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Artificial intelligence (AI) is constantly reshaping everything we do. Across industries, it is changing the way we do work, but that rapid expansion can’t continue without bumping up against real tangible limitations.

Most notably, planning for hyper scaled data centers across the world and increasingly complex cloud computing infrastructures and AI systems are leading to difficult conversations around energy pricing, generation and availability.

Around the world, electricity consumption is increasing at some of the fastest rates seen in decades, and there are no signs of it slowing down. The International Energy Agency (IEA) projects global electricity demand growth of 3.3% in 2025 and 3.7% in 2026, driven heavily by those same data centers, AI deployment, and other advanced industrial expansion.

The IEA has also warned that electricity demand from data centers is expected to double by 2030, with AI-focused facilities alone projected to triple their power use over the same period.

The financial implications and policy blowbacks are already starting to be felt. With limited expansions of electrical grids, more consumers are fighting for less resources, causing prices to only go up. In fact, according to S&P Global, some regions with AI data centers have seen wholesale electricity prices surge by more than 250% in the past five years.

This growing tension between AI advancement and energy availability is beginning to reshape how the technology sector thinks about the future of innovation. For years, the dominant assumption was that progress in AI would mainly come from scaling model size and centralized compute infrastructure.

But the next wave of value creation will also come from AI embedded in the physical world: machines, devices, buildings, industrial assets, medical wearables, and infrastructure that continuously sense, act, and adapt. In that context, the question is not only how to train larger models, but how to process massive streams of real-world data with minimal latency and minimal energy.

That is why alternative architectures, including low-power and decentralized AI, are becoming strategically important.

What low-power AI systems are

Low-power AI are systems specifically designed to minimize the resources required for inference and online learning, particularly energy consumption, while still delivering on high-performance expectation. Rather than relying entirely on massive cloud-based infrastructure and centralized data centers that guzzle down energy, these systems prioritize resource-efficiency at every level of the technology stack, from semiconductor architecture to data processing and deployment.

Low-power AI is not a single breakthrough at model level. It is a system-design discipline that spans sensing, signal conditioning, embedded processing, semiconductor architecture, algorithm optimization, and deployment. The real challenge is to co-design hardware and software for a specific use case so that intelligence is delivered where it matters, with the lowest possible energy budget.

This is precisely where research-transfer institutions such as CSEM can contribute: by combining expertise in sensors, edge computing, ultra-efficient IC design, and application-driven system integration to translate AI into robust real-world solutions rather than generic demonstrations.

Most of the focus in AI development has been in creating systems that are trained and operated on generalized architecture, handling a wide array of tasks simultaneously. These systems are immensely powerful but rely on the same models that require copious amounts of energy to keep them functioning.

On the contrast, low-power AI systems focus on more highly specialized systems, limited in scope and capabilities to a well-defined set of tasks that allow them to be less reliant on vast infrastructure and energy resources to function.

This includes edge AI, where data is processed directly within devices and systems rather than being sent continuously to remote cloud infrastructure. That shift matters even more in the era of physical AI. When intelligence is embedded into the real world, the volume of potentially relevant data generated by sensors, machines, vehicles, buildings, and other assets becomes enormous.

Sending everything to the cloud is not only inefficient, but often too slow and too costly. Many decisions must be taken locally, in real time, with strong constraints on power, bandwidth, privacy, and reliability.

Low-power AI therefore becomes essential not just to reduce energy use, but to preprocess data close to where it is generated, extract the small fraction of information that is meaningful, and enable the broader system to be monitored and optimized for performance, resources, and health.

Perhaps most importantly, low-power systems expand where AI tools can realistically operate. Wearable medical devices, industrial sensors, remote monitoring systems, transportation infrastructure, and smart manufacturing environments all require AI systems capable of functioning within strict energy constraints.

These contexts show places where sustainability is not only a cost-effective measure, but a functional requirement. At the sub-milliwatt level, some systems can even move beyond battery dependence and become energy-autonomous, harvesting ambient energy from light, heat, or vibration to enable a true fit-and-forget lifecycle.

Why efficiency is becoming an imperative

Power generation capacity, transmission infrastructure, cooling resources, and semiconductor supply chains are all facing mounting, simultaneous pressure. The assumption that future competitiveness depends solely on building larger and more power-intensive systems may no longer hold true, with further expansion likely bringing with it exponentially higher costs.

Organizations capable of delivering efficient, highly targeted distributed AI systems could gain major strategic advantages and offers a pathway toward greater technological resilience, as their design natively makes them more resistant to fluctuations in electricity pricing, supply disruptions and geopolitical instability.

Additionally, a more sustainable option can bring value by reducing environmental impact, while still not sacrificing function. The conversation around responsible AI therefore cannot remain focused solely on software governance and ethical frameworks but needs to be talking about how systems are powered, and how and where they process information.

A strategic opportunity for smaller nations

The rise of low-power AI also bears the opportunity to redefine who can meaningfully participate in the global AI race.

The United States and China have been postured as global tentpoles when it comes to the development of AI, and subsequently massive AI investments that have followed suit.

Both are examples of large nations that have the resources to invest billions into data centers, chip production and other infrastructure. On first glance, this paradigm forces many smaller nations to miss the financial and innovation benefits of the AI movement.

But with low-power and distributed AI systems, smaller countries do not need to compete on scale alone. They can compete through specialization, precision engineering, and the ability to translate research into deployable systems for demanding applications.

My home nation of Switzerland provides a useful framework for what this looks like in practice. Similar to most nations across the world, we cannot outspend the largest economies, but we do possess strong capabilities in microelectronics, embedded intelligence, sensing technologies, and high-value industrial and medical applications.

By recognizing these strong foundations, technology transfer organizations like ours can then play an important role in bridging these unique national strengths with industrial deployment, helping transform AI from a cloud-centric paradigm into efficient intelligence embedded in the physical world.

Even for relatively small nations with limited natural resources, there is an opportunity to be a leader in AI development by embracing low-energy system design. Chip producers with less resources will have to increasingly focus on creating specialized, energy-efficient technologies optimized for targeted applications to let them compete on the global stage.

As energy constraints become more severe, demand will likely grow for AI systems capable of operating efficiently in real-world conditions rather than exclusively within massive, centralized infrastructure environments.

In many ways, low-power AI could democratize portions of the AI boom by rewarding efficiency, precision, and specialization as opposed to simply providing opportunities for regions that can match scale. It can be said that virtually every country on earth has some level of specialized technical expertise that can be bridged to an AI use case.

The next generation of AI will see a shift from chat interfaces and cloud platforms to physical systems that shape daily life and industrial productivity. In that setting, efficient local intelligence is not a secondary optimization; it is a core architectural requirement.

Physical AI will depend on the ability to sense the world continuously, interpret it selectively, and act on relevant information without moving every raw data stream through centralized infrastructure.

The democratization of AI brings with it the need for more democratized solutions and opportunities for all to participate.

Looking ahead

While the early years of the AI boom have been defined by large, multi-purpose models and increasing scale, the next chapter will likely be written by developers and ecosystems that are able to utilize precision engineering to focus on low-power distributed systems that are by nature more resilient and sustainable.

Energy availability is no longer a secondary consideration in AI development and will increasingly be one of the defining variables shaping the future of the industry, and subsequently, how the global economy is built. That reality is making low-power AI an emerging necessity.

The next generation of AI systems must be designed with these restrictions in mind, requiring advances in semiconductor design, edge computing, specialized architectures, and intelligent energy management.

Countries and companies that embrace these more efficient and targeted systems may ultimately be better positioned for long-term competitiveness than those that rely instead on growing as large as possible as quickly as possible.

The European Union’s Joint Chips Undertaking is already bringing together its member nations, as well as some outside partners like Switzerland, to develop pathways to technologies like low-power chips. With that in mind, the future of AI may not belong solely to the biggest players, but to the smartest and most efficient ones.

For the global economy, that may prove to be one of the most important transitions of the AI era which only started to come to prominence recently with the growing controversies regarding hyperscale data centers.

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Every few years, enterprise software discovers a future it insists you cannot afford to miss.

Often these claims outrun the product, and amid the hype, organizations commit time and money to innovations that never quite live up to their billing.

Agentic AI is the exception, and that is exactly why it deserves a closer look.

We're not talking about chatbots that draft emails or summarize reports. This is something entirely different: software that acts.

Agentic AI can read a company's own approval hierarchies and permissions, make decisions within finance or supply chain processes, and execute them.

The last wave of enterprise AI offered suggestions. This one does the work. For the organizations that get it right, that is a different order of advantage.

The large vendors know this, which is why they have spent the past year racing to own agentic AI. At its Sapphire conference this spring, SAP unveiled what it calls the autonomous enterprise: more than 200 specialized agents that carry out tasks across the core business functions, orchestrated by some 50 domain-specific assistants and reached through a single interface, Joule.

Oracle has been building the same capability into Fusion, its cloud suite for finance, HR and supply chain.

Unlocking the potential

There has been no shortage of talk about what that could unlock, and for many organizations the excitement is well earned: an agent that can carry out work, not just recommend it, changes what the software is for. But for most of them, already running this software, capability was never going to be the sticking point. Access is.

As the major vendors have built it, agentic AI is native to the system of record, woven into the core platform that runs the business. That is a real achievement: an agent that respects your permissions and executes a live transaction is worth far more than one bolted from outside.

But "native" also carries a second meaning – and this one never makes the keynote speech. With the agent tethered to the vendor's cloud platform, there can be no reaching it without committing to that same platform, regardless of what your systems run on today.

SAP shows how that gate works. Until this spring, Joule reached only customers on its cloud subscriptions: RISE and GROW. At Sapphire, with much of its installed base showing little sign of moving, SAP opened a door, but a narrow one. ECC and S/4HANA on-prem customers are no longer shut out, provided they commit to moving the majority of their SAP estate to Cloud ERP.

Even then, they get only a limited set of the AI capabilities rather than the full portfolio. The on-prem route, in other words, is sold on the condition that you start paying for the cloud. And for the older ECC core, a clock is running regardless: mainstream maintenance ends in December 2027, with a stay of execution to 2030 at a premium, after which the choice narrows to an unsupported system or an upgrade.

So an ECC customer that takes the on-prem route is still paying for the cloud to access AI capabilities, even as support drains out of the system that they rely on (according to a timeline they never chose).

A more direct route

Oracle arrives at the same place by a more direct route: its agents do not exist outside the cloud. The Fusion agentic applications it has rolled out this year run only inside Fusion Cloud, on Oracle's own infrastructure and within its security model. There is no on-prem edition to license. An organization still running E-Business Suite cannot switch these agents on where its systems sit today; to use them at all, that organization will have to re-platform onto Fusion.

Strip away the packaging and the structure is the same in both cases: the on-ramp to agentic AI is the migration these vendors have been trying to sell all this time. What is new is the leverage. Cost, risk and disruption have held many organizations on-prem for years – despite all the pressure to move. Could the prospect of being shut out of agentic AI be the argument that finally overcomes their objections?

The budget is already committed

The migration and the AI draw on the same budget, and the migration has first claim on it. In the Americas' SAP Users' Group, 61% of members reported that budget was the biggest challenge they faced this year, and the group's research director was blunt about the cause: the cloud ERP projects are themselves creating the pressure, with AI expected to land on the same budgets next.

The sequence is unforgiving. Pay to reach the platform, then pay again to use the AI once you are there, because the headline subscription covers only a limited band of embedded features, and the rest is metered by consumption. The capital a CIO would want to invest in building an advantage is spoken twice before a single agent has delivered a measurable outcome.

And that is before the program meets its harder test. Boards have grown tired of pilots that never reach production and spending that generates activity no one can tie to an outcome. In some cases, these will be genuine execution failures, but a program that starts short of capital, on a timetable set by someone else, is not starting from a position of control.

Who decides the order of operations?

A CIO should separate the two decisions the vendor has deliberately combined. Whether to modernize is one question. When to do it, in what order, and against which budget is another. Nothing requires that the second be dictated by an end-of-support date printed on someone else's roadmap.

The critical issue here is who holds the authority to decide how the organization's most critical systems change and when. When a vendor sets the timetable, the sequence and the price of innovation, the executive accountable for that estate is not really the one running it anymore. Restoring that authority does not mean refusing to modernize. It means refusing to let the vendor selling the upgrade also decide when and how you buy it.

This is the case for third-party software support. An independent provider maintains the existing ERP estate, keeping it secure and preserving interoperability in place of the vendor's maintenance contract – and typically at a fraction of the cost. That does two things. It removes the end-of-support date as a forcing function, because now the system will stay supported whether or not the organization moves.

And it frees the maintenance budget that would otherwise fund the vendor's marketing roadmapping, so the money can go towards AI investments the organization actually wants to make. The stable core stops being a liability to escape on the vendor's schedule and becomes what it always was: productive capital, running the business while leadership decides on its own terms where agentic AI earns its place.

From there the real options open up. An organization can hold that stable core and run agentic AI as an orchestration layer above it, reaching into the system of record without re-platforming the whole estate first.

Or it can modernize selectively – moving from proprietary databases to open-source alternatives that eliminate vendor license fees, where there’s a business case for it – on a timetable set by business value rather than a maintenance deadline. What matters is that the sequence belongs to the organization.

The case for authority

The case for holding that authority is stronger with agents than it was with assistants. Tying the behavior and the governance of a system that acts on your business to a single vendor's platform roadmap is a heavier dependency than the previous era ever required.

Governed autonomy is the right ambition, but governance an organization sets for itself is a wholly different thing from governance inherited from the platform it happens to sit on.

The vendors are right about one thing: agentic AI is a capital decision, not a software purchase. But a capital decision means choosing where the money goes and what you expect back from it. When the vendor sets the timing, the sequence and the price, that is not allocating capital, it is settling an invoice.

The autonomous enterprise may well be worth building. What a board should refuse is to let its timetable and its budget be set by the company with the most to gain from the move.

