TECH

NVIDIA DLSS 5 mod runs in a browser and even works on Apple Silicon

If you've been fascinated by NVIDIA's DLSS 5 technology, yet lack the requisite hardware or necessary gumption to try it out for yourself, you can now see a live demo right on your PC, directly in your browser, without having to manually download or install anything. The demo works in your web browser, and it uses your GPU, but it doesn't run the DLSS 5 model in real-time, so you can get it to work on just about anything with the requisite WebGPU support.

A developer going by MAAN has demonstrated NVIDIA DLSS 5 Neural Rendering running inside a web browser using WebGPU, and according to their post on X, it works on macOS too. A live demo is up now, and MAAN says people can try it with their own models as well. This is odd because NVIDIA doesn't officially support DLSS through WebGL or WebGPU at all. DLSS normally runs through NVIDIA's NGX interface or the Streamline SDK, which sits between a game and DirectX or Vulkan and needs motion vectors, depth data, and the rendered frame handed over directly. None of that maps cleanly onto a browser's pipeline, so it's not clear which approach the developer has used to get it running on a web browser.

The developer hasn't shared technical details either, but they have confirmed that the source code will be shared on GitHub soon. Performance is another question mark. VideoCardz says loading the Neural Rendering model alone took a second or two on an RTX 4090, and running on Apple Silicon apparently works but isn't fast. That probably rules out real-time gaming for now, though the concept could still be useful for 3D model previews, where a slower response time matters less. It's an early proof of concept more than anything, but getting DLSS 5 working outside NVIDIA's own supported paths entirely is still a neat trick.

Created by a developer known as MAAN and spotted by Videocardz, this web implementation of DLSS 5 is little more than a model viewer that has the ability to apply the DLSS 5 neural filter to loaded models. It offers a chance to preview the look of DLSS 5 on most platforms, as unlike the official release (currently only available in NBA 2K27), it does not require a GeForce RTX 50 series graphics card. I was able to get the demo working on my ASUS ROG Flow z13 tablet with Radeon 8060S graphics, although it didn't load on my aging Snapdragon 888-powered smartphone.

That could have been down to the amount of memory required. The models that the site includes are pretty big, but the DLSS 5 neural network itself is the largest amount of the download, totaling nearly 150 MB by itself. This is pretty large for a website, and some devices may balk at the bulk. However, the developer notes that it works on MacOS, and in theory it should work on most devices with a compatible browser.

If you keep up with DLSS news, it should come as no surprise that the page is able to run the DLSS 5 neural filter on nearly any hardware, as it's not doing anything unique to NVIDIA's hardware. Indeed, modders have gotten DLSS 5 working on older GeForce hardware, AMD and Intel hardware (including integrated graphics), and even on video feeds with no 3D information. While DLSS 5 is AI-powered, it's mostly a post-processing filter, so you can apply it to nearly anything, and it can be run on nearly anything with a little tweaking.

As Whycry notes, the interesting part of MAAN's demo is how he's wired it up to the Three.js framework. NVIDIA doesn't offer WebGL or WebGPU as supported DLSS interfaces, which means the developer has either extracted the weights and reimplemented the network or created some kind of shim to use the leaked binary directly. In any case, it's impressive stuff, and you should check it out if you have a minute.

mundophone

TECH

Iran and China use AI agents to create fake social media profiles

Iran and China have used autonomous artificial intelligence agents to create and manage networks of fake accounts on platforms such as Instagram, Facebook, X, and TikTok. These operations were identified by U.S. authorities and security researchers.

The groups combined open-source Chinese AI models with agents capable of operating software and executing tasks independently. Hundreds of these systems were reportedly employed to open profiles, generate political posts, and coordinate messaging aimed at influencing opinions and stoking online debate.

These so-called AI agents differ from conventional chatbots because they are not limited to answering questions or generating text. They can be assigned a goal and execute a sequence of actions to achieve it—such as accessing a platform, filling out forms, creating accounts, and publishing content. In the identified campaigns, this technology reportedly allowed for the automation of virtually the entire process, from profile creation to message dissemination, with minimal human involvement.

According to a report by *The New York Times*, operators in most cases disabled the safeguards that would otherwise prevent the models from creating fake accounts or manipulating online discussions.

Based on the characteristics of these operations, U.S. authorities and cybersecurity experts attributed the Iranian and Chinese campaigns to initiatives linked to their respective governments. Two other similar operations, associated with Israel, were reportedly conducted by private companies.

The campaigns were considered technically rudimentary, and there is no indication that they achieved significant reach. Nevertheless, they drew the attention of intelligence agencies, technology companies, and researchers by demonstrating how autonomous agents can reduce the cost and effort required to maintain networks of fake profiles.

Operations of this type previously relied on teams tasked with creating accounts, drafting posts, and coordinating interactions. With AI agents, a smaller number of people can control hundreds of profiles and continuously produce content. The technology also makes it possible to rapidly adapt messages to different platforms, topics, and audiences. The discovery points to a new phase in digital influence campaigns. The risk lies not only in the production of fake text, images, or videos, but in the automation of the entire infrastructure needed to distribute these materials and create the appearance of spontaneous support for specific narratives. 

