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The 2026 World Cup is already under way. Billions of eyes are on the pitch. The story that matters to anyone running a supply chain, though, is playing out in warehouses, customs terminals and distribution centers spread across three countries.
For the first time, the tournament spans three host nations: the United States, Canada and Mexico. That means millions of product lines, thousands of supplier handoffs and cross-border compliance requirements across three distinct regulatory environments, all compressed into a window with zero tolerance for error.
The operational scale of this tournament is without precedent.
Now that the group stages are live, the pressure on supply chains is real and immediate. The lessons surfacing are worth paying attention to, because they apply well beyond sport.
A live packaging stress testOfficial merchandise for an event of this scale moves across multiple countries, customs jurisdictions and retail channels simultaneously. Product identification has to work at every stage of that journey, from the manufacturer's floor to the stadium vendor's shelf. The label that cleared customs in Los Angeles may face entirely different requirements in Toronto.
The deeper problem is structural. Today's supply chains still largely operate as disconnected islands. Each site, supplier, co-packer and carrier maintains its own systems and repeatedly re-enters the same product and compliance data. That fragmentation creates built-in waste at every handoff: redundant setup, duplicate records and inconsistent label versions. Under normal conditions, these inefficiencies are costly but can be masked by day-to-day operations. Under the pressure of a live global tournament, they become critical.
Demand shifts are happening in real time. A host city that reaches the knockout stages sees fan merchandise demand surge overnight. Supply chains built on static, batch-processed labelling data are finding they cannot respond at that pace.
The speed of these shifts can be surprisingly tangible. In Atlanta, shortly after the Spain-Cape Verde match, I walked through the airport and was struck by how many people were wearing Cape Verde jerseys. In the space of a few hours, merchandise that had been relatively low-profile had become highly visible, underscoring how quickly demand signals can emerge and spread during a global event.
From labels to live dataWhat the World Cup is making visible in real time is a shift that has been under way for several years. Product identification is no longer a print-and-forget exercise. It is a live data problem.
The industry is moving from fragmented, internal systems to connected, multi-partner ecosystems. The organizations managing the tournament's supply chain most effectively are those that have made this shift: rather than each stakeholder operating in isolation and recreating the same product and compliance data from scratch, they are working within a shared, real-time environment where information flows seamlessly across systems, suppliers, customers and geographies.
The benefits of this approach are measurable. Organizations that can operate with real-time visibility and trusted data across their extended value chain can reduce delays, prevent errors at source and respond faster when disruption hits. In sectors where production downtime can exceed $1-2 million per hour, that responsiveness is not a nice-to-have but operationally critical.
The cost of disconnected systemsThe consequences of siloed product data are well understood in theory. A tournament of this scale is making them visible in practice.
When product data does not flow seamlessly across sites and trading partners, the failure surfaces in predictable ways. Rejected shipments at customs. Compliance failures that stall distribution. Production downtime while teams manually reconcile data across systems. Against the backdrop of a global event with fixed deadlines, those failures are not recoverable.
The organizations absorbing those costs right now are those still operating inside the organization perimeter - managing product identification as an internal function rather than a network-level capability. The distinction matters. Supply chain resilience increasingly depends on the ability to coordinate accurate product data across every site, trading partner, and customer - creating a connected ecosystem in which product identity can be shared, trusted, and acted upon seamlessly.
What happens after the final whistleConnected, network-driven approaches to product identification are no longer a future aspiration. The World Cup is demonstrating their value in real time, at a scale most supply chains will never encounter but from which every supply chain can learn.
The direction of travel is clear. Organizations that can rapidly adapt labelling requirements across plants and partners, share trusted product data in real time and eliminate the manual rework that comes with disconnected systems will outperform those that cannot. That is as true in retail, pharma and automotive as it is in a stadium in Los Angeles.
The World Cup will be over in a matter of weeks. The infrastructure challenges it is exposing will still be there when it ends. The organizations that use this moment to address those fundamentals will be better placed for whatever high-pressure deadline comes next.
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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.
