TechRadar News

• Whoop has opened up its Advanced Labs blood testing service
• You can now use it without a Whoop membership or wearable device
• That brings down the costs involved significantly

Whoop’s Advanced Labs program was originally designed to let owners of Whoop devices better understand their health through a series of blood tests. It was previously limited to owners of Whoop wearables who had an active membership, but access has now been expanded to users of other health devices, including the Apple Watch, the Oura Ring, and many of the best Garmin devices.

That means a far greater pool of people can now gain insights into their health by harnessing Whoop’s system of biomarker analysis. It also provides Whoop with a wider range of responses than it would have been able to collect before.

If you take part in Advanced Labs, you can put yourself through a blood testing service that gives you artificial intelligence (AI) insights and personalized recommendations that are reviewed by clinicians. It goes beyond the standard metrics you might get from rival wearables.

As well as opening up Advanced Labs, Whoop is also providing access to the Galleri cancer detection test to non-Whoop users. This is “designed to detect cancer signals associated with more than 50 types of cancer,” Whoop says. Galleri is available as part of Advanced Labs whether or not you have a Whoop membership.

Bringing down the cost

(Image credit: Whoop)

Whoop’s Advanced Labs testing starts at $150. Combine that with the cost of a Whoop membership — which is priced at $149 for your first year and $199 a year thereafter for the most basic tier — and you’d be looking at a sizeable outlay if you wanted to start using the Advanced Labs testing kits.

But now that Advanced Labs has been opened up to non-Whoop users, that cost can be brought down significantly. Owning a Whoop device — and paying its annual fee — is no longer required, although you still have to fork out for the tests themselves.

It’s worth noting that allowing users of other wearables to access Advanced Labs does not mean that Whoop will start incorporating data from your Apple Watch or smart ring into its own platform. That all remains totally separate — it’s just that Whoop’s walled garden has been breached slightly.

If you’ve been tempted by Whoop’s Advanced Labs testing but were put off by the cost involved, this might be your opportunity to take part. It could give you better insights into your health than you were previously privy to.

The Premier League is back and, watch out, because it might be sooner than you think.

The 2026/27 EPL season kicks off with Matchday 1 this Friday, August 21, with EFL Championship victors Coventry City facing EPL champions Arsenal at the Gunners' London home.

That means the FPL GW1 deadline time is Friday, August 21, 2026, at 6.30pm BST (1.30pm ET). Don't miss it!

And if you don't want to miss the games themselves, then we've got the kick off times and streaming options for all of the EPL fixtures for Matchday 1 just below.

If you're away from your regular stream and stuck in a blackout area, then don't forget that you can use a VPN to watch the match as usual.

Right now, one of the cheapest and most effective VPNs available is IPVanish and it's currently $2.19 per month.

EPL Matchday 1 2026/27 schedule

US (all times ET)

UK

August 21 (Fri)

Arsenal vs Coventry

3pm – USA Network

8pm – Sky Sports / NOW

August 22 (Sat)

Hull vs Man Utd

7.30am – USA Network

12.30pm – TNT Sports / HBO Max

Everton vs Crystal Palace

10am – USA Network

3pm – Blackout

Nottingham Forest vs Leeds

10am – NBCSN / Peacock

3pm – Blackout

Ipswich vs Sunderland

10am – Peacock

3pm – Blackout

Brentford vs Tottenham

12.30pm – NBC / Peacock

5.30pm – Sky Sports / NOW

August 23 (Sun)

Brighton vs Aston Villa

9am – Peacock

2pm – Blackout

Man City vs Bournemouth

9am – USA Network

2pm – Blackout

Newcastle vs Liverpool

11.30am – USA Network

4.30pm – Blackout

August 24 (Mon)

Fulham vs Chelsea

3pm – USA Network

8pm – Sky Sports / NOW

In the States, four of the weekend's matches are available to stream on Peacock TV. It's $12.99 per month and there are no free trials. Only two of those games are airing on cable on NBC and NBCSN.

The other six games are all on cable for those who have USA Network. Cord cutters can watch those using Hulu + Live TV, YouTube TV, DirecTV Stream and Sling TV.

Of those, Hulu + Live TV and YouTube TV have free trials for new customers, while DirecTV Stream and Sling TV offer discounts for the first month.

To watch your US EPL streams while you're traveling abroad, you'll need to use a VPN.

Watch EPL streams with IPVanish: $2.19/month
IPVanish allows you to change your apparent internet location. That means you can set your mobile or laptop to appear to be back in the States when you're away from home. That way you won't get blocked from your usual US EPL streaming services. IPVanish is $2.19 per month over two years, paid as $52.56 upfront. You can install it on unlimited simultaneous devices and you get 3GB of eSIM data to help you travel securely over the summer months and beyond.View Deal

The EPL's blackout rules in the UK mean that none of the 3pm Saturday fixtures are televised. Instead, there are usually just four televised fixtures each weekend.

You can watch the fixtures with subscriptions to Sky TV or TNT Sports. The matches on Sky are also available to stream with one-off day passes on NOW. TNT games are also on HBO Max.

You can read the full details on how to watch the Premier League all season here.

• Researcher inds ClarityCheck’s exposed 450GB database with 9M+ user images
• Leak included faces, profiles, and photos, risking identity theft and phishing abuse
• Company secured access quickly; no evidence of dark web distribution or misuse so far

An online reverse-lookup platform has inadvertently leaked millions of faces on the internet, putting people at risk of identity theft, phishing, and more, experts have warned.

Jeremiah Fowler, a cybersecurity researcher known for hunting exposed databases, recently found one totaling 450.2GB in size.

It contained exactly 9,042,977 image files - profile pictures, screenshots, and scans of physical photographs - all seemingly uploaded by the users. The images showed adults, teenagers, and even children, and were stored in folders labeled “faces” and “profiles”.

What happened?

Further investigation showed the database belonging to a company called ClarityCheck. This is a US-registered firm describing itself as a “reverse phone, email, image, vehicle lookup”, allowing users to identify unknown callers, verify online contacts, check photos, and decode vehicles using publicly available data from “trusted sources”.

It is a legitimate business whose use case grows more important by the day - cybercriminals create fake internet personas every day, and use them in all sorts of schemes, from romance scams, to fake job offers, to anything in between. To do that, they will either steal other people’s photos, obtain (or buy) them on the dark web, or generate them using artificial intelligence.

Being able to verify someone’s identity has become everyone’s essential due diligence, regardless of if it’s a personal or business matter.

How ClarityCheck responded

As soon as Fowler confirmed who owned the database, he reached out to ClarityCheck and responsibly disclosed his findings. The company responded quickly, barring further access, and thanking the researcher for his work.

“I completely understand your concerns regarding the exposure of sensitive images and the associated privacy risks. We greatly appreciate ethical researchers like you who bring these matters to our attention so we can act swiftly to protect our users' data and privacy,” the company’s representative told Fowler.

Unfortunately, without a deeper investigation on ClarityCheck’s end, there is no way of confirming exactly how long the database remained open, or if anyone accessed it before. However, so far there is no evidence of abuse, since a “ClarityCheck photo database” is currently not being distributed or sold anywhere on the dark web.

Exposing people to hackers

In a world where data theft and leaks are increasingly common, a cause that’s easiest to address, is also the one resulting in most exposures - misconfigured databases. Nowadays, almost every business harvests and stores data about their employees, partners, and customers. Most of them store these files in cloud databases, for easier access and better integration with business intelligence software.

However, cloud service providers work on a so-called “shared responsibility model”, which means they are responsible for providing industry-standard security features. Users, on the other hand, are responsible for using those features and properly configuring their databases (namely, setting up a strong password or encrypting the content). Unfortunately, many organizations don’t seem to be aware of the shared responsibility model, firmly believing it’s the service provider’s task to keep the data safe. Others simply keep these archives accessible by mistake.

