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The Digital Operational Resilience Act (DORA) came into effect on January 17, 2025. Financial services institutions (FSIs) across the EU must now fully comply with its stringent cybersecurity and operational resilience requirements. But achieving compliance is not just about meeting regulatory expectations. DORA represents a fundamental shift in how financial institutions approach digital security, ensuring they can withstand cyber threats, operational disruptions, and third-party vulnerabilities.

For firms that have already established a compliance framework, the focus now moves to long-term resilience and continuous improvement. For those still catching up, the urgency to close security gaps has never been greater. Failing to meet DORA’s requirements carries not only financial penalties but also the risk of operational restrictions and reputational damage. In this new era of cybersecurity regulation, FSIs must go beyond basic compliance measures and embed resilience into their core strategies.

A shift in cyber resilience thinking

For years, financial institutions have relied on traditional cybersecurity approaches, primarily focused on perimeter security to keep external attackers at bay. However, recent cyber incidents have made it clear that threats do not always come from outside an organization. Many damaging breaches have originated from within digital supply chains, through third-party vulnerabilities, or from internal weaknesses. In 2023, third-party attacks led to 29% of breaches with 75% of third-party breaches targeting the software and technology supply chain. This evolving threat landscape has forced financial institutions to rethink their approach. The future of cyber resilience isn’t about building higher walls - it’s about securing every layer, inside and out.

DORA mandates a resilience-first mindset, shifting the focus from prevention alone to a more comprehensive strategy that includes rapid response and recovery. It is no longer enough to defend against cyber threats; organizations must assume that breaches and disruptions will happen and ensure they can respond swiftly and effectively. This change means cybersecurity is no longer just the responsibility of IT management. It is now a board-level priority, requiring CFOs, CIOs, and risk officers to play a direct role in overseeing governance structures, risk assessments, incident response planning, and ongoing security monitoring.

The growing role of automation in compliance

With DORA now in full effect, financial institutions are also navigating additional regulatory frameworks such as the NIS2 Directive and the Cyber Resilience Act (CRA), both of which introduce further security and operational resilience requirements. The increasing complexity of compliance is prompting many organizations to turn to automation to streamline regulatory processes.

Okta’s 2024 Businesses at Work report found that data compliance tools were the fastest growing applications with 120% year-on-year growth. As firms seek to reduce the burden on their security teams while ensuring continuous adherence to evolving regulations, the rising popularity of these tools is unsurprising.

Automating security audits, compliance validation, and real-time threat detection allows financial institutions to maintain compliance efficiently while also enhancing their ability to identify and mitigate risks before they escalate into major incidents. In a landscape where regulatory expectations will only become stricter, automation is important for maintaining both security and operational efficiency.

Addressing digital supply chain risks

One of the most pressing concerns for financial institutions under DORA is the security of their digital supply chains. High-profile cyberattacks in recent years have demonstrated that vulnerabilities often originate not from within an organization's own IT infrastructure, but through weaknesses in third-party service providers, cloud platforms, and outsourced IT partners. DORA places a strong emphasis on third-party risk management, making it clear that security responsibility extends beyond a firm’s immediate network.

Ensuring supply chain resilience requires a proactive and continuous approach. FSIs must conduct regular security assessments of all external vendors, ensuring that partners adhere to the same high standards of cybersecurity and risk management. It is no longer sufficient to perform security checks only at the beginning of a partnership; ongoing monitoring and real-world scenario testing are essential to ensure that contingency plans hold up under real conditions. The ability to anticipate and respond to emerging threats within the supply chain is critical to maintaining operational stability and regulatory compliance.

Navigating post-implementation compliance challenges

While many FSIs had operational resilience frameworks in place before DORA’s enforcement date, aligning these existing efforts with the regulation’s EU-wide supervisory structure presents new challenges. Firms that have not been closely following the consultation process may struggle to adapt to these additional requirements.

At this stage, financial institutions must prioritize regular compliance evaluations to ensure that their security frameworks remain aligned with DORA’s evolving mandates. Conducting a gap analysis is critical to identifying areas where improvements are needed. Engaging with regulators, industry bodies, and technology partners can provide valuable insights into best practices and common pitfalls. Additionally, collaboration within the financial sector will be essential, as firms can learn from each other’s experiences and share strategies for maintaining long-term compliance.

The cost of non-compliance

The consequences of failing to comply with DORA are severe. Regulators now have the authority to suspend business operations, issue cease-and-desist orders, and demand access to sensitive data for compliance reviews. For critical third-party service providers, non-compliance could result in financial penalties of up to 1% of their global daily turnover for up to six months - a staggering cost that could significantly impact business operations.