First time Chinese models were used like this...US officials told the NYT that these instances marked one of the first times AI agents from Chinese-produced models were used to handle every stage of an online influence campaign, from creating new fake accounts to coordinating messaging and framing online.

Unlike proprietary AI models, the open-source models were manipulated by developers to allow the agents to evade safeguards designed to verify users and prevent the manipulation of online discourse, the US officials added.

The US intelligence community, along with cybersecurity experts, looked into the groups behind the AI-powered campaigns and traced those efforts back to state-backed Chinese and Iranian groups, as well as to Israeli campaigns stemming from two private firms.

Kyle Crichton, a fellow at the Georgetown Center for Security and Emerging Technology who studies AI and cybersecurity, told the NYT that “Agent-led campaigns” will likely become more common as the technology spreads.

“Agents can be fairly sophisticated, and so in addition to just scale, they have an ability to blend in and look more like a human user,” he said.

The investigation was published in the wake of other major technology news, as AI leaders warned of the risks of developing AI too quickly and without proper safety measures. These warnings followed public concern over recent stories of AI models going rogue and hacking other companies.

In a report last month, Meta said it had seen a “technically significant development” of bad actors using AI for malicious purposes.

AI is now “embedded within automated systems that can produce, adapt, and distribute content with minimal human intervention,” Meta added.

The investigation also traced AI-agent campaigns back to Israel...The report mentioned that IntelEye, a Tel Aviv-based company founded by former NSO Group employees, operated roughly 1,000 AI-powered fake accounts on social media to manipulate political discourse around the upcoming Israeli elections.

The company used DeepSeek, a Chinese open-source AI model, to create its agents. According to the investigation, the bots then published content both supporting and opposing Prime Minister Benjamin Netanyahu.

IntelEye disputed claims that its work was intended to influence online discussions, stating the company was conducting a defensive research experiment to expose weaknesses in AI systems.

“IntelEye does not operate influence campaigns,” co-founder Maor Sellek told the Times. “Our work with DeepSeek was conducted for defensive research and testing, to understand how models with insufficient safeguards could be misused and to improve detection of that activity.”

Sellek said IntelEye bought 10,000 social media accounts and handed over control to autonomous AI agents, though he added that only about 1,000 of the accounts were operational.

The agents used the accounts to leave comments on posts to boost engagement, although Sellek said they were not very successful.

Meta told the New York Times that the vast majority of the AI-powered agents set up on Facebook and Instagram by IntelEye, other companies, and state-backed groups had been removed by its automated detection and verification systems.

Networks of fake accounts...According to the publication, publicly available Chinese artificial intelligence models were used to create autonomous agents capable of performing online tasks. Hundreds of such agents were allegedly deployed to create networks of fake accounts on the American platforms Instagram, Facebook, X and TikTok.

The agents filled the accounts with posts about politics and current events, and also coordinated messages and online interactions. U.S. officials and cybersecurity researchers called this among the first known cases in which agents based on Chinese AI models were involved in almost the entire influence campaign process.

More current news is available on the UA.News Telegram channel Telegram. The operations were described as relatively primitive, but as demonstrating the possibility of automating tasks that previously required significant human involvement.

Iranian and Israeli operations...U.S. intelligence services, technology companies and security researchers investigated this activity and, according to The New York Times, linked the Chinese and Iranian operations to state-backed actors. Iranian AI accounts posed as ordinary Americans in major cities, shared popular memes, tagged journalists and politicians, and promoted views critical of the Republican Party.

Nearly 80,000 people followed these accounts in the first half of the year. The report also describes two Israeli campaigns linked to private companies. One of them, linked to the Tel Aviv-based company IntelEye, involved using AI agents to manage thousands of accounts and participate in discussions about upcoming elections in Israel. The company said it was a defensive research experiment to test the potential misuse of AI models.

TECH

Will AI Graduate from Tool to Lab Member? A Q&A with John Tsang

John Tsang, PhD, and his Yale lab members Jacob Kim and Ao Huang recently posed the question of whether AI immunologists are ready for prime time. Specifically, they were referring to large language models (LLMs) and whether they are as creative as humans in developing new hypotheses and methods for research in the field of immunology.

The answer, based on current studies, is no, not yet, says Tsang, Anthony N. Brady Professor of Immunobiology and professor of biomedical engineering at Yale School of Medicine. While there are functions where AI can be effective, such as summarizing relevant literature, LLMs like ChatGPT aren’t able to consistently generate truly original hypotheses, scientific ideas, or experimental approaches.

But, Tsang adds, researchers are exploring new ways of engaging LLMs that could overcome these current hurdles.

“I think there is a future—probably not too distant—where AI will be able to do such things,” says Tsang, founding director of the Yale Center for Systems and Engineering Immunology and an investigator with and the Yale lead for Biohub New York. “I see AI one day being a sort of team member, contributing ideas alongside scientists.”

We spoke to Tsang about the current state of what he calls AI immunologists and where he sees opportunities going forward.

John Tsang, PhD: An AI immunologist isn’t yet a robot that goes into the lab and does experiments. It’s an AI system that can think through concepts, propose hypotheses, suggest experiments, and interpret the data that come out of those experiments. That’s the kind of AI immunologist we’re talking about.

What do you mean when you refer to AI immunologists?

