Agregátor RSS

Bylo oznámeno, že webový prohlížeč Opera GX zaměřený na hráče počítačových her je už také na Flathubu and Snapcraftu.

Workers are facing a conundrum: They worry about the potential for their displacement by AI even as it dramatically speeds up their own productivity.

According to a new survey from Anthropic, workers in roles most likely to be taken over by AI (developers or IT workers, for instance) recognize their precarious position. Yet, perhaps naturally, they readily adopt the tools that could take their jobs, and see first-hand how well they work.

This measurement is fundamentally different from the way others are gauging AI job displacement, noted Thomas Randall, research director at Info-Tech Research Group.

While macro reports, such as those from Goldman Sachs, the International Monetary Fund (IMF), or the World Economic Forum (WEF), are asking what share of existing job tasks AI could theoretically perform in the future, “Anthropic is measuring qualitative experiences of workers in the present,” he pointed out. This “tells us how people are navigating this landscape in real time.”

The paradox of AI in the workforce

Anthropic’s survey of 81,000 Claude users gauged peoples’ “visions and fears” around advances in AI, and weighed these findings against the company’s own measurement of jobs most vulnerable to AI displacement. This was based on Claude usage data; jobs are identified as more exposed when associated tasks are significantly performed on the platform, in work-related contexts, and take up a larger share of a role.

Some occupations at risk include computer programmers, data entry keyers, market researchers, software quality assurance analysts and testers, information security analysts, and computer user support specialists.

Overall, one-fifth of respondents expressed concern about displacement, noting that their job, or at least aspects of it, is being taken over by automation. Those in jobs identified as most exposed readily recognized that fact, voicing worry three times as often as those in less at-risk positions. One software engineer remarked: “like anyone who has a white collar job these days, I’m 100% concerned, pretty much 24/7 concerned, about losing my job eventually to AI.”

Early-career respondents were also more nervous than others.

At the same time, those in the highest-paid occupations reported the largest productivity gains when using AI. This is most notably in terms of their ability to perform new tasks, which was cited by 48% of users. In addition, 40% of workers said the technology helped speed up their work, and a little more than 10% said it improved the quality of their work.

In general, enterprise usage of AI is “actually quite consistent,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. Teams are using the technology “where information is abundant and time is limited,” such as in drafting documents and code, summarizing content, responding to customer queries, navigating internal systems.

Is AI actually creating more work?

Still, not everyone thinks AI makes their jobs easier or faster. In some cases, people felt it made their work harder; for instance, project managers are assigning tickets for issues that are much more difficult to solve, Anthropic noted.

Gogia agreed that, even when tasks become easier, scope and responsibilities expand, and roles can absorb adjacent tasks. This results in a “redistribution of effort,” rather than a reduction of effort.

“Faster generation means higher expectations on quality,” he said. More output feeds into decision pipelines that are already constrained. “In some cases, the system becomes heavier, not lighter.”

Delayed impact on enterprises

 The market is rewarding those who can integrate AI into complex workflows to do more, faster, and often with better outcomes, Gogia noted. However, the most exposed tasks, including  documentation, basic coding, routine analysis, and structured support work, often “sit at the base of the experience ladder.”

These very tasks traditionally have given entry-level workers a way in, and the automation of them reduces the urgency for companies to hire them. “What you begin to lose is not the job,” said Gogia. “It is the path into the job.”

This can have a delayed impact; enterprises may not realize until years later that they do not have enough mid-level experts because they didn’t bring enough people in at lower levels. As AI plays a greater role in the workplace, there must be a “conscious effort” to rethink how people enter and grow, Gogia said. “New pathways need to be created, and they need to be deliberate.”

How enterprise leaders should adjust

As is often the case, sentiment moves faster than structural change, Gogia pointed out. Workers feel the shift almost immediately, but organizations take longer to adjust hiring, redesign roles, and rethink workforce structures.

