TECH

Quantum computing is an inevitable threat to bitcoin?

There is a question that has accompanied Bitcoin almost since quantum computing began to advance: what would happen if a sufficiently powerful machine managed to break the cryptography protecting digital currencies? This hypothesis has already raised alarms among researchers, investors, and developers. Now, two studies viewed side-by-side suggest a much more complex—and potentially reassuring—scenario, although the debate is far from over.

The quantum threat to Bitcoin is primarily linked to the elliptic curve cryptography used in its digital signatures. In theory, a sufficiently advanced quantum machine could use Shor's algorithm to solve the mathematical problem protecting certain keys.

Quantum computers and cryptography...A great amount of digital ink has been spilled on the topic of how quantum computers pose an existential threat to currently used asymmetric cryptography. We will therefore not discuss this in detail, but only explain the aspects that are relevant for the analysis in this article.

In asymmetric cryptography, a private-public key pair is generated in such a manner that the two keys have a mathematical relation between them. As the name suggests, the private key is kept as secret, while the public key is made publicly available. This allows individuals to produce a digital signature (using their private key) that can be verified by anyone who has the corresponding public key. This scheme is very common in the financial industry to prove authenticity and integrity of transactions.

The security of asymmetric cryptography is based on a mathematical principle called a “one-way function”. This principle dictates that the public key can be easily derived from the private key but not the other way around. All known (classical) algorithms to derive the private key from the public key require an astronomical amount of time to perform such a computation and are therefore not practical. However, in 1994, the mathematician Peter Shor published a quantum algorithm that can break the security assumption of the most common algorithms of asymmetric cryptography. This means that anyone with a sufficiently large quantum computer could use this algorithm to derive a private key from its corresponding public key, and thus, falsify any digital signature.

Bitcoin 101...To understand the impact of quantum computers on Bitcoin, we will start with a brief summary about how Bitcoin transactions work. Bitcoin is a decentralized system for transferring value. Unlike the banking system where it is the responsibility of a bank to provide customers with a bank account, a Bitcoin user is responsible for generating his own (random) address. By means of a simple procedure, the user's computer calculates a random Bitcoin address (related to the public key) as well as a secret (private key) that is required in order to perform transactions from this address.

Moving Bitcoins from one address to another is called a transaction. Such a transaction is similar to sending money from one bank account to another. In Bitcoin, the sender must authorize their transaction by providing a digital signature that proves they own the address where the funds are stored. Remember: someone with an operational quantum computer who has your public key could falsify this signature, and therefore potentially spend anyone’s Bitcoins!

In the Bitcoin network, the decision of which transactions are accepted into the network is ultimately left to the so called miners. Miners compete in a race to process the next batch of transactions, also called a block. Whoever wins the race, is allowed to construct the next block, awarding them new coins as they do so. Bitcoin blocks are linked to each other in a sequential manner. Together, they form a chain of blocks, also called the “blockchain”.

The victorious miner who creates a new block, is free to include whichever transaction they wish. Other miners express their agreement by building on top of blocks they agree with. In case of a disagreement, they will build on the most recently accepted block. In other words, if a rogue miner attempts to construct an invalid block, honest miners will ignore the invalid block and build on top of the most recent valid block instead.

Under certain circumstances, this would allow a private key to be derived from public information, thereby compromising funds.

A new chapter in this story has emerged with a study by researchers from Chinese universities. They calculated the resources required to solve the so-called discrete logarithm problem on 256-bit elliptic curves.

The result is striking: approximately 835 logical qubits would be required.

This estimate represents a reduction compared to previous calculations, which pointed to 1,098 or 1,175 qubits in certain configurations. For the secp256k1 curve used by Bitcoin, the researchers also arrived at a figure of 835 logical qubits, while estimating a cost of approximately 230.88 million Toffoli gates.

At first glance, reducing the resources needed for an attack seems like terrible news.

But there is another figure that completely changes the interpretation.

A potential physical barrier stands in the way of quantum computers... Physicist Tim Palmer, from the University of Oxford, is working on a formulation called Rational Quantum Mechanics. Among the implications discussed in this work is a hypothesis that is particularly relevant to large-scale quantum computing.

Investor Fred Krueger publicly linked this research to the new cryptography calculations and highlighted a potential limitation of approximately 400 coherently entangled qubits. It is precisely the difference between the two numbers that has sparked interest.

If breaking a 256-bit cryptographic curve requires around 835 logical qubits, yet there exists a fundamental physical barrier near 400 coherently entangled qubits, a quantum computer capable of executing such an attack could face an obstacle far deeper than merely scaling up its technological capacity.