Beyond regulatory penalties, the reputational damage of non-compliance may be even more devastating. The financial sector operates on trust, and any failure to meet cybersecurity standards can lead to a rapid loss of confidence from both consumers and investors. A single security lapse or compliance failure can undermine an institution’s credibility, and once trust is lost, rebuilding it can take years. FSIs must recognize that compliance is not just about avoiding fines - it is about preserving their reputation and long-term viability in an increasingly digital financial ecosystem.

The role of identity security

One of the most effective ways to strengthen cybersecurity resilience under DORA is through identity management (IAM). Research indicates that 80% of cyberattacks originate from compromised credentials, making authentication and access control a top priority for financial institutions.

A robust IAM strategy involves implementing multi-factor authentication (MFA), enforcing least-privilege access policies, and continuous monitoring for credential-based threats. The adoption of a zero-trust security model, where no user or system is automatically trusted, further enhances security by ensuring that every access request is verified before granting permissions. As cybercriminals continue to develop more sophisticated attack methods, securing user identities will remain a cornerstone of both regulatory compliance and overall cyber resilience.

An opportunity for long-term resilience

DORA has transformed the cybersecurity landscape for financial services firms. Compliance is no longer a one-time activity - it is an ongoing effort that requires constant adaptation to emerging threats and regulatory updates. Organizations that approach DORA as an opportunity to strengthen their overall cybersecurity posture will be best positioned for success.

FSIs that invest in proactive security strategies today will not only protect themselves from regulatory penalties but will also build stronger, more resilient digital ecosystems. Cyber resilience is now a business imperative, and those that take it seriously will emerge as leaders in the evolving financial landscape. Compliance in itself should not be the security strategy of any organization, but it is a rising tide that raises all ships to a better security foundation to the benefit of all.

We've set up a list of the best network monitoring tools.

This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

The four non-3D Zen 5-based AMD Ryzen processors top our leaderboard when it comes to price per core.

Data collated at the beginning of March 2025 shows that the 9900X, the 9950X, the 9700X and the 9600X are the most competitive price wise.

The Ryzen 9900X is by far, the most balanced offer of the quartet, with a low TDP per core (just 10W), a high base speed (4.4GHz) and a very reasonable price at $387.75 (or $31.56/core) at the time of writing, almost a quarter cheaper than its suggested retail price.

This is the second of several articles based on data I’ve compiled on 41 AMD Zen 4 and Zen 5 CPUs (socketed, OEM). In the rest of the series, I will be looking at the cost per core, performance per core, AMD CPUs that are getting more expensive, all this with the new Ryzen 9 9900/9950 X3D CPUs in the backdrop.

Not bad for a near-flagship CPU launched less than one year ago. The 9950X has a cost per core slightly higher, at $34.05, but is the fastest consumer CPU that AMD has to offer (until the launch of the 9950X3D).

The table of all the CPUs I have analyzed can be found at the end of this article. They have been sorted by cost per core. Some CPUs are not yet on sale at the time of writing.

A ‘value’ 192-core CPU?

At just over $10,000 from a reputable retailer (Wiredzone), the EYPC 9965 is AMD’s most expensive CPU ever launched and one that I covered extensively in a recent article.

It has 192 cores, which translates into a per-core cost of $52.26; far more than any consumer Ryzen CPUs but still a third of the cost of the most expensive AMD CPU (per core).

It delivers one of the lowest TDP per core (at just 2.6W) and the lowest TDP per GHz* (1.16W), thanks to its Zen 5c architecture, a more compact (but compatible) version of the Zen 5.

Its smaller sibling, the 96-core AMD EPYC 9655, has the largest discount I’ve seen across the 41 CPUs I’ve tracked, with a staggering 56.8% reduction from the sticker price.

It is a full Zen 5 part and as such gets a much higher TDP per core, twice the amount of cache and a faster base speed.

* Lowest TDP per GHz is calculated by taking the CPU TDP and dividng it by the number of cores x the base speed in GHz. It delivers a very rough composite efficiency metric.

The mystery of the ThreadRipper Pro 7945WX

At the other end of the spectrum, the EPYC 9175F is the most expensive AMD CPU per core costing of just under $160, that’s almost 5x that of the 9950X, which shares the same number of cores (16).

The reason why it is so expensive is that it has 32x more cache per core than an average consumer CPU (512MB) and cache is a very, very expensive commodity.

Other F-labelled EPYC CPUs trawl the bottom of my cost per core leaderboard; F stands for Fast and these CPUs are high frequency optimized parts with big cache memory.

One more thing. I’d like to draw your attention to the existence of the Ryzen Threadripper PRO 7945WX.

It is the only AMD CPU from this list that you cannot buy as it is available exclusively in workstations from Lenovo, HP and Dell.

What makes it so special for me is its high TDP per core, the highest of all the CPUs I’ve analysed.

