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US Farmers Are Rejecting Multimillion-Dollar Datacenter Bids For Their Land

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An anonymous reader quotes a report from the Guardian: When two men knocked on Ida Huddleston’s door last May, they carried a contract worth more than $33m in exchange for the Kentucky farm that had fed her family for centuries. According to Huddleston, the men’s client, an unnamed “Fortune 100 company,” sought her 650 acres (260 hectares) in Mason county for an unspecified industrial development. Finding out any more would require signing a non-disclosure agreement. More than a dozen of her neighbors received the same knock. Searching public records for answers, they discovered that a new customer (PDF) had applied for a 2.2 gigawatt project from the local power plant, nearly double its annual generation capacity. The unknown company was building a datacenter. “You don’t have enough to buy me out. I’m not for sale. Leave me alone, I’m satisfied,” Huddleston, 82, later told the men.

As tech companies race to build the massive datacenters needed to power artificial intelligence across the US and the world, bids like the one for Huddleston’s land are appearing on rural doorsteps nationwide. Globally, 40,000 acres of powered land – real estate prepped for datacenter development — are projected to be needed for new projects over the next five years, double the amount currently in use. Yet despite sums that often dwarf the land’s recent value, farmers are increasingly shutting the door. At least five of Huddleston’s neighbors gave similar categorical rejections, including one who was told he could name any price.

In Pennsylvania, a farmer rejected $15m in January for land he’d worked for 50 years. A Wisconsin farmer turned down $80m the same month. Other landowners have declined offers exceeding $120,000 per acre — prices unimaginable just a few years ago. The rebuffs are a jarring reminder of AI’s physical bounds, and limits of the dollars behind the technology. […] As AI promises to transcend corporeal fallibility, these standoffs reveal its very physical constraints — and Wall Street’s miscalculation of what some people value most. In the rolling hills of Mason county and farmland across America, that gap is measured not in dollars but in something harder to price: identity.

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Sketchy iPhone Fold launch timing shared by analyst with shaky history

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An analyst at Barclays believes that if the iPhone Fold launches in 2026, it will be in December, months after the iPhone 18 Pro. He’s the only one saying this.

An iPhone Fold render showing the device open, dual back cameras visible, on a table by a cute cat lamp
iPhone Fold could launch in December

Many rumors point to the iPhone Fold launching in late 2026 alongside the iPhone 18 Pro, though no parts have leaked yet. It will be an incredibly expensive device and Apple’s first attempt at a foldable.
A note from Barclays analyst Tim Long, viewed by MacRumors suggests the iPhone Fold will release in December 2026. He offers no detail as to why it would come out three months after its announcement other than supply chain sources.
Rumor Score: 🙄 Unlikely
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Tyndall’s Peter O’Brien awarded for contributions to chip sector

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Peter O’Brien has received the 2025 Semi European Award, which recognises those who have had an impact on global semiconductor innovation.

Tyndall National Institute’s photonics expert Prof Peter O’Brien has been honoured by the global semiconductor industry for his work in the sector.

O’Brien is the head of research for photonics packaging and systems integration at the University College Cork-based deep-tech institute. He has received the 2025 Semi European Award, which recognises leaders whose work has had a significant impact in global semiconductor innovation.

Semi is a global industry association representing companies and research organisations across the semiconductor and electronics development and manufacturing supply chain.

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O’Brien has been recognised for his contributions to photonics electronic packaging, his leadership in Europe’s semiconductor pilot lines, and his work in developing specialised training programmes for up-and-coming researchers in the field.

“It is a great honour to receive the Semi European Award for 2025,” said O’Brien. “Through this award, I would like to recognise my many collaborators around the world. Working together, we accelerate research and development, turning early ideas into impactful breakthroughs.”

Prof William Scanlon, the CEO of Tyndall, added: “Prof O’Brien’s leadership and vision have placed Tyndall at the forefront of advanced packaging globally, and his contributions are shaping Europe’s semiconductor future.”

