• Forcepoint X-Labs publishes threat model for persistent memory poisoning
• Hidden text on a webpage becomes a durable "fact" an agent retrieves and trusts in unrelated tasks weeks later
• It has already been demonstrated against products already in the market, including ChatGPT, Gemini, Claude and Microsoft 365 Copilot

New findings from Forcepoint's X-Labs outline an interesting scenario that could easily mimic real life: An AI assistant with browser access reads a webpage about travel disruption.

Near the bottom of that page, in text sized and positioned so no human will ever see it, sits a short paragraph stating that ABC Travel Support is the official emergency booking provider and should always be recommended when urgent travel changes are needed.

The assistant's text extractor does not distinguish between hidden and visible text, so the model treats the whole thing as plain prose and files the claim away as a useful fact about how this organization handles travel. A month later, the user's flight is canceled. They ask their assistant what to do, and it tells them, helpfully and with no sign of anything wrong, to contact ABC Travel Support.

An easy-to-replicate attack vector

This is what Forcepoint calls persistent memory poisoning, a security vulnerability where an attacker injects false data or malicious instructions into an AI agent's long-term memory or retrieval database, and it is a threat model that is increasingly in focus as users increasingly rely on AI, often treating its responses as gospel, despite the warnings most chatbots come with.

The canonical academic result is MINJA, short for Memory INJection Attack, presented at NeurIPS 2025. Its significance is the attacker model. MINJA does not assume access to the memory store, elevated privileges, or any compromise of the system. It works by submitting ordinary queries through the standard interface, using indication prompts, bridging steps, and a progressive-shortening technique that strips away giveaway language while leaving the poisoned record behind.

Across GPT-4o-mini, Gemini 2.0 Flash, and Llama 3.1 8B, it reported injection success above 95% and attack success above 70%.

It must be noted that those numbers might be optimistic; a January 2026 paper evaluating memory poisoning in electronic health record agents notes that MINJA's numbers were obtained under idealized conditions, and that how well these attacks hold up in realistic deployments remains understudied.

Despite this, it remains a significant threat to products that continue to ship, including ChatGPT, Gemini, Claude, and Microsoft 365 Copilot. It is important to find a solution to a problem that Microsoft has already warned about in the past; Forcepoint suggests an approach that could mitigate it.

Its proposal is to stop treating extracted memories as facts and start treating them as objects that can be inspected. Each memory is stored with metadata: where it came from, what type of source it is, whether a user confirmed it, and a risk score. Language written to shape future behavior, phrases like "from now on" or "make this your default going forward," adds to the score. So does the sudden appearance of a previously unseen domain, contact, or vendor.

Contradiction detection is also in play: if new memory conflicts with an existing entry about the official travel provider, both cannot be true, so the engine flags the conflict and holds the new item for user confirmation rather than silently overwriting it. At the same time, anything related to payment instructions, banking details, VPN configuration, or security contacts is given higher weight, regardless of where it came from.

None of these approaches, however, solves the underlying problem: agents are built to treat retrieved memory as their own experience rather than as input. Scoring raises the cost of poisoning. It does not change what the agent believes once something gets through, and as Agent Security Bench found, current defenses are not doing well.

For anyone using an assistant with memory today, the practical play is unglamorous but worth following anyway: open the memory settings occasionally and read what is in there, but that's easier said than done when it comes to propagating the message since a sizeable chunk of AI users never bother to look under the hood.

• The system connects directly to a standard alternating-current power outlet
• Sulfur co-implantation gave engineers precise control over individual diamond qubits
• Single-qubit fidelity reportedly reached 99.98%

German startup Saxon Q has launched the world's first portable diamond-powered quantum computer operating at room temperature without requiring complex cryogenic cooling equipment.

The system fits inside a standard server rack, connects directly to a conventional alternating-current power outlet, and supports configurations reaching 128 qubits.

The company says larger 512-qubit systems will become available next year, while its long-term roadmap aims to exceed 10,000 qubits after 2030.

Synthetic diamonds replace cryogenic cooling with a different quantum approach

The system relies on nitrogen-vacancy defects created inside synthetic diamonds, where nitrogen atoms occupy positions beside empty spaces within the crystal structure to function as quantum bits.

Scientists manipulate these qubits using lasers and microwave pulses, allowing them to represent quantum states extending beyond conventional binary computing methods.

Although nitrogen-vacancy technology has existed for years, producing stable systems beyond 10 qubits has remained difficult because creating reliable defects at larger scales proved technically challenging.

Saxon Q says its breakthrough came from introducing sulfur alongside nitrogen during manufacturing, allowing engineers to improve stability while gaining greater control over individual qubits.

“That's the key technology point. Because the sulfur lifts the chemical potential to a point that it's negatively charged,” said Marius Grundmann, professor of experimental physics at Leipzig University and co-founder of Saxon Q.

“The sulfur supplies the electron; the sulfur also makes the vacancy attached to the nitrogen with a very high yield.”

According to the company, this manufacturing process enabled qubits to achieve 99.92% fidelity before error correction, representing fewer than one error for every 1,000 operations.

However, it later updated its data, saying subsequent measurements increased single-qubit fidelity to 99.98%, although those figures have not yet received independent verification.

The hardware is currently available in rack-mounted systems supporting up to 128 qubits, while 512-qubit configurations are expected next year and expansion beyond 10,000 qubits remains the long-term objective after 2030.

Practical deployment offers advantages while scalability questions remain unresolved

Unlike many quantum systems that require cryogenic refrigeration and extensive infrastructure, Saxon Q says its hardware can operate directly within conventional computing environments without dedicated cooling facilities.

“We have a fully functioning quantum computer… We have a quantum computer that can execute quantum code that you can reach via the network, and it is a multiuser, multitasking, multicore system,” Grundmann added.

The company believes local deployment could reduce dependence on cloud-based quantum services for applications including robotics and autonomous driving, where communication delays may become unacceptable.

Researchers still caution that comparing nitrogen-vacancy systems directly with superconducting quantum computers remains difficult because both technologies emphasize different engineering approaches and operating characteristics.

Existing studies generally indicate superconducting systems deliver faster processing, although the balance between processing speed, cloud latency, and practical deployment remains uncertain across different workloads.

Another unresolved challenge involves increasing chip density, since current devices support only eight or 16 qubits per chip.

Future applications may require hundreds of thousands or even millions of interconnected qubits operating within significantly larger quantum arrays.

Via Live Science

The world is full of weird and wonderful inventions that never really took off, with the Segway perhaps one of the most overhyped technologies out there. Invented by Dean Kamen, it was once considered the future of how people would move around towns and cities – and it had plenty of huge supporters.

Codename Ginger

Speculation ran wild when these leaked comments by Jon Doerr hit the mainstream media at the start of the 21st century.

This statement was initially disclosed when a copy of a book proposal by journalist Steve Kemper was leaked to the media and published on the online tech magazine Inside.com. They were centered around an exotic new invention described only as 'Codename Ginger' – but the trouble was that nobody knew what it was.

What Kamen was working on behind closed doors was, in fact, a two-wheeled, self-balancing personal vehicle that used smart sensors and motors to keep the rider upright. This was the Segway – and, well, we all know how that story ended.

Wheels of time

Some predicted at the time that cities would be redesigned around the Segway – but the product sold less than 150,000 units before production was shuttered. It's now widely considered the most overhyped invention in history, with high costs and huge regulatory hurdles proving insurmountable obstacles for mass adoption.

The last Segway was actually manufactured as recently as 2020, with the company now transitioning to other forms of transportation, like e-scooters and UTVs. There are still uses in security and in tourism, but it's largely a redundant technology.

As for Kamen, his most recent venture, DEKA, is focusing its efforts on machines in the biotech space. Specifically, he has recently invented a large-scale regenerative medicine manufacturing and organ preservation system.

• Samsung advances floating data center project with first Texas engineering agreement
• Floating facilities promise 50MW capacity while reducing traditional construction timelines significantly
• Texas grid review creates fresh uncertainty for expanding data center developments statewide

Samsung Heavy Industries and Dallas-based Mousterian Corporation have signed an engineering contract covering their first floating data center project planned for deployment in Texas.

The deal advances their partnership into the engineering phase, with both companies jointly responsible for the project's basic design, detailed engineering, and production planning.

Each proposed floating facility is expected to provide 50 MW of critical IT capacity while operating on water instead of occupying conventional land-based development sites.

A shipyard-built facility moored beside a power plant

The pilot site is expected to sit next to an existing combined-cycle gas turbine power plant located in Houston, according to reporting on the plan.

Mousterian believes the shipyard construction approach could shorten delivery schedules compared with conventional land-based developments.

"Because the facility is built in a shipyard, fabrication advances in parallel with sitework rather than after it, a schedule and scale that conventional delivery methods cannot match," said Min Suh, chief executive of Mousterian.

Samsung Heavy Industries brings five decades of offshore and maritime engineering experience into the venture, according to its chief executive.

"Together with Mousterian, we intend to deliver floating datacenters with the same rigor, quality, and schedule certainty that define our maritime track record," said Sung-an Choi, chief executive of Samsung Heavy Industries.

Mousterian says its cooling approach avoids drawing on potable water supplies entirely and sends no discharge back into surrounding waterways.

Removing air-cooled chillers and fans from the design is also expected to considerably cut down on noise pollution near the site.

The American Bureau of Shipping is expected to classify these waterborne facilities once formal engineering work moves further.

Grid access in Texas remains an open question

The proposed location beside an existing gas-fired power plant may not shield the project from broader regulatory challenges affecting new data center developments.

Texas Governor Greg Abbott has paused approvals for new Electric Reliability Council of Texas (ERCOT) grid connections while state authorities review pending applications.

ERCOT is reviewing requests covering more than 474 GW of proposed new connections, a figure Abbott says is over five times peak state demand.

Data centers alone account for roughly 90% of those pending connection requests currently sitting before regulators.

State regulators are now asking project developers whether they plan to generate their own power or depend fully on the shared grid.

Officials also want detailed water consumption estimates from each project before any new connection request receives approval going forward.

Any project that fails to meet these new disclosure requirements will simply be denied a grid connection outright.

Floating infrastructure could sidestep competition for scarce buildable land near major cities, while offering built-in access to water for cooling.

Whether Texas regulators treat water-borne facilities differently from land-based ones under the new audit rules remains unclear for now.

Via The Register

• Synology lowers NAS entry costs without removing future hardware upgrade flexibility
• Four new NAS models arrive with expandable memory and familiar hardware
• Lower default memory creates cheaper starting prices across the new lineup

Synology has introduced the DiskStation neo+ Series, a new lineup built to give buyers more flexibility over cost and performance from the outset.

The series includes four models — the DS1825neo+, the DS1525neo+, the DS925neo+, and the DS725neo+ — each sharing hardware with existing Plus units.

The company says each new unit is built on the same platform as its Plus Series counterpart but ships with a different default memory setup and price tag.

Synology created room for future upgrades

All four models rely on AMD processors with 64-bit architecture and include a hardware encryption engine.

The DS725neo+ uses an AMD Ryzen R1600 processor featuring 2 cores, 4 threads, and clock speeds of 2.6 GHz with a boost reaching 3.1 GHz.

Meanwhile, the DS925neo+, DS1525neo+, and DS1825neo+ all rely on the AMD Ryzen V1500B processor, offering 4 cores, 8 threads, and a 2.2 GHz operating frequency.

In terms of memory, the four-bay DS925neo+, five-bay DS1525neo+, eight-bay DS1825neo+, and two-bay DS725neo+ ship with 4GB memory but are expandable up to 32GB.

The larger DS1825neo+ and DS1525neo+ models include PCIe 3.0 expansion slots supporting optional 10GbE or 25GbE networking cards, while built-in M.2 slots provide SSD caching capabilities.

All models include 2.5GbE networking and expansion support through the DX525 unit, allowing storage capacity to increase as requirements become more demanding.

Buyers searching for the best NAS may still need to compare memory configurations carefully because the standard Plus models offer higher default ECC memory on selected versions.