John Tsang, PhD: An AI immunologist isn’t yet a robot that goes into the lab and does experiments. It’s an AI system that can think through concepts, propose hypotheses, suggest experiments, and interpret the data that come out of those experiments. That’s the kind of AI immunologist we’re talking about.

How is AI capable in that regard now? What can it do well?

Tsang: What it can do really well is research the literature. For example, if you ask, “What's known about this molecule? What can that molecule do to this kind of cell?” AI can deliver excellent information. Basically, if you talk to any of these AI chatbots, things like Claude, Gemini, and ChatGPT, they're doing very well in this regard these days.

And where does AI still have room for improvement?

Tsang: AI is still not super effective at coming up with things that may be novel in the sense that AI may not be completely taking into account, for example, information from another field. If you don't prompt the AI and say, “Hey, have you thought about this particular knowledge in biochemistry or this particular thing over in aging? Do you think there's a connection between that and this principle?” it doesn’t do that on its own.

You note there are exciting opportunities when it comes to multi-agent approaches. Can you describe what these approaches are?

Tsang: When using AI in a classical way, you have one agent, and you have an interactive discussion with it. With a multi-agent approach, as a few research groups have now described, you ask different independent bots to take different roles. One bot could be a computational biologist, another one could be a biochemist, and yet another one could be someone who knows a lot about antibodies, for example. And then there's also a human who's involved in the team that can give the bots a high-level task and then let them talk to each other.

What is very interesting is that in examples of this type of approach so far, the majority of the interactions happen among the AI bots. The human doesn't necessarily go in and tell them what do. And researchers have found that the bots are capable of coming up with a work plan and executing it. Even when the human, while part of the team, is not doing the major coordination.

So that is quite interesting, suggesting that by having interactions among these bots—and I think the key is that they take on different roles—they are able to start to generate something that's quite emergent, not just straight answering. They can engage a multi-step plan and make decisions based on the data. That is, I think, quite intriguing.

One striking example is “The Virtual Lab,” a system developed by Biohub colleagues in which an AI principal investigator led a team of AI specialists—an immunologist, a computational biologist, and a machine learning expert—to design nanobodies (small antibody-like molecules) against a new SARS-CoV-2 variant. The team produced 92 candidate designs, and two were experimentally confirmed to bind the new variant. Notably, more than 98% of the words exchanged during the project came from the AI agents themselves; the human researcher set the goal and stayed in the loop, but the bulk of the back-and-forth largely happened among AI agents.

Would this type of approach help overcome some of the current limitations of AI?

Tsang: Yes, I think so. And I think it also overcomes what's called a context window problem. So each bot has a certain memory, and when you have a team like that, the total volume of memory becomes quite large. At the same time, the bots can start to write out answers to external files, which can get pulled back in again, extending memory further, in a way.

Another interesting thing that's emerging is what's called “world models.” So these are agents that have a much longer memory span, so they're not just working within this context window. They have external storage, and they try to provide a model of the world that the particular agent is in. I think that's also going to help.

Where do you think is the greatest potential when it comes to AI and immunology?

Tsang: I think the answer lies on two levels. First, I think it's going to be helpful in proposing and hypothesizing things that we haven't thought of yet. However, that will be based on existing data, and we still have limited data on lots of things in immunology.

For example, one project that I co-lead called the Human Immunome Project aims to capture human immunology variation across different people and across different populations. Now that kind of data simply doesn't exist yet. If you ask a chat bot or even multi-agents and say, "I know this vaccine works in the U.S. with this level of efficacy, what happens if I bring it to Tanzania?" it would have no idea because it simply doesn't have enough information and knowledge yet.

So that's where a different kind of AI, models like quantitative models of the human immune system, come in. That's another area that we are actively working on, how to develop that kind of model. Of course, once such models become available and useful, then AI agents can use them to query and answer the questions we ask.

Additionally, AI agents, I think, could form a team with us to work together on that problem, where we need to design experiments. We need to think about, given a certain number of resources and budget, what's the best first experiment, for example, what's the best first data collection exercise? Those are the kind of things that we are thinking about a lot.

How do these AI technologies intersect with education?

Tsang: I think we need to put these into education fairly quickly so students can understand how to use them and feel comfortable doing so. Plus, there are creative aspects, like the multi-agents and how to put them together.

I personally use these tools, especially when I’m starting something new. I want to get a sense of what’s known and also to potentially make novel connections. And my lab has discussed this sort of AI team approach.

Sometimes I think there's a bit of a worry about using AI to cheat, for example. That's what some educators may be thinking. But I think we have an opportunity to promote positive and productive use. And so the question is how to incorporate this technology in the most positive, effective way possible.

by: Mallory Locklear, PhD--Managing Editor—Science, Research, and Education

TECH

GrapheneOS says Google is keeping new Android 17 security features from other phones

The Android smartphone world often prides itself on its open-source nature, but the rules of the game appear to be changing drastically. GrapheneOS, a renowned privacy-focused ROM, has leveled harsh criticism at Google regarding the operating system's latest update.

At issue is the release of the new Android 17 QPR1, which began rolling out in mid-September alongside the Pixel Drop package. According to the project's developers, the search giant is intentionally blocking access to crucial features and security fixes within the AOSP (Android Open Source Project). This stance creates a massive gap between Pixel devices and the rest of the ecosystem, delaying improvements for other brands by months. It is, to say the least, concerning to see the company that founded Android increasingly closing the door on community developers.

