Technology

Businesses of all shapes and sizes are adopting AI to improve productivity and efficiency, but where they should be seeing improvements from this strategy, instead they’re seeing rising token costs, struggles with integrating tools, and more security risks.

As with all new technologies, adoption comes first and the procedures and governance are a few steps behind. Experiments are taking place in almost every industry, and the lessons learned will help guide other businesses into successful adoption and AI maturity.

But employees fear replacement and sometimes spend more time questioning the results of their AI assisted work. In the worst circumstances more work is created in trying to ensure employees trust the technology, and solving the new, unseen challenges that come with adapting an AI strategy.

AI costs and challenges in the road ahead

Rising token costs are one of the biggest challenges businesses face. Without clear ways to measure how token costs reflect performance, it’s very difficult to assess if AI spend is actually offering any performance benefits. This is especially true when new, more powerful models are being released - and staying ahead of the competition means the accompanying, ever-increasing costs is the price of doing business.

But while employees may have just finished their training, or setting up a new workflow for one AI model, introducing the next can increase complexity and harm any new productivity gains. There is therefore a balance to be struck between AI integration, its associated costs, and the productivity gains employees see.

Rampant spending and reckless adoption can turn an AI strategy from a business-boosting asset into a stress-inducing, trust-eroding liability.

Ruth Patterson, Managing Director, HP, UK & Ireland says that the businesses seeing the most success during this technological revolution aren’t necessarily integrating it at every turn. Instead, they’re “applying it to practical workflows in a way that protects data and delivers real results.”

I spoke to Patterson to understand the challenges businesses face in delivering an AI strategy that shows real results, and how organisations can tackle the challenges that come with adoption AI.

• How are enterprise attitudes towards AI token use changing in 2026?

Businesses are waking up to a simple but overlooked truth about AI: the more they use, the more it costs. As AI has become part of everyday work, the financial cost of millions of interactions, charged at a token level, has become much more visible – and not just to the IT department.

Finance leaders are watching token usage fill a sizeable chunk of their balance sheets, rightly prompting much closer scrutiny of where workloads are processed and whether every task needs to be sent to the cloud.

As the conversation moves from AI experimentation to value, leaders must now focus on building an AI strategy that's both commercially sustainable and operationally efficient. That means moving away from AI for AI’s sake to a more tailored approach that balances cost, security and performance.

• What are businesses prioritising as they move beyond initial AI experimentation? Where have businesses seen the greatest gains?

The biggest gains so far have come from AI taking repetitive tasks off people's desks. You will have heard this said a lot, but the benefits are real. Whether it's summarising meetings, drafting documents, searching internal knowledge or helping employees find information more quickly, AI really is giving people back time to focus on work that requires judgement and creativity. Employees increasingly recognise that the future of the workplace is AI-enabled, whereby their own skills augmented by digital solutions. To succeed, organisations must offer access to the right technology and create environments where people feel empowered to experiment with AI.

However, as more organisations begin to move beyond this experimentation phase, they are starting to become much more disciplined. They're asking whether AI is secure, whether employees are using approved tools and whether the technology is genuinely improving productivity rather than simply adding another application to the estate.

The businesses making the fastest progress in this environment aren't necessarily using the most AI. They’re the ones applying it to practical workflows in a way that protects data and delivers real results.

• What are the biggest challenges organisations face when deploying AI at scale?

Organisations must juggle several competing priorities as they scale deployment: how to protect sensitive data, manage operational costs, maintain performance and ensure employees trust the technology they're using.

The trust question is particularly important as AI moves further into everyday use. Employees need confidence that the tools are reliable and approved, while organisations need confidence that data is protected and whether the cost model is right.

Getting IT infrastructure tuned correctly is key to making this work at scale. One of the most important decisions that organisations need to make is which AI workloads belong in the cloud, and which make more sense running on the device. That is ultimately a business decision – as opposed to purely a technology or IT decision – because it affects performance, cost and security.

• How does on-device AI slot into an organisation's overall AI strategy?

I don't see cloud AI and on-device AI as competing approaches - I see them as complementary. Cloud services will remain essential for large-scale models and complex reasoning. But not every AI task needs to leave the device.

For everyday activities like summarisation, transcription or content creation, running AI locally can reduce dependence on cloud infrastructure and give organisations greater control over sensitive information. AI only creates value when it becomes part of everyday workflows. By adopting on-device AI-solutions, employees can reduce latency and increase productivity with the peace of mind that their data is secure. This offers an easy route-in for employees beginning to implement AI into their day-to-day workflows.

The right approach to enterprise AI strategy is pretty simple: place workloads where they make the most sense. Because once the right balance between cloud and local AI is found, organisations will find the sweet spot between cost, performance and security.

• How are business leaders evaluating the success of AI investments around productivity?

The conversation has moved beyond adoption metrics. Now leaders want to understand whether AI is creating measurable improvements in business performance. They're looking at time saved, faster decision-making, improvements to workflow efficiency and whether employees can spend more time on higher-value work.

Technology leaders are also beginning to examine the broader economics of AI. Productivity gains need to be considered alongside operational expenditure, infrastructure requirements and long-term scalability.

Ultimately, AI should reduce friction. If employees can complete work more efficiently while organisations maintain control over cost and governance, that's where the real return on investment begins to emerge.

AI-enabled digital solutions now offer benefits such as persona-based device optimisation or integrated sentiment analysis to measure employee satisfaction with digital tools. These innovations truly redefine experience management and facilitate higher employee satisfaction and productivity. It also enables leaders to stay closer to their employees and gauge whether AI is improving their experience or simply adding another layer of complexity.

• How do you expect enterprise AI infrastructure to evolve over the next few years?

We will continue to see significant investment in AI infrastructure. But the conversation needs to go beyond capacity, because capacity doesn’t create business value alone.

The next phase will be about ensuring infrastructure enables AI workloads to run where they are most effective. That means treating the endpoint as part of the AI infrastructure and using it to process selected tasks locally, alongside cloud services and edge computing. At the same time, we can expect to see greater emphasis put on AI infrastructure that supports governance, security and efficiency alongside raw compute capacity.

Together, these infrastructure shifts will start to deliver the practical value that employees and businesses expect from AI but aren’t currently seeing.

• What trends are you watching most closely in enterprise AI right now?

Three stand out for me:

First, the move towards hybrid AI architectures. Organisations are becoming much more deliberate about deciding which workloads should run in the cloud and which belong on the device.

Second, the growing focus on the economics of AI. As adoption scales, businesses are paying much closer attention to operational expenditure, infrastructure efficiency and the total cost of AI deployment.

Finally, we’re seeing the role of the endpoint evolve. For years, the PC has been framed as an access device only. Today, it has become an intelligent computing platform, capable of running AI workloads securely and efficiently.

Businesses don’t need a big AI budget to get all three right. They just need to spend time working out, task by task, where intelligence really belongs.

Businesses of all shapes and sizes are adopting AI to improve productivity and efficiency, but where they should be seeing improvements from this strategy, instead they’re seeing rising token costs, struggles with integrating tools, and more security risks.

As with all new technologies, adoption comes first and the procedures and governance are a few steps behind. Experiments are taking place in almost every industry, and the lessons learned will help guide other businesses into successful adoption and AI maturity.

But employees fear replacement and sometimes spend more time questioning the results of their AI assisted work. In the worst circumstances more work is created in trying to ensure employees trust the technology, and solving the new, unseen challenges that come with adapting an AI strategy.

AI costs and challenges in the road ahead

Rising token costs are one of the biggest challenges businesses face. Without clear ways to measure how token costs reflect performance, it’s very difficult to assess if AI spend is actually offering any performance benefits. This is especially true when new, more powerful models are being released - and staying ahead of the competition means the accompanying, ever-increasing costs is the price of doing business.

But while employees may have just finished their training, or setting up a new workflow for one AI model, introducing the next can increase complexity and harm any new productivity gains. There is therefore a balance to be struck between AI integration, its associated costs, and the productivity gains employees see.

Rampant spending and reckless adoption can turn an AI strategy from a business-boosting asset into a stress-inducing, trust-eroding liability.

Ruth Patterson, Managing Director, HP, UK & Ireland says that the businesses seeing the most success during this technological revolution aren’t necessarily integrating it at every turn. Instead, they’re “applying it to practical workflows in a way that protects data and delivers real results.”

I spoke to Patterson to understand the challenges businesses face in delivering an AI strategy that shows real results, and how organisations can tackle the challenges that come with adoption AI.

