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CISA warned that attackers are now exploiting a high-severity Apache ActiveMQ vulnerability, which was patched earlier this month after going undetected for 13 years. [...]

Sniffnet, tj. multiplatformní (Windows, macOS a Linux) open source grafická aplikace pro sledování internetového provozu, byl vydán ve verzi 1.5. V přehledu novinek je vypíchnuta identifikace aplikací komunikujících po síti.

Email threats have long outgrown spamming and obvious phishing. Attackers now exploit trust itself. They impersonate internal users, hijack legitimate threads, and abuse misconfigured configurations. Defenses like perimeter filtering or static rules are not adequate any longer. A Zero Trust model redefines the issue by eliminating implicit trust at all phases of email processing.This shift is especially important in modern Linux mail environments where services are often modular, network-exposed, and heavily dependent on correct configuration across multiple components.

Chytrý kuchyňský robot Monsieur Cuisine Smart zlevnil na 9 099 Kč. • Lidlovská alternativa k Thermomixu nabízí 16 funkcí a nevyžaduje předplatné. • Varný robot má naprosto nadšené recenze a tříletou záruku.

Zvýšené ceny SSD lákají padělatele hardwaru i k jejich výrobě. Falza se již neobjevují pouze v Japonsku, ale zavítala i do Evropy. Zdá se však, že mají různý původ, neboť nejsou totožná…

Microsoft warns that some Windows domain controllers are entering restart loops after installing the April 2026 security updates. [...]

The National Institute of Standards and Technology (NIST) has announced changes to the way it handles cybersecurity vulnerabilities and exposures (CVEs) listed in its National Vulnerability Database (NVD), stating it will only enrich those that fulfil certain conditions owing to an explosion in CVE submissions. "CVEs that do not meet those criteria will still be listed in the NVD but will not Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

23-year-old Kamerin Stokes of Memphis, Tennessee, was sentenced to 30 months in prison for selling access to tens of thousands of hacked DraftKings accounts. [...]

Pause your Mythos panic because mainstream models anyone can use already pick holes in popular software

Anthropic withheld its Mythos bug-finding model from public release due to concerns that it would enable attackers to find and exploit vulnerabilities before anyone could react.…

The productivity gains from AI are so great, companies can lay off thousands of employees and still get the same amount of work done — right? Or maybe it’s the opposite: despite all the hype, any supposed AI productivity boom is a mirage, causing employees, even  developers, to experience heavier workloads.

At the moment, the jury’s still out on whether AI use boosts or busts productivity across the workforce, despite the prediction that American business spending on AI will exceed $200 billion by the end of the year, according to one analysis. 

There’s no doubt workers are turning to AI in a variety of ways. Gallup, for instance, says nearly half of all US workers now use AI. And Hubstaff data published by Worklytics shows that 85% of professionals use the technology— but only for about 4% of their actual work time. That means 96% of work is 100% human. 

Mileage varies according to how you group employee types, too. The Federal Reserve Bank of St. Louis found that workers using AI saved 5.4% of their work hours, a 1.1% overall increase in productivity. That’s an average, with math and computer workers and within the information services industry reporting higher productivity gains. 

The Federal Reserve Bank of Atlanta, meanwhile, uncovered what it called a “productivity paradox,” in which the productivity gains people think they see aren’t reflected in measurable gains. (It sounds like AI isn’t the only one hallucinating.)

And research from Harvard Business Review (February 2026) found that AI often increases the intensity of work rather than reducing the total workload as originally promised. I’ve heard software developers, in particular, expressing this view and finding that AI is a major source of job burnout. 

All this talk about productivity can miss the qualitative dimension. A 2025 study found that using AI makes employees more innovative by giving them confidence they can handle more complex problems. 

The research goes on and on and, taken together, is more or less inconclusive. However, it’s reasonable to assume that productivity gains from any kind of new technology are likely to take time to show up. It took a decade or more with the PC revolution, for example. While these early days for AI present a mixed picture, productivity gains will surely come, and probably on a massive scale. 