“This is why expectations can become misaligned,” he noted. The reality is that most enterprises have introduced AI into existing ways of working without fundamentally changing them. Acceleration occurs in unchanged systems that still carry the same dependencies, approval chains, and coordination challenges.

Ultimately, Gogia advised, leaders must approach the shift with “intentional design.” This requires clarity, he emphasized; people need to understand how their work is expected to change. What will be enhanced? What will reduce? Where should they focus their development?

Baselines are moving: Roles may begin to look “oversized” as what used to be considered a full day’s work begins to look like half a day’s work, or what used to be considered efficient begins to look average. “AI is changing how work is done, but more importantly, it is changing what work expects from people,” said Gogia.

As well, Info-Tech’s Randall pointed out that workers who experience AI expanding what they can do by performing tasks previously outside their competence appear to relate to AI more positively than those who experience it as doing their existing job faster. So, he advised, “tech leaders should design AI deployment around capability extensions.”

Along with goal setting, managers must have support, Gogia emphasized. They set expectations and interpret strategy, and when they’re not properly equipped, “even the best tools will fall short,” he said. Measurement must also evolve; enterprises need to look at quality, sustainability, and capability development over time.

“What we are witnessing right now is not a sudden disruption,” said Gogia. “It is a gradual shift that is becoming impossible to ignore.”

The AI agent introduction frenzy continued at a torrid pace this week, with OpenAI launching what it called workspace agents in ChatGPT and Microsoft adding hosted agents to its Foundry Agent Service.

Both launched on the same day that Google both updated its Gemini Enterprise app to provide new ways for office workers to build, manage, and interact with AI agents, and launched the Gemini Enterprise Agent Platform, which the company said is designed to build, scale, govern, and optimize agents.

This trio of offerings follows Anthropic’s early April introduction of Claude Managed Agents, a suite of composable APIs for building and hosting cloud-hosted agents, which is now in public beta.

In its announcement, OpenAI said, “workspace agents are an evolution of GPTs. Powered by Codex, they can take on many of the tasks people already do at work—from preparing reports, to writing code, to responding to messages. They run in the cloud, so they can keep working even when you’re not. They’re also designed to be shared within an organization, so teams can build an agent once, use it together in ChatGPT or Slack, and improve it over time.”

Microsoft, meanwhile, stated in a blog that its latest move “brings agent-optimized compute and services designed for production-grade enterprise agents.” After its preview of hosted agents last year at Microsoft Ignite, the company said, “this refresh is a fundamentally different experience: secure per-session sandboxes with filesystem persistence, integrated identity, and scale-to-zero economics.”

Announcements are connected

Jason Andersen, principal analyst at Moor Insights & Strategy, said, “these four announcements are connected, as the frenzy around agents continues. What OpenAI is announcing is the native ability to support the creation and sharing of agents.”

This is new functionality for OpenAI, which is a bit late to the game; Google, Microsoft, Anthropic and others have had this capability for some time, and are in fact moving farther ahead with these other announcements, he said.

 “What we are seeing with Anthropic and Microsoft is that, as agents become more powerful, they will go to great lengths to solve the problem they are posed with, and sometimes that includes the agent writing code and doing other tasks,” he pointed out. “This increases complexity and concerns about agents and models being well managed while running. The hosting options both of these vendors provide are a more advanced infrastructure for agents to run.”

Right now, he added, “many agents are being treated as simply a more advanced front end. These newer options provide the ability for an agent to do things like spin up a dedicated container, and they can support semi-autonomous and, in some cases, autonomous operations. These two announcements are more infrastructure-related, whereas OpenAI is more about agent building.”

He described the Google launch as being “something in between.”

He noted, “OpenAI’s announcement is very similar to last year’s announcement of Gemini Enterprise from Google. This year, Google took steps forward to enable a management control plane for agents called Gemini Enterprise Agent Platform, which enables a much richer sharing experience and a number of management and governance capabilities.”