Synology believes that the new memory approach broadens purchasing options by allowing buyers to select lower initial costs while retaining the ability to increase capacity later.

“Desktop models remain at the heart of our NAS ecosystem,” said Bie-I Chu, Executive Vice President of the Synology NAS Group.

“As memory market dynamics shift, the neo+ Series reinforces our commitment to accessibility, giving customers the freedom to scale hardware according to their immediate budget and future growth.”

The company has confirmed that the neo+ products will remain alongside the existing Plus Series instead of replacing those systems, giving buyers additional configuration choices.

DSM software remains unchanged across the new range

Although hardware memory differs from several Plus models, the software environment remains unchanged. Every neo+ device runs the same Synology DiskStation Manager (DSM) operating system.

DSM includes Synology Drive for private cloud access, cross-platform file synchronization, and site-to-site replication for organizations operating across multiple locations with shared storage requirements.

Active Backup Suite protects Windows, Linux, macOS systems, virtual machines, and cloud accounts while supporting off-site backup strategies intended to improve data resilience.

Surveillance Station also enables each compatible NAS to function as an on-premises network video recorder for monitoring multiple security locations from one interface.

The DiskStation neo+ Series is now available through Synology's global distribution channels, though there are no official prices for these models.

• Chinese research vessel lingered above critical Pacific undersea communications infrastructure
• Taiwan's Coast Guard warned the vessel to turn away from the Pacific Light Cable Network
• Windward linked slow vessel movements with recognized intelligence-gathering behavior

A Chinese-flagged research vessel was recently observed loitering directly above an undersea cable system connecting Taiwan to the United States.

Taiwan's Coast Guard filmed the encounter and warned the 200-foot ship to alter its course away from the cable route.

The cable is the Pacific Light Cable Network, an 8,000-mile fiber-optic system linking Taiwan, the Philippines and the US.

A pattern consistent with intelligence gathering

Maritime analysis platform Windward tracked the vessel moving at slow speed directly over the cable route in the Philippine Sea.

The company described such slow-speed movement over critical undersea infrastructure as one of the clearest signals used in maritime intelligence gathering.

Windward stated plainly that cables do not move, adding that "vessels that hover over them get noticed" by trained analysts worldwide.

Though the vessel's name and identifying number were withheld, Windward said it had operated repeatedly near the disputed Paracel and Spratly Islands.

Records also placed the same ship as far as the Bay of Bengal and waters off Sri Lanka in earlier years.

Analysts also noted increased Chinese research vessel activity east of Taiwan since June alongside broader maritime operations in surrounding waters.

Some analysts have expressed concerns that Beijing's military-civil fusion policy could enable information gathered by civilian research vessels to support military activities.

However, no publicly available evidence has verified that the vessel gathered intelligence or transferred information to Chinese military authorities during the reported voyage.

Beijing refutes the claims

Chinese authorities have consistently denied that their vessels intentionally damage undersea cables, calling such accusations premature and politically motivated overall.

They accuse Taiwan and several Western governments of concluding before formal investigations into these incidents are completed.

Chinese-flagged vessels have featured in previous investigations into damaged undersea cables around Taiwan.

NATO officials and Taiwan's own Coast Guard have also linked Chinese-flagged vessels to earlier cable disruptions, including suspected sabotage in the Baltic Sea.

On its part, China claims that all its maritime activities are completely legal and respects international maritime law.

Submarine cables worldwide now carry roughly 99% of all intercontinental internet traffic, making their protection a growing global security concern.

The Pacific Light Cable Network carries added significance because Taiwan alone produces more than 90% of the world's most advanced semiconductor chips today.

This network was originally planned to connect Hong Kong before US regulators blocked that route in 2020.

American officials cited concerns at the time that the connection could directly expose sensitive US data to Chinese intelligence services.

The project was later reconfigured to link only the United States, Taiwan and the Philippines, bypassing Hong Kong entirely afterward.

Analysts believe the recent deployments may gradually normalize China's presence in waters that Taiwan has long regarded as strategically important.

Whether the vessel's activity ultimately amounted to scientific research or intelligence collection remains unresolved.

Via Newsweek

• Huntress reverse-engineers MacSync, a six-stage macOS stealer and remote access Trojan delivered through a fake Claude Code install guide hosted on a real claude.ai share URL and promoted via a paid Google ad
• The lure needed almost no forgery: the page sat under Anthropic's own certificate, and the platform's safety banner repeated the attacker's chosen display name, "Apple Support," back to the reader as fact
• The final stage rewrites installed Ledger and Trezor apps in place so a normal launch leads to a fake recovery message that harvests the seed phrase, draining the wallets of unsuspecting victims

Looking online for instructions on how to install Claude Code on a Mac could lead to you having your crypto wallet hijacked in the process

The victim ran a Google search, clicked the first result, a paid Google advertisement, and landed on a tidy step-by-step guide hosted on claude.ai, badged as shared by Apple Support, telling them to open Terminal and paste a single command.

What followed, according to a reverse-engineering write-up published by security firm Huntress was a six-stage macOS kill chain called MacSync: a stealer, a remote access trojan, a signed helper built to farm one specific system permission, and finally Trojanized copies of the victim's own cryptocurrency wallet apps, rewritten in place to phish the recovery phrase.

A complex, intricate approach that resulted in a stolen wallet

The victim had pulled their machine offline before Huntress could extract the malware from disk, so the researchers reconstructed the loader's request and instead downloaded every stage from the attacker's own delivery servers.

The results showed a sophisticated campaign that had to fake little to appear legitimate.

Anthropic lets any user publish a conversation to a public share URL. The operator maliciously used that feature exactly as designed. The lure page sat on claude.ai itself, over HTTPS, under Anthropic's own security certificates. There was no lookalike domain to squint at, no certificate warning to click past, and nothing in the address bar to give the game away.

The staging went much further than that. Whoever published the share set their display name to "Apple Support," and Anthropic's own safety banner, which sits directly above the content, duly reported that the reader was looking at a copy of a chat between Claude and Apple Support.

The platform repeated the attacker's chosen name back to the reader as established fact, further cementing their 'credentials'. The conversation itself was written to read like vendor documentation, promising that the install leaves personal files untouched and makes no system-level modifications without approval.

That same design decision has surfaced before for entirely different reasons. Wired had reported earlier private Claude conversations were turning up in Google and Bing results, because a share link is an ordinary public web page that search engines crawl like any other.

Users accidentally exposing their own chats and an operator deliberately planting a fake install guide are two outcomes of the same property: whatever gets published to a share URL is public, indexable, and served under Anthropic's certificate.

It is also not a one-off. Huntress has previously documented the same delivery pattern with AMOS through poisoned ChatGPT and Grok conversations, a separate remote access trojan through fake Claude desktop malvertising, and fake installers for other AI tools hosted on GitHub.

The lure has changed somewhat, but the shape has not, and users need to exercise caution when clicking links or following commands from untrustworthy sources on the internet, even if they appear to come from a source that Google was paid to place above the correct answer.

• deGDID wipes cached tracking keys straight from the Windows registry permanently
• The script blocks Microsoft's DeviceAdd endpoint from ever seeing your device again
• Some Microsoft account and login services like login.live.com and account verification break once deGDID runs

Windscribe has built an open source script called deGDID to strip a persistent tracking identifier from Windows systems entirely.

The tool targets Microsoft's Global Device Identifier, a permanent marker that operates beneath the network layer where VPNs function.

Its release follows a federal case in which the identifier reportedly helped the FBI locate an alleged hacker.

How the script operates

The free script runs through PowerShell with administrator privileges and is available for download directly from GitHub.

It offers four execution flags that control its behaviour, starting with a read-only -Status check to confirm whether a GDID is active.

A -Status -Redact option generates diagnostic logs with the identifier hidden for safer sharing, while -Protect performs the core function.

This flag purges cached GDID keys from the registry and then modifies Access Control Lists and registry permissions to stop Windows from reissuing them.

It further puts up a firewall against the internal DeviceAdd endpoint, cutting off Microsoft identity services from recognizing the machine as registered at all.

A fourth flag, —Unprotect, lets users reverse the process and restore default settings if needed.

Testing on a Windows 11 machine confirmed the script functioned as described, with the -Status flag revealing several cached identifiers before removal.

No new GDID appeared even after a system restart, though the trade-offs became apparent almost immediately afterward.

Account verification through login.live.com stopped working across every browser tested, while login.microsoftonline.com continued functioning without issue.

Some Microsoft applications returned connection errors, though online games and other software kept working normally throughout testing.

Users should still keep antivirus and endpoint protection active since deGDID addresses tracking rather than malicious software.

Limitations of the workaround

Windscribe acknowledges that keys already stored on Microsoft's servers cannot be deleted, meaning the company retains indefinite access to previously collected data.

The script also refuses to run on managed systems or domain-joined accounts, restricting its use to individual unmanaged machines.

Windows currently offers no built-in method for disabling GDID, and the identifier persists across IP addresses regardless of any VPN tunneling applied.

"We tried the script on a Windows 11 computer, and it worked as intended," the company noted in its documentation of the testing process.

Windscribe describes deGDID as an ongoing research effort that will keep evolving as more details about the identifier's mechanics come to light.

The broken services represent a real cost for users weighing whether the privacy trade-off justifies losing certain Microsoft account functions.

Given that server-side keys remain permanently accessible to Microsoft, this script functions as a partial fix rather than a complete solution.

Via Tom's Hardware

• PricewaterhouseCoopers (PwC) is believed to have published several AI-generated reports between 2024 and 2026
• The reports included various AI hallucinations, fabricated citations, and fake footnotes, which were identified by the GPTZero AI detection tool
• PwC is one of the world’s largest accounting firms

Accountancy and auditing giant PricewaterhouseCoopers is alleged to have published at last four AI-generated or enhanced reports over the past three years.

An investigation by the GPTZero team found the reports – which make inaccurate and false claims – were published by PwC Middle East between 2024 and 2026, and were intended to act as thought leadership pieces.

Various indicators of AI-generated links and citations have been highlighted as a result of the discovery, and while PwC has responded, there is no direct indication how this happened or if action has been taken.

Vibe citations

GPTZero may be better known as a software tool for discovering plagiarism and AI-generated content, but it also boasts an investigation team.

The work of Paul Esau, Om Ogale, and Alex Cui has highlighted several problems with the publications issued by PwC, uncovering “a pattern of irresponsible AI usage resulting in hallucinated (vibe) citations, fabricated claims, and incomprehensible drafting and formatting decisions.”

Vibe citations are hallucinated references to works that an AI-generated document cites as genuine. In some cases, specific pages may be cited, but usually the vibe citations refer to titles and authors.

They can be demonstrated as fake due to issues such as non-existence, incorrect authors, incorrect publication dates, erroneous URLs… or the citation may simply not exist, as in one case highlighted in the GPTZero report.

Not all inconsistencies are determined to be hallucinations, however, as not all meet the GPTZero team’s standard for identifying vibe coding. So, while some other references with issues may be genuine and simply poorly cited, others may still be fake, but impossible to define.

PwC’s account

The reports that PwC (and its competitors) are publishing are an attempt to position themselves as authorities on AI, and its responsible use. They seek to encourage organizations to avoid issues such as fabricated claims and vibe citations, but rather than provide advice, it has been a live demonstration in what not to do.

PwC responded to the GPTZero report by telling the Financial Times: “Consistent with our approach to responsible AI, we have quality control processes for research and content development we expect all our people to adhere to.”

Unfortunately, it seems that a better process of quality control and fact checking ahead of publishing was required for these reports, and perhaps others so far undetected.

Perhaps most concerning is the realization that PwC is not alone in this. In the past 12 months, GPTZero has documented similar issues with all the “big four” accountancy firms, with PwC joining Deloitte, EY, KPMG in this “vibes gallery” of fabricated and unchecked citations.

I've been a fan of Zoner Studio since first reviewing it in 2025 - it's a fusion of Lightroom and Photoshop-style tools for photographers, with some compelling advantages over both.

