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The AI race started off with a pretty clear direction – bigger and better. The first waves were characterized by building bigger models, but it’s all change in the world of artificial intelligence and with enterprises, SMBs and consumers all finding use cases for the technology, the focus has shifted.

Now, AI firms and model developers are looking to realize a much tougher goal. Efficiency. Cost per token, performance per watt, output per input, it’s all about driving maximum efficiency.

One clear divide is between training and inference. While training models still requires huge amounts of resources, inference efficiency is starting to improve, and one company (Rebellions) now believes an opening for inference-first hardware could create a new market.

The company’s racks are said to consume around 16-20kW, compared with around 120kW for leading GPU-based inference systems that, for many use cases, are sheer overkill.

Rebellions’ rack costs are also said to be around one-third of the price, making AI inference more accessible and helping enterprises to deploy AI more widely.

This hardware shift could be the start of truly efficient AI

Memory is also another battleground, whereby huge trillion-parameter models are testing the limits of today’s hardware and the intertwined reliance on memory and compute. Something Rebellions says it’s looking to fix by working with the likes of SK Hynix and Samsung to align multiple roadmaps, instead of having to respond to shifts in architecture.

Ultimately, today’s black-and-white chip manufacturing landscape is now evolving, and Rebellions sees two key changes happening simultaneously. Firstly, training and inference hardware is starting to differ more drastically. Secondly, aligning multiple hardware roadmaps across memory, compute and more will drive more efficiency not just across deployments, but in terms of bringing new products to market.

I spoke to Rebellions CEO Sunghyun Park allows me to understand how and why inference and training hardware are starting to separate, as well as the importance of open standards and collaboration in the drive for all-round efficiency.

• The AI chip market seems to be splitting between training-first and inference-first architectures. Why is that happening, and why now?

This split exists because training and inference are fundamentally different problems.

Training is how you build a model. It happens once, involves a small number of organizations, and rewards raw computational flexibility because the workload keeps shifting as research moves forward.

Inference is how you actually use a model: every query answered, every transaction processed, every decision an AI system makes in production. That happens billions of times a day across nearly every industry, and it’s where AI moves from R&D into revenue.

Those two jobs need different physics. Training requires maximum FLOPS. Inference requires efficiency, reliability, and economics that hold up when you’re serving users at scale.

The industry forced a training chip into that second job because that’s what existed. Now that inference has become the larger, more urgent market, that compromise no longer holds. Enterprises and governments are asking how fast they can deploy. That’s why the conversation is splitting now.

• Where does Rebellions fit in that split, and what makes your approach to AI inference fundamentally different from your competitors?

We built for inference, from day one. Most first-generation AI chip companies emerged from the 2016-2017 training boom and adapted their architectures for inference afterward.

We started in 2020 – after that wave – with inference as the only target, which meant designing around what production AI actually needs instead of retrofitting a training chip.

The numbers reflect that choice. Our racks draw 16-20kW versus roughly 120kW for leading GPU-based inference systems, about a sixth of the power, in a market where power is the binding constraint for most operators.

Acquisition cost runs around $10 million per rack versus roughly $30 million, about a third of the cost. Our chiplet-based architecture also scales out rather than betting that a single device can handle a model’s full size, which matters now that production workloads are trillion-parameter mixture-of-experts models instead of the few-hundred-million-parameter models the first generation was built around.

It’s also why our architecture is memory-centric rather than compute-centric. The chiplet approach exists to keep memory close to logic as models scale, not just to add cores.

And we have three years of production deployments behind that architecture, not pilots. That’s the hardest part to replicate: real workloads, running at scale, today.

• A year ago, everyone in AI was talking about chiplets. Now the conversation has shifted to memory. What changed?

The chiplet conversation was about architecture: breaking a chip into modular pieces that scale independently, rather than betting everything on a single monolithic die.

That mattered because it let the industry move past an assumption the first generation of accelerators made in 2016 and 2017, that a single device would always be big enough to run any model.

That assumption broke once mixture-of-experts and trillion-parameter models arrived.

The memory conversation is the layer underneath that. Once the architecture problem is solved, the constraint becomes physical: can you actually get enough high-bandwidth memory (HBM) to build what you’ve designed?

HBM is 3D-stacked memory, and how closely you can physically stack it to compute is as much of a bottleneck as raw supply.

Every AI accelerator company is competing for the same limited supply right now, and demand has outpaced what memory makers can produce. That’s the memory-logic co-design problem: architecture and memory supply are no longer separable decisions.

We’re in a different position because our investor relationships were built around supply, not just capital. Our memory partners are also investors, and we co-design our memory architecture directly against their roadmaps rather than simply purchasing off them.

Our chiplet architecture also develops against our foundry partner’s process roadmap. When the rest of the industry was fighting for allocation, we already had a seat at the design table through those relationships.

That’s memory-logic co-design, not just secured supply. The shift from chiplets to memory tracks has moved the real constraint: from architecture to physical supply.

• Your stack runs on open-source frameworks like vLLM, PyTorch, Kubernetes, and OpenShift, tools many enterprises already use. Does that make adoption relatively plug-and-play, or is that an oversimplification?

It’s mostly true, but ‘plug-and-play’ undersells how deliberate that was, and oversimplifies in one specific way.

We built entirely on open standards: vLLM, PyTorch, Kubernetes, and Red Hat OpenShift. We’re one of only two chip companies in the PyTorch Foundation, and the only AI accelerator company fully integrated with OpenShift.

A developer who already knows how to run inference on existing infrastructure already knows how to run it on ours. There’s no proprietary runtime to learn and no migration project. That part really is close to plug-and-play.

The first generation of AI chip companies each built proprietary software stacks, and hundreds of millions of dollars went into software that didn’t survive. We came to market once the open source ecosystem had matured and chose to build on it instead of forking it.

Where it oversimplifies is assuming that means zero integration work. Production deployments still require validating performance at your specific workload and scale, and that takes real engineering time, no matter how compatible the stack is.

What open standards remove is lock-in risk and retraining cost, not the deployment work itself.

• What are the biggest challenges organizations face when trying to run AI inference outside of hyperscaler platforms?

Most organizations aren’t built like hyperscalers, and much of the available inference infrastructure assumes they are.

The first challenge is physical. Most enterprises, telcos, and governments already have data centers. They can’t wait two to three years or spend $600 million-plus on new ones, and they can’t retrofit for liquid cooling without major cost and disruption.

So the practical question is whether inference hardware runs on what they already have: standard racks, air cooling, and existing power budgets.

The second is sovereignty. Organizations increasingly want to bring compute to where their data already lives, rather than move sensitive data to wherever compute is hosted.

That’s partly regulatory, partly operational, but either way, cloud-only inference creates a dependency a lot of operators are no longer comfortable with.

We built specifically for that gap. Our systems run at 4-5kW per server on standard air-cooled infrastructure, no facility redesign required.

SK Telecom has run on our hardware for nearly three years, scaling from a small cluster to close to 100 racks and now processing 50 million API transactions a day, entirely inside their existing network.

KT Cloud runs real-time inference on highway CCTV systems nationally. Both show you don’t need hyperscaler-scale infrastructure to run AI at hyperscaler-relevant volume.

• Cerebras' IPO, and potential listings from others in the space, have put AI infrastructure in the spotlight. What do these IPOs signal about the market, and where might the hype be outpacing deployment reality?

It tells the public markets that AI infrastructure is a durable, investable asset class, not just a venture-backed bet. That validation benefits the whole sector, including us: it says purpose-built AI silicon is real, differentiated from general-purpose GPUs, and worth independent capital.

Capital flowing into the sector is necessary, but it doesn’t by itself determine who wins. The companies that define the next decade of this market won’t necessarily be the most-funded ones.

They’ll be the ones with real fundamentals: production customers, deployment scale, proven economics, durable supply chain relationships. That’s a different filter than fundraising size, and it’s the one that matters once public market scrutiny starts.

We’ve been building toward that filter since 2020, with production deployments and supply relationships with our foundry and memory partners that we secured before the rest of the industry was fighting over the same allocation. The capital is a tailwind for everyone serious about inference.

Whether it gets deployed well is a separate question, and one the market will answer over the next few years.

• Looking ahead, what trends are you watching most closely across AI infrastructure and inference over the next 12–24 months?

The buildout happening inside existing infrastructure keeps accelerating. Most enterprises, telcos, and governments aren’t waiting for new data centers.

