, and geometries. 

Quality control: from visual inspection to digital monitoring...Quality control is a major reason why 3D concrete printing should be treated as a digital fabrication process rather than only a new placement method. The printed product is defined by the material, but also by the process history. Layer width, layer height, surface texture, defects, waviness, discontinuities, dimensional deviations, and local instability can all influence performance.

Recent reviews of geometry and surface inspection in 3D concrete printing discuss image-based systems, structured light, laser scanning, point clouds, and other sensing methods for monitoring printed layers and final geometry. These methods can support quality control during printing, after each layer, before assembly, and during assembly of printed components. 

Machine learning is beginning to enter this field. Recent research has explored deep-learning approaches for predicting filament cross-sectional geometry from material properties and process parameters. Such methods point toward future closed-loop systems in which simulations, sensors, and printer control are connected to reduce trial-and-error and adjust printing parameters more intelligently. Still, these tools must be validated carefully and used within the range of the data on which they were developed.

 

mundophone

 

TECH

Tiny droplets can form soft networks for brain-inspired computing

Inside the human brain, billions of individual neurons connect to form a vast network capable of rapidly processing information and electrical signals. A team of researchers and engineers at Penn State is taking inspiration from these natural computers to build soft networks of water-based droplets, connected by membranes like those found in the walls of living cells. Their networks lay the foundation for a new kind of soft computer built for wet environments, capable of sensing and processing information with ions much like silicon chips do with electrons, but at far lower energy cost.

The team immerses tiny lipid-containing droplets in oil, where lipid molecules naturally arrange themselves around each droplet. When neighboring droplets touch, these lipids form a thin, two-layer membrane between them. The soft surfaces contain voltage-sensitive channels that allow electrical signals to pass between droplets through the movement of charged atoms or molecules called ions. The membranes store charge, while the channels respond to present and recent signals. When combined, these properties allow the droplets to form a synthetic, interconnected network that the researchers call a “neuromorphic tissue.”

They detailed these tissues’ properties in a paper published in Science Advances, using laboratory measurements and computer simulations to show that interconnecting the droplets’ membranes can significantly improve the material’s ability to process complex and unpredictable signals.

According to Joseph Najem, associate professor of mechanical engineering, associate director of the Convergence Center for Living Multifunctional Materials System and co-corresponding author on the paper, integrating a computer interface or sensor in the body, for example, is not as simple as implanting circuitry because even though both electronics and biology process signals and compute, they carry information differently. Conventional electronics work by moving charge carriers like electrons through conductive materials such as metal wires, while the electrical signaling that happens in the body relies on ions moving through fluids and across cell membranes.

“Scientists and developers must create interfaces in devices that use traditional silicon computing to process and translate between electronic and ionic signals,” said Najem, who holds an additional affiliation in the Intercollege Graduate Degree Program in Materials Science and Engineering at Penn State. “However, these interfaces can delay and distort signals, while adding significant energy costs. To address this problem, we must investigate creating new computing platforms that go beyond just electronics.”

Ionic computing explores approaches to processing information using ions instead of the traditional charge carriers used in electronics. Neuromorphic computing is an approach to computing that draws inspiration from the brain’s structure and information processing functionality. The team’s new approach uses aqueous compartments, lipid membranes and peptide ion channels — structures that mimic the natural architecture of the brain — to bring both the structure and functionality of these ideas together, drawing on the nodes' interconnectivity to create electrical feedback within the material.

With this architecture, the researchers can use reservoir computing, in which a changing input produces a pattern of responses that retains traces of earlier inputs. In this network, membrane charging and ion-channel activity generate responses, and the connections allow the droplets to influence one another. Coupled nodes can accumulate information in the form of electric charge, passing it to the next node in the chain. This behavior, which also occurs in neurons, allows electricity to rapidly transfer through each node without losing overall signal clarity. The membranes between the droplets give the network short-term, fading memory, so its response to a new input depends partly on recent inputs, Najem explained.

“In traditional systems, memory and processing are separated, which consumes a lot of energy to make those actions work together,” Najem said. “In the brain, information processing and memory occur in the same place. That’s why we are seeing interest in this idea of neuromorphic computing, as it could significantly reduce the amount of energy needed for operation.”