Criminals are aware of this, and are taking advantage of the situation to steal valuable information. By using widely available tools like Shodan, Censys, or FOFA, they can scour the web for unencrypted, non-password protected databases, and exfiltrate data to be used in phishing, business email compromise, and other forms of cyberattacks.

Over the years, Fowler and other searchers have found dozens of enormous databases that have leaked sensitive data on hundreds of millions of people.

In 2026, researchers found that European cloud provider Nextcloud kept an unprotected database on the public internet, containing 367,000 records (8GB) of sensitive employee and client data.

In 2025, IMDataCenter, a Florida-based data hygiene, enhancement, and append services provider, was leaking 38GB of sensitive personal records. The unencrypted and non-password-protected database held 10,820 in total.

In 2024, sports analytics technology company TrackMan exposed sensitive customer data: 110TB and 31,602,260 records. The database had no password.

Software is being released at a pace traditional testing approaches struggle to match, while AI is moving from assisting with test design to generating, adapting, and maintaining tests across the delivery pipeline.

The question facing software testers is no longer only how to scale testing, but how to guarantee the evidence automation produces.

Fundamentally, does AI scale quality, or simply magnify inconsistency?

In this environment, the test engineer’s role is becoming more strategic. Rather than focusing on individual test cases, test professionals are increasingly expected to orchestrate quality across AI-enabled systems.

This means applying domain expertise and judgement to connect AI-generated artefacts with requirements, identify hidden risks, and ensure meaningful coverage.

As AI takes on more generation and execution work, the value of the test engineer is shifting towards governance and evidence stewardship. Without human oversight, faster delivery can create a false sense of assurance.

A fine balance: machine logic versus human control

AI-generated testing introduces new complexity for quality teams. Traditional automation executed predefined instructions, but modern AI systems can interpret requirements, generate scenarios, and make decisions about coverage. This increases speed and scale but raises questions around validity, accountability, and confidence in outcomes.

The debate has moved beyond whether AI can generate tests to whether those tests are trustworthy, explainable, and aligned with the real-world risks they are intended to uncover. This is where standardization begins to provide a foundation.

Emerging standards and methodologies for testing machine-learning-based systems – including test methodology for ML‑based systems, recent guidelines on documenting AI‑enabled systems, and novel approaches to continuous auditing‑based conformity assessment - are starting to define structured approaches for assessing robustness, bias, security, and other quality characteristics across the machine-learning lifecycle, giving teams a shared basis for judging whether AI‑generated tests are sound.

Human oversight remains the anchor of trust, ensuring automation elevates quality rather than simply increasing activity.

The era of the quality orchestrator

The test engineer’s role is evolving into that of the quality orchestrator, a profile that combines technical depth with contextual awareness to ensure AI‑produced tests are not only efficient but trustworthy.

Rather than concentrating on individual test cases, the quality orchestrator validates AI outputs, identifies gaps automation overlooks, and applies human judgement where risk or ambiguity is high. They provide the governance layer AI cannot replicate, ensuring automation strengthens quality rather than simply increasing throughput.

AI systems can identify patterns, summarize logs, and propose test scenarios at a speed far beyond human capability. Yet testing involves more than processing information. Skilled testers challenge assumptions, explore unexpected behaviors, and assess risks that may not be obvious from requirements alone.

Understanding business context and determining whether a result is meaningful requires domain knowledge and judgement beyond pattern recognition.

Adding value: where AI expectations exceed reality

AI-enabled testing delivers significant value where speed, scale, and pattern recognition matter most. It can extract requirements from standards, draft initial test cases, summarize logs, and accelerate repetitive activities that traditionally consume valuable testing time.

However, fully automated test generation remains aspirational. AI-generated tests depend heavily on high-quality requirements and documentation; ambiguity or missing context can create flawed assumptions and coverage gaps.

Consistent, structured documentation is therefore a prerequisite for dependable automation. Recent guidelines on documenting AI-enabled systems provide a practical framework for traceability and transparency, including model cards, fact sheets, and other structured artefacts, all in the service of reliable automation and regulatory alignment, most notably the EU AI Act.

While AI can accelerate generation and execution, it cannot independently confirm that tests are correct, relevant, or aligned with intended risks.

Human oversight remains essential to ensure AI-generated outputs support genuine quality rather than superficial completeness.

Continuous, context-aware, AI-enabled testing

The future of AI in testing points towards environments that are more continuous, context‑aware, and closely integrated with engineering and operations. Continuous conformance assessment is emerging as a logical extension of AI‑driven tooling, enabling systems to evaluate behavior in real time rather than at fixed milestones.

Novel approaches to continuous auditing-based conformity assessment clearly reflect this shift, replacing standalone audits with the ongoing collection of evidence and automated checks throughout an AI system’s operational life. Shift‑right practices will become increasingly important as organizations analyze real‑world logs to understand software behavior under genuine usage conditions.

The industry is also exploring AI-driven detection of standards changes and affected requirements, though strong traceability remains essential before these capabilities can be relied upon at scale. As generative and agentic AI enter the delivery pipeline, the assessment challenge will only deepen, and with it the need for common quality criteria.

This is indicative of where the discussion is in 2026, with generative and agentic AI introducing new layers of complexity that make shared quality criteria even more essential.

From test engineer to quality orchestrator: redefining the future of software testing

As AI continues to redefine software testing, confidence in quality cannot be delegated to automation. The testers who succeed will combine technical expertise with judgement and governance, orchestrating human oversight and AI capability to deliver trustworthy outcomes.

The advantage will belong to organizations that invest in orchestration capability now, in their people as much as their tooling.

AI can guarantee the speed of a pipeline; only human judgement can guarantee that the evidence it produces is reviewable, defensible, and credible.

We've reviewed, rated, and ranked the best small 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

• Xbox Wireless Controller X25 Special Edition will reportedly launch on October 20
• Leaker 'billbil-kun' claims pre-orders will open during Gamescom on August 26
• The controller will cost $79.99 / £74.99 / €79.99

The price and release date for Microsoft's 25th anniversary Xbox Wireless Controller have reportedly leaked online ahead of Gamescom 2026.

The company revealed the Xbox Wireless Controller X25 Special Edition in June, alongside the Xbox Series X25 Limited Edition, both inspired by the original Xbox and the "OG green" that players remember.

We haven't heard anything about the hardware since, but now, according to reliable leaker 'billbil-kun' of Dealabs (via IGN), the X25 controller will launch on October 20, 2026.

The leaker also claims the controller will cost $79.99 / £74.99 / €79.99 / Canadian$99.99, with pre-orders opening on August 26, coinciding with its announcement at Gamescom.

We know Xbox will be at Gamescom, but its plans for Opening Night Live, where host Geoff Keighley will take to the stage to unveil world premiere trailers for new and upcoming games, are currently unknown.

However, billbil-kun claims the X25 controller details will be featured during the event, with pre-orders opening soon after.

There are no pricing details for the Xbox Series X 25 Limited Edition. Still, Xbox previously stated in an Xbox Wire post that the console and controller will be available together in select markets as a limited-edition collection in November.

Both the console and controller are a gorgeous translucent green, allowing players to see their internal builds, with illuminated Xbox buttons and "Xbox 25" branding.

"This is just one of the ways we’re celebrating 25 years of XBOX with you, the players who have made XBOX what it is. Thank you," said Jason Ronald, VP Next Generation, in the same post.

• RCS in Google Messages has stopped working for some users
• The issue mostly seems to be affecting newly set up Samsung phones
• Various fixes have been proposed, but the simplest and weirdest seems the most reliable

RCS (Rich Communication Services) is a worthwhile upgrade to SMS — it brings encryption, read receipts, and more, and it’s accessible via Google Messages on Android. Except right now, it seems not to be working for a number of people.