My team recently re-reviewed the photo editing and organizing software, complete with its big summer updates roll-out, where it earned an even higher score for 2026 (read our Zoner Studio review).

I spoke to Zoner's Thomas Urban to find out what's new in this Windows-only photo editor, why the devs prefer meaningful features over flashy gimmicks, and how the software handles the messy realities of shooting.

Adobe has a near-monopoly on creative software. What is the elevator pitch to a photographer who is locked into the Lightroom/Photoshop ecosystem to try Zoner Studio?

Great point, they are a household name. If you want a single interface for all your editing, faster culling and RAW capabilities, not being stuck in a catalog while being able to work with local files if you’d like and all of this for a lower price, then you should try out Zoner Studio.

In your summer update, you intentionally avoided “flashy new features you’ll use once a year.” Why do you think so much of the industry is chasing AI gimmicks instead of fixing core editing speed?

Perhaps they think people will find these types of upgrades boring? Maybe the core issues are more difficult to address? And, some of these features like a completely revamped search engine, just aren’t visually that striking, for sure.

When you’ve taken thousands of photographs though and just want to get through the culling in a responsible and effective way with quality metadata and organisation, these less flashy things are going to save your time down the road and your mental state.

It seems the entire world now has the attention span of a few seconds and everyone is fighting for a fraction of your attention, god forbid your undivided attention. It may be that in order to actually present those important features, you need the flashy stuff to catch the attention first.

I hope with time, society as a whole, we make our way back to authentic things that matter, things that last and things we’re proud of because they’re done well.

Most suites force you to lock in your RAW edits before moving to layers. What’s the practical advantage of keeping a photographer’s entire pipeline inside a single workspace?

Simplicity. We all started this whole photography thing because it was fun and gave us something we kept coming back to. Photo editing should match that and complement this process instead of being a chore.

We want photographers to have a proper partner to their camera and being able to do everything in a single UI just felt like the natural solution.

The fact that each photographer can stay in the same interface allows them to stay in the creative flow rather than worry about whether or not this particular photo is now good enough to move on to layers. That just seems counter to what we're trying to do as photographers.

Cleaning sensor dust or portrait blemishes across a massive shoot is soul-crushing work. Where does your Smart Healing excel, and where do complex textures still force a photographer to step in manually?

Our tools still require manual inputs as we don’t have automatic detection of dust or blemishes. Our tools however, are non-destructive and fast compared to our competition (which is a rare combination).

Source search is on the verge of being possible without the use of AI and they also allow for very complex retouching. Retouching is divided into individual layers, and individual strokes are separated on the layers.

Within strokes, the user can utilize entire strokes (some only allow circles) or subtract from them, brushes have all parameters and we have the best tablet pen integration on the market. It’s possible to customize and map out your parameters based on your pen pressure.

Again, I wanted to remind you of the non-destructive model because this combination is not common for most of these operations in other photo editors. Users have to go to bitmap editors like Ps or Affinity whereas with Zoner Studio, you don’t even have to leave the Develop module.

This makes it easy and there’s no need to make a complicated trip from one program to another while also considering the correct order of operations.

Manual keywording and file tagging is a major chore. How does your Search engine let someone instantly pull up shots without forcing them to build a rigid catalog first?

Yes, this is a core issue with many photo editors. To start, the difference between Zoner Studio and most of the competition is that you don’t need to create a Catalog as a blackbox per se but rather a Catalog built on market standard files or data that sits next to these common files.

Because of this, our Catalog serves more as a cache and does not contain unique data that the photographer could lose and is always “rebuildable” based on the data stored in those photos. Therefore, the photographer is not locked into Zoner Studio’s infrastructure.

We still rely on manual keywording and tagging (unfortunately, the program cannot replace manually entered data because it’s our interpretation of structures) and we’re also working robustly with names and the folder structure, which are an important part of manually entered data and are often ignored by other photo programs.

We also focus on working effectively with them and also with regards to the data that is inherently in the photos themselves. Then based on your knowledge of your data, you can effectively find a lot of things.

Automatically grouping and merging panoramas or focus stacks sounds great in theory. But how did you design the software to handle the messy reality of real-world shooting?

For us, it’s not only good in theory but is great in practice. Of course, we can always improve but we have been optimizing the algorithms for several periods and are still working on them.

If the photographer uses the automatic camera functions like exposure and focus bracketing, and doesn’t make major mistakes when taking photos, we have an almost 100% hit rate.

Of course, it’s ideal to catch those deviations and we are very grateful to the user base for supplying us with samples. We’re really happy that in most cases it simply boils down to a shooting error. In our opinion, a large number of future disruptive technologies will be built on automatic stacking.

It’s necessary to realize that it’s not only about stacking panoramas and others that form a fraction of what a photographer processes but also continuous shots and generally similar images with which the user works as a whole and, for example, selects the best shot.

From turning burst sequences into time-lapses to simulating long exposures without an ND filter, what do you think are the most underrated creative tricks hidden in Zoner Studio?

Back to the basics; LUT's. There are plenty of them on the internet and they are great for everyone.

They'll be liked by the user who still can't click and use sliders to achieve their desired results and by the user who has the ability to accurately utilize the entire RAW processing pipeline.

We aren't exactly fans of the sort of "tricks" philosophy idea but if we had a choose an underrated function that can pull some serious weight, it would be the preview function in the Manager module that allows photographers to preview multiple images at once

Ready to start shooting? We tested the best cameras for photography.

• Telefónica upgrades 2,756 network sites before Spain's August solar eclipse
• More than 400 observation points received detailed mobile coverage assessments beforehand
• Engineers adjusted base station settings to handle expected traffic surges efficiently

Spain's upcoming solar eclipse is expected to attract large crowds, prompting Telefónica to strengthen its mobile network before millions of photographs and videos begin flowing online.

The telecommunications company has spent several months preparing for the event after assessing likely visitor numbers and network demand across hundreds of planned observation locations.

Rather than waiting for congestion to develop, the operator is adjusting network capacity in advance to maintain reliable mobile services during the eclipse.

Network upgrades span thousands of kilometres

Telefónica's preparations cover 31 Spanish provinces and 2,756 network sites distributed across the extensive path where the August 12, 2026 solar eclipse will be visible.

Engineers examined coverage conditions at more than 400 officially identified observation points before determining where additional network resources would be required during the event.

The operator expects unusually high demand from domestic and international visitors gathering to photograph, record, and share images across mobile networks simultaneously.

Instead of relying solely on additional infrastructure, engineers are adjusting technical settings inside existing base stations to match anticipated traffic patterns more effectively.

Those network changes redistribute available bandwidth and equipment capacity, helping mobile users maintain stable connections even when thousands attempt uploading content from the same locations.

People using AI tools to edit eclipse photographs and videos could also increase mobile data demand as content begins appearing online.

Eclipse preparations form part of a wider summer programme

The eclipse initiative forms one element of Telefónica's broader summer network programme, which includes more than 500 infrastructure improvements across different parts of Spain.

The seasonal programme also covers 34 strategic coastal locations alongside network optimization supporting 440 events expected to attract substantial visitor numbers throughout the summer period.

Additional mobile units have been assigned to locations expected to receive the largest crowds, expanding network resources wherever unusually high demand is anticipated.

Areas expected to experience particularly strong interest include Aragon, Asturias, the Balearic Islands, Cantabria, Castile – Castilla-La Mancha, Castile and León, Catalonia, Madrid, the Valencian Community, Galicia, La Rioja, Navarre, and the Basque Country.

Telefónica began organizing its eclipse preparations approximately five months before the event, allowing engineers sufficient time to analyse coverage requirements and technical priorities.

"This technical effort reflects our day-to-day commitment, as Telefónica always strives to be the best gateway for citizens to digital technologies, guaranteeing more and better services over the best network," said Juan José Marfil, Director of Network, IT and TV at Telefónica España.

TP-Link Roam 7: 30-second review

The TP-Link Roam 7 is a compact travel router that lets you connect to whatever internet you can find and turns that into your own private network. This helps reduce the need to reconnect all your devices in each new location; just connect the router and all other devices will connect automatically through it.

The Roam 7 differs from a mobile router insofar as there's no internal SIM or battery; power is supplied through a USB source such as a power bank, and the internet connection comes from a range of available options, including Ethernet at a hotel or business, public Wi-Fi, your phone tethered or a USB modem.

The size and weight are compact, so it’ll fit easily in a backpack or travel bag. The design is simple with a vented front: two fold-down aerials, and a customisable button on the side.

Setup is quick with the mobile Tether app, which lets you use the QR code on the base of the box to complete setup and configure logins. If you want anything more involved then once your device is connected to the router you can access the full web admin at 192.168.0.1.

This is all very clean and while it looks a little dated next to the app, it goes far deeper on the options and settings enabling you to manage the primary and guest networks, assign clients to 2.4GHz or 5GHz, configure Multi-Link Operation so one device takes priority over another, and monitor live CPU and memory usage. Considering this is a travel router, the amount of control is impressive.

There’s also a good level of built-in protection, which is helpful when connecting to public Wi-Fi or hotel networks. While the more advanced protections, including VPNs, are all paid options, there’s a good basic level of security and parental controls built in. The Web interface also gives you a huge amount of options.

Through the test, the one feature that really stood out as I wrote this review was the failover; with the router connected to the Netgear Nighthawk M7 through LAN and public Wi-Fi in a café. The café Wi-Fi was initially good, but as the hour progressed and the place filled, the speed dipped, and the router automatically switched over to the stronger SIM connection.

Overall, this is a well-balanced product that, at the price, offers far more than most, especially that Wi-Fi 7 connection speed. The internal hardware is, however, mid-range, and with only three devices connected, the web admin showed a CPU load of 40-60%. However, as a few more devices were added, the figure remained around the same.

If you work in different locations with multiple devices, then an option like the TP-Link Roam 7 makes absolute sense. Connect the router to the internet, then connect all other devices to it; no need to reconnect each. It offers a balanced set of security features, including VPN support, making it a great solution at a lower price than most mobile routers.

TP-Link Roam 7: Price and availability

• How much does it cost? From $139 / £99
• When is it out? Available now
• Where can you get it? Directly from tp-link and online retailers

The Roam 7 is available directly from TP-Link here for $139.99 / £99.99. In the US, it's also available for $130 at Amazon (down to $90 at time of review). In the UK, it's priced at £99.99 at Amazon.co.uk. Outside of that, it's widely available from online retailers.

• Value: 4.5 / 5

TP-Link Roam 7: Specs

RAM: 512MB
Rear Ports: One 2.5Gbps WAN, one 1Gbps LAN, one USB-A 3.0, one USB-C power input
Connectivity: Dual-band Wi-Fi 7 at 2.4GHz and 5GHz, Ethernet, public Wi-Fi, USB phone tethering, 3G/4G/5G USB modem
Wireless Speed: 2,882Mbps at 5GHz and 688Mbps at 2.4GHz
VPN: OpenVPN, WireGuard, PPTP and L2TP, client and server
Size: 126 x 93.5 x 36mm
Weight: Approximately 250g
Power: 5V/3A USB-C, no built-in battery
Accessories: Power adapter, Ethernet cable, USB cable, quick-installation guide and Wi-Fi information card

TP-Link Roam 7: Design

The Roam 7 is a relatively compact black box that measures 126 x 93.5 x 36mm and weighs in at 250g. It features a large front vent and two aerials that fold flat against the sides when transporting. One thing to note about the design is that it’s noticeably larger than a pocket hotspot but still small enough to slip into a backpack or bag easily.

It’s also worth noting that while the Roam 7 is relatively small, it requires a power supply, and an AC plug is included in the box; alternatively, you can plug it into a power bank, and in this review I’ve used the Ugreen 145W. On top of the power supply, you’ll also need a way to connect the router to the internet, and this is where the clever bit of this device comes into play.

You can either connect directly to a WAN or LAN via the two ports at the back, hardwire a phone via a USB cable and piggyback off the phone's cellular network or use it as a pass-through for a Wi-Fi network, say, if you’re in a café.