On the whole, Andersen said, “the agent space is getting very hot, and some who have been later to the party are getting on board, and those who have been investing are evolving to provide end customers more scale, operations, and security capabilities.”

Brian Jackson, principal research director at Info-Tech Research Group, said that with the flurry of announcements “we’re seeing a race to gain critical mass as the agentic platform becomes the daily work interface for the enterprise. Anthropic and OpenAI are coming at it from their AI startup positioning, while Google, Microsoft, and Amazon are leveraging their entrenched hyperscaler and enterprise platform positions.”

Jackson pointed out that the differentiation in what these tech firms offer is most clear in who they are targeting and their delivery model.

He noted that OpenAI’s Workspace Agents are designed for non-technical business teams. They provide templates for agents that can automate tasks from lead scoring to vendor research reports. Users can “prompt” their way to work automation without worrying about the behind-the-scenes mechanics – what model is being used, what APIs are being called, how data is retrieved and written, or how permissions are granted.

Anthropic is taking a different approach, he said. Rather than going directly to business users, it is providing tools to enterprise development teams to build their own agents and provide a custom interface to their users. Anthropic’s Managed Agents are a group of composable APIs that developers can use. The approach is more flexible, but it requires more effort to produce value.

Microsoft and Google, on the other hand, are both vertically integrated platforms providing an agentic layer on top of an extensive stack. Microsoft’s Foundry is similar to Anthropic’s offering, but offers even more flexibility by remaining model-agnostic and allowing developers to choose their preferred agentic framework.

New problems as the market develops

As the agentic platform market develops, Jackson observed, “we are seeing new problems crop up regarding observability. Detecting and observing agents will be rooted in the identity system used to provision them. However, since each platform uses its own identity system, it will be difficult for any one platform to see all agents created in an enterprise, or worse, those created by a rogue user (‘Shadow AI’).”

Furthermore, he added, “agentic workflows imply significantly higher AI token consumption to complete work. We are already seeing AI capacity constraints and price increases due to high demand. Because agents require multiple ‘reasoning’ steps to complete a single task, it is very hard to predict what a workflow you automate today might cost to run one year from now.”

This means that IT leaders need to decide where they will build the agentic layer of their stack. “You don’t want to get it wrong, because becoming entrenched in one platform means significant vendor lock-in,” he said. “We already worry about lock-in with systems and data, but when you add an intelligence layer, you are essentially building a brain with neuronal pathways to your workflows. It is not going to be easy to do a ‘brain transplant’ to another platform later.”

This article originally appeared on InfoWorld.

That’s a few minutes longer than it takes to fill up the average gas-powered car—but still fast enough it might not matter.

For all their promise, electric cars have always had a big drawback: Charging takes much longer than filling up a gas tank.

But the gap has been closing, and this week, Chinese battery giant CATL announced battery technology nearing parity. On Tuesday, the company said its third-generation Shenxing fast-charging battery goes from 10 percent to 98 percent charged in 6 minutes and 27 seconds.

If you’re driving an electric car around town, charging is a breeze. You probably don’t have to do it more than a couple times a month. And when you do, you can plug your car in overnight at home.

For longer trips, you’ll need a charging station. Smartphone apps can help, and drivers learn to plan ahead, but it’s still a pain. Stations aren’t abundant, and when you find one, there may be a line. A full charge will then take the better part of an hour. Most people aim for 80 percent, but even that consumes up to a half hour. EV fans may find it’s worth the trouble, but range is a sticking point for many drivers.

It’s no wonder that battery makers have been hyper-focused on energy density, which determines how far EVs can go, and charging speed. They’ve improved both in recent years. But increasing range, which involves balancing a complex mix of battery chemistries, weight, and economics, may prove a tougher tradeoff to manage than bringing charging times in line with gas-powered cars at the pump.

