Technology

The Fit List

The corner of the TechRadar site that swaps processors for press-ups, The Fit List is our regular series of fitness listicles. We explore how to improve your health in handy bite-size pieces of advice. You can read the whole series here.

When I started regularly going to the gym several years ago, I did so without bothering with the best fitness trackers. These gadgets, I thought, were only good for running, tracking my sleep and counting my steps. But I’ve since learned that I was totally wrong.

Now I keep a cheap fitness tracker as a handy weightlifting companion. That’s right, a budget option; all of the functions I need, I can use on my low-cost Xiaomi Smart Band 10, making a purchase like the Apple Watch Ultra 3 totally unnecessary.

New to the gym, or eyeing up a new wearable purchase and wondering how it could help you? Let me walk you through five small-but-mighty functions of a cheap fitness tracker which will help you out at the gym.

1. Timekeeping

(Image credit: Future)

Honestly, it’s one of the simplest features of my fitness tracker that helps my workout the most. When you start tracking a workout on any wearable, you’ll see an on-wrist timer to tell you how much time has elapsed, and I use that timer constantly.

I use this not to see how long I work out for after the fact, but to dictate how long I exercise for during each set. I make sure I take a consistent amount of time for each set to maintain the right level of intensity and, more importantly, rest for the same amount of time between sets. I'll try to keep each rest period to a minute or less.

When I first started using the gym, I could sometimes rush through sets if I had lots of energy, or waste absolutely ages of time if I didn’t. Neither of those is good practice.

With my fitness tracker’s stopwatch, I’ve been treating my body better, between sets and rest periods. It also makes it far easier to predict how long a session will be – if my work on each kit is reliable span of time, and I know what I want to do ahead of time, I know how long I’ll be at the gym. Great for planning my day, and encouraging me to try shorter workouts if I have less time instead of not working out at all.

2. Heart rate

(Image credit: Future)

One of the basic functions of any fitness tracker is that it can monitor your heart rate. They all do so, to varying degrees of accuracy, and again I find this a really useful metric to help me stay healthy on a workout.

According to the British Heart Foundation’s simple math on ideal heart rate during exercising (220 minus your age, then work out 50% to 70% of that), my heart rate should be between 95bpm and 133bpm when working out.

Suffice to say, it isn’t always that. Sometimes when I’m really pushing myself with a new heavier weight, or I’m still recovering from my trip to the gym (I always run or cycle), my heart rate will push higher. And at other times, like when I’m being sluggish (should’ve kept a better eye on that timer!) or if I’m spending my rest times typing elaborate article pitches to TechRadar’s editors [like this one! — Ed.], I might be dropping too low.

As before, having my fitness tracker update me on my heart rate during the workout can help me decide how long to rest between workouts, whether to spend my rest period doing active recovery exercises or in 'true rest', and often what workout to do next too if I don't have a pre-prescribed plan to follow. Most wearables make this even easier, by having a ‘red zone’ indicating my heart rate is too high, and a blue zone if it’s low.

3. Longitudinal fitness overview

(Image credit: Future)

Both of my previous points have been about using a fitness tracker’s stats to dictate how I plan and adapt my workout. That principle stands on a longer-term scale too: fitness trackers are almost always tied to a fitness app on your phone, which can track performance over time and provide an insight into how our exercise habits are affecting our bodies.

I can see how frequently I exercise per week, month or even year, and what different types of workout I tend to do. I can see the trends in my workouts: if I find myself going on fewer runs during winter because of the cold, I can make a conscious decision to compensate and do more treadmill runs instead. If I’m doing less weight training during the tennis season, I can add in a session or two to course-correct.

Some of these changes to a workout program are harder to notice without data, since my fitness decisions tend to be idiosyncratic and made on-the-fly, so using an app's graphs gives me useful insights I’d otherwise miss.

4. Calorie information

(Image credit: Future)

One of the many pieces of information that fitness trackers tell you after a workout, is how many calories you burned. I’m always a little skeptical of this data, and it’s best to take it as a rough estimate rather than a concrete figure, but it’s still a useful reminder of an aspect of working out lots of gym-goers forget, and a core tenet of building muscle and losing fat — calories in versus calories out.

I’ve often struggled with under-fueling; I definitely didn’t eat enough when I trained for a marathon, and find it too easy to fall into the same trap as many in thinking that ‘being healthy’ is the same as ‘eating less’. When you’re doing a lot of exercise, your body needs proper upkeep, and that means eating more than you otherwise would.

When I started exercising with a fitness tracker, I’d see how many calories I’d burn per exercise — some of the marathon training runs saw almost 2,000kcal burned, and I did one every week — and it was a lot more than I expected. This, more than any ache or groan in my body, served as an important reminder to eat more. A lot more; bigger meals, more healthy snacks, less starvation. Doing so improved my energy and made my run recovery smooth.

I know that calorie counting is a slippery slope for a lot of people, however, and I don’t advise paying too much attention to this metric if that’s you. If in doubt, listen to your body more than tech.

5. Rest and recovery

(Image credit: Future)

My golden rule with fitness trackers is that I shouldn’t let them make decisions for me. I should always defer to how my body feels, not what tech tells me. But sometimes you need a second opinion, and that’s where rest timers come in.

Different fitness trackers have different names for it, but many wearables, even budget ones, pack functions that tell you how drained you are, or how long you should wait before you exercise again. On my Xiaomi tracker it’s called Training State, and it gives you an idea of how hard you've been training recently. The best Garmin watches call it Body Battery or Training Readiness, while Google Health calls it Daily Readiness. The gist of all of these features is that they tell you how much energy your body has – and recommends a level of workout intensity for the day.

These scores use the various metrics captured by your fitness tracker, like what workouts you’ve done and how often you’ve done them, to make an informed guess as to when you should next work out. Sometimes this comes in the form of a timer, telling you how long you need to wait before you next work out, and sometimes it’s a body battery telling you how ‘charged’ you are.

I never rely on this feature over my own gut feeling, but sometimes I just can’t decide whether I’m feeling up for a run or workout. That’s when my fitness tracker steps in, to give me some extra data and push the needle in one direction or another.

France’s tax authority confirmed a cyberattack exposed taxpayer data as officials investigate the breach’s scope and an unverified 678,000-record claim.

The post Nearly 700,000 French Taxpayer Records Reportedly Stolen in Government Cyberattack appeared first on TechRepublic.

• IKEA's new 3-in-1 display is a clock, thermometer, and humidity sensor
• The display follows the release of IKEA's TIMMERFLOTTE temperature sensor
• It's only available in the UK so far, priced at £5

If you’re looking for simple, convenient, and affordable home tech, IKEA is surprisingly handy when it comes to this — and the Swedish home furnishing giant just launched a new 3-in-1 display that does more than tell the time.

As well as displaying a clock, the new ÖRNVRÅK device also shows the temperature and humidity of your home, a true triple threat. Right now, ÖRNVRÅK is available in the UK online and in-store, but we’re keeping our eyes peeled for a US rollout.

The ÖRNVRÅK’s 3-in-1 functions aren’t its only selling points, however — it costs a mere £5 — a cheap and cheerful device that’s not overly complicated. And because it’s powered by one AAA battery, you won’t have to worry about constantly charging it.

As far as its specs go, ÖRNVRÅK is a small device at just 8x3cm and incredibly lightweight, weighing just over 100g. With its IP44 resistance rating, the display can endure small splashes of water, meaning it can be placed pretty much anywhere in your home, from your kitchen to your utility room or bathroom.