Joseph explained that individual droplets can connect to as many as six neighbors in a structure reminiscent of a honeycomb, noting that various arrangements provide different patterns of connectivity. To measure the dynamics of a droplet chain, the team introduced an electrical current, recording voltages at individual nodes. These measurements were compared with a physics-based model of the network, allowing the researchers to explore how connections and differences between droplets influence overall computational performance.

Seven individual nodes can join together in a hexagonal pattern to form a structure, pictured, similar to a honeycomb. Credit: Provided by Ahmed Mohamed. All Rights Reserved.

“When the droplets join together this way, you create coupling that enables rich electrical dynamics capable of processing complex signals that change over time,” Najem explained. “In our simulations, these coupled droplets generated more accurate signal predictions than the uncoupled reservoirs we tested.”

The team assessed computational performance using simulations based on its experimentally tested model, comparing interconnected reservoirs with parallel reservoirs whose node’s responded independently. Using benchmark tests that use mathematical equations to model chaotic behavior, the team found that connected networks reduced prediction error by nearly 50% when compared to independent nodes. By reducing these errors, the team can better understand how electricity will flow in extremely complicated systems.

Although external equipment is still used to supply inputs and measure responses, Najem said, the fact that the network’s recurrent connections arise within the material without external feedback circuitry is what makes it so exciting.

“With silicon, circuitry is needed to build a computer,” Najem said. “With our approach, the circuitry is the material itself: the membranes store charge, and ion channels regulate signals between droplets. This draws inspiration from the way biological membranes contribute to information processing and memory.”

The team plans to continue investigating their material, its properties and how it can be further optimized for ionic computing. According to Najem, potential future applications include adaptive sensors, soft robotics and computing at biological interfaces.

“Using this material, we want to create a framework to enable commercially scalable ionic computing,” Najem said. “Our hope is to one day harness this technology to make Jelly-like computer chips to complement and optimize electronics in the body.”

Additional co-first authors on the work include Ahmed S. Mohamed, a postdoctoral scholar at Penn State, and Nicholas X. Armendarez, a postdoctoral fellow at the University of Texas at Austin who conducted the work at Penn State as a doctoral student. Md Sakib Hasan, an assistant professor at the University of Massachusetts Dartmouth, contributed as a co-corresponding author.

Ty Tkacik(tct5204@psu.edu) Penn State

 

DIGITAL LIFE

Five ways to beat dating algorithms at their own game

You open a dating app intending to spend ten minutes on it. An hour later, you are still swiping. You might have a few new matches, but are you any closer to meeting someone you actually like? Arguably, one of the benefits of dating apps is that they make introductions possible on a scale that would once have been hard to imagine.

They can help people meet outside their usual circles and offer a less daunting way to signal interest. But their design also shapes what we do: profiles arrive one after another, choices take seconds, and there is always the possibility that the next person will be a better match.

In our research, we examined 21 interviews with dating app users alongside public online discussions and French newspaper coverage. We found that people navigate three coexisting, sometimes competing ideas about dating. The first one is the romantic approach to dating where it is important to take time, build trust and look for a connection. The second, the shopping approach, treats dating more like browsing where a person can compare options, filter efficiently and keep their choices open. The third, the systems approach follows the app’s own rhythm: swipe, match, respond to prompts and keep engaging, without necessarily thinking about the long-term goals.

Most people do not fit neatly into just one of these approaches. Someone may hope for a lasting relationship while filtering profiles like a careful shopper. Another may enjoy casual dating but resent feeling pushed to make endless quick decisions. That tension helps explain why using an app can feel both useful and exhausting.

So how can you ‘beat’ a dating algorithm? Despite years of research dedicated to looking for determinants of online dating success, there is no proven secret combination of swipes, photos or messages that will make a platform deliver the right partner.

Ask yourself instead, how can you stop the app’s rhythm from setting the terms of your romantic life? Here’s how:

-Keep a clear picture of your intentions...Before opening the app, consider whether you want a committed relationship, casual dates or simply to meet people without deciding yet. Any of these is a valid aim. Knowing yours can help you choose whom to talk to and what to say. It can also make it easier to notice when you are following someone else’s expectations because it seems simpler than explaining your own. When a conversation starts to matter to you, share what you are looking for and ask what the other person wants, too. You need not settle everything immediately, but you should not have to guess indefinitely.

