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Iran-backed Houthis say they will blockade a waterway on the Red Sea. And, the FDA continues to investigate Taylor Farms lettuce as a source of the Cyclospora outbreak despite a retracted test.

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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 minutes

In 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 architects

The 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 math

The 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 next

A 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 familiar

One 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 carrying

It’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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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 it

In 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 Value

Amazon 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 buy

The 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.

• Shop all docking stations at Amazon

Defense secretary Pete Hegseth will appear on the Hill on Tuesday. He's looking for a nearly $90 billion funding boost, as the Iran war once again flares.

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 test

Official 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 data

What 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 systems

The 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 whistle

Connected, 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.

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President Donald Trump on Monday imposed 50% tariffs on most Canadian goods, declaring that Canada has unfairly discriminated against American autos, alcohol and dairy products.

(Image credit: Adrian Wyld)

Harrisburg is home to one of the nation's largest communities of Nepali-speaking Bhutanese refugees and their families. Through soccer, generations have found connection, belonging and a way to carry forward a shared history.

(Image credit: Maansi Srivastava)

Our free economics course for your ears is on a quest for great economics ideas.

Minisforum has two slim mini PCs discounted right now, and which one to buy comes down to how much CPU you actually need.

As part the the Black Friday in July sale, the Minisforum UM760 Slim is $600 (was $680) at Best Buy, while the beefier Minisforum UM870 Slim is $700 (was $800).

Today's top Minisforum mini PC deals

AMD Ryzen 5 7640HS (6-core/12-thread, up to 5.0GHz) with Radeon 760M graphics. 16GB RAM and a 512GB SSD, with dual M.2 slots for expansion. HDMI 2.1 and USB4 output supporting up to 8K@60Hz, Wi-Fi 6E, and Bluetooth 5.3.View Deal

AMD Ryzen 7 8745H (8-core/16-thread, up to 4.9GHz) with Radeon 780M graphics. 16GB RAM and a 512GB SSD, expandable to 96GB RAM and up to 4TB storage across dual M.2 slots. Triple display support via HDMI, DisplayPort, and USB4, dual 2.5G LAN, Wi-Fi 6E, and Bluetooth 5.3.View Deal

Should I buy it

Which to choose

✅ Choose the UM760 Slim if...

✅ Choose the UM870 if...

Workflow

Your workload is mostly everyday desktop use

You want real multi-core headroom and better integrated graphics

Connectivity

You only need to connect via HDMI and USB4

You prefer wider connectivity including dual LAN

Why we recommend these mini PC deals

Both machines share the same slim Minisforum chassis and general design philosophy, but the CPU gap between them is real rather than cosmetic.

The Ryzen 5 7640HS in the UM760 is a capable 6-core, 12-thread chip that handles everyday desktop work, browsing, and light multitasking comfortably.

The Ryzen 7 8745H in the UM870 steps up to 8 cores and 16 threads with a newer Radeon 780M GPU, and independent benchmarking has shown its multi-core performance landing in the upper third of current mini PCs, without thermal throttling under sustained load.

That extra core count and the RDNA 3-based Radeon 780M make the UM870 the meaningfully better pick for anyone doing content creation, code compiling, or casual 1080p gaming — reviewers have measured the 780M hitting 75-123 FPS in popular titles at 1080p, a tier above what the 760M in the UM760 typically manages.

Connectivity is another real difference, not just a spec-sheet footnote: the UM870 adds dual 2.5G Ethernet ports and triple display output, compared to the UM760's single display path over HDMI/USB4.

If you're setting either of these up as a home server or a multi-monitor workstation, that difference in networking and display support is worth the extra $100 on its own.

For more top picks, see our guide to the best mini PCs.

What to know before you buy

Something worth flagging on both: the blue status LED on Minisforum's UM-series machines has drawn complaints from some owners as distractingly bright in a dark room.

And if you plan to run Linux on either, the bundled Wi-Fi cards have had driver support issues reported by some reviewers — a wired Ethernet connection sidesteps that entirely.

More mini PC deals

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

Read our full reviewView Deal

The GMKtec K16 mini PC delivers Ryzen 7 7735HS performance with 32GB LPDDR5 RAM and a 1TB SSD. Featuring OCuLink eGPU support, triple-display output, dual 2.5GbE LAN, Wi-Fi 6E, and USB4, it’s a versatile compact system for gaming and creative workloads.

Read our full reviewView Deal

• See the full Black Friday in July sale at Best Buy.

The Houthis, a Shiite militia in Yemen, which is aligned with Iran, have announced a naval blockade of a key waterway, the Bab Al-Mandeb Strait.

(Image credit: Mohammed Huwais)

If you’ve done any shopping for marketing automation tools these days, you’ve probably noticed they all claim to be “powered by AI.” Apologies for splitting hairs, but that’s just not true.

A significant portion still rely primarily on rule-based automation, work identically to the platforms they replaced, triggering if/then workflows created by human engineers years ago. Give their systems an edge case to parse and you’ll soon see them send an inappropriate email, crash, or output some stale nonsense that wouldn’t matter to any living person.

Same label. Same old architecture. The problem? A rule bottleneck.

Traditional marketing automation relies on knowing rules ahead of time. A lead hits a score? Send an email. A prospect completed behaviors A, B, and C? Trigger sequence Y. Wait Z days, send the follow-up email.

A rules engine can execute these commands flawlessly – but it can only execute what it knows. When a situation arises that doesn’t fit the rules, what do you do? You update the rules.

Why this matters

Marketing is messy. Prospects take unpredictable journeys, trends come and go overnight, and audiences who loved your message last week don’t care about it this week. But rule-based systems can only improve when given new rules to fire. Engineers can’t possibly keep writing rules faster than the world changes.

The industry has been papering over this problem with AI buzzwords. Sprinkle some Neural Network magic on a rule engine, and suddenly you’ve got yourself an “AI platform.” Engineers who look past the updated sales brochures still find the same good old-fashioned if/then statements, patched up with trendy new nomenclature for the latest round of funding.

The difference between legacy automation and true agentic AI is that true agentic AI won’t just patch up the last generation of marketing automation tools – it will replace them. Agentic AI isn’t defined by capabilities so much as by the way decisions are made.

Rules engines ask, “what rule should fire next, given this input?” Agents ask, “what action should I take to get closer to my goal?” This is subtle but critical. Agent theory holds that the system knows its goal, its current context, and a list of available actions it can take.