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The global AI in IoT market has grown rapidly within the last year, with its value reaching $4.08 billion in 2025, and forecast to reach $6.45 billion by 2035. This progress reflects a wider industry shift, as with the rise of AI organizations are increasingly looking towards a more intelligent and connected future to achieve more from their IoT deployments.

Building additional defenses with AI-powered IoT security

The nature of the IoT can be complex, with devices often operating outside traditional IT security perimeters and in globally distributed environments. Within this landscape, connected devices now form the backbone of daily operations, from energy and healthcare to retail and manufacturing. As digitalization increases, so too will the critical functions that depend on connected devices.

Importantly, as deployments accelerate, so too does the potential IoT attack surface area. Due to the interconnected nature of the IoT, each device acts as a potential entry point for cybercriminals. With the UK government reporting the annual cost of cyberattacks as £14.7 billion annually, it is crucial that the entire IoT deployment is secure.

While private IP addressing and private APN approaches are useful in limiting exposure, they offer limited visibility into device behavior once traffic is flowing, particularly as deployments grow more complex.

AI, however, can see what may have previously been missed. Security has become a key area that can be elevated by AI-powered anomaly and threat detection, which detects behavior such as suspicious IPs, remote code execution, device backdoors or an abnormal port connection. This threat detection enables organizations to quickly identify the first sign of a cyberattack.

AI can even help identify the nature of attacks, including distributed denial-of-service (DDoS) and man-in-the-middle (MiTM) attacks, and device takeovers. By flagging irregularities in real time and enabling rapid corrective action, AI strengthens IoT security and supports more effective risk management, helping organizations avoid operational disruption, financial loss and reputational damage.

AI unlocks the value of IoT data

Beyond security concerns emanating from the IoT’s fragmented landscape, organizations may also struggle with data management and deriving meaningful analysis across the IoT’s web of networks, devices, cloud environments and enterprise processes.

The IoT constantly collects and transmits vast quantities of information, with a single internet-connected security camera generating around 300 GB of data per month. When these connected devices are multiplied and deployed globally on a much larger scale, organizations can be left with an overwhelming amount of data that can be difficult to utilize effectively.

Additionally, the role of the IoT is evolving. Whilst historically the IoT has purely had the role of connecting devices and gathering and transmitting data, there is growing industry pressure for intelligent solutions to inform decision-making and drive outcomes. Enterprises are now seeking more effective solutions to extract meaningful insights from IoT information, and without this, they will fail to unlock the true value of their data.

AI offers a promising solution to these challenges. AI-ready infrastructure is being increasingly prioritized by enterprises to support the collection, transmission, processing and integration of data into systems to enable AI-driven insights. The intelligent analysis AI offers enables patterns and trends in device fleets to be identified and analyzed more efficiently to help reduce manual intervention and lower costs.

Through advanced analytics and machine learning, AIoT can help organizations make better use of their data across the entire fleet, transforming data into an increasingly valuable asset that drives efficiency and long-term value.

Automation supports efficiency

Vast quantities of data across the IoT also make manual intervention expensive, ineffective and time consuming. These challenges can result in performance issues escalating unnoticed, or key device faults being missed.

The analytical and automation capabilities of AI can also support through real-time monitoring, automated fault resolution and predictive maintenance. By analyzing data on device usage, lifecycle maturity and performance, AI can identify potential issues before they arise. This enables a shift from reactive to proactive operations, reduced downtime and lower operating costs.

The benefits of AI-driven analytics are industry-wide. In manufacturing, for example, AI systems can use equipment performance data to estimate maintenance costs, consequently minimizing later expenses and downtime. In industries like healthcare, predictive maintenance also helps ensure the resilience of critical services like remote patient monitoring, where it is crucial that devices remain connected.

Rules-based automation can also support policy-driven fleet management and eSIM orchestration, with many capabilities gradually moving towards greater automation. In practice, adoption is iterative, starting with defined rules and oversight, before layering in AI-driven insights such as real-time usage, performance monitoring and anomaly detection to address issues more quickly.

As automation scales further, IoT environments will be able to respond flexibly to changing conditions with reduced manual intervention.

However, it must also be noted that as this progress continues, transparency and explainability remain crucial. Organizations must understand how decisions are made, and where human oversight remains critical as they scale.

Looking ahead to intelligent connectivity

AIoT’s influence will only grow moving forward. In fact, Transforma Insights’ forecast suggests a more than six-fold increase in connections over 10 years. Managing fleets intelligently and securely at scale, with transparency at the center of operations, must become an industry priority.

Those that can successfully integrate AI with scalable connectivity will be best positioned to move beyond simply managing devices towards truly intelligent operations. In doing so, they will unlock new efficiencies, strengthen resilience and create differentiated value in the IoT.

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Artificial intelligence has moved from experimentation to expectation at a remarkable speed. What began not that long ago as isolated pilots is now being embedded across every industry, from highly regulated sectors like financial services to those closest to the human experience, such as healthcare and the arts.

In just four years of widespread business use, these technologies have already moved from operational tools to business infrastructure, underpinning resilience and requiring the same meticulous planning and protection as any other critical system.

This shift is changing the way AI investment decisions must be made. As organizations rush to deploy AI tools, the challenge is no longer whether to invest but how to ensure investments create long-term value, while mitigating growing operational, security, and compliance risks.

Moreover, AI is not a purely digital investment. Behind every model, application, and workflow sits a physical technology estate: servers, storage, networking equipment, energy-intensive infrastructure, devices, and the supply chains that support them.

As AI adoption accelerates, enterprises risk expanding this estate without fully understanding the lifecycle consequences, from rising energy use and infrastructure refresh cycles to underutilized assets, electronic waste, and lost residual value.

Translating investment into impact

Investment in AI continues to rise exponentially, seemingly unimpeded by rising market prices or economic instability. Today, 71% of CEOs rank AI as a top investment priority, yet many still struggle to translate capital expenditure into operational value.

According to Gartner, at least 50% of AI projects are abandoned after proof of concept. Projects that do become operational often fail to deliver a return on investment, with 56% of CEOs saying they have not realized any revenue or cost benefits from AI projects.

This points to a deeper issue: AI success depends less on experimentation alone and more on the quality of the investment, governance, and capability-building decisions that follow.

The AI impact gap

As complexity increases, an investment impact gap is emerging. Leaders expect AI and the tech that supports it to deliver strategic value, improve performance, and reduce risk, but when making investment decisions, they often continue to prioritize near-term costs over the lifecycle factors that determine whether those outcomes can actually be achieved.

Without a view of the lifecycle consequences of their tech investments, as AI adoption accelerates, businesses tend to prioritize factors that are easier to quantify and act on in the short term. Our own research shows that 64% of organizations have rejected a superior technology solution because of its upfront price.

This may reduce immediate financial strain, but it can also introduce operational friction, scalability issues, and erode performance over time. As EY's Americas CTO, Dan Diasio, recently noted, "There's a very clear limit to the amount of value you can create by just focusing on productivity and cost reduction."

AI introduces entirely new cost dynamics. Enterprises must account not only for acquisition and implementation, but also for ongoing expenditure tied to usage, energy consumption, governance, compliance, infrastructure, and model evolution. These lifecycle impacts are often invisible in traditional business cases, yet they increasingly determine whether AI investments create durable value.

Good AI governance starts with accountability and visibility

Similarly, AI does not respect organizational boundaries. Its costs, risks, and value are distributed across the business, making cross-functional accountability essential. And yet, investment decisions are often assessed in silos, making it harder to build a complete view of the risks and value that emerge across the lifecycle.

Likewise, organizations need to understand the scope and reach of the systems they have in production, as well as the financial, operational, security, and environmental impacts they will have throughout their lifecycle.

Without this end-to-end view, major financial and operational blind spots can emerge at critical moments, many of which are not anticipated or planned for. While security, privacy, and compliance consistently rank among the top AI concerns, our research found fewer than half rate data protection (49%) or compliance capabilities (46%) as a high priority when making technology investment decisions.

From cost to AI-driven impact

To close this gap, businesses need to move beyond narrow assessments of upfront price and near-term ROI. These measures still matter, but they do not capture the full lifecycle consequences of AI investments, particularly as systems become embedded in critical operations and begin influencing performance, resilience, compliance, risk, reputation, and long-term value creation.

This is the thinking behind Total Cost of Impact (TCI). TCI is a new model that helps organizations evaluate technology investments through a broader lifecycle lens, assessing not only what a solution costs to buy and implement, but what it will require, enable, constrain, and expose the business to over time.

TCI assesses four core areas of technology impact - financial, operational, security and compliance, and environmental and social - encouraging businesses to understand how investment decisions made today influence outcomes over time.

When applied at the point of investment, TCI makes the trade-offs, risks, and downstream consequences that conventional procurement models often overlook visible. In doing so, it creates a common language across business functions, reducing friction, improving collaboration, and helping ensure technology investments are aligned with strategic priorities from the outset.

Importantly, TCI is an agnostic model: it can be used not only to evaluate whether AI-enabled technologies deliver business value, but also to assess how the infrastructure that supports them is procured, used, scaled, maintained, reused, and eventually retired.

Looking ahead

As AI lifecycles shorten, infrastructure demands grow, and resource constraints intensify, a lifecycle approach to technology investment is becoming a strategic necessity.

AI rollout cannot be separated from the physical infrastructure that enables it. Organizations need to understand not only what AI systems can deliver, but what they will require in energy, data, and asset governance, maintenance, refresh cycles, and end-of-life management over time.

This is why circularity must be part of the AI investment conversation. By taking an end-to-end view of technology assets, businesses can identify opportunities to extend lifespans, increase utilization, recover residual value, reduce waste, and manage end-of-life risk. Circularity is not a separate sustainability agenda; it is a practical way to reduce hidden costs, strengthen resilience, and improve the long-term impact of AI investment.

Ultimately, success with AI will depend not only on the capabilities organizations deploy, but on the quality of the decisions that support them. Those that assess the full impact of their technology decisions from the start will be better positioned to capture value, manage risk, and build resilient, future-ready digital infrastructure.

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HP Omen 16 Slim: Two-minute review

In these strange times where PC memory prices seemingly double by the day, an affordable gaming laptop is a tantalising prospect. But is it actually realistic? Enter the HP Omen 16 Slim, HP's latest mainstream gaming portable.

There are two core variants of the Omen 16, a standard-sized model and this so-called "Slim". Long story short, this Slim model is a little skinnier but still isn't exactly super compact for a 16-inch laptop, thanks to not only a fairly hefty lower screen bezel, but also a chassis that sticks out beyond the base of the display. At least the slightly tanky proportions translate into a tanky chassis with a solid feeling keyboard bed.

Anyway, it's the value proposition that matters and that means getting into the specs. CPU-wise, we're talking Intel Core Ultra 7 255H, which is a pretty modern 16-core chip from Intel's Arrow Lake generation. Here it's configured with 24GB of DDR5 in dual 12GB SoDIMMs. But various configs are available, including 16GB and 24GB.

There's also 1TB Gen 4 SSD, but with any gaming laptop surely the most important single component is the GPU and it's no surprise to find an Nvidia GeForce RTX 5060 8GB doing the pixel pumping duties.

(Image credit: Future)

It's one up from the bottom of Nvidia's RTX 50 Series laptop GPUs, but it gets the full feature set, including all the latest DLSS upscaling bells and whistles. It outputs to a 165Hz 16-inch LCD display with a native resolution of 1,920 by 1,200 pixels, making it a 16:10 aspect panel rather than the usual 16:9 ratio.

The battery is a pretty generous 70Wh item, meanwhile, and networking includes WiFi 6E, Bluetooth 5.4 and Ethernet. Slightly less impressive is the physical connectivity. Not only are you limited to just one USB-C port (plus three USB-A), it can only do a measly 10Gbps. So, the only video out is the single HDMI 2.1 port.

Of course, gaming performance is the key metric here, and the results are pretty good, albeit with one major caveat. This skinnier Omen 16 Slim makes do with 110W of Total Platform Power (TPP) and 80W maximum for the GPU, where the full-sized Omen 16 cranks that up significantly to 170W TPP. Given premium gaming laptops can supply as much as 175W and more just for the GPU, you can see how 110W for everything, including CPU, RAM and storage, can be a limiting factor.

That said, the RTX 5060 GPU is pretty efficient and well matched with the 1200p (near-enough 1080p) 165Hz display. You'll need to use DLSS upscaling to get slick frame rates in the most demanding games. But, broadly, you'll get fairly smooth results at very high detail settings. What's more, the chassis doesn't get too hot and the fan noise is moderate.

All of which means HP has done a nice job with the HP Omen 16 Slim. It's well built, mostly well specified and delivers a pretty sweet gaming experience. Whether it makes sense as a buy comes down to pricing. As configured here with 24GB of RAM, it's a little spendy. But there's an otherwise identical 16GB model that we just spotted for a whisker under $1,200. Right here in 2026, that's a very decent deal.

HP Omen 16 Slim review: Price & availability

(Image credit: Future)

• Available for £1,499 in the UK with 24GB of DDR5
• The closest US config rocks 16GB of DDR5 for $1,599
• Looks out for deals on sites like Newegg

In this precise configuration with 24GB of DDR5 RAM, the HP Omen 16 Slim weighs in at £1,399 in the UK. That's actually very good given current memory prices, right now. Indeed, you could argue it would make sense to knock that back to 16GB, keep everything else the same, and save some money.

Indeed, in the US, just that configuration is available, while the 24GB option is not. With 16GB, you're currently looking at $1,599, which is a fair bit less appealing. Currently, an otherwise identical version but with an RTX 5050 instead of the RTX 5060 can be had for $1,198 on Newegg, which implies you might be able to bag one with the 5060 and 16GB for around $1,400 at some point if you hang around and wait for a deal. At that price point, the HP Omen 16 Slim would start making a lot of sense.

