TechRadar News

• DJI has launched the Mic Mini 2S, the latest addition to its wireless mic lineup
• It features internal recording, a first for the line, plus support for four simultaneous transmitters
• Prices start at £79 / AU$149 — but there's no confirmed US launch yet

DJI has unveiled the Mic Mini 2S, a new wireless microphone system that arrives just months after the launch of the Mic Mini 2 — and brings some significant upgrades to the table.

The Mic Mini 2S is the first mini wireless mic from DJI with internal recording capabilities, expanding on the recently launched Mic Mini 2 for creators who need to reliably capture every audio detail in a compact, flexible form. The company says it's designed to handle everything from solo shoots to on-the-street interviews, podcasts, and multi-person productions.

The DJI Mic Mini 2S is available to order today, priced from £79 / AU$149 for the single transmitter and receiver bundle, or from £79 / AU$149 for the equivalent mobile receiver kit (1 TX + 1 Mobile RX + Charging Case). The full two-transmitter bundle (2 TX + 1 RX + Charging Case) costs £169 / AU$279.

A range of accessories is also up for grabs, including the transmitter on its own (£59 / AU$99), the charging case (£29 / AU$79), and the magnetic front cover sets (£19 / AU$35 for the multi-color set, £35 / AU$69 for the Victo Ngai-designed Time Series set).

(Image credit: DJI)

DJI says the Mic Mini 2S won't be launching in the US for now, with regulatory authorization still pending. That's become par for the course for the brand, which has essentially been banned from selling new products in the US since the end of last year.

Having reviewed the Mic Mini 2 back in April, I can say the jump to the 2S looks like a far bigger step-up than the minor bump from the original Mic Mini to the Mini 2 was. So without further ado, let's look at what's new.

DJI Mic Mini 2S: everything that's new

1. Internal recording
Upgrades-wise, this one's the headliner. The Mic Mini 2S transmitter packs 16GB of onboard storage, enabling it to record up to 28 hours of audio without the need for a receiver or connected camera. It supports both 24-bit and 32-bit float recording (the higher demands of the latter trimming storage to around 22 hours). It's a feature the Mic Mini 2 doesn't have at all, and one I mentioned as a notable gap when I reviewed it.

2. Four transmitters, not two
The Mic Mini 2's receiver tops out at two simultaneous transmitters. The 2S doubles that to four, opening it up to podcast interviews, small-panel discussions and multi-person shoots without the need for extra hardware. It also adds a new Quadraphonic recording mode alongside the existing Mono, Stereo and Safety Track options.

3. AI-powered noise canceling
DJI has swapped the Mic Mini 2's “active” noise cancellation for an NPU-driven AI system, still offered across two levels (Basic and Strong). DJI claims both levels now deliver clearer vocals with less distortion than their equivalents on the Mic Mini 2.

4. Fast file transfers
The 2S's charging case can pull recorded audio straight off the transmitters at up to 34MB/s, which DJI says is roughly twice the speed of the flagship Mic 3. A 30-minute 24-bit recording reportedly transfers in a zippy eight seconds.

5. A redesigned charging case
The Mic Mini 2S's case now has room for two transmitters, a receiver, the mobile receiver, and a 3.5mm audio cable all at once — a combination the Mic Mini 2's case couldn't accommodate. Invaluable if you like to keep as much kit in one place as possible.

(Image credit: DJI)

It's not a total win across the board, though. Battery life dips slightly on paper, according to DJI's spec sheet: it's 11 hours for the 2S transmitter and 10 hours for the receiver, versus 11.5 and 10.5 hours respectively on the Mic Mini 2, with total charging-case runtime down from 48 hours to 40. That's presumably the trade-off for fitting in the internal recording hardware, and while it's a fairly minor one, it's worth noting if all-day battery life matters more to you than onboard storage.

Elsewhere, lots of other stuff carries over unchanged: it comes with the same three voice tone presets (Regular, Bright, Rich), the same 400m maximum transmission range with the standard receiver, and the same direct OsmoAudio connection — now extended to the new Osmo Pocket 4P as well, and working with the likes of the Osmo Pocket 4, Osmo 360, Osmo Nano and Osmo Action 6.

DJI had confirmed that the Mic Mini 2S was on the way when it unveiled the Mic Mini 2 back in April, so its arrival isn't a huge surprise — but the scale of the upgrade is more substantial than I was expecting for what is still positioned as an entry-level product. I've been sent a review sample by DJI, so look out for my full Mic Mini 2S review very soon.

As I overheard a fan saying at this year's Italian Global Series television festival in Rimini, Star Trek was one of the first TV franchises that you had to own physically in order to see the entire thing.

With so many different iterations of the overarching IP — from the classic 1960s series to Star Trek: Voyager, and more recent additions such as Star Trek: Strange New Worlds — it's not surprising that this has been the case for almost 60 years.

But the rise and dominance of streaming services is leading to an increasing reliance on digital services. After all, why spend more of your hard-earned money on discs when so much of Star Trek is available to stream on Paramount+ at the click of a button?

However, with season 1 of Star Trek: Starfleet Academy still not having a confirmed release date for Blu-ray, 4K and DVD formats, we're reminded of the reason why physical media is still important.

There's often no guarantee that a show or movie will always be available on a streaming service or in a digital format, meaning the only way to guarantee access to content you want to watch time and time again is buying a physical copy.

But if the studios behind shows such as Starfleet Academy don't confirm physical-media release dates, what then? Should Star Trek fans be worried that the opportunity to invest will be taken away from them?

According to Jeri Ryan, who played former Borg drone Seven of Nine in Star Trek: Voyager and Star Trek: Picard, it's unlikely that this will be the case — even if the 'tech bros' are intent on taking over mainstream studios.

'There's a sheer number of collectors who want to have the physical thing'

Jeri Ryan played Seven of Nine in Star Trek: Voyager and Star Trek: Picard (Image credit: Paramount+)

"I haven't actually heard about this, but I can't imagine that physical media will go away entirely, because there are too many collectors," Ryan replies when she's asked about Star Trek's physical media presence potentially disappearing.

"These studios are primarily IP companies. More so than ever now that all of the studios have basically been taken over by tech bros as opposed to creatives. It's about dollars, and numbers, and if there's money to be made. I think there probably always will be just by the sheer number of collectors who want to have physical things in their possession.

"I can't imagine that it would get entirely done away with. Maybe I'm wrong... I hope I'm not."

This isn't the first time that Ryan has been outspoken about the entertainment industry's relationship with technology. In April 2026, she testified in support of California Senate Bill 1050, which would "target false advertising by requiring clear and conspicuous consumer disclosures whenever a synthetic digital performer is used in state advertisements."

Ryan's certainly right in thinking that many factors, from over-reliance on streaming services to the increased use of AI, could affect the future of the Star Trek franchise in ways we haven't even imagined yet.

But for physical collections, I've got a feeling that Ryan's optimism when it comes to demand means these releases won't be going anywhere. But if Paramount could hurry up and announce a disc release date for Starfleet Academy to put our minds at ease, that would be great.

• Bynario disclosed CVE‑2026‑43760, a macOS RCE flaw allowing root file creation via legacy VNC password option
• Apple patched it July 27, 2026 in macOS Tahoe 26.6 and Sonoma 14.8.8; unpatched users should disable Screen Sharing/Remote Management or the legacy VNC setting
• Reporting was delayed as Apple limited submissions due to AI‑generated bug report overload, but the company reached out directly to fix this issue

Apple has fixed a high-severity vulnerability that allowed threat actors to execute malicious code remotely (RCE), as root, on certain macOS devices - and would have probably fixed the issue even sooner; had it not been flooded with AI slop vulnerability reports.

Security researchers Bynario published an in-depth report discussing finding an RCE flaw on macOS 26.5.2 devices running on Apple Silicon M4 and M5 systems, with System Integrity Protection (SIP) enabled.

According to Bynario, the vulnerability affects Mac devices with Screen Sharing or Remote Management enabled, and with the legacy "VNC viewers may control screen with password" option turned on. For those devices, should a threat actor obtain the VNC password and authenticate to the Mac (no macOS account compromise is required, only the VNC password), they would be able to perform file-transfer operations, with root permissions, due to a logic flaw.

Use code TECHRADAR10 for 10% off

NordStellar provides businesses of all sizes with a comprehensive threat exposure management platform to bolster your cybersecurity. NordStellar actively monitors for data breaches and exposed credentials to prevent hackers gaining easy access, while simultaneously implementing a range of cybersecurity tools to keep employees and company data safe.

Use coupon code TECHRADAR10 for an additional 10% off.View Deal

Drowning in the AI flood

The attacker would then be able to create new files owned by root anywhere the system allows.

In the report, the researchers demonstrated creating a valid file inside /private/etc/sudoers.d, granting passwordless sudo privileges, and once that policy was in place, they were able to run commands as root.

In a separate report, the researchers said Apple was forced to limit the number of active bug reports individual researchers can keep open at one time, due to its security teams being flooded with AI slop reports.