Based on those three pieces of information, it can reason as to which action will bring it closer to accomplishing its overall objective. This extends far beyond executing canned responses - it’s deciding what to do.

Agentic systems maintain goals, reason over available actions, invoke tools, evaluate intermediate results, and adapt their plans as new information becomes available. The architecture is fundamentally iterative rather than purely reactive.

You know where this is going.

An agent can adapt if a campaign stops performing. It can coordinate with other agents who manage different subsets of that workflow. And it can do so without a human engineer going back into the system to rewrite the rules every time the world changes. The system manages its goals.

Why specialization matters

One important architectural decision that separates good agent implementations from the rest is specialization. Should you build one big generalist AI system to handle everything or many specialized agents, each performing their own task?

Specialization comes up often in discussions around AI, from medical doctors to Renaissance men. There is broad utility in generalization, but singular accuracy in specialization. The family doctor can handle any symptoms you throw at them. But when you need to be absolutely certain about your diagnosis, you see a specialist.

That’s because specialists aren’t smarter than the generalist – they’re just trained on narrower data. Likewise, generalist AI models aren’t going to produce great results for highly specific use cases. OpenAI’s models can write you a marketing strategy. They can craft creative assets. But they can’t produce marketing assets that:

• Fit the pixel ratio requirements of a given publisher
• Match your brand’s color palette
• Align with your target audience’s emotional affinity profile
• Incorporate mentions of trending topics from the previous day

They can’t do all of those at once, either. And you shouldn’t expect them to. For hard problems with specific solutions, you should build specialized agents (sometimes called “agent crews”) that own a narrow subset of your workflow.

One crew might specialize in strategy generation, while another focuses on creative writing. One might select publishers while another analyzes performance. Separately, these crews create atomic workflows that a generalist system would struggle to manage.

How not hosting your models affects data privacy

There’s another argument for specialized, privately hosted models that isn’t made enough: data privacy.

Whenever you use a public large language model (LLM) to write marketing copy, your data is being uploaded to someone else’s infrastructure. “We don’t use customer data for training” is easy to say but barely offers any assurance. Inputs are still being ingested, processed, stored, and handled according to what that provider’s internal policies dictate.

And those policies can change… most corporate lawyers have never looked at the data use section of public AI providers Terms of Service, let alone dissected it line-by-line.

But what about controls your organization can enforce? Do your developers scrub data for PII before generating content with an LLM? That only works if everyone in your company memorizes your data policies and uses tools responsibly. One rogue employee attaching a spreadsheet full of internal pricing to a prompt breaks your compliance.

But if the model itself is hosted privately, that’s one major source of exposure that goes away. Your data never leaves your infrastructure. There’s no ingestion point to transmit it to a third-party, no training feedback loop that will process it, and no agreement to parse about how that company will handle your data “moving forward.”

Governance is a system property

Because AI in the enterprise has reached a maturity level where governance is a legitimate concern, many teams treat it as a bulk edit at the end of AI-generated content. Have humans review and approve. That’s fine, and many teams require this today. But governance should be built into the system at a fundamental level.

Well-built agents have guardrails at every stage of the decision-making process. That means models that make predictions within set bounds. That means observability that can trace every word generated back to its origin.

That means third-party benchmarking to prove your models perform well against industry standards, not just internal testing. Governance shouldn’t just be applied to outputs – it should be inherent in the architecture.

What enterprise buyers should actually be asking about

Buying criteria for agentic AI will vary by company, but as requests for proposal accelerate to keep pace with innovation in the industry, here are a few considerations every enterprise buyer should ask about:

• Goals vs. rules - Is this system actually agentic? Or is it just automating workflows with AI tools bolted on? The first step is asking vendors point blank what their system does when it encounters data it doesn’t know how to parse. Rules engines will point to specific fallback rules that execute. Agents will talk about reassessing their goal and weighing their available actions until they decide on the next best step.
• Models and hosting - Where are the models hosted? Are they specialized and trained on domain-specific data? This answers two questions at once – vendor capability, as well as data privacy concerns.
• Long-term memory and context - Enterprise agents become dramatically more useful when they retain organizational context over time. Rather than treating every interaction as a new conversation, they can accumulate institutional knowledge, remember previous decisions, and personalize future actions while remaining within governance boundaries. Persistent memory allows agentic systems to improve continuously without requiring engineers to encode new rules after every edge case.
• Hallucination - No current LLM is immune to hallucinations. The important architectural question is how the system detects, bounds, and mitigates them before they affect downstream business processes. Specialists hallucinate less in their domain of expertise. Prediction window guardrails limit how far an AI system can go “outside the data.” Human approval gates before sending anything live catch anything that slips through.
• Governance / auditability - Can the system provide traceability for every output it generates? Is the system’s accuracy benchmarked against a third-party, or just internally verified?

The economic case for getting this right

There's an additional argument that often gets overlooked in discussions focused on capability: cost structure.

Token-based pricing from large model providers creates a fundamentally unpredictable cost model for enterprise deployments. Every question, every generation, every iteration costs tokens — and iterating toward an acceptable output for a complex campaign task can consume a significant volume of them.

Enterprise subscriptions impose usage caps that create their own operational friction. The more AI-dependent your workflows become, the more acute this pressure grows.

Organizations that own and host their own specialized models are not subject to this dynamic. There is no token meter running. The economic relationship is closer to infrastructure than to a metered service - you bear the cost of building and maintaining the system, and in return you have predictable marginal cost. For organizations at scale, that math changes substantially.

Don’t fall victim to marketing speak

AI marketing platforms will continue to flood the market with AI-sounding languages attached to rules engines. But for enterprises who truly want to deploy agentic AI, there’s a far better solution. Domain specific, privately hosted agents that don’t leave your organization exposing itself to risk.

As agentic systems mature, the organizations that differentiate between genuine autonomous architectures and AI-enhanced workflow engines will be better positioned to capture sustainable competitive advantage.

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Only about 1 in 8 nurses are men, but their share is growing. A strong job market for nurses and sustained efforts to recruit men into the field have made a difference, including in the South.

(Image credit: Andi Rice for NPR)

Lenovo Yoga Slim 7x Gen 11: Two-minute review

The Lenovo Yoga Slim 7x Gen 11 is an ultra-thin laptop with a strong spec that makes it more than capable for workday use.

It looks smart, thanks to its minimalist form. All sides are flat, and the Cosmic Blue colorway is subtle yet helps to distinguish it in this largely monochromatic sector.