It's not clear how widespread the issue is, but various posts on Reddit suggest there are issues with RCS on recent Samsung phones like the Samsung Galaxy Z Fold 8 and Samsung Galaxy S26 Ultra.

The issue most commonly seems to occur when swapping over to a new device, so people who’ve had a Samsung Galaxy S26 for a while with no issue, for instance, are seemingly less likely to run into problems than someone who just bought the phone and set it up.

But in any case, a number of Reddit users report RCS failing to connect, in some cases followed by a 3100 error code. The good news is that there are several potential fixes floating around, and one of them is quite straightforward.

That fix, as highlighted in one Reddit thread, is to turn off RCS, connect to a VPN, then turn RCS back on, and hopefully it will reactivate as normal. You can safely turn the VPN off at this point. This Reddit post says that you should connect to a VPN in Paris, France, specifically, but elsewhere, users have claimed success by using other VPN regions, so it’s unlikely you need to choose Paris — we certainly can’t see why that would make all the difference.

Anecdotally, this method seems to have the highest success rate going by the replies on Reddit, with most users exclaiming variations of “I can't believe this actually worked,” and that’s good, because not all the suggested solutions are as simple as this.

The official suggestion

Some Samsung Galaxy Z Fold 8 users are reporting RCS issues (Image credit: Richard Priday / TechRadar)

So we’d suggest trying this method first, but if it doesn’t work, then Google’s own support team posted a suggestion too. They say you should update to the latest version of Google Messages, then turn RCS off, clear data for Carrier Services, restart your phone, and finally turn RCS back on.

If that doesn’t work either, another user has posted a six-step process that involves stopping the Google Messages app, then removing your phone number from Google, followed by turning off two-step verification, turning off recovery phone, and removing Google Play Services updates, then turning Google Messages back on — after which, if RCS is now connecting, you should re-enable/update all of the above.

This feels like a bit of a nuclear option though, and not all users found this method worked for them. So hopefully the weird VPN trick will solve the issue for you. Interestingly, this trick first emerged two years ago when some users were having similar issues. So, while we’re not sure why it works, it has a long history of solving the problem.

Every major technological shift has required humans to assess where their value lies.

Computers reduced our reliance on memory, calculators changed the way we approached arithmetic and, today, AI is transforming how we research, analyze and create content. AI is also influencing how leaders communicate.

When Mark Zuckerberg experimented with AI avatars and Klarna CEO Sebastian Siemiatkowski used an AI-generated version of himself to present the company's earnings, both instances sparked an important debate.

As AI becomes increasingly capable of replicating our words, voices and our appearance, are we ever going to automate leadership itself?

When used thoughtfully, AI has enormous potential to remove repetitive work, surface valuable insights and give leaders more time to focus on strategic decision making.

Yet, as the technology becomes increasingly capable of producing polished communications and replicating human personas, organizations need to ask a more fundamental question: which aspects of leadership are uniquely human and should remain as such?

Trust can’t be generated

Leaders have the power to shape careers, make decisions that affect livelihoods and set the direction of an organization, meaning trust is fundamental to effective leadership.

The 2026 Edelman Trust Barometer found that employees trust "my CEO" significantly more than CEOs in general. That distinction is important because it shows trust is not automatically granted by a leadership title, but earned through consistent, authentic interactions.

During periods of uncertainty, employees are not simply listening to what their leaders say, but how they say it. If people are receiving communications or direction from AI, they will feel like a cog in a wheel, reflecting a timeless Maya Angelou quote: “People will forget what you said, people will forget what you did, but people will never forget how you made them feel."

Confidence, empathy, vulnerability and conviction are communicated through countless subtle signals that extend well beyond a polished speech. Pausing before answering a difficult question, acknowledging uncertainty or responding honestly to employee concerns often carry far more weight than a perfectly scripted presentation. These intuition-led moments demonstrate humanity rather than performance.

For leaders who lean on AI to generate meeting agendas, draft speeches and craft thought leadership content, the authentic, human-first details will only become more valuable.

Rather than diminishing the importance of leadership communication, AI may raise the standard by making authenticity the defining characteristic of effective leaders, giving leaders more time to focus on these people-driven moments rather than the tedious, administrative tasks.

As routine communication becomes easier to automate, the conversations that genuinely require a leader's presence will become even more significant.

Cross-border communication is more than translation

Leadership communication is significantly more complex when organizations operate across multiple countries, cultures and markets. Employees, customers and investors all bring different expectations, communication styles and social norms to the table, meaning a message designed to reassure one audience may be interpreted very differently by another.

This challenge has become more prominent as hybrid working has accelerated, with over half of employees worldwide working in hybrid environments and collaborating across borders.

While AI is already exceptionally good at translating language, translation is not complete without context. Effective leadership depends on understanding context, sentiment and intent, recognizing when to adapt a message, when greater transparency is needed and when listening is more important than speaking. These decisions are rooted in emotional intelligence, cultural awareness and lived experience rather than algorithms.

This is where AI should be viewed as an enabler rather than a replacement. It can help leaders analyze sentiment, identify emerging themes and prepare communications informed by a much broader range of information than any individual could process alone. Ultimately, however, ensuring those messages resonate with the people receiving them remains a human responsibility.

Future leaders need human mentors

The most effective leaders rely on experience and relationships to hone their skills above all else. Early-career professionals learn by observing experienced colleagues, receiving constructive feedback, navigating challenging conversations and gradually taking on greater responsibility. These everyday experiences develop judgement, resilience and communication skills long before someone reaches a leadership position.

As AI automates more of the work traditionally undertaken by junior employees, organizations should also consider what opportunities for development may be lost. If AI begins drafting every script, summarizing every meeting or even providing performance feedback, younger employees risk missing many of the mentoring moments that have historically shaped how people develop into leaders.

Rather than relying on technology to fill those gaps, businesses will need to become much more intentional about creating opportunities for coaching, feedback and real-world learning if they intend to develop the next generation of leadership talent.

The human advantage

Artificial intelligence will undoubtedly transform the way leaders communicate but it should not redefine it. Instead, embrace the technology where it has proven to be most effective: removing administrative burdens and informing decisions, giving leaders more time to engage with people, exercise judgement and build trust.

If emerging technologies, such as AI avatars, begin to replace leaders in the moments that matter most, organizations risk undermining the very trust that effective leadership depends on. AI cannot replace the authenticity that employees look for during moments of uncertainty.

The conversations that earn real trust will always be most meaningful when there is a human on both ends.

We've reviewed, rated, and ranked the best video conferencing 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

Demand for AI fluency has risen nearly sevenfold in two years, faster than demand for any other skill, according to the McKinsey Global Institute.

My conversations with technology leaders echo this: nearly every one I speak with is hiring for AI skills.

The problem, however, is that fewer can tell me what AI fluency actually looks like on the job at their organizations.

McKinsey found nearly 90% of companies have invested in AI. Fewer than 40%, however, report measurable gains.

Most AI post-mortems take a hard look at models, data and workflows.

Few look at the people hired to champion AI transformation or how and why they were chosen.

The risks of mistaking confidence for competence

A side effect of AI advancement is that it has made the ability to speak about AI use much easier. A candidate might start by naming every model, then walking you through an architecture they read about last week. In a short conversation, fluent language is almost impossible to separate from fluent practice.

Those who win over the hiring manager are those who sound the most at ease when speaking to AI. Whether they can actually use AI to do the job is a separate question, and most hiring processes never ask it.

Think of an interview as a demo on the candidate’s chosen grounds with controlled setup and their narration. On-the-job AI use is where the trouble starts: without proper AI fluency, it’s easy to lose control of messy data, missing edge cases, systems that refuse to talk to each other, or a model that hallucinates at the worst possible moment.