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
The TobenONE 11-in-1 USB-C Docking Station is on sale for $83 (was $130) at Amazon, a saving of $47.20.
Thanks to its programmable shortcut keys, dual 4K display support, and a wide selection of ports, it will be especially appealing to productivity-focused Windows users.
This docking station supports dual 4K 60Hz displays, 100W USB-C Power Delivery, Gigabit Ethernet, three 5Gbps USB ports, SD and microSD readers, programmable shortcut controls, audio jack, and dual HDMI connectivity for Windows laptops.
Dual HDMI 4K@60Hz, programmable shortcut keys and volume knob (Windows), 100W USB-C Power Delivery input, Gigabit Ethernet, USB-C and USB-A 5Gbps ports, SD/microSD card readers, 3.5mm audioView Deal
Should you buy it?✅ Buy this deal if...
You want a single USB-C dock that connects dual 4K monitors, wired networking, storage devices, and accessories while adding programmable shortcut controls to speed up repetitive Windows tasks.
❌ Skip this deal if...
You primarily use a Mac and need two independently extended external displays. The customizable shortcut features are also designed specifically for Windows systems.
Why we recommend itIn his review, our expert Mark said: "This is a clever concept, and for those with a video or photo editing workflow, it's genuinely useful."
Most budget USB-C docks follow a familiar formula, but this model takes a different approach by integrating four programmable shortcut keys and a multifunction rotary dial alongside its standard connectivity.
The controls can be configured via the TobenONE software to launch applications, take screenshots, control media playback, mute audio, or trigger custom key combinations.
Dual HDMI outputs support two independent 4K 60Hz displays on compatible Windows laptops with DisplayPort 1.4 MST support, making it well suited to coding, design, trading, and other multi-monitor workloads.
Gigabit Ethernet provides a reliable wired connection, while three 5Gbps USB ports, SD and microSD card readers, and a USB-C Power Delivery input supporting up to 100W charging complete a versatile desktop setup with a single cable.
Price Context & Historical ValueAmazon has reduced the TobenONE from $129.99 to $82.79, a 36% discount that will save you $47.20. This is as low as the dock has ever been sold, so if you've been planning to add a docking station to a home office or hybrid work setup, this is a great time to buy.
The Catch: What to know before you buyThe programmable buttons and smart control knob require the TobenONE software and are available only on Windows. Mac users can still use the dock's core functions, but customization is limited, and macOS mirrors content across two external HDMI displays rather than supporting two independently extended screens.
Our biggest criticism was bandwidth. Although compatible with USB4 and Thunderbolt hosts, the dock limits its USB data ports to 5Gbps, so fast external SSDs won't reach their full performance. It also ships without a power adapter, meaning you'll need to provide your own if you want laptop charging.
The conversation about AI in space keeps arriving at the same image: a floating supercomputer processing the world's information from 400 miles up. It’s a compelling narrative, but there’s a gap between what companies are hoping to build and what is being built today.
Today, satellites run on fixed power budgets measured in watts with strict constraints. Bandwidth is scarce enough that every byte reaching the ground has to earn its place.
Those limits push the field toward architecture that looks more like a nervous system, than a data center – lightweight models running onboard that interpret sensor data in near real time and convert observations into structured events. The event reaches the ground.
The raw pixel doesn’t.
From hours to minutesIn a conventional Earth observation pipeline, a satellite captures an image, downlinks it to a ground station, ground systems process the raw data, and the result reaches whoever needs it. Best case that happens in hours, but often it’s closer to a day.
For a disaster response team managing a flood in a low-lying river delta, or a conservation authority trying to locate the origin point of a wildfire in a remote national park, that day comes at a cost.
A satellite running inference onboard changes the math. Detection happens in seconds. What gets downlinked is a position, timestamp, or risk score. The bottleneck shifts from the space segment to the ground distribution, which is a comparatively manageable problem.
The use cases where this matters most are not the visible disasters people are watching. They’re the methane leak on a pipeline with no weekly inspection schedule, an oil spill beyond the reach of coastal patrols, a wildfire that began in a remote area before anyone had reported smoke. Onboard inference turns a passive imaging asset into an early warning system.