The build quality is excellent. The body is as premium as it comes, feeling very solid but also light and smooth to the touch. In fact, it could probably lay claim to having one of the best laptop constructions around right now. Allied to that lightness is its thinness, which is quite remarkable, although the thick rear foot underneath somewhat negates this aspect, which is a shame. What’s more, these truncated dimensions mean that you get three ports, all of which are USB-C.

(Image credit: Future)

With the new Snapdragon X2 Elite chip equipped, the Yoga Slim 7x Gen 11 is a capable all-round performer. It handles multiple browser tabs and 4K streaming with ease, as well as basic productivity. While it's not necessarily aimed at gamers and lacks a dedicated GPU, it's even capable of some light gaming; it managed to run Cyberpunk 2077 in a playable state, although don't expect top-tier performance. Heat and fan noise are generated when such workloads are conducted, but neither is too disruptive.

Another advantage of the X2 Elite chip is the improved efficiency. This is evident in the battery life of the Yoga Slim 7x Gen 11, which is one of its most impressive aspects. It lasted close to a full day when I ran a movie on a continuous loop, which ranks among the best in class. It’s also quick to charge, taking about two hours to do so.

The 2.8K display in my review unit was sharp, bright, and vivid, although it was prone to showing reflections at times. For the most part, though, these were only apparent in the most unfavorable of lighting conditions.

The keyboard is fairly easy to use, thanks to the ergonomic keycaps and their spacing. However, presses are a little heavier than you might expect, so this can take some getting used to if you’ve come from a board that requires a light touch. The touchpad is large and smooth, which makes for easy navigation, while taps and clicks are responsive and provide good feedback.

The Yoga Slim 7x Gen 11 commands a reasonably high price tag, but considering its spec and quality, it makes for good value compared to some of its less premium-feeling rivals. It might lack the ports and graphical performance of other laptops, but it’s a fine choice if portability and build quality are among your top priorities.

Lenovo Yoga Slim 7x Gen 11 review: Price & availability

(Image credit: Future)

• Starts from $999.99 / £945 / AU$1,999
• Available now in one colorway
• Reasonably expensive for the spec

The Yoga Slim 7x Gen 11 starts from $999.99 / £945 / AU$1,999 and is available now in one color: Cosmic Blue. This base spec features a Snapdragon X2 Plus X2P-42-100, 16GB of RAM, 512GB of storage, and WUXGA display. This isn't exactly cheap, and costs escalate quickly as you move up the model range. The top-spec unit, which features 32GB of RAM, 1TB of storage, and a 2.8K display, costs $2,299.99 (about £1,700 / AU$3,300).

If you’re looking for another thin and light laptop, then the HP OmniBook 7 is a very strong alternative. I was very impressed with its performance when I reviewed it, and its only missteps are the port placements and slightly harsh keyboard. It's base model is slightly cheaper than the Yoga Slim 7x Gen 11's in the US, and significantly so in the UK.

There’s also the Asus Zenbook A14, which is incredibly thin and light, and has one of the best battery lifespans around. However, it lacks the build quality and display brightness of the Yoga Slim 7x Gen 11, yet it’s significantly more expensive when comparing like-for-like models.

• Value score: 4 / 5

Lenovo Yoga Slim 7x Gen 11 review: Specs

Base spec

Max spec

Price

$999.99 / £945 / AU$1,999

$2,299.99 (about £1,700 / AU$3,300)

CPU

Snapdragon X2 Plus X2P-42-100 Processor (4.04 GHz)

Snapdragon X2 Elite X2E-80-100 Processor (4.70 GHz)

Graphics

Integrated

Integrated

RAM

16GB LPDDR5X

32GB LPDDR5X

Display

14-inch WUXGA (1920 x 1200), OLED, Glare, HDR 500 True Black, 100% DCI-P3, 400 nits, 60Hz

14-inch 2.8K WQXGA+ (2880 x 1800), OLED, Glare, HDR 1000 True Black, 100% DCI-P3, 500 nits, PureSight Pro, 120Hz

Storage

512GB SSD M.2 PCIe Gen4

1TB SSD M.2 PCIe Gen4

Ports and Connectivity

3x USB-C (USB4 40Gbps); Wi-Fi 7, Bluetooth 5.4

3x USB-C (USB4 40Gbps); Wi-Fi 7, Bluetooth 5.4

Battery

70Wh

70Wh

Weight

2.6lbs (1.2kg)

2.6lbs (1.2kg)

Dimensions

12.3 x 8.7 x 0.6 inches / 312 x 221 x 14mm

12.3 x 8.7 x 0.6 inches / 312 x 221 x 14mm

Lenovo Yoga Slim 7x Gen 11 review: Design

(Image credit: Future)

• Extremely thin
• Premium materials
• Lacks ports

The minimalist design of the Yoga Slim 7x Gen 11 is appealing, while the dark Cosmic Blue colorway rescues the unit from looking dull. The rounded sides and corners help to soften its appearance. Most sides are flat, with no extraneous bulges, save for the central camera bezel that extends beyond the rest of the lid. However, the overhang this creates actually helps you to open the lid, which is a thoughtful touch.

The body is one of the best I’ve seen in a laptop. It looks and feels premium, rivaling what the best MacBooks have to offer. It’s incredibly smooth and seems very durable. There’s some flex to the base and the lid, but they’re remarkably solid when you consider just how light the whole unit is. The lid is also very stable when open, and adjustments are smooth and easy to make.

(Image credit: Future)

Just as impressive is the thinness of the Yoga Slim 7x Gen 11. It’s certainly one of the thinnest 14-inch laptops I’ve seen, although it’s a shame the bulky bar underneath, which acts as the rear foot, somewhat undermines this aspect. This is no doubt to improve airflow from the underside vent, which is more exposed than many others.

To achieve this thinness, port selection has been compromised. There are only three ports, all of which are USB-C, which means you’ll need plenty of adapters (or a hub) if you’re going to be connecting lots of peripherals. The saving grace is that all ports have 40Gbps transfer rates, support the Power Delivery standard, and can be used to charge the laptop itself. They’re also split across both sides of the unit, which improves practicality.

On the right you’ll also find the power button and a button for disabling the webcam, which might disappoint those hoping for a physical privacy shutter.