Just because a candidate dazzles in the demo doesn’t mean they won’t stall in a production environment.

On the surface, this looks like a people problem. In reality, it’s a hiring process problem.

What does a bad AI hire really cost?

The true cost of bad AI hires typically takes time to manifest. It’s rarely on day one, and it can often take several forms. For example, a new AI model goes into production and starts hallucinating in front of a client.

Digging further into the root of this risk reveals that most companies have no shared definition of what “good” looks like for working with AI. One manager may test it one way, another may test it completely differently, and a third goes on gut feel. When the fluency bar changes with every interviewer, organizations are limited to collecting surface-level impressions rather than assessing skills.

Without that shared definition, the damage eventually reveals itself. A project stalls because the person who talked a great game can't get a model to produce anything reliable. Or worse: they can, but only for themselves. They 10x their output, then leave light documentation no one else can follow and route around security and compliance to do it.

Therein lies more risk: productivity that works for one person and breaks the system around them has wide-reaching ramifications.

Eventually, the work has to be redone and the role reopens. Project deliverables slip another quarter and the company is forced to burn more budget.

Bad hiring is not the only reason AI investment underdelivers, but it is a bigger one than most leaders admit.

Test for competence, not vocabulary

Simply adding another requirement to job descriptions isn’t going to solve this issue. Instead, it's deciding the scope of AI use required by the role, then building a process that makes job candidates demonstrate this level of use rather than describe it.

Too many companies set the standard around whether a candidate knows the tools exist. Going deeper to set the bar at independent, verified use makes the fluency gap immediately visible.

There are several changes teams can make to the hiring process to shift the measurement of AI fluency:

1. Test the work before the conversation

Put candidates in front of a scenario that mirrors the real job and score it against a fixed set of capabilities agreed in advance. Say you’re hiring for a product designer, then drop them into a live brief with a real product constraint and ask "why" at every step.

The candidate limited to fluent talking hands you forty polished directions. The one who can actually do the job tells you which one survives the edge cases, what accessibility rules are needed, and the engineer who has to build it. That's the test most interviews never run.

2. Change the interview questions

Stop asking which tools people use. Ask about the last time AI gave them a wrong answer and how they caught it, or to show a prompt that failed and how they fixed it.

Go deep on one real piece of work: what changed, what broke, what they checked, and who the output affected. None of that survives someone who has not done the work.

3. Pivot the script

Give the candidate a realistic AI task, then change it halfway through by taking a tool away, adding a constraint, or moving the goal. Strong candidates reframe and carry on, while performers stall or describe what they would theoretically do.

Ask to see the workings, not the output. Let people use AI in the assessment on the condition that they show you their workflows, automation, prompts and the reasoning behind them. Anyone can produce a polished output now. How they got there is more important.

4. Score it together

Give every interviewer the same dimensions to judge, set before the conversation, then compare evidence in a shared debrief session. The shared evidence review becomes the critical lever for the final hiring decision and fuels the definition of AI fluency for that candidate.

Today’s AI hiring continues to fall into the trap of selecting the candidate who interviews best, only to find a quarter later that they can’t deliver the actual work.

A vague hiring process may feel efficient because it asks less of the people running it. However, the business impact is real as it moves the cost downstream into deployment, where it becomes much harder to trace and far more expensive to fix.

Next time you make an AI hire, skip the questions about which tools they use. Instead, focus on the last time one of those tools failed them, and what they did about it.

We've featured the best HR 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

I’ve used and reviewed a number of the best coffee machines in Australia in the last few years, from simple one-button pod options through to more involved models offering plenty of refinement. For my money, one of the standout espresso machines I’ve had the pleasure of using is the Philips LatteGo 4400 Series, and it’s now down to a delightfully low AU$613 at Amazon for a limited time.

This compact model from Philips is a fully automatic machine, meaning you only need to touch a few buttons to be rewarded with a well-made cuppa. It also means you can start the brewing process and walk away to complete other tasks, which is an excellent use of time if you ask me.

4400 Series Fully Automatic Espresso Machine: was $1199 now $613
Amazon has already discounted the 4400 to an accessible AU$763, but is offering an extra AU$150 off via a coupon, so don’t forget to check the box. And if you have a Prime membership, your new best friend can be up and running in your kitchen within 48 hours. View Deal

The Philips LatteGo 4400 Series isn’t the first automatic espresso machine I’ve used — and it certainly won’t be the last — but it’s left a lasting impression on me for a number of reasons. Firstly, it’s just so darn easy to master. Touch-enabled buttons are clearly labelled and the small colour screen is easy to read.

Then there’s the main event: the coffee. As I said in my Philips LatteGo 4400 Series review, it consistently brews a great-tasting coffee. I did have to adjust the grind setting for the best results, but thankfully that’s an easy process that shouldn’t scare newbies.

What you’re also getting here are 12 drink presets, two customisable user profiles and an automatic milk-foaming system, which is where it gets the LatteGo name. The user profiles are particularly useful if you have more than one person in your household using the machine: just enter a profile, select a drink and then the start button, that’s it.

Where I found the LatteGo 4400 falls a bit is with its headline feature — the LatteGo milk-foaming system. This sees a milk carafe being attached to the machine, for it to then deliver hot foamed milk into your cup or mug. I personally wasn’t all that thrilled with the level of foam produced, and it certainly won’t appeal to hardcore cappuccino fans, but flat white drinkers might actually prefer it. I found I achieved much better results using the Philips Baristina Milk Frother separately, and that’s not an expensive addition to add to your coffee routine, costing just under AU$120 on Amazon.

As for the actual coffee-making part though, I simply cannot fault the 4400 Series. Navigating through the menus is as easy as can be, and I’m consistently satisfied with the results.

While this isn’t the lowest-ever price I’ve seen on this highly capable machine, it comfortably beats the price I saw during Prime Day back in July (when it was AU$878). I can’t be sure how long this discount will last, so this could be one to jump on ASAP if your kitchen is crying out for a coffee machine.

Want more suggestions?

Amazon has a few other coffee machines at discounted prices at the time of writing, including the Philips Baristina and Ninja Luxe Cafe, both of which claim spots in my guide to the best coffee machines in Australia.

Here are some other standout deals for you to consider:

• Philips Baristina – was AU$559 now AU$449.25
• Ninja Luxe Cafe (Stainless Steel) – was AU$799 now AU$619
• Breville Bambino Plus – was AU$729 now AU$498
• KitchenAid Semi Automatic – was AU$999 now AU$777

Have you noticed that ChatGPT has suddenly started dropping f-bombs? I have, and it seems that I’m not the only one.

It first happened in a casual chat, and it really caught me off guard. We were discussing Netflix’s Jujutsu Kaisen and before I had sworn in our chat, ChatGPT used the f-bomb to describe a character’s actions as “f**** awful”. Before that, I’d said things like “God damn,” in the conversation, but ChatGPT was the one to introduce the f-word. It did so completely unprompted, to add emphasis to how awful the person was, and that strikes me as a big change in its personality.

I wondered if it was doing that for other people, so I asked around on the team and they’d noticed it too. A quick online search turned up two Reddit threads almost immediately, from people who’d found the same thing.

It seems like in the last few days ChatGPT has developed a potty mouth. I actually like it — I’m not offended and for me, it makes the conversation better — but I don’t think everybody wants this. The question is, why has it started doing this now?

Here comes the science

The most interesting clue is that OpenAI's Model Spec, was updated this week. They made it public and it explicitly says the default is to avoid swearing — but crucially, that's only a guideline rather than a hard rule. And OpenAI says guidelines can be implicitly overridden by things such as “contextual cues, background knowledge, or user history.”

Its own example is that asking ChatGPT to speak like a realistic pirate implicitly overrides the no-swearing guideline. So, while I’d been having my informal conversation I’d passionately expressed the opinion that I didn’t care much for some of the characters in the show, and ChatGPT had matched the conversational tone, eventually leading it to start swearing.