What orbital constraints teach edge architectsThe tradeoffs being resolved in orbit are an extreme version of the same constraints facing any organization deploying AI outside a well-provisioned data center.
Cloud-native AI development often has a back up plan: when the model is too large add compute; when bandwidth is constrained, increase it; when latency is a problem, move the processing closer. In orbit, none of these options exist. You build within the envelope, or the system doesn’t function.
The result is a forcing function that enterprise architects rarely encounter at the same level. Industrial IoT deployments face intermittent connectivity. Autonomous systems can’t afford round-trip latency to a central server at decision time.
The shift from 'send everything, process centrally' to 'process locally, transmit what matters' is happening across multiple industries. Space is where that shift ran without a safety net.
The bandwidth mathThe data reduction numbers transmitted in real time is not just 80-90 percent. Once processing happens on the spacecraft, the reduction for the real-time layer exceeds 99 percent. This is semantic compression. The satellite sends the meaning of what it saw, not the measurement it produced.
A conventional operator downlinking hundreds of terabytes of raw imagery daily is paying bandwidth cost for data that largely contains nothing of interest. With onboard inference, what's transmitted in real time is a structured detection event: a position, a timestamp, a risk score, and perhaps a small compressed image. That is hundreds of kilobytes, not terabytes.
An operator downlinks only what warrants examination, rather than blindly dumping the full data stream.
Architecture decisions that preview what's nextA model making decisions before a human is in the loop carries different requirements than one generating recommendations for human review. Ambiguity tolerance is lower. Inference behavior needs tighter scoping. This is the same conversation that medicine and finance have been having for years.
AI-assisted diagnostics and accountability distributed across platform, model, training data, and end customer rather than concentrated in a single layer. The space industry is joining a conversation other sectors have already been having for years.
In a cloud environment, model size and computational efficiency are optimization targets. Meaning they are important, but secondary to capability. In a constrained orbital environment, they are the primary design constraint from which everything else follows. A model that cannot run within the available compute envelope is not a model that gets deployed. There is no option to add a larger instance.
A maritime patrol aircraft that previously ran random vessel inspections now works from a ranked list of targets with risk scores attached. Some alerts will be false positives which is a physical reality of any probabilistic system. But the aircraft's operational effectiveness improves substantially compared to random patrolling or no monitoring at all. The AI narrows the search.
The scaling problem is familiarOne satellite running an onboard model is a proof of concept. A constellation of hundreds running distributed inference is a different infrastructure problem as orbital AI scales.
Centralized orchestration becomes the bottleneck when constellations grow. Every decision can’t route through a ground station. Distributed inference is a requirement. Enterprise architects hit the same wall when a pilot deployment expands to thousands of edge nodes. The centralized model that worked in development becomes the thing that breaks in production.
The cloud infrastructure analogy supports this. Nobody builds a data center before launching an application. The pattern is shared infrastructure, with control at the model, mission logic, and decision layer. Those can be sovereign regardless of who owns the underlying compute.
A design principle worth carryingIt’s hard to develop constraint-based thinking in environments where adding compute is always on the table. The organizations that have built it tend to have faced conditions where it wasn’t.
The strategic advantage in edge AI over the next decade will not just be measured in the amount of compute available. It will also be measured in code deployed to the right place in the stack. Satellites are running that experiment first.
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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.
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
As part of Best Buy's Black Friday in July sale, the price of the Apple MacBook Air 13-inch (M5) has dropped to $1199 (was $1299).
Powered by Apple's M5 chip, it combines a 10-core CPU, 16GB of memory, and a 512GB SSD in a lightweight design. For my money, with the extra RAM and storage this makes it to model to choose if you're buying a MacBook Air.
It's an excellent choice for students, professionals, and anyone looking for a premium everyday laptop that's lightweight and loaded with long-term value - especially given Apple recently announced price increases across all its laptops and tablets.