They’re deploying inference into facilities they already have, and I expect that to become the bigger story even though it gets less attention than hyperscaler headlines.

Additionally, memory remains the physical constraint. HBM supply hasn’t caught up with demand, and as models keep moving toward trillion-parameter mixture-of-experts architectures, that pressure increases rather than eases.

Companies with secured, strategic supply relationships will have a real advantage over the next two years, not just a cost one.

Chiplets are how we get there. We’ve already mass-produced a highly advanced 4-chiplet package – a level of integration Nvidia has struggled to reach.

Reporting this year indicated Nvidia had built and demonstrated a four-chiplet Rubin Ultra design, then canceled it in favor of a dual-die architecture over manufacturability concerns: a four-die single package pushes roughly 7.5-8x past reticle limits on yield and cost. That’s the foundation.

The next layer we’re building on is performance optimization of HMB3E (3D-stacked memory) in close collaboration with memory and compute co-designed together from the start, not bolted on after the fact.

Also on my radar is the fact that as more companies in this space go public, capital will get valued against production fundamentals rather than funding rounds.

And the efficiency point matters: as inference gets cheaper per token, demand doesn’t shrink, it expands, because new use cases become viable at lower cost. That’s been true of every computing platform in history, and I don’t expect AI inference to be the exception.

After a 5-mile run through the California hills, this is what Samsung’s toughest watch gets right, and where it still trails behind.

If you're looking to get ahead for college in the fall, then a VPN is one of the most sensible back-to-school staples to tick off your list now. You're going to spend a lot of time connected to campus Wi-Fi and it's often not as user-friendly as it might seem.

For sure, there can be security concerns when connecting to any form or public network, which a VPN can help with, but its best use at university is to make sure you can access all the content that you usually do at home.

That's because campus Wi-Fi can be very restrictive. These are networks that have to manage huge bandwidth demands, maintain cybersecurity, and also comply with legal requirements.

That means that they often limit access to certain usage-heavy services, and sites and apps that are more in a grey area when it comes to safety and the law. Think video streaming, online gaming and torrenting, for examples, as three activities that you might find curtailed.

But, if you're using a VPN on your device, the campus network in question won't be able to see what internet sites and services you're accessing, so it will be blind to you and your activities.

It will probably be able to see that you're using a VPN, the amount of bandwidth your device is using and what your device is but your online deeds will remain private. So, if you're headed to college this year, you might want to think about getting a VPN.

Right now, IPVanish is a good choice for college students. It does a great job of keeping your digital life private and, just as importantly, it's cheap!

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At $2.19 per month, IPVanish is solid, 4-star VPN and is only short of the very best VPNs on features like server count and some streaming service unblocking that's not a priority for this use case.

It does also have a unique privacy feature which may be handy for your browsing too.

IPVanish's Secure Browser is remote browser software that runs on an IPVanish cloud server. The sites and services you then navigate to have no idea about you or your device at all. They only connect with the cloud server.

That means the session cannot be linked with you at all and no trackers, cookies nor anything else can come your way. It also protects you from any malware or any other nasty things that you stumble across. Definitely worth using when you're searching the darker corners of the web.

Along with IPVanish's standard features, that should have you covered for all the college dorm internet use cases you'll have. Give it whirl.

New reports on real-world AI deployments seem to be being published almost daily, but there's one clear message which seems to span them all – shadow AI is a major problem.

The use of unapproved or unauthorized tools by workers is a common theme regardless of business size, sector or geography, and it often stems back to one or two reasons – employers are either being too prescriptive about permitted AI tools and are giving workers a narrow window of unsuitable tools to experiment with, or they lack any clear strategy altogether.

These reports have already detailed the risks in great depth, but to summarize, using consumer-grade versions of AI apps puts sensitive and confidential workplace data at risk, be it leaks or secondary exfiltration via model training. Hence why companies invest in enterprise-grade versions with additional safeguards.

Shadow AI is a symptom of a bigger problem

Too commonly, employers consider shadow AI a disease that plagues their workers. Something that should be stamped out with more effective training or harsher consequences to breaking the rules.

But the reality is that shadow AI is more often a symptom of the boarder workplace culture, and it's the cause of this that I set out to explore when speaking with industry experts and policymakers.

Canva preaches the importance of freedom of choice – the Australian software giant gives its workers full autonomy over the models they want to use, affording them the time to identify the right tools rather than being prescribed unsuitable alternatives.

Policies only work when they're accessible

Beginning with insufficient and unsuitable policies, Zendesk Chief Legal Officer Shana Simmons explained to me in an exclusive interview that many of today's agreements and policies are far too formal and field-specific.

"AI policies often fail because they’re written for lawyers, not for the people expected to follow them," she outlined, "if people can't understand the guidance, their behavior won't change."

Simmons also explained the policies are being stored behind closed doors in hard-to-reach places, like HR folders that workers never, ever check.

"If a policy is buried in a handbook or on a website, it’s not going to reach employees when they need its," she said, noting that policies should actually form part of the UI – or in other words, where the workers already are.

Canva warns us that, "the most common mistake is treating AI training as a curriculum," whereas it should really be seen as an ongoing back-burner activity that's always developed. The company's spokesperson insisted that workers learn through fixing their own problems, not by "sitting through a course on prompting."

Unsuitable tools and taking matters into their own hands

In a bid to work out whether it's employees or employers who are at fault (or whether it's shared), I asked whether shadow AI is a reflection of worker misconduct or insufficient tooling.

In response, Simmons stressed that "most people want to do the right thing," agreeing that the most common cause of shadow AI is indeed poor tooling.

"If employees are given the tools they need and are informed of the rules and requirements in a way that’s understandable to them, and technical controls are in place to restrict the riskiest behavior, I’d expect shadow AI to be no greater a problem than any other form of employee misconduct."

The answer then isn't necessarily to approve every new AI application, but understanding why users prefer certain tools over others is key to building suitable policies and safeguards around those.

In certain, low-risk conditions, shadow AI could actually be an important and useful part of feedback, showing organizations where they're falling short and exactly where to invest, but a clear oversight over this is just as important to ensure that no leaks or other threats occur.

AI literacy can't be taught – it's learned

Clearly, then, workers need more guidance and support. But does that come in the form of training, policies, access to tools, or something else?

Simmons explained that "training alone is not very effective for developing AI fluency," though giving workers a clear direction and some initial pointers certainly serves as a helpful baseline. Zendesk, for example, has found the greatest success in giving workers time and space to experiment and become accustomed with AI on their own terms.

This particular company's stance was to pause non-urgent work and organize a dedicated internal hackathon to encourage proactive exploration. The result was a marked increase in employees' practical AI skills and better cross-team collaboration, but halting non-urgent operations altogether isn't a necessity and just reflects one initiative.

It's a similar initiative that's being piloted by Canva, which tells its 5,300+ workers to drop tools for a full week and experiment with AI.

"We give our team the room to step back, get out of business as usual, and try something genuinely new," a spokesperson said.

"Practical AI training should go beyond introductory courses and prompting techniques and create space for employees to actually use the tools in a safe, secure environment," Simmons concluded. Piloting AI tools with synthetic data (and therefore, no harmful consequences) ultimately leads to the highest levels of confidence.

'Employers own the conditions... employees own the curiosity'

Another key area where studies and reports have been split is in whose responsibility it is to upskill and re-skill, whether that's through updated policies, passive training or active experimentation.

As a C-suite exec, Simmons believes the organization should bear the brunt of the responsibility by giving workers access to tools and learning opportunities. Clearly, they must think outside the box and offer a much broader array of support: "not just training, but also ideation and experimentation through initiatives like hackathons, sandboxes, and collaboration opportunities."

But beyond that, it's totally on the workers' shoulders to "take those opportunities and run with them." After all, it's not just for the benefit of their organization, but it's also to ensure they stay relevant as work evolves in an AI-first era.

A secondary opinion by Canva also backs this up: "Employers own the conditions: the time, budget, permission to experiment... Employees own the curiosity."

• AMD's Helios beats Vera Rubin NVL72 on memory at rack level, with 31TB of HBM4 against roughly 20.7TB
• AMD also claims it beats the competition by 15% on FP4 compute on a 'per-GPU' basis even as Nvidia comes out ahead on published 'per-rack' figures
• AMD claims Helios also offers 30% more tokens per dollar spent versus the competition

AMD has launched its Helios rackscale offering, outlining five comparisons in which it claims wins over what it calls "the leading competitive solution."