That's consistent with a broader direction OpenAI has publicly acknowledged ChatGPT is moving in. Recent model updates have emphasized conversational quality, personalization and adapting tone contextually. OpenAI's model notes specifically describe personality updates designed to make ChatGPT “more conversational” and better at adapting its tone to context.

OpenAI has previously explained that ChatGPT’s personality isn't simply produced by one prompt somewhere. Model behavior is shaped through training, baseline instructions and user feedback, and seemingly small personality adjustments can have unintended effects. This was something it discussed very openly after the notorious GPT-4o sycophancy update.

It broke my mental model

Whether OpenAI deliberately made ChatGPT more willing to swear or it's simply an unintended consequence of making it more conversational, I don't know. But it’s a significant change in its behaviour either way.

Language matters, and when ChatGPT swore at me, I noticed immediately because it broke my mental model of how ChatGPT talks. It suddenly felt less like the carefully neutral AI assistant I'd been using for years and more like someone matching the tone of an informal conversation.

I happen to prefer this version of ChatGPT. Other people undoubtedly won't. And that's the strange thing about increasingly conversational AI: relatively small changes to the way it talks can make the thing on the other side of the screen feel surprisingly different.

For now, mine seems to have developed a potty mouth and I'm okay with that.

Now that AI has quickly become embedded in global enterprise operations, it has the ability to impact everything from data analysis to decision-making and even security.

Most conversations, however, focus on AI capability, power, productivity, and accuracy, but leave out one major issue.

While everyone is focused on what happens as AI is implemented, very few are asking what happens when access to AI capability suddenly disappears.

A prominent example of this is the debate around Anthropic restoring access to its Fable and Mythos AI models, which primarily revolved around compliance timelines and export control mechanics.

Yet, few have questioned why so many organizations discovered that one external decision, out of their control, removed a business-critical capability seemingly overnight. In short, AI access disruptions are a symptom of a much larger operational resilience issue, and expose an overlooked governance gap around dependency.

As organizations integrate AI deeper into their business operations, they need to shift their focus from exploring whether AI is secure enough right now to start asking whether their organizations can even continue operating if or when those very AI services become unavailable.

Security does not equal resilience

These little discussed topics bring up an important point that security and resilience are not synonymous.

Security prevents and protects systems from compromise. This keeps attackers from gaining access, reduces the number of vulnerabilities, and defends against malicious entities - all crucial elements of security operations. Resilience is the ability to continue business operations when systems, services, or data become unavailable, regardless of the cause.

We tend to associate resilience with situations such as cyberattacks or IT infrastructure failures. AI has changed the threat landscape for organizations, how they work with AI, and protect themselves from it. Organizations must now increasingly plan for disrupted access to critical tools and functions caused by geopolitical decisions, regulations, or changes made by technology providers themselves.

A service doesn't have to be hacked to become unavailable. A policy decision on the other side of the world can have the exact same operational effect. We saw exactly that with Anthropic.

With this in mind, resilience has to be built into how the enterprise operates and include any new AI infrastructure, so business continuity is ensured even through policy interference.

AI Creates a New Kind of Vendor Dependency

Where traditional software dependency usually involves a single or small amount of vendors, enterprise AI often depends on an interconnected ecosystem that organizations do not own or control. Every additional layer in the AI ecosystem represents a dependency, and therefore a potential point of failure.

This creates four risk factors that leadership needs to be mindful of:

Data sovereignty: Enterprise data may be processed under legal jurisdictions the organization doesn't control, with limited visibility into who can access it or whether it feeds future model training

Model sovereignty: Organizations often have little to no control over model availability, feature capabilities and changes, or access decisions, leaving them exposed if a provider suddenly decides to restrict access or withdraw capabilities.

Infrastructure dependency: Much of today’s enterprise AI ecosystem relies on a small handful of cloud providers operating under specific national jurisdictions.

AI supply chain risks: An interconnected system of foundation models, cloud platforms, and software vendors means disruption at even one layer can quickly cascade across the wider technology stack.

These factors increasingly depend on geopolitics rather than technology.

AI Governance is a Boardroom Issue

The reality is that a vendor contract alone cannot guarantee uninterrupted access to the tools and platforms that an enterprise has invested in. But governance frameworks haven’t truly evolved to account for this issue. Only newly emerging frameworks like NIS2 and DORA recognize that resilience must go beyond fending off cybersecurity threats.

Best practice for an organization as they approach vendor contracts and governance frameworks of their own is to understand where dependencies lie across suppliers and develop contingency plans that allow them to operate smoothly through eras of disruption.

Whether the dependency is within an AI platform, ITSM solution, a CRM, or another business critical technology, organizations should assess how they would continue operating if access changed overnight. AI should be subjected to the same scrutiny as any other critical third-party vendors.

Begin Resilience Frameworks Before the Next Disruption

On top of this, boards should be cautious about accepting AI capability claims at face value. Organizations should require evidence that vendor claims deliver measurable outcomes.

While AI can quickly identify an overwhelming amount of potential vulnerabilities, discovery alone does not improve resilience. Human expertise here remains essential to validate findings, prioritize fixes based on order of immediate business impact and ensure resources are focused where true risk exists.

The biggest lesson from recent AI disruption is how many organizations have underestimated their dependence on technologies they don’t have assured control over. And with renewed conversation from U.S. legislators around a potential AI “kill switch,” this has to be top of mind.

Business leaders must recognize that with all the opportunity AI unlocks, the risk of vendor dependency is close to follow. If I were head of technology at a major enterprise today, I would ensure teams across the entire technology and security departments understand where critical AI capabilities originate, the dependencies that exist across the supply chain, and how operations can remain resilient if access changed overnight.

Ultimately, the future of successful enterprise AI use will be determined by organizations baking governance and resilience strategies into business plans so that through commercial, political, and operational disruptions, business can continue as securely as usual.

Our rankings of the best cloud backup platforms.

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

• NATO is planning to fortify the border with Russia and its allies with a network of sensors and drones, with AI assistance
• The Eastern Flank Deterrence Initiative will track threats along the NATO borders with Russia and Belarus
• The strategy mirrors the steps taken by Ukraine to bolster its defenses against Russian aggression

NATO’s concerns about its border with Russia and Belarus have grown into plans to deploy sensors and drones along 3,500 kilometers, with an AI element to assist in threat detection and tracking.

Known as the Eastern Flank Deterrence Initiative, the AI will be used to control fleets of drones, but the AI’s role in the kill chain will not involve decision-making.

Instead, human operators will be given the final say on whether a drone can strike a target.

Millions of drones

The plans follow a similar approach in Ukraine, where the borders with Russia and Belarus are closely observed by AI-managed drones. But the move towards AI looks set to evolve the procedural kill chain into a kill web, where information provided by drones and sensors with AI oversight is provided to a human operator for a decision.

The Eastern Flank Deterrence Initiative (EFDI) is a fully fledged operation, complete with a data backbone, for which US Army Maj. Ben Schiff is the product manager. The EFDI Data Backbone is described as a “digital ‘nervous system’” for the project, which connects not just the sensors and drones but also satellites and the information systems of NATO members.

Automation of the drones is mainly concerned with the “boring” jobs, like hovering over target areas to detect incursions. But the drones don’t attack unprompted. Instead, this is brought to the human operative for the final decision.

“So the humans are deciding what the drone does, but they don't necessarily need to take the action of flying it because we don't have a million pilots,” said Schiff. “We can't fly a million drones at the same time.”