• Design score: 4 / 5

Lenovo Yoga Slim 7x Gen 11 review: Performance

(Image credit: Future)

• Capable everyday performance
• Light gaming possible
• Keys are a little heavy

Lenovo Yoga Slim 7x Gen 11 benchmarks

3DMark: Night Raid: 40,637; Fire Strike: 7,911; Steel Nomad: 1,051; Solar Bay: 21,754; Solar Bay Unlimited: 22,518; Solar Bay Extreme: 2,491; Solar Bay Extreme Unlimited: 2,526
Geekbench 6.5: Multicore: 20,362; Single-core: 3,815
Cinebench R23: Multi Core: 13,071; Cinebench R24: Single Core: 90; Multi Core: 723
Crossmark: Overall: 1,896; Productivity: 1,703; Creativity: 2,171; Responsiveness: 1,742
Passmark Overall: 7,821.2; CPU: 30,341.4; 2D Graphics: 594.2; 3D Graphics: 6,632.9; Memory: 3,429.5; Disk: 51,620.7
BlackMagicDisk: Read: 5,214MB/s; Write: 4,947MB/s
HandBrake 4K to 1080p: 63.68fps
Total War: Warhammer III: 1080p, Medium: 43fps
Total War: Warhammer III: 1800p, Ultra: 11fps
Battery Life (TechRadar movie test): 23 hours and 30 minutes

For everyday use, the Yoga Slim 7x Gen 11 is certainly fast enough. It handled many of the tasks I threw at it with aplomb, from basic productivity to 4K streaming. With the 32GB of RAM in my unit, it also handled multi-tab web browsing with ease.

No doubt a lot of this performance comes from the new Snapdragon X2 Elite chip inside the Yoga Slim 7x Gen 11. Qualcomm claims this chip is a stronger performer than Intel equivalents, and based on our testing this appears to be true. For instance, it beat the Geekbench Single Core benchmark score of the HP OmniBook 7, with its Intel Core 5 220H, by over a thousand points, and doubled its Multi Core score.

When it comes to gaming, the Yoga Slim 7x Gen 11 was less competent, but that's hardly a surprise — it's not really aimed at wooing gamers, given it doesn’t have a dedicated GPU. However, I still managed to run Cyberpunk 2077 in a playable state, albeit with ray tracing disabled and resolution scaling set to Balanced. It wasn’t exactly a smooth experience, so it's not likely to be for you if you're in the market for a dedicated gaming laptop, but if you want a productivity powerhouse with just a little casual gaming on the side, it should suffice.

As expected, the fans can be heard whirring away in such cases, but the noise isn’t too disruptive. Heat is also generated, and temperatures can get quite high in places, although thankfully these hotspots are confined to the top and underside of the base.

(Image credit: Future)

The display in my review unit was crisp, thanks to its 2880 x 1800 resolution, and the high levels of brightness ensure content is viewable in most conditions. There were times when reflections showed on screen, but only when the lighting in my environment was particularly unsuitable.

The keys in the Yoga Slim 7x Gen 11 adopt Lenovo’s trademark shape, which are very ergonomic. The generous spacing between them also makes for a forgiving typing experience. However, they’re a little heavier and travel further than you might expect, which might throw off those who prefer a light and snappy response. The touchpad is suitably large and incredibly smooth, making it conducive to cursor navigation. Taps are responsive without being overly sensitive, and clicks are easy to perform and provide plenty of feedback.

• Performance score: 4 / 5

Lenovo Yoga Slim 7x Gen 11 review: Battery life

(Image credit: Future)

The battery life of the Yoga Slim 7x Gen 11 is very impressive. No doubt that aforementioned new Snapdragon chip plays a major part here, due to its improved efficiency. It managed to last just under 24 hours in our movie playback test, putting it ahead of many other laptops in this sector. This may be partly due to that aforementioned chipset, which Snapdragon claims brings improved efficiency. Charging is a quick affair, too, as it took about two hours to fully replenish.

However, there are those that can match and even outlast it, such as the HP OmniBook 7, which managed 26 hours in the same test. And then there’s the Asus Zenbook A14, which managed a staggering 28 hours and 25 minutes.

• Battery life score: 4.5 / 5

Should I buy the Lenovo Yoga Slim 7x Gen 11?Scorecard

Attributes

Notes

Rating

Value

It seems expensive, but when you consider its design and spec, it’s reasonable.

4 / 5

Design

I couldn’t help but marvel at its thin and premium design. The port selection is disappointing, though.

4 / 5

Performance

Great for everyday tasks, and it can even handle light gaming. The display is vivid if a little reflective, and it can get hot under load.

4 / 5

Battery life

Among the best in class, it really can last a full day. It’s quick to charge, too.

4.5 / 5

Total Score

The Yoga Slim 7x Gen 11 is great if you need a thin yet powerful Windows machine that lasts all day unplugged. A few areas are less impressive, but it’s still a good value proposition.

4 / 5

Buy it if…

You want one of the thinnest laptops around
That bulky foot underneath might spoil things a little, but the Yoga Slim 7x Gen 11 is still incredibly thin.

You want a premium design
It looks premium, and the body feels exquisite and hardwearing. It’s also quite light.

Don't buy it if…

You want lots of ports
There are only three ports on board, and all are USB-C, so you'll need a hub if you have lots of connections to make.

You want light keys
Although they’re comfortable to use, the keys on the Yoga Slim 7x Gen 11 are a little heavier than on many other laptops, which might take some getting used to.

Lenovo Yoga Slim 7x Gen 11 review: Also consider

HP OmniBook 7 14-inch (2025)
Like the Yoga Slim 7x Gen 11, the OmniBook 7 is an incredibly thin and light laptop. Not only that, I was very impressed with how well it handled all kinds of workloads, and it has a superb battery life to boot. Read our full HP OmniBook 7 14-inch (2025) review.

Asus Zenbook A14
Another portable hero, the A14 is even lighter than the Yoga Slim 7x Gen 11. However, its battery life is even better; in fact, it’s the most enduring laptop I’ve ever tested, lasting a staggering 28 hours in our movie playback test. Its build isn’t as premium as the Yoga Slim 7x Gen 11’s, and its display isn’t as bright, but it’s still a capable machine. Read our full Asus Zenbook A14 review.

(Image credit: Future)How I tested the Lenovo Yoga Slim 7x Gen 11

• Tested for several days
• Used for a variety of tasks
• Plentiful laptop experience

I tested the Yoga Slim 7x Gen 11 for several days, during which time I used it for a variety of tasks, from productivity and browsing to streaming and gaming.

I also ran our series of benchmark tests, designed to assess every aspect of a laptop's performance. I also ran a movie on a continuous loop to test the battery life.

I've been using laptops for decades, and have reviewed a large number of them, from small budget models to large gaming machines.