While the company skipped naming Nvidia, the market leader's Vera Rubin-based NVL72 rack-scale solution is the only real competitor to Helios and the one it continues to compare itself against.

While its memory claims hold, its GPU FP4 claim might fall short when comparing rack to rack, and many of its calculations are based on peak performance rather than Nvidia's published numbers, making Helios an interesting "win" but one that does encourage potential adopters to look more closely.

A numbers game that continues to grow complex even as AMD ekes out some wins

While AMD claims a 15% win versus Nvidia's Rubin on a per-GPU basis for FP4 compute, its 72-GPU rack-scale solution falls short of Nvidia's published rack-level numbers: 2.9 exaflops versus 3.6. AMD does not specify whether it is counting Nvidia's individual dies or its two-die packages, and the distinction matters: against dies the gap runs in AMD's favor by far more than 15%, while against packages AMD trails.

The two figures also use different formats, AMD's MXFP4 against Nvidia's NVFP4, so they are not measuring identical arithmetic.

This might, however, be indicative of a very real situation that hampers both vendors' headline numbers: real-world FP4 workloads rarely reach hardware peak ratings due to memory movement constraints, scheduling overheads, and software kernel efficiency. AMD conceded as much at its own event, putting measured FP4 throughput at roughly half its peak rating.

Nvidia may sustain more of its peak thanks to its custom Vera CPU, a mature NVLink 6 software stack and a larger pool of what it calls fast memory, 75 TB per rack once 54 TB of LPDDR5X is counted alongside 20.7 TB of HBM4. AMD's 31 TB is all HBM, which is better suited to models that must be held entirely in high-bandwidth memory, and Helios offers higher capacity and bandwidth per accelerator at 432 GB and 23.3 TB/s.

On raw scale-up fabric, the two are level, both delivering 3.6 TB/s per accelerator and 260 TB/s per rack.

Despite this, Helios is an exceptionally strong product on paper, and it may be the first time AMD has produced a credible rack-scale answer to Nvidia since the AI race began. Seventy-two MI455X accelerators, 18 EPYC Venice CPUs, 31 TB of HBM4, UALink over Ethernet inside the rack and Ultra Ethernet out, on an OCP Open Rack Wide chassis with merchant Broadcom switch silicon, is a serious response to a company that had a two-year head start on the form factor.

More importantly, its fabric specifications are publicly available, enabling hyperscalers to build customized variants that meet their requirements. Nvidia's platform offers no equivalent latitude.

AMD frames openness as the platform's central advantage, with Vamsi Boppana, senior vice president of AI at AMD, saying that Helios "brings together leadership compute, high-performance networking and open software in a unified rackscale platform."

The more important question for AMD, however, might be memory supply. Nvidia has had Vera Rubin in full production since Q1 with partner availability this half, while AMD's first Helios deployments are not due until Q4. AMD may be further gated by HBM4 supply, much of which is reported to be already committed to hyperscalers, which could keep its deployment volumes well below Nvidia's this year.

Discussions about technological sovereignty in Europe can tend towards doomerism. It is easy to see why. A handful of hyper-scalers in the US and China control the foundational infrastructure of the modern world. The reliance on these technologies from companies and governments in Europe grows with each week that passes.

Near-total dependence on foreign-hosted and trained AI models presents a massive national security risk. If there isn’t a sovereign layer to your infrastructure, you don’t have control over the future. What if the plug is pulled or if security is compromised?

These are the nightmare scenarios being discussed in boardrooms and government departments across the continent.

And yet, Europe has many reasons to feel optimistic in its pursuit of sovereignty. Perhaps the LLM ship has sailed, but it is what comes next that is truly exciting.

In areas such as quantum computing and robotics – and their application across industries including healthcare and climate science – there is evidence that Europe’s combination of engineering talent and deep customer/market knowledge is laying the foundation for the next wave of society-shaping innovation.

This goes to the heart of what AI sovereignty really means: developing a technology that is both a great product, with no compromise on efficiency and scalability, while also solving the biggest possible problems of today or tomorrow.

A time for action

Now is the time to act. Sovereignty, particularly AI sovereignty, is an absolute priority for European governments and, increasingly, for customers and the public as geopolitical tensions escalate. We are already seeing many examples of companies in Europe building parallel IT infrastructure where they would usually be dependent on hyperscalers. There is also a growing trend towards on-premise cloud and data solutions, in a bid to create sovereign solutions.

So the question is, can we, those of us outside the US and China, build the best products that solve the most pressing problems? The answer is a resounding yes, but we need to ensure that our approach isn’t purely defensive. True sovereignty is about more than having the right level of regulation to protect ourselves against external systems we can’t control. If we want to be sustainable, we need to build alternatives that are at least as good; if we compromise on that, it will fail.

If we take healthcare as an example of sovereignty. It is one of the most sensitive industries when it comes to data, given how personal and confidential medical data is. If your life were dependent on an AI solution that could help define the best personalized or predictive care, and that solution was not sovereign, would you hesitate? Of course not. It is a big problem, but there are companies out there nearing a solution.

A fantastic example of this is the French scale-up ALAN, which is increasingly disrupting the whole medical insurance market thanks to a truly end-to-end designed business process. We need to play to our strengths in these use-case areas where technology has a life-changing impact. In June this year, it announced that it had raised €480m, valuing the company at €5.5bn.

Hindering factors

There are, of course, some factors that might hinder our progress, such as overregulation and the constraints that entrepreneurs face. We cannot ignore the challenge of funding; the US benefits from a $30 trillion pension fund that flows massively into the PE and VC markets. We are far away from that in Europe, and we must find solutions urgently.

There are some positive developments, such as the European Union’s flagship €95.5 billion R&D funding program, Horizon Europe, which supports 200-300 groundbreaking scientific discoveries annually across the EU and the UK. But these initiatives are still relatively small and only represent a step in the right direction.

And Europe’s unique digital market is still not as easy to address as the single US or Chinese market. From my experience on the boards of several European scaleups, many of them find it easier to attack the US market once they’ve gained momentum in their home country than to attack another European country. Ultimately, all of these problems need to be solved.

It is also vital that we choose our battles. We shouldn’t be chasing after American or Chinese LLMs or hyperscalers. We will waste time trying to play catch-up. The reality is that we have a proven track record in use case-driven areas, such as healthcare, and that is where we must focus.

Our approach must be to anticipate where the market is moving. Two areas that are emerging as potentially revolutionary are quantum and robotics. The US is already hedging its bets. President Trump recently signed a bill mandating that the US will have a quantum computer by 2028 and requiring that cyber systems adopt post-quantum crypto-consistent solutions. This is what sovereignty is: it’s not hyperscalers; it’s technology that will significantly impact everyone's lives.

The good news is that in Europe, we have many examples of startups producing world-leading technology in quantum and robotics. Ultimately, the success of sovereignty will depend on our ability to build the best products in Europe to solve our greatest problems. We won’t be sustainable if the ‘sovereign’ alternative isn’t as good as the solutions developed elsewhere. In Europe, we have all the tools to achieve sovereignty. But we must act now.

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This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

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Are you a fan of CD Projekt Red's The Witcher game series? Have you ever fantasized about what type of character you'd be if you were somehow sucked through a magic portal and found yourself in the fantasy world of The Continent? Do you think you'd be a princess, a bard, a sorceress, or even a witcher?

A brand-new The Witcher 3: Wild Hunt expansion, Songs of the Past, has been announced, and it's launching sometime next year. While we don't have plot details yet, the story will once again follow Geralt of Rivia ahead of The Witcher 4.

This means an all-new journey to experience with everyone's favorite witcher, and, hopefully, one that will feature the return of the rest of the gang, like Ciri, Yennefer, and Dandelion.

CD Projekt Red has promised to share more about Songs of the Past next month, so we'll have to wait and see.

In the meantime, I've put together a fun little personality quiz that will help you figure out which Witcher character is your kindred spirit. Are you the protective, titular witcher himself? His daughter, Child of Destiny and cabale fighter, Ciri, or the fierce sorceress Yennefer? Maybe you're the merciless King of the Wild Hunt...

Everyone has a favorite, but which character are you really? Find out below.

Did you get the character you expected or someone completely opposite to your expectations? Let us know in the comments!

Claude shared chats containing medical, corporate, and credential data appeared in Google Search, raising new questions about public AI links.