• How are enterprise attitudes towards AI token use changing in 2026?

Businesses are waking up to a simple but overlooked truth about AI: the more they use, the more it costs. As AI has become part of everyday work, the financial cost of millions of interactions, charged at a token level, has become much more visible – and not just to the IT department.

Finance leaders are watching token usage fill a sizeable chunk of their balance sheets, rightly prompting much closer scrutiny of where workloads are processed and whether every task needs to be sent to the cloud.

As the conversation moves from AI experimentation to value, leaders must now focus on building an AI strategy that's both commercially sustainable and operationally efficient. That means moving away from AI for AI’s sake to a more tailored approach that balances cost, security and performance.

• What are businesses prioritising as they move beyond initial AI experimentation? Where have businesses seen the greatest gains?

The biggest gains so far have come from AI taking repetitive tasks off people's desks. You will have heard this said a lot, but the benefits are real. Whether it's summarising meetings, drafting documents, searching internal knowledge or helping employees find information more quickly, AI really is giving people back time to focus on work that requires judgement and creativity. Employees increasingly recognise that the future of the workplace is AI-enabled, whereby their own skills augmented by digital solutions. To succeed, organisations must offer access to the right technology and create environments where people feel empowered to experiment with AI.

However, as more organisations begin to move beyond this experimentation phase, they are starting to become much more disciplined. They're asking whether AI is secure, whether employees are using approved tools and whether the technology is genuinely improving productivity rather than simply adding another application to the estate.

The businesses making the fastest progress in this environment aren't necessarily using the most AI. They’re the ones applying it to practical workflows in a way that protects data and delivers real results.

• What are the biggest challenges organisations face when deploying AI at scale?

Organisations must juggle several competing priorities as they scale deployment: how to protect sensitive data, manage operational costs, maintain performance and ensure employees trust the technology they're using.

The trust question is particularly important as AI moves further into everyday use. Employees need confidence that the tools are reliable and approved, while organisations need confidence that data is protected and whether the cost model is right.

Getting IT infrastructure tuned correctly is key to making this work at scale. One of the most important decisions that organisations need to make is which AI workloads belong in the cloud, and which make more sense running on the device. That is ultimately a business decision – as opposed to purely a technology or IT decision – because it affects performance, cost and security.

• How does on-device AI slot into an organisation's overall AI strategy?

I don't see cloud AI and on-device AI as competing approaches - I see them as complementary. Cloud services will remain essential for large-scale models and complex reasoning. But not every AI task needs to leave the device.

For everyday activities like summarisation, transcription or content creation, running AI locally can reduce dependence on cloud infrastructure and give organisations greater control over sensitive information. AI only creates value when it becomes part of everyday workflows. By adopting on-device AI-solutions, employees can reduce latency and increase productivity with the peace of mind that their data is secure. This offers an easy route-in for employees beginning to implement AI into their day-to-day workflows.

The right approach to enterprise AI strategy is pretty simple: place workloads where they make the most sense. Because once the right balance between cloud and local AI is found, organisations will find the sweet spot between cost, performance and security.

• How are business leaders evaluating the success of AI investments around productivity?

The conversation has moved beyond adoption metrics. Now leaders want to understand whether AI is creating measurable improvements in business performance. They're looking at time saved, faster decision-making, improvements to workflow efficiency and whether employees can spend more time on higher-value work.

Technology leaders are also beginning to examine the broader economics of AI. Productivity gains need to be considered alongside operational expenditure, infrastructure requirements and long-term scalability.

Ultimately, AI should reduce friction. If employees can complete work more efficiently while organisations maintain control over cost and governance, that's where the real return on investment begins to emerge.

AI-enabled digital solutions now offer benefits such as persona-based device optimisation or integrated sentiment analysis to measure employee satisfaction with digital tools. These innovations truly redefine experience management and facilitate higher employee satisfaction and productivity. It also enables leaders to stay closer to their employees and gauge whether AI is improving their experience or simply adding another layer of complexity.

• How do you expect enterprise AI infrastructure to evolve over the next few years?

We will continue to see significant investment in AI infrastructure. But the conversation needs to go beyond capacity, because capacity doesn’t create business value alone.

The next phase will be about ensuring infrastructure enables AI workloads to run where they are most effective. That means treating the endpoint as part of the AI infrastructure and using it to process selected tasks locally, alongside cloud services and edge computing. At the same time, we can expect to see greater emphasis put on AI infrastructure that supports governance, security and efficiency alongside raw compute capacity.

Together, these infrastructure shifts will start to deliver the practical value that employees and businesses expect from AI but aren’t currently seeing.

• What trends are you watching most closely in enterprise AI right now?

Three stand out for me:

First, the move towards hybrid AI architectures. Organisations are becoming much more deliberate about deciding which workloads should run in the cloud and which belong on the device.

Second, the growing focus on the economics of AI. As adoption scales, businesses are paying much closer attention to operational expenditure, infrastructure efficiency and the total cost of AI deployment.

Finally, we’re seeing the role of the endpoint evolve. For years, the PC has been framed as an access device only. Today, it has become an intelligent computing platform, capable of running AI workloads securely and efficiently.

Businesses don’t need a big AI budget to get all three right. They just need to spend time working out, task by task, where intelligence really belongs.

• Apple Fitness+ could be getting Peloton-style live workouts
• The fitness platform has been 'under review' since earlier this year
• Spotify recently expanded into fitness, so Apple might one-up its rival with live workouts

Something is going on at Apple Fitness+, the tech giant’s flagship fitness service, and it could be hinting at a new Peloton-style live workout hub.

A job listing posted by Apple advertises a Studio Producer role for Fitness+, implying there’s about to be a new upgrade coming to the health service, which is asking for a candidate with a "strong foundation in live or live-to-tape multi-camera production and a genuine curiosity about where live content is headed next."

Apple Fitness+ was introduced back in 2020, offering subscribers a one-stop shop of on-demand workout videos run by in-house fitness experts, integrating real-time workout metrics from your Apple Watch. Apple Fitness+ doesn’t host live workouts with live leaderboards in the way that Peloton does, so an upgrade could give the service the new lease of life it needs.

Despite its rich library of pre-recorded workout videos and guided meditations, Apple Fitness+ has been declining over the years. Earlier this year, we reported that Apple Fitness+ was ‘under review’, with the possibility of the firm merging with the existing Apple Health app to offer a combined subscription, but as it stands, the future of Apple Fitness+ is still undecided.

Bloomberg’s ($/£) Mark Gurman said that "there’s no telling what will end up happening," so not even seasoned leakers are able to predict where it’s going. A direct partnership with a fitness giant like Peloton hasn’t been hinted at, but since Spotify recently ventured into the fitness world, Apple Fitness+ might have some fresh competition.

Spotify's Fitness hub packs on-demand workout videos from Peloton, but it has yet to jump on the live workout streaming opportunity (Image credit: Future)

A few weeks ago, Spotify expanded its services into fitness and I sat down with its developer, Austin Lamon, who described it as a “natural extension of how people already use Spotify today”. The new Fitness hub packs curated Running Mode playlists, as well as a section packed with pre-recorded workout guides from Peloton’s flagship personal trainers — and it’s all included in the Premium plan on top of access to music, audiobooks, and podcasts.

Though Apple Fitness+ got there first with its own roster of on-demand workout videos, Spotify has combined its Fitness hub with services that subscribers are already paying for, whereas Apple’s music and fitness platforms are separate entities. And yes, Spotify is prone to a price hike every now and then, but its new fitness content doesn’t come with any add-on costs.

The only thing that Spotify Fitness hasn’t jumped on yet is the idea of hosting its own live workouts, which opens a door for Apple Fitness+ to snatch an opportunity. If Apple started pumping out its own Peloton-style live workouts, it could give the service an edge over rivals and give iOS fans a new reason to stay put. Perhaps we'll find out more during Apple's upcoming product launch in September?

Google Meet can use Gemini to capture in-person conversations and turn them into a Google Doc with summaries and action items.

The post How to Take Notes for In-Person Meetings With Google Meet appeared first on TechRepublic.

TP-Link BE6300 One Minute Review

I liked TP-Link’s BE3200 range extender, which helped to tackle the Wi-Fi deadspot in my office quickly and easily, and at a competitive price.

However, the BE3200 is very much an entry-level option for Wi-Fi 7, with a relatively modest top speed of 3.2Gbps. If you’re looking for something a little faster for gaming or streaming 4K video then you could consider the BE6300 model, which doubles that speed without doubling the price.