HP Omen 16 Slim review: Specs

Base spec

Review config

Price

$1,189 / £1,100 (estimated UK price)

£1,399 / $1,799 (estimated US price)

CPU

Intel Core Ultra 7 255H

Intel Core Ultra 7 255H

Graphics

Nvidia GeForce RTX 5060 Laptop GPU

Nvidia GeForce RTX 5060 Laptop GPU

RAM

16GB DDR5

24GB DDR5

Display

16-inch 1200p (1,920 x 1,200), 144Hz, IPS, 300 nits

16-inch 1200p (1,920 x 1,200), 165Hz, IPS, 400 nits

Storage

1TB NVMe SSD PCIe Gen4

1TB NVMe SSD PCIe Gen4

Ports and Connectivity

1x USB Type-C 10Gbps (USB Power Delivery, DisplayPort 1.4), 3x USB Type-A 5Gbps, 1x ethernet, 1x headphone/mic combo jack, 1x HDMI 2.1, Wi-Fi 76E Bluetooth 5.4

1x USB Type-C 10Gbps (USB Power Delivery, DisplayPort 1.4), 2x USB Type-A 5Gbps, 1x ethernet, 1x headphone/mic combo jack, 1x HDMI 2.1, Wi-Fi 76E Bluetooth 5.4

Battery

70Whr

70Whr

Weight

5.35lbs / 2.425kg)

5.35lbs / 2.425kg

Dimensions

14.1 x 10.6 x 0.9 inches / 35.75 x 26.9 x 2.27 cm

14.1 x 10.6 x 0.9 inches / 35.75 x 26.9 x 2.27 cm

HP Omen 16 Slim review: Design

(Image credit: Future)

• Not actually that "Slim"
• Solid, well-built chassis
• Connectivity is only OK
• Cooling is well executed

The HP Omen 16 Slim is mostly plastic, just the bottom cover is metal. But it still feels very solid and durable. The Keyboard is nice and stable, with only a touch of flex when pushed hard. The keyboard, while we're on the subject, has four configurable RGB lighting zones, but at this price point, you're not going to get per-key lighting.

HP has done a nice job with the cooling, too. The twin fans draw air from the bottom cover and push it out the back and do so without making too much noise. However, despite the "Slim" branding, this is not a super compact laptop.

(Image credit: Future)

Coming in at 1.99cm to 2.27cm thick, where the standard Omen 16 measures between 2.39cm and 2.54cm, the 16 Slim is thinner than its sibling. But it's still not that compact.

Thanks to a rather large chine bezel and the way the rear of the chassis sticks out beyond the screen hinge, the Omen 16 Slim actually has a larger footprint than many laptops that trade on pure performance rather than portability and also tips the scales at 2.42kg. So, it's no featherweight.

In that context, the somewhat limited connectivity is a touch disappointing. You get three USB-A ports, but just one USB-C and even the latter is limited to 10Gbps, though it does at least support video out and charging the laptop itself.

HP Omen 16 Slim review: Performance

(Image credit: Future)

• Decent frame rates at 1200p with DLSS
• Seriously quick SSD
• Fan noise is reasonable

HP Omen 16 Slim benchmarks

3DMark: Night Raid: 57,311; Fire Strike: 25,198; Time Spy: 10,454; Port Royal: 6,372
Geekbench 6: Multicore: 14,468; Single-core: 3,114
Cinebench R24: Single Core: 134; Multi Core: 2,713
Crossmark: Overall: 1,992; Productivity: 1,888; Creativity: 2,182; Responsiveness: 1,783
Passmark Overall: 10,817; CPU: 38,631; 2D Graphics: 819; 3D Graphics: 17,174; Memory: 3,094; Disk: 36,232
CrystalDiskMark: Read: 7,025MB/s; Write: 5,895MB/s
Shadow of the Tomb Raider: (1200p, High): 132fps; (1200p, High, DLSS Quality): 139fps
Total War: Warhammer III: (1200p, Ultra): 83fps; (1200p, Low): 205fps
Cyberpunk 2077: (1200p, Ultra, no RT): 70fps; (1200p, RT Ultra, DLSS Balanced): 51fps
Doom: Dark Ages: (1200p, Ultra): 57fps; (1200p, Ultra, DLSS Quality): 83fps
Battery Life (TechRadar movie test): 12 hours and 32 minutes

Intel's Core Ultra 7 255H is very much a known quantity, what with its eight Performance and eight Efficient CPU cores. It's all you need for gaming in CPU terms, so the HP Omen 16 Slim's critical gaming component compared with the competition among the best gaming laptops is that Nvidia RTX 5060 GPU and how well it's been implemented.

This thinner HP Omen 16 Slim model does suffer slightly in the wattage department, with the Total Platform Power shared across the GPU, CPU, memory and more capped at 110W. But that doesn't absolutely hobble gaming performance.

For instance, running at the 16-inch display's native 1,920 by 1,200 resolution, you can crank Cyberpunk 2077 right up, with Ultra RT settings and Quality DLSS scaling and still get 44fps average frame rate.

(Image credit: Future)

OK, that's not spectacular. But it is acceptable for a single-player title. Switch off the ray tracing and you'll be looking at frame rates nearer 100. Moreover, in less demanding games, let's say something a little older like Shadow of the Tomb Raider, you can hit over 130fps even without DLSS upscaling and really make the most of the 165Hz refresh rate of the 16-inch display.

Speaking of the screen, the 16:10 aspect is a little odd and adds a little GPU load versus a vanilla 1080p panel. But it's reasonably vibrant, with nice-ish colours and contrast and decent 3ms response times. It's nothing special, but probably what you'd expect at this price point.

Just note the panel reviewed here is the upgrade 400 nit option. The base display is only 300 nits, runs at 144Hz and has much less colour fidelity. Avoid that if at all possible.

(Image credit: Future)

The 16 Slim's thermal performance is pretty impressive, too. It doesn't get too hot under load and the fan noise is reasonable. Well, it is unless you enable "Unleashed Mode" in the Omen Gaming Hub app. That cranks up the fans, but doesn't do much for frame rates.

Elsewhere, the SSD is particularly nippy, clocking over 7GB/s for reads and nearly 6GB/s for writes. For what is pitched as a value-orientated system, that's impressive. As for other elements, the sound quality from the speakers is tolerable at this price point, but the volume could be a little higher, and the webcam quality is OK.

In terms of physical performance, the chassis feels robust, even though only the bottom cover is made of metal, and the keyboard base is nice and solid with minimal flex. As for the trackpad, it's mechanical and doesn't have niceties like a glass cover. But it's large and works well enough. It's also worth noting that the memory, storage and Wi-Fi cards are all upgradeable, which is welcome in an age where too many laptops have everything soldered on.

HP Omen 16 Slim review: Battery life

(Image credit: Future)

• Surprisingly good for a budget gaming laptop
• USB-C charging is supported
• Not actually all that portable

Undoubtedly one of the HP Omen 16 Slim's more impressive aspects is battery life. OK, you're not going to get more than a couple of hours of light gaming off the mains, but for watching video, this laptop has some real legs.

Our local video playback test netted over 12 and a half hours with the screen at half brightness. That's a realistic setting for use on, say, a plane, albeit you might want to wind the screen up a little in bright daylight.

Whatever, that's a very impressive result for a gaming laptop and makes this portable much more plausible as an all-round computing device to take with you on trips and outings. The caveat to that is that the fairly large proportions and relatively hefty 2.4kg kerb weight mean this isn't a laptop you're going to sling into a small shoulder bag and forget about.

Should I buy the HP Omen 16 Slim?HP Omen 16 Slim: Scorecard

Attributes

Notes

Rating

Value

Prices are pretty variable depending on spec and location. But certain Omen 16 Slim configurations do look appealing. You may need to keep your scanners peeled for a good deal, however.

4 / 5

Design

The "Slim" branding suggests something very compact, but this is actually a fairly beefy 16-inch laptop. That said, it is solidly built with a decent keyboard. It's just a pity the connectivity isn't a touch more comprehensive.

4 / 5

Performance

This is a value-orientated gaming laptop, so expectations should be kept in check. But the RTX 5060 GPU matches well with the 16-inch display and you can run pretty much any title at high detail settings with the aid of DLSS upscaling.

3.5 / 5

Battery life

If there's one metric where the HP Omen 16 Slim delivers beyond expectations, it's battery life. You can expect over 12 hours of video playback, which is well beyond what most affordable gaming laptops can manage.

5 / 5

Total Score

Much depends on the price you can bag on the HP Omen 16 Slim. But if you can find a good deal, you'll get good performance, solid build and a nice all-round package.

4 / 5

Buy the HP Omen 16 Slim if…

You want decent value in today's weird market
Nothing's cheap when it comes to gaming PCs, these days. But the HP Omen 16 Slim is definitely decent value in the current context of spiralling memory prices.

You want good battery life
Most gaming laptops struggle away from the mains. But the HP Omen 16 Slim will do 12 hours-plus of video playback on the battery.

Don't buy it if…

You're looking for great portability
Despite the "Slim" branding, this is not a particularly compact or light 16-inch laptop. But it is solid and feels built to last.

You need cutting-edge performance
The HP Omen 16 Slim puts out decent frame rates for a mainstream laptop. But its RTX 5060 GPU is limited to 80W and this is not a high-end machine.View Deal

HP Omen 16 Slim review: Also consider

Asus V16
If you're looking for something even cheaper, try the Asus V16. It uses older Nvidia RTX 30 and 40 Series GPUs to achieve a few savings. But it's surprisingly well built and very portable for a gaming laptop. Read our full Asus V16 review.

Medion Erazer Major 16 X1
If you want more performance without breaking the bank, try the Medion Erazer Major 16 X1 and its RTX 5070 Ti GPU. It's heavy, feels and bit cheap and the battery life is poor. But it'll kick out some very impressive frame rates. Read our full Medion Erazer Major 16 X1 review.

How I tested the HP Omen 16 Slim HX

(Image credit: Future)

• Used as my daily rig for two weeks
• Threw my Steam library at
• Got forensic with the screen and build quality

I've been round the block when it comes to gaming laptops. My first portable ran a GeForce 2 Go, if you can remember back that far. My current lappie runs an RTX 4080, and there have been plenty more in between. ANd those are just my personal machines. I've long since lost count of how many gaming laptops I've reviewed. There are always a few knocking around.

Over the years, I've learned a lot about what makes a good gaming laptop and I applied that to the HP Omen 16 Slim over a couple of weeks. That means throwing everything in my Steam Library at it, from classic strategy titles like Total War to graphics fests including Cyberpunk 2077. All round performance, including the SSD, is also important, as is battery life for what is, ultimately, meant to be a portable machine.

I'm also very picky when it comes to screen quality and keyboard feel and have something of a forensic obsession with laptop build quality. You have to be realistic at this price point, but build quality is particularly important to gaming laptop longevity. Bendy chassis usually end up in flakey performance, eventually.

• First reviewed: July 2026
• Read more about how we test

The nominations are in - and the finalists for the 2026 edition of the Power 50 can now be revealed.

Part of the Mobile Industry Awards 2026, the Power 50 highlights the most important and influential figures in the UK mobile industry during the past year.

The finalists for the Power 50 2026 have now been decided, with the final standings set to be revealed at the awards.

(Please note: these names are ranked A- Z by first name and not the final rankings.)

View the 2026 Power 50 here!

The winner, recognised with the Power 50 award at the Mobile Industry Awards on September 17 2026, is selected following in-depth interviews with key senior figures across the industry, from operators and retailers to manufacturers and distributors.

It rewards those individuals who inspire their businesses with their values, but also have influence beyond existing roles, serving as an inspiration to the rest of the trade.

To make the Power 50 list, an executive needs to be bold in their leadership and vision, and have exceeded expectations in the different categories we have chosen.

Power 50 Previous Winners 

• 2025: Max Taylor, CEO, VodafoneThree
• 2024: Marc Allera, CEO - EE and Consumer Division at BT Group
• 2023: Ahmed Essam, CEO, Vodafone UK
• 2022: Lutz Schüler, CEO, Virgin Media O2
• 2021: Marc Allera, CEO, EE and BT Consumer
• 2020: Nick Jeffrey, CEO, Vodafone
• 2019: Marc Allera, CEO, EE and BT Consumer
• 2018: Marc Allera, CEO, EE and BT Consumer
• 2017: David Dyson, CEO Three UK
• 2016: Sebastian James, Group CEO, Dixons Carphone
• 2015: Sebastian James, Group CEO, Dixons Carphone
• 2014: Andrew Harrison, CEO, Carphone Warehouse
• 2013: Olaf Swantee, CEO, EE
• 2012: Simon Stanford, VP of UK & Ireland telecommunications and networks division, Samsung UK
• 2011: Guy Laurence, CEO, Vodafone UK
• 2010: Tom Alexander, CEO, Everything Everywhere
• 2009: Andrew Harrison, CEO, Carphone Warehouse
• 2008: Kevin Russell, CEO, Three UK

• For more information on the Power 50, or to get more involved in the Mobile Industry Awards 2026, visit the awards site here

Most fraud strategies fail before a fraudster is ever detected, because they evaluate documents, rather than people.

Consider the operational reality: a customer signs up for a fintech service. Their government-issued ID clears every document check. Their selfie matches. Their name passes the database lookup. Onboarding completes.

What no one detected was that the identity didn’t belong to a person. It was assembled by generative AI in minutes — a synthetic construction engineered to pass exactly the checks that were run.

The account sits dormant for weeks, builds behavioral credibility, then executes a scheme that costs the platform tens of thousands of pounds. Deepfake-enabled fraud alone caused more than $200 million in losses in the first quarter of last year.

This is not a stress-test scenario. Synthetic identity fraud is the operating condition for every business that onboards customers digitally. And the industry’s default response — collect an ID, verify it, move on — was not designed for it.

The document-first blind spot

The document-first model has a structural flaw in that it only responds to fraud after a document arrives. Intent, behavioral context and the coherence of a user’s digital existence sit outside its scope entirely. Identity verification becomes a single event rather than a continuous assessment.