At 6.21W, this Zen 4 part is 5.5X more power hungry than the EPYC 9965 (or 3x that of the 9900X, a similar 12-core CPU).

Maybe that’s because it has the highest base speed of any CPU in the list (jointly with the 9800X3D) and is built on an older manufacturing technology.

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In a world where Generative AI (GenAI) is reshaping the global business landscape, mastering faster decision-making at the right time at the right pace has become a crucial competency for leaders seeking to maintain a competitive edge. GenAI is rapidly becoming an integral part in business operations, driven by its ability to streamline processes, enhance effectiveness, improve competitiveness, and lead to significant cost reductions and revenue enhancements.

According to a recent IDC report, enterprise spending on GenAI globally will grow by 30% in 2024 – $40 billion from an estimated US$16 billion in 2023. Spending is also expected to swell to more than $150 billion by 2027, with the banking, retail, and professional services industries being the top spenders. This spending is bound to increase significantly beyond the estimates in coming years.

The latest developments in the field include the broad adoption from open public models to private models to pre-trained open source AI models to untrained open source and custom models. The significant investments are in both infrastructure and AI-enhanced products and services.

Enterprises are moving beyond initial experiments with GenAI and towards aggressive infrastructure and trained data model building, aiming for a transformation that integrates GenAI at the core of digital business activities. This strategic integration is expected to provide a competitive edge and catalyze a shift towards more dynamic, efficient, and innovative business environments.

For business leaders, the challenge now lies not just in adopting these technologies but in integrating GenAI with human intuition to optimize faster and right decision-making processes. This nuanced approach ensures that businesses keep pace with technological changes and stay ahead of their competitors. Understanding and harnessing the power of GenAI is essential for any organization aiming to thrive in this transformative era.

Strategic Implications of GenAI

GenAI stands distinct with its ability to create new content, ranging from text to images, from existing data sets, a stark contrast to other AI technologies that primarily analyze or interpret data. This capability positions GenAI as a revolutionary technology in enhancing business strategy, across global tech giants. 

In marketing, for instance, GenAI enables the creation of highly personalized content that resonates with diverse customer segments, dramatically improving engagement rates. Product development also benefits from GenAI as it can suggest innovative product features or designs by analysing current market trends and consumer feedback. Moreover, in customer service, it enhances responsiveness and personalization, as seen in AI chatbots that provide real-time, context-aware solutions to customer queries. These examples illustrate significant efficiency gains and competitive advantages, marking GenAI as a transformative force across business functions. 

One significant example of strategic implication of GenAI is in the telecommunications industry where it helps to optimize network performance and management. GenAI models analyze vast amounts of data from network operations, including traffic patterns, equipment health, and historical performance metrics. These models can simulate various scenarios to predict potential network failures or degradations before they occur.

GenAI plays a crucial role by creating synthetic datasets and simulations that mirror real-world network conditions. This allows telecom operators to test and validate maintenance strategies, capacity planning, and network upgrades without disrupting actual service. By simulating different traffic loads and failure conditions, AI can recommend optimal configurations and preemptive actions, leading to reduced downtime, improved service quality, and cost savings on emergency repairs. The predictive insights generated by AI ensure that the network remains resilient and capable of handling increasing data demands.

Challenges in AI-driven Decision-Making

Integrating GenAI into business decision-making processes presents nuanced challenges, necessitating a balanced approach to utilizing AI outputs. In complex scenarios, the efficacy of GenAI hinges on the model’s training adequacy. If a model is not sufficiently trained for a specific task, human intervention becomes crucial, as human expertise can surpass undertrained AI models in navigating intricate decisions. Conversely, when models are well-trained, they can outperform humans by delivering consistent and data-driven insights at scale.

Therefore, the integration of GenAI requires astute judgment to discern when to rely on AI and when to defer to human judgment. This balance ensures that decision-making processes harness the strengths of both AI and human intelligence, leveraging AI for efficiency and precision, while capitalizing on human intuition and experience in areas where AI’s training may fall short. This nuanced approach is essential for maximizing the potential of GenAI in business contexts.

Another notable challenge associated with integrating GenAI into business decision-making processes is data privacy. GenAI systems require vast amounts of data to train and operate effectively. This reliance on large data sets raises concerns about compliance with global data protection regulations such as GDPR in Europe or CCPA in California, which mandate strict guidelines on data usage, storage, and privacy. In the telecommunications industry, GenAI could be used to analyze customer call data to improve service offerings or personalize marketing strategies.

However, this data often contains sensitive personal information. Ensuring that GenAI applications comply with data protection laws requires robust anonymization techniques and secure data handling practices. Failing to adhere to these regulations can result in substantial fines and damage to the organization's reputation, illustrating the complexity and risk associated with deploying GenAI in sectors with stringent privacy requirements. There are multiple data masking and data anonymization solutions available in the market which needs to be applied if there are PII in the data before using them for training the models.

