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If I could sum up Rick and Morty season 9 in one word... I couldn't. It's physically impossible to sump up the chaos that is the Smith's family life in so few words.
All we've got to go off of this week is a name: Tom Sawyer. We can assume that this will somehow relate to the classic Mark Twain novel The Adventures of Tom Sawyer, but does this mean Morty is also Huckleberry Finn? Watch this space.
Regardless, when does Rick and Morty season 9 episode 10 arrive on Adult Swim, Hulu, and HBO Max?
What time can I watch Rick and Morty season 9 episode 10 on Adult Swim, Hulu, and HBO Max?In the US, Rick and Morty season 9 episode 10 will debut on Adult Swim on Sunday, July 26 at 8pm PT / 11pm ET.
Viewers elsewhere, as well as in the US, have two streaming options: Hulu and HBO Max. Episodes should appear on these platforms 24 hours after they've aired on Adult Swim, meaning you can expect episode 10 to land on Monday, July 27. These are the timings you need to be aware of for the latter date:
- 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
After this week's entry, they won't. Rick and Morty season 9 comprises 10 episodes so, once its finale has aired, there'll be no more chapters to enjoy until a 10th season is potentially greenlit.
- Xteink has just launched the X4 Pro
- This tiny ereader can stick to the back of your phone, and its main upgrades include a light and a touchscreen
- New open source ereader software called FreeInk has also been announced
While small smartphones have largely gone out of fashion, it seems there’s an appetite for absolutely tiny ereaders. The Xteink brand is one of the biggest small-screen successes in this space, and its new model, the Xteink X4 Pro, is set to potentially be its best yet.
This ereader has just a 4.3-inch screen, with dimensions of 111 x 69 x 5.95mm, making it even smaller than the iPhone 13 mini, so it's small enough to fit in even a tiny pocket.
That makes it roughly the same size as the Xteink X4, and, like that device, this can also be magnetically attached to the back of a phone, essentially turning your handset into a dual-screen device, with the second display being a more eye-friendly way to read.
But where the Xteink X4 Pro has its predecessor beat is in replacing clunky buttons with a touchscreen, and — perhaps even more importantly — adding a front light, so you can read on it in the dark.
This also has a much higher-capacity 1,100mAh battery (up from 650mAh), and yet somehow weighs marginally less at 72g.
A low price and lacking apps(Image credit: Xteink)The Xteink X4 Pro is out now, and you can order it directly from Xteink’s site for $109 / £84 / AU$159, though a launch discount temporarily brings it down to $99 / £76 / AU$145.
That makes it a bit more expensive than its predecessor, and it replaces USB-C charging with a magnetic pogo charger, which won’t appeal to everyone. But arguably the main downside of this device is that it doesn’t support Android. No current Xteink devices do — though the upcoming Xteink S4 will. That means you can’t get the Kindle or Kobo apps on them, so you have to sideload books.
It also means they run Xteink’s own software, which receives mixed reviews. However, as long as you get a developer edition of the Xteink X4 Pro, you’re free to change the firmware, and there are some third-party options that may work better, including the newly-announced FreeInk.
This is open-source software that’s specifically listed as supporting the X3 and X4, among other devices. So while this won’t give you access to the Kindle app, some users might find it preferable to Xteink’s own software.
Across industries, organizations are increasingly tracking AI usage through dashboards, token utilization and platform engagement metrics. Some of the most visible companies in the world have stood up leaderboards ranking employee AI use; others have tied AI adoption directly to raises and promotions.
The intent is reasonable - leaders want a signal that the investment is landing.
But we’ve reached a point where managers, executives and boards carry a false assumption that AI usage means a more AI-ready workforce. Usage and capability are not the same thing.
Recent research, based on a survey of 2,000 workers across the U.S. and U.K., suggests many organizations are measuring AI adoption faster than employees are learning to use it effectively. While 46% of employees report using AI tools at work, nearly half have received no formal AI training and 56% have no clear path for developing AI-related skills.