The team behind GrapheneOS did not mince words when analyzing the inner workings of this new version. They claim Google intentionally withheld new developer APIs and security patches, keeping them exclusive to its own Pixel smartphones.

The major issue is that partner manufacturers and custom ROM creators are effectively left with their hands tied until December, when the code is expected to finally be released to AOSP with the arrival of Android 17 QPR2. This means millions of users on other brands remain exposed to vulnerabilities that Google has already patched on its own devices—raising serious ethical questions.

To understand the gravity of the situation, here are the key technical points raised by the security team:

Critical vulnerability fixes included in the September Pixel bulletin were not integrated into the general Android security bulletin.

New APIs essential for developers are restricted and completely out of reach for third parties.

Deep system changes force community projects to resort to reverse engineering in order to adapt the software in time. This is the first time since the days of the old Android Honeycomb that a version has introduced new APIs without simultaneously sharing them via AOSP.

GrapheneOS—which had already begun moving away from Google hardware to form a more transparent development alliance with Motorola—views this situation as a clear-cut anti-competitive move. It is hard to disagree when one considers the obvious imbalance this creates in the current mobile market.

For now, the development team reports that while they have managed to adapt much of their code for the new Android 17 QPR1, they lack the legal authorization to officially distribute it. Their workaround has been to port firmware, drivers, and other technical components from the Pixel directly to the standard Android 17 base—a genuine engineering puzzle.

It remains to be seen whether Google will maintain this strategy of distancing itself in the coming years or if community pressure will force a change in direction. One thing is certain: the promise of an Android platform that is perfectly uniform and open to everyone is gradually becoming a mirage, accessible only to those who invest in a Pixel.

GrapheneOS has accused Google of deliberately withholding crucial APIs and Security fixes from AOSP in the Android 17 QPR1 update. The decision affects Android manufacturers, with the new security measures potentially never arriving on phones other than Google Pixel devices.

Google Is Withholding AOSP code...According to GrapheneOS, Android 17 QPR1 marks the first time since Android Honeycomb that Google has introduced new developer APIs without simultaneously publishing the corresponding code to AOSP.

This means that the new APIs that promote better privacy and security can never be implemented on other Android smartphones, as Google has officially made them exclusive.

The GrapheneOS team stated they had their code ported prior to the September 15, 2026 release but currently lack permission to publish it. The complaint arrives on top of developers already being forced into the tedious process of backporting Pixel firmware, userspace drivers, and HALs directly to the older Android 17 framework.

This is because Google has essentially stopped publishing device trees, source code and binaries for Pixel phones, essentially attempting to kill third-party custom ROM support.

However, projects like Graphene and LineageOS have held on, albeit still limited to the Pixel 9 series. As a result of this change, even a year after the Pixel 10's launch, the phones are still not supported by the LineageOS team. 

Critical security repercussions...The accusations also extend to critical device safety. The September 2026 Pixel Update Bulletin mentions several vulnerability fixes that are conspicuously missing from the standard Android Security Bulletin.

GrapheneOS claims that many of these fixes apply to core platform components used by non-Pixel devices. By holding these ecosystem-wide patches back until the Android 17 QPR2 release in December, Google is giving its Pixel hardware an exclusive security advantage over other Android OEMs.

While GrapheneOS acknowledges that Pixels are still useful for their project due to their baseline security features, they warn that Google's new strategy makes supporting the devices significantly more difficult.

That said, with the upcoming GrapheneOS-Motorola partnership, the project expects its upcoming collaboration to streamline software development, as that partnership will provide direct access to official firmware and driver code.

This is clearly not a good outlook on Google's part, especially considering how the company doesn't hesitate to market Android security and privacy. If anything, these actions suggest otherwise, and delaying new security APIs and features is quite contradictory.

mundophone

APPLE

iPhone 18 Pro battery life disappoints vs iPhone 17 Pro in gaming

Thanks to bigger batteries and the more power-efficient Apple A20 Pro SoC, Apple promises some excellent battery life gains for the iPhone 18 Pro and the iPhone 18 Pro Max. However, Dave2D shows that the battery life improvements aren't as impressive as some might've imagined.

Apple makes some bold claims regarding the battery life of the iPhone 18 Pro and the iPhone 18 Pro Max. Apple officially claims 45 hours of video playback for the eSIM-only iPhone 18 Pro Max and 36 hours for the iPhone 18 Pro. While Apple doesn’t officially advertise the battery capacities, reports suggest that the eSIM-only iPhone 18 Pro and the iPhone 18 Pro Max carry a 4,288 mAh and 5,567 mAh battery, respectively.

However, despite packing larger batteries and a more efficient A20 Pro SoC, Dave2D shows that the battery life gain in demanding use cases remains small for the iPhone 18 Pro Max and non-existent for the iPhone 18 Pro.

The YouTuber reports that, in Genshin Impact at 600 nits of screen brightness, the Apple iPhone 18 Pro Max only lasted 23 minutes longer than the iPhone 17 Pro Max. So, the 9.4% bigger battery is undoubtedly helping here.