The post Claude Shared Chats Appeared in Google Search, Exposing Sensitive Data appeared first on TechRepublic.

Compare ChromeOS Flex and Chromebooks by cost, app support, security, battery life, compatibility, and long-term value for old PC users.

The post ChromeOS Flex or Chromebook: Which Is the Better Value? appeared first on TechRepublic.

The data center industry has found itself in unchartered waters recently. The boom in demand for AI tools and services has led to a 17% increase in electricity consumption. And despite a rapid decline in power consumption per AI task, energy consumption is set to triple for AI-focused facilities.

Recent industry research also reveals the average cost of unplanned downtime has climbed to $9,000 per minute, or $540,000 per hour. For large enterprises, major outages can exceed $5 million per hour when all factors are considered.

Even more sobering: 60% of small and medium businesses that experience catastrophic data loss close within six months.

For data centers, power is non-negotiable, and it’s clear that for those who can take control of their power supply, success in this AI boom can be achieved.

But the challenges are also clear. Europe’s existing, and ageing, grid infrastructure is becoming unreliable under the demand of data centers, renewables, and electric vehicles.

Data centers will not always be the priority, but the need for consistent operations cannot falter. Not if businesses want to stay competitive in a crowd that is growing.

The ripples of change

The AI boom is redefining success and failure within the energy sector. And the standout feature of the winners is the access to a reliable power supply. Bloom Energy’s 2026 power report, which looks at developments in the US data center market, indicates a clear trend from areas where the grid is strained to those with ample supply.

Traditional leaders are making way for the new, with data showing that areas like California, Oregon, Iowa, and Nebraska are set to lose up to 50% of their market share due to tighter power availability. Meanwhile Texas’ data center load is set to more than double by 2028. A reliable power supply is not only an advantage, but is king, when considering the construction of new facilities. 

For nearly 20 years, I’ve worked in the sector, on a variety of projects and continents. One I’ve learnt is that the age-old idiom "when America sneezes, the world catches a cold” is true. The lessons of this dramatic shift in the US market should be understood and acted on by leaders in Europe sooner rather than later. 

And the question for European data center operators is obvious – how can you stay take control of your own power availability and stay afloat?

From dependence to independence

The answer for many has been independence away from grid energy. Rooted in the need to avoid grid capacity challenges and delays due to waiting lists, Bloom’s report indicates  that up to a third of US data centers are expected to be fully off-grid by 2030. And this data on decentralization is growing. The IEA has observed a sharp increase in orders for gas turbines, with the intention to power data centers directly, circumventing a grid connection. 

But this specific solution isn’t without its drawbacks. For example, gas turbines alone struggle to support the large swings in demand that are induced by AI training and modelling.

Increasingly, businesses are finding Battery Energy Storage Systems (BESS) are key to overcoming these sudden waves of demand. BESS can act as a buffer for the load swings AI produces when implemented as a supplementary power source. And the IEA forecasts that up to 25GW of battery storage could be installed in data centers globally by 2030. 

At Aggreko, we’re already deploying this option for a number of our partners across Europe, ranging from 10MW all the way up to loads in excess of 100MW. For example, when a major colocation provider in Dublin found that they would not be able to connect their data center to the grid until several months after the site came online, we were called upon to bridge the gap in the meantime. 

We supplied 12 next-generation gas (NGG) generators  totaling 14MWe, including 10kV of switchgears, transformers, and auxiliary equipment that allowed the site to operate entirely independent of the grid. This was supported by a 1MW BESS, providing resilience against energy swings with the added benefits of improved power quality, emission reduction, and fuel savings. As a result, the site was online right away, compared to the estimated two-year delay for a grid connection. 

Staying ahead of the curve

The pace of development has been faster than anticipated for most operators, which has led to dramatic shifts in the way they approach power and cooling.

So, secure your energy supply, start decentralizing, and stay on top of new technologies, such as BESS, and you might just find your company winning the AI boom.

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This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

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US technology companies are resisting broad controls on Chinese AI models while enterprise teams weigh lower costs against security and governance concerns.

The post Silicon Valley Splits Over Chinese AI as Washington Weighs New Controls appeared first on TechRepublic.

Whether it’s a vacuum cleaner, a hand dryer, an air purifier, or a fan, Dyson products rarely look like anything else on the market — and that’s particularly apparent at the company’s campus in Malmesbury, southwest England, where images of recent launches decorate the walls like modern art. The sprawling complex, in a particularly beautiful patch of Wiltshire countryside, is home to Dyson’s global Research, Design and Development (RDD) center, and the Dyson Institute, where students from around the world study while working alongside engineers on live projects. I visited the center to meet the company’s Chief Engineer Jake Dyson — son of founder Sir James Dyson — and learn more about the company’s approach to design.

Jake Dyson didn’t join his father’s business immediately; instead he set up a workshop and made a name for himself in the world of industrial lighting. Sitting in his office, I asked whether his experience in that sector had contributed to his work at Dyson today.

“Yes, it comes down to identifying problems and solving them,” he explained. “When LEDs first entered the market, I realized people weren’t cooling them properly. The promise of LEDs is that they should last a lifetime, but in reality they were being treated like disposable lightbulbs. I visited Osram in Asia, and they explained that if you keep the diode temperature below about 50C [120F, you can maintain brightness, color quality, and lifespan. That became my goal.

Dyson's unique product designs take center stage at the company's campus in Malmesbury, UK (Image credit: Getty Images / Bloomberg)

“I looked at how satellites manage heat. In space, temperatures swing from extremely hot to extremely cold, so they need precise thermal control. I applied similar thinking by designing systems that passively dissipate heat. For example, the heat moves away from the chip and is cooled by airflow, maintaining a stable temperature even at high power. That process, spotting a problem and solving it, is what drives everything.”

That problem-solving approach has always been the driving force behind the Dyson brand, and explains its unusual portfolio of products; its engineers have never been afraid to venture into new areas when there’s a problem to be solved.

“Sometimes it comes from frustration," said Jake Dyson. "'This product is rubbish, how can we make it better?’ Other times it’s curiosity: ‘Why does this work the way it does?’”

Problem-first design

James Dyson’s book Invention: A Life of Learning Through Failure, describes how the problem-first process led to the creation of many of the company’s most iconic and recognizable products — starting with a humble wheelbarrow. Dyson and his wife Deidre were renovating a house in Gloucestershire, England, and wanted to create a garden, but the traditional tools proved frustrating.

“I used a navy barrow in anger and its limitations became increasingly clear,” writes Dyson. “Cement slopped out of it. Its tubular legs sunk into the ground. It was hard to steer. Its sharp edges damaged doorframes. The more I used it, the more I realized that nobody had really thought about these problems or bothered to fix them.”

After much experimentation with shapes, materials, and manufacturing processes, the result was the Ballbarrow: a molded plastic bucket on a steel frame, with the conventional wheel replaced by a pneumatic ball made from EVA (ethylene-vinyl acetate). This spread the weight of the load more evenly on soft ground, and was easier to maneuver than a wheel — and in bright orange, it looked unlike anything else at the time.

Dyson products are always designed to solve a problem — the Airblade hand dryer was created to reduce waste from paper towels in public bathrooms (Image credit: Getty Images, Gado)

The product itself was a success, but due to a series of poor business decisions, Dyson senior eventually lost control of the company he had founded around it, Kirk-Dyson, and was ultimately kicked out by the other shareholders.

“I had lost five years of work by not valuing my creation,” he wrote. “I had failed to protect the one thing that was most valuable to me.”

It was a painful experience, but one he learned from as he pressed on with identifying and solving problems — starting with the creation of the first cyclonic vacuum cleaner, which he designed after realizing that dust bags don’t just serve to collect dust — they also act as filters that block airflow, drastically reducing suction power.

“I remembered the same clogging problem on the calico cloth with the powder coating in the Ballbarrow factory and the giant cyclone we had made to solve it,” he wrote. “What if I could develop a much smaller version and replace the clogging bag in a vacuum cleaner?”

Thousands of prototypes later (5,127 to be precise), he had the world’s first bagless vacuum — and a vast collection of patents to protect it.