That architecture now has a measurable cost. GenAI-enabled fraud losses in the U.S. will reach $40 billion by 2027, up from $12.3 billion in 2023 — a compound annual growth rate of 32%. The dark web has already industrialized the supply side: scam kits that produce deepfake videos and synthetic documents sell for as little as $20, placing sophisticated fraud tools within reach of anyone willing to pay.

The damage shows up at the portfolio level. Synthetic identity fraud, fabricated identities assembled from combinations of real and invented data, accounts for 10 to 15% of charge-offs in a typical unsecured lending portfolio. That is not a tail risk. It is built into the cost structure of credit.

From KYC to proof of personhood

The problem is not that document verification is imperfect. The problem is that document verification asks the wrong question. Proof of personhood reframes the objective entirely. Rather than asking "Is this ID real?" it asks "Is there a real, unique human being behind this transaction?" And it maintains that inquiry continuously across the customer lifecycle rather than resolving it once at onboarding.

The distinction matters operationally. Fraudsters engineer synthetic identities specifically to clear document checks. A convincing fake ID passes the same OCR and liveness detection as a legitimate one. What it cannot reproduce — not convincingly, not at scale — is the full context of a real person’s digital existence. That context is where proof of personhood lives and where effective verification has to operate.

A real person leaves years of accumulated traces across the digital economy: email addresses registered to real services, phone numbers tied to consistent carriers, devices with histories spanning multiple sessions and networks, and behavioral patterns shaped by how actual humans navigate products.

None of these traces is individually decisive. But their presence or their absence tells a story that a fabricated identity, however polished its documents, has not had time to build.

What a multi-signal assessment looks like

No single signal tells the full story. Effective identity assessment requires multiple independent signals that together reveal a coherent identity or expose its absence.

Explicit signals form the foundation. eKYC and eID database verification cross-references user-provided information against authoritative government records, confirming that the identity exists and belongs to the person claiming it. Document authentication validates submitted IDs. Biometric liveness checks confirm a real person in real time.

These explicit signals are necessary but insufficient. Implicit signals provide the surrounding context and a fraudster finds them far harder to fabricate. Digital footprint analysis examines whether an email address carries a real history, whether social media accounts show genuine activity and whether a phone number maps to real-world patterns. Device intelligence surfaces anomalies in hardware and software environments. Behavioral signals flag interaction patterns that deviate from how humans actually behave.

The value of layering these signals is combinatorial. A fraudster can fabricate a convincing document. Simultaneously manufacturing a years-old email address, a coherent social presence, a clean device fingerprint and natural behavioral rhythms is a different order of problem. Each additional signal compounds the cost and complexity of fabrication until spoofing the full picture becomes economically unviable.

Operating across borders

For businesses with multinational customer bases, multi-layered verification is a structural requirement. The regulatory landscape is moving fast. The European Union’s Digital Identity Wallet, which all member states must support by December 2026, will reshape how consumer identity functions across the bloc. National eID programs are expanding across Asia, Africa and Latin America on varying timelines and to varying technical standards. The World Bank estimates 850 million people still lack official government identification — a reality that makes digital coverage and inclusion inseparable priorities.

Businesses that operate effectively in this environment will need verification coverage broad enough to match their customer base and adaptable enough to keep pace with shifting regulatory requirements. eKYC and eID checks across national and regional schemes are the baseline. The operational advantage lies in what surrounds them; the implicit signals that distinguish a genuine applicant from a synthetic one, regardless of which jurisdiction’s ID scheme they present.

Verifying the person, not the ID

Risk assessment should be well underway before a document is ever presented. When digital footprint, device intelligence and behavioral signals work at first contact, synthetic identities are filtered before they reach your most expensive verification steps — and legitimate users clear faster because the system already has context.

That architecture only works when fraud, IDV and AML share a unified data environment instead of operating in parallel. The organizations building that capability now set themselves up to absorb fewer losses and onboard the customers that their competitors cannot confidently clear.

Best Identity Theft Protection: tried and tested protection from Aura, Norton, Experian, and more.

This article was produced as part of TechRadar Pro Perspectives, our channel to 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/pro/perspectives-how-to-submit

You all know what a guitar amplifier is. Big, intimidating stacks of speakers and dials, designed for some nice loud guitar playing. But do they work as well for other people's guitars; i.e., can a big powerful block of music-playing kit double as a home speaker for recorded music?

It may seem like a stupid question, but the more I thought about it, the more I realized that speakers and amps are the same thing... kinda. And for people who don't have the money to buy a guitar amp and a home speaker, could buying the former kill two birds with one stone? I had to find out.

Now, I’m not talking about a guitar amp-esque speaker made by a brand traditionally known for its amps (and before these new Bluetooth speaker things became so jolly well lucrative), such as the Marshall Stanmore IV or the Orange Box. No, I’m talking about a real power-speaker designed for guitars, with jacks and dials galore and no Bluetooth in sight.

Specifically, I’m talking about my Line 6 Spider IV amp, which I have owned for at least half of my life, thus far.

Now, I know Line 6 has a mixed reputation, making it an odd choice to incorporate into a home sound system. Its speakers aren't even for serious guitarists — they're for edgy 14-year-olds without much cash who just want goofy levels of distortion. In my defense, when I bought it, I was an edge 14-year-old without much cash, who just wanted goofy levels of distortion. And it still works fine for what I need.

For the tech-heads among you, my Spider IV offers 75W of power. For the purposes of this article, you should know it has a 3.5mm and a 6.35mm input, and I played music from my turntable as well as some Edifier speakers (the same set-up as in my recent article about setting up a new turntable, so an Edifier M90 and Sony PS-LX310BT). The amp was getting incorporated into an existing hi-fi system, not replacing it.

The other important point of context before we get started is that I had to make a conscious effort to turn off my brain before undergoing these experiments. In many parts of my rational brain, I could tell that a guitar amp wouldn't function smoothly as a speaker; they're not designed to hit a wide range of frequencies, just the ones guitars play at, and they're not intended to be connected to a chain of other players. If I switched off the scientific part of my cranium, I could just mess around with wires, have some fun, and see what happened.

Turning my amp into a subwoofer

(Image credit: Future)

With my first experiment, I decided to start small: I’d see if my amp could function as a subwoofer for my Edifier M90s. I wanted to use its wattage just to give me some extra 'oomph', even though it’s not a bass amp.

First, I used a 3.5mm to 3.5mm cable to connect the M90s, from their Sub Out port, to the amp’s smaller input… and nothing happened. I couldn’t hear a peep from the Line 6, though oddly, the amp’s volume control adjusted the output from the speakers.

Next, I took the risky move of using a 3.5mm to 6.35mm to connect the Sub Out port to the amp’s primary input — risky because I was using an unbalanced stereo cable across devices really not meant for this use, and I wasn’t sure what was going to happen.

I turned the Line 6’s dials all the way down, plugged the cable in, and popped on some Morgan Wallen before slowly turning the volume up.

The resulting speaker sound is best described as ‘rhythmic, distorted pulses’: I was getting something, but it sounded more like asthmatic coughs than music. The amp was clearly connected, and outputting something, but it was just rhythmic fuzz. I suppose that's the Line 6 effect.

I spent quite a while fiddling with the dials, but it was impossible to reduce the drive, while also retaining enough volume for the amp to be audible when I wasn’t putting my ear to it. So it wasn't good for most music, but turn the volume up while listening to a rock track, and the pulses sounded like the strums of a really keen rhythm guitarist trying to play along. This was pretty nostalgic for me, because when I bought the Line 6, that's basically exactly what I was doing.

I tried the same experiment with my turntable, which was connected to the Edifiers via Bluetooth, and found the same result, albeit watered-down.

How about as a giant aux speaker?

(Image credit: Future)

Perhaps I was trying to run before I could walk? For my next experiment, I decided to make things a bit more simple. I connected the iPad I was using as a music player, to a USB-C to 3.5mm converter, and then plugged in the aforementioned 3.5mm to 6.35mm cable which led to the guitar amp’s primary input.

I could hear music! I mean, if you could call it that. The Line 6 not being a bass amp became patently clear here, because I was basically just getting treble. It was surreal; I've tested speakers and earbuds with barely any bass, but never any with literally no low frequencies to speak of.

Plus, a weird phase effect was causing noticeable oscillations in the music, providing a powerful attack but fast fade. It was most audible in Post Malone’s voice (I’d moved on to Post Malone at this point) – it sounded like he’d learnt Mongolian Throat Singing.

The resulting sound was odd, and didn't match even the affordable Tribit Stormbox Micro 3 I use in the shower, but at least the guitar amp gave me some control over it: I could use the dials to add a bit of warmth, and some fuzz to mask the lack of bass.

Then I had a brainwave: I returned to the 3.5mm to 3.5mm cable from the first section, and music sounded much better. There was finally some bass (not much), and Malone was back to his regular singing voice. It was the closest to a speaker I'd managed to bring the amp.

It wasn’t perfect, though: Line 6 amps can sound quite tinny, and I felt like I was listening to music on some cheap earbuds you get free with a flight… just 75W ones that'd annoy the downstairs neighbor.

Let's add some drive

(Image credit: Future)

Obviously, the thing missing from my set-up was more things, so I whacked out a Pigtronix Fat Drive to add to the mix. You can probably tell what this does; it’s obviously a vital addition to an amp which is slated for offering nothing but drive.

Here’s how the set-up looked now: a USB-C to 3.5mm converter plugged into my iPad, leading to a 3.5mm to 6.35mm cable, which was plugged into the Fat Drive pedal, which was connected to another 6.35mm to 6.35mm cable, which led into my amp. That music was getting quite a few air miles before being played.

You can probably imagine what more drive did to already-too-distorted music; it sounded like it was playing from behind a waterfall. Add to that the phase effect that playing music from the 6.35mm port provided, and it was even more of a write-off than before.

I wasn't expecting sudden audio clarity, though. As with the rest of this piece, it was just a fun experiment to try.

Unfortunately, there was a casualty to this experiment: my Fat Drive pedal conked out the day after the test, and I’ve not been able to revive it. I don’t blame my tests, as much as the several years of misuse and poor management prior to them, but I’d still like to pour one out for the Pigtronix.

Lessons learnt

(Image credit: Future)

Obviously, I didn’t go into this experiment thinking that a guitar amplifier would make for a genuine home speaker alternative. That’s like thinking a pottery kiln is a good microwave replacement; it technically does the same thing, but things are going to get a little crunchy.

But I was hoping it'd turn into a fun, albeit messy, party speaker in a pinch, in case I had people over and suddenly wanted room-filling sound. Sure, I could buy such a device, but given how expensive tech is (and how much space that'd take if I already own an amp), who needs that?

I always knew, too, that some useful features would be missing. My amp doesn't allow for a wireless connection, for example, and it's far from being portable.

The solution is already out there: recent Bluetooth speakers released under the Fender name (though made by by a different company called Riffsound) had an XLR port that doubles as a 6.35mm jack, so you can plug a guitar and use it as an amp, even though it's technically a speaker.

Given how many guitar companies now make consumer tech (Marshall, Fender, Orange; Yamaha does too but it's an everything-music company really), it'd be great to see this kind of feature again. Or, at the very least, some way of blurring the borders between amp and speaker. Not all of us have the space or money for both.

In an era increasingly dominated by AI and automation, it’s still incredible just how much construction work remains manual. The contrast is most stark in data centers – facilities built to power cutting-edge technology, but whose delivery is generally still slowed down by major fragmentation and time-consuming manual tasks.

One of the industry’s biggest challenges seems to be capturing and understanding what’s actually happening on a project. Project managers can spend countless hours walking sites, checking completed work and coordinating contractors operating to different schedules. When documentation falls behind, small issues can go unnoticed and eventually develop into costly delays.

While the industry is finally starting to get to grips with this technology, construction is presenting own on challenges. Autonomy works best within fixed parameters and with a limited number of variables, but live sites offer the opposite – changing plans, moving materials, new structures being built and multiple trades working alongside each other.

Construction is where automation fails – is now the turning point?

These are the exact conditions that have made automation ineffective in construction. But automation isn’t impractical or impossible, it just means vendors will need to focus on the tasks where machines can deliver the best results.

Progress capturing, side documentation and routine inspections are some of the areas where automation could work best, and better still, companies like OpenSpace argue much of this work can actually be done outside of normal operating hours to both reduce disruption, and to reduce exposure to a live and dynamic environment.

Data centers could be a good proving ground for this, with large floor plans, repeatable layouts and intense schedule pressures. With up-to-date data from overnight checks, for example, leaders could assess progress and identify areas that need extra attention.

But collecting that data is only the first step, because the AI running behind the scenes need to be able to interpret site imagery to cross-reference it with site plans, drawings, schedules and other information held across other systems.

In this Q&A, OpenSpace CEO Jeevan Kalanithi explains why construction automation is starting to gain momentum, why ‘good enough’ may indeed be good enough without needing immediate perfection, and how automated monitoring could actually help soften the blow of ongoing labor shortages.

• Construction has traditionally been one of the least automated industries. Why is that beginning to change now?

I actually think the idea that construction is slow to adopt technology is a bit of a misconception. Builders adopt tools that genuinely make their jobs easier – they've just been waiting for technology that understands how they actually work.

Construction is fundamentally different from industries like manufacturing. Every project is unique. Jobsites change every day. Teams are working in environments that are constantly evolving, with dozens of trades operating simultaneously. That's a much harder environment to automate than a factory or warehouse where conditions are highly controlled.

For a long time, most construction software focused on documents, schedules and reports because that's what computers could understand. But construction isn't really a document problem – it's a physical-world problem. The most important information lives on the jobsite itself: what's been built, what's changed, where work is progressing and where risks are emerging.

What's changing now is that AI is becoming capable of understanding the physical world. Instead of asking people to manually document what's happening, AI can interpret images and other real-world data to understand the state of a project. That makes robotics and automation much more practical because they fit naturally into how builders already work instead of forcing them to adopt entirely new processes.

