Is it finally time to upgrade that aging Apple Watch that you’re charging twice a day? I have some great news for you! The Apple Watch Series 11 is marked down at major retailers like Amazon and Best Buy to as low as $300 for the base version, or $400 for the upgraded GPS + Cellular version, depending on your finish and included band choice.

While we’ve enjoyed previous generations of Apple Watch, the Series 11 made one of the most major improvements yet to the popular line of smartwatches. With the upgraded battery and extra optimization features, the standard Apple Watch can now last an entire day and then some on a single charge. That’s great news for anyone who wants to get the most out of their watch during the day, and then use it to track their sleep at night.

That improved battery life also lets you take advantage of all the updated health tracking features found on the Apple Watch Series 11. The biggest new feature is monitoring for high blood pressure, which will keep track of your vitals over a two week period before alerting you if it thinks you should see a doctor. While it isn’t an official medical device, it’s cleared by the FDA, and at least gives you some helpful information from information it was already gathering anyway.

Otherwise, it has all the features you’d expect from an Apple Watch, including sleep tracking, fitness features for different sports and activities, and a close connection to your iPhone for messages and notifications. If you opt for the version that includes a cellular connection, it features both satellite and 5G messaging, with helpful tips for getting the best connection right on the watch.

I spotted the marked down GPS-only Apple Watch Series 11 at Amazon and Best Buy, with quite a few color and band options in stock at both retailers. Both Amazon and Best Buy also had the versions that support a cellular connection as well, albeit with fewer options at the discounted price. If you’re curious about all the changes to the latest version, make sure to swing by our full review for a complete hands-on experience.

While quantum computers continue to slowly grind towards usefulness, some are pursuing a different approach—analog quantum simulation. This path doesn’t offer complete control of single bits of quantum information, known as qubits—it is not a universal quantum computer. Instead, quantum simulators directly mimic complex, difficult-to-access things, like individual molecules, chemical reactions, or novel materials. What analog quantum simulation lacks in flexibility, it makes up for in feasibility: quantum simulators are ready now.

“Instead of using qubits, as you would typically in a quantum computer, we just directly encode the problem into the geometry and structure of the array itself,” says Sam Gorman, quantum systems engineering lead at Sydney-based start-up Silicon Quantum Computing.

Yesterday, Silicon Quantum Computing unveiled its Quantum Twins product, a silicon quantum simulator, which is now available to customers through direct contract. Simultaneously, the team demonstrated that their device, made up of fifteen thousand quantum dots, can simulate an often-studied transition of a material from an insulator to a metal, and all the states between. They published their work this week in the journal Nature.

“We can do things now that we think nobody else in the world can do,” Gorman says.

The powerful process

Though the product announcement came yesterday, the team at Silicon Quantum Computing established its Precision Atom Qubit Manufacturing process following the startup’s establishment in 2017, building on the academic work that the company’s founder, Michelle Simmons, led for over 25 years. The underlying technology is a manufacturing process for placing single phosphorus atoms in silicon with sub-nanometer precision.

“We have a 38-stage process,” Simmons says, for patterning phosphorus atoms into silicon. The process starts with a silicon substrate, which gets coated with a layer of hydrogen. Then, using a scanning-tunneling microscope, individual hydrogen atoms are knocked off the surface, exposing the silicon underneath. The surface is then dosed with phosphine gas, which adsorbs to the surface only in places where the silicon is exposed. With the help of a low temperature thermal anneal, the phosphorus atom is then incorporated into the silicon crystal. Then, layers of silicon are grown on top.

“It’s done in ultra-high vacuum. So it’s a very pure, very clean system,” Simmons says. “It’s a fully monolithic chip that we make with that sub-nanometer precision. In 2014, we figured out how to make markers in the chip so that we can then come back and find where we put the atoms within the device to make contacts. Those contacts are then made at the same length scale as the atoms and dots.”

Though the team is able to place single atoms of phosphorus, they use clusters of ten to fifty such atoms to make up a so-called register for these application-specific chips. These registers act like quantum dots, preserving quantum properties of the individual atoms. The registers are controlled by a gate voltage from contacts placed atop the chip, and interactions between registers can be tuned by precisely controlling the distances between them.

While the company is also pursuing more traditional quantum computing using this technology, they realized they already had the capacity to do useful simulations in the analog domain by putting thousands of registers on a single chip and measuring global properties, without controlling individual qubits.