• First reviewed: July 2026
• Read more about how we test

Lenovo Yoga Slim 7x Gen 11: Two-minute review

The Lenovo Yoga Slim 7x Gen 11 is an ultra-thin laptop with a strong spec that makes it more than capable for workday use.

It looks smart, thanks to its minimalist form. All sides are flat, and the Cosmic Blue colorway is subtle yet helps to distinguish it in this largely monochromatic sector.

The build quality is excellent. The body is as premium as it comes, feeling very solid but also light and smooth to the touch. In fact, it could probably lay claim to having one of the best laptop constructions around right now. Allied to that lightness is its thinness, which is quite remarkable, although the thick rear foot underneath somewhat negates this aspect, which is a shame. What’s more, these truncated dimensions mean that you get three ports, all of which are USB-C.

(Image credit: Future)

With the new Snapdragon X2 Elite chip equipped, the Yoga Slim 7x Gen 11 is a capable all-round performer. It handles multiple browser tabs and 4K streaming with ease, as well as basic productivity. While it's not necessarily aimed at gamers and lacks a dedicated GPU, it's even capable of some light gaming; it managed to run Cyberpunk 2077 in a playable state, although don't expect top-tier performance. Heat and fan noise are generated when such workloads are conducted, but neither is too disruptive.

Another advantage of the X2 Elite chip is the improved efficiency. This is evident in the battery life of the Yoga Slim 7x Gen 11, which is one of its most impressive aspects. It lasted close to a full day when I ran a movie on a continuous loop, which ranks among the best in class. It’s also quick to charge, taking about two hours to do so.

The 2.8K display in my review unit was sharp, bright, and vivid, although it was prone to showing reflections at times. For the most part, though, these were only apparent in the most unfavorable of lighting conditions.

The keyboard is fairly easy to use, thanks to the ergonomic keycaps and their spacing. However, presses are a little heavier than you might expect, so this can take some getting used to if you’ve come from a board that requires a light touch. The touchpad is large and smooth, which makes for easy navigation, while taps and clicks are responsive and provide good feedback.

The Yoga Slim 7x Gen 11 commands a reasonably high price tag, but considering its spec and quality, it makes for good value compared to some of its less premium-feeling rivals. It might lack the ports and graphical performance of other laptops, but it’s a fine choice if portability and build quality are among your top priorities.

Lenovo Yoga Slim 7x Gen 11 review: Price & availability

(Image credit: Future)

• Starts from $999.99 / £945 / AU$1,999
• Available now in one colorway
• Reasonably expensive for the spec

The Yoga Slim 7x Gen 11 starts from $999.99 / £945 / AU$1,999 and is available now in one color: Cosmic Blue. This base spec features a Snapdragon X2 Plus X2P-42-100, 16GB of RAM, 512GB of storage, and WUXGA display. This isn't exactly cheap, and costs escalate quickly as you move up the model range. The top-spec unit, which features 32GB of RAM, 1TB of storage, and a 2.8K display, costs $2,299.99 (about £1,700 / AU$3,300).

If you’re looking for another thin and light laptop, then the HP OmniBook 7 is a very strong alternative. I was very impressed with its performance when I reviewed it, and its only missteps are the port placements and slightly harsh keyboard. It's base model is slightly cheaper than the Yoga Slim 7x Gen 11's in the US, and significantly so in the UK.

There’s also the Asus Zenbook A14, which is incredibly thin and light, and has one of the best battery lifespans around. However, it lacks the build quality and display brightness of the Yoga Slim 7x Gen 11, yet it’s significantly more expensive when comparing like-for-like models.

• Value score: 4 / 5

Lenovo Yoga Slim 7x Gen 11 review: Specs

Base spec

Max spec

Price

$999.99 / £945 / AU$1,999

$2,299.99 (about £1,700 / AU$3,300)

CPU

Snapdragon X2 Plus X2P-42-100 Processor (4.04 GHz)

Snapdragon X2 Elite X2E-80-100 Processor (4.70 GHz)

Graphics

Integrated

Integrated

RAM

16GB LPDDR5X

32GB LPDDR5X

Display

14-inch WUXGA (1920 x 1200), OLED, Glare, HDR 500 True Black, 100% DCI-P3, 400 nits, 60Hz

14-inch 2.8K WQXGA+ (2880 x 1800), OLED, Glare, HDR 1000 True Black, 100% DCI-P3, 500 nits, PureSight Pro, 120Hz

Storage

512GB SSD M.2 PCIe Gen4

1TB SSD M.2 PCIe Gen4

Ports and Connectivity

3x USB-C (USB4 40Gbps); Wi-Fi 7, Bluetooth 5.4

3x USB-C (USB4 40Gbps); Wi-Fi 7, Bluetooth 5.4

Battery

70Wh

70Wh

Weight

2.6lbs (1.2kg)

2.6lbs (1.2kg)

Dimensions

12.3 x 8.7 x 0.6 inches / 312 x 221 x 14mm

12.3 x 8.7 x 0.6 inches / 312 x 221 x 14mm

Lenovo Yoga Slim 7x Gen 11 review: Design

(Image credit: Future)

• Extremely thin
• Premium materials
• Lacks ports

The minimalist design of the Yoga Slim 7x Gen 11 is appealing, while the dark Cosmic Blue colorway rescues the unit from looking dull. The rounded sides and corners help to soften its appearance. Most sides are flat, with no extraneous bulges, save for the central camera bezel that extends beyond the rest of the lid. However, the overhang this creates actually helps you to open the lid, which is a thoughtful touch.

The body is one of the best I’ve seen in a laptop. It looks and feels premium, rivaling what the best MacBooks have to offer. It’s incredibly smooth and seems very durable. There’s some flex to the base and the lid, but they’re remarkably solid when you consider just how light the whole unit is. The lid is also very stable when open, and adjustments are smooth and easy to make.

(Image credit: Future)

Just as impressive is the thinness of the Yoga Slim 7x Gen 11. It’s certainly one of the thinnest 14-inch laptops I’ve seen, although it’s a shame the bulky bar underneath, which acts as the rear foot, somewhat undermines this aspect. This is no doubt to improve airflow from the underside vent, which is more exposed than many others.

To achieve this thinness, port selection has been compromised. There are only three ports, all of which are USB-C, which means you’ll need plenty of adapters (or a hub) if you’re going to be connecting lots of peripherals. The saving grace is that all ports have 40Gbps transfer rates, support the Power Delivery standard, and can be used to charge the laptop itself. They’re also split across both sides of the unit, which improves practicality.