The kill web

Traditionally, drone strikes have been overseen by a procedural kill chain, whereby information is assessed by a remote operator ahead of a decision being made to attack a target. The EFDI program is unifying data to progress NATO’s approach from a kill chain to “kill web.” This means the information accumulated on the target – from drones but also sensors and satellites, as well as member country information – can be quickly passed to operators. But the devices assembling the data need to be able to work together.

Maj. Matt Blubaugh, a spokesperson for US Army Europe and Africa, says "The value of a drone, sensor, or other capability is not determined solely by its individual performance [but] its effectiveness depends on how well it integrates into the broader operational ecosystem."

The model has already been proved in combat, such as taking out an Iranian Shahed drone in Kuwait.

The Ninja Crispi Pro arrived in Australia only in June this year, and I immediately wanted it. You see, I'd been eyeing the standard Ninja Crispi for a while because the nonstick coating of the trays in my two-drawer air fryer was coming off. My research told me that it wasn't toxic, but I was still concerned.

So it was serendipitous that the larger Crispi Pro came along just when I was ready to ditch my old air fryer — and at a price that wasn't all that much more over the standard Crispi. For AU$399 RRP, it may not be 'cheap' but it sure is good value considering how well it's performed in the last couple of months I've had mine. So much so that I don't regret ditching my previous air fryer that was more versatile thanks to a steam functionality as well.

Right now, the Crispi Pro is going slightly cheaper as well, down to AU$322 on Amazon and at JB Hi-Fi, and available in four colour options.

Don't be fooled by Amazon's list price — Ninja released the Crispi Pro for AU$499 initially, but it's been available for AU$399 RRP since its June launch. Even though it's a small saving, it's absolutely worthwhile even at full price, not just because of the non-toxic glass containers, but also its overall performance.View Deal

I may not have done TechRadar's Ninja Crispi Pro review, but I wholeheartedly agree with its high star rating and, for me, it comes down to the glass containers more than the performance — all the best air fryers in Australia offer excellent cooking results, but few are as safe as glass.

Glass is also remarkably easy to clean — just chuck it in the dishwasher without even having to think about it or soak it in the sink if you have caked-in food on the sides and a light scrubbing will have it sparkling again.

I also appreciate the bigger container's massive 5.7L capacity, something that most two-drawer air fryers don't offer (for context, you get about 4L to 4.5L at most in the average dual-basket models). You don't have to worry about picking the smallest chicken to roast, for example, and can make fries or wedges for up to 10 people.

(Image credit: Karen Freeman / Future)

The smaller 2.3L container is fine for making dishes for a single person, but I would be careful with what I put in there. Chicken drumsticks, for example, have a little height to them and when I tried cooking two in the smaller container, the raw and cooked results scraped against the rubber gasket around the top heating element of the appliance every time I took the container out or replaced it on the modular stand.

Sitting higher within the machine also means that the food is close to the heating element on the top of the appliance, so if the food you're cooking splutters, you'll need to ensure you clean the heating element after each use to prevent burns and subsequent deterioration in performance.

However, 'flatter' foods like a fillet of fish, fries or some vegetables for roasting is fine in the smaller basket.

Ninja also has a 3.2L container as well, but you'll need to buy that separately. Personally, I'd have preferred the 3.2L and 5.7L dishes to be the default options, with the 2.3L being the additional purchase, but that's not a complaint in any way; it's a reflection of my own needs.

Karen Freeman / FutureKaren Freeman / FutureKaren Freeman / Future

The fact that all the containers come with airtight lids is also a tick in my books. Got leftovers? Leave them in the container, cover and pop in the fridge, then just reheat in the air fryer itself. That's less cleaning up!

Despite the two containers, the Crispi Pro doesn't take up too much space on the kitchen counter. In fact, its footprint is about half that of my previous dual-basket air fryer, and most of us would be able to stow away a small glass container in a drawer or cabinet. And I now no longer worry about black bits contaminating my food.

If you're keen on an air fryer upgrade, I'd highly recommend the Ninja Crispi Pro but, if you don't need to feed an army, even the standard Ninja Crispi is a good option (it just has smaller containers).

• Texas Robotaxi passenger films car hitting plastic traffic posts
• Company claims "impeccable" Robotaxi safety record
• Tesla is preparing Cybercabs for public roads imminently

A Tesla Robotaxi user in Austin, Texas, has captured the moment their driverless ride failed to navigate a curb extension, marked off by plastic traffic posts, turning directly into them and carrying on with the journey.

The incident came not long after Tesla’s head of AI told investors the Robotaxi program has an “impeccable safety record” with “zero notable incidents,” according to Electrek.

The footage, posted to Reddit, shows the vehicle creeping forward, backing up, creeping forward again, and then driving right through the posts.

An audible thud can be heard as the vehicle hits the posts, while the passenger can be heard saying, "It hit em, it hit em."

The Robotaxi in question didn’t have a safety operator behind the wheel, although it is unclear whether a remote operator was called upon to authorize the decision to drive through the marked-off area.

Tesla Robotaxi Rams through several Bollards and keeps going. from r/SelfDrivingCars

Tesla’s decision to eschew “costly” LiDAR in favor of high-definition cameras and other sensors has been met with plenty of criticism, but this particular incident appears to indicate a potential issue with Tesla’s mapping, rather than its sensors.

According to Electrek, Redditors used the Texas Orthopedics building visible in the video’s frame to pin down the exact corner where the incident took place on Google Maps, and Street View shows those posts have been in place since at least February 2024.

Tesla's choice not to use a proprietary high-definition mapping system, as Waymo does, means it is largely up to the autonomous vehicle's on-board compute power to make on-the-fly decisions. Here, it looked like it failed.

Despite this, VP of AI Ashok Elluswamy defended Tesla’s Robotaxi safety record at the company's Q2 earnings call, claiming that the company has driven “more than 380,000 miles” with “zero notable incidents.”

Analysis: Tesla readies its Cybercab for public roads

(Image credit: Tesla)

Despite Tesla accumulating a fraction of the unsupervised autonomous ride-hailing miles compared to its closest rival, Waymo (220 million plays Tesla's 380,000), it has been reported that it could start trialing its fully autonomous Cybercab on public roads this month.

As a reminder, the Cybercab is a purpose-built, fully autonomous taxi that rolls off the production line without traditional pedals or a steering wheel, leaving every part of the driving experience to computers, cameras, and a handful of sensors.

The Next Web reports that Tesla’s own employees will be the first to test Cybercab on public roads, although it could start introducing the vehicles into its Austin autonomous fleet soon thereafter.

However, Tesla’s Austin fleet is reported to have shrunk to around 17 cars, down from about 25 in the spring, while those that still exist are operating in a tightly controlled, geofenced area that is a fraction of the size of the area Waymo is working with.

While every autonomous ride-hailing service naturally deals with issues and incidents on a daily basis, the recent video clip from Austin shows that Tesla still has plenty of work to do in order to back up that “impeccable” safety record claim… and boost consumer confidence in a Cybercab service that lacks any physical controls.

• Japan has now launched the second U.S. surveillance payload under the program
• The payload will feed near-real-time orbital data to U.S. forces
• Mission Delta 2 will operate the American payload after deployment

The United States Space Force and Japan have completed a bilateral satellite launch meant to strengthen surveillance capabilities across the Pacific region and beyond.

A US space domain awareness payload was carried aboard Japan's Quasi-Zenith Satellite 7, launched from Tanegashima Space Center.

This mission marked the second and final launch under the Quasi-Zenith Satellite System-Hosted Payload program, described as the first bilateral U.S.-Japan cooperative effort built specifically around national security.

A wider watch over the Indo-Pacific

The US payload will be operated by Combat Forces Command's Mission Delta 2, a unit that identifies and tracks objects across contested orbital terrain.

Mission Delta 2 is headquartered at Peterson Space Force Base in Colorado and maintains a presence at 11 separate operating locations worldwide.