• Robotics in construction has been discussed for years but has seen relatively limited adoption. What factors are contributing to the increased interest in autonomous and semi-autonomous systems on jobsites today?

The conversation has become much more practical.

Ten years ago, people asked whether robots would replace construction workers or build entire buildings autonomously. Today the question is much simpler: How can robots help experienced builders work more efficiently? That's an important shift because construction has always adopted tools that solve real problems.

The biggest opportunities today are around repetitive, time-consuming tasks like documenting progress, capturing site conditions or performing routine inspections. Those activities are incredibly valuable, but they're not necessarily the best use of a superintendent's or project engineer's time.

I think of robotics a bit like the introduction of nail guns. Nail guns didn't replace carpenters – they helped carpenters work faster and more consistently. Robotics is following a similar path. The goal isn't to automate construction. It's to automate specific tasks that allow skilled professionals to spend more time coordinating work, solving problems and making decisions.

We're already seeing that with the robotics companies that we integrate with, each approaching different aspects of autonomy. Rather than trying to solve every problem, they're proving that robots can reliably perform specific tasks that create immediate value on today's jobsites.

• Data centers and other large-scale projects have been cited as promising early use cases for construction robotics. What characteristics make those environments more suitable for autonomous technologies?

Data centers are actually a great example of where robotics and AI can demonstrate value today because they combine three characteristics that work well for autonomous systems.

First, they're relatively structured environments. Compared to a renovation project or an occupied hospital, data centers typically have large floor plates, repeatable layouts and fewer unexpected obstacles, making them easier for robots to navigate.

Second, they're incredibly schedule-sensitive. AI infrastructure is expanding at an unprecedented pace, and every day matters. Owners and contractors need continuous visibility into progress because hundreds of activities are happening simultaneously.

Finally, they're highly repetitive. The same systems and construction sequences occur over and over, which allows robotics to operate more consistently and makes it easier to measure progress over time.

Autonomous data capture is particularly valuable in these environments because it creates a consistent visual record without disrupting work during the day. That gives project teams objective information about what's actually happening on site, helping them identify issues earlier, coordinate more effectively and keep stakeholders aligned.

• Construction sites are highly dynamic environments. What are some of the biggest technical and operational challenges robots face when deployed on active projects?

Construction sites are probably one of the hardest environments you could ask an autonomous system to operate in.

Unlike a warehouse, where everything is designed to be predictable, construction sites change constantly. Materials move. Equipment gets relocated. Walls appear. Doors that were open yesterday might be closed today. You also have dozens or sometimes hundreds of people working alongside autonomous systems, so safety has to remain the highest priority.

There are practical challenges as well. Connectivity isn't always reliable. GPS often doesn't work indoors. Navigation has to account for changing layouts and temporary obstacles that don't exist in more controlled environments.

That's why I think we'll continue seeing supervised autonomy for quite some time. Humans are still remarkably good at adapting to unexpected situations, and construction has plenty of them.

One lesson we've learned is that "good enough" often beats "technically perfect." Builders don't need the most sophisticated robot, they need one that's reliable enough to show up every day, operate safely and consistently, and fit into the way projects already run.

• As robotics adoption grows, what role does visual intelligence play in helping machines understand and navigate the physical world?

Whether imagery comes from a person carrying a 360° camera, a robot, a drone or another autonomous system, collecting the data is really only the first step. The real challenge is understanding what that data means.

AI has become remarkably good at understanding language, but if it's going to operate in the real-world economy, it also needs to understand physical places, physical assets and physical work.

Construction has always had information about what was supposed to happen – drawings, BIM models, schedules and specifications. What's historically been much harder is understanding what actually happened in the field.

That's where visual intelligence comes in. AI can compare what's been built against what was intended to be built, measure progress over time, identify potential issues and surface insights that help project teams make better decisions.

Our philosophy is simple: we don't really care how the data gets collected. Whether it comes from a phone, a 360° camera, a drone, a laser scanner or a robotics platform from one of our robotics partners, our goal is to bring that information together into a common understanding of the jobsite.

Today, our customers have captured imagery across more than 70 billion square feet of construction. That scale creates an opportunity not only to help builders today, but also to help train and validate the next generation of AI systems that need to understand the physical world.

• There's often concern that robotics could replace workers. How are builders currently thinking about the relationship between automation and the existing construction workforce?

I think that's a very understandable concern, but it doesn't reflect what we're seeing on jobsites.

The builders we work with aren't trying to replace experienced people. If anything, they're trying to figure out how to help those people do more.

Construction has faced labor shortages for decades, and demand continues to outpace the available workforce. There simply aren't enough skilled people entering the trades to meet the amount of work that needs to get done. That's especially true as we build more data centers, manufacturing facilities and infrastructure.

Robotics and AI help address that challenge by taking on repetitive work like routine documentation, progress capture or inspections, allowing experienced professionals to spend more time coordinating work, solving problems and applying their expertise.

The best technology doesn't replace people – it amplifies what people are already good at. That's how I think construction will continue to adopt AI-powered tools and robotics. They become another set of tools that help experienced teams make better decisions, work more efficiently and get more done with the resources they already have.

• The concept of "human-in-the-loop" robotics is gaining attention across industries. How does that approach apply within construction environments?

I think human-in-the-loop is going to be the dominant model for construction for quite some time.

Construction is simply too dynamic to expect fully autonomous systems to handle every situation. Something unexpected happens every day – a blocked corridor, a relocated piece of equipment, a new safety barrier, an area that's suddenly inaccessible. Humans are still exceptionally good at recognizing those situations and adapting in real time.

What we're seeing today is a very practical division of responsibilities. Robots handle routine, repetitive tasks consistently, while people provide judgment, context and intervention whenever it's needed.

Some of our robotics partners are already operating this way, where autonomous systems perform most of the work but remain remotely supervised so a person can step in if something unexpected occurs.

I don't think that's a compromise. I think it's actually the right model. The goal isn't autonomy for its own sake. The goal is giving project teams better information while maintaining the flexibility and judgment that complex construction projects require.

• Some contractors are experimenting with autonomous data capture during off-hours, such as overnight or before crews arrive. What lessons are emerging from those early deployments?

The biggest lesson has been consistency.

One of the challenges with manual documentation is that it depends on people having the time to do it. On a busy project, it's easy for documentation to become a lower priority because everyone's focused on solving immediate problems.

Autonomous capture changes that. Robots can document the site on a predictable schedule, often overnight or before crews arrive, creating a consistent visual record every day without interrupting construction activities.

That consistency gives teams a much clearer understanding of how a project is progressing over time. It also makes documentation much more resilient. If a superintendent is tied up or a project engineer is out that day, the visual record doesn't stop. Everyone still has access to current information about what's happening on site.

Capturing a visual record has always been a natural byproduct of walking a site with a 360° camera. It's also a very natural task to hand to a robot. That allows experienced people to spend more of their time interpreting information and making decisions instead of simply collecting data.

• Looking ahead, what indicators should the industry watch to better understand whether construction robotics is moving beyond pilot programs and toward broader adoption?

I don't think the biggest indicator will be the number of robots on jobsites. It will be whether contractors keep using them after the pilot is over.

Construction is a very practical industry. Builders don't adopt technology because it's exciting, they adopt it because it saves time, reduces risk or helps projects run more smoothly. If a technology creates more work than it eliminates, it won't last.

We'll also know the industry has reached the next stage when robotics becomes just another way of collecting project information. Builders shouldn't have to think about whether data came from a person, a robot, a drone or another autonomous system. They should simply have access to an accurate, up-to-date understanding of what's happening on their projects.

Ultimately, I think the story is bigger than robotics. The real-world economy doesn't run on documents alone. It runs on physical places, physical assets and physical work. If AI is going to create value there, it has to understand reality, not just language.

That's why we say agents need eyes. When AI can reliably see, understand and reason about what's happening in the physical world, robotics becomes much more than automation. It becomes a new way for people to interact with the built environment and make better decisions. I think that's the transition we'll be talking about over the next decade.

A relative newcomer in the industry, DataImpulse has grabbed a slice of the proxy pie thanks to its wallet-friendly, high-performing service. From more than 90 million ethically-sourced residential IPs across 195 locations to mobile and datacenter proxies, users gain access to a speedy and reliable global network.

The company specializes in creating custom proxy services for businesses, though individual users who aim to access data from anywhere and collect the information they need will have no problem finding something for themselves.

Arguably, the biggest selling point is a highly competitive, pay-as-you-go pricing model with traffic that never expires. DataImpulse throws in free country-level targeting for every plan (more granular targeting options are also available), and full support for HTTP, HTTPS, and SOCKS5 proxy protocols across all of its proxy networks.

Plans and Pricing

DataImpulse operates on a transparent pay-as-you-go model where traffic never expires. In case you’re unfamiliar with the concept, the bandwidth you buy here is yours until your scrapers actually consume the bytes, whether that takes days or months.

The price structure is based on the type of proxy and the amount of traffic.

For instance, residential proxies start at a flat $1 per GB for a 5GB starter block, and go up to 50GB at the same dollar-to-gigabyte ratio. If you ramp up your scraping to the Advanced tier, the cost drops down to $0.80 per GB for an 800GB package, with custom enterprise pricing scaling down even further for terabyte-scale deployments - though you’ll have to pony up at least $4000 for the option.

Datacenter proxies start at a mere $0.50 per GB for new users, slipping down to $0.45 per GB when upgrading to a larger $450 volume pool. Once again, you can get a custom price per gigabyte.

Gaining access to mobile proxies kicks off at $2 per GB for the entry-level 5GB option. The price goes down to $1.6 per GB for bulk operations, with custom pricing available for truly massive projects.

Finally, premium residential proxies will set you back $5 per GB via 1GB and 10GB plans. And yes, there is a custom option too with a $20k starting point.

It’s worth mentioning that DataImpulse doesn’t play favorites with its plans. Each includes free country-level geo-targeting, rotating and sticky sessions, HTTP(S) and SOCKS5 support, full programmatic API access, 24/7 support, and unrestricted concurrent threads. There are no overage penalties or hidden connection fees, which means your infrastructure bill will match your actual code execution down to a T.

Features

If you decide to give DataImpulse a go, your first step will be to register, either the old-fashioned way or through your Google, GitHub, or LinkedIn profile. A classic dashboard layout will greet you, where all the important stuff is located in the left sidebar.

(Image credit: DataImpulse )

The usage chart and details will be your starting point. Once you settle on a plan, you can begin with the proxy configuration by setting the targeting options down to the ASN (Autonomous System Number) and IP rotation interval, which you can save altogether as a dedicated configuration.

You have to choose between a sticky or rotating proxy, as well as the protocol in charge. Sticky proxies are port-based (IP addresses are bound to a specific port for a specific timeframe), and the rotation interval can range from 1 to 120 minutes. Rotating proxies change the IP address automatically with each new request.

From here, it’s also possible to top up your balance by choosing the number of GBs you want to add in the event you need more traffic.

Here’s a more detailed look at DataImpulse’s offering:

Residential proxies

Arguably the workhorse of the platform, the standard residential proxy pool provides on-demand access to a sizable footprint of over 90 million active IP addresses across 195 countries. DataImpulse proudly markets its pool as entirely ethically sourced, which means all nodes join the network legally and transparently.

As a result, the IPs maintain a pristine reputation score and are drastically less likely to be blacklisted by major CDN platforms. In other words, when your script routes data through these nodes, target web servers view the connection as an ordinary home user browsing a site, rather than an automated bot. In our tests, residential proxies delivered a consistently high scraping success rate.

Premium residential proxies

DataImpulse labels these as ultra-responsive IP addresses with minimal latency that deliver better reliability and security compared to regular residential proxies. So, you should see faster connection speeds and more precise geo-targeting, especially considering you get full customization options to tweak your scripts.

Moreover, upgrading to the premium residential line significantly reduces the risk of encountering unexpected access blocks or sudden CAPTCHAs. Since this is a premium offering, it includes additional operational perks, such as a dedicated account manager to help optimize your efforts and resolve any doubts or issues.

Datacenter proxies

When targeting open public directories or basic landing pages (basically, anything that isn’t buffed up with sophisticated anti-bot frameworks), residential IPs can be an expensive overkill. To that extent, DataImpulse offers 20 million datacenter IPs in 195+ countries to tide you over in a cost-efficient manner.

With a response time of less than 100 milliseconds and the fact that these IPs connect directly to high-speed enterprise data systems, this tier is built for pure speed and instances where you need to process thousands of requests per second. You can also configure your IP address to be active for up to 30 minutes.

Mobile proxies

For the most challenging anti-bot systems, like social networks or review platforms, DataImpulse provides rotating and sticky mobile proxies. Pulling from a pool of over 16 million real 3G, 4G, 5G, and LTE cellular towers across 195 locations, these carry a distinct security advantage due to the same public-facing IP to thousands of mobile devices simultaneously.

Hence, web firewalls seldom block a mobile IP address outright since doing so would mean blocking hundreds of legitimate human users on that cell tower network. The largest pool of DataImpulse’s mobile IPs is in India (1.57 million), Saudi Arabia (444k), Italy (257k), Morocco (239k), while the United States is covered with 129k IPs. You can have a single address remain active for up to two hours.

Traffic that never expires

Perhaps the defining USP separating DataImpulse from a chunk of its competitors is the fact that your purchased pay-as-you-go traffic never expires.

In most cases, you’ll wind up with monthly billing subscriptions or restrictions on how long your traffic is valid for. If you have unused bandwidth, it’s gone for good. With DataImpulse, it remains completely active until your crawlers actively consume the bytes. Having peace of mind that you can use data as long as you need without worrying about losing access makes the model a great fit for small businesses and dev teams with limited budgetary and operational width.