To navigate these complexities, leaders must stay abreast of the latest developments in GenAI by engaging with ongoing education, participating in industry forums, and fostering partnerships with AI ethics boards. By doing so, they can implement GenAI's capabilities responsibly and effectively, ensuring that their strategic decisions are both innovative and ethically sound. 

Embracing the Epoch of GenAI: A Strategic Imperative 

In the vanguard of technological evolution, the strategic integration of GenAI stands as a linchpin for redefining decision-making and operational efficiency within forward-looking businesses. This leap towards GenAI adoption is not merely an enhancement but a transformative shift that sets enterprises apart in today's competitive landscape. 

To harness GenAI’s full capabilities, establishing a solid business case is crucial before implementation. This involves identifying key objectives and anticipated benefits aligned with business goals. Conducting a Proof of Concept (PoC) or pilot project helps validate GenAI’s potential, demonstrating tangible results and addressing any challenges. By doing so, businesses can ensure a strategic, well-informed adoption of GenAI, optimizing its impact and value.

In a nutshell, diving into GenAI isn't just about keeping up with the latest tech trends. It's about seizing an opportunity to redefine how your business operates, making decisions smarter and faster than ever before. The future is about those who adapt, and with GenAI, that future is bright.

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This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

We’ve reached a point of obfuscated redundancy in the auto space. Many manufacturers pushing out new models incorporate aspects calling on Tesla’s minimalism – but that means the axing of many important things, like the instrument cluster, tactile buttons and dials, and a focus on customization in the infotainment system, with this trend more noticeable in the EV space. Among the axing of supposed non-essentials is the humble full-size spare tyre – which is why it’s cause for celebration whenever it returns.

The Chery Omoda E5, sold in the UK as simply the Omoda E5 and originating from China, is the car we’re celebrating today. I was diplomatic in my initial impressions article on the E5, noting that it’s likely a better fit for value-savvy Aussies than Britons, who have a greater variety at the car’s price point. It lacks a competitive angle on driving comfort, boot capacity, and DC recharge time, though its price to range ratio is considerable and the features offered in the slightly more expensive trim are notable.

Across both trims in Australia and the UK, there is one standout feature – the full-sized spare tyre. There’s also the expected internal space for it to be stored under the boot mat in the back.

A spare tyre is a rarity among new cars, especially for EVs. It’s a feature only typically found in cars built for off-roading lifestyles, such as heavy-duty SUVs and utes, but you’ll find exceptions here and there, like the 2022 Subaru BRZ.

But it should be a greater consideration for drivers and manufacturers alike. Roadside assistance, though often reliable, especially for drivers who may not be comfortable repairing a spare on the roadside, can often be time consuming when your car is capable of carrying a spare. So let’s talk about why it’s great that the Omoda E5 has included it.

Spare a spare?

(Image credit: Behold! The spare tyre found in the Chery Omoda E5. Zachariah Kelly / TechRadar)

The spare tyre is an inclusion that has largely been lost over time, with the argument often going that the space is better used to improve volume capacity, that it adds room for a larger battery or fuel tank, or that it adds unnecessary weight. Of the more than 30 EVs I’ve reviewed, only two have featured a full-sized spare tyre – the Omoda E5 and the 2024 Hyundai Kona EV (though there are a handful of other EVs offering spares).

I’m not going to fly in the face of removing redundancy, but a spare tyre is the furthest thing from it. This is the kind of thing where the term ‘have it and not need it, rather than need it and not need it’ applies.

The most common argument against the provisioned spare tyre is that it’s unnecessary. In many cases, a space–saver tyre is enough to get the job done, or a puncture repair kit would suffice.

A puncture repair kit will only work for holes smaller than 3mm (per Drive, who interviewed a former roadside assistance mechanic on the topic), and even then, the tyre may have been further damaged by driving at low pressure. Meanwhile, a space saver limits your speed to 80km/h (49mph) and can only be used for a short distance. These are solutions that could work in the city, but are unideal if you live far out from town.

Within reason, tyres can be repaired for general road use after a tread puncture, but a puncture to the tyre wall is often unlikely to be repairable. A full-size spare tyre gives the owner greater agency over their car. As it’s specced in-line with the rest of the car’s tyres, it can be fitted and used ongoing as if things were normal – though it would be best to replace (or repair the spare) at your earliest convenience.

But obviously roadside assistance can be preferred. People who are less nimble might not want to bend their back to get the wheel out of the back of the car, or might not want to kneel down to replace the tyre if it’s too heavy.