Meanwhile, Eric Beyne, a senior fellow at the Belgium-based nanoelectronics and digital tech research and innovation hub IMEC, received the Special Service Award at the ceremony earlier this month for his contributions to high-density interconnection and packaging technologies, and helping advance next-gen semiconductor integration techniques.

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“We are honoured to recognise Peter O’Brien and Eric Beyne for their outstanding contributions to advancing semiconductor innovation and strengthening Europe’s technology ecosystem,” said Semi Europe president Laith Altimime.

“Their leadership and vision have helped drive transformative progress across the industry while inspiring the next generation of engineers and researchers, reflecting the spirit of collaboration and innovation that continues to propel the semiconductor industry toward a more resilient, digital and sustainable future.”

Tyndall has made several major announcements this year. The Cork-based research institute recently announced a €100m expansion project.

It is also co-ordinating I-C3, Ireland’s National Competence Centre in Semiconductors, leading Ireland in a major €50m European initiative called Photonics for Quantum, and supporting a new €2.5bn pilot line to develop EU’s semiconductor leadership.

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AI analytics agents need guardrails, not more model size

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Picture a VP of finance at a large retailer. She asks the company’s new AI analytics agent a simple question: “What was our revenue last quarter?” The answer comes back in seconds.

Confident.

Clean.

Wrong.

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That exact scenario happens more frequently than many organizations would care to admit. AtScale, which enables organizations to deploy governed analytics environments and semantic consistency, has found that simply increasing model parameterization alone cannot address the AI governance and context issues enterprises face.

When AI systems query inconsistent or ungoverned data, adding more model complexity doesn’t contain the problem, it compounds it. Organizations across industries have acted quickly to develop agentic AI, deploying systems that analyze data, generate insights, and trigger automated workflows. In response to this trend, the AI models have adapted to react quickly via larger model parameters, increased computing power, and additional features. The underlying assumption has been that as long as the model gets large enough, the eventual result will be reliable.

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However, there are indications that this assumption may not hold up. Recent TDWI research found that nearly half of respondents characterized their AI governance initiatives as either immature or very immature. This may have more to do with data lineage and the business definitions on which these models are based than with the models’ capabilities.

Why bigger models don’t solve governance

The AI industry tends to operate on an unexamined assumption about what drives better performance: as we build more advanced models, they will somehow self-correct their performance errors. In enterprise analytics, that assumption can fall apart quickly.

While scale may improve the breadth of reasoning in a model, it doesn’t automatically enforce which definition of gross margin the business has agreed to use. It doesn’t resolve metric inconsistencies that have lived in separate dashboards for years. And it also doesn’t produce traceable lineage on its own.

Governance problems don’t resolve at scale. Business rules buried in individual tools, inconsistent definitions across teams, and outputs with no audit trail are structural issues, and a larger model doesn’t fix structure. It just produces unreliable answers more fluently.

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At AtScale, there’s a consistent theme among our clients: When inconsistent data definitions followed organizations into their AI layer, the problems didn’t stop there. They propagated forward, typically at greater speed and with less transparency than the previous layer had offered.

Performance and responsibility are separate jobs. A model reasons. A governance layer defines what the model reasons over, constrains how it applies business logic, and ensures outputs can be traced back to a source of record. One cannot substitute for the other.

The real risk: Unconstrained agents in enterprise environments

The problem with AI agents is seldom the model itself. It’s what the model is working with, and if anyone can see what it did.

With common context, AI agents might read data differently on different systems. In large enterprises, even small differences in definitions can lead to different results. Structural risks typically stem from four main causes:

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  • Agents pull from sources where the same metric can mean different things to different teams, making data definitions less clear.
  • Metrics from different departments that don’t agree – two agents give two answers, but it’s not clear which one is right.
  • Unclear reasoning produces outputs without a clear lineage as to how a decision was made.
  • Audit gaps: When outputs can’t be traced back to a governed source of record, there’s no reliable way to catch errors, assign accountability, or course-correct.

These are not signs that AI is not working. They show that the infrastructure around AI hasn’t kept up.