(Image credit: IKEA)

It’s also multi-faceted when it comes to attachments. Its magnetic back allows it to stick to surfaces such as your fridge door, or you can use its built-in hook attachment to hang it on the wall, or use it as a stand for flat surfaces.

In recent years, IKEA has really taken advantage of the demand for cheap home devices that just get the job done. Earlier this year, the retailer launched its £5 Matter-supported TIMMERFLOTTE temperature sensor, which fits perfectly into your existing smart home ecosystem. The company also has a cheap lineup of audio devices, most notably its colorful KALLSUP Bluetooth speaker — which packs a punch for just £10.

While the TIMMERFLOTTE sensor has the smart home bells and whistles, this isn’t the case for the new ÖRNVRÅK display. Instead, it keeps things simple, swapping the smart home connectivity for the digital clock face.

If you’re all about smart home connectivity when it comes to IKEA’s devices, this might not be the product for you, but what the ÖRNVRÅK display does that the TIMMERFLOTTE sensor doesn't is show the time, temperature, and humidity simultaneously. ÖRNVRÅK removes the need to swipe through each reading individually, which gives it the edge when appealing to those who value simplicity over smart features.

D23 Expo 2026 has come and gone — and, like past editions of the Ultimate Disney Fan Experience, there were plenty of big announcements concerning Marvel and Star Wars.

As the dust settles on the aforementioned fan convention and said reveals, there's no better time to take stock of what was shown, and rank each announcement based on how exciting they were. So, read on for my take on all six reveals — and, once you reach the end, let me know if you agree with my ranking by leaving a comment!

6. Ryan Gosling's Star Wars: Starfighter character name revealed

welcome to our galaxy, Ryan.Star Wars: Starfighter, directed by Shawn Levy and starring Ryan Gosling, arrives in theaters May 28, 2027. pic.twitter.com/3wHmjoqbARAugust 15, 2026

I feel bad putting Star Wars: Starfighter at the bottom of my ranking because it's a film I'm really looking forward to seeing next May. Nevertheless, Lucasfilm only offered us the tiniest of crumbs about its next big-screen offering, which is why it doesn't feature higher on this list.

So, what was revealed? The name of leading man Ryan Gosling's character, which is Kade Auberon. As names go in the hugely popular sci-fi franchise, that's a pretty cool one to have, but it's still small fry in comparison to the announcements I've yet to cover.

Interestingly, a first teaser for Starfighter was also unveiled at the fan convention — and, if it had been officially released online, I'd have placed this entry higher. For what it's worth, the teaser has been leaked, but I won't be sharing any links because I don't want to get in trouble with Lucasfilm or Disney.

While we wait for it to properly hit the internet, find out everything we know so far about Star Wars: Starfighter.

5. Your Friendly Neighborhood Spider-Man season 2 release month and first-look details

First look at ‘YOUR FRIENDLY NEIGHBORHOOD SPIDER-MAN’ Season 2.Releasing in January on Disney+ pic.twitter.com/NzKnicqmBjAugust 15, 2026

Your Friendly Neighborhood Spider-Man (YFNSM) might not everyone's cup of tea. However, aside from its unusual animation and art style, which took me a long time to get used to, I actually enjoyed the Disney+ animated show's debut season.

So, even though every other Marvel announcement at this Year's D23 was better and/or more exciting to a lot of people, I'm really looking forward to YFNSM season 2's launch in January 2027.

Based on the above artwork and first-look footage, there's a lot to get excited about, including Spider-Man bonding with the symbiote and getting his iconic black suit, confirmation that new and returning villains will appear, and the revelation that Spider-Gwen is set to join the show.

4. Star Wars: Ahsoka season 2 release date and first trailer

Three years have passed since Star Wars: Ahsoka's first season premiered on Disney+, one of the world's best streaming services. But, while it's been plenty of months since filming wrapped on its sophomore installment, we've still got a long wait on our hands for it to arrive. That's because Star Wars: Ahsoka season 2 won't arrive until January 20, 2027.

Disappointing though that is, Lucasfilm has sought to appease us by releasing season 2's first trailer — and, I don't know about you, but it looks much, much better than its sluggish forebear did. Okay, trailers can be deceiving, but if Ahsoka season 2 marries huge space battles with more intimate sci-fi drama, I'll be very happy.

While we wait for it to be released, get the lowdown on Star Wars: Ahsoka season 2.

3. VisionQuest first trailer and story details unveiled

The final part of the Marvel Cinematic Universe's (MCU) only TV trilogy to date, VisionQuest will wrap up the story that began with hit series WandaVision and continued with 2024's Agatha All Along.

With VisionQuest set to come out on October 14, it's high time we saw some actual footage from it. It was kind of Marvel to not only oblige at D23 Expo 2026, then, but knock our socks off with what appears to be a cracking third act to this Wanda Maximoff and Vision-led narrative.

That's not all we've learned about the Paul Bettany-fronted show, either, because the Marvel Phase 6 TV show's official story synopsis has also been revealed.

"Vision, rebooted and having escaped from those who sought to weaponize him, has been in hiding," the plot brief reads. "Searching for new meaning, he consults the AI personas embedded in his programming, including F.R.I.D.A.Y., E.D.I.T.H., J.A.R.V.I.S., and the infamous Ultron.

"His discreet existence ends when a bounty placed on his head thrusts him on the run with Thomas Shepard, a mysterious boy who may be Vision’s son, reincarnate. As Vision evades capture, he must confront his nature, resist Ultron's influence, and unravel the enigma that is his young companion if he’s to survive."

For the full scoop on the final MCU TV project of the year, find out what we know so far about VisionQuest.

2. Avengers: Doomsday trailer #2

You wait months for an Avengers: Doomsday trailer and, like London buses, two arrive in quick succession. Less than a month after the first trailer for Avengers: Doomsday broke the internet, Marvel has released another one for the hotly anticipated team-up movie — and it's further raised our collective hopes that the fifth Avengers film will be as epic as the three of its four predecessors (sorry, Avengers: Age of Ultron...) were.

There's plenty in this new teaser to excite fans ahead of one of 2026's most exciting new movies, too. From a fresh look at Doctor Doom's duel with Thor, and more teases concerning the former's backstory and motivations, to the gravity of the situation that our heroes face and their desperate attempt to delay the inevitable — that being, Doom's likely victory in Avengers 5 — and December 18 can't come soon enough.

1. Marvel's new X-Men movie cast reveal and release date announcement

The X-Men are coming to the MCU:Sadie Sink is Jean GreyKit Connor is CyclopsChristopher Abbott is Professor Charles XavierSamara Weaving is Emma FrostInde Navarrette is RogueMaya Boyd is StormAdam Driver is Nathaniel MilburyOnly in theaters May 5, 2028. pic.twitter.com/ZCd4f7I1W7August 15, 2026

Quite frankly, there was only one thing that could top Avengers: Doomsday's new trailer release at D23 Expo 2026: some official news on Marvel's new X-Men movie.

After weeks of speculation that included numerous casting rumors and gossip that Marvel would unveil the stars of the MCU's first mutant-led film at D23 Expo 2026, the comic titan did just that, too.

We already knew that Sadie Sink's Jean Grey would be joined by Kit Connor's Cyclops and Samara Weaving's Emma Frost in the Marvel Phase 7 flick. Meanwhile, Obsession star Inde Navarrette had all but teased her involvement after confirming she'd met X-Men movie director Jason Schreier.