Once fully operational, the payload is expected to deliver near real-time data to the Space Surveillance Network, expanding awareness of the geosynchronous orbit above the Indo-Pacific.

Both US payloads under the program were designed and built by MIT Lincoln Laboratory in Lexington, Massachusetts, while the Japanese host satellites came from Mitsubishi Electric Corporation in Kamakura.

The arrangement traces back to a December 2020 agreement between the two nations to jointly execute the hosted-payload program.

Officials describe the effort as evidence of a maturing alliance built around shared access to orbital infrastructure.

"The successful launch of QZS-7 is a powerful testament to the enduring strength and rapid modernization of the U.S.-Japan alliance," said Col. Bryon McClain, Space Force portfolio acquisition executive for Space Combat Power.

He added that merging capabilities in satellite communications helps maintain security in a contested space domain.

A project years in the making

According to the program’s officials, the launch was preceded by two years of integration work between U.S. and Japanese engineering teams.

"QZSS-HP is a key contributor to help us meet the threat of peer adversaries in space," said Col. Gina Peterson, deputy commander of Mission Delta 2 for Space Domain Awareness.

Brig. Gen. Brian Denaro, commander of U.S. Space Forces Pacific, called the U.S.-Japan alliance one of the country's greatest strategic advantages in the region.

He said the program shows how the two militaries can build interoperability from design through launch.

Neither government has disclosed the cost of the two-satellite program or a timeline for when the second payload will reach full operational status.

Space Force officials treat the launch primarily as an alliance milestone rather than a specific technical breakthrough.

That distinction matters because surveillance gains from a single hosted payload are likely to be incremental rather than transformative on their own.

The program's real test will be whether it produces usable, timely data that changes how the US and Japan respond to activity in geosynchronous orbit.

• New version of Siri Remote appears in latest Apple code
• There are no details of how it might differ from the current one
• One popular wish, customizable buttons, is already possible

Apple's latest product leaks include good news for Apple TV owners like me: a new Siri remote is coming. The current one is rather divisive, with some people comparing it to the Magic Mouse as an example of Apple style over substance; I definitely think the current one is perhaps a little too sharp-edged and minimalist, and hitting the on-screen Skip button with it is often more miss than hit.

The leak was spotted by MacRumors in the code of the macOS 26.7 release candidate, which is the last version of code given to developers and beta testers before the final and official release. In the code there's a reference to 'ATVRemote1,5', which appears to be the same new remote control that Bloomberg's Mark Gurman previously described as part of Apple's 2026 product road map.

There are no details of what this new remote might do, but Redditors have plenty of good ideas — and three in particular would be on a lot of my fellow Apple TV owners' wishlists.

The features we'd like to see in the new Apple TV remote

If you don't need the mute button you can remap it. (Image credit: Future)

Over on r/apple, the very first suggestion is the one that's top of my wishlist too: a dedicated Source button.

Like Redditor Masam10, who like me has their Apple TV 4K connected to a Samsung TV, "A source button would mean I could officially drop my Samsung remote and throw it away. 9/10 times my Apple TV works just fine with HDMI CEC, but every now and then it will get a bit funky and not work and I have to use the Samsung remote to swap inputs," they explain.

The second suggestion, by the fantastically named 'handtoglandwombat', is a speaker for Apple's Find My feature. That'd be very welcome too, given the current Apple TV remote has a habit of disappearing down any available nook in your sofa. Some even have their remote in a case that holds an AirTag for this very reason.

Lastly, another interesting suggestion is the addition of a customizable action button — and the discussion of that unearthed an Apple tip I didn't know about, even though it's been there for a few years: you don't need to wait for a new remote to customize your buttons.

You can sacrifice one of your remote buttons, such as the Mute button, by changing what it does in Settings > Remotes and Devices > Volume Control > Learn New Device. Follow the instructions and when you get to the bit where it says press and hold the mute button, don't. Press the source/input button on your TV remote instead.

The downside, of course, is that you no longer have a mute button. But you might think that's a price worth paying to have a source/input button instead.

The next generation Apple TV is expected to arrive this Fall alongside a new HomePod mini, so we shouldn't have too long to wait for the new Siri remote — although whether it has all of these features remains to be seen.

Thinking of buying a new TV?

Try our TV size and model finder! You tell it how far you sit from your TV, we'll tell you what size to buy based on viewing angle advice from image quality experts, and we'll recommend our three top TVs at that size for different prices.

• Ukraine’s new ground robot can carry 210 kilograms across rough terrain
• The Gnom L2 can travel 50 kilometers on one charge
• Remote control keeps operators away from dangerous frontline supply routes

Ukrainian robotics company Temerland has rolled out a new Unmanned Ground Vehicle (UGV) called the Gnom L2, engineered to haul substantial loads over difficult terrain.

Engineers rate its cargo capacity at 210 kilograms, equivalent to 463 pounds, while top speed reaches 12 kilometers, or 7 miles, per hour.

A single charge reportedly lets the machine cover 50 kilometers, close to 31 miles, before it needs to return for resupply or recharging.

A heavier vehicle for frontline logistics

The company equipped the L2 with higher-capacity batteries to support longer movements while carrying substantial loads.

Its cargo basket has also been reinforced, while a combination of wheels and tracks helps the vehicle move across uneven ground.

Operators control the machine remotely using specialized HotRC equipment, keeping humans involved in decisions about its movement.

The machine does not support autonomous control; thus, it can not independently navigate battlefields without operator input.

The Gnom L2 is currently under field evaluation, so its published specifications may change as testing provides more engineering data.

The manufacturer has not disclosed production numbers, battlefield deployment figures, unit costs, or detailed results from combat operations.

These factors will be important in determining how well the L2 can perform repeated resupply missions in demanding conditions.

However, its payload could allow military units to transport ammunition, equipment and other heavy supplies while reducing soldiers’ exposure during routine logistics work.

Its range could also support trips between supply points and forward positions, although real-world endurance will depend on terrain, cargo weight and operating conditions

One platform, several battlefield roles

The L2 joins several other Gnom variants developed by Temerland for reconnaissance, engineering, evacuation, and support missions.

The company previously introduced the Gnom ND-Recon, which uses six cameras and six high-gain microphones for reconnaissance.

It can also carry a tethered drone with a charging station, while other equipment can provide defensive functions.

Temerland is also developing a Gnom version equipped with a robotic arm for emergency evacuation, mine clearance and engineering tasks.

The range of configurations shows how the company is adapting one robotic platform for different military needs.

For the L2, the focus is moving heavy loads through difficult terrain while keeping personnel away from some routine transport risks.

However, questions remain about its performance when communications are disrupted, or electronic warfare interferes with control links.

Production capacity, Ukrainian deployment numbers and operational frequency are also unknown.

Because the L2 is still undergoing trials, there is no certainty that it will ever be deployed in real combat situations.

Its long-term value will therefore depend on whether it can reliably deliver heavy loads under the unpredictable conditions of active combat zones.

Via Drone Front

• Morgan County's 12-month moratorium has no enforcement power thanks to Dillon’s Rule limiting its ability to act against a proposed 50 MW Bitcoin mine by VoltCore
• The town of Somerville never adopted zoning, and the site sits outside the town limits, leaving its 18-month moratorium equally unenforceable
• The developer trades under three different corporate identities, was refused easements by all four affected landowners, and is routing utilities through the county right-of-way instead

Somerville, Alabama, a town with fewer than 800 residents and the state's oldest surviving courthouse, had no reason to think about land use law until this summer.

However, a 50 Megawatt data center designed to mine cryptocurrencies spanning 15 acres could now disrupt life in an otherwise idyllic town.

This is despite there being two different moratoriums in place, one from Morgan County and another from Somerville's town council, to prevent its construction, moves that lack legal teeth to follow through.

Local governments limited by an arcane Iowa Supreme Court ruling?