Today's best MacBook Air deal13.6-inch 2560 x 1664 display, Apple M5 processor with 10-core CPU and 8-core GPU, 16GB memory, 512GB SSD, two Thunderbolt 4 ports, backlit keyboard.View Deal
Should you buy it?✅ Buy this deal if...
You want a lightweight premium laptop with strong everyday performance, and enough memory and storage for years of work, study, and creative tasks. Battery life impressed us during testing, lasting around 15.5 hours of web use and comfortably getting through a full working day on a single charge.
❌ Skip this deal if...
You need a larger display, more ports than Thunderbolt 4 provides, or dedicated graphics for demanding professional workloads and modern PC gaming. In that case, check out the MacBook Pro instead.
Why we recommend itIn his review, our laptop expert Lance said the latest MacBook Air was "the best ultraportable I've ever used". He noted it delivers an outstanding balance of performance, battery life, and portability, handling everything from everyday productivity to photo editing, 8K video editing, and even gaming without feeling out of its depth.
Despite its slim, fanless design, he found it surprisingly difficult to push the M5 chip to its limits, even with demanding creative applications and heavy multitasking.
The 10-core CPU, 8-core GPU, and 16GB of memory provide plenty of performance for work, creative projects, and everyday computing, while the upgraded 512GB SSD delivers faster storage than previous entry-level MacBook Air models, helping applications launch quickly and improving file transfers.
At just 2.7 pounds, it's easy to carry every day, while the premium aluminum chassis feels exceptionally solid despite its lightweight design.
Price Context & Historical ValueAlthough the $100 discount isn't huge, deals on Apple's newest MacBook Air models are relatively rare - especially with Apple recently hiking prices across all its devices. With the upgraded 512GB base storage and M5 processor, Apple's latest ultraportable is a must-have.
The Catch: What to know before you buyThis configuration offers excellent everyday performance, but it isn't designed for intensive 3D rendering, high-end gaming, or workloads that benefit from dedicated graphics.
Connectivity is also limited to two Thunderbolt 4 ports, so you may need a hub or dock if you regularly connect multiple wired accessories.
Robot lawn mowers are growing in popularity, and with good reason. They're getting more advanced in features, easier to set up and use, and can be a huge time- and effort-saver... provided you pick the right model. There are plenty of options to choose from, spanning a range of prices and capabilities, and if you're new to the lawnbot world then it can be tricky to figure out which one you need.
This article is here to guide you through that process. I've pulled together five big questions to help you narrow down your options. And for added expertise, I've enlisted the help of KK Yin, Marketing Director at leading lawnbot brand Mammotion, whose insights you'll find throughout the piece.
#1. Do I need all-wheel drive?"Everyone has a different environment for their lawn. So the first thing a customer should do is choose which kind of power system they need — for example, all-wheel drive or rear-wheel drive, or some the brands have front-wheel drive," says KK.
Rear-wheel drive will be perfectly fine if you have a relatively even and flat lawn, and will have no issue scaling relatively gentle inclines. However, if you have a very steep or uneven lawn, all-wheel drive (AWD) will help ensure the lawnbot maintains its grip without churning up your grass. Front-wheel drive is really only for flat lawns, and is a less common power system.
AWD tends to be reserved for larger, more premium robot mowers. However, at the start of this year Segway Navimow launched compact AWD model, with the suggestion that it would treat your turf more gently.
All-wheel drive improves a lawnbot's climbing ability (Image credit: Future)#2. Does my lawn have shaded areas?Next, KK suggests figuring out which navigation method is best suited to the environment. "A customer should look at if they have shaded areas, the house is tall or they have trees in the garden. If they do, I suggest they don't choose an RTK positioning system. They should maybe choose a LiDAR model," says KK.
There are two major navigation systems for robot mowers. RTK, which uses satellites, can work very well if the lawnbot has a wide, uninterrupted view of the sky. However, if there are obstructions — such as those listed by KK — in the way, these can block the signal and lead to the lawnbot getting lost.