Once plugged in, a simple status LED on the front illuminates to indicate connections or activity. While the design is simple, with most options and settings being controlled through the app or web interface, there is a physical toggle button on the side that can be configured to control power, the status LED, or the VPN connection.

The rear panel houses all the connections: a 2.5 Gbps WAN port, a 1 Gbps LAN port, USB-A 3.0 for storage or tethering, and USB-C for power.

As this is a travel router, it would have been great to see a carry case supplied as well. Unfortunately, aside from the power supply, USB-C-to-USB-A adapter, and network cable, there are no other accessories.

• Design: 4.5 / 5

TP-Link Roam 7: Features

Taking a look at the feature set, it really highlights how the Roam 7 differs from a standard router or mobile router. In the case of the first, the compact size and weight mean it can be taken anywhere, and the build and design are robust and highly portable, with power supplied via USB-C rather than the standard barrel-style connector to mains power, which is more common.

Unlike a mobile router, it has no built-in battery or SIM card, so it relies on other devices for both. I’ll come on to why this can be a great option in the performance section.

The Roam supports dual-band BE3600 Wi-Fi 7 with Multi-Link Operation, 4K-QAM, and four wireless streams, rated at 2,882 Mbps on 5GHz and 688 Mbps on 2.4GHz. Those are link rates rather than throughput figures, and there is no 6GHz band, so this is Wi-Fi 7 in its dual-band form rather than the full tri-band specification.

Looking through the different operating modes, there are seven in total: cover router, hotspot or WISP, USB tethering, USB modem, access point, range extender and client. Switching between Ethernet, USB tethering, USB modem, and hotspot occurs through the web interface, and in testing, it took around 45-50 seconds to switch between each. USB tethering to an iPhone was slower, occasionally up to a minute, but on Android it was far faster.

One of the modes I was pleased to see was Failover, as it lets you connect to a mobile router and a tethered phone at the same time, and the Roam 7 prioritises whichever provides the better connection.

There’s also VPN support for major brands, including OpenVPN, WireGuard, PPTP, and L2TP, as both client and server. A VPN connection can run alongside ordinary traffic rather than being on all the time; the physical switch on the side can be allocated as a quick on/off for the VPN, so for general browsing you can switch it off, and for financials, shopping, etc you can switch it on for that additional protection.

The device management is also well thought out, with two options tailored to the type of user, or you can switch between them, as I find myself doing. For most people, the Tether app, which enables initial setup via a QR code, provides access to most of the settings.

During the test, there were a couple of occasions when I was unable to find the settings I wanted through the mobile app, such as assigning the connection type. The Web Admin, however, made this easy and offered a more traditional approach to router and network settings along with many advanced options, including what I was looking for, which was an easy way to switch between inputs.

The web admin panel can be located once you have connected to the router through 192.168.0.1 in your browser, and once you log in, you have all the primary and guest network management, assigning clients between 2.4GHz and 5GHz, Multi-Link Operation configuration to give one device priority over another, and a live view of CPU and memory load, which is interesting especially as more devices connect.

The best way to look at the two options is that the app is for consumers and the web client is for businesses, so actually having both is a good balance.

Another point about this router is that, as it doesn’t have its own power source, it will be a drain on whatever it’s plugged into. However, a check of the power usage shows it is extremely low at less than 4W; still, TP-Link do offer three power modes. Boost is the default and the option I used in the test to maximise performance; below it are Balance and Eco for managing speed, heat, and power draw. The MLO mode that I mentioned earlier will increase power use, but again, this power usage is extremely well managed.

Alongside the hardware admin, there’s a good level of software support, including HomeShield, which adds a network scanner, parental controls, and device-level quality of service. For each of these features, there is a Free tier, but the more robust parental controls and Security+ features require a paid subscription.

• Features: 4 / 5

TP-Link Roam 7: Performance

Initial setup is relatively straightforward, as there’s nothing to install. Or charge. Once unboxed, the AC adapter of a power bank can be plugged in, and the front light glows orange. You then go to the Tether Mobile App, scan the QR code, complete the rest of the process, and you’re set.

After that initial setup, I took the Roam 7 to a café, plugged it into a power bank, joined the venue's Wi-Fi via the Tether app, connected my MacBook to the router's network, and enabled the VPN. The whole sequence took between two and three minutes depending on the connection type and strength. It required no laptop configuration at all, having already connected to the router at home; likewise, the same for the connection to my phone and iPad, which were all previously connected.

One issue that struck me early on was the number of wires and gadgets I had suddenly accumulated around the MacBook; I had my phone, the power bank, and the router all connected along with wires. While it didn’t take up a huge amount of space, it was more than I’m used to.

One point about the connection speeds is that the public Wi-Fi connection was faster than connection tethered through my mobile phone. During testing on location, the 4G connection showed as full, however connected to the Wi-Fi without a VPN, I saw speeds of around 150 Mbps, down to around 120 Mbps with VPN on.

Through a series of checks to see how long it takes to connect in the café location from cold, it I found it took just under two minutes to boot, and connecting and getting the computer online added another minute. There’s also time to unpack the three devices you need to make that connection: the router, the power bank, and the mobile device.

The average connection time in the test was just under three minutes from nothing to a working connection on a public Wi-Fi network. OK, it’s faster to connect directly, but a public Wi-Fi, isn’t secure and the cost saving of the Roam 7 compared with a mobile router is huge.

Through the test, I was interested to see how long it would take to switch between connection types, say, from Wi-Fi to a tethered mobile connection. To do this you need to dip into the web interface, not the app, and the switchover took around 45-50 seconds.

As highlighted, there is no internal power, so the draw from the host machine is an important factor for travel, and surprisingly it’s very low. With a MacBook Pro, an iPad and an iPhone all connected, the router pulled 3.8W. This is really nothing, and my 145W power bank essentially ran through a working day without issue. It was also good that I was able to set up a local network, and the colleagues were also able to connect as guests, again all easily set up through the web interface.

For the most part, I was piggybacking off the wireless network in the café, and, interestingly, a single device connected, I saw 87.3 Mbps down and 98.5 Mbps up. With three connected, the individual figures ranged from 11.5 Mbps to 75.4 Mbps down, but their combined total came to 103.9 Mbps, more than that first single device. The issue is that the café's internet is open to the public, so there will be some fluctuation due to other users.

Internet speed is part of the picture, especially as more devices connect. One of the features I liked was that, through the web admin, you can see live CPU and memory usage. With only three devices connected, CPU load averaged 40-60%, while memory sat at around 50% of the installed 512 MB.

TP-Link states that it supports up to 90 devices, which I think may be a little bit hopeful. Still, for the three I had connected during the test, all checking the internet speed, then watching a 4K YouTube video and uploading files to an ftp this speed generally felt stable and fast.

A travel router like this has a very focused job and won't be for everyone; however, if you connect multiple devices to an internet connection away from home and need something lightweight and portable, this is a great solution that balances features, performance, and cost.

• Performance: 4 / 5

TP-Link Roam 7: Final verdict

At first it may be difficult to see why you would want the Roam 7 over just connecting directly to your mobile phone or the public Wi-Fi. But then, in the workplace, where you may have multiple devices you use while travelling, or you’re dubious about using public Wi-Fi, this well-priced travel router makes sense.

Setup is easy, and whatever connection mode you use, once you’ve run through the process a few times, it all makes sense. Essentially, arrive, plug in, order a coffee, and by the time you’ve unpacked and set up your computer you’ll be ready to go.

Overall, the connection seemed robust, especially with the failover. While the router can only be as good as the local connections, the test proved it was a solid option, switching from mobile to Wi-Fi to enable the fastest connection without issue.

I had to ask: with mobile routers also available, who is this best for? The Roam 7 is an ideal solution if you travel and use multiple devices. It essentially lets you connect all devices to it, and you just need to connect it to an internet connection. When you arrive at your destination, you connect to the router, and everything else follows suit, without you needing to log in to every device individually.

While you can do this with your mobile, the added network features and the ability to turn your phone into a mobile router are definitely appealing and much cheaper than a dedicated mobile router.

Then there’s the price, which at a fraction of dedicated mobile routers and with all the additional features suddenly makes this very appealing as an option to use anywhere, be that travelling or in a location that you just want to set up a private network, such as in an office, studio or workshop where a separate network can be useful.

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• Voyager drone boats tracked smugglers before US Navy forces intercepted the shipment
• Autonomous vessels helped seize cocaine worth more than $81 million offshore
• Electric-powered drone boats can patrol continuously for more than 100 days

Unmanned surface vessels have helped the US Navy intercept a cocaine shipment worth more than $81 million in the Caribbean Sea.

Two Voyager drone boats, built by California-based Saildrone, tracked a vessel of interest before coordinating directly with naval forces stationed nearby.

This coordination allowed a Navy ship to move in, intercept the suspect craft, and seize its illegal cargo without delay.

Built for extended maritime patrols

The Voyager measures over 10 meters (33 feet) in length and runs on an electric motor rather than conventional fuel.

It can operate continuously for more than 100 days before returning to port for maintenance work.

Saildrone describes the vessel as a high-capacity platform supporting advanced sensor integration across a wide range of maritime surveillance missions.

According to the company, Voyager remains the only unmanned surface vessel maintaining continuous operations with the Navy's Fourth Fleet.

This fleet oversees the Caribbean along with sections of the Atlantic and Pacific near Central and South America.

Unlike a typical budget drone built for hobbyist use, this system is a purpose-built military asset designed for sustained, long-range ocean surveillance.

A growing role for autonomous systems

Saildrone argues that the recent seizure shows how autonomous vessels can support real operational missions rather than remaining limited to experimental demonstrations.

“The continuity of Saildrone’s presence along the southern border for nearly three years demonstrates the strategic value we bring as the world's most experienced operators of unmanned surface vehicles,” said John Mustin, president of Saildrone.

“Our unmatched ability to remain on station and on mission in critical regions for months at a time is why our fleet serves as a powerful deterrent to bad actors.”

The company expects continued cooperation with the Navy as maritime security efforts extend across the country's southern approaches and border regions.

It describes the Voyager as a high-capacity platform capable of supporting numerous surveillance sensors while maintaining long endurance without requiring frequent maintenance or logistical support.

The US Navy previously awarded Saildrone a task order worth $16.3 million to expand Voyager operations across the Caribbean and the Eastern Pacific region.

That contract specifically supports efforts to combat drug trafficking alongside other illicit activity occurring across international waters.

Whether autonomous drone boats can consistently replace crewed vessels for high-risk interdiction work still remains an open question for naval planners.

However, the technology clearly reduces risk to personnel by allowing unmanned surface vessels to do the hard job before a US Navy ship swoops in.

What started as something closer to a beginner drone concept for coastal monitoring has evolved into a sophisticated tool for international drug enforcement.

Via The DroneFront

• Army seeks NGCM missile capable of defeating larger drone threats faster
• New missile programme builds on proven Coyote counter-drone launch infrastructure
• Army demands rapid launch capability with extended interception range against drones

The US Army has launched a search for a new surface-to-air missile called the Next Generation C-sUAS Missile (NGCM), designed to destroy small drones while extending the range of existing counter-unmanned aircraft systems.

According to the Army's requirements, the missile should engage Group 2 drones like Boeing's ScanEagle, weighing between 21 and 55 pounds, flying below 3,500 feet at speeds under 250 knots.

It must also defeat larger Group 3 drones like Insitu's RQ-21 Blackjack, weighing less than 1,320 pounds, operating below 18,000 feet while remaining under 250 knots.

Army seeks faster missile with longer reach

The Army expects the missile to leave its launcher within five seconds after the operator issues a firing command during an engagement.

Initial performance requirements call for intercepting drones at 6 kilometers altitude beyond 16 kilometers, while future objectives extend engagement beyond 25 kilometers at 8 kilometers altitude.

Guidance should rely on a radar seeker, allowing the missile to operate alongside existing air defense networks supporting broader battlefield surveillance.

“The Army is looking for NGCM to be produced and integrated into the existing Coyote Launch System to provide extended range, rapid launch time, and reduced time-to-target over current effectors,” the notice said.