The Dyson Supersonic was created to solve the problem of heavy and cumbersome hair dryers, with a lighter motor and a center of gravity that sits in your palm (Image credit: Future)

Dyson’s understanding of airflow and cyclonic technology has informed almost all of the company’s subsequent products; but like the vacuum, each one started with a problem. The Dyson Airblade hand dryer was created to reduce waste paper towels; the Airmultiplier fan solved the issue of ‘choppy’ air from conventional fan blades; the Pure Hot+Cool purifier was made to tackle indoor air pollution; and the Supersonic hairdryer solved the problem of heavy and uncomfortable hairdryers with weighty motors.

In every case, the form of the finished product was dictated by the problem it was designed to solve — even if the result looked totally unlike established versions of the device.

“That’s why Dyson products often look unusual; they’re built around their function,” said Jake Dyson in his Malmesbury office. “They’re also beautiful. A hair dryer has a hole through it because of how the airflow works. Fans and other products expose their engineering principles through the way they look — we’re not hiding how things work, we’re expressing it.”

Creative color

Those unusual designs are often set off by equally unusual color schemes, which tend to highlight buttons, switches, removable canisters, and other functional parts.

“We have a team here, CMF, which is colors, materials and finishes, that look into the appearance but also materials of our products," said Jake Dyson.

The CMF team doesn’t just draw on experience from successful projects, but also failed ones like the cancelled Dyson electric car, which provided finish and material ideas for many of the company’s health and beauty devices.

Most recently, CMF lent its expertise to Dyson's first hand-held fan — the Dyson HushHet Mini Cool — which launched just in time for a series of heatwaves in the UK. It sold out almost immediately, and after testing it myself, I can see why; it’s compact, much more powerful than its closest rival, the Shark ChillPill, and more affordable to boot.

“We’ve seen strong demand [for the HushHet Mini Cool], and it’s one of those products where people don’t initially realize they need it but once they try it, they understand the value,” said Jake Dyson. “It’s designed to be reusable, not disposable like cheaper alternatives. I've seen people with the cheap plastic fans that break very [easily], but we wanted ours to be well engineered, durable, quiet and efficient.

The Dyson HushJet Mini Cool fan is the company's latest product, and proved enormously popular during a hot British summer (Image credit: Future)

“We’re working on scaling production, but demand has been very strong, so availability can sometimes be limited. That said, it is coming back to market.”

So what does the future hold? The company is investigating ways to use AI where it will actually add value, helping machines interpret data and make better decisions, and Jake Dyson says that "vision systems and new product directions" are the most exciting areas for him.

"We’ve historically been very strong in mechanical engineering motors, airflow, performance. But now, adding cameras and vision systems allows machines to detect what they’re looking at, understand it and act accordingly.

"That opens up entirely new categories of products and capabilities, so we’re moving from purely mechanical devices to machines that can see, think, and respond, and that’s where the next wave of innovation is coming from."

It'll be fascinating to see which problems the company will be looking to solve with those new technologies, and there's no way of knowing what the next generation of Dyson products will look like as the company expands into new areas. We'll just have to wait and see — but it's definitely not going to be boring.

Microsoft has been battling to truly establish itself in the device market for years now, with its Surface suite covering everything from foldable smartphones to 2-in-1s up to more traditional laptops and desktops - all the way up to the enormous Surface Hub (RIP).

But the company has seemingly always fallen short - whether it's battery life, falling short on power, or the unavoidable lock-in with the Microsoft 365 experience.

However its latest collection of Surface for Business releases, framed squarely at work and enterprise users, looked to address all of that, and having been using one for the last few weeks now, I can safely say, Microsoft may finally have cracked the formula for a great working laptop at last.

Going hands-on

Microsoft has positioned the Surface Laptop for Business squarely at enterprise customers rather than consumers, and it's a substantial refresh over the previous generation, with the biggest improvements around AI, security, manageability, battery life and repairability.

The device is light and portable, weighing in at just over 1.35kg, and its slim build (just 0.69in in width) means it slipped easily into a rucksack or carry-on bag.

It's a stylish device to look at as well - the polished black anodized aluminium build is far more striking than other identikit dull business laptops around today, with a well-designed keyboard and touchpad that offer more than enough space.

(Image credit: Future / Mike Moore)

But where Microsoft is looking to take a real step forward with the Surface Laptop for Business is in hardware, where the company has equipped the device with Intel Core Ultra (Series 3) processor, up to 64 GB LPDDR5X RAM and up to 1 TB removable Gen4 SSD.

Crucially though, as with many modern devices, it also features AI-specific hardware, with an Intel AI Boost NPU delivering 50 TOPS of AI performance.

Along with offering local AI processing on the device, the NPU looks to perform a number of other tasks, from improved battery life to greater Windows performance. This is a decent level of performance, but if you're looking for tasks such as CAD, 3D rendering or AI model training, you may want something a bit more powerful, with a dedicated Nvidia GPU.

This looks to boost productivity and efficiency across the board - and I can say this was definitely true when I was working on the go.

Sadly I wasn't doing particularly AI-heavy work to really test it out, but I was able to take the device with me on a week-long overseas work trip, using it in conference keynotes, remote interviews, and site visits, and it absolutely ticked all my boxes when it came to responsiveness, usefulness and battery life.

At home however, it wasn't quite the same story.

It should also have meant the device was well-placed to be the centerpiece of my home office set-up, but unfortunately I frequently found issues when trying to connect a range of devices, from monitors to Bluetooth keyboards - this may have been a driver-led issue, but it was frustrating for quite some time.

The lack of ports may be an issue for some users - as there is just one USB-A connection, and two USB-C ports, which might be an issue for some creators. I use a docking station for my set-up, so for my usage I was largely OK - however as mentioned, even this wasn't always responsive - and it's a shame Microsoft has ditched the USB-A port on the charger block cable as well, as this has definitely saved me in the past with previous Surface devices.

(Image credit: Future / Mike Moore)

Microsoft has also introduced a more advanced haptic touchpad for the Surface Laptop for Business, promising a more consistent click feel and improved gesture support, as well as customizable feedback. Although slightly smaller than my usual work device (a HP EliteBook) I found the touchpad incredibly responsive and interactive, making navigation between different apps and windows a breeze.

My device was equipped with the new integrated privacy display - a feature which gained a lot of attention when it was included in the latest Samsung Galaxy flagship smartphone earlier this year.

Toggled on via the alternate F1 button function, the privacy display instantly makes the screen difficult to read from side angles - no need for an external magnetic privacy filter any more.

This is obviously pretty useful for those workers accessing private or proprietary information, stopping snoopers or spies from catching a glimpse, but it also offers an anti-glare coating which should be a hit with everyone.

I loved it - but will you?

Will it be the ideal device for everyone? Probably not, as being a Surface device means it is closely tied-in with the Microsoft ecosystem - so if you use Microsoft 365 at work (which I don't) you'll have a much smoother set-up and overall experience.

The price will also be a sticking point for some shoppers, as my model starts from £1,599 - with the top spec hitting £2,499 - probably out of reach for most start-ups and SMBs.

But overall, I was incredibly impressed by the Surface Laptop for Business, easily the best Microsoft device I've used for work by a mile - and one I hope to get to use again sometime in the future.

Gaming is edging closer towards a divisive new normal, with Sony set to stop releasing physical game discs for PlayStation consoles in 2028, and Rockstar Games announcing that physical copies of Grand Theft Auto 6 will come with a download code in the box, rather than a disc.

Unsurprisingly, gamers attached to their physical media aren’t happy with either development, and the backlash online has been fierce, with PlayStation’s social media posts being met with furious demands for Sony to reverse course.

Sony’s plans, and the fact that Microsoft is seemingly set to following suit, also mean GTA 6, one of the most anticipated games of all-time, likely won’t ever be available as a physical copy.

I spoke with Katarzyna Jakubiec, the chief business officer at G2A, an online marketplace that provides digital game keys. She told me that interest in PlayStation Store gift cards was at record levels on the site before Sony’s and Rockstar's announcements.

That’s great for marketplaces like G2A, but not so much for gamers. If Sony’s plans do come into effect in 2028, gamers will increasingly be forced into buying gift cards (and already are for GTA 6) as one of the few options for cheaper digital purchases.

Appreciating discs while they last... (Image credit: Future / Isaiah Williams)

That partially explains the increasing popularity of PlayStation Store gift cards on marketplaces like Loaded and G2A, especially since physical copies of games in general are becoming less common. But despite potential growth in terms of G2A's site visitors and earnings once 2028 arrives, Jakubiec is sympathetic to the objections of gamers and their ability to make a purchasing choice is evident.

