Agregátor RSS

Apple iPhone 18 Pro a 18 Pro Max jsou po všech směrech evolucí minulé generace • Vypadají stejně, jen mají moderní čip, zmenšený Dynamic Island • Hlavní novinkou je variabilní clona fotoaparátu pro regulaci světla

Organizátoři konference LinuxDays zveřejnili program letošního ročníku a spustili registraci návštěvníků. LinuxDays 2026 se uskuteční 3. a 4. října v areálu ČVUT v pražských Dejvicích, na Fakultě informačních technologií. Těšit se můžete na 70 přednášek a workshopů od 66 přednášejících. Konference bude rozdělena do pěti sálů s různou kapacitou. Vstup na LinuxDays je jako obvykle zdarma, stačí včas vyplnit registrační formulář. Opět je možné si na akci zakoupit oběd, ale je třeba to udělat předem, na místě už to nebude možné.

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) has confirmed that ransomware gangs are also exploiting a critical WatchGuard Firebox firewall vulnerability, which it flagged as actively exploited in December. [...]

Google Chrome 153 byl prohlášen za stabilní. Nejnovější stabilní verze 153.0.8010.36 přináší řadu novinek. Podrobný přehled v poznámkách k vydání. Opraveno bylo 230 bezpečnostních chyb. Vylepšeny byly také nástroje pro vývojáře.

Apple včera ukázal světu nové iPhony Pro, Pro Max a Duo, v jejichž nitru najdeme čipy A20 Pro. Poprvé v mobilu vidíme procesory vyrobené pokročilým 2nm procesem od TSMC a první čísla naznačují zajímavý výkonnostní skok. Ostatně sám výrobce říkal, že v mobilech nabídne výkon desktopu. Příliš ...

Microsoft says the September 2026 Patch Tuesday updates fix a known issue causing desktop settings to be lost or reset on some Windows devices. [...]

Podle statistik prodeje GPU, kterou zpracovala společnost John Peddie Research (JPR) překvapivě na jaře letošního roku stouply dodávky o 10 %. Důvody jsou však jiné, než by se mohlo zdát…

Nearly one in ten of the internet-facing LiteLLM servers that Wiz Research scanned in February accepted sk-1234, the example admin key in LiteLLM's own setup guide. LiteLLM is an open-source AI gateway, the software a company puts between its applications and the model providers it pays for. That key is the gateway's administrator credential. Anyone who holds it can read every

Nearly one in ten of the internet-facing LiteLLM servers that Wiz Research scanned in February accepted sk-1234, the example admin key in LiteLLM's own setup guide. LiteLLM is an open-source AI gateway, the software a company puts between its applications and the model providers it pays for. That key is the gateway's administrator credential. Anyone who holds it can read every Swati Khandelwalhttp://www.blogger.com/profile/[email protected]

Anthropic on Wednesday disclosed a fourth incident in which its artificial intelligence (AI) model broke into real third-party systems, marking the latest in a growing list of cases that have raised concerns about the security risks posed by autonomous AI agents. The AI company said the incident dates back to January 2026 and involved an early version of Claude Opus 4.6 that breached "

Anthropic on Wednesday disclosed a fourth incident in which its artificial intelligence (AI) model broke into real third-party systems, marking the latest in a growing list of cases that have raised concerns about the security risks posed by autonomous AI agents. The AI company said the incident dates back to January 2026 and involved an early version of Claude Opus 4.6 that breached "Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