As always, though, Marvel had a few surprises up its sleeve. Not only did it officially announce Navarrette would portray Rogue, it also confirmed Christopher Abbott (Girls) would feature as Charles Xavier/Professor X, and that Maya Boyd (Merrily We Roll Along) is on board as Ororo Munroe/Storm. Perhaps the biggest shock of all, though, was that none other than Adam Driver would finally join the MCU as Nathaniel Millbury — or, to give his proper name, Nathaniel Essex, aka the villainous Mister Sinister.

Oh, and did I mention that the MCU's first X-Men film was confirmed to be releasing on May 8, 2028? No? Well, it will. Can someone invent time travel, so we can fast-forward to 2028, please?

For more on the forthcoming mutant flick, read up on everything we know so far about Marvel's new X-Men movie. Again, be aware that some information, especially in the cast section, will be out of date until I update it.

• The Honor Robot Phone is attracting lots of attention on social media
• Commenters are calling its built-in gimbal an "innovation" and arguing that customers "are tired of seeing the same phone every year"
• Some critics argue it could be fragile or hard to clean

Earlier this month, Phones Editor Axel Metz travelled to Shenzhen, China, to be among the first to try the Honor Robot Phone.

This upcoming flagship from Chinese smartphone giant Honor features a built-in gimbal camera that can pop out of a special rear panel for enhanced video stabilization, along with high-tech features like AI-powered subject tracking and advanced shooting modes.

It's one of the most distinctive and exciting smartphones we've seen in years, and it's likely to cost less than you might think if it does reach Western markets. It officially launched in China for ¥9,999, which comes to around $1,500 / £1,100 / AU$2,100.

There is a big catch, however. Honor used to be a Huawei subsidiary and, while its devices are no longer subject to sanctions following its separation in 2020, they're still not officially sold in the US.

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That fact hasn't stopped our US audience from expressing their excitement for the Robot Phone on TikTok and Facebook, where a brief hands-on video we posted last week has already reached more than a million views and accumulated thousands of likes and comments.

One top-rated comment reads: "This is why Chinese companies get banned in the US. Our tech is so behind."

"US companies can't compete," reads another. "That's why they just banned them."

Other users praised the unique concept of a gimbal built into a phone. "That's what I call innovation. People are tired of seeing the same phone every year, like the iPhone," wrote one commenter.

Another posted: "This right here is innovation light-years ahead of iPhone."

"Americans only dream of phones like these," wrote one wistful commenter.

There are also plenty of sceptics, with some users citing durability concerns and others arguing that the gimbal would be hard to keep clean. "External moving parts are never a good idea," wrote one critic.

There's currently no official word on whether the Honor Robot Phone will be available in the US, UK, or Australia, but it certainly seems like there's a lot of interest. Hopefully Honor makes an announcement soon.

In the meantime, you can read what Axel thought after two hours with the Honor Robot Phone, or our interview about the Honor and ARRI collaboration behind its camera.

The Pixel 11 Pro is one the new additions to Google's flagship phone lineup, sitting alongside the Pixel 11, Pixel 11 Pro XL and Pixel 11 Pro Fold (we've tried them all out, and were impressed). The Pro's screen is the same 6.3-inch size as the standard Pixel 11, but is brighter and higher-resolution. The build quality of the phone is also a step up over the basic model, and the rear camera setup is more advanced too.

If you've decided this is the phone for you and you've taken advantage of one of the Pixel 11 Pro preorder deals, now's the time to get a case sorted. Below, I've rounded up a selection of my favorites, in the US and in the UK.

My go-to brands for reliable, tough cases are Otterbox and Spigen. Those cases are generally on the pricier side, so I've included some cheap-and-cheerful Amazon alternatives. These tend to come from weird, no-name brands, so quality can be hit-and-miss. I've stuck to the options that come backed with reassuring reviews. And I've also thrown in a selection of fun cases from Casetify, which is the best place to look for quirky options to add some personality to your phone.

US cases

FNTCASE Pixel 11 Pro Case Compatible With Magsafe

CASETiFY Sunset wave case

Temdan Pixel 11 Pro Case with built-In invisible Kickstand

Spigen Ultra Hybrid Magfit for Pixel 11 Pro Case, Clear White

Spigen Ultra Hybrid Magfit for Pixel 11 Pro Case, Frost Black

OtterBox Pixel 11 Pro Lumen Series Case — Clear

Spigen Tough Armor Magfit for Pixel 11 Pro Case, Black

CASETiFY Blue Reflections case

Spigen Tough Armor Magfit for Pixel 11 Pro Case, Abyss Green

CASETiFY Wave Trip - Blue Voltage MagSafe case

Spigen Nano Pop Magfit for Pixel 11 Pro Case, Black Sesame

OtterBox Pixel 11 Pro Defender Series Pro Case - Black

UK cases

Spigen Nanopop Magfit Case for Google Pixel 11 Pro - Blueberry Navy

TOCOL Google Pixel 11 Pro Case, Magsafe Compatible, Alpine Green

FNTCASE Google Pixel 11 Pro Case, Fit for Magsafe

Spigen Ultra Hybrid Magfit Case for Google Pixel 11 Pro - Clear White

Ringke Google Pixel 11 Pro Case, Onyx (Dark Green)

CASETiFY Happy Croissants case

OtterBox Pixel 11 Pro Lumen Series Case - Clear

CASETiFY Hibis-Kiss case

JETech Magnetic Matte Case for Google Pixel 11 Pro, Black

CASETiFY New York Subway Metro case

Spigen Liquid Air Magfit Case for Google Pixel 11 Pro - Matte Black

OtterBox Pixel 11 Pro Defender Series Pro Case - Black

Slop. The word that people have settled on, and it fits.

LinkedIn posts that say nothing. Investor decks assembled from prompts. Sales outreach that feels personalized but went to thousands of people. Conference presentations that look polished but collapse when someone asks a substantive question.

Nobody in a position of power seems to want to say this clearly, so here it is: we are making it ourselves.

People racing to scale content with AI tools are burning through the one thing that makes any of this worth doing, which is the reasonable expectation that when a person puts their name on something, a person thought about it.

When the thinking did not happen, the name is borrowed credibility, and audiences tend to feel the difference.

A denial of service attack on professional attention

Think about what high-volume, low-signal communication actually does to the people on the receiving end. It is a denial of service attack. Done at scale, they do not just waste time. They degrade the channel for everyone trying to use it honestly.

Trust in professional life is not a soft value. It is the mechanism by which work gets done. Slop corrodes that mechanism slowly, quietly reshaping expectations until the default posture toward professional content shifts from curiosity to suspicion.

The deck nobody built

Anyone who spent serious time at an event this year noticed it. You sit in the room and thirty minutes in you realize the presenter has not actually field-tested any of what is on the screen. The model made it coherent. Coherence is not the same as true.

The tell is always the same. Someone asks a follow-up question that goes one layer below the framework and the answer drifts. Not because the presenter is unintelligent. Because there was no thinking to retrieve.

This matters because a presentation is a claim: I know something about this, I worked it out, and it is worth your time. An AI-assembled deck without the underlying experience is not a weak presentation. It is a lie. It borrows the authority of hard-won perspective without having won it.