"GTA 6 pre-orders have reinforced a trend we were already seeing rather than changing the market overnight," Jakubiec said. "During the pre-sale period, PlayStation gift card purchases on G2A reached record levels and continue to grow as anticipation for the launch builds, showing that many players are planning their purchases well ahead of release rather than waiting until launch day.

"While more players are embracing digital formats for the flexibility and convenience they offer, it's equally clear that physical copies continue to mean a great deal to many gamers, whether that's because of ownership, collection, or simply the experience of buying a physical game.

"We don't see this as an either-or conversation, and in an ideal world, people should have the choice to buy games in the format that suits them best. Physical and digital both have an important place within gaming, and different people will always have different preferences."

(Image credit: Rockstar Games / Sony)

Unfortunately, that's not how Sony sees it; in fact, Sony's stance aligns with the idea that more consumers are purchasing more games digitally, as it makes the controversial decision to end game discs based on 'shifting trends in consumer preference', suggesting that physical game copies are becoming obsolete.

However, the reaction online and from key game industry figures like Dan Houser (Rockstar Games co-founder), who is no longer working at the game studio, indicate that gamers aren't, and frankly, never will be, done with physical game copies — and Jakubiec shares the same sentiment.

"The reaction has been just as telling as the decision itself," she adds. "It highlights how passionate and diverse the gaming community is, and why major industry decisions need to consider the different ways people choose to buy, own, and experience their games.

She says that whether Sony decides to reconsider its plans is "ultimately for them to determine" — which seems unlikely, given that Sony has so far refused to reverse its decision, and shows no sign of changing its mind.

Jakubiec adds, "Whatever direction the industry takes, moments like this reinforce the need to listen to players and communicate those decisions clearly."

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(Image credit: Future)

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When I first caught a glimpse of The Adventures of Elliot: The Millennium Tales, it sparked a great sense of anticipation within me. After all, Square Enix and Claytechworks were collaborating to bring a brand new HD-2D RPG to the table, which appeared to combine sprinklings of classic Zelda titles with the visual, sonic, and environmental grandeur from series such as Mana and Dragon Quest.

Review info

Platform reviewed: PS5
Available on: PS5, Nintendo Switch 2, Xbox Series X and Series S, PC
Release date: June 18, 2026

I’m a huge fan of the HD-2D graphical style, and massively enjoyed recent releases such as Final Fantasy: The Ivalice Chronicles and Dragon Quest 1 & 2 HD-2D Remake, but I still wasn’t quite sure if I’d love The Adventures of Elliot. Was the action combat going to be polished and engaging enough? Was the world going to deliver the spectacle and appeal conjured up with other series? Would the narrative have me hooked?

Well, after playing the game for more than 25 hours now, I have an answer to all of those questions. Here’s what I made of The Adventures of Elliot: The Millennium Tales.

The good: combat that exceeds expectations

(Image credit: Square Enix)

Let’s start by addressing my curiosity surrounding combat in The Adventures of Elliot — just how good is it? Well, I’m pleased to report that I had a lot of fun with the action in this game. It clearly pulls on 2D Zelda, with real-time combat that challenges you to use a variety of weapons to overcome your foes.

You can equip two weapons at once, and while I typically used my sword for close range attacks and bow for projectiles, I found genuine utility in a lot of equipment, be that bombs, a hammer, spear, and more. Combat feels fluid, responsive, and well balanced. You can also defend or parry with a shield, and using this to avoid big damage can be crucial.

There’s a pretty fast, high-octane feel to battles, big or small, and by defeating multiple foes in a row, you can build up a streak to obtain better drops. This adds a layer of fun to combat, and gives you a genuine reason to seek out and destroy random creatures in your vicinity — I had a lot of fun pushing myself to get that streak as high as possible.

Combat is absolutely at its best during boss fights, though. These can offer genuine challenges, and often require you to switch up attacks, defend with care, and employ a variety of weapons to get the win. The satisfaction I got when evading a robotic titan’s assault and slashing it to smithereens with my blade was nothing short of exhilarating.

Best bit

(Image credit: Square Enix)

The highlight of this game is without question its majestic boss battles. Whether I was taking on Minister Kaifried or a bunch of deadly robotic guardians, I enjoyed making use of my full arsenal of weapons in order to emerge victorious.

What’s more, you can customize weapons with something called Magicite, which imparts specific abilities to help you wipe out the opposition with greater ease. This is executed very well, and helps you to raise attack power, unlock elemental attacks, and extend the reach of your attacks, for instance.

By spending more Tul (the in-universe currency), you can use more Magicite, and this helps you scale in terms of power as the game unfolds, making progression feel natural and well-paced.

Other gameplay elements are solid too. Platforming isn’t a massive part of the game, but feels precise and smooth. Your companion for most of the journey, Faie, also has abilities such as dashing and warping, which make traversing environments and taking down enemies even more varied and seamless, and you can unlock more of — and improve on — said abilities as the game progresses.

The not-so good: a narrative missing its spark

(Image credit: Square Enix)

So, the gameplay in The Adventures of Elliot is a hit, in my book. The brilliant bosses and close contests against frogs, robots, slugs, and more kept me coming back for more. But unfortunately, some things made me feel reluctant to indulge in long, uninterrupted play sessions — namely, the game’s narrative and dialogue.

Simply put, the story in The Adventures of Elliot lacks the spark that I was looking for. It often feels flat, lacking moments of surprise and suspense, and its largely predictable plot points paired with sluggish and dull dialogue meant that I was tempted, at times, to skip through a few scenes — something I never do with story-driven RPGs.

On top of this, the cast of characters is surprisingly weak for a Square Enix game. The protagonist, Elliot, feels somewhat hollow, and spends much of the game telling people to follow their heart, chase their dreams, and to believe in themselves. To be blunt, it feels a bit sappy, and despite his striking appearance, he’s actually quite an uninteresting lead.

(Image credit: Square Enix)

A lot of the other characters are written in a slightly wooden way, too. Elliot will meet them, they’ll reveal something that troubles them — be that isolation, missing a loved one, or seeking connection with others — the hero will do something to assist them, and then you move on. As a result, characters often lack nuance or depth, and it feels hard to care about the various individuals involved.

Like a lot of other players have pointed out online, your companion, Faie, is also rather irritating. She speaks up…a lot…and her hand-holdy, pointless interjections can feel grating. You can mute your companion, thankfully, which is a good thing given that the fairy’s high-pitched tone is still haunting me.

Much of the game is centered around time travel, another element that could’ve been handled more effectively in my view. A lot of the environments look identical across different eras, and enemy variety can be pretty limited across time as well.

I did like the discoveries you could make across different ages, though, and hunting for new weapons in the various dungeons, and general exploration, was pretty enjoyable. My critique here, however, is that the puzzles within various areas are very easy, and require little effort to overcome. Therefore, anyone seeking out the ingenious design of classic Zelda dungeons may be left wanting more.

Final thoughts: a new IP with growing pains

(Image credit: Square Enix)

The Adventures of Elliot still nails a lot of the fundamentals, with a beautiful soundtrack, gorgeous HD-2D visuals, and a neat UI. But when I look at the full package, I’m left feeling conflicted.

While the combat is slick and enticing, the underwhelming story and lack of variation in environments and enemies slightly disappointed me.

Although I still had a decent time with The Adventures of Elliot, and I enjoyed its delicious HD-2D graphics and high-octane battles, it’s clear that the new IP has gone through a few growing pains. And unfortunately, its forgettable characters and lacking dialogue bring the overall experience down a touch, meaning it doesn’t quite hit the highest of heights.

Should you play The Adventures of Elliot: The Millennium Tales?

(Image credit: Square Enix)Play it if...

You love classic Zelda combat
If you’re a sucker for combat in 2D Zelda games, then this title will surely hit the spot for you. The pace of battle and numerous weapon types keep combat feeling varied and exciting throughout the game’s runtime.

You’re a fan of the HD-2D visual style
If, like me, you’ve enjoyed the HD-2D visual style before, you'll almost certainly love it again here. The game is full of beautiful backdrops and environments, and the expressive 16-bit style sprites really pop.

Don't play it if...

You’re expecting a gripping story
The biggest weakness of this title is its underwhelming story, with dull dialogue and an uninspired cast of characters holding the overall experience back from greatness.

You want tough puzzles
Although in-game dungeons hold some highly entertaining boss fights, reaching them can often feel like a formality. That’s largely because puzzles are very straightforward, with little challenge involved.