Getting by sparingly

(Image credit: Zachariah Kelly / TechRadar)

It’s hard to think of a spare tyre as anything less than necessary in rural and regional areas. You can get by in cities where a puncture repair kit or space-saver could be relied on for a short distance, but places like regional New South Wales in Australia may not have a tyre shop or mechanic for a great distance.

And to give yourself peace of mind on the road, especially if you live far out of town or are roadtripping, it’s probably not a bad idea to pick up a compatible full-size spare tyre, to save yourself from calling up roadside assistance.

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I love diving into learning about new things and falling down research rabbit holes, but sometimes I just need a quick, efficient answer to a question or a concise guide to a task. If I’m trying to figure out how long to roast chicken or whether Pluto has been reinstated as a planet, I want a short list of bullet points and a simple yes or no.

So, while ChatGPT's Deep Research feature has proven to be an amazing researcher that is great when I want to immerse myself in a topic, I haven't made it my default tool with the AI chatbot. The AI model's database, as well as its search tool, resolve pretty much any day-to-day question or issue I might ask it. I don't need a formal report on how to make a meal that takes 10 minutes to compile. But, I do find the comprehensive answers from Deep Research viscerally appealing, so I decided it was worth comparing it to the standard (GPT-4o) ChatGPT model and giving it a few prompts that I could imagine submitting on a whim or with little long-term need.

Beef Wellington

(Image credit: ChatGPT Screenshots)

For the first test, I wanted to see how both models would handle a classic, somewhat intimidating recipe: Beef Wellington. This isn’t the kind of dish you can just throw together on a weeknight. It’s a time-consuming, multi-step process that requires patience and precision. If there was ever a meal where Deep Research might prove useful, this was it. I asked both models: “Can you give me a simple recipe for kosher Beef Wellington?”

Regular ChatGPT responded almost instantly with a straightforward, well-structured recipe. It listed ingredients in clear measurements, broke the process down into manageable steps, and offered a few helpful tips to avoid common pitfalls. It was exactly what I needed in a recipe. Deep Research took a full ten minutes and had a very long, complex mini-cookbook centered on the dish. I had multiple versions of Beef Wellington, which did all adhere to my specific requests, but ranged from a Jamie Geller-inspired method to a 19th-century traditional preparation with some substitutions. That's not counting the extra suggestions about decorations and an analysis of various types of puff pastry and their butter-to-flour ratios. If I'm honest, I loved it as a piece of trivia obsession. But, if I wanted to actually just make the dish, it was a bit too much like those recipe blogs where you have to scroll past someone's entire life story just to get to the ingredients list.

TV time

(Image credit: ChatGPT Screenshots)

For the second test, I wanted to see if Deep Research could help me buy a TV so I kept it simple with: “What should I consider when buying a new TV?”

Regular ChatGPT gave me a quick and clear answer. It broke things down into screen size, resolution, display type, smart features, and ports. It told me that 4K is standard, 8K is overkill, OLED has better contrast, HDMI 2.1 is great for gaming, and budget matters. I felt like I had a decent grasp of what to look for, and I could have easily walked into a store with that information.

Deep Research had its usual extra questions about what's important to me, but it was faster this time, only six minutes before delivering a full report on several TVs. Except rather than a simple pros and cons list, I got a lot of unnecessary detail on things like OLED vs. QLED panels, the reason TV refresh rates affect video games, and the impact of compression algorithms on streaming quality. Again, this was all incredibly informative, but entirely unnecessary for my purposes. And unlike Beef Wellington, I'm not going to keep coming back to the TV buying guide on a semi-regular basis.

Telescope look

(Image credit: ChatGPT Screenshots)

For the final test, I decided to get a little more academic in light of my recent decision to pursue astronomy more seriously as a hobby. Still keeping it brief, I asked, “How does a telescope work?”

Regular ChatGPT responded instantly with a simple, digestible answer. Telescopes gather and magnify light using either lenses (refracting telescopes) or mirrors (reflecting telescopes). It briefly touched on magnification, resolution, and light-gathering power, making it easy to understand without getting too technical.

Deep Research gave me a report of a kind I might have written in high school. After asking how technical I wanted my answer, and me responding that I didn't want it to be technical, I waited about eight minutes for a lengthy discussion of optics, the development of different kinds of telescopes, including radio telescopes, and the mechanisms behind how they all work. The report even included a guide on buying your first telescope and a discussion on atmospheric distortion in ground-based observations. It was answering questions I hadn't asked. Admittedly, I might do so at some point so the anticipation of follow-up queries wasn't a huge negative in this instance. Still, a couple of sentences about mirrors would have been just fine in the moment.