LiDAR is the system used by robot vacuums, and is relatively new in the robot mower market. LiDAR builds a picture of its surroundings by bouncing light off objects, so it's not well suited to wide-open lawns with no objects in them.
Broadly speaking, RTK will be a good choice in big, open yards, whereas LiDAR might be a better fit if you have a smaller, more cluttered yard. LiDAR models are often pricier, so if you can get away with an RTK bot, you might prefer to go with that option.
Overhead obstructions can cause issues if the robot relies on satellites for navigation (Image credit: Future)#3. How big is my lawn?Many lawnbot models are available in a few different iterations aimed at different-sized lawns (typically indicated by a number after the product name). The difference is usually the battery capacity — a bot designed for a larger area will have a bigger battery, enabling it to cover more ground before having to return to its dock to recharge, thus making it more efficient.
"I think most users figure out that in one week they will mow maybe two or three times. So they need to work out how long it will take a product to mow their lawn once," suggests KK. With that information, you can work out if your mower will be efficient enough to keep up with your grass-trimming demands.
#4. Do I have more than one mowing zone?"The next point, I think, is about multi-zone management," says KK. "Not all people have one big lawn — it's divided by the house, or the garden is split into different areas. That means they need to manage more than one mowing zone."
Pretty much every lawnbot will be able to mow multiple separate lawns, with the ability to create different maps in your app. However, some have more advanced multi-zone capabilities than others. One bot might be intelligent enough to handle a schedule where the lawnbot spends the morning in Zone A and moves to Zone B in the evening, and it might even be able to work out the optimal mowing patterns and cadence.
#5. How much setup do I want to do?If you end up with a satellite-based lawnbot (see point #2), you might need to decide between a bot that comes with a separate antenna and one that does not. This RTK station is required to make the satellite information more accurate — so satellite positioning on its own might tell you where a bot is to within a meter or two, but add in a separate RTK receiver and it's more like centimeters.
Traditionally, you'd have this receiver in your own back yard. This adds an extra level of complexity to setup — the reciever needs to sit high up, with direct line of sight to satellites in the sky, and it also needs a power source. However, increasingly, we're seeing lawnbots that use a big, centralized RTK receiver that covers a large area. Mammotion calls this NetRTK. It's definitely a more low-effort option.
Some RTK lawnbots require you to install a separate receiver in your yard (Image credit: Future)There are further features to look out for if you want the most straightforward setup. Some modern robot mowers have cameras and AI features that enable them to instantly create a map of their location, so you can just pop it on the floor and it'll be able to start mowing pretty much instantly. KK suggests these kinds of features are more of a nice-to-have, and advises focusing on the first four points first.
Reviews round-upWe've tested plenty of excellent lawnbots at TechRadar. Here's a taster of some of our favorites — tap the 'View details' button for more information, plus a link to our full reviews.
Mammotion LUBA 3 AWD 3000 Mammotion Yuka Mini Robot Lawn Mower Mammotion YUKA Mini 2 1000 Robot Lawn Mower with LiDAR Segway Navimow i210E LiDAR Pro Segway Navimow X3 Series TerraMow V1000 Robot Lawn Mower ANTHBOT Genie Smart AI Robot Lawn Mower Eufy E15 Robot Lawn Mower Mammotion LUBA 2 AWDRead our full review
- Only 34% of organizations say they trust their agents' actions
- 77% of businesses in 'agentic chaos' have deployed agents
- Those organizations are focus on the wrong thing (models and tools)
New Boomi data has claimed while 86% of enterprises have evolved from agentic AI pilots to actual production-level deployment, only 34% trust the actions their agents are taking.
However those trust issues don't stem from model intelligence – Boomi argues that data quality, integrations, governance and controls could be to blame.
The research splits respondents into distinct categories, with the bottom quartile for readiness referred to as experiencing 'agentic chaos'. Among those in agentic chaos, as many as 77% are still moving AI agents into production, highlighting a worrying gap.
AI trust is entirely in the hands of enterprisesWith more than three in four of the enterprises in agentic chaos still pushing ahead with their plans, Boomi warns they could face unexpected costs from compliance fines, lost customers and operational downtime.