Meanwhile, one slice of the American workforce is already seeing giant gains — remote workers. 

Why AI is working for those working from home

As I’ve argued in this space many times, remote work is a boon for companies in most circumstances. The reasons for this bullish stance are both numerous and, to me, intuitive to the point of being obvious. 

Here are three: 

• Employees have more time because they don’t waste time commuting
• Flex hours are more likely with remote work, so employees can better manage work-life balance, making them happier and more committed to their jobs
• Remote work reduces interruptions, facilitating “deep work,” which, according to deep work expert Cal Newport, is the more valuable type of work for companies 

Now, a new study has added another major benefit for companies in allowing employees to work remotely: AI. 

The study by Michael Blank, a faculty fellow at the Stanford Institute for Economic Policy Research (SIEPR), and colleagues found that AI has a much higher impact in the home than in the office. The study looked at internet browsing data of more than 200,000 U.S. households. 

One reason is surprising: AI helps work-from-home (WFH) employees with both professional and personal tasks, making them more productive at both. The study shows that AI helps people save time and complete tasks much more efficiently when working, planning travel, shopping, figuring out how to fix things around the house and more. 

WFH employees have an AI advantage over office workers, according to the study, because they have the autonomy to integrate AI into their flow without corporate oversight and control. 

Also: Remote employees are more likely to task-switch during the day, alternating between work and personal tasks, something AI facilitates through increased automation. 

Interestingly, the researchers found that employees are taking time saved and using it for more leisure time, as opposed to doing more work or learning new skills. This particular fact is a mixed bag for employers, because while they’re not realizing productivity gains in terms of work performed, they are benefiting from happier employees less prone to dissatisfaction and burnout. 

Blank’s major note of caution is that he found younger people with higher incomes saw the highest productivity gains with AI use at home. He fears a growing “digital divide” between higher and lower income groups and younger and older workers.

It’s about the autonomy as much as the technology

I want to be very clear about the great revelation of this study. It does not look directly at higher productivity with the use of AI for work tasks. Nor does it necessarily conclude that only WFH remote employees can see these gains. 

What it found is that people with high autonomy are the ones who see  the biggest productivity gains from the use of AI in general. WFH employees have the highest autonomy, so they’re seeing real improvements in increased leisure time. 

Just as the benefits of “flex work” are not about flexibility in location but in the use of time, flexibility in the use of AI drives productivity. 

I’ve been beating the flex work drum for years, and now during the AI revolution I’d like to add autonomy to that mix. Whether employees are working in offices full or part-time, from home full or part-time or as digital nomads full or part-time, in 2026 it appears that the highest productivity and employee satisfaction gains come from maximizing flex work and AI autonomy. 

AI disclosure: I don’t use AI for writing. The words you see here are mine. I do use a variety of AI tools via Kagi Assistant (disclosure: my son works at Kagi) — backed up by both Kagi Search, Google Search, as well as phone calls to research and fact-check. I use a word processing application called Lex, which has AI tools, and after writing use Lex’s grammar checking tools to find typos and errors and suggest word changes. Here’s why I disclose my AI use and encourage you to do the same.

Threat actors are exploiting three recently disclosed Windows security vulnerabilities in attacks aimed at gaining SYSTEM or elevated administrator permissions. [...]

An international law enforcement operation has taken down 53 domains and arrested four people in connection with commercial distributed denial-of-service (DDoS) operations that were used by more than 75,000 cybercriminals. The ongoing effort, dubbed Operation PowerOFF, disrupted access to the DDoS-for-hire services, took down the technical infrastructure supporting them, and obtained access to Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

Obyčejné půdní houby získaly od bakterií gen pro tvorbu speciálních bílkovin • Vyloučené proteiny fungují vysoko v mracích jako účinná krystalizační jádra • Objev nabízí obrovský ekologický potenciál pro umělé vyvolávání dešťových srážek

Ryzen 7 5800X3D se vrátí na trh. Současná poptávka po procesorech kompatibilních s pamětmi DDR4 přiměla AMD obnovit výrobu této (prakticky již) legendy. Objeví se znovu jako výroční edice…

Na všech herních platformách je každou chvíli nějaká slevová akce. Každý týden proto vybíráme ty nejatraktivnější, které by vám neměly uniknout. Pokud chcete získat hry zdarma nebo s výhodnou slevou, podívejte se na aktuální přehled akcí!