Both range extenders keep the price down by sticking with dual-band Wi-Fi, which uses just the 2.4GHz and 5.0GHz frequency bands, but omits the high-speed 6.0GHz band that is available with the fastest Wi-Fi 7 models.

But, as the name suggests, the BE6300 is still almost twice as fast as the BE3200, stepping up to a top speed of 6.3Gbps, which should be more than fast enough for most home users.

As you’ve probably guessed, the term ‘BE6300’ refers to the speed of the range extender, but it’s also known by the model number RE403BE, so keep your eyes peeled if you’re buying online.

(Image credit: Future)TP-Link BE6300 review: Price & release date

• Where is it available? US-only
• Available Now
• Price: $149.99

The BE6300 is currently only available in the US. You can’t buy it direct from TP-Link either, so you’ll need to shop around online, with the average price sitting at around $149.99.

Oddly, though, there is a similar model called the BE6500 (model RE605BE) available in the UK and Australia. This is slightly faster than the BE6300 that we review here, with a top speed of 6.5Gbps, and costs £188.00/AU$265.

TP-Link BE6300 review: Specs

Price

$149.99

Wi-Fi

Wi-Fi 7 (2.4GHz/5.0GHz)

Wi-Fi Speed

6.3Gbps

Ports

1x 2.5Gbps Ethernet

Antennae

4

Dimensions (HxWxD)

161 x 81 x 39mm

TP-Link BE6300 review: Design

Unsurprisingly, the BE6300 looks very similar to its little brother, with a chunky, white rectangular design that plugs straight into any convenient mains power supply.

However, the extra speed of this model means that it requires a little more power and the BE5300 is therefore quite a bit larger, and also sprouts an extra pair of antennae – four in total - to boost performance.

The body of the BE6300 measures 161mm high, but unfolding the top and bottom antennae extends that to a full 261mm. It’s wider too, at 81mm, which means that the bulky device could block any adjacent power sockets if you have two or more sockets that sit close together.

You’ll also need plenty of room if you want to use the Ethernet port that's located on the right-hand side of the range extender. Mind you, it’s possible to plug the BE6300 into a mains extender, rather than plugging it straight into a wall socket, although that’s not a very neat solution as it leaves you with additional cables trailing on the floor.

(Image credit: Future)TP-Link BE6300 review: Features

As mentioned, the BE6300 provides dual-band Wi-Fi 7, using the 2.4GHz and 5.0GHz frequency bands with a top speed of 6.3Gbps.

It may not support the high-speed 6.0GHz band, but it does include a Wi-Fi 7 feature called MLO – multi-link operation – that allows computers and mobile devices with Wi-Fi 7 to boost performance by transmitting data on both bands simultaneously. The Ethernet port on this model is faster too, now running at 2.5Gbps.

As we’ve seen before, TP-Link’s Tether app is well designed and easy to use, making it easy to get started even if you’ve never used a range extender before. It recommends that you initially plug the BE6300 into a power socket close to your main broadband router in order to avoid any interference during the initial set-up process.

The app is then able to automatically detect the range extender and guides you through the process of connecting it to both the 2.4GHz and 5.0GHz networks set up by your main router.

If you want to keep things simple then you can tell the range extender to just use the same network name and password as your original network, or you can give the range extender a new name and password for extra security.

Once that’s done you can unplug the range extender and move it closer to the room where you need to boost the Wi-Fi signal. The app can help here too, by checking the strength of the Wi-Fi signal so that you can find the best spot to place it.

The app can also show you all the devices that are connected to the range extender, and allows you to block devices, such as a child’s tablet or smartphone at night-time. There’s also an Eco mode that helps to reduce power consumption, and you can schedule a time for the range extender to turn itself off as well.

TP-Link BE6300 review: Performance

I have a Wi-Fi deadspot in an office at the back of my building, and when I tested the less expensive BE3200, I found that its 2.4GHz band couldn’t quite achieve the full speed of 150Mbps provided by my office broadband service.

That was no longer a problem when I set up the BE6300 in a nearby hallway, and it was easily able to maintain a smooth 150Mbps for both the Ookla Speed Test and for file downloads from Steam. Needless to say, the faster 5.0GHz band worked equally well, cruising along at 150Mbps throughout all my tests.

Not everyone will need a range extender with a speed of 6.3Gbps, so many homes will still get perfectly good performance from a less expensive range extender such as the BE3200. But, if you do need strong performance for gaming or streaming video, then the BE6300 fits the bill at a competitive price.

Should you buy the TP-Link BE6300?

Attribute

Notes

Score

Features

It’s dual-band only, but good performance and ease of use make it suitable for many homes

4/5

Performance

It’s a modest performer compared to tri-band rivals, but still fast enough for gaming and streaming video

4/5

Design

The bulky design could cause problems with some power sockets

3.5/5

Value

It's a good dual-band range extender at a competitive price

4/5

Buy it if…

You’ve got an annoying deadspot
A range extender is a good option for improving the Wi-Fi signal in just one or two rooms, or an outdoor location such as a garden or parking space.View Deal

You’re a gamer
It’s certainly not the fastest range extender you can buy, but a speed of 6.3Gbps means that the BE6300 can handle gaming and streaming video with ease.View Deal

Don’t buy it if…

You’re on a budget
Not everyone needs Wi-Fi 7, or 6.3Gbps performance, so there are plenty of range extenders using the older Wi-Fi 6 that can still boost your Wi-Fi for less than $100.View Deal

You need top performance
The BE6300 performs well, but its dual-band Wi-Fi is really just mid-range for Wi-Fi 7. There are faster tri-band range extenders that can hit 10Gbps or more.View Deal

Also consider

TP-Link BE10000
A speed of 6.3Gbps will be fine for most homes, but if you really need top performance then TP-Link’s tri-band BE1000 range extender adds support for the high-speed 6.0GHz band, which boosts the Wi-Fi speed to 10Gbps.

Read our full TP-Link BE10000 reviewView Deal

Asus RP-AX58
Wi-Fi 6 has still got some life in it, and this low-cost range extender provides dual-band Wi-Fi running at 3Gbps, which will be perfectly adequate for email, web browsing and bingeing Netflix.

Read our full Asus RP-AX58 reviewView Deal

• Read TechRadar’s reviews guarantee
• First reviewed August 2026

Hard drives remain one of the most affordable ways to add terabytes of storage to a PC, server, or NAS, particularly where speed isn't a priority. The 4TB Seagate IronWolf (ST4000VN006) hard drive is currently $187 (was $280) at Newegg.

This is a standard 3.5-inch SATA hard drive that can be installed in a compatible desktop, workstation, server, or enclosure. What you get over a basic desktop drive is hardware and firmware designed to cope particularly well with continuous operation and multi-drive NAS environments.

It offers CMR recording, a 5400 RPM spindle speed, 256MB cache, and SATA 6Gb/s interface. It's rated for 24/7 operation and workloads of up to 180TB per year. Seagate's AgileArray technology is designed to improve reliability and RAID performance in compatible multi-bay systems.

4TB Ironwolf SHard Drive: was $279.99 now $187
This Seagate IronWolf HDD provides 4TB of CMR storage, a 5400 RPM spindle speed, 256MB cache, SATA 6Gb/s connectivity, 24/7 operation, and a 180TB/year workload rating.View Deal

Why we recommend it

In his 4-star review of the 8TB model (ST8000VN004), our expert Mark said: "IronWolf drives feature CMR technology and AgileArray firmware, ensuring smooth RAID performance, reduced vibration, and efficient power management. The 180TB per year workload rating, the RV sensors, the three-year warranty, and the bundled Rescue Data Recovery Services have all persisted as defining features of the consumer tier."

Flexibility is a huge part of the HDD's appeal. Its standard 3.5-inch SATA interface means it can provide conventional storage in compatible PCs and other systems, and its NAS-specific features are particularly handy when it's running continuously or alongside other drives.

CMR recording means it's well suited to sustained writing and RAID environments, and the 180TB/year workload rating makes the drive better equipped for heavier storage duties than a typical everyday HDD.

You also get Seagate's IronWolf Health Management on supported NAS hardware, which can provide drive-health monitoring and recommendations. Seagate includes three years of Rescue Data Recovery Services as well, although availability varies by country.

For more options, take a look at our roundups of the best desktop and laptop hard drives and best NAS hard drives.