Deep thoughts

After running these tests, my opinion of Deep Research as a powerful AI tool with impressive results remains, but I feel much more aware of its excesses in the context of regular ChatGPT use. The reports it generates are detailed, well-organized, and surprisingly well-written. For a random tour of interesting information, it's pretty great, but I much more often just need an answer, not a thesis. Sometimes a shallow dip is preferable to a deep dive.

If the regular ChatGPT approach is accurate and does in seconds what takes Deep Research several minutes and a lot of unnecessary context to provide, that's going to be my preference 99 times out of a hundred. Sometimes, less is more. That being said, Deep Research's shopping advice would be great for a much bigger purchase than a TV, like a car, or even when looking for a house. But for everyday things, Deep Research is just doing too much. I don't need a jet engine for an electric scooter, but, for a transcontinental flight, that jet engine is good to have on-hand.

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• The latest audacious display launch from Thanko certainly pushes boundaries
• Perfect for working on the move - if you have the space
• New double 24-inch monitor can be extended up to 270 degrees

Doubling up on monitors is a surefire way to help drive productivity, but Japanese brand Thanko now seems to have taken things to the next level.

The firm has released a new double 24-inch monitor, and it’s quite the site to behold, as users can expand the monitor into two screens, connected in one single unit.

For those that are short on desk space, it’s a very handy piece of equipment and can be extended up to 270 degrees. The monitor’s measurements come in at 542 x 17 x 650mm (when unfolded) and 542 x 25 x 323mm (when folded).

Getting flexible

Admittedly, it doesn’t quite meet the mark with port options, featuring just a single HDMI slot and two USB-C ports - and one of these is for power supply.

It also boasts an array of ports and features, including a single HDMI port and two USB-C ports, although admittedly one of these is for power supply. These are complemented by a 3.5mm headphone jack as well as two 2W speakers.

From a performance perspective, it also gives users a maximum refresh rate of 100Hz alongside a response speed of 14ms.

The monitor has been touted as a ‘portable’ monitor. Given it weighs some 5kg, or roughly 11lbs, it could make for a great piece of equipment if you're on the move.

You can get your hands on the dual monitor for around ¥62,800 ($420).

Pushing boundaries

This isn’t the first Thanko product to push boundaries, as in February 2022, the gadget maker unveiled an audacious vertical display which allowed users to keep tabs on social media feeds.

The Thanko TL Portrait Display was designed to complement a laptop or desktop display - boasting a display size of 7.9 inches, the compact monitor fitted neatly alongside a laptop, according to reports at the time from Tom’s Hardware.

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• Rocket Enterprise SSD delivers up to 7,000MB/s read speeds with low-latency performance
• Features a 2.5 million-hour MTBF and an ultra-low bit error rate
• Supports both U.2 and U.3 interfaces, making it incompatible with standard desktop motherboards

Sabrent has introduced its first large SSD, the Rocket Enterprise PCIe 4.0 U.2/U.3 NVMe SSD, designed for enterprise — including data centers and large-scale operations by offering up to 30.72TB of storage, just like Micron's 9550 NVMe enterprise SSD, released in 2024.

Sabrent's product listing notes the device is not intended for consumer use, but businesses requiring high-speed, high-endurance storage solutions.

The new SSD delivers speeds of up to 7,000MB/s for sequential reads and 6,800MB/s for sequential writes and also provides up to 1,600K IOPS for 4K random reads, delivering the speed required for AI tools, server applications, and large-scale data management.

Performance tailored for enterprise workloads

The Rocket Enterprise PCIe 4.0 offers enterprise features like namespaces and power loss protection with an endurance rating of one DWPD.

The highest capacity model, at 30.72TB, can handle over 56PB of written data over its lifespan, and it also features a bit error rate (UBER) of less than one sector per 10^18 bits read, ensuring data integrity.

In terms of reliability, the SSD boasts a mean time between failures (MTBF) of 2.5 million hours, reducing the likelihood of unexpected failures. To maintain performance, the SSD offers sustained low-latency 4K random reads and writes.

It operates efficiently, consuming 21W during active use and just 6W while idle.

The SSD supports both U.2 and U.3 interfaces, which can be used simultaneously to ensure compatibility with a wide range of enterprise storage systems. However, this form factor makes it incompatible with standard desktop motherboards, which typically use M.2 or SATA connections.

Even if you could use it in a consumer setup, you might want to give it a second thought — the largest 30.72GB model of the Rocket Enterprise PCIe 4.0 is priced at just under $4,500.

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Volkswagen teased its tiny, affordable electric city car last month, when it released a series of gloomy images that hinted at a sportier, more aggressive EV to kickstart its line-up of battery-powered passenger cars.

Dubbed the ID.EVERY1 (we know, it’s a terrible name), the show car has now been revealed in all its boxy, flared-arch glory.