The prototype that is not a product

For most of software's history, the gap between a prototype and a product was real and substantial. Building a finished product required engineering time, design iteration, infrastructure decisions – all the unglamorous work that separates a demo from production. That gap created useful friction.

AI has compressed that gap in ways that are genuinely exciting and genuinely dangerous. You can now generate a working interface, complete with plausible data and coherent navigation, in a matter of hours. Teams can test market appetite and validate direction before committing to a full build. That is legitimately valuable.

What is not legitimate is presenting that prototype as though the hard questions have already been answered. Can it scale? What happens at the edges? What does it cost to run? These are not details to resolve later. They are the product.

A prototype shown honestly invites conversation. A prototype shown as a finished product forecloses it, and the interest on that debt gets paid in credibility.

Thinking versus the appearance of thinking

The fundamental choice we make every day, often without recognizing it is this: are we using AI to think better or to avoid thinking?

There is a version of AI-assisted work where the human is still the author. The thinking happened. The judgment was applied. Then there is the version that produces slop. The deck assembled via prompts. The prototype shown without the word prototype appearing anywhere. No thinking happened. No perspective was shared. An audience was addressed but did not receive anything.

Getting ahead of the trust crisis means being honest about which version we are practicing and having the discipline to stop calling the second one a strategy.

The bill has started to arrive. Response rates to AI-drafted cold outreach have collapsed. Investors reviewing AI-generated pitch decks are getting quicker at identifying when the strategic narrative was assembled rather than discovered. In product, the reckoning takes the form of churn.

The cruelest part is that the organizations doing the flooding harm not only themselves but everyone trying to communicate honestly in the same space. Individual rationality producing collective ruin, one generated paragraph at a time.

There is a longer consequence that rarely gets named. Frontier AI models were trained on the accumulated output of human knowledge-sharing: forums, papers, articles, the slow sediment of people working things out in public. New questions posted on Stack Overflow are down almost 80% year over year. If the knowledge commons are replaced by AI-generated output, we are not building on a base of human insight. We are iterating on a fixed one.

Slop does not just erode trust in the present. It risks stagnating the knowledge base that the future depends on.

What we owe the people we are trying to reach

As AI drives content toward abundance, credibility becomes the only real differentiator, and it is the scarcest resource in the communication ecosystem. The organizations and individuals who protect it now will hold something that becomes more valuable as the surrounding environment degrades.

Worth the time to actually write the thing, work through the strategy, build the product before calling it one. Worth asking whether what you are about to put in front of people reflects your thinking or just a model's prediction of your thinking. And sometimes it is worth saying nothing rather than saying the algorithmically optimal version of nothing.

Trust has always been the currency of professional influence. Right now, a lot of people are flooding the market with counterfeits. The people on the receiving end know. They may not say so in the meeting, or click unsubscribe, or walk out of the session. But something shifts. The next email gets opened a little more slowly. The next deck gets a little less benefit of the doubt. The next demo gets a harder question in the room.

Credibility does not collapse all at once. It drains, quietly, one hollow interaction at a time. And by the time you notice the account is empty, the withdrawals have been happening for a long time.

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

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• Car Thing display dongles are currently e-waste...
• ... but this new software online lets you revive them
• Created by fan engineers to bring back old tech

It's been several years since Spotify killed off its Car Thing auto dongle, bricking devices which customers owned, and some users have never forgiven the brand for that 'Car Thing is being discontinued' pop-up on their displays. But, even though it's been four years since the brand announced the end of the road for Car Thing, fans have finally found a way to bring it back.

Over the last few months, an engineer has been working on a revival project, and they finally have it — in the form of Mira. This is a software you can download onto your old Car Thing, to bring back its functionality in your vehicle.

You can download the software via Github, and get it working on your Car Thing by connecting it to your phone or computer, but you don't need a permanent install of anything onto your device (other than Spotify, obviously).

Then, you'll be able to keep using your Car Thing to add Spotify playback to your smart system-less car, just as the streaming giant intended when it unveiled the thing.

It's not quite Car Thing...

... but it's close. The designer has shared a list of features available in Mira, and it's pretty extensive. You can use voice commands, see album artwork and lyrics, use gesture controls to navigate, use Car Thing's physical dial and see the transcripts of podcasts.

Apparently there are a few Bluetooth connection bugs in the current version of the software, and Reddit commenters on the announcement post are clearly waiting for Apple Music and YouTube Music support. But Mira's mere existence is great news in making up for Spotify's failings.

Perhaps best of all in this 'pay-for-everything' existence is that Mira is free to download and use, with no rolling subscription fee (so, just whatever you pay for Spotify and your device, obviously).

I didn't have 'Car Thing coming back to life' on my 2026 bingo card, but now that it has, I'm not surprised Spotify had nothing to do with it. The brand has been too busy essentially swerving the AI slop problem (then belatedly backpedaling and trying to contain the issue) to work on any hardware projects. So it makes sense that buyers would have to step in if they wanted to get their Car Things back on the road.

• Microsoft Excel is pulling the COPILOT () function
• Function allowed users to describe tasks in natural language, including as part of a formula
• It never reached public launch, and has now been overtaken by other tools

Microsoft is dropping its COPILOT function in Excel as part of its wide-ranging overhauls to the AI platform.

In a Message Center alert, the company warned users that the tool will be pulled on September 14, 2026.

First announced in August 2025 and originally set for launch in January 2027, the tool has been in testing for over a year with Frontier and Insider users, but Microsoft has apparently decided not to move forward with its service.

Copilot dismissed

At launch, Microsoft had described the COPILOT () function as helping users to describe tasks in natural language, including as part of a formula, before receiving the results created by Copilot, in their cells, without needing to open up a separate Copilot window

It proved an initial hit for tasks such as text summarization, classification, content generation, and looking up information on the Internet.

Some examples of =COPILOT's use cases include summarizing customer feedback, categorizing data, integrating external knowledge and formatting.

In its most simple form, a function might look like "=COPILOT(prompt_part1, [context1])" – though context is optional.

Microsoft did say the function should not be used for numerical calculations, which might have been a major use cases for many - as well as saying users should avoid using it for gathering and finding information already stored inside a workbook or for content subject due to legal and compliance requirements.

So what's next? Microsoft explained that it is removing the COPILOT() function as it believes Excel cam already provide other, more effective ways to access the same AI capabilities.

Users can continue working with Copilot through both the Copilot side panel and the floating Dynamic Action Button (DAB), making a dedicated function slightly redundant.

The move comes as Microsoft makes a number of changes to AI capabilities within Excel as user demands continue to evolve and change.

This includes a new update to Copilot in Excel aimed specifically at finance workers, positioning the spreadsheet software as an AI-powered tool for financial modelling, forecasting and reporting.

Via Neowin

One small phone attachment can spot cams, leaks, insulation problems and more. I was amazed when I tried it at my house.

AI conversations have moved past the point of curiosity.

Boards and leadership teams are no longer asking what AI might eventually do.

They are asking where it is actually working, what measurable value it is creating - and why so many promising experiments still fail to become durable operating advantages.

Across industries, companies have invested heavily in AI pilots, proofs of concept and impressive demos.

Yet many remain stuck in what I think of as pilot purgatory: the place where a tool works in a controlled environment but never survives contact with the complexity, exceptions and accountability required in production.

The problem usually isn’t the model

In my experience, AI initiatives rarely fail because the underlying technology is not powerful enough. They fail because of how the technology is applied. A model can be impressive in a sandbox and still be irrelevant to the business if it is not embedded into a real workflow, connected to the right data, governed appropriately and measured against outcomes that matter.