Since they already hit that threshold by submitting more than 50 bugs in three weeks, the researchers were unable to report this RCE flaw sooner. However, they explained that Apple reached out to Bynario directly to review, and later patch, the flaw.

The bug is now tracked as CVE-2026-43760 and was given a severity score of 8.6/10 (high).

Apple released the updates on July 27, 2026, addressing the bug on macOS Tahoe 26.6 and macOS Sonoma 14.8.8.

Those who cannot patch should disable the legacy "VNC viewers may control screen with password" option or disable Screen Sharing and Remote Management entirely.

As telemetry becomes central to how modern platforms are built, trained, and automated, its perceived future utility has driven a dramatic increase in collection and retention. A recent study shows telemetry volumes tripling in many enterprises over the past year, with agentic AI expected to drive nearly 10x more growth expected within the next two years.

Clearly, telemetry is starting to look less like infrastructure and more like capital. The mistake is treating it as a pure asset and overlooking the other side of the ledger. Exploding log volumes and rising costs are the most tangible inconveniences, and often the first triggers for concern, but they are merely symptoms.

The bigger problem is that telemetry gradually becomes a system of knowledge that exceeds an organization’s ability to understand, govern, or explain it and, therefore, to reliably bound its cost, risk, and downstream use.

When AI systems both produce telemetry and consume it

Traditional telemetry was largely retrospective. It described what happened. In AI, it starts feeding back into the system. A session log captured to troubleshoot a crash today may become training data tomorrow, evolve into a model feature later, and eventually drive automated decisions without human supervision.

In essence, the same data serves multiple functions throughout its lifecycle, many of which have little to do with why it was collected in the first place.

This sets a loop in motion. The system produces telemetry, the AI consumes it, which produces new signals and predictions, and those create an appetite for still more telemetry. As a result, the value organizations place on data keeps growing, often ahead of any clear understanding of how it will be used.

Once telemetry becomes a form of organizational memory over time, you are past just discussing observability. You are now dealing with governance, cost, and control.

Telemetry retention is fundamentally biased by asymmetry

It’s easy to justify the seemingly “small cost” of storing another terabyte when weighed against the hypothetical cost of discarding it, which can seem enormous, because someone can always argue that the data you threw away might have been a golden ticket.

The prospect of regretting its deletion feels potentially irreversible. Could it have solved a problem? Could it have trained a model or shown you an opportunity you missed? Caving to uncertainty, most organizations just keep everything.

The flaw in this reasoning is a myopic focus on the asset that ignores its liabilities. Every retained dataset carries ongoing storage and governance costs, security and compliance obligations, and discovery risk if you ever land in litigation. Each new dataset can also be combined with existing ones, amplifying its informational value in ways that were never foreseen.

Is there a guarantee that organizations will never regret a deletion? No, but there can be a clear rationale. You knowingly forgo some option value because the expected benefits of keeping the data do not outweigh the costs and risks of holding it. That is a decision you can stand behind later, even if it turns out the data might have been useful.

Asking whether something could be useful someday is not helpful, because almost anything clears that bar. It’s better to ask what specific capability you are keeping it for. If you have a clear answer, retain the data and govern it accordingly.

For CTOs, the blind spot is treating telemetry growth as a scaling problem

As important as ingestion pipelines, storage, query speed, and tooling are, what often catches people off guard is that telemetry turns into a body of knowledge that no single person in the company actually understands. While most teams can tell you where their data lives, far fewer can explain what it reveals once you start putting it together.

The math here is quite unforgiving because exposure does not grow one stream at a time. Every new source can be matched against every source you already have, so the number of possible combinations spirals into something no longer tractable.

Consider clicks, session length, support tickets, device IDs, login records, or approximate location – on their own, none of it is sensitive, and nobody thinks much of collecting any of it. But put them together, and you can reconstruct someone’s daily routine, flag changes in their financial behavior, or predict upcoming life events.

The sensitivity is not attributed to any single stream but is born out of correlation across streams. In reality, most organizations have never fully explored what those correlations could actually enable.

For a CTO, the thing to worry about is not the size of the data, but whether you can still explain what your organization knows and where that knowledge came from. Give every stream an owner, a stated purpose, and a date it expires. If a stream cannot answer what it improves, then it has no business being retained indefinitely. A rule like that will do more to reduce risk than any amount of clever storage engineering.

For CFOs, the blind spot is filing telemetry under infrastructure cost

What makes telemetry different from other infrastructure is that it compounds. More telemetry produces more analysis, which creates new use cases, which extend retention and increase demand for more storage, processing, tooling, and oversight. Before long, what started as a small storage expense has become an ongoing commitment.

The next year’s cloud bill may seem like the thing you should brace for, but at least you can put a number on it. What’s more concerning is that telemetry can become an ever-growing liability with unpredictable cost, risk, and duration. Unglamorous as it may be, the fix is down to the same thing – focus on the bounded business outcomes it produces.

Can you take any category of telemetry you hold and say plainly why it exists, what value it earns, how long it should live, and what happens to it when it stops being useful?

What would a bounded telemetry architecture look like in an AI-driven world?

It starts by overturning the most basic assumption that you have to collect and keep everything until it turns out to be absolutely impractical. Instead, assume most things should not be collected and that nothing stays forever unless there is a reason for it to.

The effect is a change in default behavior. When every stream is set up with a clear purpose, owner, retention policy, and expected outcome, data starts following a lifecycle instead of piling up. Raw events might exist briefly at full detail, collapse into aggregates after that, and, once they are no longer useful for decisions, eventually disappear.

Thinking in lifecycle terms reframes data as something that interacts across systems rather than existing as isolated stores. That’s why the boundary you draw is not around a single database, but around what the combined system is allowed to infer and act on. What you collect should be based on the decisions you want to improve.

The complication in an AI setting is that these systems do need substantial telemetry to work, so it is not as simple as collecting less. The challenge is to collect enough while still keeping control over what the system learns from and what it ultimately produces.

We've featured the best business intelligence platform.

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

• Astell & Kern is launching an 'entry-level' DAP to supersede the SR35
• We'll have to believe it for now, because no actual price has been provided
• Highlights include EnviroTune ambient noise awareness and an Octa-DAC mode

Astell & Kern has straightened things out. We gave its wonky, quirky SR35 digital audio player a glowing five-star review, and now a successor is here in a more conventional form factor.

The A&K PD5 is the new 'best' in question, and very much an upcoming contender for our best high-res media player list. And we'll decide whether it is, as soon as we learn a few key details about it such as when it's actually coming out and how much it'll cost.

Pitched as an entry-level audio player, this hopefully won't cost a huge amount, but we'll have to wait to find out. It's been unveiled as part of an AV show in Hong Kong, and it'll be making its way around a few other similar shows in Shenzhen and Southern California before its wider release.

So we don't know much about it, but there are some key selling points which Astell & Kern have unveiled — and very promising they are too.

It's always listening

(Image credit: Astell&Kern)

The PD5's biggest spec is that it packs in eight DACs at once, and you can choose between 4-DAC or 8-DAC playback. The former lets you listen for longer (28 hours, to be precise), the latter allows you to enjoy higher-quality listening. A&K refers to 'class-defining performance'. Woof.

Another tool here is EnviroTune, which apparently is always listening to you... and, specifically, ambient noise around you. Then it can tweak the sound you're hearing to cater for different environments. It sounds like a proto-ANC, in other words.

For raw specs, the PD5 plays back in 32-bit/384kHz PCM and DSD256, with support for dual-band Wi-Fi, Bluetooth 5.3 and a vast range of codecs. The player runs on Android, though Astell & Kern didn't say which build of Android it'll run.

The player is housed in an aluminum shell, but A&K didn't provide dimensions, so it's impossible to tell how it compares size-wise to the SR35.

The real question here is still the price. This might be Astell & Kern's best entry-level audio player of all time, but its definition of 'entry-level' might vary wildly from yours. Evidently, we'll have to wait and see what the chips fall.

The use of AI across finance functions is soaring as it quickly becomes a key tool for getting the job done.

As such, businesses are investing heavily and spending continues to rise, meaning adoption has more than doubled since 2024.

But governance isn’t keeping pace, as almost half (49%) of UK finance leaders admit their organization has gaps in its AI governance strategy.

That’s a concern for two reasons. One, because governance is a compliance exercise, and two, because it underpins how confidently businesses can adopt AI at scale.

Without clear guardrails, employees will naturally start making their own decisions about which tools to use and how to use them – creating ripe conditions for Shadow AI to emerge and thrive.

The widening adoption-governance gap

I speak from experience when I say that AI is and will continue to be transformative for the finance function.

And for an industry that is largely accepting of the tried and tested status quo, it’s genuinely encouraging to see how positively leaders in the finance space view AI. Our research showed that 8 in 10 (83%) believe it will play an important role in helping them achieve their business goals.

What’s less encouraging, and somewhat worrying, is that almost a quarter (23%) say they have little to no AI governance measures in place. And that disconnect really matters.