-Choose when to stop browsing...One participant in our study, Rose, imposed a swiping ban on herself so she could focus on a few people she was genuinely interested in. Her choice did not reveal a flaw in the app’s code. It changed how she used the app. If you find yourself collecting matches without talking to them, try pausing new searches long enough to give existing conversations your attention.

Use filters and profiles as tools, not verdicts...Some people in our study carefully shaped their profiles, used several apps or filtered matches to save time. These tactics can be useful, particularly when they reflect what you genuinely value. But a profile offers only a partial view of a person. A match is a reason to begin a conversation, not evidence that a relationship will work. Critically, a lack of matches is not a measure of your worth. Research shows that low match rates on dating apps may lead to negative mental health outcomes because the apps create a false equivalence between algorithmic compatibility and personal worth.

-Take promising connections beyond the swipe cycle...Participants seeking closeness often wanted more than an exchange of quick reactions. Some moved conversations off the app or made plans to meet, rather than continuing to browse indefinitely. You can do this at a pace that feels comfortable to both people. The point is to give a connection a chance to develop in a setting where the next profile is not constantly competing for attention.

-The importance of psychological sovereignty...One participant in our study, Alan, deleted an app when dating began to feel like a game, though he sometimes returned later. Taking a break need not mean giving up on meeting someone. It can be a way to check whether the app is still helping you pursue what you want. We call this reclaiming psychological sovereignty. Put plainly, it means keeping hold of your ability to decide what matters to you, how much attention you give the app and when you have had enough. You cannot control who appears on your screen or how the platform ranks profiles, but you can decide what you do next.

Dating apps can open doors, but it is important to remember that you are the one who is in control. You get to choose whether to walk through this door, keep looking, or put your phone away, and no algorithm can make this decision for you.

Dating app algorithms are fundamentally built to keep you on the platform rather than help you leave it, since their revenue relies heavily on your active screen time, ad impressions, and premium subscriptions.

To "beat" these algorithms, you can either manipulate the code to boost your profile visibility (playing the technical game) or reclaim your psychological sovereignty to stop the app's rhythm from dictating your love life (playing the human game).

Here are the five most effective ways to beat dating algorithms at their own game:

1. Be strict and selective with your swipes...Swiping right on every single profile out of frustration will severely damage your hidden desirability score (often rooted in the classic "Elo" ranking system).

• How it works: When you swipe right indiscriminately, the algorithm flags you as a bot or a low-effort user and lowers your profile visibility.

• The fix: Only swipe right on people you genuinely want to talk to. High-selectivity tells the system you are a high-value user, which improves the caliber of profiles you are shown.

2. Guard your time and pause new searches...The interface is intentionally designed to present an infinite loop of options, making you believe the "perfect" match is just one more swipe away.

• How it works: Collecting matches without messaging them triggers an algorithmic confirmation loop where you continuously browse but rarely convert actions into real conversations.

• The fix: Set strict limits on your daily screen time. If you have a few active matches, pause your profile discovery or stop swiping entirely until you have explored those existing conversations.

3. Log In daily but reply quickly...Dating apps prioritize active, highly responsive accounts because they want to avoid matching users with dead or abandoned profiles.

• How it works: Apps like Bumble and Tinder reward "permanent presence." If you open the app frequently and reply to messages fast, the algorithm boosts your visibility in other users' active feeds.

• The fix: Spend at least 5 to 10 minutes a day on the app actively engaging. When you get a match, send a quick, thoughtful opener or reply promptly to signal to the algorithm that you are a highly engaged participant.

4. Treat the profile as an introduction, not a verdict...Algorithms try to cluster users based on physical appearance and superficial data tags, which can lock you into a very narrow loop of recommendations.

• How it works: A digital profile only offers a heavily curated, partial snapshot of a human being. Over-filtering based on checklist items creates a false sense of algorithmic compatibility.

• The fix: Use filters loosely. Treat a match simply as a green light to start talking, not evidence that the relationship will work. Move the conversation off the app or schedule a low-stakes, in-person date (like coffee or a walk) reasonably quickly to test real-world chemistry before algorithmic fatigue sets in.