Price context & historical value

Newegg has slashed the price from $279.99 to $187, a $92.99 saving, equivalent to roughly 33%. This isn't the lowest price we've ever seen for the ST4000VN006, which has sold for considerably lower in the past (it was $169.99 in May of this year), but it's still a solid offer.

Should you buy it?

✅ Buy the Seagate IronWolf 4TB if...

You need plenty of inexpensive storage for files that don't require SSD speeds. 4TB gives you a decent room for photos, videos, backups, documents, games, and other large collections.

It's an even stronger fit if that storage finds its way into a NAS. IronWolf drives are designed specifically with always-on and multi-bay environments in mind, so you can use this as an ordinary storage drive today without ruling out a NAS or home-server role later.

❌ Skip the Seagate IronWolf 4TB if...

You need high-speed primary storage for Windows, applications, or workloads that constantly move large amounts of data. A 5400 RPM mechanical drive can't compete with an SSD for speed so its strengths are capacity, and continuous operation.

4TB might also be too small if you're filling a NAS with a limited number of bays. A four-bay system populated entirely with 4TB drives will give you just 16TB of raw capacity before accounting for RAID, so larger drives could make more sense depending on your personal needs.

The Catch: What to know before you buy

This is a 5400 RPM mechanical hard drive, so will be good to use for bulk storage, backups, media libraries, servers, and NAS duties rather than something where a speedy SSD would be a smarter choice.

If you are buying it specifically for a NAS, check the manufacturer's compatibility list for the drive before ordering. Compatibility between a particular NAS enclosure and individual drive models shouldn't be assumed.

Hard drives remain one of the most affordable ways to add terabytes of storage to a PC, server, or NAS, particularly where speed isn't a priority. The 4TB Seagate IronWolf (ST4000VN006) hard drive is currently $187 (was $280) at Newegg.

This is a standard 3.5-inch SATA hard drive that can be installed in a compatible desktop, workstation, server, or enclosure. What you get over a basic desktop drive is hardware and firmware designed to cope particularly well with continuous operation and multi-drive NAS environments.

It offers CMR recording, a 5400 RPM spindle speed, 256MB cache, and SATA 6Gb/s interface. It's rated for 24/7 operation and workloads of up to 180TB per year. Seagate's AgileArray technology is designed to improve reliability and RAID performance in compatible multi-bay systems.

4TB Ironwolf SHard Drive: was $279.99 now $187
This Seagate IronWolf HDD provides 4TB of CMR storage, a 5400 RPM spindle speed, 256MB cache, SATA 6Gb/s connectivity, 24/7 operation, and a 180TB/year workload rating.View Deal

Why we recommend it

In his 4-star review of the 8TB model (ST8000VN004), our expert Mark said: "IronWolf drives feature CMR technology and AgileArray firmware, ensuring smooth RAID performance, reduced vibration, and efficient power management. The 180TB per year workload rating, the RV sensors, the three-year warranty, and the bundled Rescue Data Recovery Services have all persisted as defining features of the consumer tier."

Flexibility is a huge part of the HDD's appeal. Its standard 3.5-inch SATA interface means it can provide conventional storage in compatible PCs and other systems, and its NAS-specific features are particularly handy when it's running continuously or alongside other drives.

CMR recording means it's well suited to sustained writing and RAID environments, and the 180TB/year workload rating makes the drive better equipped for heavier storage duties than a typical everyday HDD.

You also get Seagate's IronWolf Health Management on supported NAS hardware, which can provide drive-health monitoring and recommendations. Seagate includes three years of Rescue Data Recovery Services as well, although availability varies by country.

For more options, take a look at our roundups of the best desktop and laptop hard drives and best NAS hard drives.

Price context & historical value

Newegg has slashed the price from $279.99 to $187, a $92.99 saving, equivalent to roughly 33%. This isn't the lowest price we've ever seen for the ST4000VN006, which has sold for considerably lower in the past (it was $169.99 in May of this year), but it's still a solid offer.

Should you buy it?

✅ Buy the Seagate IronWolf 4TB if...

You need plenty of inexpensive storage for files that don't require SSD speeds. 4TB gives you a decent room for photos, videos, backups, documents, games, and other large collections.

It's an even stronger fit if that storage finds its way into a NAS. IronWolf drives are designed specifically with always-on and multi-bay environments in mind, so you can use this as an ordinary storage drive today without ruling out a NAS or home-server role later.

❌ Skip the Seagate IronWolf 4TB if...

You need high-speed primary storage for Windows, applications, or workloads that constantly move large amounts of data. A 5400 RPM mechanical drive can't compete with an SSD for speed so its strengths are capacity, and continuous operation.

4TB might also be too small if you're filling a NAS with a limited number of bays. A four-bay system populated entirely with 4TB drives will give you just 16TB of raw capacity before accounting for RAID, so larger drives could make more sense depending on your personal needs.

The Catch: What to know before you buy

This is a 5400 RPM mechanical hard drive, so will be good to use for bulk storage, backups, media libraries, servers, and NAS duties rather than something where a speedy SSD would be a smarter choice.

If you are buying it specifically for a NAS, check the manufacturer's compatibility list for the drive before ordering. Compatibility between a particular NAS enclosure and individual drive models shouldn't be assumed.

• CVE‑2026‑65400 macOS Screen Sharing flaw exploited for cryptojacking within days of disclosure
• Attackers gained root via exposed port 5900 and deployed Monero miners using XMRig
• Apple patched in Sequoia 15.7.9, Sonoma 14.8.9, Tahoe 26.6.1; users urged to update immediately

Less than a week after being publicly disclosed, a macOS vulnerability plaguing Screen Sharing was observed as being used in cryptojacking attacks.

Alfredo Pesoli, a security researcher from Bynario, discovered an authentication issue in macOS Screen Sharing and reported it to Apple. Screen Sharing is a built-in macOS tool that allows users to remotely connect, and use, another Mac device. It is similar to third-party tools such as AnyDesk or TeamViewer and comes in rather handy for IT teams accessing Macs stored in closets or used by remote and home-working employees.

The bug allows a remote attacker to bypass authentication and gain access to a vulnerable Mac device without valid credentials. It apparently stems from a logic issue in the Screen Sharing server’s authentication process, affecting systems where the service is exposed to the internet.

The Netherlands issue a warning

Soon after disclosure, Apple released an out-of-bound fix, signaling that this is, indeed, a dangerous vulnerability. “Apple does not ship an update out of band unless something is critical,” security researchers Calif said in their technical writeup. The National Vulnerability Database (NVD) assigned it an identifier - CVE-2026-65400 - and gave it a severity rating of 9.6/10 (critical).

Approximately at the same time the patch was released, the flaw was also showcased at the 2026 Black Hat conference, with a video demonstration was made public a few days later.

Apple said it fixed it with improved state management, addressing the bug in macOS Sequoia 15.7.9, macOS Sonoma 14.8.9, macOS Tahoe 26.6.1.

Now, less than a week after the disclosure, researchers are saying the bug is being leveraged in actual cyberattacks, with Dutch security officials being first to react

“The NCSC has received a report showing that active abuse of this vulnerability has been observed on several systems on which port 5900 was accessible from the internet,” the Netherlands National Cyber Security Centrum (NCSC) said in a machine-translated report. “In all these cases, root access was gained on the affected system and a Monero crypto miner was placed.”

Why Monero?

Monero is considered an “altcoin” - a cryptocurrency built as an alternative to Bitcoin. It is one of the oldest active altcoins out there, having been launched more than 12 years ago. Most cryptocurrencies rarely live through a single four-year bitcoin cycle but Monero, just like Ethereum, Litecoin, Solana, and a handful of others, endures.

It is similar to Bitcoin because it, too, can be “mined” (unlike Ethereum, for example). It differs on the privacy front. Unlike Bitcoin, whose transactions are recorded on a public ledger and can often be traced, Monero is designed to obscure the sender, recipient, and the amount of transactions. This privacy feature has, unfortunately, also attracted criminals.

Another key feature that made crooks choose Monero for their cryptojackers is the fact that the altcoin uses a proof-of-work (mining) algorithm optimized for general-purpose CPUs, making mining relatively profitable on ordinary servers, desktops, and cloud machines.

Although it was not specifically stated, it is safe to assume that in this incident, the attackers were deploying XMRig. It is, by far, the most popular cryptojacker and one that mines primarily Monero (its ticker is XMR).