In other words, if you can travel the same distance and charge or gas up in roughly the same amount of time, the two become interchangeable on long trips. (This also depends, of course, on infrastructure—more on that below.)

CATL has been pushing the boundaries of charging speeds with its Shenxing line of fast-charging batteries, first announced in 2023. The company is the world’s largest EV battery manufacturer. Its products power EVs in China but also American brands including Tesla and Ford.

The numbers are hard to compare generation to generation and company to company, as the specs reported vary. The second-generation Shenxing battery, announced last year, charged from 5 percent to 80 percent in 15 minutes, according to the Financial Times. Then in March of this year, rival battery maker BYD said its Blade 2.0 model charged 10 percent to 97 percent in 9 minutes.

Notching nearly a full charge in under 10 minutes was already an impressive mark.

But on Tuesday, CATL one-upped BYD with its third-generation Shenxing, which takes a full charge in a little over six minutes. At a maximum legal rate of 10 gallons per minute at gas stations in the US, that’s still a few minutes longer than it takes to fill up most gas-powered cars. But it might also be fast enough not to matter. Big gas-powered trucks are already in the same range. And CATL said charging to 80 percent takes just 3 minutes and 44 seconds—which is nearly a wash.

“This effectively closes the gap with ICE [internal combustion engine] vehicles,” Bernstein analysts wrote in a note quoted by the Wall Street Journal.

Fast-charging batteries have shorter lifespans due to excess heat. But CATL said it’s tamed the heat by decreasing the amount produced in operation, more effectively bleeding it off, and controlling how and when it’s generated. The battery retains over 90 percent capacity after 1,000 charging cycles.

“The boundaries of electrochemistry are still far from being reached, and the possibilities of materials science are still far from being exhausted,” CATL founder and CEO, Robin Zeng, told reporters and investors, per the Financial Times.

With 6-minute charging times, it’s easy to imagine charging station lines evaporating. Instead of drivers grabbing a meal while their car takes up real estate, they’d breeze in and out, like at a gas station.

That vision will take time to materialize, however. There are still far fewer charging stations than there are gas pumps. And those that do exist won’t include chargers that handle the bleeding edge anytime soon.

As for the batteries themselves, splashy press releases don’t usually translate to near-term availability and might not match real-world performance. The third-generation Shenxing isn’t likely to hit roads right away. When it does, it could show up in Chinese models first, be pricey (like BYD’s latest offering), and require fancy new chargers.

Still, it’s no longer theoretical: EVs can compete with the convenience of traditional cars at the gas station.

The post CATL’s New EV Battery Charges in Six Minutes appeared first on SingularityHub.

Další kosmologický model navrhuje, jak se obejít bez temné energie. Gravitační vlny jako možný původ temné hmoty. 5 věcí, které sonda Juice zjistila o mezihvězdné kometě 3I/ATLAS. I obyčejná neutralizace dokáže ještě přinést překvapení. Tajemství černých děr by mohlo skrývat v 7dimenzionální geometrii.

Ministerstvo školství si připravilo nepěkné překvapení. Stát se dosud tvářil, že bude platit povinné plavecké výcviky a v dubnu to vzal zpět. Školy a zřizovatelé teď řeší, kde vezmou peníze na jejich zaplacení.

Na světě je nové vydání Ubuntu, které je současně pravidelnou LTS verzí, tedy tou, která nechává svého uživatele v klidu nejméně 10 let od vydání. Nové Ubuntu je podařeným mixem novinek evolučních i revolučních.

Quaise Energy míří za obnovitelnou energií do zemských hlubin. Spoléhají na to, že superhorké horniny, jejichž teplota překračuje 300 °C, mohou poskytnout spoustu geotermální energie. V současné době již staví pilotní projekt Obsidian, který by měl v roce 2030 dosáhnout výkonu 50 MW.