5. Feed the algorithm fresh data (or hit reset)...The data profiles have on you is historical; if your preferences change, the algorithm can take a very long time to adapt.

• How it works: The system rewards dynamic content and penalizes stagnant bios or years-old photos.

• The fix: Regularly refresh your prompts, change your primary lifestyle tags, and upload new, high-quality photos. If the algorithm has completely pigeonholed you into a low-quality tier, the ultimate bypass is to fully delete your account, wait a few days to avoid a shadowban, and start fresh with a clean history

mundophone

 

TECH

AMD releases powerful new mini-PC with 160 GB VRAM, 10 GbE and AMD Gorgon Halo

AMD has released a new mini-PC in the US. Arriving in the wake of recent Nvidia RTX Spark announcements, the new AMD Ryzen AI Halo Developer Platform hopes to surpass its predecessor with 192 GB RAM, of which 160 GB can be assigned as VRAM, and the Ryzen AI Max+ Pro 495 with the new Radeon 8065.

AMD has joined the increasing number of OEMs to launch a Gorgon Halo-backed mini-PC. For context, the new mini-PC has arrived less than a week after Asus and Dell revealed new details about their first Nvidia RTX Spark-backed mini-PCs. As with AceMagic, GMKtec and Minisforum, among others, AMD has adopted the Ryzen AI Max+ Pro 495 with the Radeon 8065S rather than lesser Gorgon Halo alternatives.

Visually, the new Ryzen AI Halo Developer Platform is unchanged from its Strix Halo-based predecessor (curr. $3,999 on Amazon). Likewise, AMD's mini-PC continues to find itself with four USB Type-C ports and 10 Gigabit Ethernet. Incidentally, AMD complements the latter with Bluetooth 5.4 and Wi-Fi 7 connectivity too.

Switching to the Gorgon Halo platform provides the Developer Platform with modest CPU performance improvements according to our benchmarks. Similarly, the Radeon 8065S is only a minor revision of the existing Radeon 8060S with the same RDNA 3.5-based 40 Compute Units, despite the name change.

The flagship SKU, the Ryzen AI Max+ PRO 495, will be inside the next generation of the new Ryzen AI Halo platform. It is a powerful 16-core 32-thread part with a base and boost clock frequency of 3.1 and 5 GHz respectively. It packs 80MB of cache memory. All this will be powered within a configurable TDP (cTDP) package of 45 to 120 watts. As always with Max APUs, the new 400 series also has a big chunk of die allocated to the on-board graphics with 40 RDNA 3.5-based CUs (compute units). The 495 will also feature an NPU built on the XDNA 2 architecture and is capable of 55 TOPS.

Aside from the 495, AMD is also introducing the Ryzen AI Max+ PRO 490 and Max+ PRO 485. These pack fewer CPU cores at 12 cores and 8 cores. The GPU CU count also comes down from 40 to 32; the NPUs are slightly less capable with 50 TOPS output though all of them still qualify for Copilot+ PCs.

Compared to the Ryzen AI Max PRO 300 series, the 400 series supports 50% higher LLM processing handling at 300 billion+ parameters (the world's first x86 CPU capable of doing so), and to help do this, memory support goes up from 128GB on the 300 series to 192GB on the 400 series. Out of the 192 Gigs, up to 160 GB can be used as VRAM, whereas previously, the 300 series could use up to 96GB as VRAM. These will be available in Q3 2026 (towards the end of this year).

With that being said, Gorgon Halo supports up to 192 GB of RAM, compared to 128 GB for Strix Halo. On the one hand, this change provides the newer Developer Platform with 50% more RAM than its predecessor. On the other hand, the mini-PC can assign up to 160 GB of this unified memory as VRAM to the Radeon 8065S.

Consequently, the latest Developer Platform has up to 66% more VRAM at its disposal, too. Unfortunately, these improvements come at a steep financial penalty. At the time of writing, AMD charges $7,299 for the new Developer Platform with 192 GB of RAM and 2 TB of storage. Please see the company's Micro Center listing for more details.