Too often, governance is viewed as something to tackle only once adoption of new technology is well underway. But it has to be built alongside adoption. There’s often a fear, not always unfounded, that governance can slow innovation. But that’s not always a bad thing, because the point of governance is to make sure innovation happens safely and in a way that business can measure and trust.

Without it, AI adoption can quickly become fragmented, increasing a business’ exposure to compliance and security risks that will only intensify as AI becomes more deeply embedded.

When processes create friction, people will find another way

While governance gaps are directly linked to organizational risk, they also shape employee behavior. If approved tools are difficult to access, limited, or policies aren’t clear, people will look for another way to get the job done. Employees as a whole want to embrace the productivity benefits of AI and won’t let a lack of formal guidance stop them.

And that’s exactly what research tells us.

More than a quarter (27%) of UK employees admit to purchasing AI tools for work without approval in the last year. More broadly, 67% say they regularly bend rules or find loopholes to access company money, while 27% report missing business opportunities because of delays accessing spending.

These findings aren’t suggestive of employees deliberately trying to undermine company policy. More often, it’s a sign that existing processes aren’t keeping pace with the way people want to work.

That’s where shadow IT starts to emerge.

Shadow AI is a symptom of a wider governance problem

It’s easy to think of Shadow AI as the problem itself. But in reality, it’s usually a symptom of something bigger.

The concern is that this unchecked use of AI can lead to data leakage, compliance failures, poor record-keeping and inconsistent decision-making. So, it’s an important problem to nip it in the bud before it spirals out of control.

When employees feel they need to work around approved processes to stay productive, businesses quickly lose visibility over which AI tools are being used and how company data is being handled. Finance teams can quickly lose track of where money is being spent.

That creates a practical challenge for finance leaders, while businesses can find themselves managing duplicate tools, fragmented AI adoption, unmanaged spend and inconsistent governance. Exposure to scrutiny and compliance risks can also increase exponentially.

The longer those issues go unaddressed, the harder they become to unwind.

Good governance enables AI

The goal is never to slow AI adoption or place unnecessary barriers in front of employees. When governance is implemented well, it makes the approved route the easiest and best way to support employees in new, more productive ways of working.

It means making sure employees have access to tools that help them work effectively, putting clear policies in place for how to use them, and making it clear what’s expected.

When governance supports productivity and innovation, helping employees to address areas of friction in their roles instead of adding to it, they’re far less likely to look elsewhere for solutions.

We've listed the best business software.

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 can now sync your Fitbit data from Google Health to Apple Health
• Previously, data could only flow in the other direction
• The change makes it easier to manage your fitness info in one place

A quiet update from Google has the potential to make a big difference for fans of all the best Fitbits who also use an iPhone. Instead of spreading your health data across multiple apps — or having to rely on Google Health alone — you can now use Apple’s own solution for wearables that are spread across different platforms.

The change in question involves a new feature found in the latest Google Health update on iOS. Specifically, data stored in Google Health can now be synced to the Apple Health app, including data such as “exercise records, sleep, vitals, steps, and more,” according to Google.

That’s important news for a couple of reasons. The first is that it finally opens up Google Health data to a wider audience. Previously, you could sync Apple Health data to Google Health, but you couldn’t send it back the other way, meaning anything saved in Google Health was siloed off and isolated. Now, it’s much more accessible to iOS users, especially those who prefer the Apple Health app.

The second factor is that it now means Apple fans — or those who would rather use Apple Health instead of Google Health — no longer need to compromise if they have any Fitbit wearables. From now on, those users can happily manage their Apple and Fitbit devices from one place inside Apple Health, instead of having to either use multiple apps or Google’s offering alone.

Apple Health is all you need

(Image credit: Google)

To sync your data from Google Health to Apple Health, open the Google Health app, then tap your profile icon and select Partner Apps. Choose Apple Health, and follow the on-screen prompts to send your data across to Apple’s platform.

As mentioned previously, one group of people who will benefit from this change are those who use two or more wearables that operate on different platforms. Before, if these users wanted to get all their data into Apple Health, they had to use a third-party app or workaround. Now, things are much simpler.

The change seems to have gone down well with Fitbit users online. Posting on Reddit, for example, one user commended Google, saying: “Good job from Google, I didn't think they would actually add that feature anytime soon.”

Another happily noted that “Google did it quite fast. Nice to see when they care about customers.” A third, meanwhile, lamented that cross-platform functionality like this “Should have been there a long time ago.”

If you’re an iOS user with a Fitbit device, you no longer need to stick to Google Health to keep track of all your health and fitness information. With this change, Apple Health is all you need.

Jump to...

Mini PCs under $600
Mini PCs over $600

A mini PC is a physically small yet very powerful productivity booster. It’s like compressing the performance of a high-end desktop into a compact device that fits in your palm. You can carry the mini PC around easily and simply connect it to a monitor.

Personally, a mini PC has been one of my best purchases, but choosing the right one was an uphill battle. Between weighing performance, compactness, energy efficiency, and price, selecting the best options from numerous choices felt daunting, but I ultimately narrowed it down after extensive research and testing.

I tested many devices to curate this list, including some of the best mini PCs you can get, so each recommendation is backed up by data. Whether your main consideration is the price range, performance, speed, compactness, or energy consumption, get ready to strike the right balance and make an excellent choice.

Best mini PC deals : Quick links

• Best Buy: Save on Geekom, Minisforum, and more
• Walmart: Big savings on budget mini PCs
• Newegg: Discounts on fully specced and barebones mini PCs
• Geekom: Up to 50% off mini PCs for summer
• GMKtec: Save on powerful desktop machines

Best mini PC deals under $600

AMD Ryzen 5 7640HS | 32GB DDR5 | 1TB SSD

This compact mini PC features an AMD Ryzen 5 7640HS processor, 32GB DDR5 RAM, a 1TB SSD, triple 4K display support, USB4, dual 2.5G Ethernet, Wi-Fi 6E, and Windows 11 Pro pre-installed.

Check our full reviewView Deal

AMD Ryzen 7 8745HS | 16GB DDR5 | 1TB SSD

Powered by an AMD Ryzen 7 processor, this mini PC packs 16GB DDR5 memory, a 1TB NVMe SSD, USB4, WiFi 6E, 2.5G LAN, and supports up to 8K displays.

Read our full reviewView Deal

AMD Ryzen 7 6800H | 16GB DDR5 | 1TB SSD

The Geekom A6 mini PC packs an AMD Ryzen 7 6800H processor, Radeon 680M graphics, and fast DDR5 into a compact aluminum chassis, delivering high performance without taking over your desk.

See our full reviewView Deal

AMD Ryzen 9 6900HX | 24GB LPDDR5 | 1TB SSD

AMD Ryzen 9 6900HX (8-core/16-thread, up to 4.9GHz), Radeon 680M graphics, 24GB LPDDR5X-4800 RAM, 1TB PCIe 4.0 SSD (expandable to 4TB), dual Gigabit Ethernet, Wi-Fi 6E, Bluetooth 5.3, and triple 4K display output via HDMI, DisplayPort, and USB-C.

Read our reviewView Deal

Ryzen 7 7735HS | 24GB LPDDR5 | 500GB SSD

A solid Ryzen 7 mini PC that will tick most boxes for most people looking for a sub-$500 machine. Soldered RAM might be a sticking point for some, though. View Deal

AMD Ryzen 5 3500U | 16GB DDR4 | 512GB SSD

AMD Ryzen 5 3500U (4-core/8-thread, up to 3.7GHz) with Radeon Vega 8 graphics, 16GB dual-channel DDR4 RAM, and a 512GB PCIe 3.0 NVMe SSD. Dual M.2 2280 slots support up to 16TB of total storage, and RAM is upgradeable to 64GB.

Read more in our full reviewView Deal

AMD Ryzen 5 7430U | 16GB DDR4 | 512GB SSD

This Windows 11 Pro mini PC is well-suited for general home and office use, which is where it excelled when we tested it out. Specs-wise, it features an efficient AMD Ryzen 5 7430U processor, 16GB RAM, and 512GB SSD. View Deal

Intel Core Ultra 7 256V | 16GB LPDDR5 | 512GBSSD

The NucBox K13 is a superb mini PC equipped with an Intel Core Ultra 7 256V processor, 16GB of LPDDR5X memory, and a 512GB SSD. Interestingly, it boasts AI acceleration up to 115 TOPS, dual USB4 ports, 5G Ethernet, and support for triple 4K displays. In our review we called it a "thoughtfully engineered slice of modern computing." View Deal

Best mini PC deals over $600

AMD Ryzen 7 7735HS | 32GB LPDDR5 | 1TB SSD

The GMKtec K16 mini PC delivers Ryzen 7 7735HS performance with 32GB LPDDR5 RAM and a 1TB SSD. Featuring OCuLink eGPU support, triple-display output, dual 2.5GbE LAN, and USB4, it’s a versatile compact system for gaming and creative workloads.