The battery life improvement was much more noticeable in lighter routine usage, as the iPhone 18 Pro Max lasted for 17 hours and 19 minutes while browsing Reddit at 600 nits. The iPhone 17 Pro Max lasted for 15 hours and 36 minutes in the same scenario.

The iPhone 18 Pro and the iPhone 17 Pro have roughly identical battery capacities, as there is only a 36 mAh difference between the two. So, any battery life improvements we see on the iPhone 18 Pro will primarily come down to the efficiency gains provided by the Apple A20 Pro.

Coming in at 14 hours and 27 minutes, the iPhone 18 Pro lasted almost an hour longer than the iPhone 17 Pro in Dave2D’s Reddit loop test. However, the iPhone 18 Pro actually fell behind the iPhone 17 Pro while playing Genshin Impact, as the iPhone 18 Pro's battery life was 11 minutes shorter.

In short, the battery life upgrades enjoyed by the new iPhone 18 Pro and 18 Pro Max don’t seem to be that big. So, you might be disappointed if you were hoping for much better battery life.

iPhone 18 Pro battery numbers are both good and bad news for Samsung...Apple announced the iPhone 18 Pro and iPhone 18 Pro Max last week, and it had plenty to say about them. The company doesn’t traditionally share battery capacity, though, but a regulatory source has now revealed these details and more.

The European Union’s EPREL database has revealed that the iPhone 18 Pro has a 4,056mAh battery, a negligible upgrade over the iPhone 17 Pro’s 3,988mAh battery. On the other hand, the iPhone 18 Pro Max is listed with a 5,391mAh battery, which is a significant upgrade over the iPhone 17 Pro Max’s 4,823mAh battery.

It’s worth noting that global iPhones usually have slightly smaller batteries than US models, as the global variants still offer physical SIM slots. For example, the iPhone 17 Pro has a 4,252mAh battery in the US, while the Pro Max has a 5,088mAh battery. So don’t be surprised if the iPhone 18 Pro and Pro Max have even larger batteries in the US than the EU’s database indicates.

Apple is also banking on the 2nm A20 Pro chip to deliver plenty of efficiency gains across both models. Nevertheless, it’s clear that the iPhone 18 Pro Max is probably the one to get if you want the longest battery life possible in an iPhone. It also has a bigger battery than all recent Galaxy flagships. However, many other Android brands are now using 7,000mAh, 8,000mAh, or even 10,000mAh silicon-carbon batteries in their phones for even longer endurance.

The EPREL database also reveals disappointing news if you were hoping for better long-term battery health. The iPhone 18 Pro series offers 1,000 charging cycles before reaching an effective 80% capacity. This means you’ll basically lose 20% of your battery’s capacity after roughly three years. That’s in line with recent iPhones and Pixel phones, but short of rival devices like the Samsung Galaxy S26 series (1,200 cycles), Motorola Edge 70 Pro (1,200), OnePlus 15 (1,100 to 1,400), and Xiaomi 17 series (1,600).

There is good news for charging times, though. Xiaobai’s Tech Reviews (h/t: Ice Universe) reports that the iPhone 18 Pro Max in particular is the fastest-charging iPhone ever. It can apparently reach 100% in 55 minutes, with a peak speed of 52W. There are faster-charging Android phones out there, but this seems a little faster than the Pixel 11 Pro XL.

mundophone

TECH

Heat directly converted to cooling in electricity-free refrigeration

Motor-free refrigeration...The basic cooling principle used in refrigerators, air conditioning systems, and data centers has remained unchanged for over a century: an electric-powered compressor transfers heat—carried by a refrigerant fluid—from one location to another. As the demand for cooling constantly rises, heating and cooling currently account for nearly half of global energy consumption.

So, what if we replaced the electric motors used in these cooling technologies with heat itself?

This is precisely the concept being tested by Yi-Ting Hsiau and colleagues from the Karlsruhe Institute of Technology (Germany) and the University of Tsukuba (Japan).

Two ultra-thin films made of a special metal alloy—known as a shape-memory alloy, the same type used in artificial muscles—interact to convert thermal energy into mechanical work, a task currently performed by electric motors. This mechanical work is then converted into cooling.

Beyond saving energy, this interaction opens up new possibilities for waste heat recovery and the use of solar energy in solid-state cooling systems.

The proof-of-concept prototype features two ultra-thin nickel-titanium films that perform complementary functions.

The first film utilizes the shape-memory effect: upon heating, it begins to contract, converting thermal energy directly into mechanical work without the need for an electric motor. This movement is immediately transferred to the second film, where cyclic loading and unloading induce reversible changes in the metal's crystal structure, generating cooling.

In this way, heat replaces the electric actuator previously used in elastocaloric cooling systems.

"The crucial innovation lies in combining two complementary functions of shape-memory alloys: one film converts heat into mechanical work, and the other converts that work into cooling," explained Professor Jingyuan Xu. Thus, we have established a new approach to solid-state cooling, opening up interesting possibilities for harnessing waste heat and solar energy.