Perhaps most concerning, 17% admit they are pretending to use AI at work. If organizations mistake AI usage for capability and readiness, they risk building strategies and processes for a workforce that doesn’t actually have the skills to execute them.
The gap isn't a talent problem; it's a systems problem between technology adoption and workforce development. Solving it means CIOs and HR leaders must move beyond coordination and take joint accountability for translating AI usage into true workforce capability.
The measurement trapAs AI becomes embedded into everyday work, leaders are looking for ways to track progress. Dashboards, usage reports, prompt counts and engagement metrics seem to offer an obvious way to demonstrate momentum. But activity is not the same as capability.
An employee generating 10 prompts daily may appear highly engaged. That doesn’t mean they know how to provide effective inputs, evaluate outputs, recognize hallucinations, or apply AI responsibly in ways that meaningfully improve performance.
When organizations treat activity as a proxy for competency, leaders develop a false sense of confidence about workforce readiness while critical capability gaps remain hidden beneath the surface.
Don't just agentify the messThere’s a parallel trap on the technology side, where there’s a race to “agentify” everything, wrapping an agent around every existing process and SKU.
But automating a broken workflow simply produces a faster broken workflow. The point isn’t to agentify the mess. It’s to rethink the work first, then apply AI to what matters.
The same discipline applies to how we measure return. Automation and the productivity gains are real, but treating efficiency as the finish line badly undersells the opportunity.
The larger prize is transformation: moving the topline and the bottom line, not just shaving cost per task. Organizations that aim only at incremental productivity will capture a fraction of what AI can actually deliver.
The visibility gap nobody is talking aboutThis creates a new challenge for CIOs and HR leaders. Most organizations can now see who is using AI tools. Far fewer can see whether employees are using them effectively.
The next phase of AI transformation will not be determined by access to tools, which most organizations have already solved. It will be determined by whether employees possess the judgment, confidence and skills necessary to use those tools productively and impactfully.
Without that visibility, organizations risk optimizing for adoption metrics while underinvesting in the development that generates long-term business value. Technology procurement lives with one team. Learning and skills data lives in another. Performance data often lives somewhere else entirely.
Consequently, organizations struggle to connect AI usage with business outcomes.
This is a CIO problem as much as an HR one.
Joint accountability, not coordinationThe conversation I’m having with peers is about moving from coordination to joint accountability. Coordination means IT and HR talk to each other. Joint accountability means they own the same outcome together; specifically, whether the workforce can execute the organization’s AI strategy.
Forget using AI. Are employees using it effectively enough to have a measurable impact on the business?
That reframe changes where decisions get made and who makes them. HR leaders understand what capabilities the business will need and where the development gaps are widening. CIOs understand how AI tools are deployed, where agents sit in the workflow, and where technical infrastructure can support learning at the point of work.
Neither function can solve the problem alone.
The skills that endureThrough all this churn - new models, new tools, new agents every quarter - one thing stays durable: domain expertise expressed as work. The specific, task-level skills that make someone effective at their core job don’t depreciate the way a given tool does. As AI transforms how work gets done, those domain-grounded skills are what compound and carry forward.
When organizations examine why AI adoption often stalls or remains shallow, the same issue tends to surface: AI is deployed without being meaningfully anchored to the skills and tasks of the workforce. Adoption becomes activity - visible, but not compounding.
Addressing this requires a shift in focus. AI needs to be connected directly to how work is actually performed and improved. When adoption is tied to real tasks and outcomes, it becomes a mechanism for continuously strengthening underlying skills, rather than just increasing tool usage.
What CIOs need to ownThe AI-readiness conversation has largely focused on technology deployment. The harder question is whether organizations are building the workforce capabilities necessary to translate adoption into results.
For CIOs, that means taking ownership of something that extends beyond technology infrastructure. It’s creating the systems, partnerships and feedback loops that allow their organizations to build capability at the speed AI is evolving, with visibility into the AI skills that their people are developing.
The most successful organisations will have CIO and HR leaders jointly turning AI usage into sustained workforce capability and measurable business value. They give employees not just the tools, but the support to use them effectively. That is what the AI-empowered workforce of the future looks like.