Po kritice podpory OC pouze na deskách s čipsetem Z890 a oficiálním příslibu cenově dostupnější procesorů s podporou přetaktování se dozvídáme, že taktování nabídnou dva čipsety pro Nova Lake…

Legit-looking website, camera-on interviews, jokes about backdoors ... it worked

EXCLUSIVE  It all started with a LinkedIn message, as so many employment scams do these days.…

Posted by Thomas Brunner, Yu-Han Liu, Moni Pande

At Google, our Threat Intelligence teams are dedicated to staying ahead of real-world adversarial activity, proactively monitoring emerging threats before they can impact users. Right now, Indirect Prompt Injection (IPI) is a top priority for the security community, anticipating it as a primary attack vector for adversaries to target and compromise AI agents. But while the danger of IPI is widely discussed, are threat actors actually exploiting this vector today – and if so, how?

To answer these questions and to uncover real-world abuse, we initiated a broad sweep of the public web to monitor for known indirect prompt injection patterns. This is what we found. 

The threat of indirect prompt injection

Unlike a direct injection where a user "jailbreaks" a chatbot, IPI occurs when an AI system processes content—like a website, email, or document—that contains malicious instructions. When the AI reads this poisoned content, it may silently follow the attacker's commands instead of the user's original intent.

This is not a new area of concern for us and Google has been working tirelessly to combat these threats. Our efforts involve cross-functional collaboration between researchers at Google DeepMind (GDM) and defenders like the Google Threat Intelligence Group (GTIG). We have previously detailed our work in this area and researchers have further highlighted the evolving nature of these vulnerabilities.

Despite this collective focus, a fundamental question remains: to what degree are real-world malicious actors currently operationalizing these attacks?

Proactive monitoring at GoogleThe landscape of IPI on the web

There are many channels through which attackers might try to send prompt injections. However, one location is particularly easy to observe - the public web. Here, threat actors may simply seed prompt injections on websites in hope of corrupting AI systems that browse them.

Public research confirms these attacks are possible; consequently, we should expect real-world adversaries to exploit these vulnerabilities to cause harm.

Thus, we ask a basic question: What outcomes are real attackers trying to achieve today?

For ease of access and reproducibility, we chose to use Common Crawl, which is a large repository of crawled websites from the English-speaking web. Common Crawl provides monthly snapshots of 2-3 billion pages each. These are mostly static websites, which includes self-published content such as blogs, forums and comments on these sites, but as a caveat it does not contain most social media content (e.g., LinkedIn, Facebook, X, …) as Common Crawl skips websites with login walls and anti-crawl directives.

This means that, while prompt injections have been observed on social media, we reserve these for an upcoming separate study. For a first look, we can observe prompt injections even in standard HTML, for which Common Crawl conveniently provides not just the source, but also the parsed plaintext.

The challenge of false positives

The task of scanning large amounts of documents for prompt injections may sound simple, but in reality is hindered by an overwhelming number of false positive detections.

Early experiments revealed a significant volume of "benign" prompt injection text, which illustrates the complexity of distinguishing between functional threats and harmless content. Many prompt injections were found in research papers, educational blog posts, or security articles discussing this very topic. 

False positives: Most prompt injections in web content tend to be education material for researchers. (Source: GitHub/swisskyrepo)

When searching for prompt injections naively, the majority of detections are benign content – false positives in our case. Therefore, we opted for a coarse-to-fine filtering approach:

• Pattern Matching: We initially identified candidate pages by searching for a range of popular prompt injection signatures, like “ignore … instructions”, “if you are an AI”, etc.

• LLM-Based Classification: These candidates were then processed by Gemini to classify the intent of the suspicious text, and to understand whether they were part of the overall document narrative or suspiciously out of place.

• Human Validation: A final round of manual review was conducted on the classified results to ensure high confidence in our findings.

While this approach is not exhaustive and might miss uncommon signatures, it can serve as a starting point for understanding the quality of prompt injections in the wild. 