On the right you’ll also find the power button and a button for disabling the webcam, which might disappoint those hoping for a physical privacy shutter.

• Design score: 4 / 5

Lenovo Yoga Slim 7x Gen 11 review: Performance

(Image credit: Future)

• Capable everyday performance
• Light gaming possible
• Keys are a little heavy

Lenovo Yoga Slim 7x Gen 11 benchmarks

3DMark: Night Raid: 40,637; Fire Strike: 7,911; Steel Nomad: 1,051; Solar Bay: 21,754; Solar Bay Unlimited: 22,518; Solar Bay Extreme: 2,491; Solar Bay Extreme Unlimited: 2,526
Geekbench 6.5: Multicore: 20,362; Single-core: 3,815
Cinebench R23: Multi Core: 13,071; Cinebench R24: Single Core: 90; Multi Core: 723
Crossmark: Overall: 1,896; Productivity: 1,703; Creativity: 2,171; Responsiveness: 1,742
Passmark Overall: 7,821.2; CPU: 30,341.4; 2D Graphics: 594.2; 3D Graphics: 6,632.9; Memory: 3,429.5; Disk: 51,620.7
BlackMagicDisk: Read: 5,214MB/s; Write: 4,947MB/s
HandBrake 4K to 1080p: 63.68fps
Total War: Warhammer III: 1080p, Medium: 43fps
Total War: Warhammer III: 1800p, Ultra: 11fps
Battery Life (TechRadar movie test): 23 hours and 30 minutes

For everyday use, the Yoga Slim 7x Gen 11 is certainly fast enough. It handled many of the tasks I threw at it with aplomb, from basic productivity to 4K streaming. With the 32GB of RAM in my unit, it also handled multi-tab web browsing with ease.

No doubt a lot of this performance comes from the new Snapdragon X2 Elite chip inside the Yoga Slim 7x Gen 11. Qualcomm claims this chip is a stronger performer than Intel equivalents, and based on our testing this appears to be true. For instance, it beat the Geekbench Single Core benchmark score of the HP OmniBook 7, with its Intel Core 5 220H, by over a thousand points, and doubled its Multi Core score.

When it comes to gaming, the Yoga Slim 7x Gen 11 was less competent, but that's hardly a surprise — it's not really aimed at wooing gamers, given it doesn’t have a dedicated GPU. However, I still managed to run Cyberpunk 2077 in a playable state, albeit with ray tracing disabled and resolution scaling set to Balanced. It wasn’t exactly a smooth experience, so it's not likely to be for you if you're in the market for a dedicated gaming laptop, but if you want a productivity powerhouse with just a little casual gaming on the side, it should suffice.

As expected, the fans can be heard whirring away in such cases, but the noise isn’t too disruptive. Heat is also generated, and temperatures can get quite high in places, although thankfully these hotspots are confined to the top and underside of the base.

(Image credit: Future)

The display in my review unit was crisp, thanks to its 2880 x 1800 resolution, and the high levels of brightness ensure content is viewable in most conditions. There were times when reflections showed on screen, but only when the lighting in my environment was particularly unsuitable.

The keys in the Yoga Slim 7x Gen 11 adopt Lenovo’s trademark shape, which are very ergonomic. The generous spacing between them also makes for a forgiving typing experience. However, they’re a little heavier and travel further than you might expect, which might throw off those who prefer a light and snappy response. The touchpad is suitably large and incredibly smooth, making it conducive to cursor navigation. Taps are responsive without being overly sensitive, and clicks are easy to perform and provide plenty of feedback.

• Performance score: 4 / 5

Lenovo Yoga Slim 7x Gen 11 review: Battery life

(Image credit: Future)

The battery life of the Yoga Slim 7x Gen 11 is very impressive. No doubt that aforementioned new Snapdragon chip plays a major part here, due to its improved efficiency. It managed to last just under 24 hours in our movie playback test, putting it ahead of many other laptops in this sector. This may be partly due to that aforementioned chipset, which Snapdragon claims brings improved efficiency. Charging is a quick affair, too, as it took about two hours to fully replenish.

However, there are those that can match and even outlast it, such as the HP OmniBook 7, which managed 26 hours in the same test. And then there’s the Asus Zenbook A14, which managed a staggering 28 hours and 25 minutes.

• Battery life score: 4.5 / 5

Should I buy the Lenovo Yoga Slim 7x Gen 11?Scorecard

Attributes

Notes

Rating

Value

It seems expensive, but when you consider its design and spec, it’s reasonable.

4 / 5

Design

I couldn’t help but marvel at its thin and premium design. The port selection is disappointing, though.

4 / 5

Performance

Great for everyday tasks, and it can even handle light gaming. The display is vivid if a little reflective, and it can get hot under load.

4 / 5

Battery life

Among the best in class, it really can last a full day. It’s quick to charge, too.

4.5 / 5

Total Score

The Yoga Slim 7x Gen 11 is great if you need a thin yet powerful Windows machine that lasts all day unplugged. A few areas are less impressive, but it’s still a good value proposition.

4 / 5

Buy it if…

You want one of the thinnest laptops around
That bulky foot underneath might spoil things a little, but the Yoga Slim 7x Gen 11 is still incredibly thin.

You want a premium design
It looks premium, and the body feels exquisite and hardwearing. It’s also quite light.

Don't buy it if…

You want lots of ports
There are only three ports on board, and all are USB-C, so you'll need a hub if you have lots of connections to make.

You want light keys
Although they’re comfortable to use, the keys on the Yoga Slim 7x Gen 11 are a little heavier than on many other laptops, which might take some getting used to.

Lenovo Yoga Slim 7x Gen 11 review: Also consider

HP OmniBook 7 14-inch (2025)
Like the Yoga Slim 7x Gen 11, the OmniBook 7 is an incredibly thin and light laptop. Not only that, I was very impressed with how well it handled all kinds of workloads, and it has a superb battery life to boot. Read our full HP OmniBook 7 14-inch (2025) review.

Asus Zenbook A14
Another portable hero, the A14 is even lighter than the Yoga Slim 7x Gen 11. However, its battery life is even better; in fact, it’s the most enduring laptop I’ve ever tested, lasting a staggering 28 hours in our movie playback test. Its build isn’t as premium as the Yoga Slim 7x Gen 11’s, and its display isn’t as bright, but it’s still a capable machine. Read our full Asus Zenbook A14 review.