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- OpenAI researchers confirm an AI agent escaped sandbox, exploited zero‑days, and attacked Hugging Face
- Controlled experiment with GPT‑5.6 Sol showed autonomous chaining of vulnerabilities and credential theft
- Security experts call it unprecedented, urging stronger AI governance, accountability, and protection models
OpenAI has confirmed one of its AI agents broke out of a sandbox, found and exploited zero-day vulnerabilities to gain access to the open internet, and then attacked a platform.
Not just any platform too - the agent was able to breach Hugging Face, one of the biggest AI and machine learning companies on the Internet today.
The good news is that this was a controlled experiment done by white hat researchers. The bad news is that if it could be done by researchers - it could probably be done by malicious actors, too.
Whatever it takesIn a blog post explaining the incident, OpenAI revealed the experiment was part of its testing of GPT‑5.6 Sol and an “even more capable pre-release model” to see how well they would perform on the ExploitGym benchmark.
ExploitGym is a cybersecurity benchmark that measures if an AI agent can turn a known software vulnerability into a real, working exploit. OpenAI ran it in a “highly isolated environment, with network access constrained to the ability to install packages through an internally hosted third-party software that acts as a proxy and cache for package registries.”
But the models found a way through. They identified and chained vulnerabilities in the package registry cache proxy to obtain open internet access and then attacked Hugging Face, reasoning that the solutions for the ExploitGym benchmark might be found there.
“In one example, the model chained together multiple attack vectors, including using stolen credentials and zero-day vulnerabilities to find a remote code execution path on the Hugging Face servers,” OpenAI said.
The security community is up in arms over what OpenAI called, "an unprecedented cyber incident,” while Ansgar Dodt, VP Product Management, Software Monetization at Thales said this “demands a fundamental rethink of software protection.”
Bill Conner, president and CEO of AI integration and automation expert Jitterbit, said that while investing in AI is “critically important,” “overly aggressive policy cannot compromise AI accountability, transparency and data privacy.”
“To lead in AI, governments and organizations must lead with principles. Responsible AI governance isn’t a side note but the foundation of lasting global influence.”
Cybersecurity risks in water infrastructure have consequences that reach far beyond systems and networks.
Across the sector, the systems responsible for treatment and distribution are becoming more connected. Pumps, sensors and control environments that once operated in isolation are now linked to wider networks, often in the name of efficiency or modernization.
The problem is that these systems were never built with continuous exposure to cyber threats in mind and connecting them has introduced new attack surfaces that are difficult to maintain visibility of and control.
At the same time, many facilities have crept towards an increasingly blurred line between IT and operational technology (OT). Driven by incremental needs and very practical limits on systemic refresh, these systems have been added to and grown more complex over time rather than being designed securely from the ground up.
As a result, they become more tightly interconnected, with access stretching further across networks and systems than it should. Once an attacker breaches a system, it becomes far easier to move laterally across the network and get closer to critical infrastructure.
Meanwhile, cyber threats continue to encroach on the water sector, with attacks becoming increasingly frequent and their potential impact is hard to ignore. Disruptions to drinking water treatment or control systems can quickly escalate, interrupting supply or affecting water quality and, in turn, the communities that depend on them.
Structural challenges in securing water systemsSecuring water infrastructure is made more difficult by the operational realities many providers face. Technology environments have expanded over time, often without dedicated cybersecurity resources growing at the same pace, making it harder to maintain consistent oversight across increasingly ageing and disparate systems.
Many organizations are also balancing modern hyper-connected digital management expectations with the ongoing operation of originally isolated, long-established systems. This places additional pressure on teams responsible for maintaining both resilience and day-to-day continuity.
Another persistent challenge is the divide between IT and operational technology (OT) teams. Because these environments have traditionally evolved separately, with different design approaches, responsibilities, priorities and expertise, they are not always closely aligned, which can slow decision-making and create gaps in visibility during an incident.