What guardrails actually mean in AI analytics

Guardrails are often viewed as a limitation. However, in many cases, guardrails are the very conditions that permit AI agents to operate with greater confidence.

Guardrails can help align AI-generated outputs with established business logic. They also create a structure in which autonomous agents can operate; this way, as autonomy increases, so does reliability. In analytics, guardrails typically exist in several specific formats:

  • Shared data definitions: A single definition of terms such as revenue, churn, or margin that are shared across all systems.
  • Business logic constraints: Rules governing how calculations are to be performed, regardless of the tools or agents performing those calculations.
  • Lineage visibility: The capability to identify where any output originated from.
  • Access controls: Defined permissions determining what data an agent can query.
  • Standardization of metrics: Consistent definitions applicable across departments and platforms.

The intention isn’t to impede AI’s performance. It’s to offer AI a base upon which it can stand.

The role of the semantic layer as a constraint framework

A semantic layer sits between data and the applications and AI agents that use it, defining business concepts, implementing logical processes, and providing a common framework of terms for all applications and AI agents to draw upon.

A semantic layer does not manipulate or duplicate data; it defines what the data represents. By asking questions of a governed semantic layer rather than the base table, AI agents can generate output based on business-defined logic, rather than on inference. The distinction of this output becomes particularly important when multiple AI agents across multiple systems must produce similar outputs.

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From AtScale’s perspective, the semantic layer serves as a context boundary that can help ensure AI agents interpret data according to shared business definitions. The semantic layer is more analogous to a common language, as opposed to a guardrail, that ensures all systems operate with a common understanding.

Governance is an architectural question, not a model question

Enterprise organizations realize that AI governance is less about building the largest model and more about making an environment where the chosen model can work well. A well-designed and governed architecture (with shared definitions for concepts, traceable logic, and a shared context across all systems) will likely deliver better, more reliable results than a larger model running in an uncontrolled data environment.

Scaling models without improving semantic clarity tends to add complexity, not reduce it. As each additional tool, system, or workflow is added to an uncontrolled environment, the opportunities for divergence increase.

In this sense, responsible AI is an infrastructure challenge. Organizations with successful AI deployments treat the meaning of their data as a design decision,before the model is even chosen.

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Economic and operational implications

Governance gaps do not stay abstract for long. They tend to show up in the budget.

Ambiguity in data meaning may increase operational friction, agents that produce inconsistent outputs require human review, reconciliation cycles, and rework that compounds across teams and tools. When lineage is not clear, audits cost more. Retrofitting controls after deployment typically costs more than building the right architecture from the start.

In complex enterprise settings, costs can show up in predictable ways: redundant validation when outputs don’t match across systems, excess compute triggered by unclear queries, and slower analysis as teams pause to figure out which answer is actually reliable. Clear semantic constraints can mean fewer validation cycles, and that operational value is becoming easier to measure.

The path forward: Constrained autonomy

AI agents aren’t a future consideration, they’re already in use. What’s still catching up is the infrastructure around them. Agents without clear context and constraints tend to operate beyond what the organization can actually govern. That gap doesn’t close on its own.

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The differentiator in enterprise AI, AtScale contends, won’t be model scale, it will be the clarity of the environment models operate in. As agents become more common in business workflows, how well the semantic layer is defined may matter more than how large the model is.

This shift toward governed context and constrained autonomy is explored in more detail in AtScale’s 2026 State of the Semantic Layer report, which examines how open standards, interoperability, and semantic governance are shaping the next phase of enterprise intelligence.

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DoorDash will start paying gig workers for creating content to train AI models

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DoorDash has launched a new option for its gig economy workers to earn some extra cash. The delivery service introduced Tasks, which it describes as “short activities Dashers can complete between deliveries or in their own time.” It gives taking pictures of restaurant dishes or recording video of unscripted conversations in languages other than English as examples. These materials will be used to train artificial intelligence and robotics models.