In other words, it would not simply be a matter of waiting for better computers.

Physics itself could impose a limit.

This interpretation, however, hinges on important conditions. Palmer’s hypothesis does not definitively establish that no quantum system can surpass this threshold, nor does it prove in isolation that Bitcoin is permanently secure.

For now, it remains a theoretical possibility contrasted against another estimate.

Logical qubits are not the same as the qubits advertised by companies...There is yet another essential detail to understanding why these numbers can be misleading.

The 835 qubits mentioned in the study are logical qubits, not merely physical qubits.

Real-world quantum computers suffer from noise and errors. Constructing a single reliable logical qubit—protected by error-correction systems—may require many physical qubits.

This means that a machine capable of carrying out a cryptographically relevant attack would need to be far more sophisticated than a computer simply advertised as having 835 qubits.

The researchers themselves acknowledge that quantum algorithms relevant to cryptography continue to face limitations regarding hardware, error correction, and the need to maintain sufficiently low error rates.

Consequently, the scenario of a quantum computer stealing bitcoins does not appear imminent.

However, this does not mean developers are ignoring the issue.

The technical community has been exploring ways to make the protocol more resilient should cryptographically relevant computers actually emerge.

One such initiative is BIP-360, a proposal introducing a new type of output called Pay-to-Merkle-Root, or P2MR. Its goal is to reduce exposure to certain long-term quantum attacks by removing a vulnerable spending path associated with elliptic curve cryptography.

The proposal itself makes it clear that this alone would not resolve all possible attacks.

More comprehensive protection might eventually require the introduction of post-quantum signature schemes. The idea is to allow Bitcoin to evolve gradually as the actual threat level becomes clearer.

This precaution is important because there are still many unknowns.

By March 2026, a Google Quantum AI study had already reignited the discussion by indicating that the resources required to attack the encryption used by Bitcoin could be significantly lower than previous estimates suggested. Even so, there is currently no quantum machine capable of executing such an attack under real-world conditions.

The threat has not vanished, but the story has become more complex...It would be premature to declare Bitcoin definitively secure against quantum computers.

These new studies do not settle the debate; in fact, they add another layer of uncertainty.

On one hand, researchers continue to find ways to reduce the theoretical resources needed to break cryptographic systems. On the other, there are hypotheses suggesting that fundamental physical limitations could prevent quantum computers from reaching the necessary scale.

While this contest plays out in laboratories, Bitcoin developers are working on alternatives to avoid relying on a single bet regarding the future.

Perhaps this is the most important conclusion.

Bitcoin’s security in the face of quantum computing depends on more than just determining whether a machine capable of cracking its encryption might one day exist. There is also a race to modify defenses before such a machine appears.

And, following these new calculations, a threat that once seemed to hinge solely on time and technological progress has raised a far more intriguing question: what if there is a barrier that even quantum computers cannot cross?

mundophone

ACER

Acer releases new 16-inch laptop with 120 Hz OLED, 32 GB RAM and Intel Arc B390 graphics

Acer just announced the Vero 16 laptop, which has been designed with repairability in mind. To that end, the computer features removable port covers, a detachable external hinge and a latch on the bottom that can be opened without tools. There's even an engraving on the exterior that shows the location of the battery and motherboard. All of this combined should make service calls and installing upgrades much simpler than usual.

This looks to be an extremely capable laptop, beyond repairability. It can be outfitted with up to the Intel Core Ultra X9 processor 388H, 32GB of RAM and 512GB of SSD storage. The computer ships with up to a 16-inch 3K OLED display and comes with a nice 71Wh battery. There's a 5MP IR camera with a built-in privacy shutter, an SD card reader, an HDMI 2.1 port and a pair of Thunderbolt 4-rated USB-C ports.

It's also billed as a Copilot+ PC, so it has all of those AI tools that nobody really uses. This includes stuff like Windows Studio Effects, Cocreator and the Click to Do platform.

The Vero 16 will be available early next year, but only in Europe, the Middle East and Africa (EMEA) at launch. We don't know the price just yet, or when it's slated to come to America.

This isn't the only big Acer news of the day. The company also just refreshed its Swift series of laptops. The new Swift Air 16 is still thin, but is a bit heavier than the previous generation. It's powered by up to an Intel Core 7 processor 350 and will be available in most territories by early 2027.

The new Swift Blade 14 is an extremely light and thin laptop, clocking in at 1.76 pounds and just a half-inch thick when closed. It can also be outfitted with up to an Intel Core 7 processor 350. It'll be available for purchase in December but, again, only in EMEA.