How to stay safe

The best way to go about it is to install the patch Apple just released. This effectively plugs the hole and makes the device secure. Those who are unable to deploy the patch immediately should block Screen Sharing and enable it only when it is actually needed and used. To do that, users can go to System Settings > General > Sharing and toggle the Screen Sharing switch off.

Finally, it is worth mentioning that the NCSC stressed the crooks could only exploit the flaw when the target device’s port 5900 is exposed to the internet. Therefore, setting routers and firewalls to block the port can also work, although we’d only recommend it as a last resort. Installing the patch is still the best way to go.

Right now, no groups claimed responsibility for this attack, and there is no evidence it is being used for anything else. In theory, though, it can also be used for data exfiltration, malware deployment, and possibly even ransomware attacks.

• CVE‑2026‑65400 macOS Screen Sharing flaw exploited for cryptojacking within days of disclosure
• Attackers gained root via exposed port 5900 and deployed Monero miners using XMRig
• Apple patched in Sequoia 15.7.9, Sonoma 14.8.9, Tahoe 26.6.1; users urged to update immediately

Less than a week after being publicly disclosed, a macOS vulnerability plaguing Screen Sharing was observed as being used in cryptojacking attacks.

Alfredo Pesoli, a security researcher from Bynario, discovered an authentication issue in macOS Screen Sharing and reported it to Apple. Screen Sharing is a built-in macOS tool that allows users to remotely connect, and use, another Mac device. It is similar to third-party tools such as AnyDesk or TeamViewer and comes in rather handy for IT teams accessing Macs stored in closets or used by remote and home-working employees.

The bug allows a remote attacker to bypass authentication and gain access to a vulnerable Mac device without valid credentials. It apparently stems from a logic issue in the Screen Sharing server’s authentication process, affecting systems where the service is exposed to the internet.

The Netherlands issue a warning

Soon after disclosure, Apple released an out-of-bound fix, signaling that this is, indeed, a dangerous vulnerability. “Apple does not ship an update out of band unless something is critical,” security researchers Calif said in their technical writeup. The National Vulnerability Database (NVD) assigned it an identifier - CVE-2026-65400 - and gave it a severity rating of 9.6/10 (critical).

Approximately at the same time the patch was released, the flaw was also showcased at the 2026 Black Hat conference, with a video demonstration was made public a few days later.

Apple said it fixed it with improved state management, addressing the bug in macOS Sequoia 15.7.9, macOS Sonoma 14.8.9, macOS Tahoe 26.6.1.

Now, less than a week after the disclosure, researchers are saying the bug is being leveraged in actual cyberattacks, with Dutch security officials being first to react

“The NCSC has received a report showing that active abuse of this vulnerability has been observed on several systems on which port 5900 was accessible from the internet,” the Netherlands National Cyber Security Centrum (NCSC) said in a machine-translated report. “In all these cases, root access was gained on the affected system and a Monero crypto miner was placed.”

Why Monero?

Monero is considered an “altcoin” - a cryptocurrency built as an alternative to Bitcoin. It is one of the oldest active altcoins out there, having been launched more than 12 years ago. Most cryptocurrencies rarely live through a single four-year bitcoin cycle but Monero, just like Ethereum, Litecoin, Solana, and a handful of others, endures.

It is similar to Bitcoin because it, too, can be “mined” (unlike Ethereum, for example). It differs on the privacy front. Unlike Bitcoin, whose transactions are recorded on a public ledger and can often be traced, Monero is designed to obscure the sender, recipient, and the amount of transactions. This privacy feature has, unfortunately, also attracted criminals.

Another key feature that made crooks choose Monero for their cryptojackers is the fact that the altcoin uses a proof-of-work (mining) algorithm optimized for general-purpose CPUs, making mining relatively profitable on ordinary servers, desktops, and cloud machines.

Although it was not specifically stated, it is safe to assume that in this incident, the attackers were deploying XMRig. It is, by far, the most popular cryptojacker and one that mines primarily Monero (its ticker is XMR).

How to stay safe

The best way to go about it is to install the patch Apple just released. This effectively plugs the hole and makes the device secure. Those who are unable to deploy the patch immediately should block Screen Sharing and enable it only when it is actually needed and used. To do that, users can go to System Settings > General > Sharing and toggle the Screen Sharing switch off.

Finally, it is worth mentioning that the NCSC stressed the crooks could only exploit the flaw when the target device’s port 5900 is exposed to the internet. Therefore, setting routers and firewalls to block the port can also work, although we’d only recommend it as a last resort. Installing the patch is still the best way to go.

Right now, no groups claimed responsibility for this attack, and there is no evidence it is being used for anything else. In theory, though, it can also be used for data exfiltration, malware deployment, and possibly even ransomware attacks.

• GPU price hikes are coming later this year, PC Partner Group warns
• The parent company of Zotac notes that entry-level GPUs will suffer 'more severe shortages'
• A manufacturer of gaming monitors, KTC, has also warned on price hikes

PC gamers should be aware that there are further signs that GPUs could get more expensive, and gaming monitors too, so you might want to consider buying now rather than waiting.

First off, the gloomy GPU news which comes from PC Partner Group (PC PG), an Asian graphics card giant based in Singapore and the parent company of Zotac, Inno3D, and Manli.

VideoCardz reports that PC PG just published its latest fiscal results that came with a warning on GPU pricing as part of the trends and conditions which are likely to affect the company's finances in the upcoming year.

PC PG notes that "graphics memory costs are expected to rise further, leading to a substantial increase in VGA Card costs in the second half of the year." VGA Card simply refers to discrete PC graphics cards, and although it's a well outdated term now, it's one still used behind the scenes in the industry.

PC PG says that shipments of GPUs "declined substantially" in the first half of this year, and that they're likely to drop further in the second half of 2026. The firm writes that: "Beyond the price increases, entry-level VGA Cards are expected to face even more severe shortages, which may further drive ASP [the 'average selling price'] upward in the second half of 2026."

The company also highlights that other PC components, including system RAM and CPUs, are taking longer to get hold of.

VideoCardz also noticed that monitor pricing could be on an upward trajectory, too, as manufacturer KTC has issued a notice of a likely 'product price adjustment' in the future.

KTC observes that the "production cost of monitors has increased significantly" and that it can no longer absorb those pressures, therefore: "KTC's monitor product line may see a price increase in the future."

While that's just a 'may' rather than a 'will' or a certainty, it sounds like there's a high likelihood of it happening. Particularly because KTC advises: "For users with a clear need to purchase a device [monitor] in the near future, the current pricing window offers excellent value for money, and we recommend planning your purchase time accordingly."

And at the end of its statement, KTC talks about price adjustments being a "necessary measure", so it sounds pretty much like they're coming no matter what.

Analysis: is anything immune to the component crisis?

(Image credit: Alastair Jennings)

This isn't the only warning on monitor price increases, as AOC recently said that the rising cost of components for its products will mean price hikes starting from this month. Is this the start of a growing trend? The bigger monitor producers will, of course, have more financial wherewithal to weather component rises, but it may only be so long before even those firms start to jack up prices.

The way things are going, I wouldn't bet against it, and this latest news has people on social media asking what isn't immune to the RAM crisis – and what might we expect to see hit next?

How about pricier PC cases, one Redditor jokes: "Wouldn't you enjoy a simple case costing like $300?"

The crisis does feel rather relentless at this point, but the clearest worry is now GPUs. PC Gamer is tracking graphics card prices on a weekly basis, and there have been some clear cost increases coming through in the last few weeks, which suggests that PC PG's prediction is on the money here. And there are multiple other rumors of late warning that graphics cards are set for considerable price hikes. For me, this noise is becoming too consistent to ignore.

As I recently advised, if you need a graphics card upgrade, or think you will in the next year or two, I believe now is the right time to get in and buy. It's particularly notable that according to this latest rumor, entry-level GPUs are mentioned as apparently coming under more pressure, meaning that mainstream buyers are going to be suffering.

The effects of pricier video RAM, and other pressures besides, are no longer going to be mostly about high-end (or 16GB) cards becoming ridiculously expensive, and we could see some unfortunate hikes with lower-end GPUs.

There's very little good news of late across the gamut of PC components, and as we've already seen today, RAM and SSDs are getting even more expensive.

• GPU price hikes are coming later this year, PC Partner Group warns
• The parent company of Zotac notes that entry-level GPUs will suffer 'more severe shortages'
• A manufacturer of gaming monitors, KTC, has also warned on price hikes

PC gamers should be aware that there are further signs that GPUs could get more expensive, and gaming monitors too, so you might want to consider buying now rather than waiting.