Read our full reviewView Deal

AMD Ryzen 9 8945HS | 32GB DDR5 | 1TB SSD

The A8 MAX packs Ryzen 9 8945HS power, 32GB DDR5 RAM, and a 1TB SSD into a compact mini PC. With USB4, dual 2.5GbE LAN, and 8K output, it’s built for AI, design, gaming, and productivity.

Read our full reviewView Deal

Intel Core Ultra 9 285H | 32GB DDR5 | 1TB SSD

Powered by an Intel Core Ultra 9 285H processor, this GMKtec mini PC pairs 32GB DDR5 memory with a 1TB PCIe 4.0 SSD, OCuLink, three M.2 slots, and support for four 8K displays.

Read our full reviewView Deal

AMD Ryzen AI 9 HX 370 | 32GB DDR5 | 1TB SSD

Powered by AMD Ryzen AI 9 HX 370, this Geekom mini PC combines 32GB DDR5 memory, a 1TB SSD, WiFi 7, USB4, 8K output, and 80 TOPS AI performance for demanding workloads.

Read our full reviewView Deal

AMD Ryzen 7 8845HS | 32GB DDR5 | 2TB SSD

The K8 Plus packs a high-performance Ryzen 7 8845HS processor, 32GB RAM, and a 2TB PCIe 4.0 SSD for gaming, video editing, and demanding workloads. With OCuLink eGPU support, dual 2.5GbE LAN, USB4, and 8K display output, it’s a powerful compact desktop alternative.

Read our full reviewView Deal

Intel Core Ultra 9 (Series 2) ARL-HX | 32GB DDR5 | 1TB SSD | Nvidia GeForce RTX 5070 Mobile GPU

With a dedicated GPU, this gaming mini PC ready for demanding projects out of the box. In our review, we noted it has plenty of ports and upgrade options. But it's an expensive option.View Deal

AMD Ryzen AI 9 HX-370 | 32GB DDR5 | 1TB SSD

An absolute powerhouse AI mini PC workstation with the ultra-powerful HX 370 chip and OcuLink support. In our review, we loved the performance here, but it's a shame the RAM is soldered. View Deal

Intel Core i7-14700 | 16GB DDR5 | 512GB SSD

A true business mini PC well-suited to the office, HP's Elite Mini pairs a 14th Gen Intel Core i7-14700, with Intel UHD 770 graphics. 16GB DDR5 RAM and a 512GB PCIe SSD, both user-upgradeable via two SO-DIMM slots and dual M.2 slots. View Deal

He's a legendary superhero, a pop culture icon, the star of comic books, movies, TV shows, video games and more, but how well do you really know Spider-Man?

Ever since we've seen the web-slinger's first outing in comic book form, we've seen various takes on the character: different human alter egos, different actors taking the role on, different powers and villains.

There's a lot about the character to get your head around, and you should be sure to brush up on how to watch the Spider-Man movies in order and our ranking of the best Spider-Man movies if you need to do some research.

But how well-read and well-watched a fan are you? To find out, we've designed this 30-question quiz, that'll ask you all about Spider-Man and his (and, sometimes, her) media outings.

The questions will test your knowledge on the breadth of the media empire, more than specific details from Spider-Man's world. You don't need to know what Doc Oc was a doctor of, or when Uncle Ben's bed-time is!

We'll start with 10 easy questions, and then move into 10 medium ones, and finish with 10 difficult ones for the experts. So, let's find out just how much you know:

If you need to brush up on your Spider-Man, let's find you some schoolwork:

If you've been considering Photoshop, Premiere Pro, Illustrator, or any of Adobe's other professional creative applications, and haven't signed up before, you can save 50% on the Creative Cloud Pro plan for your first year.

This cuts the monthly cost from $69.99 to $34.99 (or from $91.99 to $45.99 in Canada) on an annual plan billed monthly. And the subscription comes with more than 20 industry-leading creative apps, alongside 4,000 monthly generative AI credits for premium features such as Text to Video.

Subscribers also gain unlimited access to standard AI tools like Generative Fill, Adobe Fonts, Adobe Stock assets, tutorials, collaboration tools through Frame.io, and integrated AI models from Adobe, Google, OpenAI, and others. The promotion for new subscribers ends August 10.

Today's top Adobe Creative Cloud Pro deal

Save 50% for the first year
This deal is available to new subscribers signing up for the annual plan billed monthly, reducing the price from $69.99 to just $34.99 per month for the first year. Creative Cloud Pro includes more than 20 Adobe applications, including Photoshop, Illustrator, Premiere Pro, Acrobat Pro, and Firefly, alongside 4,000 monthly generative AI credits and unlimited access to standard AI features such as Generative Fill.View Deal

Why this is a great deal

Although there are many other creative tools available, Adobe continues to lead the way for professionals, and Creative Cloud Pro remains the easiest way to access its entire ecosystem.

I personally wouldn't use anything else and there's a good reason why Adobe's apps always come out top in our round ups of the best photo editors, best video editing software and best PDF editors.

The Pro subscription includes more than 20 applications, from industry staples like Photoshop, Illustrator, Premiere Pro, and Acrobat Pro to specialist tools such as After Effects, Audition, Lightroom, and InDesign, all designed to work seamlessly together.

Beyond the apps themselves, Creative Cloud Pro includes Adobe Firefly, 4,000 monthly generative AI credits for premium features such as Text to Video, unlimited access to standard AI tools like Generative Fill, plus Adobe Fonts, Adobe Stock assets, tutorials, and collaboration features through Frame.io.

At 50% off, this is one of Adobe's best ever offers for new subscribers in the US and Canada.

AI spending is forecast to reach $40.74 billion by 2030.

While AI adoption is accelerating on both sides of the Atlantic, UK and US retailers are taking differing approaches. US organizations are using AI to unlock new revenue opportunities and reshape the customer experience. UK retailers have largely focused on what AI can do internally.

There are understandable reasons for the differing approaches. But UK retailers risk falling behind if they don't broaden their strategy to focus on embedding AI across the full customer journey.

The Transatlantic AI divide: Operational efficiency vs revenue growth

For UK retailers, early AI adoption has been cautious. The focus has been on internal operational efficiencies, such as creating marketing content, handling surface-level customer service and answering post-purchase queries.

US retailers, on the other hand, are embedding AI directly into the shopping journey itself. A third of US adults now use AI agents when shopping online, such as Amazon’s Rufus, to discover or research products. In-chat checkout experiences are becoming increasingly popular. US retailers are positioning AI more like a digital sales assistant than a back-office tool.

While both approaches have their merits, there is a risk UK retailers get left behind. The gap won’t happen overnight. It will emerge quietly, starting with share of attention and then showing up in revenue.

Global retailers using AI for discovery, conversion, and lifetime value will start owning key decision moments. UK brands focused on operational efficiency may find themselves absent from the journeys that matter most.

Compliance vs experimentation

UK retailers didn’t arrive at caution arbitrarily. Regulation and the consumer expectations shaped it. GDPR means shoppers expect clear consent, transparency on data use, and control before engaging with AI-driven experiences.

That’s why many UK retailers started with safer operational use cases, including automating support, enhancing content and streamlining fulfilment, before fully reinventing the ecommerce journey. Explainability and trust came before experimentation.

In the US, fewer regulatory guardrails made it easier for retailers to test AI tools publicly, iterate quickly and showcase value directly to consumers. The UK’s position isn’t a weakness. It’s a different starting point.

By ensuring customer-facing AI functions are both compliant and secure, UK retailers can both unlock new revenue and strengthen consumer confidence, rather than risking the need to roll back features due to compliance concerns.

Turning AI into a commercial growth engine

The faster US adoption has been less about technology and more about how the internal conversation is framed. The boardroom conversation isn’t “How do we save cost?” — it’s “How do we acquire smarter, convert faster and grow lifetime value?”.

US retailers treat AI as a growth lever across the full customer journey, from personalizing discovery and optimizing pricing, to guiding promotions, streamlining checkout and automating lifecycle communications. AI is framed as a revenue driver, not an operational nicety.

UK retailers have strong operational foundations. The shift is about pointing that discipline toward growth, opt-in recommendations and assisted checkouts. Controlled experiments that build insight and customer confidence at the same time.

For these experiments to work, the product data underneath them has to be ready: rich specifications, reviews, imagery and supporting content that gives AI something real to reference during conversational discovery and search.

Predictive personalization can prioritize high-value customers and surface relevant offers. Controlled agentic shopping experiences build familiarity and insight over time. Dynamic pricing and AI-driven lifecycle communications improve both conversion and retention. None of this requires abandoning the trust UK retailers have built.

The risk of falling behind may be gradual, but it is real

UK retail has the foundations. Now they need to be built upon.

Customer acquisition costs will rise if competitors are winning the discovery moment first. Conversions will happen earlier – and elsewhere – if the journey isn’t being shaped. Lifetime value will lag as AI-driven retention compounds faster for the brands that moved sooner.

Cumulative gaps are harder to close than visible ones. The retailers that combine strong governance and trusted data practices with genuine customer-facing AI innovation are the ones that will compete well in what comes next.

We've featured the best ecommerce hosting.