Acer has quietly updated the options with which one of its lightweight 16-inch laptops is available. Previously restricted to the Core Ultra 9 386H, the latest Swift Go 16 can now be purchased with Arc B390 graphics via the Core Ultra X7 358H and Core Ultra X9 388H in some markets across East Asia and Southeast Asia.

A few months have passed since Acer started selling the new Swift Go 16 globally. Initially, the company claimed that it would be offering its lightweight 16-inch laptop with up to the Core Ultra 9 386H, a 16-core processor with a 4-core Xe3 iGPU. However, we have now spotted Acer selling its latest Swift Go 16 with Arc B390-backed processors.

For context, we reviewed the Swift 16 earlier this year with the Arc B390 and Core Ultra X7 358H (curr. $1,269 on Amazon). According to our benchmarks, these processors should provide Acer's 16-inch laptop with twice the GPU performance thanks to their Arc B390 iGPU. Conversely, CPU performance broadly remains unchanged from the Core Ultra 9 386H.

Acer Vero Aluminum is made from a purposeful blend of 50% post-industrial recycled aluminum and 50% low-carbon aluminum, then finished with a powder-coated surface for a refined tactile feel and a grounded, natural look.

Acer Vero 16 is designed around Acer’s circular economy approach, considering environmental impact across the full product lifecycle, from materials and manufacturing to distribution, use, and end-of-life recycling.

Intel Inside®...Your world is on your laptop. That’s why it delivers the power and efficiency to make, play, and finish it all on one charge. With performance built for mobility and seamless compatibility, your go-to apps and games always run smoothly.

Built for repairability...A more serviceable design makes key components easier to access, repair, and replace, helping extend the device’s usable life while supporting a more thoughtful approach to long-term ownership.

It's also built for longevity, with a MIL-STD-810H certified chassis that's made primarily from low-carbon aluminum. The battery and SSD are user-replaceable, which should increase the lifespan of the unit. The company promises that users will be able to upgrade with new processors "for at least two generations."

At the time of writing, a Core Ultra X7 358H-powered Swift Go 16 starts at SGD 2,499 (~$1,972) in Singapore. For reference, this configuration also includes 16 GB of RAM, a 1 TB SSD and a 1200p OLED display that outputs at 60 Hz. Alternatively, Acer offers the Core Ultra X7 358H with an 1800p OLED display, which boasts a 120 Hz refresh rate and 500 nits peak brightness.

Moreover, this SKU contains 32 GB of RAM. Unfortunately, these upgrades increase the Swift Go's price by over 30% to SGD 3,299 (~$2,605) and to HKD 15,398 in Hong Kong. Incidentally, a Core Ultra X9 388H variant costs SGD 3,599 (~$2,840) with the same display and memory as Acer's more expensive Core Ultra X7 358H SKU.

Currently, we only have found Arc B390-powered SKUs in Hong Kong and Singapore. Please note that while an apparent Best Buy US listing exists, this is actually the Swift 16 mislabelled as a Swift Go 16. At this stage, it is unclear whether Acer will offer the Swift Go 16 with the Core Ultra X7 358H or Core Ultra X9 388H in Europe, either.

 

by mundophone

TECH

Artificial Intelligence and the cognitive cost of convenience

The theme of the KES Summit 2026, held from August 25 to 27 in Trancoso, was "Conjugated Intelligences." Impeccably produced, the event brought together around 200 participants and nine speakers—André Alves and Lucas Liedke (Float), Heather Collins, Oliver Stuenkel, Alysson Muotri, Duda Franklin, Nilton Bonder, Kaká Werá, and this columnist (Dora Kaufman)—as well as a CEO panel featuring Rafaela Rezende (Decolar) and Alexandre Guerrero (Eletromídia).

Recognized as one of these conjugated intelligences, artificial intelligence (AI) permeated the entire event. Two insights stood out: the relational nature of the technology—where both ideation and implementation are co-creation processes between humans and AI—and the potential cognitive decline resulting from the intensive use of AI, exacerbated by a tendency among leaders to prioritize optimization and convenience over creative capacity. As Heather Collins put it: “AI should think with you, not for you.”

This tension between the convenience offered by AI and the cost it may impose on our ability to think and create finds empirical support in three studies conducted in distinct geographic and educational contexts, yet yielding convergent conclusions: intensive use of generative AI is associated with a reduction in critical thinking skills and learning outcomes. Analyzed together—a quantitative study with 666 participants in the UK, a longitudinal study involving over 26,000 Chinese secondary school students, and a survey on AI adoption at an elite American college—the three studies paint a coherent picture of the cognitive risks associated with the automation of reasoning.