That is why access to AI is no longer a differentiator. Anyone can buy access to models or integrate a third-party tool. The real advantage lies in the things that can’t be bought off the shelf: proprietary data, deep domain expertise, and the discipline to continuously improve AI once it is operating at scale.

For us, those principles come together in our Lean AI approach, rooted in a Lean operating model that drives continuous improvement through testing, learning, and acting. Instead of chasing technology for technology’s sake, our Lean AI approach helps us move AI beyond experimentation and into production, where it can improve service, boost productivity and create real business value.

Production AI requires discipline, not experimentation for its own sake

This is where many organizations get stuck. They treat AI as a portfolio of experiments instead of an operating capability. Organizations that successfully operationalize AI tend to do the opposite.

They prioritize AI opportunities based on business value and points of operational friction, identifying manual, repetitive and high-volume work. Then, they build and deploy agents where automation can improve speed, accuracy, scalability or service quality across the entire customer workflow.

There is no hobby AI in this model. Every deployment needs a clear business case, a workflow owner, measurement, feedback loops, and a plan to scale. That discipline is especially important with agentic AI, because agents operate with more autonomy than traditional software.

Progress is not always linear. Systems improve, encounter new edge cases, retrench and improve again. Human oversight isn’t a temporary bridge either; it is part of the architecture.

The application layer is where the moat gets built

The AI ecosystem is often described in layers, from the underlying IT infrastructure and large language models to the applications built on top of them. Those foundational layers are essential, but they are not where most enterprises will build durable, competitive moats.

The real advantage comes at the application layer—where AI is integrated into workflows, systems, exceptions, data and human judgment that define how a business actually runs. If an enterprise does not own or deeply control that layer, it risks turning AI into another generic capability rather than a competitive advantage.

Take supply chain logistics as one example. Moving a single shipment isn’t a linear task. It may require coordinating moves by truck and ship and rail, customs documentation in multiple countries, handoffs at multiple facilities, and weather and market conditions that change by the hour. A generic AI tool does not understand that workflow out of the box.

Context is the hard part

Every industry has its own data and context that powers it. In supply chains, that context lives in historical pricing patterns, warehouse operations, customer-specific policies, shipment characteristics, driver performance, market cycles and the judgment of people who have solved messy freight problems for years.

That context cannot simply be purchased. It has to be collected, structured, governed and applied. AI becomes more effective when it’s built into your technology platform and can learn from those realities rather than relying on generic information alone. Just as important, employees add institutional knowledge through continuous feedback, teaching AI agents the same way they would train a new operations employee.

Take something as seemingly straightforward as scheduling a truck to pick up freight. On the surface, it sounds like a narrow task. In practice, it requires understanding customer requirements, freight characteristics, facility policies, loading dock constraints, appointment systems and exceptions that may vary by location. An AI agent can only automate that work reliably if it has been engineered with the right context and oversight.

The companies that get the most from AI will be the ones that move beyond pilots and treat it as an operating model. That means starting with real business problems, owning the application layer where differentiation happens, feeding agents with proprietary context, keeping humans in the loop and measuring outcomes relentlessly.

AI will not reward the companies with the most demos. It will reward those that can operationalize learning faster than their competitors.

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AI spend made headlines again recently with the Claude Fable 5 model from Anthropic. Before security concerns led to the model being suspended, there were also cost concerns. Anthropic says Fable costs $10 or approximately €9 per million input tokens and $50 per million output tokens. This is double the price of the company’s previously most expensive model, Claude Opus 4.8.

Posts soon began to pop up on LinkedIn, showing just how quickly teams were going through their tokens and, as a result, their budget. There are some caveats here. Namely, that Fable 5 is an advanced model and, for most businesses, won’t need to run non-stop or be used for every task.

But therein lies a key issue: AI use is accelerating and models are evolving. But the level of control and visibility businesses have over how much is being spent, by who and for what is lagging behind.

How AI consumption became a finance problem

There is a massive shift within the UK software market toward AI and specifically Anthropic’s ecosystem. Proprietary data from Pleo looking at the top tech merchants based on number of spending customers, shows that Anthropic (Claude) surged from 12th place in Q4 2025 to 7th in Q1 2026. Meanwhile, the average spend per customer increased +43.0% in this time.

This rapid climb signals that Anthropic has reached enterprise maturity in the UK market with businesses moving beyond the experimentation phase. But while this reflects growing confidence in AI adoption, it also presents some financial challenges.

On the whole, AI has redefined how the workplace runs, but it is not a free trial. The cost of tokens has gone up, and new models that can achieve what was seemingly unthinkable a few years ago come with a price tag to match. The new challenge for business leaders is to leverage these technologies but also limit rampant spending.

This is why many organizations are turning to their finance teams. Finance has the visibility to dig into the details and map AI use across the organization, whether it quietly shows up as a subscription renewal or a new budget request. But more than that, they can be instrumental in ensuring teams embrace open conversations, not just OpenAI.

AI activity does not translate to AI value

Just about every organization will have developed transformational ways of using AI tools. But, whether they know it or not, there will be wasteful ones too.

When it comes to inefficient use, some of the major culprits include asking AI agents open-ended questions, model mismatch where tokens are burned unnecessarily; and duplicate tools, resulting from shadow AI and overlapping subscriptions. These prevent businesses from seeing the full picture; one that is, in all probability, very expensive.

User literacy can improve this. But for finance teams they must start with the grey area of AI consumption. Two teams might show as active AI users, but one that’s using an LLM to produce more content faster is doing something fundamentally different to one that’s using it for peripheral productivity tasks. In fact, only 29% of European SMEs using Gen AI are doing so in core business activities.

To improve the control they have over AI, organizations must start by elevating their visibility from who is using AI, to who is using it to become smarter, faster and more productive.

How to regain control over AI use

A complete view of AI spend is essential, regardless of whether costs are rising.

Breaking spend down by department, team and budget helps identify both disproportionate usage and areas where adoption may be lagging. These should be combined with performance metrics such as the time-to-first-draft on marketing content; code review cycle times in engineering; support ticket resolution time in customer support; and so on.

This combination of spend and performance can reveal whether AI investment is translating into measurable productivity gains and not just higher software costs.

Visibility should also extend to model-level usage. As mentioned before, the cost difference between frontier reasoning models and lighter alternatives can be tenfold. Monitoring model and vendor usage alongside token consumption helps organizations route routine tasks to lower-cost options, maximize ROI and reduce unnecessary spend.

Finance teams should therefore expand reporting and budgeting frameworks to include AI-specific metrics. A key question at month-end is whether AI-enabled teams are increasing output and capacity without increasing headcount. This provides a clear headline for AI's impact, can justify investment and distinguish between high-value and low-value AI usage.

Ultimately, effective control over AI is not about costs alone. It is about understanding where AI is creating value and ensuring investment is aligned with business outcomes.

AI control is at your fingertips

The good news is that none of these metrics require a sophisticated AI analytics stack. Finance teams should already have the tools for real-time visibility into what’s being spent and where. All that’s needed now is to fold AI into the mix and collaborate with other departments to measure and improve its ROI.

The outcome is that organizations control AI use through oversight, without restricting spend, adoption or innovation through lengthy procurement processes. Spend policies, category controls and clear approval thresholds control what is spent, and everything is measured.