In smaller providers, cybersecurity responsibilities may sit with operational staff whose primary expertise lies in running facilities rather than managing cyber risk. Larger organizations may have more specialized cyber teams, but greater separation between functions still introduces coordination challenges and the risk of operational blind spots.
Connectivity without constraintThe growing use of cloud computing platforms and remote access tools has brought clear operational advantages to critical infrastructure like water systems. However, it has also reinforced a default position of keeping systems online at all times, often without a genuine operational imperative for continuous connectivity.
This “always-connected” approach can unnecessarily increase exposure, particularly as more assets become reachable across wider networks. Without clear control over the time windows when systems need to be accessible, organizations may be creating more risk than expected, certainly more than is required.
A stronger, resilient approach starts with recognizing that security grows by making connectivity intentional. Not every system needs to remain online continuously, and limiting unnecessary access significantly improves security outcomes.
This can be achieved by creating stronger separation between critical systems and the wider network, using controls that allow connections to be enabled only when required while maintaining essential operations. In this model, connectivity is actively managed to define resilience on demand.
Containment as a first line of defenseIn the event of a vulnerability or compromise, response speed is critical, notably in environments where interconnected systems enable threats to spread rapidly across the network. Without effective connection controls in place, attackers can exploit this unconstrained accessibility to extend their reach before a full response is underway.
The ability to isolate systems in real time helps change this state. Segmenting critical parts of the network helps limit lateral movement, and deeply segmenting down to high criticality digital elements enables organizations to contain threats far more substantially and focus their efforts on speeding up the incident response.
Having this level of control helps limit the spread of disruption and supports a more structured response. It also creates clear, demonstrable evidence of how risk is being managed - something that’s becoming increasingly important as regulatory scrutiny and cyber insurance requirements become more demanding.
Moving to controlled accessThe most resilient model possible with physical connection control treats access to critical systems as fully conditional. Rather than keeping them permanently online, connections can be limited to where, when and why they are required for business reasons. As risk levels change, this can be refined or tightened at will.
This lowers both risk and potential impact, minimizing loss, while preserving the flexibility required for day-to-day operations.
For water providers, deliberately managing connectivity and segmenting networks at an infrastructure level should be a priority for resilience. Clearer boundaries and reduced unnecessary access make it easier to protect infrastructure that plays a vital role in public safety.
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Samsung's second big phone launch of the year is almost here — and while the leaks have given us lots of hints, we're here to untangle all of the official news on the its new foldables and smartwatches. And who knows, maybe Samsung will have a surprise in store too.
According to the rumors, it's going to be a particularly big day for foldables. We're expecting to see the Samsung Galaxy Z Fold 8, Galaxy Z Flip 8, , and Galaxy Z Fold 8 Ultra all make their debuts — and the Z Fold 8 could also be a sneak peak of what to expect from the incoming iPhone Ultra.
But if you've been waiting for a sequel to the Samsung Galaxy Watch Ultra or Galaxy Watch 8, it'll worth tuning in for those too. Both are expected to get successors today, when the event kicks off at 9am ET / 6am PT / 2pm BST / 11pm AEST on July 22.
So if you want to see what Samsung has in store today, join us for all of our live reactions and first impressions from inside the London event...
Welcome to Galaxy Unpacked 2026 (the sequel)Hello, and welcome to our liveblog for Samsung's second big phone launch of the year, Galaxy Unpacked 2026.
We'll be reporting live from the London event, which kicks off at 9am ET / 6am PT / 2pm BST / 11pm AEST. But first, it's time to take you through all of the last-minute rumors on what we're expecting to see — and we certainly haven't been short of leaks...
The Z Fold 8 is winning the hype battleHey guys, let's guess what he's holding? pic.twitter.com/H9jwX1OCPtJuly 21, 2026
The Galazy Z Fold 8 is emerging as the potential star of today's Unpacked event — and the foldable has just appeared in a new teaser poster (above).
It's the Z Fold 8's wider, passport-style design, which will likely separate it from the taller Fold 8 Ultra, that makes it the one I'm most interested in from today's launches. Partly because it also probably gives us an early peak of the iPhone Ultra's likely form factor.