Cooling and heating account for nearly half of global energy demand1, and the ongoing miniaturization of electronic and photonic devices makes efficient thermal management increasingly critical. Vapour compression cooling dominates the market, but its reliance on high global warming potential refrigerants accounts for more than 40% of global energy-related CO2 emissions2 and its bulky components, such as compressors, make it incompatible with miniaturized systems. Thermoelectric devices have been widely deployed to address the demand for miniaturized cooling3, but their efficiency is only 10–15% of the theoretical reversed Carnot limit, roughly one-quarter that of modern vapour compression systems4, which greatly constrains their applicability. Elastocaloric cooling, driven by stress-induced martensitic transformations in shape memory alloys (SMAs) or crystallization transitions in polymers, has emerged as the most promising alternative9,10, eliminating volatile refrigerants and offering theoretical efficiencies of up to 84% of the Carnot limit11.

Most recent research on elastocaloric cooling has focused on bulk SMA geometries for macroscale applications, such as SMA tubes, which are coupled with heat-transfer fluids and regenerator designs to achieve high cooling power and a large temperature span. Macroscale elastocaloric devices have already demonstrated temperature spans of up to 75 K and 1,284 W cooling power at zero temperature lift. By contrast, for miniature-scale cooling, film geometries offer promising prospects: their small mass and large surface-to-volume ratio enhance heat transfer, enabling higher cycling frequencies (for example, 4 Hz) and a high specific cooling power (SCP) of up to 19 W g−1. Moreover, film-based elastocaloric devices can avoid the use of heat-transfer fluids by employing solid-to-solid mechanical contact for heat exchange17,18, minimizing system complexity and allowing operation in confined spaces. To further enhance performance, cascading can increase the temperature span, while parallelization boosts cooling power, together enabling device upscaling for higher overall output.

Currently, however, elastocaloric cooling prototypes typically rely on electrically powered actuators, making them electricity-dependent systems that continue to incur indirect CO2 emissions during operation as most electricity is still generated from fossil fuels. In addition, current devices typically use electromechanical motors or hydraulic actuators as drivetrains to generate the large forces required to load superelastic SMA refrigerants. However, high-force actuators are commonly designed for long stroke operation, resulting in relatively low force/displacement ratios (defined here as the ratio between maximum output force and stroke length). In contrast, superelastic SMA refrigerants require stresses on the order of several hundred megapascals while undergoing strain of only a few percent, thereby demanding a high force over limited displacement and corresponding to a high force/displacement ratio. This mismatch in actuator–refrigerant mechanical characteristics, together with the substantial mass of conventional drivetrains relative to thin-film refrigerants, leads to inefficient drivetrain utilization and oversized actuation systems26,27. A key challenge, therefore, is to develop elastocaloric cooling systems that minimize electricity dependence while employing mechanically well-matched actuators.

In this work, we developed a film-based, heat-driven elastocaloric cooling prototype by integrating thermally powered shape memory actuator films with superelastic SMA refrigerant films, offering the potential to harness low-temperature heat sources, such as waste heat, to deliver electricity-free cooling. The SMA-based thermal actuator provides an improved force/displacement ratio of 14.5 N mm−1, compared with 1.1 N mm−1 for the commercial electromechanical actuator used in this study (Supplementary Note 1), as a result of the intrinsic shape recovery behaviour of high-temperature SMAs, which enables high force generation over a limited stroke without bulky transmission systems. While a regenerative heat-driven elastocaloric concept has been proposed through thermodynamic modelling and numerical simulations at the macroscopic scale26, in this study, we experimentally realized thermally powered shape memory actuation directly coupled with a thin-film elastocaloric refrigerant architecture at miniature scale. The integrated prototype achieves a temperature span of 12.9 K at the material level and 4.0 K at the device level under Joule-heated thermal actuation at 86 °C. Operation driven by an external heat source was further validated, achieving a device-level temperature span of 2.2 K. This approach addresses the key limitations of electrically driven systems and illustrates the feasibility of using thermal energy as the driving source for thin-film solid-state elastocaloric cooling.

Construction of a heat-driven elastocaloric cooling system...Our prototype integrates two mechanically coupled units: a thermal actuation unit and a cooling unit (Fig. 1a). Thin-film SMAs were chosen for their high surface-to-volume ratio, which promotes rapid heat transfer and enables efficient cycling. The actuation unit employs a 22-µm-thick TiNi SMA film that generates force through the one-way shape memory effect when heated by thermal input. The cooling unit consists of a 26.5-µm-thick TiNiFe superelastic SMA film that functions as the elastocaloric refrigerant. Heat sinks are attached to both units to enable efficient heat rejection, while a polymer coupling element transfers the actuator force directly to the refrigerant film. The two films are mounted in series on low-friction sliders guided on rails, ensuring precise longitudinal motion (Fig. 1b). Further details of device fabrication and construction are provided in the Methods and Supplementary Fig. 1.