But crucially, teams don’t slow down as a result. The only difference is that AI is optimized for impact.

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The legal sector’s use of AI is maturing, and its use is going beyond simply being an external chatbot or standalone tool. The focus is shifting to embedding AI into everyday legal work, giving it access to the documents, matters, knowledge and systems lawyers use every day.

As firms increase their investment in AI technology, they need to look beyond what AI models can generate and focus on a more practical question: how do those models connect to the information lawyers rely on, where does that information sit, and how is access governed?

That connection challenge is why Model Context Protocol (MCP) has started to attract so much attention.

Separating the standard from the assumptions

MCP is an open standard that gives AI tools a more consistent way to connect to external systems, data sources and applications. For all the excitement around MCP, firms need to be clear about what it does, and just as importantly, what it does not do. Otherwise, there is a risk that firms either overestimate what MCP can solve on its own or dismiss it as just another technical acronym.

The reality sits somewhere in the middle. MCP can help AI tools connect to the systems and data sources firms already use, but it does not automatically solve challenges around governance, integration, permissions or legal context. With misconceptions starting to spread across the legal sector, here are five common myths to clear up.

Myth 1: MCP is only for Claude

Although MCP was created by Anthropic, it is not just a Claude feature. It is an open-source framework and is now becoming part of the broader conversation around how AI agents connect to external tools, data sources and systems.

That distinction matters for law firms and legal organizations generally. If MCP is treated as a single-vendor feature, it can be dismissed as something tied to one model or product roadmap. But as a broader connectivity standard, firms need to think about how it fits into their wider AI strategy, integration architecture and governance model.

Myth 2: MCP replaces APIs

APIs still matter. They remain the backbone of platform-to-platform connections, allowing software systems to exchange data and trigger actions. MCP does something different: it acts as a protocol layer on top of APIs, giving AI agents a more standard way to discover and interact with approved legal systems, tools and data sources.

Put simply, MCP is closer to a universal adapter for AI, than an alternative to APIs. Just as USB gives different devices a common way to connect, MCP gives AI tools a more consistent way to understand what systems and functions are available to them, and how they can interact with those systems.

But it does not remove the need for APIs, authentication, system owners or clear rules about what AI tools can and cannot access.

Myth 3: MCP means moving documents into AI tools

There is a common misconception that connecting AI to legal systems means copying a large volume of documents into external AI platforms. In practice, AI should only be given controlled access to governed systems. Documents, precedents and matter files can remain within the firm’s trusted environment, with AI tools using MCP to retrieve only the information they are authorized to access.

Rather than creating another uncontrolled copy of sensitive material, firms can let AI work with the right information while existing permissions, security policies and governance controls remain intact. To hit the right balance, legal IT leaders should be asking “Where does the data stay, what is exposed, and how is access controlled?

Myth 4: All MCP integrations are the same

As MCP becomes more common, there will be a temptation to treat any MCP-compatible integration as broadly equivalent. That would be a mistake. MCP standardizes the connection, rather than the value of what comes through it. One integration may provide a basic route to retrieve files.

Another may provide richer information about permissions, matter relationships, document history, metadata and audit trails. Both may be MCP-compatible, but they will not deliver the same outcome.

Law firms need to focus on what the AI receives, whether access rights are enforced and whether activity can be audited.

Myth 5: MCP automatically gives AI legal context

MCP creates the route into systems, but it does not decide what the AI receives or understands. Connection does not mean context. In legal, this is more than a simple retrieval problem. An AI tool does not just need access to a document in a DMS. It needs to understand the matter, client, permissions, version history, related work and institutional knowledge around it.

For example, an AI tool may be able to find a precedent agreement, but does it know whether that precedent is current, whether it belongs to a similar matter, whether it reflects the firm’s preferred position, or whether the lawyer has permission to access the related material? Without that context, AI may generate an answer, but the answer may not be reliable enough for legal work.

That is why MCP should be seen as the access layer, not the intelligence layer. It can help AI tools connect to legal systems, but the value comes from what those systems expose through MCP: governed, matter-aware and permission-sensitive context.

Connection is only half the story

MCP gives law firms a more standard way to connect AI tools to the systems they already use. But legal AI depends on more than connection. The firms that benefit most will be those that treat MCP as the starting point, not the destination. The quality of the information, the controls around it and the context that gives it meaning will determine whether connected AI makes a genuine impact in legal work.

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Enterprise AI strategy spent two years chasing a single objective: reach the frontier before competitors do.

The default path was a public cloud account, an API key from OpenAI or Anthropic, and a willingness to absorb cost in exchange for speed.

That reality is now running out of road.

The numbers tell the story. Gartner forecasts worldwide AI spending reaching $2.52 trillion in 2026, up 44% year on year, with $1.37 trillion of that flowing into AI infrastructure alone.

In fact, in mid-2025, they claimed that procurement of AI had entered a “Trough of Disillusionment,” where scaling depends on predictable ROI, rather than visionary pilots.

The pressure has now shifted from how fast enterprises can pilot AI to whether they can sustain, govern, and defend it in production.

The Race to the Front is Over – Now Comes the Bill

We are now past AI 1.0, where simple access to cutting-edge AI was the differentiator. Now it’s AI 2.0’s turn, where inference economics, data gravity, latency and control decide the outcomes. Token prices have fallen almost tenfold annually since 2021, but AI spend overall by organizations has increased. That’s because more capable models have enabled greater ambition.

Anthropic, OpenAI, and Mistral are now stratifying offerings between flagship reasoners and lower-cost workhorses precisely because customers refuse to pay flagship prices for every task. McKinsey’s 2025 State of AI survey confirms the pattern - adoption is increasing, but impact at scale remains elusive for most organizations.

Now CIOs have stopped asking which model, but where each workload needs to run and how much it’s going to cost.

Inference Cost Inflation

Banks delivering the next best action are a good example: the in-app, in-branch, or call-center recommendation served in milliseconds against a customer’s live context. The best banks prove that personalization at this layer can lift revenue by 5-15%. To give a firsthand example, a global bank we work with launched an AI assistant that has already resolved more than 1.5 million customer inquiries in its first year, driving huge efficiencies.

But the inference economics are unforgiving at this scale. A single agentic decision can chain five to twenty model calls, each carrying its own context window. The cost gap between £0.50 and £3 per million input tokens seems trivial in a single-turn demo. Spread across hundreds of millions of customer events, it becomes the difference between a money-making feature and a money-burning one.

This isn’t a hypothetical either. Uber’s 5,000-strong engineering team’s use of Claude Code burned through the company’s entire annual AI budget in the first four months of this year. And AI companies are responding to this market shift. Decagon, after re-architecting onto an open-source multi-model stack on NVIDIA Blackwell, dropped cost per voice query by sixfold. Next best action isn’t a marketing decision anymore; it’s an economic decision.

Organizations making the structural shift now will outcompete those treating model selection as an afterthought.

Complexity Doesn’t Disappear, It Just Moves

The hardest lesson of the past 18 months is that model commoditization does not reduce enterprise complexity but relocates it. Open weights from Mistral or DeepSeek cut experimentation cost, but orchestration, governance, evaluation, and integration burdens move up the stack and sit with the buyer.

Enterprise leaders should be measuring unit economics per useful task, operational burden per deployed agent, and the ratio of inference spent on the governance scaffolding around it. That ratio is typically 1:5 or worse.

A second architectural shift is arriving: sub-quadratic attention.