We're expecting to see a 5.5-inch cover display and 7.6-inch inner display, both with a 120Hz refresh rate. The teaser poster matches some of the leaked dummy units that we saw earlier this year (below).
First look at dummy units of the Samsung Galaxy Z Fold8, Z Fold8 Wide, and Z Flip8. The Fold8 Wide lines up closely with the expected size of the iPhone Fold. pic.twitter.com/NVP3agwsrMApril 27, 2026
What are you most excited to see?We've done a full rundown of everything we're expecting to see at Unpacked 2026, so that's worth a read as we get closer to kick-off (in just over two hours).
But the short version is that we're expecting three foldables (a Z Fold 8, Z Fold 8 Ultra and Z Flip 8), plus two Galaxy Watches (the Watch Ultra 2 and Watch 9), and (potentially) also Samsung's first smart glasses.
Which one are you most excited about? Let us know in the poll below...
We've landed at UnpackedFutureFutureWe're very keen here at TechRadar, so we've already arrived at the Galaxy Unpacked event in London, and it's very... quiet.
That's understandable, as we're still two hours away from kick-off and many international attendees will be coming from hotels much closer to the start time.
A London-based Unpacked is a rarity — last year, Samsung did a mix of US-based and virtual events, while this year's earlier Unpacked in February was in San Francisco.
You have to go back to 2012 for the last time London hosted Samsung's main event, which saw the arrival of the Galaxy S III...
FutureFutureWill Samsung finally solve foldable creases?pic.twitter.com/Ck4rIXaXxAJuly 22, 2026
There's a good chance that we'll see another big step towards crease-free foldables today. We recently visited Samsung Display's secret labs where it talked to us, among many other things, about its new "Flex Titanium Technology".
This includes a special structure at the fold to give added flexibility and help reduce crease visibility. We've recently more leaks on that theme — and while the image above doesn't look like a particularly fair comparison in terms of lighting, I'm hoping this new feature lives up to the hyper when we see it in person later.
If you host an event in London...(Image credit: Future)The umbrellas are already out at the Galaxy Unpacked 2026 queue. We've had what feels like months without rain in London, but right on queue the dark clouds are forming.
It's now just over 90 minutes till kick-off time, so hopefully we'll be ushered inside soon...
- TikTok is releasing a new tool to help users identify AI spam
- It says that AI spam “crowds out original creators”
- Industry insider suspects that ad revenue may be the real motivator
If you're getting tired of AI slop on social media, you're not alone. Even TikTok, which has spent the last couple of years rolling out AI creation tools, is now testing new ways to detect AI-generated spam and teaching users how to recognize AI content.
TikTok says it is testing improved detection systems to target accounts dedicated to posting AI-generated spam that “crowds out original creators”, and it is also creating a new guide that helps its users use AI tools responsibly. TikTok is launching a new in-app hub within the next few weeks where you can learn practical skills for spotting AI-generated content when you search for AI-related terms.
(Image credit: Future)Is it really a moral stance?We spoke to Donatas Smailys — CEO of Billo, the largest UGC creator marketing platform in the US — about the new tool. His take is that TikTok is fighting AI content because it doesn't perform, and that hurts TikTok's ad business too.
"It would be naïve to read this as TikTok taking a moral stance. The platforms can see in their own data what audiences respond to, and they're quietly rebuilding their rules around real people.”
It strikes me as slightly ironic that TikTok, which has spent the last couple of years pushing AI creation tools, is now simultaneously educating people to recognize AI content and testing systems to suppress AI spam.
If TikTok's own data suggests AI-generated spam is harming engagement or advertising performance, then it may be the saving grace for the rest of us who are tired of AI slop ruining the experience of social media. Frankly, at this point I’ll take anything that reduces the amount of AI videos that get served to me, for whatever reason.