The entry-level configuration packs ample power into a tiny 150 x 150 x 43 mm chassis. This unit utilizes the Ryzen AI Max+ 395 processor, featuring 16 Zen 5 cores and an XDNA 2 NPU capable of 50 TOPS. The package includes 128 GB of LPDDR5X unified memory. Unlike its main competitor from NVIDIA, which limits users to Linux environments, AMD's system relies on x86-64 architecture. This ensures full compatibility with Windows and Linux operating systems right out of the box.

Alongside the physical workstation, the tech giant detailed its broader Ryzen AI Max 400 series processor lineup. Leading the portfolio is the flagship Ryzen AI Max+ PRO 495, featuring 16 cores capable of boosting up to 5 GHz. Under the hood, the chip packs 80 MB of cache and an upgraded NPU that reaches 55 TOPS. Graphics performance also sees a significant boost, thanks to 40 RDNA 3.5 compute units.

Memory allocation is the new processor family's standout feature. The top-tier model supports an unprecedented 192 GB of unified memory. Users can allocate up to 160 GB exclusively as VRAM. This massive capacity makes the 400 series the first x86 client processor capable of locally running AI models with over 300 billion parameters, virtually eliminating the need for external server farms for heavy machine learning tasks.

The high initial cost targets professional developers seeking long-term savings. Heavy reliance on remote AI agents often results in monthly bills of around $750. By bringing these workloads in-house, the hardware investment pays for itself in about six months. The premium package also includes pre-installed software playbooks and access to developer programs, accelerating the entire setup process.

mundophone

 

TECH

Microsoft Maia 300 AI chip arrives with monstrous 288 GB HBM4 memory and 18 TB/s bandwidth

Microsoft has unveiled the Maia 300, its third-generation AI accelerator featuring 288 GB of HBM4 memory, 18 TB/s bandwidth and up to 13.8 PFLOPS of FP4 compute performance. The chip uses an advanced multi-chiplet design built around TSMC's N2P manufacturing process and will enter production deployment in 2027.

Microsoft has officially unveiled the Maia 300, its latest custom-designed AI accelerator intended to reduce the company's dependence on Nvidia and AMD hardware. It arrives shortly after OpenAI debuted its in-house silicon, Jalapeno.  The chip succeeds the Maia 200, which launched earlier this year. The first Maia chip, the Maia 100, surfaced in 2023. 

The Maia 300 uses a multi-chiplet design comprising a main compute section manufactured using TSMC's advanced N2P (2 nm) process. The package also incorporates separate memory and I/O chiplets, allowing Microsoft to manufacture different components using processes suited to their respective requirements. According to Reuters, some of the supporting silicon uses a less expensive 3 nm manufacturing process.

The accelerator delivers up to 13.8 PFLOPS of FP4 compute performance and 6.9 PFLOPS at FP8 precision. Its SoC power rating stands at 950 W, substantially higher than the Maia 200's 750 W rating. Microsoft has equipped the Maia 300 with eight HBM4 stacks, offering a combined 288 GB of memory and 18 TB/s of bandwidth. Each stack contributes 36 GB of capacity, with the HBM4 memory operating at 9 Gb/s per pin. For comparison, the Maia 200 features 216 GB of HBM3E memory with 7 TB/s of bandwidth, making the new accelerator useful for memory-intensive inference workloads.

Microsoft has also upgraded the accelerator's interconnect capabilities. The Maia 300 delivers 3.2 TB/s of bidirectional scale-up bandwidth and uses a two-tier Ethernet-based network with a custom AI transport protocol. A single rack accommodates 96 Maia 300 accelerators, while the scale-up architecture supports clusters containing approximately 12,000 accelerators.

Beyond its raw specifications, Microsoft claims Maia 300 can deliver 40% more tokens per megawatt than leading GPU-based AI systems. A fully populated 96-accelerator configuration reportedly offers up to seven times the rack-scale token throughput of Maia 200. However, the company has not provided sufficient details about the competing GPU configurations to independently establish the extent of its efficiency advantage.

Microsoft intends to begin deploying Maia 300 in production environments in 2027, with plans to eventually scale the infrastructure to multiple gigawatts of capacity. Reuters reports that the company could deploy more than one million Maia 300 chips next year, highlighting its ambitions to become less dependent on third-party AI accelerators.