Approaches from DeepSeek, Google, and Cartesia are collapsing the cost of long-context reasoning by orders of magnitude, with recent benchmarks showing 100x to 300x cost reductions at comparable accuracy.

Large banks will now be able to run whole-portfolio risk modelling, multi-decade fraud detection and cross-jurisdiction Know-Your-Customer (KYC) as single-pass operations - no more chunked retrieval workarounds.

Telcos can make network operations, predictive maintenance and multi-year customer journey reasoning more economically viable at scale. And manufacturers can move full-plant simulation and supply-chain disruption forecasting from periodic batch jobs to continuous reasoning.

The architecture that wins will not be the one with the cheapest token. It will be the one that places compute closest to the data, under the right jurisdiction, with governance that holds.

Sustainable, sovereign, controlled - that’s the new triad. The enterprises that build for it now will define the next decade.

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Artificial intelligence (AI) is rapidly becoming embedded in the modern workplace, with employees are increasingly turning to AI tools to work more efficiently and boost productivity.

This growing demand for faster, more effective ways of working is driving the rise of shadow AI - the use of AI tools outside approved organizational controls and governance frameworks – which results in organizations quickly losing visibility into data usage and potential risks.

The scale of this challenge is significant. While 90% of executives are confident in their organizations' visibility into AI tools, just 52% of employees admit to using AI tools without approval, often through personal accounts.

As a result, organizations are left grappling with a widening gap between AI adoption and AI governance.

The next frontier of AI risk

When AI is used without formal oversight, it can bypass governance controls, increasing the risk of errors, regulatory breaches and sensitive data leakages. Organizations are most exposed when AI is already influencing business-critical activities, from customer service and operational decision-making to software development and content creation.

The challenge will intensify as businesses move beyond large language models, which generate information, to large action models and agentic systems that can take action. These systems can diagnose issues, recommend actions and execute workflows with minimum human input, increasing both the speed and scale at which mistakes occur.

A shadow agent operating outside approved governance frameworks could trigger harmful actions before organizations have the visibility and governance capabilities needed to intervene.

There is also a longer-term risk that future AI systems will be trained on synthetic or lower-quality data, weakening performance and decision-making over time. Transparency and traceability will be critical to maintaining accountability, protecting ethical standards and preserving the effectiveness of AI systems as adoption continues to accelerate.

AI governance as an enabler

What works is AI governance that enables innovation while putting clear guardrails in place that are integrated, transparent, auditable, and aligned with existing risk and compliance frameworks. If AI is to be used safely, firms must be able to successfully identify exactly what went wrong and why when issues arise.

In practice, mature governance starts with an approved AI tool stack that provides safe and trusted options for common use cases. This should be supported by risk-based policies that make clear the data being handled, what can and cannot be shared, which tools are permitted, and where human approval is required.

Low-risk tasks such as drafting or summarizing content should not be governed in the same way as high-risk uses involving customer data, regulated information or business-critical decisions.

Training is equally important. The challenge, beyond only enforcing controls, involves helping employees understand why those controls exist and how to use AI responsibly. As agents increasingly diagnose issues, recommend actions, and execute workflows with minimum input, human oversight and approval processes must scale alongside them.

Interoperability will be critical to making this workable at scale, allowing organizations to operate across jurisdictions and multiple AI models without repeatedly rebuilding governance processes and systems from scratch.

Making responsible adoption the easy choice

For security and compliance leaders, the goal should be to make responsible AI adoption the path of least resistance. Employees turn to shadow AI when approved tools are unavailable, difficult to access or fail to meet their needs. Companies that focus solely on restricting usage risk driving activity further underground and losing out on the efficiency and innovation gains that AI can deliver.

Organizations that successfully balance AI productivity and control over their systems recognize that shadow AI use is often a symptom of unmet demand. Employees typically turn to unauthorized tools because they are easier to access, faster to use or better suited to the task at hand. Rather than focusing on restrictions alone, leaders should understand where AI is already being used across the business and ensure approved alternatives are available for the most common use cases.

With three-quarters of office professionals saying they would be likely to look for a new job that offered better AI skills development, firms that combine governance with opportunities to build AI skills are likely to see stronger adoption of approved tools and, as a result, less reliance on shadow AI.

Building an AI-enabled culture means giving employees the tools, knowledge and confidence to innovate within clear boundaries. By doing so, shadow AI can be reduced while the speed and agility that workers increasingly expect is maintained.

The organizations best positioned to succeed

The businesses that strike the right balance for AI success will be those that view governance as a foundation for AI adoption and not a barrier to it. By making the secure, approved path the easiest path, shadow AI risk is reduced without sacrificing productivity.

Embedding strong governance, supported by trusted and well-managed data foundations, avoids costly mistakes and allows AI to be deployed and scaled with greater safety and confidence.

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Lanterns has finally premiered on HBO Max — and, in a move reminiscent of Invincible's first-ever episode on Prime Video, the new DC Universe (DCU) TV show wastes no time in delivering an almighty shock in its first chapter.

Indeed, the sci-fi show's premiere, titled 'Pilot', ends with a moment that'll completely stun viewers and likely upset DC comic book fanatics.

I'm about to dig into the biggest talking point from Lanterns' debut episode, so consider this your one and only warning: full spoilers immediately follow after the poll below. Turn back now if you haven't seen 'Pilot' yet, or else.

Is Kyle Chandler's Hal Jordan really dead in Lanterns?

Green Lantern fans and DC purists are going to have a lot to say about this... (Image credit: John Johnson/HBO Max)

It certainly seems that way. Of course, this could be a massive fake out — after all, there are all manner of shapeshifting alien species that can impersonate humans in DC Comics, so it's possible that this is the case here. Nonetheless, I believe that Chandler's Jordan has taken his last breath in the DCU.

Okay, but how did we get here? For starters, this season's premiere wasn't shy about teasing Jordan's demise. Indeed, minutes before his death, which takes place during the show's 2026 storyline, is shown, Jordan, Aaron Pierre's John Stewart, and Kelly Macdonald's Sheriff Kerry Kane survive an attack from a suicidal bomber at Rushville's police station.

This incident, which happens as part of Lanterns' 2016 storyline, is caused by an alien masquerading as a human truck driver called Waylon Sanders. As the unidentified extraterrestrial reveals, their skeleton is retrofitted with a biometric neutron device that they can activate with a single thought.

Hal Jordan interrogates Waylon Sanders, who the former correctly identifies as an alien (Image credit: John Johnson/HBO Max)

Prior to detonating the explosive gadget, Sanders goads Jordan over the latter's fearless nature, which Sanders interprets as Jordan wanting someone or something to kill him. In that respect, then, the DCU Chapter One series telegraphs his death before it happens.

Rather than bump off Jordan in 2016, though, Lanterns withholds his passing until a decade later. Indeed, Jordan uses his power ring to form a protective bubble around Sanders just before the assailant detonates his skeletal device, thereby restricting the blast radius and saving the lives of everyone nearby.

But, that's not the end of the story. Five minutes before 'Pilot' ends, we skip ahead to 2026, which reteams us with Stewart as he heads to the same college football field that he and Jordan first met Sheriff Kane, and where their 2016 investigation into the deaths of four football fans began.

There, Stewart reunites with Kane and, after a bit of small talk about Kane's now-teenage son Noah, they head to a section of the bleacher seating where Jordan's snow-covered corpse is eventually revealed to be sitting.