Study 1: The AI ​​Learning Penalty in Chinese Secondary Education (June 2026, “The Generative AI Learning Penalty: Evidence from Chinese Secondary Education,” by David Stromberg, Victor Lei, and Yanhui Wu). This study features the most robust design from a causal perspective. Based on administrative data from 26,811 students in a Central Chinese county—tracked over 30 months—researchers isolated the causal effect of using generative AI. Key findings:

Productivity paradox: AI usage boosted homework grades by 18% and cut task completion time by 30% (from 64 to 45 minutes), yet led to an approximately 20% drop in performance on exams taken without AI within just six months.

Mechanism: 81% of users engaged in "homework outsourcing" by copying ready-made answers. The 20% who maintained study times comparable to non-users did not suffer significant losses—evidence that the issue lies in usage patterns, not the technology itself.

Heterogeneity: Greater losses occurred in Social Sciences (-27%) and STEM (-22%); impacts were more severe among boys and, notably, among previously high-performing students (-24% vs. -16% for low-performing students).

Study 2: AI Adoption and Equity in Higher Education (Middlebury College / IZA) (August 2025, “Generative AI in Higher Education: Evidence from an Elite College,” Zara Contractor and Germán Reyes).

The study by the IZA Institute of Labor Economics at Middlebury College does not directly measure academic performance but rather the speed at which generative AI spread and its implications for educational equity. Adoption rates surged from less than 10% in early 2023 to over 80% by late 2024. Key findings:

Near-universal adoption, though uneven across disciplines: 91.1% in Natural Sciences, compared to just 57.4% in Languages ​​and 48.6% in Literature. Demographic disparities: men use it more than women (88.7% vs. 78.4%); Black (92.3%) and Asian (91.3%) students adopt it more than White students (80.2%); students with a GPA below the median adopt it more (87.1%) than high achievers (80.3%)—a counterintuitive pattern that may indicate a risk of dependency among the most vulnerable.

Augmentation vs. Automation: 61.2% of usage cases involve "augmentation" (AI acting as an on-demand tutor to explain concepts or review texts), while 41.9% involve "automation" (replacing effort, such as writing entire essays). The authors warn that using automation under time pressure poses the greatest threat to human capital development.

Governance failure: only 10.1% of students are aware that the university offers free access to Microsoft Copilot Premium—an information gap that perpetuates inequality, as lower-income students rely on inferior free solutions.

Institutional policy dilemma: outright bans reduced reported usage by 37.8% but created a "prisoner's dilemma" that penalizes rule-followers while rewarding covert use. Only 32.6% of students know how to cite AI correctly, and 19.2% consider their course rules confusing or nonexistent.

Study 3: AI, Cognitive Offloading, and Critical Thinking (September 2025)...This is the only one of the three studies focused on the general population, combining quantitative research and qualitative interviews with 666 participants in the UK across various age groups and educational levels. Key findings:

Core correlations: the more people delegate mental tasks to external resources, the less they exercise evaluation, analysis, and inference.

Proven mediation: statistical analyses demonstrated that a significant portion of the impairment to critical thinking stems from the mechanism of "outsourcing" mental tasks to AI.

Age gradient: young people aged 17 to 25 showed greater reliance on AI and lower critical thinking scores; adults over 46 tend to choose traditional methods and maintain higher scores.

Education as a "cognitive antibody": individuals with master’s or doctoral degrees maintain analytical skepticism and verify sources even when using AI, whereas those with lower levels of education tend to accept AI responses without question.

Under different labels—"homework outsourcing" (China), "automation versus augmentation" (Middlebury), and "cognitive offloading" (Gerlich)—the three studies describe essentially the same behavior: using AI to obtain a final product without going through the mental process that fosters learning. In all three cases, the issue is not the technology itself but the pattern of use: those who turn to AI to verify, question, and complement their own judgment ("augmenters") do not suffer the losses observed among "automatizers" or "outsourcers."

Taken together, the three studies suggest that integrating generative AI into education requires an urgent recalibration of incentives: placing less weight on automatable out-of-class tasks and more on in-person, closed-book assessments; monitoring effort rather than just results; and establishing a clear pedagogical distinction between tools that teach one how to think and those that merely provide answers. The risk identified by all the authors is not the technology itself, but the possibility of an entire generation developing what one of the studies calls "cognitive debt"—an apparent productivity today that translates into a genuine inability to think, analyze, and solve complex problems in the future. This is a warning that applies equally to the classroom and to organizational processes.

Excessive reliance on artificial intelligence tools significantly reduces brain activity, recall accuracy, and critical thinking scores through a process known as cognitive offloading.