Scraping

Unlike some of its peers, DataImpulse leans heavily into a developer-first, DIY network architecture. This means there is no web scraping API, as the vendor’s focus is solely on providing raw proxy connections, rather than managed scraping tools. You must write your own code to manage requests, parse the HTML, handle retries, and bypass CAPTCHAs.

On the plus side, such a model brings financial and structural flexibility for teams writing custom data collectors. By routing scripts through DataImpulse’s proxy layer, they can plug the proxies directly into any automation framework or headless browser engine using basic credentials or token structures.

(Image credit: DataImpulse)

In that regard, DataImpulse provides a Gateway API to programmatically manage, authenticate, and automate the provisioning of their global proxy pools. From there, you can dynamically generate proxy lists, rotate IP addresses, track bandwidth consumption and request volumes, programmatically set advanced targeting filters, and more.

There are also comprehensive integration blueprints for popular developer frameworks and third-party apps. Some of these include Scrapy, Puppeteer, Selenium, Shadowrocket, Multilogin, AdsPower, Playwright, and Zapier, to name a few. The platform also provides short code snippets for Python, Node.js, PHP, C#, Go, Ruby, and cURL to set up proxies. All of the above is neatly documented with built-in support and code examples.

Advanced geo-targeting and session controls

Country-level geo-targeting across all 195 covered regions is built natively into the base $1/GB price floor with zero add-on activation fees. For more accurate data parsing, there are State, City, ZIP code, and ASN filter toggles.

When it comes to rotating sessions, the DataImpulse gateway dynamically inserts a randomized IP address on every network request your scraper submits. For standard HTTP/HTTPS traffic, you simply point your scripts to gw.dataimpulse.com on port 823. In case your stack relies on the more robust SOCKS5 protocol to handle raw TCP tunnels or high-performance scraping, you use port 824.

For situations where your code requires session continuity (like managing a social profile or handling account-tied logins), you can bind your traffic to a dedicated port range anywhere from port 10000 through 20000. Doing so locks a single IP address to your active thread for up to 120 minutes, though the system gracefully defaults to a 30-minute window if no interval is set.

Ease of Use

There’s not much to say here. Most importantly, the dashboard allows you to move effortlessly around via a single-page command center tailored for fast deployment. That said, the arrangement of certain sections could be better.

For starters, the section ‘Resources’ that features the onboarding guide is tucked away in the upper right corner of your respective plan, instead of being the first thing you see. The same place holds the links for documentation and tutorials.

Then, the ‘Proxy Locations’ section provides a table overview of supported countries for each of the four types of proxies available on DataImpulse. There’s no additional functionality or details to it, which feels like a missed opportunity.

(Image credit: DataImpulse)

Acting as a de facto homepage, there is a filterable real-time line chart that plots your request volume across custom date ranges and breaks down traffic metrics by total requests, charged traffic, and total spend.

The dashboard also logs individual session activity in an audit table showing the timestamp, targeted host, consumed traffic, and error/success states, with the option to export this data directly to a CSV report.

In terms of generating your target connection parameters, the dashboard provides a straightforward copy-paste system where you can grab your master username and password strings.

You can also manage whitelisted IPs via a simple menu. Just save your scraping server's public IP address within your dashboard profile, and the gateway will automatically recognize your incoming traffic tunnels, thus shaving valuable milliseconds off your script's execution times.

Customer Support

One of the things DataImpulse singles out is its 24/7 personalized, human support. The company proudly mentions that you’ll get an answer in under 3 minutes, courtesy of an ever-present chat icon in the lower right corner. Our experience was no different - we got a tidy reply in less than a minute.

If you prefer self-service support, the company provides a thorough Documentation page. Here, you’ll find various technical reference materials, API endpoints, authentication methods, and basic proxy setup instructions.

(Image credit: DataImpulse)

Covering the more practical elements is the Tutorials page. It acts as an extensive step-by-step engineering handbook, packed with detailed screenshots, videos, configuration blueprints, and copy-pasteable code snippets for integrating proxies across dozens of operating systems, scraping frameworks, and headless browsers.

For customers deploying huge data operations at the custom enterprise level, DataImpulse boosts its support to include dedicated account managers and custom feature engineering tailored specifically to the customer’s unique data requirements.

The Competition

Truth be told, proxy services are tough competition. DataImpulse has robust providers like Bright Data, Oxylabs, and Decodo playing in the same field, though its unexpiring pay-as-you-go traffic and a highly disruptive $1 per GB residential proxy baseline certainly make it stand out. Nonetheless, the main problem is a lack of out-of-the-box software tools or any sort of target templates, leaving all the coding execution to your team.

Final Verdict

If anything, DataImpulse proves you don’t have to pay a premium to get access to a reliable and ethically sourced proxy network. In no small part thanks to its price tag and data that never expires, the company has created an accessible, developer-first platform that is finely tuned to the needs of individual developers, startups, dev squads, and growing small businesses.

So, if you have the know-how and are looking to drastically slash data collection costs, DataImpulse will more than suffice. But, if you’re not much of a technical user and require a fully managed, hands-off scraping solution, it will likely be too bare-bones for your liking.

• Take a look at our guide to the best proxy services

AI has sparked a race to build ever more powerful infrastructure, with headlines dominated by GPUs, networking and energy demand.

But behind the scenes, another part of the technology stack is undergoing an equally significant transformation: storage.

As AI models grow larger and enterprises retain more data for training, inference and retrieval, storage is no longer simply where information resides.

It has become an active part of AI infrastructure, responsible for feeding compute efficiently, supporting vast datasets and helping organizations balance performance with cost.

That shift has fundamentally changed what primary storage looks like in modern cloud environments.

Historically, primary storage meant tightly coupled block or file systems sitting close to compute.

Today's hyperscale cloud providers have taken a different approach, treating object storage as the persistent system of record while software coordinates how data is stored, protected and accessed across distributed infrastructure.

The move to cloud computing

The move to cloud computing didn't simply increase the amount of storage organizations needed. It fundamentally changed how storage had to work.

Traditional enterprise storage was built around tightly coupled systems where applications interacted directly with file systems. That model worked well when infrastructure was relatively contained. At hyperscale, however, coordinating millions or billions of files across distributed environments introduces complexity that limits scalability.

Cloud providers responded by redesigning storage around software-defined architectures that separate how data is managed from where it is physically stored. Rather than treating storage devices as isolated resources, modern cloud platforms orchestrate entire fleets of storage through software, allowing them to scale far beyond the limits of traditional enterprise architectures.

AI has only accelerated this transition. Training large models, serving inference and retaining ever-growing datasets all place sustained demands on shared infrastructure, making software-defined storage more important than ever.

Although every hyperscale platform has evolved differently, the same architectural ideas appear time and again. Four principles in particular have become fundamental to supporting AI at scale.

1. Object storage is designed around sequential data movement

Traditional cloud storage systems frequently modify files in place. At hyperscale, continually updating data across distributed environments becomes increasingly difficult to manage.

Object storage takes a different approach. Rather than overwriting existing data, new versions are typically written as separate objects. That naturally favors large, sequential data flows, allowing storage systems to operate more efficiently as datasets continue to grow.

As AI workloads generate larger datasets, frequent checkpointing and continuous data movement, designing storage around sequential throughput becomes increasingly important.

2. Metadata has become a software challenge

As organizations store billions or even trillions of objects, tracking where everything lives becomes just as important as storing the data itself.

Rather than asking storage devices to manage both data and metadata, cloud platforms increasingly separate these responsibilities. Dedicated software layers handle object locations, namespaces and system coordination while storage media focuses on delivering scalable capacity.

Separating these functions makes infrastructure easier to scale while allowing storage systems to concentrate on what they do best: storing vast amounts of data efficiently.

3. Resilience comes from architecture, not individual devices

Cloud providers assume that, somewhere across thousands of servers and storage devices, failures will happen. Rather than relying on individual hardware to eliminate every fault, modern architectures distribute data intelligently across large storage pools.

Techniques such as erasure coding allow platforms to recover data efficiently while maintaining availability and reducing the overhead associated with traditional replication strategies.

The result is infrastructure that remains resilient even as AI workloads continue to grow in size and complexity.

4. Intelligent data staging protects performance

AI rarely produces smooth, predictable storage workloads. Training jobs, inference requests and application traffic often arrive in bursts, placing sudden pressure on infrastructure.

Rather than writing every request directly to capacity storage, cloud platforms increasingly use flash and memory as staging layers. These absorb incoming traffic before organizing data into larger, more efficient writes.

This allows organizations to maintain responsive applications while making better use of high-capacity storage as AI workloads become increasingly demanding.

Software has become the defining layer

Taken together, these four principles point to a broader industry shift. Storage performance is no longer determined solely by faster hardware. Increasingly, it is software that decides how efficiently infrastructure performs by orchestrating data movement, coordinating metadata and balancing workloads across distributed systems.

That evolution has fundamentally changed the role of primary storage. Rather than serving individual applications or servers, storage increasingly underpins shared object platforms that support analytics, cloud services and AI workloads simultaneously.

In other words, primary storage is no longer defined simply by where data resides. It is defined by how effectively software enables that data to move, scale and remain available across distributed infrastructure.

A new definition of primary storage

The rise of AI has accelerated changes that cloud providers have been making for years. Primary storage is no longer defined by proximity to compute or by storage hardware alone. Increasingly, it is software-managed, globally distributed and designed to balance performance, resilience and economics at enormous scale.

For organizations investing in AI, storage decisions can no longer focus purely on capacity or latency. Understanding how software, data movement and storage media work together has become just as important.

The organizations that succeed won't necessarily be those deploying the most hardware. They'll be those that build storage architectures capable of feeding AI workloads efficiently while keeping infrastructure scalable, resilient and economically sustainable.

In modern cloud environments, primary storage is no longer simply where data lives. It has become one of the technologies that determines how effectively AI can scale.

We've reviewed, rated, and ranked the best business cloud storage.

This article was produced as part of TechRadar Pro Perspectives, our channel to 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/pro/perspectives-how-to-submit

Having been put on Konami’s backburner for over a decade, Silent Hill’s resurgence has been nothing short of extraordinary.

A tepid start with Ascension and The Short Message was counterbalanced by the excellent Silent Hill 2 remake from Bloober Team and the homegrown Japanese horrors of Silent Hill f from NeoBards. The series seems like it’s now heading further in the right direction with the next game, Silent Hill: Townfall.

Early this month I played three hours of Silent Hill: Townfall and spoke with both Screen Burn director Jon McKellan and Konami series producer Motoi Okamoto about the game’s approach to retro tech, the Scottish setting, and cultural authenticity.

Going retro

(Image credit: Konami)

Silent Hill: Townfall follows a man named Simon Ordell in the fictional town of St. Amelia, Scotland. As he wakes up, he finds the entire place shrouded in fog. Slowly walking around, I eventually pick up a pocked CRTV television, which is used for both gameplay and narrative reasons. It's Townfall’s primary mechanic and star of the show. Through it, you can see old static video recordings of Simon’s mysterious partner, Zoe Ellis, who is seemingly his connection to this island.

The radio’s screen also typically shows you the next place to go, which means it’s up to you to search around town until you find the correct spot. Additionally, you can hold up the radio to see through walls to check a monster’s position. It’s a wonderful twist on a common trope. In similar games, protagonists sometimes come with a ‘radar’ sense so that players can stay stealthy and avoid enemy detection. Implementing the radar into the radio itself is not only clever, but reinforces that Simon is, after all, just human, in an unrelenting world filled with monsters.

The time period that Townfall takes place in is paramount to its horror themes. A puzzle hints that the current year is 1996, which is where the mass adoption of technologies like the internet and home dial-up started happening. The transition to an increasingly connected world filled people with anxieties. Simon’s CRTV radio is just advanced enough, but is nowhere near the cutting edge technology we see today.

“The world was on a technological cusp around the mid 90s, where the internet was starting to become a thing. But you were too early to have Google Maps in your pocket and you didn't have this magic bullet for all the problems that might come up,” McKellan explained.

He continued, “So with analog tech, you feel like you can't quite rely on it. It’s a little bit uncertain, and I think that's a great place to be for horror.”

Another theme that was prominently was medicine and healthcare. As I strolled through St. Amecia, I saw protest signs littered around the town square with phrases about “taking this town back”.

I picked up collectibles and notes about St. Amecia’s local hospital, vaguely hinting at some sort of institution providing “protection.” As an American citizen in a country with mainly for-profit healthcare, my mind immediately began trying to connect the dots and figure out the story’s direction. Scotland has a public system, but perhaps some private healthcare provider came in and tried to take over? And what about the “protection” aspect? Did Simon, or someone else, snitch?

The medical symbolism doesn’t end there. Zoe herself is a nurse, and Simon also has an IV bag strapped to his arm. With ingredients found scattered throughout town, he can craft items Resident Evil-style. These include items like blood bags for his IV, which let him resurrect with a sliver of health after being killed by an enemy.

Thankfully, Townfall’s combat feels much smoother compared to Silent Hill f’s. In the latter, there always felt like a second of awkward hit stun when your melee weapons made contact with enemies. Here, smashing planks over their heads feels satisfying. The way their bodies all of sudden go limp simultaneously gives me a feeling of surprise relief every time.

Simon also eventually gets a powerful pistol that one-hit kills many monsters, rivalling even Grace Ashcroft’s Requiem from Resident Evil Requiem. The downside is that it makes a ton of noise when fired, so enemies nearby will zero in on your location. With limited ammo, it’s definitely your last resort weapon, and it’s gratifying every time it goes off.

McKellen ensured that social commentary would be at the heart of the story, similar to how Silent Hill f’s overarching message was around gender expectations. “There are a few different threads in this game that start to coalesce as the game progresses, and I think it takes people in kind of unexpected places,” he explained. McKellen is hoping that people (like me) have an idea of what’s happening, then excitedly twist our expectations.

So why does Simon have a cannula in his hand, and why does it bring him back when he dies? They’re not just gameplay gimmicks—they represent something. McKellen adds, “There's a lot behind it, and we pride ourselves as developers that everything in the game has some relevance. Nothing is there just because or to fill a void.”