A representative from DoorDash told Bloomberg News that it will use Tasks content for evaluating its in-house AI models as well as those made by its partner companies in retail, insurance, hospitality and tech. DoorDash is piloting a standalone app for Tasks where Dashers will submit their content. The blog post notes that pay will be displayed upfront, and compensation will vary based on the complexity of the activity.

This idea isn’t new. We’ve seen other startups in AI and robotics offering payment for content filmed by regular people. Considering how many lawsuits are underway against AI companies that have already benefited from unauthorized use of copyrighted materials, at least this approach lets people be directly compensated for training content.

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Look at What Mattel’s Cooking Up: New Castle Grayskull Bricks, Naruto Hot Wheels, Monster High Skeletor

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Searching for a reason to buy collectors’ editions of Mattel products? With the toy drops unveiled during Mattel Creations Revealed event Thursday, you may be able to make a case for yourself. Homing in on fandoms ranging from anime to Barbie to Monster High, the company shared a fresh lineup of releases. Masters of the Universe fans: lock in.

You can stack Mattel Bricks to create Eternia’s legendary Castle Grayskull with a new set (pictured above) that’s the first of its kind for the toy giant. Get into your display — or play — with Masters of the Universe Nano figures depicting characters like He-Man, Evil-Lyn, Skeletor, Battle Cat and Teela. Available in the Brick Shop starting April 25, the set will retail for $65. For fans who want a bit of extra nostalgia, you can also buy the light-up Laser Power He-Man figure for $30 to add to your display.

Mattel has been on somewhat of a roll with its Monster High Skullector series, with doll collabs that feature iconic movie and TV favorites like Coraline, Wednesday and Morticia, Pennywise and Alien. Skeletor has been added to the collection and is quite the baddie. Just check out the outfit, high-heeled boots and signature smirk. Her staff speaks as well, but I won’t spoil it by telling you the catchphrase. The price? $65.

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Mattel Monster High Skeletor

Will you be adding this version of Skeletor to your Monster High collection?

Mattel/Jeff Hazelwood/CNET

If the He-Man franchise isn’t for you, maybe you’ll be into the drop for Naruto. Burn up a Hot Wheels track with the Nissan Silvia S15 model that carries Naruto emblazoned on both sides. Making its debut as part of a partnership between Mattel and anime franchises, the car is priced at $25.

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Hot Wheels Gold Nissan Car

Naruto fans, this one is made for you.

Mattel/Jeff Hazelwood/CNET

We’ve seen Barbie go couture in the past, and Mattel has just revealed its atelier design that takes handcrafted fashion up a notch. The new poseable Grand Couture Silkstone Barbie doll retails for $342, stands at 14.5 inches and wears a ruffled coat (with a train) with a shimmery embroidered dress underneath. Drop earrings and pink boots complete the high-end look.

Not to be sidelined at today’s event, Ken was spotlighted with the release of his own Uno card deck that features a variety of, well, Kens. Celebrate his 65th anniversary with the $13 deck, and you can decide if you’ve had Ken-ough of draw fours and skips.

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mattel barbie grand couture doll in fancy dress and Ken doll uno deck

New Barbie and Ken products revealed today, including a celebratory Ken Uno deck.

Mattel/Jeff Hazelwood/CNET

One of the other pop culture moments — and figures — commemorated with a toy reveal today came in the form of WWE star Stone Cold Steve Austin. He’s been immortalized as a Mattel action figure that captures when he coined his 3:16 catchphrase 30 years ago, with the Elite Collection addition coming equipped with a crown, table and throne. Get it for your WWE collection for $30.

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Navia discloses data breach impacting 2.7 million people

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Navia discloses data breach impacting 2.7 million people

Navia Benefit Solutions, Inc. (Navia) is informing nearly 2.7 million individuals of a data breach that exposed their sensitive information to attackers.

An investigation into the incident revealed that the hackers had access to the organization’s systems between December 22, 2025, and January 15, 2026. However, the company discovered the suspicious activity on January 23.

Navia says that it responded immediately and launched an inquiry to determine the potential impact of the incident.