(Image credit: Future)How I tested the Lenovo Yoga Slim 7x Gen 11

• Tested for several days
• Used for a variety of tasks
• Plentiful laptop experience

I tested the Yoga Slim 7x Gen 11 for several days, during which time I used it for a variety of tasks, from productivity and browsing to streaming and gaming.

I also ran our series of benchmark tests, designed to assess every aspect of a laptop's performance. I also ran a movie on a continuous loop to test the battery life.

I've been using laptops for decades, and have reviewed a large number of them, from small budget models to large gaming machines.

• First reviewed: July 2026
• Read more about how we test

Data center design has been shaped by a familiar set of priorities for years: keep systems available, resilient and predictable in any condition. Just like the electrical grid that powers these sites, they have been engineered to provide a highly consistent service regardless of what happens, even when individual components fail.

This has meant operators build layers of redundancy into power, cooling and network infrastructure.

However, artificial intelligence has changed the story. While organizations are racing to roll out AI tools at scale, the data center industry is discovering that not all workloads have the same infrastructure requirements.

For instance, training a large language model, running real-time inference, supporting enterprise applications and processing business-critical transactions each place very different demands on the underlying infrastructure.

Today, one data center doesn’t need to serve every purpose equally and we’re increasingly seeing that facilities can be both flexible and tailored to specific workload requirements.

The end of the traditional model

Historically, 99.999% uptime was non-negotiable. Data centers have traditionally powered systems like banks, emergency networks and customer-facing digital services, requiring continuous availability.

In these types of environments, where outages could have an extreme impact (from high financial losses to putting real lives at risk) this approach makes sense. Since operators couldn’t always predict which applications would be truly mission-critical, many facilities were built to the highest resilience standards by default.

But AI has changed this. “One-size-fits-all” redundancy isn’t necessary anymore. Different models, training and inference processes each require totally different service levels. For example, facilities for AI training workloads are being designed without backup generators, complex redundancy systems or high-tier architecture.

The good news is that there is a growing understanding of the distinction between environments needed for AI training and AI inference. Training facilities are increasingly being located wherever power is available.

The primary constraints are energy supply, cooling capacity and speed of deployment. In many cases, maximizing compute density and accelerating delivery timelines are more important than achieving the highest possible redundancy levels.

Inference infrastructure presents a different set of priorities. These workloads are often deployed closer to users and support services that people interact with daily. In these scenarios, latency, availability and customer experience become notably more important, creating a stronger case for resilient infrastructure and geographically distributed architectures.

Precision resilience to support an industry under pressure

It’s clear, therefore, that reliability still matters. However, infrastructure requirements vary significantly depending on the service being supported. In today’s age of AI, ‘precision resilience’ should be the focus, e.g., redundancy matching how workloads actually behave, rather than relying on legacy design assumptions.

The key challenge here for operators is determining where resilience delivers genuine business value and where it simply adds cost and complexity.

In a time when developers are facing a huge amount of pressure amid labor shortages, with demand outpacing supply, defaulting to ultra-resilient, high-tier designs for every AI deployment only intensifies challenges.

The industry is also expected to deliver capacity faster than ever before, while battling an ongoing power gap, meaning large-scale developments are increasingly difficult to execute. In this landscape, overengineering infrastructure can have unintended consequences.

Every additional layer of redundancy consumes capital and increases complexity. This is triggering an increased focus on efficiency, not just in terms of energy consumption, but in how capital is allocated throughout a project. Operators are looking to design infrastructure that maximizes the value generated by every watt of available power.

The role of upgradability

As operators move away from this one-size-fits-all redundancy to optimize their bottom line, it’s crucial that their facilities can adapt as workload requirements change.

While inference is expected to account for a growing share of AI demand, the landscape continues to evolve and it’s difficult to predict which workloads, densities and cooling requirements will dominate in the future. Infrastructure that can accommodate changes in compute technologies will be better positioned to support the next generation of AI applications.

Flexibility and fungibility are therefore the new non-negotiables in data center design. How is this made possible? Increasingly, developers are using ‘building blocks’ constructed off-site in factory environments, and then later assembling them on site to create an adaptable facility that can forever evolve, grow and shift.

This approach reduces the need to make every resilience decision upfront and builds with tomorrow’s changes in mind. In the coming years, we will see a shift towards multiple types of facilities, each developed for a different purpose.

These will range from energy-optimized training campuses built close to power sources, to distributed inference sites where uptime and latency directly affect user experience, alongside hybrid environments supporting both AI and traditional workloads. Yet they should all be built with flexibility front of mind to ensure they can evolve as requirements change.

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• The number of companies using AI is up, but the number of tools per company hasn't grown much
• ONS data also confirms that AI doesn't seem to be having a major impact on headcount
• SMBs could be lacking a clear business use case for AI

New ONS data has claimed that while AI adoption has expanded rapidly across UK businesses, the actual depth of adoption is still limited, with most firms using just a small number of tools to improve existing processes rather than totally overhauling their businesses.

Among the businesses surveyed that had adopted AI, the number of tools they used only increased marginally from 1.4 to 1.6 in the three years leading up to 2026.

The ONS also revealed that, while 60% of larger businesses have used AI to improve business operations, no negative impacts on headcount have been observed.

AI adoption might be growing, but that's not the full story

The data mostly implies experimentation rather than widespread transformation, with just 17% of SMBs (0-9 employees) reporting extensive AI use, however the report warns that many small businesses are merely trying general-purpose AI tools rather than making substantial investments into tech that can truly unlock new levels of productivity.

What's less clear is why SMBs are falling behind their enterprise counterparts, because while 7-14% of all business sizes cited cost as a barrier, 41% of SMBs with 10+ employees said they didn't have any major barrier concerns.

Rather than being held back by costs, infrastructure or capability, the ONS data actually implies that SMBs might actually be lacking compelling business cases to use AI altogether.

All in all, the most recent data confirms that while SMBs haven't exactly ignored AI, they're still stuck in the experimentation stage. The readiness to use automation tech is very much there, but ability to identify a genuine use case is potentially holding back deeper deployment.

AI is now part of the operating system of SaaS. It shapes how products are built, how teams collaborate, and how quickly ideas move from concept to launch.

But speed isn’t the most important shift. The real change is that AI is reducing the coordination cost inside organizations.

Work that once required multiple layers of approvals, handoffs, and alignment can now move more directly between the people closest to the problem. And as that friction drops, something more fundamental starts to change: how companies are structured.

Projects that once demanded large teams, heavy investment, and long development timelines can now be delivered by smaller groups using AI tools to accelerate execution.