What this means for the futureThe wider issue here is that if TikTok is beginning to treat low-quality AI spam as a platform problem, then it should raise some red flags for advertisers who heavily rely on synthetic influencers and AI creators. If low-quality AI spam was improving the platform, TikTok would have little incentive to invest in detecting and suppressing it.
TikTok actually has pretty stringent rules on AI content on its platform already. It’s quite common to see the label “Contains AI-generated content” on TikTok videos.
It seems that regulators are moving in the same direction. “New York now requires disclosure of synthetic performers in ads”, says Smailys “with penalties for those who don't comply. And my guess is that more states will follow. But the loss of trust is an even bigger risk here. Once your audience feels tricked, no disclosure label wins them back."
While the rise of AI slop seems unstoppable at the moment, I wonder whether we'll eventually look back at 2026 as the year platforms realized that flooding people's feeds with AI-generated content wasn't sustainable and that what users really wanted all along was more real people.
Quantum computing has fascinated the tech world for several decades. That’s because, while classical computing is binary, quantum bits (qubits), made from atoms, electrons and superconductors, can be in both “0” and “1” at the same time, providing computational power that is inaccessible to classical computers.
The implications of this computational power will be far-reaching, such as accelerating drug discovery, optimizing global supply chains, and revolutionizing material science for clean energy. If that’s not interesting enough, the new frontier of hybridizing quantum and classical computing generates yet another source of excitement for the field.
Humanity has embedded computing into the fundamental fabric of daily life. It underpins our progress in an enormous number of fields, as well as impacts our day-to-day lives more than almost anything else.
While quantum computers are fascinating on their own, it is becoming increasingly clear that their integration into data centers - treating them as a crucial piece of the broader computing puzzle - represents one of the most interesting frontiers in science and technology today.
Hybrid computing: already a reality and key in the futureThe idea that quantum and classical computing will work together is not a future vision. Quantum computers already rely on tight integration with classical computing, which plays a critical role in enabling their operation.
For example, extensive classical computing is used for generating the control signals that operate the quantum hardware and to calibrate quantum hardware and control systems. In this scenario, classical computers are finding thousands of different system parameters and retuning them constantly as they drift over time.
Very compute-intensive classical algorithms are also used for decoding errors during quantum error correction, which is critical for enabling stable quantum computation and the scaling of quantum systems. Another key use case is that hybrid quantum-classical algorithms rely on interleaving quantum and classical computation to solve complex problems.
In the future, this hybridization will have a growing impact on computing applications. QPUs will act as specialized accelerators within classical HPC and AI workflows, complementing CPUs and GPUs rather than replacing them. Quantum systems will sit alongside CPUs and GPUs in data centers and be deployed for exactly the type of problems they are best suited to solve.
In fact, this is already starting to happen - some of the most important recent quantum demonstrations have relied on quantum and classical systems operating in close coordination. For example, RIKEN and IBM scientists recently achieved one of the largest quantum simulations of iron-sulfur clusters through closed-loop data exchange between a co-located IBM Quantum Heron processor and RIKEN's Fugaku supercomputer.
This kind of hybrid exchange between quantum and classical systems, rather than quantum computing operating as an isolated, standalone resource, is the model we expect to become standard as the technology matures.
The final, and perhaps most intriguing frontier in quantum-classical hybridization is its relationship to Artificial Intelligence. Compute infrastructure is being used to train AI models, while hardware capabilities limit what models can do in various ways. Integrating quantum hardware into this compute fabric may allow new data to be generated for AI models as well as new engines for AI inference.
Moreover, AI tools may help us break some of the abstraction layers we have created, which can remove further limitations on how we use quantum hardware to solve problems.
Quantum computers don't run themselvesGiven the above, as quantum computing moves from laboratory demonstrations toward practical applications, success will depend not only on better quantum processors but on how effectively quantum and classical computing work together.
The latency and bandwidth of the quantum-classical links will play a critical role in how performant this integration is and will determine how deeply connected the quantum and classical processing can become. This matters because qubits hold their state only briefly - the faster the classical system can read, process and respond, the more complex a calculation it can support before that state is lost.