What happens to the brain:

-Reduced Neural Activation: Studies measuring EEG brain activity show that heavy AI use diminishes electrical connectivity and brain engagement compared to traditional searching or independent problem-solving.

-Weaker Memory Encoding: When an external tool generates ready-made answers, the brain skips the "productive struggle" required to encode and retain information deeply.

-Loss of Epistemic Authority: Over time, continuous outsourcing can lead to cognitive dependency, reducing a person's independent capacity to verify facts, evaluate biases, and think critically.

Preserving cognitive health:

-Use AI as a Co-Pilot: Treat AI outputs as a first draft or a brainstorming partner rather than absolute truth.

-Embrace Productive Struggle: Attempt to solve problems, outline arguments, or retrieve facts from memory before turning to automated tools.

-Verify and Cross-Check: Actively research multiple primary sources to maintain analytical reasoning skills

mundophone

IFA 2026

Acer SFF RTX Spark: Acer’s compact AI powerhouse

Acer used its global "next@Acer" press conference, held on the IFA 2026 stage, to unveil its latest engineering project: the Acer SFF RTX Spark. This small-form-factor (SFF) desktop was designed from the ground up to meet the demands of the new technological wave.

With the rapid rise of personal AI agents, having sufficient desktop processing power has become essential. The brand aims to move away from total reliance on the cloud, ensuring that all heavy lifting can be performed natively and without bottlenecks.

This announcement marks an interesting shift in the small desktop PC segment. Instead of relying on massive towers for complex tasks, the manufacturer is betting on a balance between energy efficiency and raw performance for users who require immediate productivity.

Standing beside it, the Acer SFF RTX Spark design takes a different shape entirely, built around NVIDIA’s RTX Spark superchip rather than Intel silicon. Vented panels wrap a narrow vertical shell accented with a warm metallic stripe, giving the design a presence that reads more like a piece of audio equipment than a workstation, while a fold-out stand lets it stand upright on a desk instead of lying flat.

Underneath that shell, Acer packs up to a 6,144-core Blackwell RTX GPU, up to a 20-core NVIDIA Grace CPU, up to 128 GB of unified memory, and up to 1 petaflop of AI compute, numbers usually reserved for server racks. Paired with NVIDIA’s full-stack AI platform and its complete suite of RTX technologies, the design targets creators, developers, and gamers alike.

Acer hasn’t confirmed pricing or a release window for the SFF RTX Spark design, while the Veriton RI110 arrives in North America during the fourth quarter of 2026 and reaches EMEA in the first quarter of 2027. Together, the two machines make a case that agentic AI doesn’t need a data center to feel powerful, just a well-engineered box small enough to tuck beside a monitor and forget about.

A compact AI powerhouse...At the heart of this machine lies the new NVIDIA RTX Spark superchip, developed specifically to power the next generation of Windows PC experiments. The platform caters to everyone from demanding content creators and language model programmers to gamers who refuse to compromise on maximum graphics settings.

To support this ambition without relying on external data centers, the desktop delivers formidable performance. The ability to run AI agents locally is—to say the least—impressive for such a small device, while also ensuring that sensitive data remains strictly private:

AI processing capability of up to 1 petaflop

NVIDIA Blackwell RTX GPU with up to 6,144 cores

20-core NVIDIA Grace CPU

128 GB of high-speed unified memory

NVIDIA full-stack AI platform and complete RTX technology ecosystem

The inclusion of 128 GB of unified memory alongside the Blackwell architecture and Grace CPU eliminates traditional transfer bottlenecks between the processor and the graphics card. It is a tailor-made solution for developers who need to load heavy models directly into system memory without sacrificing day-to-day fluidity.

Despite all the technical specifications detailed during the event in Berlin, Acer chose to keep launch dates and official pricing under wraps. The company only confirmed that retail availability for the Acer SFF RTX Spark will be publicly announced at a later date.

NVIDIA RTX Spark...NVIDIA RTX Spark redefines personal computing, combining personal agents, advanced content creation and high-performance gaming in a new platform engineered from the ground up for the next wave of Windows PC experiences. Designed for creators, AI developers and gamers, NVIDIA RTX Spark brings NVIDIA’s full-stack AI platform and full suite of RTX technologies to compact desktops.

It features up to a 6,144-core Blackwell RTX GPU, up to a 20-core NVIDIA Grace CPU, up to 1 petaflop of AI compute, and up to 128 GB of unified memory, paired with NVIDIA's full-stack AI platform and full suite of RTX technologies to accelerate AI innovation at the edge.