A recently disclosed high-severity security flaw in Apache ActiveMQ Classic has come under active exploitation in the wild, per the U.S. Cybersecurity and Infrastructure Security Agency (CISA). To that end, the agency has added the vulnerability, tracked as CVE-2026-34197 (CVSS score: 8.8), to its Known Exploited Vulnerabilities (KEV) catalog, requiring Federal Civilian Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

Anthropic has today released a new, improved Claude model, Opus 4.7, but has deliberately built it to be less capable than the highly-anticipated Claude Mythos.

Anthropic calls Opus 4.7 a “notable improvement” over Opus 4.6, offering advanced software engineering capabilities and improved visioning, memory, instruction-following, and financial analysis.

However, the yet-to-be-released (and inadvertently leaked) Mythos seems to overshadow the Opus 4.7 release. Interestingly, Anthropic itself is downplaying Opus 4.7 to an extent, calling it “not as advanced” and “less broadly capable” than the Claude Mythos Preview.

The Opus upgrade also comes on the heels of the launch of Project Glasswing, Anthropic’s security initiative that uses Claude Mythos Preview to identify and fix cybersecurity vulnerabilities.

“For once in technological history, a product is being released with a marketing message that is focused more on what it does not do than on what it does,” said technology analyst Carmi Levy. “Anthropic’s messaging makes it clear that Opus 4.7 is a safer model, with capabilities that are deliberately dialed down compared to Mythos.”

‘Not fully ideal’ in some safety scenarios

Anthropic touts Opus 4.7’s “substantially better” instruction-following compared to Opus 4.6, its ability to handle complex, long-running tasks, and the “precise attention” it pays to instructions. Users report that they’re able to hand off their “hardest coding work” to the model, whose memory is better than that of prior versions. It can remember notes across long, multi-session work and apply them to new tasks, thus requiring less up-front context.

Opus 4.7 has 3x more vision capabilities than prior models, Anthropic said, accepting high-resolution images of up to 2,576 pixels. This allows the model to support multimodal tasks requiring fine visual detail, such as computer-use agents analyzing dense screenshots or extracting data from complex diagrams.

Further, the company reported that Opus 4.7 is a more effective financial analyst, producing “rigorous analyses and models” and more professional presentations.

Opus 4.7 is relatively on par with its predecessor in safety, Anthropic said, showing low rates of concerning behavior such as “deception, sycophancy, and cooperation with misuse.” However, the company pointed out, while it improves in areas like honesty and resistance to malicious prompt injection, it is “modestly weaker” than Opus 4.6 elsewhere, such as in responding to harmful prompts, and is “not fully ideal in its behavior.”

Opus 4.7 comes amidst intense anticipation of the release of Claude Mythos, a general-purpose frontier model that Anthropic calls the “best-aligned” of all the models it has trained. Interestingly, in its release blog today, the company revealed that Mythos Preview scored better than Opus 4.7 on a few major benchmarks, in some cases by more than ten percentage points.

The Mythos Preview boasted higher scores on SWE-Bench Pro and SWE-Bench Verified (agentic coding); Humanity’s Last Exam (multidisciplinary reasoning); and agentic search (BrowseComp), while the two had relatively the same scores for agentic computer use, graduate-level reasoning, and visual reasoning.

Opus 4.7 is available in all Claude products and in its API, as well as in Amazon Bedrock, Google Cloud’s Vertex AI, and Microsoft Foundry. Pricing remains the same as Opus 4.6: $5 per million input tokens, and $25 per million output tokens.