“The thing that’s quite unique is we can do that very quickly,” Simmons says. “We put 250,000 of these registers [on a chip] in eight hours, and we can turn a chip design around in a week.”

What to simulate

Back in 2022, the team at Silicon Quantum Computing used a previous version of this same technology to simulate a molecule of polyacetylene. The chemical is made up of carbon atoms with alternating single and double bonds, and, crucially, its conductivity changes drastically depending on whether the chain is cut on a single or double bond. In order to accurately simulate single and double carbon bonds, the team had to control the distances of their registers to sub-nanometer precision. By tuning the gate voltages of each quantum dot, the researchers reproduced the jump in conductivity.

Now, they’ve demonstrated the quantum twin technology on a much larger problem—the metal-insulator transition of a two-dimensional material. Where the polyacetylene molecule required ten registers, the new model used 15,000. The metal-insulator model is important because, in most cases, it cannot be simulated on a classical computer. At the extremes—in the fully metal or fully insulating phase—the physics can be simplified and made accessible to classical computing. But in the murky intermediate regime, the full quantum complexity of each electron plays a role, and the problem is classically intractable. “That is the part which is challenging for classical computing. But we can actually put our system into this regime quite easily,” Gorman says.

The metal-insulator model was a proof of concept. Now, Gorman says, the team can design a quantum twin for almost any two-dimensional problem.

“Now that we’ve demonstrated that the device is behaving as we predict, we’re looking at high-impact issues or outstanding problems,” says Gorman. The team plans to investigate things like unconventional superconductivity, the origins of magnetism, and materials interfaces such as those that occur in batteries.

Although the initial applications will most likely be in the scientific domain, Simmons is hopeful that Quantum Twins will eventually be useful for industrial applications such as drug discovery. “If you look at different drugs, they’re actually very similar to polyacetylene. They’re carbon chains, and they have functional groups. So, understanding how to map it [onto our simulator] is a unique challenge. But that’s definitely an area we’re going to focus on,” she says. “We’re excited at the potential possibilities.”

It’s fourth period in the auto lab at Vel Phillips Memorial High School in Madison, Wisconsin, and a dozen students maneuver between nearly as many cars.

At one bay, a junior adjusts the valves of an oxygen-acetylene torch and holds the flame to a suspended Subaru’s front axle to loosen its rusty bolts. Steps away, two classmates tease each other in Spanish as they finish replacing the brakes on a red Saab. Teacher Miles Tokheim moves calmly through the shop, checking students’ work and offering pointers.

After extensive renovations, the lab reopened last year with more room and tools for young mechanics-in-training. What visitors can’t see is the class recently got an upgrade, too: college credit.

Through dual enrollment, high schoolers who pass the course now earn five credits for free at Madison College and skip the class if they later enroll. Classes like these are increasingly common in Wisconsin and across the country. They’ve allowed more high schoolers to earn college credit, reducing their education costs and giving them a head start on their career goals.

Wisconsin lawmakers and education officials want more high schoolers to have this opportunity. But these classes need teachers with the qualifications of college instructors, and those teachers are in short supply.

That leaves many students — disproportionately, those in less-affluent areas — without classes that make a college education more attainable.

“What’s at stake is access to opportunity, especially for high school students at Title I, lower-income high schools, rural high schools … It’s really been an on-ramp for so many students,” said John Fink, who studies dual enrollment at Columbia University’s Community College Research Center. “But we also know that many students are left behind.”

To teach the auto class, Tokheim had to apply to become a Madison College instructor. As a certified auto service technician with a master’s degree, the veteran teacher met the college’s requirements for the course.

But for many teachers, teaching dual enrollment would require enrolling in graduate school, even if they already have a master’s degree. That, school leaders say, is a hard sell, despite the state offering to reimburse districts for the cost. Teachers in Wisconsin often don’t make much more money teaching advanced courses the way they do in some other states, and adding these courses doesn’t raise a school’s state rating.

“You’re asking people who are well educated to begin with to go back to school, which takes time and effort, and their reward for that is they get to teach a dual-credit class,” said Mark McQuade, Appleton Area School District’s assistant superintendent of assessment, curriculum and instruction.

High Standards, Short Supply

Nationwide, the number of high schoolers earning college credit has skyrocketed in recent years. In Wisconsin, the tally has more than doubled, with students notching experience in subjects ranging from manufacturing to business.

Most earn credit from their local technical college without leaving their high school campus. In the 2023-24 school year, one in three community college students in the state was a high schooler.