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

Democratization may be the AI industry's favorite word, and one that, in fairness, it has earned the right to use.

At the beginning of the decade, building anything with machine learning meant a large research team and a significant budget, and that put it out of reach for many.

Today, however, the barrier to using advanced AI has fallen almost entirely, and what used to be a quiet technology in the background has been thrust to the forefront of everyday work and everyday conversation.

But access is just the surface layer. Underneath it sits another layer which, arguably, matters more than access to AI itself, and that layer is training.

Unlike access, model training has not been democratized at all. The industry has made it easy to consume a model and almost impossible to make one.

This needs to change.

A gatekeeper to innovation

If we look closely at what "democratization" has really delivered thus far, we see that what has opened up is only consumption. Anybody can now interact directly with an AI model, even without a technical background, and can build an app in an afternoon with minimal effort. Open-weight releases mean anyone can even run a model on their own hardware, free of an API. These are genuine advances that have helped define the AI-powered world we now live in.

What has not open up is the ability to train a model. Currently, producing a frontier model still requires capital measured in billions, scarce specialist talent that only a handful of labs can attract and retain, vast datasets, and retraining cycles that have to be repeated every six to 12 months just to stay current.

It is a cycle that is stifling progress, and a barrier that no startup, cash strapped healthcare organization, or mid-sized manufacturer can clear. It functions as a gatekeeper to innovation, and it is one that well-funded organizations have every commercial incentive to keep locked, because the scarcity of training capability is precisely what protects their market position.

There is a dichotomy. The cost of using a model has fallen toward zero. The cost of making one has climbed toward the limits of what private capital can sustain.

Can open source help?

Some might argue that the answer is to turn to open-weight models. Open weights, the argument goes, solve the ownership problem. You can download the model, run it, fine-tune it, and deploy it without asking anyone's permission. This is not democratized training though. It may be closer, but owning the weights is not the same as owning the model itself.

An open-weight model remains the frozen output of a training process run by the organization that produced it. What users receive is the result of that process. It is true that they can somewhat adjust the model. But doing that effectively demands expertise, compute, and clean data. And as the world moves on, the model ages and retraining is needed.

So despite their advantages on the surface, open weights do not offer users the ability to produce and continuously improve a model. That ability is still held by the labs that produced it.

The change that could revolutionize innovation

In my view, if democratization reaches the training layer and allows users the genuine ability to create, train, and own a model, several important structural changes follow that could meaningfully accelerate innovation across industries.

Ownership survives the vendor. If a model is trained by its user on the data they own, the weights are theirs without licensing restrictions, it will not evaporate if the company that gave them the tools to build it disappears. The relationship stops being a subscription. That single change alone removes the dependency the current market is built upon.

Models must learn continuously. If training is something that can be done continuously rather than a cyclical and centralized event, the model does not have to freeze at deployment. It can keep learning from new data in production without expensive retraining cycles and without a team of specialists being needed to run it. The workarounds become unnecessary because the underlying limitation is gone.

When control of what a model learns and how occurs at user level, the chain of responsibility is legible. That is the kind of traceability regulators are expecting and that black-box frontier models simply cannot provide.

These are the distributed benefits that make "democratization" the right word at last. If the ability to train a model is widely held, then the value, control, and responsibility are widely held too. General Learning Intelligence treats learning as part of the architecture rather than a cost only the largest labs can bear.

Access to models is already cheap and getting cheaper. However, access to training is not and that is where the next phase of competition will be decided.

We've reviewed, rated, and ranked the best laptops for programming: for professional programmers, coders, software engineers, and developers.

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

Artificial intelligence (AI) is reshaping the scale and complexity of data center infrastructure.

Traditional data center facilities were designed around relatively steady CPU workloads and predictable growth in power demand, allowing developers to secure energy supply alongside growing demand, cooling systems based on known and mature technology and infrastructure capacity with a reasonable degree of certainty.

AI workloads, however, demand far more power with greater energy density.

Electricity consumption from data centers has grown at 12% per year over the last five years and expected demand growth, particularly in AI training data centers, is set to drive a substantial increase in power demand.

Meeting this demand, while maintaining efficiency, is pushing developers toward gigawatt-scale data centers and with the timeframe for delivering these facilities rapidly compressing, the ability to deliver new infrastructure efficiently is increasingly critical.

In addition, as the scale of these developments grows, so does the complexity of delivering them. Grid interconnections can delay timelines by years, equipment supply chains are stretched, and projects must meet stringent reliability targets while navigating regulatory, environmental and community requirements that vary by region and country.

For owners and developers, the challenge is no longer simply constructing another data center building. The next generation of AI data centers requires a fully integrated approach across power, cooling, transmission, water, digital systems and long-term operations. Success depends on designing these facilities as resilient, flexible and energy-optimized industrial campuses.

Balancing site trade-offs to unlock faster delivery

Site selection is one of the clearest expressions of this dynamic where teams are typically assessing a series of imperfect options, each with its own advantages and constraints. For example, one site may offer lower cost land but lack the existing infrastructure required to support large scale development, while another may provide access to grid power but at a significantly higher cost or with timelines that delay delivery.

In practice, few locations offer everything required, and selecting a site becomes an exercise in understanding what should be prioritized, what can be mitigated, and what must be accepted.

Factors like water availability, land constraints, fiber connectivity, permitting timelines and social license to operate are all deeply important to success. Developers must consider how to optimize within these constraints. Where grid power is unavailable or delayed, for example, off grid or hybrid energy solutions may be introduced.

While these approaches can accelerate delivery, they also bring different capital requirements, financing structures, and operational considerations that must be carefully weighed.

Combining power solutions can accelerate bringing capacity online more efficiently

As AI workloads drive unprecedented levels of demand, power strategies also require a reassessment against expected scale timelines. Grid supply does offer lower long-term energy costs, stability and resilience advantages eventually but hinges on capacity constraints, and extended interconnection timelines.

In contrast, behind the meter generation, such as gas turbines or reciprocating engines, can be deployed more quickly and provide greater operational control. This, however, comes with higher upfront capital requirements, higher operational costs, fuel dependencies and more complex permitting considerations.

As speed-to-market is a key competitive driver, many large-scale developments are willing to pay a premium for off-grid or hybrid architectures, including battery storage and integration of renewables where accessible.

These systems are coordinated through microgrid controls, allowing operators to manage load variability, maintain resilience through islanding, and optimize overall system performance. The final configuration is shaped by how factors such as time to market, grid availability, resilience, and overall cost evolve.

Rethinking cooling can support high-density AI and optimize when energy is used

With this increase in power demands comes a corresponding increase in heat generation. The physics and economics of air cooling are struggling to keep pace with the thermal loads generated by AI workloads, forcing a shift toward alternative solutions.

One solution is liquid cooling, which is gaining traction as a more effective way to manage higher heat loads. Transferring heat more efficiently, it enables facilities to operate at the densities required by AI infrastructure. However, it does also introduce new dependencies, particularly around liquid cooling solutions and the infrastructure required to support it.

At the same time, taking a broader view of cooling opens up new opportunities. Cooling systems can be integrated with wider power infrastructure, excess heat can be connected to industrial processes that can utilize it and waste heat from data centers can be repurposed for applications such as district heating, which is already quite common in the Nordics.

Approaching cooling in this way allows developers to design systems that make better use of energy and create additional value through heat reuse and integration with surrounding infrastructure.

Additionally, thermal energy storage gives AI data centers the ability to shift cooling demand away from peak periods by producing chilled water when electricity is cheaper or more available and using it later when loads are highest.

This creates valuable demand response capability, allowing the facility to reduce its grid draw during periods of system stress, lower demand charges and support utility programs without impacting data center operations. In combination with batteries and advanced controls, thermal storage can help stabilize both the data center and the surrounding grid.

Early efforts on permitting can identify the fastest development route and avoid delays

Permitting and regulatory considerations sit alongside these technical decisions, shaping what is possible and how quickly projects can move forward. Requirements vary by region, country and project type, but in all cases, they influence how projects must be designed from the outset.

For example, grid connected developments may be constrained by connection approvals and capacity limits, while sites incorporating on-site generation may require air quality or emissions permits that influence technology choices. Land use restrictions, environmental approvals and community considerations can further shape site layout, development timelines and even overall project viability.

Addressing these requirements early, and in parallel with technical and commercial decision making, is therefore as important as those other factors. When permitting is treated as part of the initial planning process, it allows projects to be structured in a way that is both deliverable and aligned with regulatory expectations from the beginning.

This, in turn, reinforces the need for a coordinated approach across the full range of stakeholders involved. Energy providers, technology companies, developers, regulators and local communities each play a role in shaping outcomes, and the interaction between them becomes a critical factor in how effectively projects can progress.

Having the right expertise in place to connect these elements enables developers to navigate this complexity more effectively, ensuring that decisions made early on are aligned across disciplines. This early alignment helps create a more integrated delivery pathway, reducing friction between project phases and supporting smoother progression from planning through to construction and execution.