Powered by NVIDIA RTX Spark at its core, the Acer SFF RTX Spark offers major compute power in a space-saving footprint for tapping into agentic AI running locally. The design is built to deliver up to 1 petaflop of AI performance and up to 128 GB of high-speed unified memory, delivering a combination of compute and data privacy.

Availability of RTX Spark™ devices from Acer will be announced at a future date.

 

by mundophone

TECH

Tornyol: the mosquito-hunting drone

A mosquito is an awkward target for a machine. It is tiny, fast, difficult to see and constantly changing direction. Now, a Paris startup has shown a 40-gram drone chasing a flying insect through the air and striking it without a human guiding the final move. The catch is important: the insect was a moth, not a mosquito. The demonstration is a real engineering milestone, but it is still far from proving that tiny drones can reliably clear mosquitoes from homes, gardens or cities.

A palm-sized drone has just crossed a strange engineering threshold: it hunted down a flying insect and struck it in mid-air without a pilot guiding the final move.

The Paris startup behind it calls the test a major step toward automated mosquito control. But there is an important detail. The insect was a moth, not a mosquito. And the demonstration used some tracking and computing equipment that will eventually need to be packed into a much smaller autonomous system.

Imagine entering your bedroom at night without having to search for a mosquito hiding behind the curtain, clap your hands in the dark, or immediately resort to repellents and insecticides. Instead, a small device begins monitoring the room on its own, identifies suspicious movement, and gives chase. The concept sounds like something out of a sci-fi movie, but it is already being developed for home use—bringing with it challenges as fascinating as the technology itself.

The proposal combines sensors, artificial intelligence, and autonomous navigation to turn a small gadget into a sort of aerial home sentinel.

Mosquitoes are small, fast, and hard to spot. To make matters worse, they can quickly vanish from sight, especially in bedrooms, living rooms, and other spaces cluttered with obstacles.

It is precisely this problem that a new technology aims to tackle in an unconventional way.

Instead of dispersing chemicals into the air or relying on stationary traps, the system uses a small flying device capable of navigating the space autonomously. Before starting, the user defines the area to be protected, allowing the device to map out the environment.

From there, it can conduct short patrols and analyze its surroundings. Artificial intelligence comes into play to distinguish a mosquito from other objects or movements in the room.

Upon identifying a potential target, the device adjusts its flight path and attempts to track the insect in mid-air.

The company behind the project is Tornyol; it is banking on this specific combination of sensors, AI, and autonomous movement to create an alternative to traditional methods.

The device weighs approximately 40 grams and is designed to operate in a home environment without requiring constant supervision.

The concept is simple to grasp but extremely complex to execute: creating a machine small enough to maneuver inside a house yet intelligent enough to track down one of the smallest and most unpredictable enemies in existence. The technology is impressive in demonstrations, but there is a significant limitation that could determine whether this futuristic idea actually works in everyday life.

Despite all the technology involved, there is a very concrete obstacle: power.

Currently, each charge allows for about three minutes of flight time. Afterward, the device must automatically return to its station and spend approximately 30 minutes recharging.

This creates a considerable gap between the time the equipment can spend patrolling and the time required to get back into action.

In practice, the system could carry out short patrols throughout the day rather than staying airborne constantly. Even so, this limitation is a key issue the company needs to address before turning the concept into a truly efficient product for the home.

Tornyol is already working on a potential solution: an automatic battery-swapping system that would reduce downtime at the station and allow for significantly longer operating periods.

Another challenge involves safety.

The sonar is not as sophisticated as the camera you'd usually find on many drones, but it is built for one purpose.

The concept is one that Alex Toussaint, who created the Tornyol, discussed publicly back at the 2024 Superconference, an event for hackers.

There, he explained how any drone's fast-turning propellers could chop up a mosquito, but a human pilot would be unlikely to have the precision to track the insect.

Add in some off-the-shelf (to electronics enthusiasts, at least) components and Toussaint found that some tiny microphones could pick up the sonar return as well as the human range they were built for, so he just needed to get to work on the software.

The core element is the same component as you'd find used as a reversing sensor on many vehicles. The other sensor available to the drone's AI is a microphone to pick up the mosquito's sound and position it using the Doppler effect.

These have been upgraded to a base station with LeSonar2 phased array sonar, 380 smartphone microphones and a Artix-7 FPGA to map the world in 3D. The drone, then, isn't working entirely alone, but receiving advice from a computer

This gives it the ability to track 0.1mm movements in a range of 26ft (8m) – very precise indeed!