The rise of micro-SaaS businesses is one clear example, with small teams able to build and scale products with a level of speed that would have been difficult to imagine a few years ago.

This isn’t just about building faster. It’s changing what scale actually looks like.

From experimentation to infrastructure

Today, AI is embedded directly into product development, engineering, growth, and support. It’s no longer something teams experiment with on the side.

This has brought about a fundamental change: individual contributors can move faster, make decisions earlier, and deliver more on their own.

That has a direct impact on how teams scale.

And increasingly, the companies with an edge are not the ones with the biggest teams, but the ones that can remove friction and make better decisions faster.

Why scale no longer means more layers

Traditionally, growth came with added complexity. More customers meant more people. More people meant more managers, more processes, and more coordination.

At a certain point, coordination becomes a job in itself.

AI starts to break that pattern and bottleneck, freeing up time for quality decisions. When a product manager can analyze user feedback, draft a roadmap, and collaborate more directly with engineering using AI tools, you reduce the need for multiple handoffs. When a growth team can produce, test, and iterate on campaigns faster, execution accelerates without increasing headcount at the same pace.

It doesn’t remove the need for structure. But it does reduce the need for layers whose main role is coordination.

And that opens the door to a different model of scaling: one that is lighter, more direct, and more focused on making the right decisions, not just executing faster.

The return of the “contribution era”

What we’re starting to see is a shift back toward what could be called a contribution-led model. For a long time, SaaS organizations leaned heavily into management structures. That made sense when scaling meant handling more complexity across teams, regions, and products.

Now, as AI lowers the cost of execution, the balance starts to shift again: the biggest advantage AI creates is not productivity but organizational simplification.

Strong individual contributors who can own a problem and drive it to completion become even more valuable. They don’t need to wait for as much coordination. They can test, build, and iterate independently. And they can do it while staying closely connected to the outcome. Importantly, this isn’t about removing managers. It’s about rebalancing the system.

What builder-led really looks like in practice

A builder-led model doesn’t mean everyone is an engineer, and it doesn’t mean structure disappears.

It means the people closest to the work have more autonomy to move it forward.

You see this already across teams:

• Product teams prototyping faster using AI-assisted tools
• Growth teams running more experiments with shorter feedback cycles
• Support teams handling higher volumes while focusing human attention where it matters most

In each case, AI is not replacing people. It’s increasing their speed and range.

And when that happens consistently, the bottleneck shifts. It’s no longer capacity. It’s clarity and decision quality: knowing what to work on, what to prioritize, and where to invest time.

This is where leadership becomes even more important, not less.

Instead of focusing on overseeing activity or managing layers of communication, leaders have to focus on creating the right conditions for execution.

In practice, it often looks like:

• Fewer approval steps
• More direct communication between teams
• More emphasis on outcomes rather than process

Leaders still set the direction and make the hard decisions. But they rely more on capable contributors to carry things forward.

In many cases, the most effective leaders are those who can still contribute when needed, not just coordinate others.

Hiring for ownership, not just specialization

This shift also changes how companies think about hiring.

Specialists remain essential. But, if smaller teams can deliver more, the focus moves toward people who combine expertise with ownership, and have a strong ability to make good decisions in fast-moving environments. There’s growing value in hiring people who can operate with autonomy, make decisions, and adapt as things change.

In a builder-led environment, the question is less “what is your lane?” and more “how effectively can you solve the problems in front of you?”

That doesn’t mean everyone needs to do everything. It means teams benefit from individuals who can connect dots, move across boundaries, and take responsibility for outcomes.

Building smarter, not just bigger

It’s important to stay grounded in how this shift plays out. AI won’t fix weak strategy or unclear thinking, and layering it onto already complex processes can sometimes create new friction rather than remove it.

At Weglot, we've seen teams ship projects with significantly fewer handoffs than two years ago. Marketing can prototype ideas faster, product teams can validate concepts earlier, and engineers spend less time on repetitive tasks.

Our support team is another good example. Over time, they've built a suite of AI-powered tools including a case summarizer, customer profiler, drafting assistant, internal copilot, knowledge base, and AI chatbots. Together, these tools help agents access context faster, learn from previous cases, and resolve more requests independently.

The biggest change isn't speed itself. It's the reduction in coordination overhead, which is ultimately a more sustainable way of scaling.

Smaller, highly capable teams with clear ownership tend to stay closer to the product and the customer and can adapt more quickly when things change. We’re already seeing that in micro-SaaS businesses, but the same thinking applies more broadly.

AI will continue to evolve, but one direction is becoming clear. The companies that will stand out are not necessarily the ones that grow headcount fastest. They’re the ones that stay focused, reduce friction, and make it easier for their best people to build and deliver impact.

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When Joseph Lamont Williams refused to leave the public park where he has lived for years, he was charged with a felony. Camping bans are on the rise as states seek to crack down on homelessness.

(Image credit: Erica Lynn for NPR)

Arizona Democrats choose who can try to flip a Republican-held House seat and Republicans pick their nominee to try to unseat a Democratic governor.

(Image credit: Ross D. Franklin)

What do you mean Heartstopper Forever hasn't actually ended? Alongside the fact that the new movie is now streaming on Netflix, the streaming service has slyly announced a "companion piece" — Heartstopper: Ending on a Hi.

Obviously, we learned exactly what would happen to the gang once the cameras stopped rolling, and were left happy in the belief that Nick (Kit Connor) and Charlie (Joe Locke) definitely weren't breaking up, despite how much their terrible communication threatened to do just that.

So... what is actually coming out way? And when does Heartstopper: Ending on a Hi arrive on Netflix?

What time can I watch Heartstopper: Ending on a Hi on Netflix?

(Image credit: Netflix)

Heartstopper Forever drops on Netflix on July 24, 2026.

As for the exact time, it should be the standard 12am PT release that we saw for the movie.

For global regions, here's when you need to be prepared:

• US – 12am PT / 3am ET
• Canada – 12am PT / 3am ET
• UK – 8am BST
• India – 12:30pm IST
• Singapore – 3pm SGT
• Australia – 6pm AEST
• New Zealand – 8pm NZDT

What exactly is Heartstopper: Ending on a Hi?

(Image credit: Netflix)

Heartstopper: Ending on a Hi is an official 35-minute behind-the-scenes documentary special.

Netflix describes it as a "love letter to the franchise, featuring unseen archival footage, cast interviews, and fan reactions following the final Heartstopper: Forever film."

Don't take the title as a sign of something more to come, though... it's a reference to the very first time Nick and Charlie met in season 1.