Chiplet design and efficiency breakthroughs...Architecturally, the Maia 300 takes a fundamentally different technical approach from Nvidia. Nvidia's AI accelerators have traditionally centered on a single large compute die surrounded by high-bandwidth memory. The Maia 300, by contrast, employs a chiplet design, splitting compute, memory controllers, and input/output functions into multiple dies integrated into a single package.

The core compute chiplets are fabricated on Taiwan Semiconductor Manufacturing's (2330.TW) most advanced 2-nanometer process (N2P node), while other chiplets handling data movement and transmission use the more cost-effective 3-nanometer process. This hybrid process strategy allows Microsoft to pursue maximum performance on critical compute units while controlling costs on auxiliary functions.

On the memory front, the Maia 300 packs eight stacks of HBM4 high-bandwidth memory, totaling 288GB of capacity with transfer bandwidth of 18TB per second. By comparison, the previous-generation Maia 200 featured 216GB of HBM3E memory with 7TB per second of bandwidth. The new chip delivers roughly 33% more memory capacity and 157% more bandwidth — a critical upgrade for modern AI workloads that require long-context processing and multi-step reasoning.

In terms of raw compute, the Maia 300 delivers up to 13.8 PFLOPS at FP4 precision and 6.9 PFLOPS at FP8. Per-chip power consumption is approximately 950 watts, up from the Maia 200's 750 watts, but Microsoft emphasizes that the real benefit comes from system-level optimization.

40% higher output per megawatt...Scott Guthrie, executive vice president of Microsoft's Cloud and AI group, said the Maia 300 processes approximately 40% more tokens per megawatt of power consumed than "leading GPU systems." In a fully loaded rack configuration with 96 accelerators, rack-level token throughput reaches seven times that of the Maia 200.

However, Microsoft did not specify which GPU system served as the comparison baseline. Nvidia's latest GB300 AI server rack packs 72 GPUs with power consumption of roughly 142 kilowatts, while the Maia 300's 96-accelerator rack draws approximately 167 kilowatts. With specification details of the comparison target not fully disclosed, outside observers have difficulty independently verifying the actual substance of the claimed 40% efficiency advantage.

Rani Borkar, corporate vice president of Azure Hardware Systems and Infrastructure, told reporters: "We believe we have built the best product for these workloads. We will back that confidence with action by deploying Maia at gigawatt scale starting next year." She added that through holistic system optimization, Microsoft aims to ensure every watt of power, every dollar invested, and every byte of memory supports more AI compute.

Prioritizing internal services, not external rental...Unlike Google and Amazon, which have opened their in-house chip compute capacity to cloud customers, Microsoft has no current plans to rent out Maia compute directly to external clients. Borkar was blunt: "Our own AI services business is large enough. We want to serve ourselves first."

The logic of this approach is straightforward: Microsoft's internal AI service compute demands are enormous. From Copilot to Azure OpenAI Service, every offering requires massive inference capacity. If in-house chips can satisfy this demand, the savings on GPU procurement and operating costs flow directly to AI business margins. For investors, Maia's commercial value lies not in how much direct revenue chip rental can generate, but in whether it can effectively lower Microsoft's per-unit AI infrastructure costs.

Microsoft's capital expenditure this year is expected to exceed $175 billion, with the vast majority directed toward AI infrastructure. At this scale of spending, maximizing compute efficiency per dollar has become a core concern for both Microsoft management and investors. The large-scale deployment of the Maia 300 is one of Microsoft's answers to that challenge.

The co-opetition dynamic with Nvidia...Microsoft stressed that expanding Maia deployment is "not about cutting other vendors' products, but about raising the overall processing capacity of the infrastructure." Andrew Wall, a business lead on the project, said in an interview that Maia's expansion "is not about replacing Nvidia products, but about increasing the overall compute capacity of the infrastructure." This framing has practical considerations: Microsoft remains one of Nvidia's largest customers, and the two companies' collaboration on AI infrastructure will not change in the near term.

But from an industry trend perspective, Microsoft, Google, Amazon, and AI developers like OpenAI have all accelerated investment in custom silicon recently, all aiming to gain more bargaining power and technological autonomy in an AI compute market dominated by Nvidia. OpenAI recently unveiled its own chip, Jalapeno, signaling that AI model developers are also integrating downstream.