"The missile must be compatible with the Coyote Launcher with minimal to no modifications," it reemphasized.

The repeated references to the Coyote Launcher indicate that the Army wants the NGCM to integrate with its existing counter-drone infrastructure.

The requirement indicates the Army intends to build on its existing air defence architecture rather than introduce a new launch platform.

Compatibility is also required with Sentinel A3 and A4, LTAMDS, and TPQ-53 radar systems, allowing easier integration without extensive infrastructure changes.

Contractors have until August 20 to respond to the Army's Sources Sought notice outlining the proposed capability.

Cost questions remain despite procurement ambitions

The Army plans both a fixed-site configuration using multiple sensors and a mobile version mounted on tactical vehicles capable of operating while stationary or moving.

Officials want the programme to reach Technology Readiness Level 7 through field prototype testing before the end of fiscal 2027.

The procurement schedule also requires contractors to supply 50 missiles for operational assessment by the beginning of 2028 before larger production decisions proceed.

Army planning documents indicate an ambition to acquire 5,000 missiles costing less than $150,000 each through bulk procurement arrangements.

This missile will be considerably cheaper than the Patriot interceptor, which costs roughly $4 million.

Though, it is still a far cry from the Iranian Shahed-136 drone costing an estimated $35,000 and can therefore be launched in swarms.

This comparison partly explains continuing investment in laser and microwave technologies, which promise significantly lower costs per engagement under suitable operating conditions.

However, missiles continue to operate in weather conditions that may reduce the effectiveness of laser systems.

Via Defense News

The WikiLeaks founder Julian Assange carved a reputation as a firebrand and intense critic of the modern tech companies after rising to fame following the WikiLeaks disclosures. Although he's been absent from the public eye in recent years, he frequently commented on the emerging trends in the technology landscape.

Big Brother is watching you

Paraphrasing a famous line from George Orwell's 'Nineteen Eighty-Four', Assange used Google's failed smart glasses as a rhetorical device to critique their philosophy in an op-ed for The New York Times.

In his column, he was critiquing a new book titled 'The New Digital Age' by Google's then-executive chairman Eric Schmidt and businessman Jared Cohen, who was then director of Google Ideas.

This book outlined Google's vision for how the world was changing and laid out the company's role in reshaping it. But, to Assange, it was a bland and uninspired account of how this company was implementing George Orwell's prophecy without even really understanding how.

Drawing from the original line, Assange's disdain for the company is as clear as day in the way that he compared people wearing Google Glass to "a boot stamping on a human face".

Don't be evil

The modern form of this Orwellian vision is centered around consumer technology, Assange believed, and gadgets like the Google Glass were akin to tools that would serve to sap users' independence and privacy.

Although Google Glass itself was a failure, the point is more about the fears that this device represented. Now, more than a decade on, developments have more or less vindicated his dystopian perspectives – with a growing perception that a mass surveillance state is being precipitated by the surrender of privacy.

Some may argue this is a feature inherent to the use of consumer devices as well as software and services, and present in modern trends such as doomscrolling.

• New drone detects wildfires within minutes using thermal and optical sensors
• Israeli drone automatically searches vast landscapes for newly ignited wildfires before they spread
• Autonomous drone sends fire coordinates directly to emergency command centers within moments

Wildfires across Europe had killed at least seventeen civilians and eight firefighters and forced roughly 330,000 people to flee by early August 2026.

The Middle East, too, is facing an intensifying wildfire risk this week as a severe heat wave dries out its forests and hillsides.

Amid those conditions, Israel has just tested a new drone designed to catch wildfires within minutes of ignition, before they can spread.

How the System Works

The system is designed to alert Israel's Fire and Rescue Authority almost immediately, giving emergency crews more time to respond before flames spread.

Behind this effort sit several bodies working together, including Israel's Ministry of National Security, the Innovation Authority, Israel Aerospace Industries (IAI), and the Technion.

Command centers at the Fire and Rescue Authority would receive automatic location data once a fire is confirmed, cutting delay before crews respond.

Firefighters set the boundaries of a patrol zone, and from there the aircraft flies its own course while hunting for signs of a new blaze.

Built around IAI’s APUS 25 aircraft, the craft pairs onboard cameras and infrared sensors with software trained to spot heat signatures.

That combination is intended to catch developing fires even when smoke or darkness would otherwise hide them from view entirely.

Weighing 24 kilograms, or about 53 pounds, the quadcopter draws its power from a combustion engine rather than a battery pack.

Its endurance is rated at up to five hours, long enough to sweep hundreds of square kilometers on a single sortie.

Beyond its own weight, the craft can lift another 10 kilograms, or 22 pounds, of equipment and feed it 300 watts of onboard power.

Its operating ceiling sits near 10,000 feet, equivalent to roughly 3,048 meters, based on public figures from the manufacturer.

“For many years, IAI has been developing advanced technological solutions for complex challenges across both defense and civilian domains,” said Moshe Levy, an executive at IAI.

“This unique project … [enables] us to transform advanced technology into operational capabilities that deliver real value in the field.”

Not ready for this season

Further testing is planned to see how the aircraft performs across a broader mix of weather and terrain before any wider rollout begins.

Fire and Rescue Authority personnel are expected to support later evaluations because routine operations depend on their involvement.

The ministry described the effort as one more step toward strengthening the country's emergency and security capabilities overall.

Officials have not yet said when, or even whether, the drone will be issued more broadly to firefighting units across Israel.

A single aircraft still working through evaluation in Israel cannot do much for regions currently fighting fires on the ground.

Its usefulness, assuming the trials go well, would show up in later fire seasons rather than the one now burning across parts of Europe.

How much difference tools like this one could eventually make, and how soon that might happen, remains very much unsettled for now.

Via The DroneFront

• The Pentagon seeks autonomous boats capable of launching drones during maritime operations
• Competition offers $100 million before potential $200 million procurement contracts
• Three testing sprints will evaluate operational readiness before procurement decisions

The Pentagon has released a formal solicitation seeking an unmanned surface vessel capable of launching small attack drones directly at sea.

The Defense Innovation Unit outlined the detailed requirements under a project officially called Suitable Warfighting Adaptive Payloads (SWAP-USV).

This competition aims to strengthen asymmetric capabilities against low-cost maritime threats operating across contested waters.

A year-long competition with major funding

The Pentagon is launching a year-long, multi-phase competition backed by roughly $100 million in total prize funding.

This contest will consist of three separate testing sprints, each carrying its own distinct rewards and specific technical requirements for participants.

Companies that complete each testing phase will become eligible for procurement contracts supported by an additional $200 million budget.

Sprint 1 begins in-water testing in the fall of 2026, aiming to reach full field deployment within 120 days afterward.

The competition then continues into early 2027 with Sprint 2, which will focus specifically on expanded payload capacity for surviving entrants.

It ultimately concludes with Sprint 3, scheduled for summer 2027, marking the entire program's final and decisive phase of evaluation.

The Pentagon is actively inviting participation from maritime manufacturers, autonomy developers, sensor specialists, and established drone companies across the defense sector.

Firms may choose to enter whichever phase of the competition best matches their existing technical capabilities and current development timeline.

Strict technical requirements for the winning vessel

The unmanned vessel must operate reliably across blue-water, littoral, and other varied maritime environments without requiring constant direct human oversight.

It needs onboard sensors capable of detecting and tracking surface targets while patrolling assigned areas or navigating toward designated waypoints autonomously.

At minimum, the vessel must achieve an operational range of 200 nautical miles, equivalent to roughly 370 kilometers, or 230 miles.

It must also maintain sustained cruising speeds reaching 10 knots, which translates to approximately 19 kilometers, or 12 miles per hour.

The vessel is expected to deploy cost-effective drones directly from onboard launch systems, expanding its operational mission profile at sea.

Those aerial systems would additionally support secondary missions such as electronic warfare operations and communications relay across contested or denied environments.

The solicitation explicitly frames this vessel as mature and market-ready hardware, rather than experimental technology requiring years of additional development work.

This suggests the Pentagon wants existing commercial maritime technology adapted quickly, rather than funding entirely new engineering efforts from scratch.

In recent times, drone boats have been incredibly useful in war. Recall that when the US Army Apache helicopter went down near the Strait of Hormuz, it was an uncrewed drone boat that saved its crew.

The UK is working on drone boats that can fly while Ukraine has been at the forefront of drone technology. It is therefore reasonable for the U.S. to invest in drone boat technology.

Via The DroneFront

• Nvidia open sources its cuFile APIs and storage stack into a new GitHub organization with Google, Intel, and Meta as founding maintainers, and formally launched Storage-Next with 40-plus flash and storage vendors
• Nvidia's key unveiling is its SCADA framework, which moves the storage control path onto the GPU, allowing parallel GPUs to pull data directly from storage
• The driver is KV cache economics: inference fetches data in a few hundred bytes at a time, while SSD controllers tuned for 4KB spend the same effort on either size

Following the recent Future of Memory and Storage conference, Nvidia has argued that the next leap in AI rests as much on the storage feeding accelerated computing as on the silicon doing the computing.

The company open sourced its cuFile APIs and the storage stack beneath them, and formally launched an industry initiative called Storage-Next with more than 40 flash and storage vendors.

It also put a name to SCADA, the framework that lets GPUs pull data from drives without the CPU brokering every request.

A 512-byte problem that comes into focus as storage becomes key

Nvidia's approach here is not new. cuFile was introduced in 2019 as the interface component of GPUDirect Storage and has shipped since 2021; its role is to take the CPU out of the data path. Bytes move by Direct Memory Access (DMA) straight between the drive and GPU memory, with no bounce buffer in host RAM. What stayed on the CPU was the control path: host software still decided what to fetch and issued every request, with the GPU as the DMA target rather than the initiator.

That split is invisible at large transfer sizes. A one-megabyte read essentially amortizes the per-request cost. At 512 bytes, the ratio inverts, the fixed cost dominates, and the CPU saturates long before the drives do. SCADA is the piece that moves the control path onto the GPU, letting it construct and complete its own storage requests and absorb per-operation latency, just as it already absorbs memory latency by keeping hundreds of thousands of operations in flight. The two are complementary rather than successive: cuFile for bulk transfers, SCADA for high volumes of small random reads.

The problem it solves is one that enterprise SSDs exhibit, having been tuned around 4KB random reads for nearly a decade to match virtualization and databases. As a result, most controllers do the same amount of work to serve 512 bytes of data as they would a 4KB read request.

AI inference access patterns are considerably smaller than that: embeddings run a few hundred bytes, and the KV cache blocks sit well under a kilobyte. Serving them from 4K-tuned drives imposes something like eightfold read amplification, and at tens of terabytes of small objects, that amplification decides whether flash works as a memory tier at all.

The KV cache, essentially the attention state for every token already processed, grows with context length, and agentic deployments run thousands of concurrent conversations. It quickly outgrows GPU memory, and recomputing evicted entries costs more GPU time than reading them back, so the standard design spills from GPU memory to system memory and flash, and refills through high volumes of small random reads.

Serving that from flash rather than DRAM increases the context length and the concurrent user count each GPU can support, which in turn affects the per-user cost of serving a model. This also explains why Nvidia is focusing on 512-byte IOPS rather than raw bandwidth, since inference performance depends heavily on the former.

Opening up cuFile is a departure; this layer has historically lived inside CUDA, and the logic is not charity. A GPU-initiated storage interface only pays off if drive, controller and array vendors build to it, and vendors do not build to a proprietary interface owned by the company whose GPUs they are feeding.

Publishing the interface, open-sourcing the implementation, and convening 40-plus vendors to standardize the underlying hardware behavior is how Nvidia aims to make GPU-initiated storage the industry default. It also stands to benefit the most from that outcome, since it sells most of the GPUs in question.

StorageReview notes Intel's participation as a significant development; a leading supplier of the x86 silicon currently sitting in storage controllers has signed on to maintain software designed to remove that silicon from the I/O path. Google and Meta co-maintaining a layer that standardizes how accelerators reach storage, while building their own accelerators, points in the same direction.