PWNED Welcome back to PWNED, the weekly column where we highlight examples of how not to handle your security. This week’s tale of woe comes from a very unhealthy part of the healthcare sector. Have a story about someone leaving a gaping hole in their network? Share it with us at [email protected]. Anonymity is available upon request. Our story comes courtesy of Chris Kirksey, founder and CEO of Direction, a digital marketing and SEO company that works in the healthcare industry. He also does security audits of his clients’ systems. Last year, Kirksey was checking out a dental practice’s systems and noticed something strange. There were three accounts that had admin access to the patient database, including one that belonged to a scheduling company the dentists had stopped using all the way back in 2021. The account had been active for at least three years and could access 4,000 patient records. Leaving an unnecessary account with access to protected health information created a potential HIPAA compliance risk, particularly if someone no longer authorized to view the data could still get to it. The office manager responsible for using the system didn’t even know that this dangerous login existed. Apparently, a contractor who set up the account never told anybody, then left the company. Because no one knew that the account existed, no one knew to kill it. Kirksey immediately set about getting rid of all three admin accounts he found on the dental practice’s system. He then set up new policies for his client. “I built a permanent rule after that,” he said. “Every vendor relationship that ends now triggers an automatic access shutdown and the full list gets reviewed twice a year no matter what.” Since the incident, Kirksey has found similar security holes at six other healthcare practices he has worked with. Yikes! “Everyone worries about the sticky note with a password on it or the file just called passwords.xls, because those get caught fast and make a good story,” he told us. “Nobody worries about the login they forgot even exists, and that is usually the one still wide open years later, causing real, unseen damage.” The lesson here is pretty straightforward. You need to see all of the accounts that have access to your data and make sure that they all have a reason to exist. Conduct regular audits, even if nothing seems wrong. And, as we’ve seen before, zombie accounts can kill. When an employee or contractor leaves, check not only which accounts they used, but also which accounts they created while doing the job. ®

Trezor warned customers on Wednesday that threat actors who breached its third-party email provider are targeting them in phishing attacks. [...]

Brněnský manažer Jiří Tobola podruhé prodal zahraničnímu kupci vlastní firmu, tentokrát Logmanager dánské skupině Guardsix. • Tobola dovedl Logmanager k obratu 130 milionů korun a spoléhal spíš na tradiční B2B prodej než na rizikový kapitál. • Za úspěchem stojí i brněnský inovační ekosystém ...

Praha prověří možnost vybudování 22 kilometrů dlouhé okružní linky metra E • Plánované spojení propojí okrajové čtvrti a uleví přeplněnému centru metropole • Finální podobu i vysoké náklady projektu posoudí studie proveditelnosti

Navzdory tomu, že Intel dlouhodobě a opakovaně hovořil o vydání Nova Lake v roce 2026 a toto stanovisko nikdy nedementoval, se podle informace od partnerů Intelu s letošním vydáním nepočítá…

AI is evolving faster than most people, even those building it, could even fathom, and its impact on the workforce and the economy is, at this point, really anyone’s guess.

Researchers from The Anthropic Institute are offering a few possibilities: They have built a nuanced framework looking at how AI might impact jobs, unemployment, and gross domestic product (GDP) growth between now and 2030.

They posit three potential scenarios for an AI-augmented future: “modest,” “substantial,” and “extreme,” and have created an interactive tool where users can explore how productive, or disruptive, AI will become in the workplace, based on their predictions of how they will work in 2030.

“Which of these worlds we are heading toward may become clearer within a year or two, and preparing for potential disruption seems to us the prudent course,” the researchers noted.

The goal of their work is to inform debate as AI becomes more powerful and capable. “AI is likely to reshape the US and global economies in profound ways in the coming decade, but how, and by how much, is extraordinarily uncertain,” they wrote.

How different scenarios could play out

If you add up every single task performed by people, machines, and software, the US has created a staggering $30 trillion in value over just the last year, the Anthropic researchers estimated. Their model and the corresponding tool are a way to explore how AI impacts tasks that contribute to the economy, the tasks it augments and creates, impacts on productivity, and speed of adoption.

“The answers to these questions have direct effects on GDP, the labor market, and the share of the pie taken home by workers,” they wrote.

Under their definition of “modest” change, AI will add less than half a point to GDP by 2030, meaning it will increase the growth rate of the national economy by just 0.5%, and will raise unemployment by just a tenth of a point, a minor shift. In this future, it’s difficult to see AI’s impact in macroeconomic data; change is steady but gradual, similar to that of the internet. “It drives real economic gains, but they’re within the historical norm for new technologies,” the researchers noted.

In the “substantial” scenario, AI will be capable of doing half of all knowledge work by 2030, the majority of it autonomously. Still, it wouldn’t be adopted for all work; in fact, most knowledge work tasks would still be completed without AI. Correspondingly, the economy would grow at twice its normal rate, but even as some non-knowledge workers see gains, wages for knowledge workers wouldn’t rise.

In this case, “AI makes a bigger impact than the internet, or the railroad,” the researchers wrote. Reallocation could be costly, but it is in line with what the US labor market has historically absorbed.

In the “extreme” scenario, of course, AI would be more productive than humans on the majority of knowledge work tasks, would do all of them autonomously, and subsequently would create no new knowledge tasks for humans.