Alabama is one of 31 states that follow Dillon's Rule, a principle written into its 1901 constitution, which holds that local governments may exercise only the powers the legislature grants them.

County commissions in Alabama cannot zone property, pass ordinances, or raise taxes without first going to Montgomery for local legislation. Morgan County has no zoning in its unincorporated areas because it has never been given the authority to create any.

In a nutshell, the developer found the perfect loophole: a lack of rules that would allow the county to enforce zoning in its own domain.

Morgan County Commission Chairman Ray Long has been unusually candid about how little his own resolution, which was passed unanimously after residents collected 450 signatures, accomplishes, calling it a 'message' rather than an actual mechanism to force the developer to vacate its plans. When asked what tools were left, he said the county could essentially tell the company, "We really wish you wouldn't do it."

Somerville's position is only marginally better: Municipalities in Alabama do hold zoning power, and a long list of cities have used it this year, but Somerville never adopted any in the past. Mayor Darren Tucker told The Decatur Daily he had pushed his council to pass zoning for years, and it had failed every time, meaning nothing would stop a data center from buying land inside the town limits and building on it.

The 18-month moratorium the council approved on June 30 is the town's attempt to buy time to do exactly that, and part of the plan is to ask the Alabama League of Municipalities whether a town without zoning can legally block a data center in the first place.

The company building out the data center is VoltCore, but the original buyer of the 15-acre parcel is Texas-based Sovereign Global Solutions Inc., which later transferred it to Alabama-based Sovereign Gazelle LLC. Residents reportedly could not reach the listed agent for the latter two and also noted that the former's phone and email contacts were invalid. A third LLC also linked to the data center, Project Avalanche, was created on the fourth of August.

The developer, which needed utility connections for a proposed 10-acre facility on the 15-acre parcel, was denied access by four landowners; it offered a mix of cash, a gas line to their houses, and free internet connectivity, among other perks. It responded by pushing for a right-of-way approach that cannot be legally turned down.

This is not an isolated case; more than 500 moratoriums have been documented across 42 states, and South Dakota has passed a law restricting installations of 10 MW or more, with proposals moving in New York, Oklahoma, and North Carolina. Developers are also fighting back, however, and despite $130+ billion in canceled AI data center projects so far, plenty more is to come as AI buildouts continue unabated in a never-ending hunt for cheap land, power, and access to water.

The lesson a developer takes from the situation in Somerville is ironically that public opposition in an unincorporated county is legally inert, and that the cheapest land, the cheapest power, and the weakest local government often tend to be one and the same. A fix might be in the works, but in this case it could come months or years after the concrete is already poured.

• New analysis links aggressive driving to premature EV battery degradation 
• Chinese researchers found optimizing driving style can prolong battery life  
• Researchers used AI modeling to link driving style to battery wear

Researchers at Shanghai Dianji University in China have linked aggressive driving styles to premature EV battery degradation, concluding that optimizing driving style is an “effective way to alleviate the aging of power batteries and extend cycle life”.

The researchers pored over a year’s worth of driving data from 15 EVs in Guangzhou during 2022, gathering data every 10 seconds on metrics such as speed, current and state of charge to determine the overall smoothness of acceleration and deceleration, as well as the overall intensity of driving.

Once gathered, the data was compiled to create profiles, with the smoothest category dubbed ‘eco-driving’ and the most aggressive category housing those scenarios where the vehicle was pulling very high electrical current from the battery, and included situations such as repeated high-torque demands at relatively low speeds, according to Science Alert.

But this is where it gets clever, because instead of simply looking for correlations between factors like acceleration and battery stress, the researchers developed machine learning frameworks that could separate driving behavior from other factors that can adversely affect long-term battery performance, such as temperature, the battery’s state of charge and time spent idling.

While the paper is clear to point out that the dataset didn't have directly measured long-term battery state-of-health or capacity-fade data, instead using a model to predict long-term degradation, it did find that the most aggressive driving category produced a battery aging rate 18.4 times higher than the most economical driving style.

Longer term, the research group’s battery-aging model estimated a 53.22% capacity fade after 1,000 cycles for the most aggressive driving style, compared with 21.15% for the most economical.

This doesn’t necessarily mean that the fastest drivers are going to wear out their EV batteries quicker, but it could suggest those that regularly launch their electric vehicles away from traffic lights or constantly plant the throttle only to slam on the brakes a few seconds later could be putting substantially more stress on their battery packs.

Analysis: too many variables for concrete evidence

(Image credit: Tesla)

Admittedly, I’m not claiming to be on the same academic level as the researchers from Shanghai Dianji University, but it feels like there are a lot of confounding variables that could also speed up battery degradation.

The way in which an EV is charged has often been cited as a potential contributing factor to premature battery degradation, although a study by Recurrent (via CleanTechnica) found that there was no significant difference in range degradation between Teslas that fast charge more than 90% of the time and those that fast charge less than 10% of the time.

However, this could be a different case entirely if an EV owner was to exclusively charge via 400kW or higher outlets, as opposed to Tesla’s 250kW max charging speeds.

Similarly, factors like vehicle load, the terrain and gradient of roads regularly used by owners and even traffic conditions could all be contributing factors to diminishing range over time.

That said, it’s generally wise to drive as smoothly as possible, both in order to maximize EV range and keep your passengers from your spoiling your box-fresh EV interior.

But now, long-term battery health could also be a consideration for a lighter approach to the accelerator pedal.

Kids aren't the only ones who need new shoes for the school year. August is a perfect time to pick up a pair of sneakers, and Amazon's back-to-school sale is slashing prices on top-rated models from brands like Nike, Hoka, New Balance, Adidas, and Skechers.

Below are popular women's and men's running shoe models on sale, with savings up to 35% and prices starting at just $54. I've listed a wide range of athletic shoes, including extra support for runners and casual walking sneakers for everyday wear. Every model listed below is highly rated on Amazon, and some are included in our best running shoes guide.

You'll find links to popular sneaker brands below if you want to browse Amazon's site, followed by my pick of today's best back-to-school deals on running shoes. Keep in mind these are limited-time offers, and today's discount lets you kick off the new school year with a new (discounted) pair of shoes.

Amazon's back-to-school running shoe sale

• Nike running shoes
• Brooks running shoes
• New Balance running shoes
• Hoka running shoes
• Sketcher running shoes

The best Women's back-to-school running shoe deals

Skechers Skechers Women's Glide-Step Altus Hands-Free Slip-Ins Sneakers

Nike Nike Women's Revolution 8 Road Running Shoes

NORTIV 8 Nortiv 8 Women's Walking Shoes

New Balance New Balance Women's Dynasoft Tektrel V1 Suede Trail Running Shoe

New Balance New Balance Women's Fresh Foam Roav Running Shoe

New Balance New Balance Women's 327 Sneakers

Brooks Brooks Women Ghost Max 3 Running Shoe

HOKA Hoka Women's Clifton 10 Running Shoe

Saucony Saucony Women's Endorphin Speed 5 Sneaker

The best Men's back-to-school running shoe deals

New Balance New Balance Men's Fresh Foam Arishi V4 Running Shoe

adidas Adidas Men's Swift Run Shoe

New Balance New Balance Men's 515 V3 Sneaker, Reflection/white/aluminum Grey, 11

New Balance New Balance 574 Unisex Shoes

Nike Nike Men's Air Max Alpha Trainer 6 Workout Shoes, White/white-Black-Gum Medium Brown, 10

New Balance New Balance Men's 408 V1 Sneakers

adidas Adidas Men's Adizero Pacer Running Shoe

Skechers Skechers Men's Go Walk Glide Step 2.0 Zalor Hands Free Slip-Ins Sneaker, White/black, 10.5

Brooks Brooks Men’s Ghost 17 Neutral Running Shoe