Fig1: a, Schematic of the device, comprising a thermal actuation unit (left) and a cooling unit (right), coupled mechanically but thermally isolated. The actuator film (TiNi) is heated to cyclically load and unload the refrigerant film (TiNiFe). Movements ∆x1–3 indicate the motions of individual components: ∆x1 represents the coupled stroke between the two units, ∆x2 is the motion of the heat sink/source of the cooling unit and ∆x3 is the motion of the heat sink of the actuation unit. b, Three-dimensional rendering of the prototype, showing the actuator film, refrigerant film and heat exchangers. The actuator converts absorbed thermal energy into mechanical work that drives the cooling cycle. c, Thermomechanical behaviour of SMAs. The actuator operates via the shape memory effect (red), while the refrigerant exploits stress-induced superelasticity (blue). The dashed curves and labelled points correspond to the operation steps in d. As, austenite start temperature; Af, austenite finish temperature; Ms, martensite start temperature; Mf, martensite finish temperature. d, Schematic showing the four steps of the heat-driven elastocaloric cooling cycle. (1) Thermal input raises the actuator film temperature above Af (orange curve 1 in c), causing contraction and loading the refrigerant film, which undergoes stress-induced austenite–martensite transformation with associated heating (light blue curve 1 in c). (2) The refrigerant film contacts the heat sink of the cooling unit, releasing heat until returning to ambient temperature (both points 2 in c). (3) The actuator film cools to ambient temperature via its heat sink (orange curve 3 in c), unloading the refrigerant film, which undergoes the reverse transformation accompanied by cooling (light blue curve 3 in c). (4) The refrigerant film contacts the heat source, absorbing heat and returning to ambient temperature (both points 4 in c).

The fundamental concept of this work is to combine the shape memory effect and superelasticity of SMAs to realize thermally driven actuation for elastocaloric cooling. The red curve illustrates the one-way shape memory effect: when pre-strained in the martensitic state (temperature (T) < martensite finish temperature (Mf)), an SMA can generate an actuation force upon heating above the austenite finish temperature (Af) as it recovers its pre-set length. The blue curve represents the superelastic characteristic of an SMA with Af below room temperature. Under external loading, these materials undergo a stress-induced martensitic transformation that releases latent heat, while unloading triggers the reverse transformation that absorbs heat. This reversible process constitutes the elastocaloric effect35.

The operation cycle of the device consists of four steps. The labelled orange and light-blue dashed curves in Fig. 1c correspond to the four operating steps, illustrating the thermomechanical behaviours of the actuator (shape memory effect) and refrigerant (superelastic effect) films. Both films are initially pre-strained to enable actuation by the shape memory effect. In step 1, the thermal input raises the actuator film above its Af temperature, transforming it from martensite to austenite and generating sufficient force to elongate the refrigerant film. This loading induces a stress-driven martensitic transformation in the refrigerant, accompanied by heat release. In step 2, the heated refrigerant film contacts the heat sink of the cooling unit, where the released heat is rejected. In step 3, the actuator film is cooled to ambient temperature via its heat sink, returning to its martensitic state with reduced force. The refrigerant film then recovers to its original length through superelastic unloading, undergoing the reverse transformation and cooling below ambient temperature. In step 4, the cooled refrigerant film contacts the heat source, absorbs heat from the target region and returns to ambient temperature. Repetition of this four-step cycle transfers heat from the cold side (heat source of the cooling unit) to the hot side (heat sink of the cooling unit), thereby establishing a measurable temperature span across the device.

Solid-state cooling...With the actuator at a temperature of 86°C, the prototype achieved a temperature difference of 4°C at the component level, while the temperature change within the elastocaloric refrigerant was nearly 13°C. The system also operated reliably with an external heat source supplying 130°C, demonstrating its ability to function with real-world heat sources.

Thus, the prototype experimentally demonstrates the concept's viability for the first time.

The configuration used in the demonstration was designed as a feasibility study and is, therefore, not yet optimized for maximum cooling capacity. The team is already working on connecting multiple films in parallel to increase cooling capacity.

Potential applications range from cooling computer processors—which could utilize their own waste heat for this purpose—to cooling sensitive electronic components in automobiles by leveraging heat from the propulsion and transmission systems.

"We believe this is just the beginning," said Xu. "By scaling up this technology, we aim to develop compact cooling systems that harness abundant heat sources for sustainable cooling."

Researchers have built the first heat-driven solid-state cooling system that uses ultra-thin shape memory alloy films to convert waste heat directly into cooling without an electric motor, as published in Nature Energy by scientists from the Karlsruhe Institute of Technology (KIT) and the University of Tsukuba. This breakthrough is further documented in Nature, establishing a new way to utilize thermal energy for eco-friendly thermal management

How the system works...The prototype pairs two paper-thin, mechanically connected metal films that perform complementary tasks:

The actuator film: A 22-micrometer nickel-titanium shape memory film that acts as an internal engine. When it gets warm, it contracts sharply and turns thermal energy into mechanical pulling force

The refrigerant film: A 26.5-micrometer superelastic TiNiFe film connected to the first film. The pulling force stretches and relaxes this second film cyclically

The elastocaloric effect: Releasing the mechanical load causes a sudden shift in the metal's crystal structure, which absorbs heat and generates cold

Performance and metrics:

At 86 °C (187 °F) Joule Heating: The refrigerant film achieves a temperature span of 12.9 K, while the overall device reaches 4.0 K with a cooling power of 2.79 mW

At 130 °C (266 °F) external heat source: The system maintains a stable device-level temperature span of 2.2 K and a cooling power of 2.09 mW.

Efficiency: The tiny actuator foil generates a high force-to-displacement ratio of 14.5 N/mm, beating comparable commercial electric actuators by more than ten times

Potential applications:

Computers: Cooling high-powered processors by using their own waste heat to drive the cycle.

Automotive: Managing sensitive electronics in vehicles using leftover heat from propulsion and transmission systems.