Unbelievably for a company that has recorded its first air-to-air kill, it is planning on rolling it out for U.S.-based customers "in the next few weeks," though at the moment a deposit of $100 and either a payment plan or a $1,100 final price are possibly as off-putting as that vagueness.

On the plus side, it seems very unlikely that this tech will successfully achieve the eradication of an entire species anytime soon, which is good because there would undoubtedly be unforeseen effects.

For one thing, mosquito larvae consume organic matter in water, and might actually help clean some lakes and water near the places the flying insects are most known for spreading disease. Species which eat them, like the mosquitofish, and some migratory birds, could see serious population falls too.

A flying device inside a home must be able to coexist with people, children, and pets. For this reason, the design incorporates shielded propellers, obstacle-avoidance mechanisms, and systems intended to maintain a safe distance from the home's occupants.

The device's low weight was also chosen to minimize the consequences of any potential collision.

For now, however, the product is still under development. The company plans to begin initial shipments in the United States in 2027, with an announced price of around $100.

If the technology can overcome its current limitations, fighting mosquitoes could take on a very different look: instead of hunting for the insect around the house, you might simply be able to let a small machine do the work for you.

 

mundophone

SAMSUNG

Leaked Exynos 2700 die shot reveals what Samsung has improved over the Exynos 2600 for the upcoming Galaxy S27 series

A leaked die shot of Samsung’s Exynos 2700, expected in 2027, points to a revamped CPU design, a large NPU, 24 MB of SLC, LPDDR6 support, and an improved Xclipse 970 GPU.

Last week, details emerged regarding the Exynos 2700—internally codenamed "Ulysses"—indicating that the chipset may feature a Samsung Xclipse 970 GPU based on a next-generation AMD graphics architecture. This information is particularly significant given that the platform appeared in the One UI 9 Beta 7 code in association with the upcoming Galaxy S27 series.

Furthermore, reports point to a configuration featuring 6 WGPs—equivalent to 12 Compute Units (CUs)—and up to 1,536 stream processors. The GPU may also operate at frequencies between 1.45 GHz and 1.50 GHz, with manufacturing handled by Samsung’s 2nm SF2P process.

Estimates place FP32 performance between approximately 6.2 and 6.8 TFLOPS, while FP16 operations could reach 12.4 to 13.6 TFLOPS. However, the final version of the product could approach 8 TFLOPS in FP32 if Samsung boosts the specifications during the validation phase.

SemiAnalysis has published what it describes as an exclusive first look at Samsung's Exynos 2700, the smartphone SoC expected to arrive in 2027 using Samsung Foundry's SF2P process. The leaked die shot outlines a layout with a large NPU block, 24 MB of SLC, and an Xclipse 970 GPU with eight WGPs, the same WGP count as the Exynos 2600's Xclipse 960. As with the Exynos 2600, the modem remains a separate component rather than being integrated into the SoC.

A revised CPU core layout...The most notable change is on the CPU side. SemiAnalysis's CPU IP annotations and assumptions have been corrected by X user Piglin (@Shulker1024). Samsung appears to be doubling its prime (Ultra) core count to two for this generation, joining a trend that is now widespread across the industry. Apple has used two prime cores for years, Qualcomm has done so since the original Snapdragon 8 Elite, and Xiaomi's Xring O3 continues the dual prime setup already present in its O1 predecessor. The leaked Dimensity 9600 Pro would mark MediaTek's first use of two prime cores, while the upcoming Snapdragon 8 Elite Gen 6 Extreme likely continues Qualcomm's existing approach. The Exynos 2700 pairs two different Arm core families rather than using two matching prime cores. One prime and middle core pairing uses Arm's C1-Ultra (3.36 GHz) and four C1-Pro cores (2.88 GHz), while the other uses Arm's C2-Ultra (4.24 GHz) and four C2-Pro cores (3.74 GHz), for a total of 10 CPU cores. Running the older C1 cores at a lower clock speed alongside the newer C2 cores at a higher clock speed suggests that Samsung is using C1 for sustained efficiency and C2 for peak performance rather than deploying two symmetric prime cores.

That dual prime core approach addresses a structural issue that hardware analysis channel Geekerwan found in the Exynos 2600's 1+9 cores layout. Unlike its competitors, the Exynos 2600 has only a single prime core and nine middle cores, with no intermediate "big" core tier to bridge the gap. When CPU demand exceeded what the lower clocked C1-Pro cores could handle but did not require the C1-Ultra, Samsung had to clock three of the middle cores up to 3.25 GHz to compensate, pushing them well outside their efficient operating range and consuming disproportionate amounts of power. A second, lower clocked prime core on the Exynos 2700 would give the scheduler a clearer high performance option before it has to use the largest core.