Education and state leaders have welcomed the trend, pointing to the potential benefits: Students who take dual-enrollment classes are more likely to enroll in college after high school. Theycan save hundreds or thousands of dollars on college tuition and fees. If they do enroll in college, they spend less time completing a degree.

“It also proves to the kids — to some of our kids that are first-generation — that they can do college work,” McQuade said.

But not all students get these advantages. Many Wisconsin schools offer very few dual-enrollment courses, or none at all. A July Wisconsin Policy Forum analysis showed small, urban or high-poverty schools are least likely to offer the classes.

Wisconsin Watch talked to leaders in five school districts. All said the shortage of qualified teachers was one of the biggest barriers to growing their dual-enrollment programs.

In 2015, the Higher Learning Commission, which oversees and evaluates the state’s technical colleges, released new guidelines about instructor qualifications. The new policy required many of Wisconsin’s dual-enrollment teachers to have a master’s degree and at least 18 graduate credits in the subject they teach, just like college instructors.

In 2023, the commission walked back the new policy.

By then, colleges across the state had already adopted the higher standard.

Meanwhile, Wisconsin high schools have struggled to hire and retain teachers, even without college credit involved. Four in 10 new teachers stop teaching or leave the state within six years, a 2024 Department of Public Instruction analysis shows.

The subject-specific prerequisite is much different from the graduate education K-12 teachers have historically sought: the kind that would help them become principals or administrators, said Eric Conn, Green Bay Area Public Schools’ director of curricular pathways and post-secondary partnerships.

“To advance in education, it wasn’t about getting a master’s in a subject area. It was getting a master’s in education to develop into educational administration or educational technology,” Conn said. For teachers who already have a master’s degree, he said, going back to school just to teach one or two new classes is “a large ask.”

Funding Tempts Few

When the Higher Learning Commission announced the heightened requirements in 2015, leaders of the Wisconsin Technical College System sounded the alarm. They warned that 85 percent of the instructors currently teaching these classes could be disqualified, whittling students’ college credit opportunities.

Wisconsin education leaders called on the Legislature to allocate millions of dollars to help teachers get the training they’d need — and they agreed. In 2017, lawmakers created a grant program to reimburse school districts for teachers’ graduate tuition. But of the $500,000 available every year, hundreds of thousands go unused.

“Nobody’s ever, ever requested this funding and been denied because of a funding shortage,” said Tammie DeVooght Blaney, executive secretary of the Higher Educational Aids Board, which manages the grant.

Tuition and fees for a single graduate credit at a Universities of Wisconsin school can cost over $800, putting the total cost of 18 graduate credits at around $15,000. For teachers who don’t already have a master’s degree, the cost is even steeper. The state grant requires teachers or districts to front the cost and apply for reimbursement yearly, with no guarantee they’ll get it.

A handful of Green Bay teachers have used the grant, Conn said, but many just aren’t interested in returning to school, even if it’s free.

The district offers 50 dual-enrollment courses, but he’d like to offer classes in more core subjects, which help students meet general college education requirements. There just aren’t enough teachers qualified to teach college sciences and math to offer the same options across the district’s four high schools.

Teachers are busy, and not just in the classroom, said Jon Shelton, president of AFT-Wisconsin, one of the state’s teachers unions. Many already spend extra hours coaching, grading or leading after-school activities. Those who do go back to school typically enroll in one class at a time, he said, meaning they could be studying for several years.

Pros and Cons

The financial perks for teachers returning to school for dual-enrollment credentials are dubious at best.

Some teachers get a salary bump for obtaining a master’s degree, and some earn modest bonuses for teaching dual enrollment. But many teachers make no more than they would have without the extra training.

“There’s no incentive,” said Tokheim, the Madison auto instructor, who receives a $50 yearly stipend for teaching the college course. In contrast to his standard classes, his dual-enrollment class required him to attend two kinds of training.

There’s little incentive for schools either. They receive no extra state funding to offer college-level courses. Plus, the classes don’t factor into their state report card score, which measures students’ standardized test performance and graduation preparation, among other things.

Leaders at Central High School in Sheboygan wish it did. At that school, where the majority of students are Latino and almost all are low-income, one in three students took dual-enrollment courses in the 2023-24 school year. Still, the state gave the school a failing grade.

“It’s an afterthought in our report card, and it’s always the thing that we can celebrate,” Principal Joshua Kestell said.

So why would a teacher take on the added schooling?