Accessibility features

There are a number of ways to customize the experience in The Adventures of Elliot: The Millennium Tales. There are a handful of text languages, and you can swap between English or Japanese voices. You can alter text display speed, and set dialogue to auto if you want to watch scenes unfurl naturally.

There are a range of difficulty modes too, and you can remap controls to your liking for a more custom experience. Unfortunately, there’s no colorblind mode, or similar.

(Image credit: Square Enix)How I reviewed Dragon Quest I & II HD-2D Remake

(Image credit: Square Enix)

I spent more than 25 hours playing through the main story and side quests in The Adventures of Elliot: The Millennium Tales. I played on Normal difficulty in this instance.

For the most part, I played the game on my PS5, which is connected up to my Sky Glass Gen 2 TV and Marshall Heston 120 soundbar. However, I occasionally dipped into the title on my PS Portal, and used the Sennheiser CX 80U to enjoy in-game audio while on the go.

More generally, I’ve reviewed a wide range of games here at TechRadar, though my main focus has been on RPGs, including Square Enix titles like Final Fantasy: The Ivalice Chronicles and Dragon Quest 1 & 2 HD-2D Remake.

First reviewed: July 2026

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Vibe coding lets you create everything from one-page websites to complex applications simply by explaining what you want in plain English.

This relatively new technology is an undeniable game-changer, tearing down barriers, helping small businesses and entrepreneurs build tools that just would not have been accessible before.

But is vibe coding really all it is made out to be?

I caught up with Nikita Obukhov, Founder and CEO of website-building platform Tilda, to get his thoughts on how vibe coding is, and isn't, going to change how we approach website building. We also dive into some of the risks associated with vibe coding and hear some advice on where it can be best applied to help you grow your business.

How is vibe coding challenging the more established drag-and-drop website builder space?

The rate of progress in neural networks is insane. Everything is moving very fast: new top-tier models arrive every six months, and what looked impossible a year ago is now generated at really good, really stable quality. Naturally, for us — and for every website builder out there — that's stressful, a zone of discomfort.

Tilda made building a website accessible to a non-professional. Before that, you had to deploy WordPress or some CMS, and if you weren't a technical specialist, you couldn't really do it properly on your own.

Now building a site has been democratised and is as accessible as editing an ordinary Google Doc. The whole concept of the modern website builder is exactly that: letting anyone create a site without technical skills. Now, what AI generates simplifies the process even further.

For site builders, this is a moment of discomfort and stress. And it sets off a search: where can site builders still be useful? It strongly affects the overall product roadmap.

I can't say that the search is finished. We're in the middle of working it out, of finding the point: why not just go to the neural network — why go to a site builder as well? It's a process of transformation, and we hope we'll come through it and stay useful to the user.

How does vibe coding a website differ from using an AI website builder?

Vibe coding brings in editing by voice: you tell the agent in text or out loud what you want, and the agent generates it. It's genuinely new.

The term "vibe coding" is itself very blurry. You can call it vibe coding when you simply ask a chat interface to generate code and get plain HTML back; then there's vibe coding where an agent builds you a full site out of several files, spins up a virtual environment on localhost so you can test, and lets you connect databases if you need more than a static site.

Site builders brought visual editing — making editing simpler without touching code. Vibe coding brings in editing by voice: you tell the agent in text or out loud what you want, and the agent generates it. It's genuinely new.

Vibe coding lets you work in your own infrastructure. Most often you either pick a service to host your generated code and deploy, or you go the classic route: take a virtual server and set up deployment there.

With an AI website builder, all the interaction happens on the platform itself. Even though the neural network underneath is effectively the same in both cases (both vibe coding and the builder are usually running some top-tier model under the hood), that's exactly where the fundamental difference lies: either you work entirely in your own — or rather, rented — environment, or you work inside the website builder's ecosystem.

Both have their pros and cons. Pure vibe coding gives you the most control. You're no longer limited by the site builder's platform, but it adds complexity. You need to think about things most people don't anticipate when they start.

Take image loading. You need to know the current best practice: images should ideally sit on a CDN. So now you also have to work out how to deploy your images to a CDN. With a website builder, all the code and everything else lives on the builder's infrastructure, so you never think about it.

Second, protection from DDoS attacks. If your business has any visibility at all, taking down a site on an ordinary VPS is very easy. So you have to think about how to protect it. Fortunately, Cloudflare has a nominally free tier, but it's still something to think about, because DDoS attacks are fairly common.

Ultimately, with vibe coding, you write everything yourself via an agent that simplifies a lot for you; you barely touch the code. With a builder, you work inside the site builder's ecosystem, using the interface — and, naturally, using prompting to create the design as well.

Will vibe coding eventually replace drag-and-drop website builders?

Realistically, I think we'll end up with both. Vibe coding still has a difficult entry point; it's slower and harder. Site builders are integrating vibe coding themselves, Tilda included: we've released a vibe coding tool called Vibe Block, where you generate blocks or entire pages from a prompt in exactly the same way.

But site builders go further. I don't believe they'll disappear entirely. It's a whole platform; site builders take hosting off your hands completely and give you a convenient tool for controlling things not only by voice but graphically.

On top of that, AI may not fully understand you, may not quite do what you need if you want your own high-quality, distinctive solution. Take Zero Block, for instance: it's a Photoshop or Figma equivalent, a fully graphical interface where designers work and produce exactly what the client needs. That's hard to achieve through vibe coding.

For ordinary users, a website builder is simply faster. It absorbs a huge amount of what you'd otherwise have to do yourself. An ordinary entrepreneur running a small organisation has no need to get into server hosting or how to issue a certificate. They have other things to do. So they'll go to a site builder regardless.

Greater control isn’t always a good thing. How does Tilda set guardrails to protect against poor design decisions?

Tilda doesn't constrain you at all and, unfortunately, doesn't protect you from bad design decisions.

If a user doesn’t have graphic design skills — they use the ready-made blocks from the block library, and that protects them. Those are good, proven design decisions that stop you from making a complete mess. And as your level rises and you're no longer afraid of free-form design, Zero Block lets you make anything at all.

Users are protected in a lot of places from bad practice, because a great deal of niche, purely technical work sits under the hood. By default, all images load lazily. Under the hood, they're also adapted, converted to modern formats, and compressed. The platform takes all that nonsense on itself.

Vibe coding offers users an opportunity to build complex tools using plain-English prompts. Does using it for simple tasks like landing page creation risk overcomplicating things?

Vibe-coding a landing page from scratch is completely pointless.

In my view, vibe-coding a landing page from scratch is completely pointless — regardless of the website builder. On one hand, as a user, it's interesting. Plenty of people who love technology will get a kick out of going through it.

But if you look under the hood — do the site's visitors actually get any benefit from it being vibe-coded rather than built on a site builder? No. On the contrary, there are more opportunities to make a mistake.

For example, how do you share editing rights? That's a simple thing a site builder always gives you out of the box. With a website builder, you can let someone manage products but not page content, for example. That's a perfectly ordinary question of permissions management, and it protects your site. With vibe coding, you still have to work out how to do it properly and grant those rights.

In my opinion, vibe code is excessive for most sites. It’s brilliant for building micro-SaaS. But for building landing pages or simple sales material? Absolutely not, because it still ends up cheaper and faster on a builder, even if in this current wave of enthusiasm it doesn't feel that way.

Vibe coding outputs often need repetitive tweaking to make them fit for purpose. Is vibe coding really the time saver it is made out to be?

On one hand, a neural network gives you freedom, but on the other, you have to be able to articulate what you want. That's a problem. Users try vibe code, write something, and aren't happy with the result because the neural network generated it badly. Then you have to sit there prompting and fiddling to get the quality you want.

A site builder still works very well when you don't know what you want. You get a large block library and a large template library — you can pick a style visually and see exactly what you're going to get, rather than waiting for it all to generate and cycling through ten attempts.

The biggest problem with prompting, of course, is the time between iterations. You wait while the neural network generates and regenerates the code, and that's slow. Sometimes it's very hard to make exactly the change an interface would let you make easily. Explaining in a prompt that you want this changed to that can be devilishly hard. In the end, you're spending time on something as trivial as recolouring a button: five seconds in the interface, whereas here you write a prompt, it thinks, it regenerates the style — that's a minute.