Fixing the GPU bandwidth bottleneck...LPDDR6 support also appears to be confirmed by the die's PHY layout, raising the SoC's memory-bandwidth ceiling. That matters because the Exynos 2600's Xclipse 960 GPU was previously found to be bandwidth constrained rather than compute constrained. According to Geekerwan, the Xclipse 960 occupied roughly 23% of the Exynos 2600's total die area around 46% more than Qualcomm's Adreno GPU in a comparable SoC and offered roughly 7 TFLOPS of theoretical throughput. With mobile LPDDR bandwidth capping out at more than 80 GB/s and only 2 MB of GPU L2 cache onboard, Geekerwan's analysis found that the GPU regularly hit a memory wall under bandwidth heavy workloads, leaving compute resources idle while waiting for memory.

Whether Samsung has increased the Xclipse 970's GPU L2 cache beyond that 2 MB figure remains unconfirmed in the SemiAnalysis leak, but there are signs pointing in that direction. The proportion of the GPU block occupied by the eight WGPs appears visually smaller relative to the total GPU die area than it does on the Exynos 2600's Xclipse 960. This suggests that Samsung may have allocated more of the GPU footprint to uncore logic in this generation, including L2 cache and other supporting blocks such as raster and cache coherency interfaces. No die measurements were provided for the GPU block, and the image's resolution is not high enough to estimate the actual L2 capacity from its area alone. Still, the apparent shift in proportions is a reasonable indication that the uncore area and potentially the L2 cache has grown. If that translates into a larger GPU L2 cache, it could help alleviate the memory bottleneck Geekerwan found in the Exynos 2600, alongside the additional external bandwidth provided by LPDDR6. Still, as with all coverage of engineering samples and pre-launch leaks, these specifications are subject to change before devices powered by the Exynos 2700 reach retail.

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TECH

Nature-inspired 3D printing could improve large-scale renewable energy storage

Researchers have created a 3D-printed electrode that could help make it easier and safer to store large amounts of renewable energy generated by wind and solar farms.

Led by University of Waterloo professor Dr. Maxime van der Heijden, the research team drew inspiration from natural structures to redesign a key component of redox flow batteries (RFBs), a technology that can store electricity for later use. The new design helps battery liquid move more efficiently, allowing the chemical reactions that store and release energy to occur more effectively.

Redox flow batteries work differently from the lithium-ion batteries commonly found in phones, electric vehicles and many energy-storage systems.

These batteries use water-based electrolytes rather than the flammable materials found in lithium-ion batteries, making them a potentially safer alternative for large-scale energy storage.

Redox flow batteries are a complementary technology to lithium-ion batteries for large -scale energy storage applications. Their water-based electrolytes make them a safer option for storing renewable energy at the scale needed to supply, for example, communities and the electrical grid with continuous renewable energy.

“Instead of storing energy in solid materials, they store energy in liquid electrolytes held in external tanks,” said van der Heijden, a chemical engineering professor at Waterloo. “The amount of stored energy can be increased simply by using larger tanks, making them well-suited for large-scale renewable energy storage and grid applications.”

Professor Maxime van der Heijden holding a 3D printed structure that will be converted into an electrode through heat treatment. Credit: University of Waterloo

That flexibility could become increasingly important as more electricity comes from renewable sources. Wind and solar power are intermittent, as they do not always produce electricity when it is needed, creating a need for technologies that can store excess energy and return it to the grid later.

Researchers used 3D printing to create porous RFB electrodes, enabling precise control over their structure and fluid flow.

“With 3D printing, we can design the internal structure of an electrode in ways that are difficult to achieve using conventional manufacturing,” said van der Heijden. “That gives us much greater control over how the liquid moves through the battery and reaches the surfaces where the energy-storing reactions take place.”

A key innovation was the use of triply periodic minimal surface (TPMS) geometries, complex, repeating three-dimensional shapes that can resemble structures found in nature.

The researchers tested several TPMS designs and found that one known as the “diamond” geometry worked best, increasing performance by 52 per cent.

They then used a digital light-processing 3D printer to produce the porous structures, which were heat-treated to form conductive carbon electrodes capable of carrying electricity.

The team successfully tested the electrodes in laboratory flow cell experiments and in a working vanadium redox flow battery, demonstrating that the 3D-printed designs can function in an operating battery. The proof of concept could help pave the way for more efficient redox flow batteries designed for large-scale energy storage.

Future research will focus on increasing the electrodes’ surface area, improving manufacturing methods and exploring advanced design tools to create even more effective electrode structures.

by University of Waterloo