What sets Opus 4.7 apart

Claude Opus is being branded in the industry as a “practical frontier” model, and represents Anthropic’s “most capable intelligent and multifaceted automation model,” said Yaz Palanichamy, senior advisory analyst at Info-Tech Research Group. Its core use cases include complex coding, deep research, and comprehensive agentic workflows.

The model’s core product differentiators have to do with how well-coordinated and composable its embedded algorithms are at scaling up various operational use case scenarios, he explained.

Claude Opus 4.7 is a “technically inclined” platform requiring a fair amount of deep personalization to fine-tune prompts and generate work outputs, he noted. It retains a strong lead over rival Google Gemini in terms of applied engineering use cases, even though Gemini 3.1 Pro has a larger context window (2M tokens versus Claude’s 1M tokens), although, he said, “certain [comparable] models do tend to converge on raw reasoning.”

The 4.7 update moves Opus beyond basic chatbot workflows, and positions it as more of “a copilot for complex, technical roles,” Levy noted. “It’s more capable than ever, and an even better copilot for knowledge workers.” At the same time, it poses less risk, making it a “carefully calculated compromise.”

He also pointed out that the Opus 4.7 release comes just two months after Opus 4.6 was introduced. That itself is “a signal of just how overheated the AI development cycle has become, and how brutally competitive the market now is.”

A guinea pig for Mythos?

Last week, Anthropic also announced Project Glasswing, which applies Mythos Preview to defensive security. The company is working with enterprises like AWS and Google, as well as with 30-plus cybersecurity organizations, on the initiative, and claims that Glasswing has already discovered “thousands” of high-severity vulnerabilities, including some in every major operating system and web browser.

Anthropic is intentionally keeping Claude Mythos Preview’s release limited, first testing new cyber safeguards on “less capable models.” This includes Opus 4.7, whose cyber capabilities are not as advanced as those in Mythos. In fact, during training, Anthropic experimented to “differentially reduce” these capabilities, the company acknowledged.

Opus 4.7 has safeguards that automatically detect and block requests that suggest “prohibited or high-risk” cybersecurity uses, Anthropic explained. Lessons learned will be applied to Mythos models.

This is “an admission of sorts that the new model is somewhat intentionally dumber than its higher-end stablemate,” Levy observed, “all in an attempt to reinforce its cyber risk detection and blocking bona fides.”

From a marketing perspective, this allows Anthropic to position Opus 4.7 as an ideal balance between capability and risk, he noted, but without all the “cybersecurity baggage” of the limited availability higher-end model.

Mythos may very well be the “ultimate sacrificial lamb” at the root of broader Opus 4.7 mass adoption, Levy said. Even in the “increasing likelihood” that Mythos is never publicly released, it will serve as “an ideal means of glorifying Opus as the one model that strikes the ideal compromise for most enterprise decision-makers.”

Palanichamy agreed, noting that Opus 4.7 could serve as a public-facing guinea pig to live-test and fine-tune the automated cybersecurity safeguards that will ultimately “become a mandatory precursory requirement for an eventual broader release of Mythos-class frontier models.”

The European Commission this week requested, but did not order Google to allow third party search engines in Europe access to its search data as a means to comply with the Digital Markets Act (DMA), legislation the Commission describes as a law designed to “make the markets in the digital sector fairer and more contestable.”

Google was sent a set of proposed measures on Wednesday that, according to a release, would grant third party search engines, including Qwant from France, Mojeek, based in the UK, swisscows from Switzerland, and Ecosia, Good, and metaGer, all headquartered in Germany, the ability to access search data, such as ranking, query, and click and view data “on fair, reasonable and non-discriminatory terms.”

In a statement, Teresa Ribera, executive vice-president for Clean, Just and Competitive Transition with the Commission, said that the decision “sets out the specifications we expect Google to follow to comply with its obligations under the [DMA]. Data is a key input for online search and for developing new services, including AI.”

The measures themselves cover several areas, including the scope of the search data Google must share, the means and frequency by which it must happen, and parameters for “setting fair, reasonable and non-discriminatory prices for search data.”