Turning AI demand into operational capacity at the speed and scale the market requires

The importance of this becomes clearer when looking at how these challenges play out in practice. In Texas, for example, early engineering work on a gigawatt scale AI training data center helped define the infrastructure strategy for one of the largest behind the meter energy systems supporting AI workloads.

The project includes 5 GW of gas generation capacity, up to 1.25 GW of solar PV, utility scale battery storage and a microgrid supporting 20 buildings totaling 10 million square feet, each designed for around 250 MW of power demand.

Projects of this scale reflect the sheer pace and ambition of AI demand, but ultimately, success comes down to how effectively that demand is translated into deliverable infrastructure. That means making early decisions that can withstand real world constraints, from power availability and permitting through to long term operational performance.

Bringing these elements together into a coherent strategy, and aligning the stakeholders needed to deliver it, is what will enable projects to move at the speed and scale the market now requires.

We've featured the best database software.

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

• UK’s Police National Legal Database (PNLD) breach leaks data of 100k+ criminal justice professionals
• Threat group ExfilSquad claimed responsibility, posting 1.9 GB of stolen records on the dark web and demanding ransom
• PNLD notified NCA and ICO, hired specialists, and confirmed passwords weren’t compromised but contact details exposed

The UK’s Police National Legal Database (PNLD) suffered a cyberattack recently, in which it allegedly lost sensitive data on more than 100,000 criminal justice professionals.

In a short press release, PNLD confirmed the breach, saying it happened over a weekend. The threat actors, which were not named in the announcement, were said to have taken names, organizations, and work email addresses belonging to police officers, staff, government partners, and customers.

The announcement also said the stolen information was already published on the dark web, adding that there is “no evidence to suggest that passwords or other security credentials have been compromised.” How the attackers worked their way in was not disclosed in the announcement.

ExfilSquad takes the blame

Following the breach, PNLD hired cyber-security specialists, and notified the National Crime Agency, which started their investigation into the incident.

“All affected organizations were contacted in the days following the incident and provided with further information and guidance,” the announcement reads. “The Information Commissioner’s Office (ICO) has also been notified.”

At the same time, threat actors calling themselves ExfilSquad claimed responsibility for the attack, BleepingComputer reported. The group alleges it stole 135,000 contact records, sharing samples to support their claims. They also said they demanded a ransom in exchange for keeping the data safe.

In the dark web post, ExfilSquad said it obtained 1.9 GB of data, which includes information belonging to 114,000 PNLD subscribers and 21,000 Ask the Police users.

‘Ask the Police’ is a public-facing website where users can find answers to hundreds of commonly asked policing or legal questions.

ExfilSquad is a relatively new threat actor that's not known for any major attacks so far. Prior to the PNLD incident, it claimed the attack against Analog Devices, a US semiconductor company.

• Advanced Full Fibre Broadband from EE offers download speeds of 2.3Gbps and 8Gbps
• Speeds are potentially 222 times faster than standard superfast fibre
• Initially available in Guildford and Woking, expanding as Openreach rolls out its XGS-PON glass fibre network

Following a successful trial in early 2026, EE has launched its new Advanced Full Fibre Broadband plans, advertising download speeds of 2.3Gbps and 8Gbps. This makes EE the first provider to sell access to the Openreach XGS-PON glass fibre network.

The plans are initially only available in the Guildford and Woking areas, two Surrey towns in the London commuter belt, where the glass fibre network is installed and active.

EE is targeting users who rely on video conferencing, remote working, and content upload, and with multiple Internet of Things devices, as well as online gamers and streaming consumers. Speeds are up to 222 times faster than the standard superfast fibre broadband plans.

Two plans for EE’s XGS-PON fibre

Customers can access two plans from EE. The 2.3Gbps Advanced Full Fibre Broadband plan starts at £54.99 per month, while the 8Gbps plan is available from £74.99 a month.

No upload speeds have been stated, nor are they listed on the sign-up page, but they can be expected to be similarly fast.

XGS-PON (10-Gigabit-capable Symmetric Passive Optical Network) is a high speed, high-bandwidth data network standard, currently available in various forms across Europe, North America, and other regions. In the UK, it is used in the nexfibre, CityFibre, and Openreach networks, with EE using the latter to provide these plans.

With streaming speeds supporting 4K and 8K video, these plans are likely to be popular where available, and support for up to 190 devices suggests the plans will include routers suited to IoT and smart home applications.

Will faster fibre improve British broadband?

“Today marks a major milestone for EE as we become the first major UK provider to offer broadband speeds of up to 8Gbps on next-generation XGS-PON technology," noted Luciano Oliveira, Director of Product, Home and TV at EE.

"As homes become more connected and customers place greater demands on their broadband, we're investing in the technologies that will power the next generation of digital experiences.”

EE regularly wins recognition as the UK’s most popular network, with its business built on mobile (where its 4G, 5G and 5GSA speeds cover more than 90% of the UK) and an advertising campaign featuring Hollywood actor Kevin Bacon.

Unlike Virgin Media which owns its own fibre network, EE’s domestic and business broadband plans rely on third party infrastructure, mostly provided by Openreach, and due to the differences in connections across the country (and the legacy copper lines that remain in place in some locations), struggles to offer the same speeds for all customers.

This is a problem faced by all providers who sell access to Openreach lines, so EE’s immediate competitors will no doubt be watching with interest.

Patagonia is one of the biggest names in outdoor clothing, and especially impressive when it comes to sustainability — great news if you want to protect those beautiful natural landscapes you're planning on hiking over or camping in this summer.

I'm a big fan of Patagonia's products, but they don't come especially cheap. So I was particularly pleased, when hunting for kit for my next adventure, to uncover a goldmine of Patagonia discounts at Cotswold Outdoor. There are deals on a wide range of kit for every weather eventuality — important if you're planning on spending any time in the UK, where you can never be quite sure if you'll be facing sweltering heat or a torrential downpour.

Below, I've rounded up my pick of the best discounts across the men's and women's ranges, including fleeces, waterproofs, base layers, tees and shorts, with a few accessories thrown in for good measure. Happy exploring!

Outdoor kit for summer adventures

Patagonia Men's Boulder Fork Rain Jacket

Patagonia Men's Nano-Air Light Hybrid Hoodie Jacket

Patagonia Women's Classic Retro-X Fleece Jacket

Patagonia Men's Baggies 5" Shorts

Patagonia Women's Torrentshell 3l Jacket

Patagonia Women's R1 Fleece Jacket

Patagonia Women's Classic Retro-X Vest

Patagonia Women's Retro Pile Marsupial Fleece

Patagonia Women's Triolet Jacket

Patagonia Unisex Terrebonne Cap

Patagonia Women's Capilene Cool Daily Long Sleeve T-Shirt - Boardshort Logo

Patagonia Women's Unity Fitz Easy Cut Responsibili T-Shirt

Patagonia Women's Lightweight Synchilla Snap-T Fleece

Patagonia Women's Granite Crest 3l Jacket

Patagonia Black Hole Duffel Bag - 40l

Patagonia Men's Retro Pile Fleece Jacket

Patagonia Men's R1 Air Zip Neck Fleece

Patagonia Men's Fun Hoggers Shorts

Patagonia Men's Corduroy Volley Shorts

Patagonia Women's Better Sweater Fleece Jacket

It’s hardly a surprise that Apple had to increase the prices of its iPads, Macs and other devices last month amid ongoing RAM and storage shortages, and I wouldn’t be surprised if the upcoming iPhones follow suit — but it’s not completely doom and gloom for Apple fans.

Apple’s wearables weren’t impacted by these price hikes, which is good news for anyone looking to upgrade to a watchOS 27-compatible device (as there will only be a select few left) or if you’re keen to get a swanky new watch to track your workouts.

The Apple Watch 11 was already going at a fantastic price of AU$429 during Prime Day last month, but surprise, surprise, it’s ever-so-slightly cheaper now, and it’s not even sale season! The Apple Watch Series 11 with the 42mm chassis and GPS-only option is now AU$2 cheaper, making this the lowest-ever price on Amazon Australia — that’s 37% off in case you were wondering.

This price beats the AU$429 we saw during Prime Day, officially making this 37% discount the best we’ve seen for the Apple Watch Series 11 in Australia. The 42mm is also the only size discounted for the GPS-only version, so you’ll have to get the LTE option if you want the larger 46mm model, which is also discounted to AU$677, or 25% off, but there’s very limited stock.View Deal

Our Apple Watch Series 11 review called it “the most capable and best-looking” mainline Apple Watch yet, thanks to its larger battery capacity and brighter, more power-efficient and more scratch-resistant Always-On Retina LTPO3 OLED display compared to its predecessor.

Apple rates both the 42mm and 46mm models’ battery life at 24 hours of typical use and up to 38 hours in Low Power Mode, with battery capacity up 9% and 11%, respectively. Our reviewer found the claim checks out, with tests getting a full day and a half of battery life with light use, but the Low Power Mode makes it go even longer.