First off, the gloomy GPU news which comes from PC Partner Group (PC PG), an Asian graphics card giant based in Singapore and the parent company of Zotac, Inno3D, and Manli.

VideoCardz reports that PC PG just published its latest fiscal results that came with a warning on GPU pricing as part of the trends and conditions which are likely to affect the company's finances in the upcoming year.

PC PG notes that "graphics memory costs are expected to rise further, leading to a substantial increase in VGA Card costs in the second half of the year." VGA Card simply refers to discrete PC graphics cards, and although it's a well outdated term now, it's one still used behind the scenes in the industry.

PC PG says that shipments of GPUs "declined substantially" in the first half of this year, and that they're likely to drop further in the second half of 2026. The firm writes that: "Beyond the price increases, entry-level VGA Cards are expected to face even more severe shortages, which may further drive ASP [the 'average selling price'] upward in the second half of 2026."

The company also highlights that other PC components, including system RAM and CPUs, are taking longer to get hold of.

VideoCardz also noticed that monitor pricing could be on an upward trajectory, too, as manufacturer KTC has issued a notice of a likely 'product price adjustment' in the future.

KTC observes that the "production cost of monitors has increased significantly" and that it can no longer absorb those pressures, therefore: "KTC's monitor product line may see a price increase in the future."

While that's just a 'may' rather than a 'will' or a certainty, it sounds like there's a high likelihood of it happening. Particularly because KTC advises: "For users with a clear need to purchase a device [monitor] in the near future, the current pricing window offers excellent value for money, and we recommend planning your purchase time accordingly."

And at the end of its statement, KTC talks about price adjustments being a "necessary measure", so it sounds pretty much like they're coming no matter what.

Analysis: is anything immune to the component crisis?

(Image credit: Alastair Jennings)

This isn't the only warning on monitor price increases, as AOC recently said that the rising cost of components for its products will mean price hikes starting from this month. Is this the start of a growing trend? The bigger monitor producers will, of course, have more financial wherewithal to weather component rises, but it may only be so long before even those firms start to jack up prices.

The way things are going, I wouldn't bet against it, and this latest news has people on social media asking what isn't immune to the RAM crisis – and what might we expect to see hit next?

How about pricier PC cases, one Redditor jokes: "Wouldn't you enjoy a simple case costing like $300?"

The crisis does feel rather relentless at this point, but the clearest worry is now GPUs. PC Gamer is tracking graphics card prices on a weekly basis, and there have been some clear cost increases coming through in the last few weeks, which suggests that PC PG's prediction is on the money here. And there are multiple other rumors of late warning that graphics cards are set for considerable price hikes. For me, this noise is becoming too consistent to ignore.

As I recently advised, if you need a graphics card upgrade, or think you will in the next year or two, I believe now is the right time to get in and buy. It's particularly notable that according to this latest rumor, entry-level GPUs are mentioned as apparently coming under more pressure, meaning that mainstream buyers are going to be suffering.

The effects of pricier video RAM, and other pressures besides, are no longer going to be mostly about high-end (or 16GB) cards becoming ridiculously expensive, and we could see some unfortunate hikes with lower-end GPUs.

There's very little good news of late across the gamut of PC components, and as we've already seen today, RAM and SSDs are getting even more expensive.

This week, OpenAI added Google Drive to your ChatGPT Library. You could already connect to Google Drive, but now it means you can select a whole Drive folder and ask ChatGPT to look for answers working across all the files it contains.

This can save a lot of time compared to uploading each file individually to ChatGPT’s library and asking ChatGPT to work with it, especially for working with images. Now they’re effectively all in there already.

Now, I don’t know if you’re anything like me, but I tend to treat the root folder of my Google Drive like a very messy desktop. I’ve had my Google account for years, and I just chuck files in there without really organizing them into folders unless I really have to.

The result is that it has built up into one big mess of old and new files floating about in a kind of digital soup. I’m sure there are important things in there that I’m at risk of forgetting about forever.

So, when I heard that ChatGPT can now look at the documents your Drive folder contains and organize them for you too, I was intrigued.

10 years of junk

To test this out, I thought I’d ask ChatGPT to connect to my Google Drive and take a look at what it could find:

“I've got a load of files in my Google Drive that aren't in folders — go through everything here and identify things I’ve forgotten, unfinished tasks, contradictions, deadlines, duplicate work and anything that looks like it still needs action. Also suggest how it could be organized and what files should go where.”

The result was impressive. It thought for what felt like about 30 seconds, then came back with some results. I had to re-authorize it to connect to my Drive a couple of times first, but everything went smoothly.

It turns out that I have a real penchant for saving files called “Untitled document” into Drive. Chat was able to go through them all and work out what they were about and what was important.

It found a lot of material from various books I’d been writing and suggested how to organize them together. Old invoices were also collected, and there were a lot of forgotten idea fragments that it thought were worth reviewing.

It also found duplicate files and multiple versions of “household_budget_tracker”. Sadly, I never got that project off the ground!

A better structure

The whole process led me to discover things from the last 10 years that I’d pretty much forgotten about, and Chat suggested a folder structure for me to use to keep them more organized. Its ability to spot half-finished ideas and documents from years ago was incredibly useful. In particular, it found the outline of a book I'd started in 2018 and hadn't thought about for years. I've actually started looking at a few of those old writing projects again because of it.

But analyzing the mess and proposing a structure isn’t the only trick ChatGPT can do. I discovered it could actually make those changes itself. Just tell ChatGPT to create a new folder and put a load of files into it, and it will actually do it. So, for example, I asked, “Can you make a folder called Invoices and put all my invoices in there?” All this was possible from within ChatGPT, and I didn’t even need to touch Google Drive myself.

Sadly, the new features aren’t available to ChatGPT Free or Go users. You’ll need a Plus, Pro, or Business account. Another new feature is that it can keep Docs, Sheets, and Slides from your Drive open alongside the conversation while you interrogate them, but today I was more interested in how ChatGPT could help me organize the mess that is my Google Drive.

Seeing ChatGPT casually surface abandoned writing projects from years ago was a slightly strange experience, but I finally feel like I’ve cleaned out my digital junk drawer and got a handle on 10 years of clutter. Perhaps the most useful thing ChatGPT did wasn't organizing my Google Drive. It made the fact that I hadn't organized it properly for the last decade matter a lot less.

This week, OpenAI added Google Drive to your ChatGPT Library. You could already connect to Google Drive, but now it means you can select a whole Drive folder and ask ChatGPT to look for answers working across all the files it contains.

This can save a lot of time compared to uploading each file individually to ChatGPT’s library and asking ChatGPT to work with it, especially for working with images. Now they’re effectively all in there already.

Now, I don’t know if you’re anything like me, but I tend to treat the root folder of my Google Drive like a very messy desktop. I’ve had my Google account for years, and I just chuck files in there without really organizing them into folders unless I really have to.

The result is that it has built up into one big mess of old and new files floating about in a kind of digital soup. I’m sure there are important things in there that I’m at risk of forgetting about forever.

So, when I heard that ChatGPT can now look at the documents your Drive folder contains and organize them for you too, I was intrigued.

10 years of junk

To test this out, I thought I’d ask ChatGPT to connect to my Google Drive and take a look at what it could find:

“I've got a load of files in my Google Drive that aren't in folders — go through everything here and identify things I’ve forgotten, unfinished tasks, contradictions, deadlines, duplicate work and anything that looks like it still needs action. Also suggest how it could be organized and what files should go where.”

The result was impressive. It thought for what felt like about 30 seconds, then came back with some results. I had to re-authorize it to connect to my Drive a couple of times first, but everything went smoothly.

It turns out that I have a real penchant for saving files called “Untitled document” into Drive. Chat was able to go through them all and work out what they were about and what was important.

It found a lot of material from various books I’d been writing and suggested how to organize them together. Old invoices were also collected, and there were a lot of forgotten idea fragments that it thought were worth reviewing.

It also found duplicate files and multiple versions of “household_budget_tracker”. Sadly, I never got that project off the ground!

A better structure

The whole process led me to discover things from the last 10 years that I’d pretty much forgotten about, and Chat suggested a folder structure for me to use to keep them more organized. Its ability to spot half-finished ideas and documents from years ago was incredibly useful. In particular, it found the outline of a book I'd started in 2018 and hadn't thought about for years. I've actually started looking at a few of those old writing projects again because of it.