Crafting different Silent Hills

(Image credit: Konami)

Over the years, the setting for most of the Silent Hill games were, well, Silent Hill. But recently, Konami has expanded settings for the series to explore other areas. Silent Hill: The Short Message introduced “Silent Hill” as a phenomenon, where similar psychological and experiences occur outside of the titular town in Kettenstadt, Germany. Silent Hill f took the series back to its hometown in the fictional town of Ebisugaoka in 1960s Japan.

“Our current approach in branding is to introduce that concept and show similar types of psychological experiences and narratives that place outside of the boundaries of Silent Hill,” Okamoto said.

Screen Burn, headquartered in Glasgow, Scotland, has a wide age range within its developers. Some grew up with the older games, while others recently discovered it through the newer releases. But the big lesson that the team learned about what’s essential to Silent Hill’s DNA is the understated nature of the stories. They’re not explicit, with plenty of room for interpretation but still feeling satisfied after finishing the game.

“You can walk away from a Silent Hill game feeling like you know something, but whether you know everything or need to discuss that with someone else was one of the big things that came out,” McKellan explained. “People were saying that after every game they would end up talking to someone else about it and having different theories.”

(Image credit: Konami)

The studio’s last game was the sci-fi horror adventure Observation (which is also one of my favorites that you should check out), and the team grew from the game before that, Stories Untold. Both of them had technology as a major theme to convey gameplay, with Stories Untold having players interacting with computers and Observation switching between different cameras in a space station.

You can see the theme of technology rearing its head again in Townfall through the analog CRTV, as well as the phone booth players call to manually save their progress.

Townfall is also Screen Burn’s biggest game to date. The most notable departure is the freedom that players have in exploring the town. “Townfall feels like a Screen Burn game unleashed. We’re taking our narrative sensibilities, puzzle design, and attention to detail, and blowing that out to something much larger with the support of Konami and Annapurna,” said McKellan.

“So if you've played our previous games, you'll probably see a lot of common ground there.”

The future of Silent Hill

(Image credit: Konami)

Looking ahead, Bloober Team’s remake of the first Silent Hill is set to come out after Townfall. Okamoto understood the worry players had about the series potentially becoming annualized, perhaps resulting in fatigue or lapse in quality. That's why Konami was open to new takes on the iconic horror franchise. The Short Message focused on social media, Silent Hill f was distinctly Japanese, and now Townfall is in Scotland.

With the rising cost of game development, it’s a wise idea to have other studios take on notable IP rather than just having your internal studios handle them. Not only does this result in more games being released, but outside talent can revitalize interest. I mean, Bloober Team and NeoBards are proof enough.

Okamoto, however, explained that this method was more about portraying cultural authenticity than anything else, and dependent on the kinds of projects Konami is working on.

“For example, the Metal Gear team has their own philosophy and approach for how they develop games,” Okamoto said. “At least from our perspective, with the goal being trying to create a wider, richer, and more culturally varied universe for Silent Hill, we feel that partnering with third-party developers works best.”

Silent Hill: Townfall launches on September 24 for PC and PlayStation 5.

With all the talk of superchips driving AI inference in the data center and the likes of the Spark RTX architecture bringing Windows AI capabilities to unprecedented heights, it’s easy to overlook how much room there is for improvement in some of the most challenging device classes when it comes to power management: IoT and wearables.

For products like environmental sensors that may have to relay information from remote areas, a battery life measured in years can be essential since. On the other hand, small wearables such as rings, watches, and pendants require smaller batteries while their biomarker monitoring functions

Two of the largest SoC vendors, however, have taken aim at one of these markets with major upgrades to their product lines aimed at them.

Genio gears up for physical AI

Mediatek has long targeted the IoT market, which includes a mélange of applications such as digital signage, smart city sensors, retail, and industrial robotics. The last of those is increasingly turning to physical AI, where the intelligence of the machines can match their ability to lift and move items.

The company seeks to accelerate the market with its new Genio Pro 5100 IoT chip, which is built on a state-of-the-art 3 nm process, combines CPU performance that rivals that of Intel’s Core chips, ARM’s Immortalis GPU architecture, and a powerful NPU.

With many IoT applications focused on analyzing images, the chip can support up to 16 full HD camera inputs or or two 4K inputs (both at 30 frames per second) as well as Vision Langauge Models optimized for the platform.

Robotics is one of the fastest growing applications for these kinds of chips. MediaTek is supporting both Linux and the ROS (Robotics Operating System) for applications in factory automation, transportation and logistics, and hospitality and intends to support the line for seven to 12 years.

While the Genio Pro doesn’t integrate wireless connectivity, modules are available for Wi-Fi and Bluetooth as well as cellular, including low-power 5G RedCap.

A new breed of wearables

Qualcomm has been active in the wearables market for years (and even once released its own smartwatch called Toq, although that was intended more to highlight its since-abandoned Mirasol display technology).

Variants of its W5 chip have powered the last few generations of Pixel watches. The company’s new Snapdragon Elite represents a significant jump in performance from the previous W5, which, like MediaTek’s current-generation Genio IoT chip, will stay in the market for more cost-sensitive devices.

The new SoC, which like the Genio Pro is based on a 3 nm process node, boasts improvements in four main areas: micropower connectivity, low-power islands, performance, and user experience. With wearables expected to instantly apply AI processing to inputs that surround us, Qualcomm has implemented a dual-NPU architecture.

The main NPU can handle the same level of token processing as a two-year-old smartphone (about 18 to 20 tokens per second for a roughly 1-billion parameter model). However, a new eNPU is designed to take on tasks that require fast responses like voice activation and active noise cancellation while allowing for days of usage between charges.

(Image credit: Future / Cas Kulk)

The new chip also supports Qualcomm’s micropower Wi-Fi (which uses 80% lower power than the previous generation) as well as the low-power 5G RedCap cellular standard and satellite connectivity.

This flexibility is intended to help drive the next wave of wearable applications such as health and fitness coaching, lifeblogging, on-device translation, and USB-based authentication using standards such as Aliro, a sister standard of Matter.

At Mobile World Congress, Samsung, Motorola, and Google announced support for the Snapdragon Wear Elite, with the first noting that the new chip will be used in at least one version of the Galaxy Watch 9.

However, we should see far more devices based on both company’s leaps in 2027 and beyond, bringing new levels of intelligence to products that are constantly with us and many that work invisibly on our behalf.

• Just 27% of Gen Z respondents use antivirus software for mobile devices, and are less likely to regularly change passwords
• 57% of Gen Z spend more time online than offline, Kaspersky finds
• And only 28% regularly backup important personal data stored on their phones

Gen Z has no awareness of cybersecurity, online safety principles, or the risks of not changing your password. With just over half (52%) of respondents admitting they have had devices, data, or online accounts attacked, only 27% actually employ standard countermeasures like mobile antivirus tools.

In a study of 7200 respondents from 18 countries, the company also found only 28% regularly backup important personal data stored on their phones.

An average Gen Z-er appears to rely almost completely on their smartphone, which is where they store the information they value most, apparently without cloud backups or syncing in place. The survey has shown that social media accounts have been hacked (17%) and gaming accounts lost (12%), but smartphones have further risks to personal privacy if the correct precautions are not taken.

Gen Z needs to appreciate the risks

Access to personal photographs, identity documents, email, financial details, and of course social media profiles can be acquired via a compromised smartphone, opening the victim to a host of targeted attacks. Direct financial attacks can be made, identity theft, and more, depending on how successful the attacker is. Keeping the device out of an attackers reach, rather than inadvertently sharing its contents, is the safer course of action.

Irina Ermilova, Vice President for Consumer Product Management at Kaspersky, looked to address the apparent disconnect between a generation that has grown up with internet access and portable digital tech, and its lack of cybersecurity nous.

“Gen Z has grown up online, so digital services often feel intuitive and familiar to them. However, familiarity should not be confused with security expertise," she noted. "Being able to navigate apps, platforms and devices confidently does not necessarily mean being able to identify scams, manage passwords securely or protect personal data.”

“The findings show that cybersecurity tools and habits need to become as natural part of everyday digital life as messaging, gaming or using social media.”

Training Gen Z to find digital threats

(Image credit: Kaspersky)

Looking for a solution to this lack of cyber-risk awareness, Kaspersky has launched an interactive online game aimed at Gen Z users. Case 404 is a "cyber-detective adventure" in the point-and-click mold, set in the future with fictional cases that have been inspired by actual digital threats.

In playing the game, Kaspersky hopes that Gen Z users will spot the scams and phishing attempts, and take that knowledge with them into the real world and stay safe and secure online.

• AMD and Cerebras will split inference across two machines, with Helios racks handling prompt processing and the Wafer-Scale Engine generating tokens, available through Cerebras Cloud in H2 2026
• Nvidia is also doing something similar by licensing AI chip startup Groq's SRAM decode technology for $20 billion
• The move sees AMD and Cerebras claim 5x higher tokens per watt versus a standalone Cerebras WSE configuration

AMD and Cerebras Systems have announced a technical partnership which pairs the former's Helios rackscale system with the latter's Wafer-Scale Engine in what both companies call a disaggregated inference solution.

The move has enabled a combined AMD Helios and Cerebras WSE configuration to deliver up to five times the tokens per second per watt (TPS/W) in internal testing by both chip designers.

The move aims to address a Cerebras WSE efficiency challenge by offloading prompt processing to AMD's rackscale offering.

An efficiency gains-centric play?

Both AMD and Cerebras Systems are painting the news as a win, and it very well might be, given the latter's efficiency gains in play and the former's ability to get access to SRAM decode technology without spending the $20 billion Nvidia shelled out at the end of last year for a non-exclusive deal.

It must, however, be noted that the efficiency claims of 5 tokens per second per watt are compared against an existing Cerebras WSE (Wafer-Scale Engine) as the baseline, while running the open-source Kimi 2.6 1T model, making them impressive, but without a direct comparison to figures for an Nvidia rackscale offering, one that lacks context, especially when efficiency is the metric.

The idea itself is sound and well established in the industry, with WSE known to struggle with the 'prefill' part of the equation while handling the 'decode' segment relatively well, essentially substituting AMD's hardware where Cerebras' equipment falls short.

The choice of Kimi 2.6, however, deserves a second look. Moonshot AI's model, released on 20 April 2026, is a mixture-of-experts design with one trillion total parameters but only 32 billion active per token, and it ships natively in INT4. At INT4, the full weight set runs to roughly 500 GB. A single Cerebras wafer holds 44 GB. Even before KV cache, a Cerebras-only deployment needs somewhere north of a dozen wafers just to hold the model, while one Helios rack could hold it around sixty times over.

That asymmetry means the five-times figure is measured on a model that is close to the least favorable for a WSE-only configuration. A dense model small enough to sit resident on a handful of wafers could flatter Cerebras considerably more. None of this makes the number wrong, but it does make the case for additional testing to demonstrate both its strengths and weaknesses for different models.

A partnership without numbers, for now

More importantly, the absence of any financial information might very well be a future story, especially at a time when there are increasing concerns about 'circular financing' in an industry where Nvidia's recent move to backstop OpenAI's data center purchases was seen as a net negative by Wall St, which is already concerned about AI spend and the sustainability of such transactions.

AMD has also, in the past (and more recently with Anthropic), linked purchases of its own hardware to investments or stakes it would take in AI companies, moves that the market welcomed earlier but might view with a bit more hostility lately.

The announcement comes at a time when Cerebras might need it more than AMD: Cerebras listed on Nasdaq in May, priced at $185, opened at $350, and closed its first day at $311.07 before falling back to around $227 by late June 2026.

AMD stock, on the other hand, is up 121.48% year-to-date (YTD) as investors continue to bet heavily on its new Instinct AI processors, and the Cerebras partnership allows it to further consolidate its gains, as this might be seen as another vote of confidence in its current direction by one of its prospective customers.

• IVCM petition asked Imperial County judge to overturn IID's refusal to sell it roughly 287 million gallons to cool a proposed 330 MW data center
• The company pledged in writing as recently as February that the project would not touch Colorado River water
• The case turns on whether fallowed farmland water can be reassigned to industry, a precedent worth far more than the volume in dispute

A recent petition filed in Imperial County Superior Court has asked a judge to order a public utility to start selling water to the largest data center project in the state of California.

The volume at issue is modest by the standards of the Colorado River: 880 acre-feet a year, which the petition itself calculates at ~0.03 percent of the Imperial Irrigation District's 3.1 million acre-foot entitlement.

The legal precedent that it would set if such a motion was to be granted might however have far-reaching implications beyond the relatively minuscule requirement that Imperial Valley Computer Manufacturing (IVCM) is currently gunning for.

A prolonged dispute over water that is exacerbated by who the customer is

The Imperial Irrigation District, a local agency that supplies water from the Colorado River in Imperial Valley, has declined IVCM's request to provide approximately 287 million gallons of water for its upcoming 330MW data center, the largest in the state.

The developer, Sebastian Rucci, spoke to Business Insider, stating that the project would not add to demands on the Colorado River as it would effectively stop irrigating nearby farmland to balance its consumption, calling it a "zero impact" situation.

IID rejected the application on May 1, 2026, nine days after it was submitted. The stated ground was Regulation 21, which governs small-parcel service and bars new connections within 300 feet of an accessible potable water supply; the district redirected IVCM to the City of Imperial.

With the City of Imperial already locked in a legal battle with IVCM over the very existence of the $10 billion project, citing inadequate public notice and lack of compliance with the California Environmental Quality Act (CEQA), it is unlikely to be a place where the data center builder is going to find any relief, and it has turned to courts of law to get what it feels is its fair share.

(Image credit: Microsoft)

The subsequent legal action by IVCM might be a litmus test for how such interactions could unfold in the future, at a time when there is considerable blowback from communities living near such data centers, who see them as resource-greedy and driving up water and power prices, especially in drought-stricken regions.