So making changes through prompts is fairly tiring. Which is why we see the future in synergy: you get the first result with vibe code, then refine the details through the interface.

Are there any security issues users should be aware of when using vibe coding to create websites?

If you're selling products, say, vibe-coding an online store…well, good luck.

First, security in the sense of things simply working: a neural network can break your project, taking it from working to non-working through some internal error or problem. A neural network is a roulette wheel — it can hit the jackpot or lose everything. It's much the same here.

Second, you still need to write the code correctly. If you're selling products, say, vibe-coding an online store…well, good luck. I wouldn't risk it, because there are price calculations, stock checks, a lot of things we've been doing for a very long time.

A neural network is a roulette wheel — it can hit the jackpot or lose everything.

Equally, you don't always understand what it has written. If you're building in interaction with users and personal data and taking it further, you're creating risk for the users who trust you with that data. If you've also vibe-coded some mini-CRM of your own inside, that adds to the exposure.

If you have a static site — just a landing page that displays things — there's nothing much there; a static site is hard to do anything with. But once you start processing orders or submissions inside it, or accumulating user data, that puts your service at serious risk, and the risk is a certainty. We see it in practice, and it shows up in the news: people get a fast result, and it turns out to be unreliable. So you have to assess the risks soberly, and it's better to avoid them.

The iPhone 7 isn’t remembered as being a particularly important iPhone release, but it did represent several ‘firsts’ for Apple. It was the first iPhone to lose the traditional headphone jack, the first to boast IP67 water resistance, and the first to swap the mechanical Home button for a pressure-sensitive equivalent.

The iPhone 7 was also the first standard-sized iPhone to feature Optical Image Stabilization (OIS), which uses physical gyroscopes to counteract shake-induced blur, and has been a feature of every iPhone released since (that’s 35 models and counting).

Why am I writing about the iPhone 7? Because it arrived almost a decade ago — on September 16, 2016, to be precise — and because I came across TechRadar’s iPhone 7 review sample during a recent clearout of our office cupboard.

I’m currently using the iPhone Air — released on September 19, 2025 — as my daily phone, which is a similarly thin and lightweight iPhone with only one rear camera, and so I thought it would be fun (and nostalgic!) to compare the camera capabilities of these two devices to see how far Apple’s camera hardware has come in 10 years. The answer, as you can imagine, is 'very far'.

Specs

Before I jump into the side-by-side comparisons, here’s a table detailing the key camera specs of the iPhone 7 and iPhone Air:

iPhone 7

iPhone Air

Rear camera:

12MP, f/1.8, 28mm

48MP, f/1.6, 26mm

Front-facing camera:

7MP, f/2.2, 32mm

18MP, f/1.9, 20mm

It's worth noting that the iPhone Air defaults to shooting in 24MP, rather than 48MP, via a 'Fusion' process that merges 12 high-dynamic-range pixels and 12 low-dynamic-range pixels into a single 24MP shot. You can choose to shoot in 48MP on new iPhones like the iPhone Air, but I stuck to the default option for this comparison.

Photo gallery

Right, onto the side-by-side photos. I took both phones on a walk around the neighborhood, comparing their wide-shooting capabilities, ability to capture color, digital zoom capabilities (neither device has a dedicated telephoto zoom), and low-light shooting capabilities.

iPhone 7FutureiPhone AirFuture

First shot: a tree on the sidewalk. This isn't a particularly demanding scenario, but at first glance, both phones appear to have captured a similarly detailed image. Apple's approach to color science doesn't appear to have changed all that much in 10 years, either (at least in this example — more on color science later).

If you zoom in, though, the iPhone Air's shot is clearly superior. Check out the detail on the property nameplate to the left of the tree, for instance, or the paving stones in the foreground. The iPhone 7 smears over details it can't capture, while the iPhone Air's sensor picks up lots more information. These are subtle differences, but the iPhone Air's shot is the better of the two.

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This is another example of subtle differences. The two images look similar at first glance, but the iPhone Air captures more brick, metal, and pavement detail than the iPhone 7. The dog is just as cute in both images, mind you.

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Pub time! Again, the iPhone 7 does a decent job here, but the detail and color of the main building are more real-looking in the iPhone Air's image. If you zoom in on the main Holly Bush logo or the Hollybush House sign, you'll notice the difference in clarity. The shadows in the latter photo are also more pronounced, which speaks to the iPhone Air's superior dynamic range.

iPhone 7FutureiPhone AirFuture

How about a butterfly? The iPhone Air's image is clearly the richer of the two. The leaves, branches, and pattern on the butterfly itself are more detailed in the shot captured with Apple's newer phone, while the iPhone 7's image is softer, almost as if there's a streak of sunblock on the lens. This is the first shot where I think, "Yeah, that photo was shot on an old iPhone."

iPhone 7FutureiPhone AirFuture

This is a tricky one. The petal detail on the main flower is slightly better on the iPhone Air shot, and the color of the rear wall is more accurate, too. But the iPhone 7 keeps more foreground detail intact (see the green leaves on the left and the red label at the bottom). We'll call it a draw.

iPhone 7FutureiPhone AirFuture

And here we come to the best example of Apple's modern approach to color science in action. On newer iPhones, Apple prioritizes style over realism, occasionally warming things up with more saturation, so shots have an almost yellowish quality. As you can see above, the ice cream is literally a different color in both pictures.

The iPhone 7's approach to this image is too cold — I chose the salted caramel flavor, and the older iPhone saps all the fun and warmth out of that decision. It makes the ice cream look unappetizing and the weather miserable. The iPhone Air's photo looks immeasurably warmer, and while my surroundings weren't quite that yellow, I'd rather exist in this sun-kissed world than in the iPhone 7's gloomy alternative.

iPhone 7FutureiPhone AirFuture

Another big win for the iPhone Air here. Neither phone has a dedicated zoom camera, so I had to employ digital zoom in both cases (around 4x), and the iPhone Air captures far, far more detail than the iPhone 7.

iPhone 7FutureiPhone AirFuture

So far (the zoom example above notwithstanding), the iPhone 7 has delivered less detailed but still largely usable images versus the iPhone Air. That stops when you switch to low-light scenarios.

In this example, the iPhone 7 just can't handle the bright light of my IKEA donut lamp — you can't even tell that it's a donut at all. The iPhone Air, meanwhile, captures the charming shape of the lamp itself as well as details in the shadows it creates. Look at the pattern on the cupboard door — it's simply not visible in the photo captured by the iPhone 7.

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Again, the difference is night and day here (almost literally). The candle in the iPhone 7 photo is extremely blown out — no pun intended — while the iPhone Air more accurately recreates what I was seeing with my eyes (read: light).

iPhone 7FutureiPhone AirFuture

If this were a competition to see which phone could best recreate the neo-noir visual style, the iPhone 7 would win (the first photo is very Lynchian). Alas, it's not, and so here we have a great example of just how far the iPhone's night photography skills have come. The iPhone Air photo is more detailed, has better dynamic range, and has more accurate colors — try and read the number plates in the iPhone 7 photo, and tell me I'm wrong.

iPhone 7FutureiPhone AirFuture

Oh look, it's me! I'm actually quite impressed with the detail served up by the iPhone 7 here — the hairs on my head and face are equally visible in both examples — but the iPhone Air more accurately captures the whites of my eyes and the details of my complexion (read: the blemishes. Sigh.)

In fact, now I'm looking at the iPhone 7 photo again — specifically, my black eyes — I look a bit like a Great White Shark who's enjoying his last hours as a human. Maybe that's what Apple was going for in 2016?

Verdict

(Image credit: Future)

Surprise! The iPhone Air captured a better photo than the iPhone 7 in almost every example. That was to be expected, and, if you're spending close to four figures on one of the best iPhones in 2026, hoped for.

Apple's latest single-camera iPhone delivers superior details, colors, dynamic range, and digital zoom clarity than its predecessor 10 generations removed, and if that wasn't the case, you'd be worried for the company's future.

But in writing this comparison, I was pleasantly surprised by how well the iPhone 7 held up against the iPhone Air. At first glance, it delivered comparable photos on several occasions, only revealing itself to be a 10-year-old device when I zoomed in and dug into the details (or lack thereof). The low-light examples were a different story, but again, that's to be expected.

So, yes, iPhone photography has come a long way since 2016, but if you find yourself forced to use a banged-up old iPhone 7 for a few days (for whatever reason), it won't be totally incapable of capturing usable photos.