Health tracking is more comprehensive with the Series 11, adding blood-pressure monitoring to the usual arrhythmic heart-rate alerts, ECG, wrist temperature, respiratory rate and cycle tracking. Hearing health and Sleep Score are other notable additions, but these are also available on the cheaper SE 3 and have been rolled out to older models too.

Admittedly, the Apple Watch Series 11 won’t be much of an upgrade over the Series 10 because of the very similar hardware (and watchOS 27 compatibility), but upgrading from the Series 8 or older models, or even the Watch SE and SE 2, will see a significant performance jump and a longer support period.

And at this price, you don’t even need to wait till Black Friday as we don’t see it getting much cheaper than this in a few months’ time.

Did you expect film and TV's Christine Baranski to be an AI overlord in an alternate universe in Stuart Fails to Save the Universe episode 2? No? Me neither.

I wouldn't at all be surprised if this week's episode 3 ups the ante even further. While details are largely being kept under wraps, we can expect Stuart (Kevin Sussman), Denise (Lauren Lapkus), Kripke (John Ross Bowie) and Bert (Brian Posehn) to a more magical realm, with a wizarding world of a comic book store to boot.

Consider me strapped in. So, when does Stuart Fails to Save the Universe episode 3 arrive on HBO Max?

What time can I watch Stuart Fails to Save the Universe episode 3 on HBO Max?

For US viewers, Stuart Fails to Save the Universe episode 3 will drop on Thursday, August 6 at 6pm PT/ 9pm ET.

Internationally, you're looking out for these timings:

• US – 6pm PT / 9pm ET
• Canada – 6pm PT / 9pm ET
• UK – Friday, August 7 at 2am BST
• India – Friday, August 7 at 6:30am IST
• Singapore – Friday, August 7 at 9am SGT
• Australia – Friday, August 7 at 11am AEDT
• New Zealand – Friday, August 7 at 12pm NZDT

When do new episodes of Stuart Fails to Save the Universe come out?

(Image credit: HBO Max)

New episodes of Stuart Fails to Save the Universe will make landfall every Thursday in the US and on Fridays everywhere else. Here are the all-important dates you need to know about:

• Episode 1: out now
• Episode 2: out now
• Episode 3: August 6
• Episode 4: August 13
• Episode 5: August 20
• Episode 6: August 27
• Episode 7: September 3
• Episode 8: September 10
• Episode 9: September 17
• Episode 10: September 24

• LinkedIn will hold GPU investment and its compute and storage footprint flat rather than expanding its AI data centers
• The company says it roughly doubled the efficiency of its existing GPUs in six months through accumulated improvements to utilization, model distillation, and workload allocation, not a single breakthrough
• This makes it an exception to the rule under owner Microsoft's umbrella

LinkedIn has revealed it does not plan to spend aggressively on expanding its AI data centers over its next fiscal year - instead keeping GPU investment flat and holding its compute and storage footprint roughly where it is.

The stated reason is not restraint for its own sake: the company says it has found ways to get about twice as much out of its existing GPUs over the past six months and intends to spend that headroom on new features rather than new hardware.

Speaking to Wired, Erran Berger, LinkedIn's engineering CTO, framed the goal as keeping the compute footprint flat or close to it while still shipping more compute-hungry things into production - all while acknowledging this is an unusual position to take publicly right now.

Skipping the norm, both at the industry-level and the parent company

LinkedIn has been wholly owned by Microsoft since its December 2016 acquisition of the company for $26.2 billion.

Despite being part of the software giant, LinkedIn's approach seems vastly different from that of a company that recently saw its shares rally hard as it showcased AI spending finally resulting in measurable returns.

Microsoft also recently closed its own fiscal year, reporting $41 billion in capital expenditure in the most recent quarter alone. It added 31 data centers in that quarter and 88 across the year, and expects to spend more than $50 billion in the current quarter.

This draws an interesting parallel: while one part of Microsoft is deploying capital at a rate few companies in history have matched, a subsidiary with more than a billion users has decided to sit out the year, citing efficiency gains. This makes it a considerably more interesting approach than what would otherwise be a "company shows AI discipline" affair.

While parallels exist, it is prudent to point out that Microsoft's spending is overwhelmingly driven by Azure customer demand, and specifically by capacity it has contracted to supply to OpenAI, rather than by internal product workloads, while LinkedIn's compute is a rounding error against that.

Despite that, it offers an interesting alternative perspective when a large consumer platform can add generative AI features for a year without adding hardware; still, it runs counter to the prevailing assumption that AI features and capacity growth are inseparable.

What makes this possible for LinkedIn?

The answer runs back to a decision most coverage treated as an embarrassment at the time.

LinkedIn announced in 2019 that it would migrate its infrastructure onto Azure under a project codenamed Blueshift. In 2022, it quietly shelved that plan. An internal memo at the time cited Azure's own demand pressures and said LinkedIn would focus on scaling its on-premises infrastructure instead; subsequent reporting established that the migration had also run into difficulties because LinkedIn's in-house tooling did not transfer cleanly to Azure.

LinkedIn instead committed to its own data centers in Oregon, Texas, and Virginia.

LinkedIn's CTO for infrastructure, Raghu Hiremagalur, now argues that owning the full stack is precisely what makes this year's plan feasible, because the company can instrument every layer and treat efficiency as a standing investment rather than a one-off cost-cutting exercise.

“I really want to double underscore that for a company of our scale, to say a full year we're going to do this with no incremental storage and compute is no small feat, but it's taken a ton of work to get there," Hiremagalur said.

Reading what, in 2022, was a retreat as a 2026 advantage is self-serving, but it is not obviously wrong. The efficiency work itself is described as an accumulation rather than a breakthrough: better GPU utilization and workload allocation, distilling larger models into smaller ones, and rethinking how work is divided across training, inference, storage, and systems design.

Hiremagalur has also said the cost of serving each query had been climbing steadily while stored data was doubling annually, which he characterized as unsustainable. That is the more revealing framing. The efficiency push reads less like a strategic choice about the AI market and more like a company that looked at its own cost curve and decided it had to bend.

It makes it worth pointing out that LinkedIn has not really solved anything; It is that a platform of this size has publicly said out loud that its compute constraint is deliberate, at a moment when the four largest US hyperscalers have committed to something in the region of $600 billion to $700 billion of capital expenditure for the calendar year between them.

Almost every incentive in the industry currently runs toward announcing capacity rather than efficiency, making it an interesting outlier in a field dominated by daily capex announcements.

The more useful question is whether the approach holds. If it does, the argument that AI product ambition requires proportional growth in hardware gets meaningfully weaker. If it does not, this will read as an efficiency drive that met the hardware demands of a real product roadmap but failed to do so at a time when AI spending is increasingly scrutinized, even as LinkedIn itself recently allowed users to mark what they feel is 'AI slop'.

Beast of Reincarnation is a fantastic and immersive action-adventure role-playing game (RPG) with an emotional narrative that keeps you guessing at the end of every chapter. Game Freak, the developer responsible for the beloved Pokémon games, has taken a step into a completely new direction and done so in fine fashion.

Review info

Platform reviewed: PC
Available on: PS5, Xbox Series X|S, PC
Release date: August 4, 2026

Throughout the journey, you'll encounter formidable foes, from basic enemies to mini and major bosses, in combat encounters with an excellent feel of ebb and flow. Game Freak has nailed the concept of traversing a rundown yet beautiful post-apocalyptic Japan, with abilities that let players explore multiple regions and uncover a plethora of secrets.

I didn't expect Beast of Reincarnation to deliver an immersive and engaging experience to this degree; certainly not within the story, combat, and atmospheric aspects of the experience.

While it's far from perfect, due to some quality-of-life issues, a slightly overstayed welcome, and effectively little to no cosmetic customization, it's still a game I expect to keep playing long after rolling the credits.

What it means to be human

(Image credit: Game Freak)

In Beast of Reincarnation, a post-apocalyptic Japan set in the year 4026 is center stage. The parasitic entity or force known as the blight has taken over the world, corrupting animals, humans, and just about every living creature there is.

You play as Emma, a blight-stained young girl alongside her blighted wolf-dog companion, Koo. As a purifier, Emma is humanity's last hope to defeat the beast of reincarnation and restore the world from blight. She will need to absorb Nushi, the corrupt (or blight-stained) monsters that come in all different shapes, species, and sizes.

During the journey, Emma will discover what it's like to be human and consider a very specific aspect of her nature — avoiding spoilers, you'll slowly unravel what that means for Emma, those closest to her, and, frankly, humanity's fate.

While Beast of Reincarnation is focused on the restoration of the world, one of its central themes is love and what one will do to embrace it. It's very much an emotional rollercoaster, full of surprises that I genuinely couldn't see coming, and by the time the credits rolled, I was clapping and tearing up just a little.

Beast of Reincarnation also features a rapport system, specifically with Koo and other supporting characters like Kagura. Essentially, the more interactions you have with these characters, such as feeding Koo when hungry, can increase the rapport, resulting in benefits such as extra skill or equipment slots.

The rapport with these characters directly ties into the story, specifically with how they respond to Emma or help. Fortunately, it doesn't take too much effort to increase rapport, and with the emotional weight of Emma's story arc, you'll want to interact with other characters as much as possible.