The technology would “drive a completely transformed, unprecedented economy” arising from recursively self-improving AI. GDP growth would rise to 15% per year, but nearly one in five cognitive workers would be unemployed, and their relative wage would fall “immensely.”

The conundrum is that resources to compensate unemployed or under-paid workers will exist, but it’s unclear whether they would be fairly allocated. Mechanisms by which people can benefit from a much richer economy (retraining, income support, or universal basic income, for example) would become a question of economic policy.

“Whether and how those resources reach the people who bear the cost is not something growth delivers by itself,” the researchers wrote.

What users think

As well as developing the framework, the Anthropic researchers conducted a survey among roughly 11,000 Americans, asking them to predict AI use, productivity gains, automation versus augmentation, and displaced work.

They found that, in the main, public expectations land around the “substantial” scenario. That is, GDP would be 10% higher by 2030 than it would be without AI, and the overall unemployment rate would rise to around 5%.

Roughly 10% of respondents, on the other hand, had views in line with the “extreme” scenario.

Anyone can generate their own forecast using the researchers’ interactive tool, answering questions like: “Out of every 100 instances of a task AI can do in 2030, how many will AI actually be doing?”, “How many will be fully automated?”, or  “How much more gets done in an hour in 2030, compared with doing the tasks without AI?” The tool then responds, mapping their predictions to one of the three scenarios.

“Ultimately, what the economy looks like in 2030 depends on many factors, like what AI can do, and how companies and workers choose to adopt it,” the researchers wrote. “It also depends on how the financial benefit of this technology is shared.”

The between-the-lines reality

Sanchit Vir Gogia, chief analyst at Greyhound Research, emphasized that the Anthropic research “maps the conditions under which very different futures appear, it does not schedule destiny.”

He sees the distribution result, rather than the unemployment result, as the serious finding. In the extreme case, GDP is 32.4% above the no AI path, and the cognitive wage bill is 31% below it. Labor’s share of income falls from 60% to 45.2%, and capital income rises 81.4 %. That means a full 15% of GDP is captured as ROI rather than being paid out in labor costs.

In other words, he pointed out: “A richer economy is not automatically a fairer one.” Capability, diffusion, productivity, automation, and occupational friction all have to arrive together.

“AI will touch a large and rising share of knowledge work and will execute a much smaller share under independent authority,” he said. There is no single honest adoption percentage, because worker use, company use, technical exposure, and executed task instances are four different measurements.

Lessons from the research

Enterprises can take important lessons from the research as they deploy AI and consider its impact on their systems, workflows, and workforce, Gogia said.

“For enterprises, the binding variable is permission to delegate,” he noted. “A model that can draft a payment instruction is not thereby permitted to move money.”

His firm identifies five recurring concerns that come up in enterprise conversations: Durable returns after the full cost of deployment, control over authority being granted, augmentation quietly becoming substitution, erosion of professional formation, and fairness of how gains and risks land.

Some of those changes are progressing faster than the governance around them, he observed. Once a system can inspect customer data, change configurations, or act on workforce records, autonomy has stopped being a feature and has instead become an allocation of institutional authority.

“And the tasks easiest to automate are frequently the tasks through which judgement is learned,” he noted.

Open-source hra Shattered Pixel Dungeon (Wikipedie) byla vydána ve verzi 4.0.0. Přehled novinek v příspěvku na blogu.

Gartner on Wednesday said that it expects 30% of the positions eliminated by AI-related layoffs to be refilled by 2029, suggesting that the initial terminations were ill-advised and excessive.

“When business and IT executives look back on the early AI era, they will realize their greatest mistake was believing that work automation was the point, when workforce amplification was the opportunity,” said Tori Paulman, VP analyst at Gartner. “The competitive advantage will go to the CIOs and business executives who build an AI-shaped organization where AI value compounds by reshaping roles and allowing workflows to cross traditional boundaries, increasing velocity and reducing friction.”  

The Gartner report noted that it is finding that the cuts “deplete talent pipelines and erode institutional knowledge.” Beyond the immediate workforce disruptions associated with any mass layoff, companies will also face steep increases in costs for recruitment, training, and onboarding.

It also predicted that, by 2027, “75% of organizations that prioritize capturing AI productivity gains as cost savings will be eclipsed by competitors that aggressively reinvest those gains into innovation, modernization and upskilling.”