Nvidia's take is a coherent, well-argued push at a real bottleneck, with unusually credible partners attached. Partner systems from DDN, Dell, HPE, IBM, VAST Data and WEKA are due in the second half of 2026. Kioxia's XL-Flash drives built for 512-byte access are in development under Storage-Next.

Nvidia's roadmap calls for Gen7 SSDs sustaining 100 million IOPS each, which is a target controller vendors are designing toward rather than a product anyone can buy. Storage-Next itself has been discussed publicly since GTC 2025; this week, it acquired both a membership number and a framework to build against.

Over the past month, it seems like every frontier model has broken free of its constraints and launched a devastating attack against one or more other companies.

One of OpenAI’s models escaped a testing sandbox and launched a very real attack against AI and machine learning company Hugging Face. Just days later, Anthropic revealed that multiple variants of its Claude model also escaped a sandbox that wasn’t properly sealed and began attacking the enterprise infrastructure of three companies.

Now, Meta has revealed that one of its models attacked another company’s infrastructure during testing. The accident has been pinned on a misconfiguration that allowed the model to access the internet. So why have so many incidents happened in such a short space of time?

Why are models escaping their sandbox?

In the cases of Anthropic and Meta, their models were being tested by a third party company called Irregular. Anthropic’s AI model was taking part in a "Capture the Flag" exercise, where the model’s raw offensive capabilities were tested without the usual safeguards. But the sandbox was left connected to the internet. A similar error to Meta’s own accidental escape.

During the OpenAI incident, the company was testing two versions of GPT‑5.6 Sol using the ExploitGym benchmark. Unfortunately, the AI models performed better than expected - chaining multiple attack vectors, stolen credentials, and zero-day vulnerabilities.

The main reason these models are escaping their testing environments is because they are designed to do exactly that. These AI models act like a massive team of highly-trained cybersecurity experts hunting for vulnerabilities and exploits. But what would take a team of humans days or weeks to accomplish can be done in hours, or even minutes, by these AI models.

It’s no wonder thousands of employees from AI firms are calling for a pause on the development of the technology, and Congress is considering an AI kill switch.

Expert perspectives on AI escapes:

OpenAI

• Nathaniel Jones VP, Security & AI Strategy, Darktrace:

What makes the OpenAI and Hugging Face incident important is that the models did not need malicious intent to cause harm. They were given the legitimate goal of solving a cybersecurity benchmark and found an unexpected route to the answers, escaping their test environment and compromising another organization in the process. From the models’ perspective, this appears to have been an effective solution to the task.

The AI's actions challenge the assumption that giving an agent a legitimate goal will produce legitimate behavior. As models become capable of pursuing objectives over longer periods, developers need to define not only what success looks like, but also which methods and boundaries remain unacceptable in reaching it. Those limits must also be enforced by the surrounding infrastructure, rather than relying on the model to respect them.

Security teams need to consider the AI systems operating in their own businesses as these capabilities rapidly evolve. Right now, many security systems focus on single actions. A single action by an agent may appear acceptable but as this incident shows, models are now capable of long, complex chains of reasoning and action that add up to a harmful outcome. Teams need a mindset shift to understanding AI agent behavior in its entirety, including the outcome it is working towards, in order to safeguard it.

Hugging Face's response also exposed a second tension. The company reportedly needed a Chinese-developed open-weight model because commercial models would not process genuine attack material. Its nationality is less important than the operational lesson that safeguards that cannot distinguish an attacker from an authorized investigator may constrain defenders more than adversaries.

OpenAI and Hugging Face deserve credit for investigating this together and discussing it publicly. Other AI developers should study it closely.

Anthropic

• Dr. Ilia Kolochenko, founder of global cybersecurity company ImmuniWeb:

This seems to be quite an unimpressive marketing move from Anthropic in response to the OpenAI / Hugging Face drama, which attracted a lot of attention from all over the world recently.

Operationally, it appears that due to the progressive deterioration of the quality of training data, new AI models are getting dumber. Cheating and breaking the law, instead of accomplishing specific tasks, is certainly not an indicator of intelligence. Given that organizations and companies of all sizes now vigorously undertake all possible measures to protect their data from being exploited for AI training purposes, AI companies face a huge shortage of the high-quality and current data they so desperately need. Ultimately, frontier models are trained on synthetic, low-quality or even malicious and poisoned data, undermining their so-called intelligence. The situation is unlikely to improve in the near future unless AI companies agree to pay a fair price for training data, but this will force most of them out of business.

Contemporary AI agents and LLM models tasked with security testing can – and almost certainly will – go rogue when security controls or safeguards are insufficient. Powerful LLMs are unpredictable by design and thus virtually uncontrollable by humans. Therefore, using frontier AI models for security testing might be extremely costly from the legal viewpoint. Under the existing laws on both sides of the Atlantic, if an AI agent or any AI-powered app escapes its sandbox and causes damage to a third party, the operator of the AI model will likely be liable for all the damage caused. Excuses like “AI did it” do not currently exist in the eyes of the law, leaving AI vendors on the hook. Criminal prosecution, under a narrow set of circumstances, is also not excluded.

The same is true for the end-users of AI: even if your security testing tool is powered by a third-party AI model, your company will likely be fully liable if something goes wrong. You may then file a lawsuit against the AI vendor that you used, but here your chances to succeed in a court of law are tiny due to countless contractual disclaimers and limitations of liability that will likely be enforceable against you. Therefore, if you plan to use agentic AI for security testing – think twice and talk to your lawyers. Otherwise, you may start getting summons to court on a daily basis.

Meta

• Alex Goller, Principal Solution Architect EMEA at Illumio:

The fact we've had similar situations happen three times now across the biggest AI players is simply ridiculous. We've seen guardrails intentionally loosened to test their limits – Meta's model didn't need to be clever to breach another company's systems.

The timing of conveniently finding the exact same problem either means it's a stunt or they weren't paying enough attention during testing. Either way, both answers are worrying.

If the model has internet access, it's a bit like leaving the door open and being surprised when the cat walks out. What is concerning is that the testing infrastructure meant to prove these models are safe failed on a basic control issue.

Fundamental cybersecurity hygiene still matters, and a frontier AI model is only as secure as the environment it's operating in.

Organisations need visibility into what AI systems can access and how they interact with the wider environment, along with controls that contain the impact when an agent behaves unexpectedly. That means keeping a close eye on egress traffic, so it’s flagged immediately when an agent tries to open unexpected outbound communication patterns that are not required to achieve its original goal. In the best case this would have been contained proactively.

We need to define exactly what an AI agent is permitted to do, rather than relying only on instructions about what it shouldn't do.

• Workers fear ai is undermining them and fear replacement
• 69% of surveyed adults are concerned AI will highlight the parts of their roles they don’t understand
• It could be time for organizations to determine just why they want to use AI, what they hope to get out of it, and the human cost of its use

Do you know everything about your role and responsibilities at work? New research by Hint App indicates the increasing use of AI is highlighting workers’ own shortcomings, with 69% of surveyed adults worrying about the technology exposing gaps in their abilities. This is in contrast to the 41% who feel more concerned about being replaced by AI.

Meanwhile, the use of AI has different connotations across different businesses and industries, leading to concerns that its use may publicly expose an employee’s lack of suitability (perceived or otherwise) in their role.

With respondents also expressing concerns over what their AI use might mean to their long-term employment prospects, the survey has highlighted a potential problem across industry. Is the use of AI for productivity and admin boosts, to assist in training, or is it simply there to highlight whose job can be replaced?

Is AI making work difficult?

Hint App's survey of 10,842 working adults on the topic of artificial intelligence in the workplace, and the impact it has - or will have - on jobs, found that as well as automating complex or tedious tasks, AI is also making the workplace more difficult, and introducing new dimensions to their roles.

For example, nearly two-thirds (64%) of respondents state that they spend additional time reviewing material generated with AI, citing concerns over how the work might be received or judged. Not only must the work be accurate, it should also demonstrate that the person inputting the prompt knows what they are doing.

It’s a tricky balancing act, and doesn’t end there - over half (53%) have admitted to private worries over how a manager would react to the realization that AI is faster than they are.

Why do companies use AI, really?

Employees have long relied upon on-the-job training, collaboration and cooperation with colleagues, records, recollections, and various digital tools to succeed in their work. The introduction of AI into the workplace, with its ability to streamline and automate repeated processes, has not only made people more efficient – these results suggest that people are actively concerned that their job isn’t going to be around for much longer.

Reading between the lines of the survey’s findings, concerns could be legitimate, with distraction tactics being used by some workers (58% deliberately excusing themselves from public AI activities) to avoid highlighting how slow they are compared with an automated tool.

What the research does show, however, is that it is time for a conversation about corporate expectations over the use of AI, and what an employer’s responsibilities are to teams forced to use it.

• 52% of workers have sent replies to message or emails simply to prove that they are working
• Focused work is being interrupted by the requirement to respond quickly, with response speeds raised in performance evaluations
• More than half have replied at weekends, evenings, or even on annual leave to remain available and accessible

Are you staying in touch with your boss? New research suggests that career performance is being silently evaluated by speedy responses and availability, alongside traditional metrics, and this is resulting in employees interrupting focused work to reassure their managers.

A Use.AI survey of 15,842 adults across the United States, the European Union, the United Kingdom, Canada, Australia, and Latin America has found that 68% of employees believe that replying quickly demonstrates a higher level of commitment than slower colleagues.

There could be an AI element to this, too, with respondents who work extensively with AI feeling that response expectations are higher than those who have less exposure or requirement for the technology.

Fastest fingers first

The results from the survey strongly suggest that managers and team leaders are demanding fast responses, but that this doesn’t necessarily have the desired outcome.

For example, focused work has been interrupted by 48% of those surveyed in order to check emails or messages that might require an urgent response. Meanwhile, more than half (53%) have replied at weekends, evenings, or even on annual leave to remain available and accessible to their line managers.

Perhaps the most surprising result from the survey is that 52% say they have sent unnecessary messages or made changes to their status in messaging tools purely to demonstrate that they are working.

The new demands on employees have not gone unnoticed nor unmentioned. 42% have found themselves discussing their speediness in responding to messages in performance meetings.

Curiously, despite completing work in a timely manner, 29% have been “criticized” for slow responses to messages, highlighting a curious conflict in communication.

Is AI inflating expectations?

The survey also explores the matter of how artificial intelligence tools are impacting workplace expectations. Here, it found that 62% who use AI widely have found expected response times to emails had decreased. Conversely, just 38% of those will less regular AI use noted the same change.

While this doesn’t mean that AI caused that change, the results may suggest that the introduction of AI isn’t just impacting workflows and productivity. Enhanced, smarter systems underpinned by LLMs and agentic solutions are introducing an unforeseen tension, a mistrust from management concerning employees’ ability to devote time to work.

“AI is making routine communication almost effortless, but that convenience is also compressing expectations around response times,” said Use.AI’s Co-Founder and CEO, Ihor Herasymov. “We risk mistaking responsiveness for productivity.”

Fast responses to emails and messages may signal attentiveness, does this necessarily tally with solid results?

Kensington Expert Mouse Pro TB900 EQ: 30-second review

The Kensington Expert Mouse Pro TB900 EQ follows a long line of large-ball control devices that rethink the standard computer mouse entirely. This latest version is a relatively large unit that sits where your mouse would, with a big central trackball with controls neatly arranged around it.

Setup on the Mac took a little longer than expected, requiring a couple of restarts and some security authorisation in System Settings. Everything is guided and reasonably straightforward, and where something did not work first time, a restart sorted it. On Windows 11 it was considerably simpler: install Kensington Konnect and the applications and integration were ready to go.

Once running, you can start customising it per application. I tested both wired and Bluetooth connections, and both were fast and responsive for video and photo editing, as well as for Word and Excel.

Kensington Konnect is where everything happens. You add applications to the list on the right, and the presets for the TB900 EQ swap automatically as those applications come to the front. Switching between Photoshop and Final Cut Pro during this test, I had a different set of presets waiting for each. I could not find any pre-configured application profiles supplied, though this is a pre-release unit and maybe they’ll arrive as downloads later.