Aside from being incredibly adorable, Koo is easily one of the best companions in gaming, providing support in and out of intense combat sequences. (Video Credit: Game Freak)

Game Freak's inspirations for Beast of Reincarnation are clear as day. It almost certainly draws from PlatinumGames' Nier: Automata and Shift Up's Stellar Blade, and Square Enix's Final Fantasy 16.

Again, without spoiling story details, Emma will often encounter human non-player characters (NPCs) or even Golems (who were once human) that will label her 'stained' and act disgusted. It's very reminiscent of how Final Fantasy 16's Clive Rosfield was treated early on in the story, often labelled 'branded'.

That alone creates an interesting predicament; Emma is humanity's only hope, yet humans essentially despise her due to her blight-stained nature. It dives into some of the story's darker and more lonely aspects, especially when acknowledging Emma's emotional vulnerability.

While the narrative kept me invested, wanting to find out what happens next, I feel the game's runtime could've been shorter. There are constant cliffhangers at the end of each chapter, where answers to questions about Emma, the post-apocalyptic world, and the beast of reincarnation itself are unnecessarily withheld.

Ultimately, many of the cliffhangers and small-scale cutscenes feel like padding, rather than ones to help progress the story. I appreciated some of those 'padded' dialogue moments, giving Emma and other characters a little more depth — so it's not all bad, but it's worth noting if you hate long-winded cutscenes.

(Image credit: Game Freak)

Outside of the story, Beast of Reincarnation's soundtrack is undoubtedly one of its strongest assets, and I couldn't stop thinking about Nier Automata's orchestral, melancholic, yet magnificent soundtrack. While traversing many of Game Freak's incredible in-game regions, I was accompanied by amazing vocals, which manage to fit each scenario perfectly.

The only frustration I had with Beast of Reincarnation's original soundtrack is that it can get repetitive as you progress. Sometimes, the same soundtrack from a previous chapter would play again in a new environment, and unless you're in combat or watching a cutscene, the music never stops — and it's one of the only complaints I had for Stellar Blade too.

Nonetheless, it's still a fantastic listen, especially in the final chapters, where I was particularly blown away by the soundtrack playing during the final boss battle — and I'm already itching to start my second playthrough just to experience it again.

Spectacular Sekiro-like combat

Just imagine those satisfying sword-clashing sounds from Sekiro, but then dial it up to 11. That's exactly what you get in Beast of Reincarnation during combat. (Video Credit: Game Freak)

Simply put, Game Freak absolutely nailed the combat. Much like traditional Final Fantasy games (i.e., Final Fantasy 7 Remake), Beast of Reincarnation features a blend of real-time action and turn-based combat, where players control Emma engaging in fierce sword battles while also turning to Koo for blight attacks.

The more you pull off successful parries, the more Florescence Points (FP) you gain for Koo to select and pull off special attacks. At the same time, the more you parry, the more an enemy's stun gauge increases, causing a stagger and an opening for a finishing blow.

In this manner, Beast of Reincarnation's combat is what I would describe as Sekiro-like, with From Software's Sekiro: Shadows Die Twice featuring some of the best sword combat in games.

Parrying is key for Emma in almost all combat encounters. Players do have the option to dodge attacks, and need to for unbloackables. However, parrying will allow you to overcome all foes once they're stunned, and will also help you maximize the efficacy of Koo.

Also, if you thought Sekiro's sword clashing sound effects were great, Beast of Reincarnation gives it a run for its money. Game Freak's sound design for combat encounters single-handedly elevates enemy and boss encounters to another level; one duo boss encounter in particular left me hyped on the edge of my seat, in what was a challenging but extremely immersive back-and-forth clash.

One annoying issue, though, is one supporting character with Emma, who constantly says, "Remember to use Koo's skills too." This happens in every single combat encounter whenever Koo's FP meter is full, and it drove me wild on more than a few occasions.

Emma herself does the same when finding a new campsite or spotting an enemy, and it's one of those minor quality-of-life aspects that can very quickly become a major one, since it's bothersome after playing for hours.

There were many times I could choose to be stealthy, but decided against it. Why? The back-and-forth sword clashes are incredible! (Video Credit: Game Freak)

Throughout the game, you'll find different armor and swords to use, each with unique properties like elemental effects for the latter that will come in handy against enemies with different weaknesses.

There are two skill trees, one dedicated to Emma and another for Koo, and there are plenty of abilities to unlock. I finished the game at the 20-hour mark, and I only had around half the skills in both trees unlocked, which should give you an idea of just how much there is to add to Emma and Koo's skill set.

Fortunately, that will aid a second playthrough perfectly; and yes, Beast of Reincarnation has a New Game Plus run ready to go as soon as you finish the game, with a new difficulty (which I won't name... it's a potential spoiler). There's a fair amount of replay value, since almost all regions are explorable, with side content like mini bosses waiting to be found.

The latter makes traversal very important, and fortunately, it's a very strong highlight. Emma's hair allows her to reach high terrain and find loot that is pivotal to weapon progression, and finding new ingredients for Kagura to use for cooking.

I was quite disappointed to see that the new armor found for Emma has absolutely no cosmetic changes. It feels like a massive missed opportunity for Game Freak to give players a deeper layer of customization, besides equipping gear simply for better stat boosts.

But how does it perform?

4026 post-apocalyptic Japan is really a beautiful place to explore, but does Game Freak's ambitious action-adventure RPG run well enough to enjoy the landscape? (Video Credit: Game Freak)

I played Beast of Reincarnation on my main rig, using an Nvidia GeForce RTX 4080 Super, at 4K and 1440p. Instead of the 3440x1440 ultrawide display resolution, I was limited to 2560x1440, as the game doesn't have any 21:9 aspect ratio support — in 2026, this is unacceptable and, frankly, a massive disappointment for PC players.

At 4K, using DLSS (unclear which model it's using) quality, I was able to stay above 60 frames per second (fps), often between the 75 to 90fps range. I did have occasional frame rate dips, typically when there were numerous enemies on screen, but for Unreal Engine standards, it's not horrible performance.

At 1440p, it consistently stayed above the 90fps mark, and again, aside from odd moments of frame rate dips, performance wasn't a major concern for me.

Performance on the Lenovo Legion Go S Z1 Extreme was a little rougher than I expected. I could barely reach a stable 60fps even with the lowest graphics preset and FSR 3 performance enabled. What's worse is that the upscaler is constantly reset every time you restart the game.

Hopefully, Game Freak will include ultrawide support and FSR 4 in a future update, as only FSR 3.1 games can use FSR 4 via AMD's Adrenalin software.

Should you play Beast of Reincarnation?Play it if...

You're looking for a less punishing Sekiro-style game
Beast of Reincarnation scratches the itch of parry-based combat without the unforgiving difficulty of Sekiro: Shadows Die Twice, and there are multiple difficulties, including one unlocked for New Game Plus for a far more challenging experience.

You're a fan of gripping stories set in post-apocalyptic worlds
Much like Nier: Automata and Stellar Blade, Beast of Reincarnation features a post-apocalyptic world to explore, with regions overgrown with nature and monstrous enemies to battle.

Don't play it if...

You want an action-adventure RPG with cosmetic options
If you're expecting to find any cosmetic options in Beast of Reincarnation, you'll be severely disappointed. While there is new gear and weapons to find, Emma's look remains the same throughout the game's runtime.

You hate a narrative with info-dumping
While Beast of Reincarnation's narrative is an interesting and immersive one, plot twists and surprises can be slightly underwhelming, as some are told via character explanations instead of shown.

Accessibility features

Beast of Reincarnation allows players to adjust controller button configurations and key bindings. Only English and Japanese are available as voice-over languages — I tested both throughout my playthrough.

There are no field-of-view options, which it desperately needs. The camera angle during some boss fights can easily be the cause of defeat, as sometimes enemies are too big, and you can't see Emma while they're still attacking you.

There are options to show speaker names via subtitles, but there isn't an option to make subtitles bigger. It's a little problematic as subtitles are small, and sometimes bright scenery in games can make it difficult to read.

Menu navigation with a mouse also isn't very good, as it requires the right mouse button to exit each section, instead of simply selecting another on the fly.

How I reviewed Beast of Reincarnation

(Image credit: Game Freak)

I tested Beast of Reincarnation on my main RTX 4080 Super rig, my second SteamOS AMD Radeon RX 9060 XT 16 GB powered PC, and the Lenovo Legion Go S Z1 Extreme. With the RX 9060 XT 16GB, it was a struggle to hit 60fps at 4K without using FSR 3 and lowering some graphics settings.

I used my TCL QM8 55-inch mini-LED 4K TV and my LG UltraGear 45GS95QE ultrawide OLED, despite the lack of ultrawide support from Game Freak.

Game Freak notes that while Beast of Reincarnation is on Steam, it's working on optimizing the game for the Steam Deck, so expect potential performance improvements at launch or in the future.

First reviewed July 2026.