What we found

Our analysis revealed a range of attempts that, if successful, would try to manipulate AI systems browsing the website. Most of the prompt injections we observed fall into these categories:

• Harmless pranks

• Helpful guidance

• Search engine optimization (SEO)

• Deterring AI agents 

• Malicious

• Data exfiltration

• Destruction

Harmless Prank

This class of prompt injection aims to cause mostly harmless side effects in AI assistants reading the website. We found many instances of this – consider the source code of this website, which contains an invisible prompt injection that instructs agents reading the website to change their conversational tone:

Helpful Guidance

We also observed website authors who wanted to exert control over AI summaries in order to provide the best service to their readers. We consider this a benign example, since the prompt injection does not attempt to prevent AI summary, but instead instructs it to add relevant context.

We note that this example could easily turn malicious if the instruction tried to add misinformation or attempted to redirect the user to third party websites.

Search Engine Optimization (SEO)Some websites include prompt injections for the purpose of SEO, trying to manipulate AI assistants into promoting their business over others:
While the above example is simple, we have also started to see more sophisticated SEO prompt injection attempts. Consider the intricate prompt below, which was seemingly generated by an automated SEO suite and inserted into website text:Deterring AI agents

Some websites try to prevent retrieval by AI agents via prompt injection. There exist many examples of “If you are an AI, then do not crawl this website”. However, we also observed more insidious implementations: 

This injection tries to lure AI readers onto a separate page which, when opened, streams an infinite amount of text that never finishes loading. In this way, the author might hope to waste resources or cause timeout errors during the processing of their website.

Malicious: Exfiltration

We were able to observe a small number of prompt injections that aim at theft of data. However, for this class of attacks, sophistication seemed much lower. Consider this example:

As we can see, this is a website author performing an experiment. We did not observe significant amounts of advanced attacks (e.g. using known exfiltration prompts published by security researchers in 2025). This seems to indicate that attackers have yet not productionized this research at scale.

Malicious: Destruction

Finally, we observed a number of websites that attempt to vandalize the machine of anyone using AI assistants. If executed, the commands in this example would try to delete all files on the user’s machine:

While potentially devastating, we consider this simple injection unlikely to succeed, which makes it similar to those in the other categories: We mostly found individual website authors who seemed to be running experiments or pranks, without replicating advanced IPI strategies found in recently published research. 

What does this mean?

Our results indicate that attackers are experimenting with IPI on the web. While the observed activity suggests limited sophistication, this might be only part of the bigger picture.

For one, we scanned only an archive of the public web (CommonCrawl), which does not capture major social media sites. Additionally, even though sophistication was low, we observed an uptick in detections over time: We saw a relative increase of 32% in the malicious category between November 2025 and February 2026, repeating the scan on multiple versions of the archive. This upward trend indicates growing interest in IPI attacks. 

In general, threat actors tend to engage based on cost/benefit considerations. In the past, IPI attacks were considered exotic and difficult. And even when compromised, AI systems often were not able to execute malicious actions reliably.

We believe that this could change soon. Today’s AI systems are much more capable, increasing their value as targets, while threat actors have simultaneously begun automating their operations with agentic AI, bringing down the cost of attack. As a result, we expect both the scale and sophistication of attempted IPI attacks to grow in the near future.

Moving forward

Our findings indicate that, while past attempts at IPI attacks on the web have been low in sophistication, their upward trend suggests that the threat is maturing and will soon grow in both scale and complexity.

At Google, we are prepared to face this emergent threat, as we continue to invest in hardening our AI models and products. Our dedicated red teams have been relentlessly pressure-testing our systems to ensure Gemini is robust to adversarial manipulation, and our AI Vulnerability Reward Program allows external researchers to participate. 

Finally, Google’s established ability to process global-scale data in real-time allows us to identify and neutralize threats before they can impact users. We remain committed to keeping the Internet safe and will continue to share intelligence with the community.