The developer's plan to "buy and dry", as per Michael Cohen, a senior fellow at the Pacific Institute focusing on Colorado River Basin water use, might actually have made matters worse, with it being seen as detrimental to jobs in the area even as individual landowners profit from the exercise.

The water suit is one of at least three fronts, and arguably the least immediately consequential.

The City of Imperial's CEQA challenge to the project's exemption is pending. On June 16 2026, the county imposed a 45-day moratorium on data center approvals; on July 14 it extended that to a full year, blocking permits until June 2027 while an advisory committee rewrites zoning rules.

Rucci called the first moratorium defective and sought a restraining order against it, and has said he will challenge the second. Even a clean win on the water petition would result in supply permission for a facility the county currently cannot permit.

What is being litigated is whether an irrigation district chartered to serve farms can lawfully decline to serve an industry, and whether fallowing counts as conservation when the county is the buyer but not when a data center is.

One thing is for certain: the ruling will be read closely by every developer eyeing the West's agricultural water, which is roughly what the valley is afraid of. The gallons are a rounding error on the Colorado, but the precedent it sets here may determine everything for the region.

Looking for a different day?

A new Quordle puzzle appears at midnight each day for your time zone – which means that some people are always playing 'today's game' while others are playing 'yesterday's'. If you're looking for Tuesday's puzzle instead then click here: Quordle hints and answers for Tuesday, July 28 (game #1646).

Quordle was one of the original Wordle alternatives and is still going strong now more than 1,500 games later. It offers a genuine challenge, though, so read on if you need some Quordle hints today — or scroll down further for the answers.

Enjoy playing word games? You can also check out my NYT Connections today and NYT Strands today pages for hints and answers for those puzzles, while Marc's Wordle today column covers the original viral word game.

SPOILER WARNING: Information about Quordle today is below, so don't read on if you don't want to know the answers.

Quordle today (game #1647) — hint #1 — VowelsHow many different vowels are in Quordle today?

• The number of different vowels in Quordle today is 4*.

* Note that by vowel we mean the five standard vowels (A, E, I, O, U), not Y (which is sometimes counted as a vowel too).

Quordle today (game #1647) — hint #2 — repeated lettersDo any of today's Quordle answers contain repeated letters?

• The number of Quordle answers containing a repeated letter today is 1.

Quordle today (game #1647) — hint #3 — uncommon lettersDo the letters Q, Z, X or J appear in Quordle today?

• No. None of Q, Z, X or J appear among today's Quordle answers.

Quordle today (game #1647) — hint #4 — starting letters (1)Do any of today's Quordle puzzles start with the same letter?

• The number of today's Quordle answers starting with the same letter is 2.

If you just want to know the answers at this stage, simply scroll down. If you're not ready yet then here's one more clue to make things a lot easier:

Quordle today (game #1647) — hint #5 — starting letters (2)What letters do today's Quordle answers start with?

• G

• A

• A

• L

Right, the answers are below, so DO NOT SCROLL ANY FURTHER IF YOU DON'T WANT TO SEE THEM.

Quordle today (game #1647) — the answers

(Image credit: Merriam-Webster)

The answers to today's Quordle, game #1647, are…

• GAWKY
• ALLOY
• AUDIT
• LATCH

Another game where I came close to losing.

As it is common with words ending in Y I was convinced that i was looking for a word with a double letter, but after trying the unlikely “gammy” and “gappy”, I fortunately ran out of options and arrived at the correct answer.

Daily Sequence today (game #1647) — the answers

(Image credit: Merriam-Webster)

The answers to today's Quordle Daily Sequence, game #1647, are…

• PRIOR
• WHINE
• VENOM
• MEATY

Quordle answers: The past 20

• Quordle #1646, Tuesday, 28 July: SMITE, ROACH, RUDDY, GLOBE
• Quordle #1645, Monday, 27 July: TAFFY, FIGHT, GUILD, WAGER
• Quordle #1644, Sunday, 26 July: STOMP, LIMIT, FUNKY, STEAD
• Quordle #1643, Saturday, 25 July: SALON, SHEEN, BURST, GOURD
• Quordle #1642, Friday, 24 July: DOLLY, EDIFY, LAGER, PRANK
• Quordle #1641, Thursday, 23 July: LADEN, EVICT, WOVEN, SHIRE
• Quordle #1640, Wednesday, 22 July: COYLY, SINGE, AWASH, AWOKE
• Quordle #1639, Tuesday, 21 July: TRUSS, AXIAL, SWINE, THING
• Quordle #1638, Monday, 20 July: ICING, SLICK, GAILY, RISEN
• Quordle #1637, Sunday, 19 July: SPURT, ACRID, THRUM, BLEEP
• Quordle #1636, Saturday, 18 July: KNAVE, TIGHT, BLEAT, CHAOS
• Quordle #1635, Friday, 17 July: CUMIN, PALER, GRASS, INBOX
• Quordle #1634, Thursday, 16 July: IGLOO, PLAIT, YEAST, AROMA
• Quordle #1633, Wednesday, 15 July: PROXY, BRICK, BROTH, SURGE
• Quordle #1632, Tuesday, 14 July: EBONY, AVOID, RODEO, CUTIE
• Quordle #1631, Monday, 13 July: VOICE, BISON, BURNT, BUILT
• Quordle #1630, Sunday, 12 July: STAIN, BINGO, LARVA, DICEY
• Quordle #1629, Saturday, 11 July: WORTH, PRONG, DINGO, DRUID
• Quordle #1628, Friday, 10 July: GREET, STEAK, DUSKY, HAUTE
• Quordle #1627, Thursday, 9 July: CRUMP, PHONE, SHINY, STONY

Looking for a different day?

A new NYT Connections puzzle appears at midnight each day for your time zone – which means that some people are always playing 'today's game' while others are playing 'yesterday's'. If you're looking for Tuesday's puzzle instead then click here: NYT Connections hints and answers for Tuesday, July 28 (game #1143).

Good morning! Let's play Connections, the NYT's clever word game that challenges you to group answers in various categories. It can be tough, so read on if you need Connections hints.

What should you do once you've finished? Why, play some more word games of course. I've also got daily Strands hints and answers and Quordle hints and answers articles if you need help for those too, while Marc's Wordle today page covers the original viral word game.

SPOILER WARNING: Information about NYT Connections today is below, so don't read on if you don't want to know the answers.

NYT Connections today (game #1144) - today's words

(Image credit: New York Times)

Today's NYT Connections words are…

• CORK
• PEARL
• BASIC
• STAPLE
• MID
• WORD
• CHALK
• WHITE
• BLACK
• VANILLA
• GEM
• NAIL
• RIVET
• NUGGET
• SCREW
• PLAIN

NYT Connections today (game #1144) - hint #1 - group hints

What are some clues for today's NYT Connections groups?

• YELLOW: Attach to a wall with these
• GREEN: Same old, same old
• BLUE: Deliverance of insight
• PURPLE: Teachers use these

Need more clues?

We're firmly in spoiler territory now, but read on if you want to know what the four theme answers are for today's NYT Connections puzzles…

NYT Connections today (game #1144) - hint #2 - group answers

What are the answers for today's NYT Connections groups?

• YELLOW: FASTENERS
• GREEN: NONDESCRIPT
• BLUE: UNITS OF WISDOM
• PURPLE: ____BOARDS IN A CLASSROOM

Right, the answers are below, so DO NOT SCROLL ANY FURTHER IF YOU DON'T WANT TO SEE THEM.

NYT Connections today (game #1144) - the answers

(Image credit: New York Times)

The answers to today's Connections, game #1144, are…

• YELLOW: FASTENERS: NAIL, RIVET, SCREW, STAPLE
• GREEN: NONDESCRIPT: BASIC, MID, PLAIN, VANILLA
• BLUE: UNITS OF WISDOM: GEM, NUGGET, PEARL, WORD
• PURPLE: ____BOARDS IN A CLASSROOM: BLACK, CHALK, CORK, WHITE

• My rating: Hard
• My score: 3 mistakes

I made hard work of a relatively simple, trap-free game.

My first two errors came in collecting the group of NONDESCRIPT words first, including STAPLE instead of MID — which is understandable, as STAPLE describes something regular and average, and then WHITE which is not understandable, although whiteness does imply boring, which is where I think my brain went.

Sadly, my errors didn’t end there as I initially thought UNITS OF WISDOM was a collection of valuable things, so after connecting GEM, NUGGET and PEARL I added CORK as it is a fairly expensive material.

Yesterday's NYT Connections answers (Tuesday, July 28, 2026, game #1143)

• YELLOW: COMMAND: CAPTAIN, DIRECT, HEAD, LEAD
• GREEN: HOW DO YOU LIKE THEM APPLES: EMPIRE, FUJI, GALA, HONEYCRISP
• BLUE: SHOE BRANDS STARTING WITH "C": CAMPER, CLARKS, CONVERSE, CROCS
• PURPLE: STARTING WITH PALINDROMIC NAMES: ANNAPOLIS, BOBSLED, EVEREST, OTTOMAN

What is NYT Connections?

NYT Connections is one of several increasingly popular word games made by the New York Times. It challenges you to find groups of four items that share something in common, and each group has a different difficulty level: green is easy, yellow a little harder, blue often quite tough and purple usually very difficult.

On the plus side, you don't technically need to solve the final one, as you'll be able to answer that one by a process of elimination. What's more, you can make up to four mistakes, which gives you a little bit of breathing room.

It's a little more involved than something like Wordle, however, and there are plenty of opportunities for the game to trip you up with tricks. For instance, watch out for homophones and other word games that could disguise the answers.

It's playable for free via the NYT Games site on desktop or mobile.

Looking for a different day?

A new NYT Strands puzzle appears at midnight each day for your time zone – which means that some people are always playing 'today's game' while others are playing 'yesterday's'. If you're looking for Tuesday's puzzle instead then click here: NYT Strands hints and answers for Tuesday, July 28 (game #877).

Strands is the NYT's latest word game after the likes of Wordle, Spelling Bee and Connections – and it's great fun. It can be difficult, though, so read on for my Strands hints.

Want more word-based fun? Then check out my NYT Connections today and Quordle today pages for hints and answers for those games, and Marc's Wordle today page for the original viral word game.

SPOILER WARNING: Information about NYT Strands today is below, so don't read on if you don't want to know the answers.

NYT Strands today (game #878) - hint #1 - today's themeWhat is the theme of today's NYT Strands?

• Today's NYT Strands theme is… A made man

NYT Strands today (game #878) - hint #2 - clue words

Play any of these words to unlock the in-game hints system.

• QUEST
• NEST
• BRAT
• MANIC
• STIR
• FETA

NYT Strands today (game #878) - hint #3 - spangram lettersHow many letters are in today's spangram?

• Spangram has 10 letters

NYT Strands today (game #878) - hint #4 - spangram positionWhat are two sides of the board that today's spangram touches?

• First side: left, 5th row

• Last side: right, 5th row

Right, the answers are below, so DO NOT SCROLL ANY FURTHER IF YOU DON'T WANT TO SEE THEM.

NYT Strands today (game #878) - the answers

(Image credit: New York Times)

The answers to today's Strands, game #878, are…

• DOLL
• PUPPET
• DUMMY
• MANNEQUIN
• FIGURINE
• STATUE
• SPANGRAM: FABRICATED

• My rating: Easy
• My score: Perfect

A made man could mean a titan of industry, a mobster or if taken literally a scarecrow or indeed a PUPPET.

My first two words had double letters, a pattern that continued with DUMMY and the slightly more challenging MANNEQUIN — a word that I would have struggled to spell without the helper.

With the search virtually cracked I saw FABRICATED — although for full disclosure I should reveal that I missed it completely while collecting “fabric” as a non-game word.

Yesterday's NYT Strands answers (Tuesday, July 28, game #877)

• COMPLETE
• UNDEVIDED
• FULL
• TOTAL
• WHOLE
• ENTIRE
• SPANGRAM: ITSALLTHERE

What is NYT Strands?

Strands is the NYT's not-so-new-any-more word game, following Wordle and Connections. It's now a fully fledged member of the NYT's games stable that has been running for a year and which can be played on the NYT Games site on desktop or mobile.

I've got a full guide to how to play NYT Strands, complete with tips for solving it, so check that out if you're struggling to beat it each day.

For a long period during the early 21st century, Apple was the envy of the technology industry – pumping out a series of excellent products and seemingly unable to put a foot wrong. When the iPad came along, it had many detractors, chief among them the former CEO of its industry rival, Microsoft.

Pegged back

Bill Gates was a huge advocate for the tablet computer and had long attempted to thrust this form factor into the mainstream.

Quote of the day

This article is part of TechRadar Pro's QOTD project to provide an insight into the minds of the brightest and most recognized figures in the technology industry today and in years gone by. Read the full series here.

However, in comments to the now-defunct web magazine BNET, the Microsoft co-founder played down the impact he felt that Apple's first edition of the iPad would have. One month after its launch, he suggested it wouldn't catch on because many users would long for physical elements in such a device.

The iPad, however, proved a stellar success in the months and years that followed, so much so that Microsoft followed this trend in 2012 with the first edition of its now popular Surface device, called Surface with Windows RT.

A man ahead of his time

Although Microsoft's attempt to compete with Apple in the tablet market felt reactive at the time, the company, under Gates' leadership, played a big role in first introducing this strange form factor to the world.

The origins of the tablet are disputed, with Steve Jobs even envisioning such a device in 1983 – and even earlier examples in papers, proposals and blueprints. The first tablet with pen input and handwriting recognition, however, was the 1987 Linus Technologies Write-Top. But this type of device didn't catch on.

More than 10 years later, Microsoft attempted to reinvigorate interest in the tablet with its Microsoft Tablet PC, with the first device launching in 2003. Again, the devices were a commercial failure. Although Apple was the first to bring this concept back from the dead, today's Microsoft Surface Pro devices are outstanding in their own right and have earned a place in a very competitive market.