Mustafa Suleyman, CEO of Microsoft AI, noted: "We have to work across the full stack, all the way down to the silicon level. At this scale, we need a platform we own and can co-design with our own models. That's why running Microsoft AI models on Maia chips is a critical focus for our team." These remarks reveal the ultimate goal of Microsoft's custom silicon strategy: not merely hardware substitution, but hardware-software co-design — building a fully autonomous AI value chain from silicon to models to services.

Maia 300 deployment will begin in the United States next year before gradually expanding to data centers worldwide, including Japan. Microsoft's goal is to make Maia the "primary AI inference system" for its own products and services, meaning the compute architecture behind Microsoft's AI services will undergo a fundamental transformation in the coming years.

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The Lancet warns of extinction risk linked to AI

An international scientific commission convened by *The Lancet* has identified the risk of extinction associated with AI—when used maliciously—as one of the primary threats to human survival by 2100. Nuclear conflict is the other scenario classified as having the potential to cause humanity's extinction.

This assessment is part of a set of 17 interconnected threats to global health, spanning diseases, climate change, economic inequalities, conflicts, and emerging technologies. Researchers emphasize that while the probability of these two extreme scenarios is low, their potential consequences warrant preventive measures.

The report by the Lancet Commission on Global Health Threats in the 21st Century, published on October 10, 2026, draws on historical data and risk analyses covering 204 countries and territories. The commission gathered input from 35 experts across the fields of health, economics, security, governance, and technology.

The researchers' concerns extend beyond the possibility of artificial intelligence systems making errors; the report also considers scenarios in which the technology could be deliberately used to cause large-scale harm.

Among the risks analyzed is the use of AI to facilitate the development of biological weapons. The commission also considers the potential effects of failures in autonomous systems and the possibility of losing human control over advanced technologies.

The analysis estimates that measures aimed at reducing risks associated with the malicious use of AI could prevent the loss of approximately 10.4 billion years of healthy life by 2100.

This estimate is neither a prediction of deaths nor an indication that an extinction scenario is likely. Rather, it represents an assessment of the potential benefits of preventive measures, taking into account the severity of the events and the likelihood of their occurrence. However, results associated with threats lacking historical precedent carry a higher degree of uncertainty than projections based on established epidemiological data.

Nuclear conflict poses a similar risk...Nuclear conflict emerges as the second threat with the potential to cause human extinction. Beyond deaths directly caused by explosions, researchers consider the consequences of potential global climate disruption, which could compromise agricultural production and trigger food crises.

According to data published by *The Lancet*, effective measures to prevent nuclear conflict could avert approximately 133 million deaths and the loss of 9.35 billion years of healthy life by the end of the century.

The commission distinguishes these extreme risks from threats that already cause substantial harm to populations, such as air pollution, antimicrobial resistance, obesity, childhood malnutrition, and climate change.

Risk of extinction: an assessment of 17 threats through 2100...To compare risks of different natures, researchers used Disability-Adjusted Life Years (DALYs). This metric combines years lost due to premature death with years lived with health issues or disability.

The methodology compares plausible high- and low-impact scenarios for each threat to assess how many years of healthy life could be preserved through prevention and intervention measures.

A threat was classified as catastrophic if it met criteria including a potential impact of at least one billion years of healthy life lost by 2100, the availability of sufficient data for analysis, and the possibility of intervention through public policy.

The commission also highlights the interconnections between these various risks. Climate change, for instance, can exacerbate food insecurity and increase pressure on healthcare systems already weakened by conflict or economic inequality. 

Researchers advocate for prevention and continuous surveillance...Based on the findings, the commission recommends three courses of action. The first involves strengthening the preparedness of health systems to handle large-scale crises. The second entails increasing investment in research and technologies capable of reducing risks. The third proposes permanent mechanisms for monitoring emerging threats.

In the field of artificial intelligence, the assessment underscores the need to consider the risks of malicious use during the development and governance of the most advanced systems.

The commission also advocates for the creation of a monitoring mechanism—named the Lancet Monitor of 21st Century Global Threats to Health—designed to regularly update the assessment of threats and support public decision-making.

The report does not present the future as inevitable. The projections seek to identify possible consequences and quantify the potential benefits of preventive measures taken in the coming decades.

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