Standing at 3,880mm in length, it measures slightly longer than the old Up! (3,600mm) and is designed to sit between the upcoming ID.2all and the current Polo.

Aside from making us want to start a petition against utterly ridiculous vehicle names, the ID.EVERY1 looks slick, with animated front and rear lamps “welcoming” owners, lower front bumpers that offer a "smile" and massive 19-inch wheels that are engulfed by chunky, flared wheel arches.

It certainly moves the game on from the slightly weedy-looking Up! or yesteryear and it offers more interior roominess, too, with space for four people and a luggage compartment volume of 305 liters.

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“A secret sauce element is the roof drawn in in the middle, as is usually known from sports cars,” says Volkswagen Head of Design Andreas Mindt.

Although this is a show car and isn’t the model that will make it on sale, VW has said that the concept reaches a top speed of 130 km/h (80mph) and is powered by a "newly developed" electric motor with 70 kW (95hp). The range is at least 250 kilometers - or around 155 miles in old money.

Very much aimed at the affordable small city car segment, which is currently dominated by things like the Fiat 500e, or the even cheaper Dacia Spring and recently-announced Leapmotor T03, it is designed to offer low-cost motoring without scrimping on VW’s reputation for quality.

Made "in Europe for Europe", the ID.1 - as we hope it will be badged - will start at around €20,000, or around £17,000 in the UK ($21,600 roughly converted).

This will see it kick off a range of nine new models by 2027 including the production version of the ID.2all and updated versions of the ID.3, ID.4 and ID.5.

VW gets back to doing what it does best

(Image credit: Volkswagen)

Despite the fact that the ID.EVERY1 will likely change considerably for this funky looking concept, it already boasts a number of neat and convenient features that help it stand out from the current crowd of basic, budget EVs.

There’s a large central infotainment system, complete with a neat row of physical buttons below that inside. The two-spoke steering wheel is also festooned with multi-function buttons that ensure not everything is committed to a fiddly touchscreen display.

Volkswagen also says that the front passenger side of the dash panel is designed to be ‘variable’. In essence, designers have created a dedicated rail that allow things, such as a tablet or a tray table, to be snapped into place.

The concept images also appear to show a small speaker system that can be clicked into place in the center console – or presumably removed and used as a Bluetooth sound system when picnicking or partying outside.

Taking a leaf out of Kia’s recent design book, the center console can also be used as an arm rest, or slid backwards and offered up as a handy stowage space for rear passengers.

Practical, useable and stylish, the ID.1 will hope to mimic the success of the long-standing Polo, perhaps not in the epic sales numbers but in the fact that it can introduce a fresh new audience to the brand with good looks, a modern interior and an attractive price.

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• Eurocom Raptor X17 laptop is built for AI, cybersecurity, and high-end simulations.
• A 17.3-inch monster with advanced cooling features
• And with 128GB RAM, even heavy workloads run smoothly

Eurocom, known for its powerful but expensive laptops, such as the Sky X4C, has released the Raptor X17, a mobile workstation which supports up to 24TB of NVMe SSD storage across three M.2 slots, including two PCIe 4.0 x4 and one PCIe 5.0 drive.

On the Raptor X17's configuration page, Eurocom says its new laptop is designed for professionals handling intensive workloads such as AI tools training, cybersecurity, and large-scale simulations.

At its heart is Intel’s Core i9-14900HX, a 24-core, 32-thread processor built for exceptional computational power, as the Intel HM770 PCIe 4.0 architecture enables high-speed data processing.

Eurocom Raptor X17 gets a power-packed upgrade

The Raptor X17 features Nvidia’s RTX 4090 mobile GPU with 9,728 CUDA cores and 304 Tensor AI cores. While the absence of an RTX 5090M may be disappointing, the RTX 4090M remains one of the most powerful mobile graphics cards available.

Users can configure storage with RAID 0, 1, or 5, optimizing for speed, redundancy, or a balance of both. The laptop also supports up to 128GB of DDR5 RAM, with speeds up to 5600MHz, ensuring smooth performance for memory-intensive applications.

Eurocom offers two display options: a 17.3-inch QHD (2560 x 1440) panel with a 240Hz refresh rate or a UHD (3840 x 2160) option with a 144Hz refresh rate.

The chassis is made from an aluminum-magnesium alloy, but this does not reduce its weight, as it comes in at 3.29 kg and 24.9 mm thick. While on the heavier side, it serves those who prioritize ruggedness and power.

For connectivity, this business laptop includes two Thunderbolt 4 ports, USB-C 3.2, three USB-A ports, HDMI 2.0, and dual Mini DisplayPort 1.4. It also features a built-in 2.5GbE Ethernet port with support for an additional LAN connection via Thunderbolt 4.