To learn more about Google’s progress and research on generative AI threat actors, attack techniques, and vulnerabilities, take a look at the following resources:

• Google Workspace’s continuous approach to mitigating indirect prompt injections (blog post) from Google’s GenAI security team

• Mitigating prompt injection attacks with a layered defense strategy (blog post) from Google’s GenAI security team

• Beyond Speculation: Data-Driven Insights into AI and Cybersecurity (RSAC 2025 conference keynote) from Google’s Threat Intelligence Group (GTIG)

• AI Threat Tracker (report) from Google’s Threat Intelligence Group (GTIG)

• Google's Approach for Secure AI Agents (white paper) from Google’s Secure AI Framework (SAIF) team

• Advancing Gemini's security safeguards (blog post) from Google’s DeepMind team

• Lessons from Defending Gemini Against Indirect Prompt Injections (white paper) from Google’s DeepMind team

Hackers are actively exploiting a critical vulnerability in the Breeze Cache plugin for WordPress that allows uploading arbitrary files on the server without authentication. [...]

Akcionáři americké mediální společnosti Warner Bros. Discovery dnes schválili převzetí firmy konkurentem Paramount Skydance za zhruba 110 miliard dolarů (téměř 2,3 bilionu Kč). Firmy se na spojení dohodly v únoru. O část společnosti Warner Bros. Discovery dříve usilovala rovněž streamovací platforma Netflix, se svou nabídkou však neuspěla. Transakci ještě budou schvalovat regulační orgány, a to nejen ve Spojených státech, ale také například v Evropské unii. Proti spojení podniků se staví řada herců, režisérů, scénáristů a dalších lidí z filmového a televizního průmyslu. Otevřený dopis odmítající fúzi podepsaly přes čtyři tisíce lidí. „Výsledkem bude méně příležitostí, méně pracovních míst, vyšší náklady a menší výběr pro diváky ve Spojených státech a po celém světě. Je znepokojivé, že by tato fúze snížila počet velkých amerických filmových studií na pouhá čtyři“ píše se v dopisu.

A relatively new ransomware family is using a novel approach to hype the strength of the encryption used to scramble files—making, or at least claiming, that it is protected against attacks by quantum computers.

Kyber, as the ransomware is called, has been around since at least last September and quickly attracted attention for the claim that it used ML-KEM, short for Module Lattice-based Key Encapsulation Mechanism and is a standard shepherded by the National Institute of Standards and Technology. The Kyber ransomware name comes from the alternate name for ML-KEM, which is also Kyber. For the rest of the article, Kyber refers to the ransomware; the algorithm is referred to as ML-KEM.

It's all about marketing

ML-KEM is an asymmetric encryption method for exchanging keys. It involves problems based on lattices, a structure in mathematics that quantum computers have no advantage in solving over classic computing. ML-KEM is designed to replace Elliptic Curve and RSA cryptosystems, both of which are based on problems that quantum computers with sufficient strength can tackle.

Read full article

Comments

Canonical vydal (email, blog, YouTube) Ubuntu 26.04 LTS Resolute Raccoon. Přehled novinek v poznámkách k vydání. Vydány byly také oficiální deriváty Edubuntu, Kubuntu, Lubuntu, Ubuntu Budgie, Ubuntu Cinnamon, Ubuntu Kylin, Ubuntu Studio, Ubuntu Unity a Xubuntu. Jedná se o 11. vydání s dlouhodobou podporou (LTS).

All the Typhoons, everywhere, all at once

A majority of China-linked threat actors are using compromised routers and IoT devices worldwide, turning this gear into proxy networks to carry out further intrusions, steal sensitive data, and disrupt victim organizations’ operations, according to a joint 10-country advisory.…

The Bitwarden CLI was briefly compromised after attackers uploaded a malicious @bitwarden/cli package to npm containing a credential-stealing payload capable of spreading to other projects. [...]