“It’s good for kids,” Tokheim said. “That’s why they get us teachers, because we care too much.”

Other potential draws: the challenge of teaching more rigorous courses and the opportunity to collaborate with college instructors.

Heather Fellner-Spetz retired two years ago from teaching English at Sevastopol High School in Sturgeon Bay. She taught college-level oral communication classes for 10 years before she retired. When the Higher Learning Commission set the heightened requirements, she was allowed to continue teaching dual enrollment while she studied for more graduate credits.

“There wasn’t much I didn’t enjoy about teaching it. It was just fabulous,” Fellner-Spetz said.

She especially liked having a college professor observe her class, and she said it was good for the students, too. “When they had other people come into the room and watch the lesson or watch them perform, it just ups the ante on pressure.”

Meanwhile, the jury is still out on whether it’s necessary for dual-enrollment teachers to have the same credentials as college professors.

“Folks running these programs generally would say that teaching a quality college course to a high school student requires a unique skill set that blends high school and college teaching, and that is not necessarily captured by the traditional (graduate coursework) standard,” Fink said.

Wisconsin educators are divided on that question. Fox Valley Technical College has kept the higher standard, limiting the number of Appleton teachers who qualify. McQuade, the Appleton leader, questions those “restrictions,” saying he believes his teachers are well qualified to teach college-level courses. A different standard tied to student performance, for example, could let his district offer more classes across each of its schools.

Schauna Rasmussen, dean of early college and workforce strategy at Madison College, said the answer isn’t to lower the standard, but to help more teachers reach it.

In October, a group of Republican Wisconsin lawmakers introduced a bill aimed at making it easier for students to find dual-enrollment opportunities. It would create a portal for families to view options and streamline application deadlines, among other changes.

It doesn’t address the shortage of qualified teachers.

“Separate legislation would likely have to be introduced addressing expanding the pool of teachers for those programs,” Chris Gonzalez, communications director for lead author State Sen. Rachael Cabral-Guevara, wrote in an email.

So far, no such legislation has been introduced.

The deep learning revolution has a curious blind spot: the spreadsheet. While Large Language Models (LLMs) have mastered the nuances of human prose and image generators have conquered the digital canvas, the structured, relational data that underpins the global economy — the rows and columns of ERP systems, CRMs, and financial ledgers — has so far been treated as just another file format similar to text or PDFs.

That’s left enterprises to forecast business outcomes using the typical bespoke, labor-intensive data science process of manual feature engineering and classic machine learning algorithms that predate modern deep learning.

But now Fundamental, a San Francisco-based AI firm co-founded by DeepMind alumni, is launching today with $255 million in total funding to bridge this gap.

Emerging from stealth, the company is debuting NEXUS, a Large Tabular Model (LTM) designed to treat business data not as a simple sequence of words, but as a complex web of non-linear relationships.

The tech: moving beyond sequential logic

Most current AI models are built on sequential logic — predicting the next word in a sentence or the next pixel in a frame.

However, enterprise data is inherently non-sequential. A customer’s churn risk isn’t just a timeline; it’s a multi-dimensional intersection of transaction frequency, support ticket sentiment, and regional economic shifts. Existing LLMs struggle with this because they are poorly suited to the size and dimensionality constraints of enterprise-scale tables.

“The most valuable data in the world lives in tables and until now there has been no good foundation model built specifically to understand it,” said Jeremy Fraenkel, CEO and Co-founder of Fundamental.

In a recent interview with VentureBeat, Fraenkel emphasized that while the AI world is obsessed with text, audio, and video, tables remain the largest modality for enterprises. “LLMs really cannot handle this type of data very well,” he explained, “and enterprises currently rely on very old-school machine learning algorithms in order to make predictions.”

NEXUS was trained on billions of real-world tabular datasets using Amazon SageMaker HyperPod. Unlike traditional XGBoost or Random Forest models, which require data scientists to manually define features — the specific variables the model should look at — NEXUS is designed to ingest raw tables directly.

It identifies latent patterns across columns and rows that human analysts might miss, effectively reading the hidden language of the grid to understand non-linear interactions.

The tokenization trap

A primary reason traditional LLMs fail at tabular data is how they process numbers. Fraenkel explains that LLMs tokenize numbers the same way they tokenize words, breaking them into smaller chunks. “The problem is they apply the same thing to numbers. Tables are, by and large, all numerical,” Fraenkel noted. “If you have a number like 2.3, the ‘2’, the ‘.’, and the ‘3’ are seen as three different tokens. That essentially means you lose the understanding of the distribution of numbers. It’s not like a calculator; you don’t always get the right answer because the model doesn’t understand the concept of numbers natively.”