On the flip side, the top quartile was categorized as being in 'agentic control', and more than half (55%) of them said they're highly confident in their agents' decisions and actions.
Additionally, the report criticizes those in agentic chaos for focusing on the wrong thing, with around half (51%) prioritizing AI model or tool maturity instead of the true causes of distrust.
"Agents can only be trusted to act on data that's been properly activated, connected, and governed, and most companies deployed agents before they did that work," CEO Steve Lucas wrote.
At the end of the day, Boomi's data implies that pressure to demonstrate AI value and ROI could actually be leading to premature deployment before the foundations are in place, leaving organizations to play catch-up in a far more inefficient way.
- Someone has made a custom version of the Companion Cube case for the Steam Machine
- This DIY effort is on Printables, and can be 3D-printed for those with the hardware and necessary skills
- There are a growing number of custom projects around the Steam Machine, from faceplates through to full-on alternative takes on the gaming PC
Remember the ill-fated Companion Cube case for the Steam Machine? It had the plug pulled on it by Valve a few weeks ago, after maker Dbrand failed to get official permission to make its hard-shell case for the gaming PC, but now the concept has returned, in DIY form.
Tom's Hardware reports that a user on Printables, Jaron, has provided files that allow you to 3D-print your own version of the Dbrand Companion Cube (as flagged on X). You can print the pieces, and a full set of assembly instructions (including smart video illustrations) and extra necessary parts (screws and so forth) are supplied.
As made clear on X, this is a fan-made creation implemented "from scratch" and it's based on images of Dbrand's Companion Cube. Whether it'll attract the attention of Dbrand's own lawyers, well, we'll just have to see, but you'd imagine that some leeway might be granted here given the situation and what happened with Valve.
The creator makes it clear that the case isn't an official product, is nothing to do with Valve, and that it doesn't use "any official assets from any game".
An additional caveat is that Jaron notes: "I have not done any extensive thermal tests but no airflow or venting is blocked, and the additional vent provided by the front face plate aperture logo can help provide a little extra airflow. So I don't believe there will be any cooling issues caused by this case."
My main concerns about the original Dbrand Companion Cube were bound up in cooling issues — especially on warm days, in confined spaces like a TV cabinet — and that remains true with this creation.
Still, if you were disappointed that Dbrand's case was cancelled, and you have a 3D printer and want to have a crack at making your own — and you're pretty decent with a soldering iron — you can now do just that.
Other Steam Machine companions(Image credit: Goldboat90 on Reddit)Since the Steam Machine came out, there's inevitably been a lot of interest around the device, despite the high price. So, as well as this clone of the abandoned Companion Cube case, what else is out there in terms of Steam Machine customization?
New faceplates (front panels) are popular, and a much easier project than the above case, such as this example on Reddit (see the image above). I really like the slat effort here, and I can see why it's gone down very well with other Redditors (although I'm not so keen on the blue one).
(Image credit: PoisonISSweet666 on Reddit)What's also quite common is the number of custom small form-factor (SFF) PCs which are DIY takes on the Steam Machine, generally featuring beefier components (and at the same time, inevitably being larger than Valve's PC). 'The Orange Box' as shared on Reddit is a particularly smart example of one such effort with a strong Valve theme, built in a SilverStone SG05BB-Lite case. It has a Ryzen 7 5800X processor inside and an RX 9060 XT graphics card to give this considerably more pep than Valve's gaming PC.
There are other custom SFF PCs on Printables and this one from 3DCatt, which looks like a sized-up Steam Machine, has very similar aesthetics to fit your living room nicely, and a finished version someone has completed went very well indeed by all accounts.
Of course, there are issues around these alternatives to the Steam Machine as I explored in detail recently, and downsides to these custom SFF builds – albeit definite positives too, better performance being the main one. A PC that looks as good as The Orange Box doesn't hurt, either, and I'm sure we'll see more of these kind of SFF spins on the Steam Machine going forward.