Sustainable HVAC: Scaling up the design by connecting multiple films in parallel to replace conventional, high-emission vapor-compression systems

Karlsruhe Institute of Technology

TECH

The new artificial intelligence war may be decided by something other than intelligence

Historically, most technological breakthroughs do not fundamentally affect the risk of conflict. There have been notable exceptions. The invention of printing helped fuel the social and religious upheavals in Europe that later contributed to the outbreak of the Thirty Years’ War in 1618. By contrast, nuclear weapons have significantly dampened the risk of great power war since World War II.

Because advanced artificial intelligence (AI) could create far-reaching social, economic, and military disruptions, it could be another exceptional technology with important implications for international security (Kissinger, Schmidt, and Mundie 2024). Analysts need to seriously consider the possibility that AI may cause changes in the international security landscape that could lead to the outbreak of wars that would not otherwise happen (Mitre and Predd 2025).

Drawing on decades of research about what conditions make wars more or less likely throughout history, we examine six hypotheses about how AI might increase the potential for major interstate war (Van Evera 1994). The hypotheses reflect different ways that AI’s effects on militaries, economies, and societies might undermine international stability, with a focus on the pathways that appear most plausible and concerning. We evaluate these hypotheses by identifying what key conditions are needed for them to be valid and then assessing the likelihood that those conditions will align in ways that would make conflict more likely.

Exploring the consequences of advanced artificial intelligence that is much more sophisticated than what exists today, the analysis assumes that AI could eventually become capable of reliably matching human performance across a wide range of cognitive tasks, which some technologists refer to as “artificial general intelligence” (Kahl 2025).

Overall, the risk that AI will directly trigger a major war appears low, especially if governments take steps to manage the technology’s use. But AI could create destabilizing shifts in the balance of power or negatively influence human strategic judgment in ways that fuel misperceptions. Fortunately, prudent government policies can help limit these risks.

For years, the race for leadership in artificial intelligence was measured almost exclusively by one question: which company created the most powerful model? That logic remains important, but a second metric is emerging that could completely change the game. Cheaper models are achieving results close to those of the most advanced systems while operating at much higher speeds and lower costs. For those developing AI agents, this difference could be decisive.

Google highlighted this shift with Gemini 3.8 Flash, introduced as its most advanced general-purpose model to date. The tool was developed specifically for programming, multi-step reasoning, and agentic tasks—scenarios where the AI ​​must use tools, verify results, and correct its own errors.

The promotional price is $0.75 per million input tokens and $3.75 per million output tokens. This rate applies through the end of 2026, after which the prices will double the following year.

The most interesting aspect, however, emerges when price and performance are analyzed together. On Artificial Analysis’s Intelligence Index, Gemini 3.8 Flash scores 59 points in high-level reasoning, placing it close to the configurations of much more expensive models.

Its speed is also noteworthy. The system reaches approximately 327 tokens per second—more than four times the median recorded among reasoning models evaluated by the platform.

There is, however, a caveat: the new Gemini uses about 30% more output tokens than its predecessor and may make more tool calls. Consequently, while the price per token remains low, the effective cost per task has risen by about 40%.

Just when it seemed Google had found a particularly aggressive balance between price and capability, Meta unveiled Muse Spark 1.3.

The model was designed for programming and long-running tasks, particularly those requiring the management of tools, files, and multiple instructions within a single context. In the "xhigh" configuration, Muse Spark 1.3 scores 61 points on the Intelligence Index—two points higher than Gemini 3.8 Flash. The "max" variant, currently limited in availability, reaches 62 points.

The cost is also competitive: $1.25 per million input tokens and $4.25 per million output tokens. According to Artificial Analysis, each task analyzed costs approximately $0.55—slightly less than Google's model.

Meta also claims that the new system uses about 20% fewer tool calls and 25% fewer tokens than the previous generation.

This doesn't automatically make Muse Spark the best model. Gemini, for instance, boasts nearly double the generation speed. Furthermore, no single benchmark can replicate every real-world use case.

Even so, the results highlight something important: the gap between cost-effective models and cutting-edge systems is narrowing rapidly.

The shift that might unsettle OpenAI and Anthropic... This is where the competition gets interesting. For a simple query, the price difference between models might seem negligible. But autonomous agents operate differently.

An AI tasked with coding an application, analyzing hundreds of documents, or conducting extensive research might make dozens of tool calls and consume millions of tokens. In that scenario, small price differences are no longer small.

More expensive models from OpenAI and Anthropic can cost several times more per million tokens. In certain comparisons, Gemini 3.8 Flash costs up to 13 times less per token while maintaining performance levels relatively close to the competition in some evaluations.

This doesn't mean Google or Meta have definitively overtaken the leaders in artificial intelligence. That isn't the point.

To compete, they may not even need to create the smartest model on the planet. It is enough to offer an AI capable of performing nearly the same tasks, with sufficient speed and at a fraction of the price.

In the era of autonomous agents, that detail could be huge. A difference of just a few points on a benchmark may matter less than the number of tasks a company can execute within the same budget.

The next great battle in artificial intelligence, therefore, may not be decided solely by who has the smartest model. The winner could be the one that delivers enough intelligence to get the job done—while spending far less to do so.

mundophone