Furthermore, tabular data is order-invariant in a way that language is not. Fraenkel uses a healthcare example to illustrate: “If I give you a table with hundreds of thousands of patients and ask you to predict which of them has diabetes, it shouldn’t matter if the first column is height and the second is weight, or vice versa.”

While LLMs are highly sensitive to the order of words in a prompt, NEXUS is architected to understand that shifting column positions should not impact the underlying prediction.

Operating at the predictive layer

Recent high-profile integrations, such as Anthropic’s Claude appearing directly within Microsoft Excel, have suggested that LLMs are already solving tables.

However, Fraenkel distinguishes Fundamental’s work as operating at a fundamentally different layer: the predictive layer. “What they are doing is essentially at the formula layer—formulas are text, they are like code,” he said. “We aren’t trying to allow you to build a financial model in Excel. We are helping you make a forecast.”

NEXUS is designed for split-second decisions where a human isn’t in the loop, such as a credit card provider determining if a transaction is fraudulent the moment you swipe.

While tools like Claude can summarize a spreadsheet, NEXUS is built to predict the next row—whether that is an equipment failure in a factory or the probability of a patient being readmitted to a hospital.

Architecture and availability

The core value proposition of Fundamental is the radical reduction of time-to-insight. Traditionally, building a predictive model could take months of manual labor.

“You have to hire an army of data scientists to build all of those data pipelines to process and clean the data,” Fraenkel explained. “If there are missing values or inconsistent data, your model won’t work. You have to build those pipelines for every single use case.”

Fundamental claims NEXUS replaces this entire manual process with just one line of code. Because the model has been pre-trained on a billion tables, it doesn’t require the same level of task-specific training or feature engineering that traditional algorithms do.

As Fundamental moves from its stealth phase into the broader market, it does so with a commercial structure designed to bypass the traditional friction of enterprise software adoption.

The company has already secured several seven-figure contracts with Fortune 100 organizations, a feat facilitated by a strategic go-to-market architecture where Amazon Web Services (AWS) serves as the seller of record on the AWS Marketplace.

This allows enterprise leaders to procure and deploy NEXUS using existing AWS credits, effectively treating predictive intelligence as a standard utility alongside compute and storage. For the engineers tasked with implementation, the experience is high-impact but low-friction; NEXUS operates via a Python-based interface at a purely predictive layer rather than a conversational one.

Developers connect raw tables directly to the model and label specific target columns—such as a credit default probability or a maintenance risk score—to trigger the forecast. The model then returns regressions or classifications directly into the enterprise data stack, functioning as a silent, high-speed engine for automated decision-making rather than a chat-based assistant.

The societal stakes: beyond the bottom line

While the commercial implications of demand forecasting and price prediction are clear, Fundamental is emphasizing the societal benefit of predictive intelligence.

The company highlights key areas where NEXUS can prevent catastrophic outcomes by identifying signals hidden in structured data.

By analyzing sensor data and maintenance records, NEXUS can predict failures like pipe corrosion. The company points to the Flint water crisis — which cost over $1 billion in repairs — as an example where predictive monitoring could have prevented life-threatening contamination.

Similarly, during the COVID-19 crisis, PPE shortages cost hospitals $323 billion in a single year. Fundamental argues that by using manufacturing and epidemiological data, NEXUS can predict shortages 4-6 weeks before peak demand, triggering emergency manufacturing in time to save lives.

On the climate front, NEXUS aims to provide 30-60 day flood and drought predictions, such as for the 2022 Pakistan floods which caused $30 billion in damages.

Finally, the model is being used to predict hospital readmission risks by analyzing patient demographics and social determinants. As the company puts it: “A single mother working two jobs shouldn’t end up back in the ER because we failed to predict she’d need follow-up care.”

Performance vs. latency

In the enterprise world, the definition of better varies by industry. For some, it is speed; for others, it is raw accuracy.

“In terms of latency, it depends on the use case,” Fraenkel explains. “If you are a researcher trying to understand what drugs to administer to a patient in Africa, latency doesn’t matter as much. You are trying to make a more accurate decision that can end up saving the most lives possible.”

In contrast, for a bank or hedge fund, even a marginal increase in accuracy translates to massive value.