Another critical aspect is software. Building infrastructure, tools, compilers and applications that orchestrate hybrid quantum-classical workflows efficiently, with high performance as well as developer experience in mind, will be critical for the industry to succeed in its mission to deliver real-world value in a timely manner.
This requires investment in the orchestration layers that allow developers to calibrate and control quantum resources as easily as they would HPC today, abstracting away much of the underlying hardware complexity.
The next phase of quantum computing will depend on bringing together advances in physics, engineering and computer science to cross the barrier from lab prototypes to useful computers.
There are many challenges ahead, from scaling the hardware and performing error correction at scale to finding the right applications to push for. The clear message is that hybrid quantum-classical systems are not a temporary stage in this journey. They are the foundation on which practical quantum computing will be built.
Enterprises and IT leaders exploring quantum shouldn’t focus on the number of qubits, but on how systems integrate with classical infrastructure. When it comes to scaling useful quantum computing and implementing hybrid computing, the control systems, error correction and software orchestration will determine whether a system can deliver reliable performance and repeatable results.
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- The Jura E10 is a new premium espresso machine from the Swiss brand
- It offers hot and cold brewing, syrup-infused foam, and Light Brew mode
- It's available now for £1,425 (about $1,910 / AU$2,720)
Jura, known for its luxury espresso machines, has just launched a new coffee maker that packs a staggering array of advanced brewing features into a surprisingly compact package. The new Jura E10 offers hot and cold brewing, syrup-infusion for flavored drinks, and a new timer that means the machine can start brewing when you wake so your favorite coffee is ready and waiting the moment you wake up.
The feature set sounds very similar to that of the Jura J10 I tested last year, which is still one of the most advanced coffee makers I've ever used. It poured a perfectly-extracted espresso, and its cold-brewed espresso was mellow and flavorful.
Its milk system was excellent as well, whether using hot or cold milk, but what impressed me most was the Sweetfoam system that mixes the syrup of your choice into the milk as it's dispensed.
Flavored coffees are usually made by pumping syrup into the cup before adding coffee and milk, which means the flavor is concentrated at the bottom. With Sweetfoam, the syrup is evenly distributed throughout the whole drink, which means you get the full flavor with every sip, and only need to use a very small quantity. You can use regular or sugar-free syrup, and during my tests I found it had the added benefit of stabilizing the milk foam so it lasts and lasts.
Perfect for small kitchensLike the J10, the new E10 offers a huge menu of hot and cold drinks, but now they're organized into a color-coded menu that makes it easier to find the one you want.
These options now include Light Brew — a new feature not available in the J10 — which uses a smaller dose of coffee and a lower brewing temperature to prepare a drink with a less intense flavor that's ready to enjoy straight away.
The coffee-timing feature means the machine will start brewing your coffee automatically exactly when you want it to — a feature you'd normally only see on drip coffee machines. However, it's worth noting that this is only available if you also pick up the Jura Wi-Fi Connect V2 module, which adds wireless connectivity to your coffee machine and costs an additional £49.95 (about $70 / AU$100).
All of these brewing functions are packed into a surprisingly slim package that will slot more easily into small kitchens:
Dimension
Jura J10
Jura E10
Width
12.5 inches / 31.8 cm
10.6 inches / 27 cm
Height
13.7 inches / 34.8 cm
13.7 inches / 34.8 cm
Depth
17.6 inches / 44.6 cm
17.6 inches / 44.6 cm
The Jura E10 is available now direct from Jura and a handful of third-party stores for £1,425 (about $1,910 / AU$2,720). It's currently only sold in Europe, but the rest of the E range is available in the US, so I'm expecting it to launch there in the coming months.
- Intel's data center business has seen tremendous growth thanks to ongoing AI demands
- Layoffs would likely target operational efficiency over division reduction
- Share prices and quarterly earnings are back to being in a good place
Intel has reportedly warned workers in its Data Center Group that another round of layoffs could be coming soon, adding to the major layoffs that already came in 2024 and 2025.