Move ‘far exceeds DMA’s original mandate’

In response to the Commission’s request, Clare Kelly, senior competition counsel for Google, said Thursday in a statement, “hundreds of millions of Europeans trust Google with their most sensitive searches, including private questions about their health, family, and finances, and the Commission’s proposal would force us to hand this data over to third parties, with dangerously ineffective privacy protections.”

The company, she said, “will continue to vigorously defend against this overreach, which far exceeds the DMA’s original mandate and jeopardizes people’s privacy and security.”

Phil Höfer, board member of SUMA-EV, which develops and runs MetaGer, said, “the planned measure might help with optimizing and developing European competitors to Google’s search service, but is not what’s needed most at this time. As long as the Commission isn’t planning on forcing Google to share their index data as well, this will not do much.”

Even better, he said, would be for the Commission “to decide to continue funding the European Open Web Index and allow European actors to build a competing infrastructure. We are convinced that without a European index, the EU will not be able to compete with American search engine giants.”

Forrester Senior Analyst Dario Maisto said the decision from the Commission is “not too timely but definitely in line with the measures Europe needs to free up businesses and citizens from risky dependencies on foreign organizations, vendors, and technologies. The final outcome is truly uncertain, though: one thing is to provide access to data to other players, one other thing is to modify users’ behaviors. We have to remember that the synonym for doing a search on the internet is actually: Google it.” 

Brian Jackson, principal research director at Info-Tech Research Group, said that opening Google’s search data to third parties could make search more specialized again, especially in high-value verticals where users want results tailored to a specific industry or service need.

Enterprise digital teams, he said, may need to optimize for multiple discovery environments rather than relying just on Google alone, and software buyers could see more choice as search and intelligence vendors build on shared data.

In addition, said Jackson, “it could revive domain-specific search models, but I think a more fragmented search ecosystem might raise manipulation risks, fraud, and poisoned results. That would make governance and monitoring much more important.”

Sanchit Vir Gogia, chief analyst at Greyhound Research, noted that, in terms of the impact on enterprises if Google shares search data under DMA, “this is being framed as a competition move, but that is not where the real impact sits. What is actually shifting here is control over how enterprise information is interpreted by machines.”

Definition of optimization is changing

For a long time, he said, “enterprises have quietly relied on the stability of a dominant discovery layer led by Google. That stability shaped everything from how content was written to how digital performance was measured. What is changing now is not just who has access to data, but how many systems can interpret that data.”

Gogia pointed out, “as alternative engines improve and start to matter, enterprises will find themselves operating in an environment where the same content can be surfaced differently, depending on which engine or AI system is doing the interpreting. That creates inconsistency, and over time, inconsistency becomes risk.”

There is, he said, also a deeper shift underneath all this: “Search is no longer just about helping users find information. It is increasingly the layer that feeds AI systems, copilots, and automated decisions. Once that layer fragments, enterprises no longer have a single reference point for how they are represented externally. That loss of coherence is subtle at first, but it builds into something much more material.”

Addressing the question of whether or not enterprises will need to optimize for multiple algorithms, he said, “the short answer is yes, but the bigger point is that the definition of optimization itself is changing. Enterprises are moving away from a world where they could tune for one dominant system into one where relevance is decided differently across multiple engines that do not follow the same rules.”

Search engines such as Qwant, Ecosia, and Mojeek, “each approach indexing and ranking differently,” Gogia said. “Some rely on their own infrastructure, others blend multiple data sources. The result is that the same piece of content can behave very differently across environments, even when nothing about the content itself has changed.”

What complicates this further, he said, “is the rise of AI-generated answers. Enterprises are no longer competing for links, they are competing to be included in summaries that may not even reveal where the information came from. That shifts the focus away from keywords and toward clarity, context, and credibility. The organizations that do well will be the ones whose content holds up across systems, not just within one.”

Interested parties have until May 1 to submit views on the proposed measures prior to a final decision, which will be binding on Google and must be adopted by July 27.