“Increasing the prediction accuracy by half a percent is worth billions of dollars for a bank,” Fraenkel says. “For different use cases, the magnitude of the percentage increase changes, but we can get you to a better performance than what you have currently.”

Ambitious vision receives big backing

The $225 million Series A, led by Oak HC/FT with participation from Salesforce Ventures, Valor Equity Partners, and Battery Ventures, signals high-conviction belief that tabular data is the next great frontier.

Notable angel investors including leaders from Perplexity, Wiz, Brex, and Datadog further validate the company’s pedigree.

Annie Lamont, Co-Founder and Managing Partner at Oak HC/FT, articulated the sentiment: “The significance of Fundamental’s model is hard to overstate—structured, relational data has yet to see the benefits of the deep learning revolution.”

Fundamental is positioning itself not just as another AI tool, but as a new category of enterprise AI. With a team of approximately 35 based in San Francisco, the company is moving away from the bespoke model era and toward a foundation model era for tables.

“Those traditional algorithms have been the same for the last 10 years; they are not improving,” Fraenkel said. “Our models keep improving. We are doing the same thing for tables that ChatGPT did for text.”

Partnering with AWS

Through a strategic partnership with Amazon Web Services (AWS), NEXUS is integrated directly into the AWS dashboard. AWS customers can deploy the model using their existing credits and infrastructure. Fraenkel describes this as a “very unique agreement,” noting Fundamental is one of only two AI companies to have established such a deep, multi-layered partnership with Amazon.

One of the most significant hurdles for enterprise AI is data privacy. Companies are often unwilling to move sensitive data to a third-party infrastructure.

To solve this, Fundamental and Amazon achieved a massive engineering feat: the ability to deploy fully encrypted models—both the architecture and the weights—directly within the customer’s own environment. “Customers can be confident the data sits with them,” Fraenkel said. “We are the first, and currently only, company to have built such a solution.”

Fundamental’s emergence is an attempt to redefine the OS for business decisions. If NEXUS performs as advertised—handling financial fraud, energy prices, and supply chain disruptions with a single, generalized model—it will mark the moment where AI finally learned to read the spreadsheets that actually run the world. The Power to Predict is no longer about looking at what happened yesterday; it is about uncovering the hidden language of tables to determine what happens tomorrow.

Conpet, Romania’s national oil pipeline operator, has disclosed that a cyberattack disrupted its business systems and took down the company’s website on Tuesday.

Conpet operates nearly 4,000 kilometers of pipeline network, supplying domestic and imported crude oil and derivatives, including gasoline and liquid ethane, to refineries nationwide.

In a Wednesday press release, the company said the incident affected its corporate IT infrastructure but didn’t disrupt its operations or its ability to fulfill its contractual obligations.

Conpet added that the cyberattack also took down its website and that it’s now investigating the incident and restoring affected systems with the help of national cybersecurity authorities.

The pipeline operator has also notified the Directorate for Investigating Organized Crime and Terrorism (DIICOT) and filed a criminal complaint regarding the incident.

“We note that the operational technologies (SCADA System and Telecommunications System) were not affected, so the company’s core business, consisting of the transport of crude oil and gasoline through the National Oil Transport System, is operating normally and there are no disruptions in its operation,” it said. “As a result of this incident, the company’s website www.conpet.ro cannot be accessed during this period.”

While the company has yet to disclose the nature of the cyberattack, the Qilin ransomware gang has claimed responsibility and added Conpet to their dark web leak site earlier today.

​The threat actors also claim they’ve stolen nearly 1TB of documents from Conpet’s compromised systems and leaked over a dozen photos of internal documents containing financial information and passport scans as proof of the breach.

Qilin emerged in August 2022 as a Ransomware-as-a-Service (RaaS) operation under the “Agenda” name. Over the last four years, it has claimed responsibility for nearly 400 victims, including high-profile organizations such as Nissan, Japanese beer company Asahi, publishing giant Lee Enterprises, pathology services provider Synnovis, and Australia’s Court Services Victoria.

BleepingComputer reached out to Conpet with questions about the incident, but a response was not immediately available.

This cyberattack follows ransomware attacks on Romanian Waters (Romania’s water management authority) and Oltenia Energy Complex (the country’s largest coal-based energy producer) in December.

In December 2024, Electrica Group (a major Romanian electricity supplier and distributor) was also breached in a Lynx ransomware attack, while over 100 Romanian hospitals were knocked offline in February 2024 after a Backmydata ransomware attack took down their healthcare management systems.

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