The company has not disclosed the number of jobs being eliminated, when they would leave, or any other finer details, but Oregon Live reports that the restructuring would reduce organizational complexity and allow the division to operate more quickly.
However, the changes still come as a surprise, with the Data Center Group currently one of Intel's stronger-performing businesses.
Intel set to lay off data center business workersMore broadly, warnings of sector layoffs come as more of a shock with data center hardware in hot demand and supply chains under immense pressure.
"As part of our broader strategy to become a more focused and efficient company, (the data center group) is aligning its organization to ensure it has the right roles and skills in place to position the business for long-term success," the company told Oregon Live.
New CEO Lip-Bu Tan had already warned of major workforce restructuring in a drive for organizational efficiency.
This comes against a healthy backdrop for Intel, with first-quarter revenue for the data center division up 22% year-over-year. In fact, Intel's company share price has recovered dramatically in recent months following support from the US government. From lows of under $20 throughout parts of 2025, company shares have since grown around 5x to $105.45 at the time of writing.
That growth is set to continue, with Intel set to announce its second-quarter results on July 23 2026, targeting revenue of $13.8 billion to $14.8 billion, up from $13.6 billion in its first quarter.
- RAM pricing in Germany has suddenly gotten a lot steeper
- The price of DDR5 memory is now 448% more costly than a year ago
- On top of that, TSMC is reportedly set to increase its chip production prices by up to 10% next year, which will mean more expensive CPUs
The RAM crisis appears to be getting considerably worse right now, based on memory pricing in one country anyway — and we've also had some bad news from a big chip maker.
First off, RAM pricing in Germany is what's been under the microscope courtesy of 3D Center (via Wccftech), which regularly takes the temperature of the market in the country, finding that July has seen a notable price hike.
This is for DDR5 RAM at retailers in Germany, and prices are now at an incredible 448% more costly than a year ago (before the memory crisis kicked off). Pricing had been at 440% at the start of 2026, but then dropped to 408% in March, and stayed at that level in April, before climbing back to 419% in June.
However, this latest move to 448% represents a bigger leap, and an all-time high for the German market.
As for the bleak news from the chip maker, that's TSMC which is going to raise the cost of its chip production by up to 10% next year, according to a report by Nikkei Asia (as flagged by PC Gamer).
The reason for TSMC having to hike prices is said to be the rising cost of materials and the equipment for manufacturing chips, as well as the need to fund expansion plans for more fabrication facilities (or fabs, as they're known).
Bigger premiums will be lumped onto those ordering high-end chips (high-performance computing or HPC silicon) from TSMC off the cuff (on top of existing orders), and in these cases there could be up to a 15% hike.
Analysis: hiking misery(Image credit: Shutterstock)The good news here, such as it is, is that TSMC is reportedly waiting until 2027 before implementing these price hikes. They could have come in sooner given the climate we're currently facing, although we should remember, this isn't official, and it's just based on what Nikkei Asia has heard from its sources. But it's all too believable, and indeed arguably inevitable that further chip price hikes are coming.
What this means is that the likes of processors – or anything with a CPU in it – are likely to get pricier next year, because manufacturers who are getting their chips from TSMC will, naturally enough, pass any cost increase (or most of it) on to the consumer. And TSMC supplies a lot of the tech industry's needs, Apple and Nvidia included (as well as AMD and Qualcomm).
Regarding the RAM situation in Germany, this is just one country, of course, but it's generally reflective of what a lot of chatter is saying – that the second half of 2026 is going to bring further price rises, and that next year is likely to be even worse. That's the broad theory floated by analysts and chip makers themselves in many cases, and the German market is simply backing this notion up.
Of course, there's not much that can be done about this, except perhaps to consider buying now rather than next year. Or at this point, you may want to look at Black Friday as a good upcoming opportunity to pounce on a hardware purchase like a PC or laptop, and that's the other thing to bear in mind here: how you proceed depends on what hardware you're looking at purchasing. I've discussed this recently in some depth, after Nvidia's CEO commented on the RAM crisis and how it's not going to be over until perhaps 2030.