To sustain peak performance under heavy workloads, Eurocom has integrated an advanced cooling system to prevent thermal throttling. The laptop ships with a 780W AC adapter.

With a starting price of $12,000, the Eurocom Raptor X17 makes the MacBook Pro M4 Max, Apple’s most expensive laptop, seem reasonably priced by comparison. However, this mobile workstation is a premium option for users who need extreme performance and configurability.

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• The drone system is priced at 16,000 yuan – or around $2,195
• Unfortunately, it is only available in mainland China for now
• Dubbed Ling Yuan, it will be available on numerous BYD models

BYD has recently announced a new partnership with China’s top drone-maker DJI, stating that it has created an innovative launch pad that can be built into a number of its electric vehicles, allowing for drone take-offs and landing directly from the vehicle.

The Ling Yuan system, as it is known, consists of a bespoke ‘hangar’ system that is housed on the roof, which folds open to reveal a built-in DJI drone, as well as an automated system that both charges and swaps out depleted battery packs.

According to CNEV Post, the unit also has on-board positioning module, which we assume allows for greater accuracy when landing back in the hangar, as well as a bespoke Ling Yuan app that allows for quick movie edits on the go.

Apparently, the system supports ‘dynamic take-offs and landings”, with the drone able to be deployed and called home while the vehicle is traveling at 25km/h (or around 15mph). The drone can then follow the vehicle at speeds up to 54km/h (33mph) to snare dynamic footage.

A video posted by Shanghai Eye on YouTube (see above) shows the drone in action, with the driver of BYD’s electric SUV simply tapping a button on the infotainment system, whereby the Ling Yuan drone hangar opens on the roof and what appears to be a DJI Air 3 shoots into the sky.

The idea is that adventurous BYD owners can capture their various road trips and automotive escapades via the drone, whether that’s action-packed video clips or drone-based group shots with epic vistas in the background.

Analysis: BYD knows what gets tech-heads excited

(Image credit: BYD)

BYD is slowly making science-fiction a reality, whether that’s through its jumping Yangwang U9 supercar, which can leap over potholes, or its recently announced Blade Runner-inspired drone system that can automatically launch from the roof of an SUV.

It's all good fun, but there’s zero word on the legality or related safety implications of launching a drone from a moving vehicle, or the potential issues with multiple drones being launched at once to work out the cause of a traffic jam up ahead, for example.

It’s also not clear whether the Ling Yuang drone system has to be ordered at the point of purchase as an optional extra, or whether customers can retrofit it to existing BYD vehicles.

Either way, it’s a slightly madcap look at the future and proof that China is constantly innovating when it comes to ensuring the next generation of electric vehicles fit into increasingly tech-heavy lifestyles.

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• Scientists have developed a new AI tool to accelerate scientific discoveries
• LLM4SD explains the reasoning behind its predictions, for transparency
• Instead of replacing standard machine learning models, LLM4SD improves them

An Australian research team led by Monash University has come up with a generative AI tool designed to speed up scientific discoveries. Called LLM4SD (Large Language Model 4 Scientific Discovery), the open source tool retrieves information, analyzes the data, and then generates hypotheses from it.

While LLMs are used in natural sciences, their role in scientific discovery remains largely unexplored, and unlike many validation tools, LLM4SD explains its reasoning, making its predictions more transparent (and hopefully cutting down on hallucinations).

PhD candidate Yizhen Zheng from Monash University’s Department of Data Science and AI explains, “Just like ChatGPT writes essays or solves math problems, our LLM4SD tool reads decades of scientific literature and analyses lab data to predict how molecules behave - answering questions like, ‘Can this drug cross the brain’s protective barrier?’ or ‘Will this compound dissolve in water?’”

Simulating scientists

LLM4SD was tested over 58 research tasks across physiology, physical chemistry, biophysics, and quantum mechanics, and outperformed leading scientific models, improving accuracy by up to 48% in predicting quantum properties crucial for materials design. Zheng said, “Apart from outperforming current validation tools that operate like a ‘black box,’ this system can explain its analysis process, predictions and results using simple rules, which can help scientists trust and act on its insights.”

PhD candidate Jiaxin Ju from Griffith University said, “Rather than replacing traditional machine learning models, LLM4SD enhances them by synthesizing knowledge and generating interpretable explanations”.

The team views the tool as essentially “simulating scientists”. Professor Geoff Webb from Monash University stressed the importance of AI’s role in research. “We are already fully immersed in the age of generative AI and we need to start harnessing this as much as possible to advance science, while ensuring we are developing it ethically,” he said.

The research, published in Nature Machine Intelligence and available to view on the arXiv pre-print server, was a collaboration between Monash University’s Faculty of Information Technology, Monash Institute of Pharmaceutical Sciences, and Griffith University.

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