But analyzing the mess and proposing a structure isn’t the only trick ChatGPT can do. I discovered it could actually make those changes itself. Just tell ChatGPT to create a new folder and put a load of files into it, and it will actually do it. So, for example, I asked, “Can you make a folder called Invoices and put all my invoices in there?” All this was possible from within ChatGPT, and I didn’t even need to touch Google Drive myself.

Sadly, the new features aren’t available to ChatGPT Free or Go users. You’ll need a Plus, Pro, or Business account. Another new feature is that it can keep Docs, Sheets, and Slides from your Drive open alongside the conversation while you interrogate them, but today I was more interested in how ChatGPT could help me organize the mess that is my Google Drive.

Seeing ChatGPT casually surface abandoned writing projects from years ago was a slightly strange experience, but I finally feel like I’ve cleaned out my digital junk drawer and got a handle on 10 years of clutter. Perhaps the most useful thing ChatGPT did wasn't organizing my Google Drive. It made the fact that I hadn't organized it properly for the last decade matter a lot less.

We've been internally holding the news that Outer Banks seasons 5 was going to be the end of Netflix's hit teen action drama for a lifetime (or that's what it feels like), but now, the end is here.

The Pogues will reunite one last time while still grieving in the aftermath of everything that went down in Morocco. Now that JJ is dead, they're actively seeking revenge against Chandler Groff while still hunting the legendary Blue Crown.

The final farewell will surely be explosive, but when does Outer Banks season 5 actually hit Netflix?

What time can I watch Outer Banks season 5 on Netflix?

Outer Banks season 5 drops on Thursday, August 20.

As for exact time, it should be the standard 12am PT / 3am ET release time that practically every other Netflix TV Original has.

For fans who don't live in North America, here's when you'll need to tune in for season 5:

• US – 12am PT / 3am ET
• Canada – 12am PT / 3am ET
• UK – 8am BST
• India – 12:30pm IST
• Singapore – 3pm SGT
• Australia – 5pm AEST
• New Zealand – 7pm NZST

When do new episodes of Outer Banks season 5 come out?

(Image credit: Netflix)

All 10 episodes of this season will be released at the same time. Unlike weekly drops on the likes of Prime Video, Apple TV, and Disney+, then, you won't need to wait around for new installment of Outer Banks season 5.

As we've already touched on, this will be the final installment of the Netflix series, too, so there won't be another season after this one.

We've been internally holding the news that Outer Banks seasons 5 was going to be the end of Netflix's hit teen action drama for a lifetime (or that's what it feels like), but now, the end is here.

The Pogues will reunite one last time while still grieving in the aftermath of everything that went down in Morocco. Now that JJ is dead, they're actively seeking revenge against Chandler Groff while still hunting the legendary Blue Crown.

The final farewell will surely be explosive, but when does Outer Banks season 5 actually hit Netflix?

What time can I watch Outer Banks season 5 on Netflix?

Outer Banks season 5 drops on Thursday, August 20.

As for exact time, it should be the standard 12am PT / 3am ET release time that practically every other Netflix TV Original has.

For fans who don't live in North America, here's when you'll need to tune in for season 5:

• US – 12am PT / 3am ET
• Canada – 12am PT / 3am ET
• UK – 8am BST
• India – 12:30pm IST
• Singapore – 3pm SGT
• Australia – 5pm AEST
• New Zealand – 7pm NZST

When do new episodes of Outer Banks season 5 come out?

(Image credit: Netflix)

All 10 episodes of this season will be released at the same time. Unlike weekly drops on the likes of Prime Video, Apple TV, and Disney+, then, you won't need to wait around for new installment of Outer Banks season 5.

As we've already touched on, this will be the final installment of the Netflix series, too, so there won't be another season after this one.

Open-weight AI does not need to beat frontier models to test the growth and margin assumptions behind OpenAI and Anthropic's soaring valuations.

The post Can Open-Weight AI Pressure OpenAI and Anthropic Valuations? appeared first on TechRepublic.

• Gears of War creator Cliff Bleszinski said, "It's not cool to like something" anymore in a recent Facebook post
• He thinks players' "minds are made up" before a game is even out, with some titles falling victim ot "dogpiling"
• He listed Highguard and the upcoming Horizon Hunters Gathering as examples

Gears of War creator Cliff Bleszinski has lamented the current online culture where players' "minds are made up" before a game is even out, thanks to the "dogpiling" of certain titles like Concord, Highguard, and the upcoming Horizon Hunters Gathering.

In a recent Facebook post, Bleszinski, who was lead designer of the first three Gears of War games, said that he had been "sucked into the rabbit hole of takes on Concord's failure" while browsing YouTube.

A live-service hero arena shooter, Concord launched in 2024 and was an unprecedented flop. Its servers were shut down after just two weeks thanks to catastrophically low sales, with the few buyers refunded. Sony shut down its developer, Firewalk Studios, the next month.

Bleszinski wrote that there is "no lack of videos trying to pin the blame on a variety of factors" like "hero shooter fatigue, ugly character designs, timing, long dev cycle." This apparently led him to videos about the subsequent failure of Highguard, a free-to-play hero shooter that came out earlier this year and was shut down after just 45 days, having struggled to maintain a player base.

He argued that "gamers glommed onto" Highguard, branding it "the next Concord" before it had even come out. "I honestly thought Highguard looked pretty good," he continued, though he admits he was skeptical whether it had a strong enough unique selling point to succeed.

"Betting on 'well now you ride a horse' as a unique selling point for a title didn't make a lot of sense to me, just like 'well you fight in low gravity' wasn't enough for LawBreakers," he said, referring to the unsuccessful sci-fi hero shooter he himself directed that released in 2017 and shut down a little over a year later.

"In some of these hit piece videos I kept seeing glimpses of a really cool-looking title that I couldn't pin down until I located it," he added. The game in question was Horizon Hunters Gathering, an upcoming co-op Horizon Zero Dawn spinoff.

"I loved Horizon and I thought this looked really compelling; I dug the art style and a PvE game in that world seems like a good fit," he wrote. "And then I read the comments. [...] It was a nonstop barrage of wanting to hate something long before it even hit."

He added: "The new world order we live in is one of 'didn't have a chance before it even came out' - minds are made up before the title even ships and folks get a chance to play it and decide for themselves. The dogpiling is real. I can't imagine being a dev and seeing this."

To wrap up, he stated: "I get it. It's not cool to like something. And, if something becomes loved, it then becomes cool to hate it." He also questioned how this situation came about, though he didn't offer any definitive conclusion.

You might argue that titles like Highguard failed to compete in a crowded market due to a lack of quality, though I think it's still easy to see what Bleszinski is talking about. Upcoming games, particularly new live-service titles, often face a barrage of negative online sentiment on platforms like X and YouTube, which threatens to dominate the conversation surrounding them in the build-up to launch.

Whether the endless videos lambasting Horizon Hunters Gathering will dramatically affect the sales of the game is to be seen when it launches for PlayStation 5 and PC in the future.

• Gears of War creator Cliff Bleszinski said, "It's not cool to like something" anymore in a recent Facebook post
• He thinks players' "minds are made up" before a game is even out, with some titles falling victim ot "dogpiling"
• He listed Highguard and the upcoming Horizon Hunters Gathering as examples

Gears of War creator Cliff Bleszinski has lamented the current online culture where players' "minds are made up" before a game is even out, thanks to the "dogpiling" of certain titles like Concord, Highguard, and the upcoming Horizon Hunters Gathering.

In a recent Facebook post, Bleszinski, who was lead designer of the first three Gears of War games, said that he had been "sucked into the rabbit hole of takes on Concord's failure" while browsing YouTube.

A live-service hero arena shooter, Concord launched in 2024 and was an unprecedented flop. Its servers were shut down after just two weeks thanks to catastrophically low sales, with the few buyers refunded. Sony shut down its developer, Firewalk Studios, the next month.

Bleszinski wrote that there is "no lack of videos trying to pin the blame on a variety of factors" like "hero shooter fatigue, ugly character designs, timing, long dev cycle." This apparently led him to videos about the subsequent failure of Highguard, a free-to-play hero shooter that came out earlier this year and was shut down after just 45 days, having struggled to maintain a player base.

He argued that "gamers glommed onto" Highguard, branding it "the next Concord" before it had even come out. "I honestly thought Highguard looked pretty good," he continued, though he admits he was skeptical whether it had a strong enough unique selling point to succeed.