Recently observed Trigona ransomware attacks are using a custom, command-line tool to steal data from compromised environments faster and more efficiently. [...]

Global IT spending is expected to rise this year to $6.31 trillion, according to a new forecast from Gartner, a 13.5% increase compared to 2025.

According to the research firm, AI is the single most important driver behind the growth, with investments in AI infrastructure, in particular, driving the trend. The data center systems segment is expected to grow by a whopping 55.8% during the year, by far the fastest growing of all categories.

At the same time, IT services continue to account for the largest share of total spending and are expected to exceed $1.87 trillion this year. Software is also showing strong growth, particularly in generative AI.

Growth is also expected in the device market, though at a significantly slower pace. Overall, the market is expected to reach approximately $856 billion, though Gartner says this growth is being slowed by rising memory prices.

Chinese market research firm Sigmaintell expects Apple to be the only company to see growth in the laptop market this year.

Overall, Sigmaintel predicts global notebook shipments will reach 181.1 million units this year, a decline of 8%. That drop will, in part, be caused by memory and component shortages and also by slowing market demand. That’s going to damage all of the notebook vendors, bar Apple,. 

Apple laptop sales expected to rise more than 20%

Sigmaintell calculates Apple will ship 28 million laptop in the year, up 21.7% from 2025. This puts Apple in third place in laptop shipments, a demand the company will be able to meet despite component shortages because of the efficient use of memory inherent to its systems. That memory efficiency acts as a protection against the impact of climbing costs, even as competitors struggle with the affects on their business.

Apple’s incoming CEO, John Ternan, is being presented as a hardware man, so he will no doubt be pleased to experience the benefit of MacBook Neo’s massive attack on the lower echelons of the market. The Neo is already generating millions of additional sales, something Apple’s diversified revenue engine, including services, can further capitalize on.

PC makers face steep decline

There’s quite stark news for PC manufacturers. The report predicts Lenovo, Dell, HP, and ASUS will see sharp sales declines and warns that the entire industry will need to quickly transition from hardware-based sales toward full ecosystem plays. 

That’s going to be extraordinarily difficult for most PC makers. Not only do most of them use operating systems they don’t build themselves, but most lack a successful range of services customers will happily choose to use. 

For the most part, while Apple offers Apple Music, competitors only offer Spotify, a situation that generates far less revenue for them. That lack of successful monetization in terms of attached income across the customer base meant less when the PC market was growing, but in an environment buffeted by multiple business challenges it becomes a vulnerability that cannot be ignored. It exposes the inherent weakness of a strategy in which hardware manufacturers rely on third parties for operating systems and services, as the lion’s share of income doesn’t reach those hardware makers. 

You can go your own way

There’s little doubt that part of the reason Apple is in such a strong position is because of its highly strategic outgoing CEO, Tim Cook, who led efforts to build a strong services business, accompanied by a wide ecosystem of complementary accessories. You don’t just buy an iPhone, you buy a Mac, AirPods, and Apple Music. You don’t just get an iPad, but you likely also acquire Apple Arcade. 

To a great extent, Apple’s strength now owes a big debt to the many years in which the company was marginalized. Forced to follow its own path, Apple deliberately developed its own unique platform-based approach. That approach meant the company remained profitable even when it held just a few percentage points of the PC market; as its market share improves, we can also see its profitability climb. 

The way that you do it

This good news may not matter as much as you might think to Apple’s leadership team. To them, while becoming the industry’s fastest-growing notebook manufacturer is nice, what matters more is crafting a platform experience that means something to the people using it. That, after all, is how to generate the high user satisfaction Apple’s platform loyalty and word-of-mouth recommendations come from.

That 16% of everyone purchasing a notebook this year will choose a Mac suggests a watershed moment for all Apple’s platforms.

You can follow me on social media! Join me on BlueSky,  LinkedIn, Mastodon, and MeWe.