In an interview with Computerworld, Paulman said that the 30% figure represents the average impact on organizations of all sizes; they estimate that the layoff boomerang for enterprises would be even higher, roughly 40%. 

Paulman said that Gartner’s research found a lot of what they called “AI washing” by executives who want/need to do layoffs for purely budgetary reasons, and will falsely blame AI for the reductions because it makes them look better.

“More than 50% of our enterprise clients have been given a number [by their bosses],” Paulman said, and have been told by senior management to find that percentage of savings from AI.

But despite widespread evidence of problems due to AI-related layoffs, such job cuts are still increasing. 

Layoffs were ‘excessive’

Other analysts and consultants agreed with the Gartner suggestion that many of these job losses attributed to AI are going to be walked back, but questioned the specific statistic. Some also noted that 70% of the AI-attributed layoffs may remain in force, which would suggest that the original terminations were mostly justified. 

However, Frank Dickson, principal analyst at Dickson Research, argued that a lot of the layoff reversals will occur in a variety of ways that will obscure the fact that they are restoring a terminated role. 

“A lot of that 70% never shows up as a clean rehire even when the original cut was wrong,” he said, pointing out that some of the losses caused service to quietly get worse, and stay poor, some of the work was contracted out or offshored, some of the roles were reconstituted with a different position or title, and some was covered by the remaining staff absorbing the load. This,” he noted, “shows up later as burnout and attrition, not as a line item on this report. None of that gets counted in the 30%, and none of it is evidence the original call was sound.”

Melody Brue, principal analyst for Moor Insights & Strategy, added that the 70% scenario “could show that a substantial share of the AI-related workforce reductions is durable,” but, she stressed, “it shouldn’t be mistaken for endorsement of how those layoffs were made. What it doesn’t show is whether the organization captured the full economic value it expected. A lower headcount is not by itself evidence of a successful AI transformation.”

Valence Howden, advisory fellow at Info-Tech Research Group, questioned the methodology behind the calculation of Gartner’s 30% figure, but he agreed with the overall sentiment that layoffs attributed to AI have been excessive.

“I’m not sure we can substantiate those numbers, since it’s much more of a guesswork statement than anything else,” he said. “I do believe the current trend is going to lead to rehiring, especially as AI governance requirements ramp up and given AI’s lack of contextual semantic understanding. We know AI has not provided the value proposition that it has been sold as providing, and unless costs are controlled, it will be cheaper to use humans to perform some of the advanced work.”

Supporting data

Dickson also raised questions about the Gartner report because it lacked comparative layoff statistics. 

“Gartner doesn’t say what the reversal rate looks like for ordinary layoffs, the ones that have nothing to do with AI,” he said. “Suppose normal cuts get walked back at 10% to 15% in a typical five-year window, which is plausible given ordinary churn and business-cycle rehiring. A 30% rate specific to AI-driven layoffs would still run well above that, and that’s a damning number. Without that comparison, 30% is just a figure floating with no anchor.”

However, Dickson pointed to various datapoints supporting the position that AI layoffs have been excessive, noting that Forrester reported that 55% of businesses “already regret AI-driven cuts and are predicting half of those layoffs get quietly reversed.” 

“Robert Half puts it at a third of hiring executives who eliminated roles for AI having already rehired. Ford, IBM, Booz Allen Hamilton, Alphabet and CSX have all walked back cuts or announced rehiring drives,” Dickson said. “Gartner’s 30% by 2029 sits comfortably inside that range.” Klarna has also walked back AI layoffs. 

A ‘major indictment’

He added that many AI layoffs amounted to a corporate version of a crash diet. “You cut fast, you look great on the next earnings call, and eighteen months later, the weight is back, plus interest, because nobody fixed why the cut was made in the first place.”

Gartner’s Paulman agreed, noting, “business and IT executives who use AI primarily as a tool for cost cutting risk making reductions that are too deep and too soon, affecting their ability to innovate their business model and compete in new markets as AI continues to mature.”

Mike Wilkes, enterprise CISO at Aikido Security, said that even if the 30% figure turns out to be accurate, it is a major indictment of the layoffs. 

“If 30% of AI-driven layoffs must be reversed, that is an enormous error rate for a strategic workforce decision,” Wilkes said. “Imagine any other major capital decision where nearly one-third had to be unwound at a premium three years later. No CFO would call that a strong outcome.”