"Betting on 'well now you ride a horse' as a unique selling point for a title didn't make a lot of sense to me, just like 'well you fight in low gravity' wasn't enough for LawBreakers," he said, referring to the unsuccessful sci-fi hero shooter he himself directed that released in 2017 and shut down a little over a year later.

"In some of these hit piece videos I kept seeing glimpses of a really cool-looking title that I couldn't pin down until I located it," he added. The game in question was Horizon Hunters Gathering, an upcoming co-op Horizon Zero Dawn spinoff.

"I loved Horizon and I thought this looked really compelling; I dug the art style and a PvE game in that world seems like a good fit," he wrote. "And then I read the comments. [...] It was a nonstop barrage of wanting to hate something long before it even hit."

He added: "The new world order we live in is one of 'didn't have a chance before it even came out' - minds are made up before the title even ships and folks get a chance to play it and decide for themselves. The dogpiling is real. I can't imagine being a dev and seeing this."

To wrap up, he stated: "I get it. It's not cool to like something. And, if something becomes loved, it then becomes cool to hate it." He also questioned how this situation came about, though he didn't offer any definitive conclusion.

You might argue that titles like Highguard failed to compete in a crowded market due to a lack of quality, though I think it's still easy to see what Bleszinski is talking about. Upcoming games, particularly new live-service titles, often face a barrage of negative online sentiment on platforms like X and YouTube, which threatens to dominate the conversation surrounding them in the build-up to launch.

Whether the endless videos lambasting Horizon Hunters Gathering will dramatically affect the sales of the game is to be seen when it launches for PlayStation 5 and PC in the future.

Artificial intelligence is rapidly reshaping cybersecurity. Much of the conversation has focused on how large language models are helping developers write code, but a more significant shift may be occurring elsewhere: security testing.

AI systems are becoming remarkably effective at identifying vulnerabilities, forcing organizations to rethink how they evaluate the security of both software and hardware products.

The reason lies in the nature of modern software. Large codebases are sprawling ecosystems of interconnected modules, third-party dependencies, legacy components, and undocumented assumptions.

Security flaws often emerge not from a single defective function, but from subtle interactions between components that may be separated by hundreds of files or years of development history. Human reviewers excel at deep analysis, but they are constrained by time and cognitive bandwidth.

AI systems, by contrast, can rapidly traverse vast amounts of code, correlate information across repositories, and identify patterns associated with known vulnerability classes.

Common software weaknesses

This capability is particularly powerful for common software weaknesses such as memory-safety issues, race conditions, authentication flaws, insecure API usage, and privilege-escalation paths. In many cases, AI is acting as an amplifier for established security techniques rather than inventing new ones. Yet the result is still significant: vulnerabilities that previously required substantial manual effort to uncover can now be identified at a much greater scale and speed.

The implications for product security are profound. Organizations can no longer assume that obscure vulnerabilities will remain undiscovered because finding them is too expensive. The cost of vulnerability discovery is falling, and it is falling for defenders and attackers alike. Security testing therefore becomes less about achieving a point-in-time assessment and more about maintaining continuous assurance throughout the development lifecycle.

However, it would be a mistake to assume that all areas of cybersecurity will be transformed equally by AI. Hardware security, particularly side-channel analysis of cryptographic implementations, presents a very different challenge.

Unlike general-purpose software systems, cryptographic implementations operate within a comparatively narrow and mathematically defined problem space. Side-channel attacks do not typically exploit unexpected program behavior or complex interactions between software components. Instead, they target subtle information leakage through physical phenomena such as execution timing, power consumption, or electromagnetic emissions.

The challenge is not understanding millions of lines of source code; it is extracting meaningful signals from carefully collected measurements and applying sophisticated statistical techniques to reveal hidden secrets.

Side-channel analysis

This distinction matters because many of the strengths that make large language models effective in software security are less relevant in side-channel analysis. LLMs excel at connecting information across large bodies of text and code, identifying relationships that humans may overlook.

Side-channel research, by contrast, already relies heavily on structured datasets, statistical processing, signal analysis, and deep domain expertise. AI can certainly accelerate parts of the workflow—from automating experimentation to assisting with data interpretation—but the advantage is typically more incremental than transformational.

That does not make hardware security less important. If anything, it highlights the need for specialized testing approaches. As AI lowers the barriers to software vulnerability discovery, organizations may be tempted to assume that automated tools alone can provide comprehensive assurance.

The reality is that different classes of vulnerabilities require different forms of expertise. An AI-assisted code review may uncover a memory corruption bug, but it is unlikely to replace the specialist knowledge required to evaluate whether a cryptographic implementation leaks secrets through power analysis.

The broader lesson is that security testing is becoming more important, not less. AI is increasing the speed at which vulnerabilities can be found, but it is not eliminating the need for expert analysis. Instead, it is changing where that expertise delivers the most value.

Organizations that combine AI-assisted testing with rigorous human-led security evaluation will be best positioned to address the evolving threat landscape—whether the target is a cloud application, an embedded device, or the cryptographic hardware that underpins digital trust.

We've reviewed, rated, and ranked the best internet security suites.

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 is rapidly reshaping cybersecurity. Much of the conversation has focused on how large language models are helping developers write code, but a more significant shift may be occurring elsewhere: security testing.

AI systems are becoming remarkably effective at identifying vulnerabilities, forcing organizations to rethink how they evaluate the security of both software and hardware products.

The reason lies in the nature of modern software. Large codebases are sprawling ecosystems of interconnected modules, third-party dependencies, legacy components, and undocumented assumptions.

Security flaws often emerge not from a single defective function, but from subtle interactions between components that may be separated by hundreds of files or years of development history. Human reviewers excel at deep analysis, but they are constrained by time and cognitive bandwidth.

AI systems, by contrast, can rapidly traverse vast amounts of code, correlate information across repositories, and identify patterns associated with known vulnerability classes.

Common software weaknesses

This capability is particularly powerful for common software weaknesses such as memory-safety issues, race conditions, authentication flaws, insecure API usage, and privilege-escalation paths. In many cases, AI is acting as an amplifier for established security techniques rather than inventing new ones. Yet the result is still significant: vulnerabilities that previously required substantial manual effort to uncover can now be identified at a much greater scale and speed.

The implications for product security are profound. Organizations can no longer assume that obscure vulnerabilities will remain undiscovered because finding them is too expensive. The cost of vulnerability discovery is falling, and it is falling for defenders and attackers alike. Security testing therefore becomes less about achieving a point-in-time assessment and more about maintaining continuous assurance throughout the development lifecycle.

However, it would be a mistake to assume that all areas of cybersecurity will be transformed equally by AI. Hardware security, particularly side-channel analysis of cryptographic implementations, presents a very different challenge.

Unlike general-purpose software systems, cryptographic implementations operate within a comparatively narrow and mathematically defined problem space. Side-channel attacks do not typically exploit unexpected program behavior or complex interactions between software components. Instead, they target subtle information leakage through physical phenomena such as execution timing, power consumption, or electromagnetic emissions.

The challenge is not understanding millions of lines of source code; it is extracting meaningful signals from carefully collected measurements and applying sophisticated statistical techniques to reveal hidden secrets.

Side-channel analysis

This distinction matters because many of the strengths that make large language models effective in software security are less relevant in side-channel analysis. LLMs excel at connecting information across large bodies of text and code, identifying relationships that humans may overlook.

Side-channel research, by contrast, already relies heavily on structured datasets, statistical processing, signal analysis, and deep domain expertise. AI can certainly accelerate parts of the workflow—from automating experimentation to assisting with data interpretation—but the advantage is typically more incremental than transformational.

That does not make hardware security less important. If anything, it highlights the need for specialized testing approaches. As AI lowers the barriers to software vulnerability discovery, organizations may be tempted to assume that automated tools alone can provide comprehensive assurance.

The reality is that different classes of vulnerabilities require different forms of expertise. An AI-assisted code review may uncover a memory corruption bug, but it is unlikely to replace the specialist knowledge required to evaluate whether a cryptographic implementation leaks secrets through power analysis.

The broader lesson is that security testing is becoming more important, not less. AI is increasing the speed at which vulnerabilities can be found, but it is not eliminating the need for expert analysis. Instead, it is changing where that expertise delivers the most value.

Organizations that combine AI-assisted testing with rigorous human-led security evaluation will be best positioned to address the evolving threat landscape—whether the target is a cloud application, an embedded device, or the cryptographic hardware that underpins digital trust.

We've reviewed, rated, and ranked the best internet security suites.

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