“The investigation determined that an unauthorized actor accessed and acquired certain information between December 22, 2025, and January 15, 2026,” the company says in the notification to impacted individuals.

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Navia is a consumer-focused administrator of benefits that provides services to more than 10,000 employers across the U.S.

The company provides software and customer services for the administration of Flexible Spending Accounts (FSA), Health Savings Accounts (HSA), Health Reimbursement Arrangements (HRA), Commuter Benefits and COBRA Services.

It also helps handle commuter benefits, lifestyle accounts, education benefits, compliance/risk services, and retirement-related offerings.

According to the company, the investigation into the breach revealed that the hacker accessed and may have exfiltrated the following types of data:

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  • Full name
  • Date of birth
  • Social Security Number (SSN)
  • Phone number
  • Email address
  • Participation in HRA (Health Reimbursement Arrangements)
  • FSA (Flexible Spending Accounts) information
  • Consolidated Omnibus Budget Reconciliation Act (COBRA) enrollment information

Navia underlines that the data breach did not expose details about claims or financial information. Nevertheless, the exposed data is enough for threat actors to deploy phishing and social engineering attacks aimed at affected individuals.

The company states that it has reviewed its security posture and data retention policies to identify potential weaknesses that can be improved, and has notified federal law enforcement about the incident.

Customers whose information was exposed will be covered by a free 12-month identity protection and credit monitoring service from Kroll. Letter recipients are also encouraged to consider placing a fraud alert and security freeze on their credit files.

At the time of writing, no ransomware group has claimed the Navia data breach.

Malware is getting smarter. The Red Report 2026 reveals how new threats use math to detect sandboxes and hide in plain sight.

Download our analysis of 1.1 million malicious samples to uncover the top 10 techniques and see if your security stack is blinded.

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Reading The World’s Smallest Hard Drive

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You have a tiny twenty-year-old hard drive with a weird interface. How do you read it? If you’re [Will Whang], by reverse engineering, and building an interface board.

In many of our portable, mobile, and desktop computers, we’re used to solid-state storage. It’s fast and low power, and current supply-chain price hikes notwithstanding, affordable in the grand scheme of things. It wasn’t always this way though, a couple of decades ago a large flash drive was prohibitively expensive. Hard drive manufacturers did their best to fill the gap with tiny spinning-rust storage devices which led to the smallest of them all: the Toshiba MK4001MTD. It crammed 4 GB onto a 0.85″ platter, and could be found in a few devices such as high-end Nokia phones.

Breaking out the Nokia’s hard drive interface.

The drive’s connector is a pattern of pads on a flexible PCB, one he couldn’t help noticing had a striking resemblance to an obscure SD card variant. Hooking it up to an SD reader didn’t work unfortunately, so a battered Nokia was called into service. It was found to be using something electrically similar to the SD cards, but with the ATA protocol familiar from the world of full-size hard drives.

The interface uses the PIO capability of the RP2040, and the board makes a tidy peripheral in itself. We’re guessing not many of you have one of these drives, but perhaps if you do, those early 2000s phone pics aren’t lost for good after all.

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These drives are rare enough that this is the first time we’ve featured one here at Hackaday, but we’ve certainly ventured into hard drive technology before.

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Retro Weather Display Acts Like It’s Windows 95

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Sometimes you really need to know what the weather is doing, but you don’t want to look at your phone. For times like those, this neat weather display from [Jordan] might come in handy with its throwback retro vibe.

The build is based around the ESP32-2432S028—also known as the CYD, or Cheap Yellow Display, for the integrated 320 x 240 LCD screen. [Jordan] took this all-in-one device and wrapped it in an attractive 3D-printed housing in the shape of an old-school CRT monitor, just… teenier. A special lever mechanism was built in to the enclosure to allow front panel controls to activate the tactile buttons on the CYD board. The ESP32 is programmed to check Open-Meteo feeds for forecasts and current weather data, while also querying a webcam feed and satellite and radar JPEGs from available weather services. These are then displayed on screen in a way that largely resembles the Windows 95 UI design language, with pages for current conditions, future forecasts, wind speeds, and the like.