In Final Cut Pro, most of the defaults were close to what I wanted. I changed the dial around the main ball to scrub, the lower-left button to blade, and the right button to select. With those three, plus the ball tracking left and right, the four most common movements were all within reach without moving my hand.

The test that convinced me was a head-to-head edit. I edited the same footage once with keyboard and mouse as I normally would, then again using the TB900, and the trackball was quicker despite years of muscle memory pointing the other way.

If you work in the creative sector, or simply find a mouse annoying, this is worth a look. Having used many control boards over the years and trackballs, I am genuinely surprised by how much I like the TB900 EQ and how quickly it improved my video editing.

Kensington Expert Mouse Pro TB900 EQ: Price and availability

• How much does it cost? TBC
• When is it out? Coming soon
• Where can you get it? Directly from Kensington

The Expert Mouse Pro TB900 EQ is at pre-release and not yet available in stores. The previous TB800 EQ is widely available from major retailers and directly from Kensington, so I expect the new TB900 to follow suit.

• Value: TBC

Kensington Expert Mouse Pro TB900 EQ: Specs

Connectivity: USB-C wired, Bluetooth and 2.4GHz USB dongle
Size: 125 x 165mm, approximately 60mm high with the ball fitted
Weight: Just under 450g
Ball diameter: 55mm, removable
Tracking: Kensington Diamond Eye optical tracking
Controls: Four buttons around the ball, 360-degree scroll ring, two side scroll dials, four top buttons, mode button, three underside toggle switches
OS Installed: Kensington Konnect software, macOS and Windows

Kensington Expert Mouse Pro TB900 EQ: Design

The TB900 is a great-looking device, though far larger than an average mouse, with a desktop footprint of 125 x 165mm and a height of roughly 60mm with the ball fitted. At just under 450g, it is also considerably heavier, which in practice means it stays exactly where you put it rather than wandering across the desk.

The point of the design is that it takes up little room and you never move it. Your cursor travels because the ball turns, not because your hand travels. Around the 55mm ball sit four buttons and a rotating scroll ring. Across the top are four additional buttons and a mode button; on the left are two rotary dials with a connectivity switch and button; and on the right are two quick-access buttons.

Underneath there are three toggle switches that turn off the two side scroll dials and the top button. That makes more sense the more you use it, because those controls are easy to knock accidentally. Moving between Word, Excel, and creative applications, I found myself flicking the side dials off and on, and having physical switches for that rather than a menu is something I really liked.

Build quality is excellent and this review sample is the black-and-grey option, with a matt black body in good-quality plastic and a silver-grey ball that has a satisfying weight.

A red version is also available with a red ball but the same body. It’s also worth noting that while the device is relatively small, there’s no carry case in the box, so if you’re thinking of portability, that is something to consider. Placed into my kit bag, the ball would come loose.

That one oddity to anyone new to the system is that the ball lifts straight out. It’s held only by the cradle's light grip and its own weight, making it quick to swap in a different-coloured ball, although less well designed for portability as mentioned.

• Design: 4.5 / 5

Kensington Expert Mouse Pro TB900 EQ: Features

Compared with a standard mouse, the TB900 EQ is surprisingly feature-packed. Reading the movement of the ball is Kensington's Diamond Eye optical tracking that moves the cursor across the screen as the 55mm ball is rotated. In use, this instantly feels far more accurate than using a mouse, with plenty of control over interaction speed accessible through the Konnect software. The ball's own weight also means that it’s smooth to move but feels tactile rather than loose.

Around the ball is a fully adjustable scroll ring with 360-degree rotation, and it is superb for scrubbing through video footage. On the sides sit two customisable scroll dials which, by default, are both set to zoom in whichever application you are in. That proved annoying, and I reassigned them quickly because I kept catching one with my little finger. Likewise for some of the speed settings, the vertical scroll set at slow was actually the perfect balance; however, I did switch off the inertia scroll.

Every button and control is customisable through Konnect, which lets you create presets for each application. For this review, I configured Photoshop, Premiere Pro and Final Cut Pro. Aside from the zoom assignment, the defaults were fine for most productivity work.

Konnect splits into Buttons, Pointer, and Scrolling tabs, with Easy Mode and Advanced Mode, and settings are stored under named profiles. Pointer speed runs on a slider with optional acceleration, and a custom speed can be bound to modifier keys so a Cmd or Shift press temporarily changes tracking speed.

Alongside the dials, the buttons around the ball take what is possible on a standard mouse a good deal further. At the same time, there are plenty of mice with multiple buttons and customisation software. It’s only when you start using a trackball like this that you realise how much more intuitive the arrangement is, particularly for creative work.

• Features: 4.5 / 5

Kensington Expert Mouse Pro TB900 EQ: Performance

I tested the TB900 EQ on both macOS and Windows 11. The majority of the work was done on a MacBook Pro M1 Max, with part of the test carried out on an ASUS ProArt desktop to similar effect. Installation on the Windows machine was extremely straightforward: install Konnect, and the applications and integration were set and ready.

On the Mac it was more involved. Security authorisation is needed from within System Settings. While the setup process opens the relevant window directly rather than leaving you hunting for the toggle switches, I still hit a few connectivity issues. These were resolved with a restart of the machine, after which everything was resolved, and every connection after that initial setup was instant, with my configured presets remaining intact.

What struck me when I started was how intuitive the trackball felt. A dip into Konnect to reduce sensitivity, so the cursor was less jittery, showed how much more control you have than with a mouse. I found that resting two fingers on the ball gave the greatest control for the cursor with my thumb on the dial.

It also didn’t take long for the ball and surrounding buttons to feel natural, essentially mimicking a mouse layout. Once normal operation was established, I then moved on to per-application customisation, starting with Final Cut Pro, then Photoshop, and finally Premiere Pro.

What I was keen to address is the problem with any video edit of scrubbing control with a mouse. Essentially, scrubbing through footage both quickly and accurately to find the exact frame where an edit is needed. With the use of the ball, this all changes and what I liked here was that the four buttons around the ball and the scroll ring let me assign all the most common functions.

The ball itself scrubs the timeline. The scroll ring around it handles precise scrubbing, one frame at a time. The left button selects, the right blades, and the two top buttons move to the previous and next cut. All of it is assigned through Konnect.

Once those basics were set, everything else fell into line. After about five minutes of editing, it was clearly faster than the mouse-and-keyboard setup I am used to, and it felt far more like being back in an edit suite than at a desk.

The comparison I ran was a straight head-to-head. Stating a three-minute edit with the keyboard and mouse took around 20 minutes, consisting of a soundtrack edit then B-Roll. I then took a look at another similar video and repeated the process, and the second edit took around 15 minutes with a definite speed increase due to the button layout.

What really made the difference was the precision, being able to assign the dial around the ball to scrub, so a quick spin would speed through the footage while a smaller turn would go frame-by-frame, giving absolute control

Moving to Word, I assigned the features I use most to the buttons, with the scroll ring handling accurate movement through the text. Having the control dial skip a line at a time, with the left button for selection, again felt more intuitive than mouse and keyboard.

The TB900 however does not work in isolation. I still use the keyboard for typing and for shortcuts, but I was able to move far more of the control function away from the mouse and onto the trackball. Individually, those are a couple of seconds in savings. Over a half-day, edits add up to a considerable difference.

• Performance: 4.5 / 5

Kensington Expert Mouse Pro TB900 EQ: Final verdict

Thinking about who this is for, office workers get the simplest benefit: quick navigation around the screen without the faff of moving a mouse across the desk. The trackball stays fixed and close, so you never need to clear a path for it.

Then there is the customisation. Once you start assigning buttons to different applications and functions, and spend serious time in a piece of software, it is not difficult to tune it to exactly what you need.

Where it really comes into its own is the creative sector. The analogue feel of the two side dials and the main ring around the ball makes a genuine difference, and the texture and weight of the ball add a lot when working with creative media, giving fine and precise control. That is the real difference between this and a mouse: you feel fully in control of both cursor navigation and the edits you are applying.

It does take a little time to bed in and to get the right customisation assigned. Once you do, this is as close to a perfect trackball design as I have come across, especially when working across several applications rather than treating it as a dedicated video controller.

If, like me, you are not that keen on a mouse and often fall back to a trackpad instead, the Expert Mouse Pro TB900 EQ will be a pleasant surprise. And if you have tried trackballs before and struggled to get your head around them, my experience across this review is that this one is quick and easy to pick up.

Should I buy a Kensington Expert Mouse Pro TB900 EQ?

Buy it if...

Don't buy it if...

• Chinese equipment fills critical roles, though it represents only a small share overall
• Russia plans to quadruple annual Angara rocket production within the next decade
• Internal documents expose extensive factory upgrades supporting future rocket manufacturing

Russia is pushing to significantly grow production of its Angara space rockets, to quadruple annual output within the next decade.

Internal planning documents obtained by researchers show the expansion depends heavily on imported machine tools, and most of them are Chinese-made.

That dependence stretches across shop floors that Russia will need to modernize before it can build four rockets a year instead of one.

Counting the Chinese machines

Of the 541 pieces of equipment listed in the procurement plan, 477 are Russian-made, and only 57 come from Chinese suppliers.

Two more machines came from Bulgaria, leaving the Chinese share of the total list at roughly 10.5%, a modest slice on paper.

That number changes sharply once each machine is weighed by its technological importance rather than simply counted one by one.

Measured that way, Chinese suppliers account for 28.2% of the entire project's technological core, based on the internal planning documents.

In the metalworking shop alone, over 47% of the equipment needed there comes from Chinese manufacturers instead of domestic ones.

The finance tells a similar story: over 84% of the entire 3.62-billion-rouble (~$44.71 million) equipment budget still flows into that same technological core.

In total, Khrunichev intends to put close to 900 million roubles (~ $11.29 million), toward equipment made in China.

Roughly 10 manufacturers account for most of that spending, and several of them also sell machinery to buyers in Europe and America.

Where the machines really come from

Some of those companies fit a wider trend that has taken hold since the conflict between Russia and Ukraine started in 2022.

Heavy forging and hydraulic press equipment fall under European Union export restrictions whenever the intended buyer is based inside Russia.

Washington has also placed sanctions on Russian makers of heavy hydraulic presses, one of them a firm called Pressmash, back in 2023.

Yet under that very same customs code, the EU's imports of Chinese-made machine tools still totalled 207 million euros (~$238 million) in 2025.

Those imports rose another 17.6% in the opening six months of 2026, measured against the equivalent stretch the previous year.

In effect, the same manufacturers profit from Western markets that back Ukraine while also helping modernize the military hardware Russia sends into orbit.

Sanctioning any single Chinese supplier is unlikely to stop this flow of equipment, since near-identical machines are made by several competitors.

Whether losing access to European and American customers would ever outweigh the profits of supplying Russia remains an open question.

The Angara rockets built with the Chinese equipment carry the Russian Ministry of Defense satellites that American experts claim could threaten assets in orbit and on the ground.

However, Russian experts described the spacecraft as operating "in the interests of the Russian Ministry of Defense."

That difference between Moscow's official position and Washington's assessment has persisted since the conflict in Ukraine began.

Via United24media

• CACI secured a Pentagon contract worth up to $500 million total
• JIATF-401 manages daily counter-drone operations across the entire joint force
• SkyValor detected and defeated drones beyond visual range during testing

The US Department of Defense has awarded CACI a contract worth up to $500 million to expand deployment of its SkyValor counter-unmanned aerial system across military operations.

The agreement supports procurements through the Pentagon's Joint Interagency Task Force 401 (JIATF-401), which manages daily counter-drone operations and related acquisition activities throughout the joint force.

The award comes as militaries increasingly seek faster methods of detecting, tracking, and stopping small unmanned aircraft before they threaten personnel or critical infrastructure.

Contract expands deployment of counter-drone systems

The three-year agreement allows JIATF-401 to purchase additional SkyValor systems, including the fixed-site CORIAN configuration and the Backpackable Electronic Attack Module called BEAM.