Who killed Hal Jordan in Lanterns episode 1? Assessing the most likely candidates

Anybody else react like this when they saw Hal Jordan's dead body? (Image credit: John Johnson/HBO Max)

Alright, so who murdered Earth's first-ever Green Lantern? We don't know, but we can speculate on who pulled the trigger.

The first two — and arguably most likely candidates — are Sheriff Kane and the stadium's groundskeeper. Kane tells Stewart that the latter is the only other individual who currently knows that Jordan is dead. However, it's incredibly unlikely that the groundskeeper will be a prominent character moving forward, so we can rule them out.

We can do likewise with Kane. Sure, she's got a firearm to hand at all times, but I just don't see her wanting to draw heat over Jordan's death, and the potential ire of Stewart, the Green Lantern Corps, and/or the Guardians of the Universe.

Did Jordan's murderer steal his ring, too? (Image credit: HBO Max)

So, who else could it be? Of the other characters we've met, I wouldn't be shocked if Garret Dillahunt's Will Macon is behind it, regardless of whether he actually committed the deed or not. Right now, though, he's formed something of an uneasy alliance with Jordan and Stewart, so a massive, relationship-breaking event would need to happen for Macon to be involved in Jordan's demise.

There are bound to be other would-be murderers who could've taken Jordan's life who we've yet to encounter, so we might be adding more names to our shortlist in the weeks ahead.

That said — and hear me out on this before you pass judgement — what if Jordan killed himself? He's a former special forces pilot, so he knows how to handle a gun. For all we know, Waylon was telling the truth about Jordan wanting is life to be over, too — especially if, as an alien, the former has some form of perceptive superpower that allows his species to read someone else.

Who do you think killed Hal Jordan? Let me know in the comment section below. And, for more on the HBO Max show, read my Lanterns review.

Samsung has had a busy few weeks with the launch of its 2026 foldable and wearable ranges, with pre-orders just wrapping up last week. If you missed out on the pre-order deals or you have your eyes set on something else from the brand, then fret not — Samsung has just unveiled a slew of deals across its product portfolio.

The latest Samsung Secret Sale event features lots of top-rated tech that we’ve reviewed here at TechRadar, with savings of up AU$1,200 or 50% with the code SECRETAUG — but it’s only running for 48 hours, with deals ending at 10am AEST on Wednesday, August 19.

One standout deal is the excellent Samsung Galaxy Watch 8 with a 50% discount, bringing it down to just AU$374.50 for the 40mm LTE model. Given that the new Watch 9 has relatively modest upgrades for AU$699 (with a current deal), this offer is hard to pass up.

Galaxy Watch 8 (40mm, LTE): was $749 now $374.50
Our tester gave the Galaxy Watch 8 a glowing review for its slim design, built-in running coach and advanced health, sleep and AI features. The new Watch 9 has a similar look with relatively modest upgrades (new processor and larger battery), so this deal is a winner if you’re after an LTE-enabled Android smartwatch.View Deal

Music Studio 7 LS70H: was $749 now $449.40
Our friends at What Hi-Fi called the Music Studio 7 “a near-perfect product in a perhaps perfect form”, thanks to its Dolby Atmos support, big stereo sound and wealth of streaming options. This deal also makes it especially tempting for those looking to buy two for a stereo setup, which the review recommends for a full music and movie system.View Deal

Galaxy Tab S10 FE (128GB, Wi-Fi): was $1099 now $649
We’re fans of the mid-range FE-series Galaxy Tab slates, with the S10 FE+ getting praise from our reviewer for its excellent display, premium build and IP68 rating — we’ve previously considered it to be one of the best Android tablets. If you’re keen on a smaller size and want 5G connectivity, Samsung has also discounted the S10 FE to AU$749.40 or 40% off.View Deal

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The Samsung Galaxy S26 Ultra was named the best Android phone ever by our reviewer, praising its design, powerful hardware, AI features, excellent cameras and the new Privacy Display. This deal is only available to the online exclusive colourways Silver Shadow and Pink Gold. Also discounted are the 512GB (AU$1,749) and 1TB (AU$2,064) models.View Deal

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Samsung’s flagship OLED TV received a perfect score in our review, with praise for its brightness, colour, gaming features and performance. At this price, it’s still an investment — and note it’s ‘only’ a 55-inch model — but given it’s rare to see savings on Samsung TVs, this is one to consider.View Deal

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Here are hints and the answers for today’s NYT Connections: Sports Edition puzzle for Aug. 17, 2026.

We heard about a Maleficent coaster, a Tomorrowland revamp, a Monsters Inc octopus sushi chef animatronic, the new Cars rides and more.

• Private drone companies can now request access to US Army testing ranges
• Companies no longer need existing government partnerships before requesting range access
• Five military facilities across America and Morocco are joining the programme

Private drone companies without existing government contracts can now request access to the US Army test ranges nationwide.

The arrangement removes a requirement for companies to have existing government partnerships before requesting access to military ranges.

Army Secretary Dan Driscoll framed the change as an effort to cut through bureaucratic obstacles facing smaller defense contractors.

What companies can access and where

Five ranges now fall under this new access system, spanning four US states and one overseas location.

Dugway Proving Ground in Utah specializes in long-range fires testing for companies developing precision strike capabilities, whilst West Cibola Range in Arizona currently focuses exclusively on drone and counter-drone system evaluations.

Camp Shelby in Mississippi and Camp Grayling in Michigan round out the domestic testing locations available.

The fifth site, the Africa Multidomain Training and Experimentation Center, sits in Morocco.

Beginning in September 2026, Camp Grayling will host a recurring test simulating degraded electromagnetic conditions found in Ukraine.

This quarterly event will let companies trial drone and counter-drone systems against jamming and disrupted signals.

To get access to any of these sites, companies will have to apply through testrange.army.mil, though the Army cannot guarantee a specific site or timeline.

"A company with a good idea shouldn't need a team of lawyers and a program of record just to prove their equipment works," said Dan Driscoll, Army Secretary.

"So we fixed that. One front door — testrange.army.mil — a real person to walk you in, and on the other side, everything the modern battlefield demands: contested airspace, degraded signals and the space to test at real scale."

The pressure driving this shift

The expanded access reflects urgency inside the Pentagon around accelerating drone and counter-drone development timelines.

The Defense Department recently formed Joint Interagency Task Force 401, a unit built to streamline counter-drone procurement.

That task force has already stood up an online marketplace connecting military buyers directly with technology suppliers.

Behind this push sits a separate concern, with reports suggesting depleted Patriot missile interceptor stockpiles nationwide.

The United States reportedly used roughly two-thirds of its Patriot interceptor inventory during its recent conflict with Iran, as Washington appears to have underestimated how quickly a prolonged confrontation with Iran could consume its most valuable air-defense interceptors.

However, Defense Secretary Pete Hegseth disputed a CNN report claiming military commanders had flagged critically low interceptor stockpiles.

Regardless of that disagreement, the Pentagon continues pressing defense contractors toward faster production of essential weapons systems, and the range initiative sits within a broader goal of hastening development for drones, counter-drone tools, and interceptors.

Leading this effort is the US Army Test and Evaluation Command, working alongside several partner organizations nationwide.

Those partners include the Mississippi and Michigan National Guard units, along with U.S. Africa Command's regional support.

For companies previously locked out by lengthy contracting requirements, this shift represents a meaningfully lower barrier to entry.

Via Defense News