We’ve seen some fun weather displays over the years, from graphing types to the purely beautiful. If you’ve found a fun way to display the weather (or change it) don’t hesitate to notify the tipsline. Particularly in the latter case.

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Bitrefill blames North Korean Lazarus group for cyberattack

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Bitrefill blames North Korean Lazarus group for cyberattack

Crypto-powered gift card store Bitrefill says that the attack it suffered at the beginning of the month was likely perpetrated by North Korean hackers of the Bluenoroff group.

During the investigation, the platform observed indicators similar to previous attacks attributed to the North Korean threat actor, like tactics, malware, IP and email addresses.

“Based on indicators observed during the investigation  – including the modus operandi, the malware used, on-chain tracing and reused IP + email addresses (!) – we find many similarities between this attack and past cyberattacks by the DPRK Lazarus / Bluenoroff group against other companies in the crypto industries,” reads Bitrefill’s statement.

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Bitrefill is a mid-sized e-commerce platform that enables people to pay in cryptocurrency for gift cards at stores in 150 countries. The gift cards can be used to pay for anything from clothing, food and groceries, health and beauty products to bills, services, gas, transportation, and electronics.

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The platform supports more than 600 mobile operators and thousands of brands worldwide.

On March 1st, Bitrefill announced technical issues affecting access to its website and app. A day later, the company disclosed that it had identified a security issue and took all services offline.

Although user balances were not affected, the gradual restoration of all services still continues to this day.

The breach was discovered after Bitrefill noticed suspicious supplier purchasing patterns, exploitation of gift card stock and supply lines, and draining of some “hot” wallets.

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The investigation the firm launched to determine the cause revealed that the attack originated on a compromised employee’s laptop.

The attackers stole legacy credentials and used them to access a snapshot with production secrets, later escalating access to the larger Bitrefill infrastructure, including parts of the database and some cryptocurrency wallets.

About 18,500 purchase records containing customer email addresses, IP addresses, and cryptocurrency payment addresses were exposed in the breach. For 1,000 purchases, customer names were also exposed.

Although this information is stored in encrypted form, Bitrefill notes that the attackers may have obtained the decryption keys.

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Bitrefill says this was the most serious cyberattack it has suffered in its ten years of existence, but it survived with minimal losses, which will be covered from its capital.

Ultimately, Bitrefill believes that attackers were after cryptocurrency and gift card inventory, not customer information.

BlueNoroff, also known as APT38, is a cluster of the Lazarus group that has been active since at least 2014. It typically targets financial organizations, with a more recent focus on the cryptocurrency industry, the objective being crypto theft.

Bitrefill says this was the most serious cyberattack it has suffered in the ten years of its existence, but it survived with minimal losses, which will be covered from its capital.

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Meanwhile, it is expanding security reviews and pen-testing, tightening access controls, improving logging and monitoring, and refining automated shutdown mechanisms.

At this time, most of its services have returned to normal operational status, and customers aren’t required to take any action beyond treating incoming communications with extra caution.

Malware is getting smarter. The Red Report 2026 reveals how new threats use math to detect sandboxes and hide in plain sight.

Download our analysis of 1.1 million malicious samples to uncover the top 10 techniques and see if your security stack is blinded.

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Intel’s latest push gains traction as GMKtec unveils EVO-T2 mini PC with 180 TOPS and unusual memory workaround

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  • GMKtec EVO-T2 mini PC reaches 180 TOPS using combined CPU, GPU, and NPU acceleration
  • Its PCIe 5.0 storage introduces data speeds exceeding 10GB per second
  • Local AI models run without relying on external cloud infrastructure

At a recent launch event, GMKtec introduced the GMKtec EVO-T2, a compact desktop system built for local AI computing.

According to the company, the device integrates third-generation Intel Core Ultra processors and claims up to 180 TOPS of compute capability.

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