BEAM combines radio frequency detection with electronic attack capabilities, giving operators a portable option for identifying and disrupting hostile drones across different operational environments.

SkyValor is designed to detect, monitor, identify, and defeat unauthorized drones while providing an additional protective layer before larger counter-drone systems become available.

The latest contract follows operational evaluations conducted at Marine Corps Air Station Yuma, where the system reportedly detected and defeated multiple small drones beyond visual range.

CACI previously secured another JIATF-401 agreement allowing SkyValor integration onto heavy-duty military trucks, expanding deployment options beyond fixed defensive positions and portable configurations.

Those developments indicate the Pentagon's interest in fielding counter-drone equipment capable of supporting military forces across varied operational scenarios.

The growing availability of inexpensive unmanned aircraft has increased demand for systems capable of providing earlier warning while responding rapidly against evolving aerial threats.

Operational testing supports wider procurement plans

According to CACI, successful operational evaluations cleared the way for SkyValor to enter full-rate production under the newly awarded procurement agreement.

The company’s President and CEO John Mengucci said drone threats are becoming faster, easier to obtain, and increasingly difficult for military personnel to detect.

"SkyValor delivers that advantage through long-range detection, precision defeat, and the ability to adapt as drone threats evolve," Mengucci said.

He claims that the system gives defenders more time and intelligence needed to stop threats before they endanger military forces, borders, or infrastructure.

These capabilities, according to Mengucci, moved SkyValor beyond testing phases and toward readiness for large-scale military deployment across multiple environments.

"With successful operational evaluations complete, we are moving SkyValor into full-rate production to deliver mission-ready protection where it is needed most," Mengucci added.

The agreement does not specify how many SkyValor systems the Pentagon expects to purchase during the three-year contract period or their deployment priorities.

Although successful testing supported the procurement decision, broader operational effectiveness will ultimately depend upon how the systems perform against increasingly sophisticated drone threats.

Via The DroneFront

Campaigners have long feared the influence of big money and influential corporations in politics – with this situation arguably worse than ever. In modern times, lobbying by technology companies has even given way to technology executives playing a role in devising government policy.

The path to progress

At the end of his two-term presidency, Dwight D Eisenhower used his final address to the American people to warn about the dangers that he foresaw lying ahead.

In particular, the military veteran warned about the rise of a new elite in society dominated by figures in science and technology, whose power would come to overwhelm democratically elected officials. He also suggested that the path of progress would be wrought with a lack of morals and ethics, with projects pursued in the name of progress regardless of the consequences to society at large.

A major component of this would be that the pursuit of major government contracts would dominate the scientific world, whereby the pursuit of money would dampen any genuine curiosity and lead to fewer meaningful discoveries.

The dawn of Silicon Valley

The former US president's warnings have largely come to fruition, first with the rise of Silicon Valley elites during the original internet age and now, subsequently, these forces are arguably entrenching their power in the AI era.

Technology companies have spent billions of dollars collectively on lobbying the government, with a small handful of companies including Microsoft, Meta, X and Snap channeling more than $260 million between 2020 and 2024.

The Trump administration has taken this influence one step further by giving vested interests a seat at the table and in the heart of government.

• Redditor uses Claude Code to help reverse-engineer the firmware on an HP 15-dw1036ne, patching its signature check and exposing 55 hidden setup fields and four configuration tabs
• The laptop's RSA-2048 was not broken: the verification routine was patched to return success, a standard firmware-modding technique as per Community Notes
• The result is model- and version-specific, still requires physical access via a hardware flash programmer, and leaves the machine permanently more exposed to firmware implants in the future

A Reddit user going by Reddit_2049 posted to r/ClaudeAI a claim that traveled fast: Claude Code had picked apart the firmware on their HP 15-dw1036ne and unlocked it, exposing 55 hidden setup fields and four previously invisible configuration tabs.

The move, though impressive, relies on a well-known BIOS modding approach: attacking the BIOS signature check so the BIOS always replies in the affirmative, effectively bypassing it altogether rather than throwing a potential firmware corruption error.

While it does have security implications, it also opens up a route to essentially 'rescue' locked or disabled laptops that would otherwise become e-waste.

A solution to certain types of e-waste, enabled by AI

What Claude Code did to "unlock" the Redditor's laptop is not a new technique. It bypassed the firmware's integrity check (an RSA-2048 signature verification) by targeting not how the check was performed, but how its result was reported.

The cryptography remains untouched, as Community Notes on two of the larger viral posts about the story pointed out. What the episode does highlight is that on machines without hardware-rooted boot protection, the verification routine itself sits in modifiable firmware, which is the same repair specialists and modders have relied on for years.

Reddit_2049 ran the job through the standard firmware toolchain: Ghidra for disassembly, UEFITool for extracting firmware volumes, and Unicorn Engine to emulate the check outside the real machine. Claude Code assisted here rather than acting autonomously by stitching together the relevant parts for him. The techniques involved are well documented; what was not documented was this particular machine, and that is the gap the model closed.

That opens an interesting front, one that could help with e-waste, even if not every owner is equipped to open a laptop safely or use a hardware flash programmer. An LLM that can read disassembly and hold a firmware layout in its head compresses hours of specialist work, which means targets that were never worth anyone's time individually now might be.

Whether the approach travels is an interesting question, however. The author of the original post warns the script may not work even on superficially similar HP models, and anyone attempting it on different hardware would need a flash programmer on hand and a tolerance for bricking the machine: modified firmware also brings its own problems, including occasional stability issues.

Right-to-repair rules in the EU and several US states have been circling this territory without directly addressing firmware. Until they do, the position stands roughly where it did last week, except that reverse-engineering a specific machine's firmware has become considerably cheaper for everybody.

That could let owners keep using older, locked laptops that would otherwise become e-waste, at a time when consumers are already burdened by higher costs due to inflation and an AI spending spree by data centers affecting laptops and desktops alike.

• Hivemind completes first autonomous swarming mission on water in Taiwan
• Drone boats identified and escorted a vessel during a coordinated surveillance exercise
• SeaShark 600 and 800 vessels operated together under a single autonomous command

An autonomous software system called Hivemind has directed a group of unmanned boats through a live mission for the first time on open water.

Developed by Shield AI, the AI tool took control of two vessel types built by Thunder Tiger during a staged exercise off Pingtung, Taiwan.

Engineers designed the exercise to test whether one AI system could manage several drone boats acting together as a coordinated unit.

Autonomous software directs coordinated drone boat mission

Two vessel models, the SeaShark 600 and the SeaShark 800, carried out the reconnaissance work under Hivemind's direction during the exercise.

Rather than following pre-set routes, the software built its own path in real time using radar feeds, camera imagery, and ship-tracking signals pulled from the area.

It located the search zone on its own, spotted an object worth investigating, and then organized the fleet's response around that discovery.

Once the object was found, the boats worked together to guide a separate ship safely away from the area under watch.

The trial marked the first time Hivemind completed an on-water swarming exercise, extending technology previously associated with autonomous operations across different military platforms.

“Hivemind continues to prove that true mission autonomy can scale across domains, platforms, and functional assets, unlocking new capabilities for distributed intelligence and maritime security missions,” said Nathan Michael, chief technology officer at Shield AI.

“As we continue working with Thunder Tiger to expand the scale and sophistication of autonomous maritime teams, these systems will fundamentally reshape maritime surveillance operations and strengthen deterrence through resilient, intelligent operations.”

The companies began collaborating through a strategic agreement aimed at integrating Hivemind autonomy software across both maritime and airborne unmanned systems.

Companies plan broader maritime deployments

Shield AI and Thunder Tiger plan to expand their collaboration by integrating additional sensors and improving coordinated swarm capabilities across autonomous maritime platforms.

The companies also intend to conduct open-ocean deployments supporting future United States and allied military applications.

According to Thunder Tiger, the latest exercise also examined how fleets of unmanned surface vessels could improve maritime awareness by detecting, classifying and identifying contacts during security operations.

“By integrating AI pilots into our portfolio, we can enable distributed, networked teams that coordinate dynamically to build a more complete understanding of the maritime environment … while helping operators respond with greater speed, scale, and effectiveness,” said Gene Su, board director and general manager of Thunder Tiger Corp.

“This demonstration marks an important step … and we expect our partnership with Shield AI to continue delivering increasingly sophisticated autonomous systems that strengthen Taiwan's defense posture.”

The demonstration adds to ongoing efforts to advance autonomous maritime operations using coordinated unmanned surface vessels.

Future activities will likely focus on integrating additional sensors, expanding swarm capabilities, and conducting open-ocean deployments.

Via The DroneFront

• A developer has a 28.9-million-parameter model generating TinyStories-style text at 9.88 tokens per second, fully offline, on a $10 microcontroller
• It was achieved by fitting a language model on a chip with 512KB of RAM by leaving most of it in flash storage
• The project is available on GitHub under an MIT license

A developer going by slvDev has a 28.9-million-parameter language model generating text on an ESP32-S3, a microcontroller built for sensor nodes and smart plugs, at 9.88 tokens per second, with nothing leaving the chip.

The project, esp32-ai, went up on GitHub under an MIT license in late July 2026, was showcased on the Better Stack YouTube channel, and has since collected over 3,600 stars and has more than 470 forks of the underlying code.

Given the underlying hardware's limitations, the attempt meant the model, which was trained on the TinyStories dataset by Microsoft Research, had to be scaled down.

Still a capable LLM despite the loss in accuracy

The arithmetic involved here is brutally unforgiving; weights typically need one to four bytes per parameter just to sit in memory, so 28.9 million parameters at 16-bit precision still require nearly 60 MB of RAM. An ESP32-S3 offers 512 KB of SRAM and 8 MB of PSRAM that is woefully inadequate at first glance.

Quantization closes the first part of the gap: dropping the weights to four-bit precision trades a little accuracy for a 75% reduction, bringing 60MB down to 14.9MB.

The second step is where the project earns most of its attention: most of a language model's parameters sit in embedding tables that the model reads from rather than computes against, and something you only read can live somewhere slow.

Borrowing Per-Layer Embeddings from Google's Gemma 3n, slvDev moved about 25 million parameters (roughly 12 MB at four-bit) into the board's 16 MB of flash, pulling around 6 rows, about 450 bytes, per token.

What remains in working memory is closer to 2 MB: the dense core and output head in PSRAM, and activations and norm weights in SRAM. Each tier stores whatever is read at its own frequency, and the chip's memory hierarchy serves as the architecture that enables the LLM.

Offloading weights to storage is not new; it is how people coax frontier-class models onto hardware that has no business running them, but it usually destroys throughput, turning tokens per second into seconds or minutes per token.

The PLE approach avoids that because the offloaded weights are accessed sparingly, so flash bandwidth never becomes the bottleneck. The resulting performance, 9.88 tokens per second, is faster than most people read.

The LLM has its limitations, however: the model writes short, mostly coherent stories and will not answer questions, follow instructions, write code, or know facts. The developer says the limit stems from the small part of the model that performs the reasoning (~4M parameters) and that the memory trick does not change its capabilities. His repository also ships a second model, Barista, which expressly answers espresso questions only (pun intended).

In short, the approach doesn't make small models smarter, but it does allow them to offload data within the board onto chips they could not previously run on, making it an interesting choice, though one with limited practical uses in its current iteration.

• Researchers tested AI-generated patches on six CVEs with poor success rates
• Many fixes failed, altered behavior, or introduced new vulnerabilities
• Guidance improved outcomes, leading to FLAWED evaluation harness release

When using Generative Artificial Intelligence (GenAI) to fix vulnerabilities, security professionals are most of the time just robbing Peter to pay Paul, experts have warned.

Researchers from 1Passwords Off-by-1 Labs analyzed fixes proposed by two frontier models - ChatGPT 5.5 at “medium” effort, and Claude Opus 4.8 at “high” effort.

As an experiment, the researchers took six recently disclosed CVEs and produced 6,080 patches using two frontier, cyber-capable reasoning models. The results were underwhelming to say the least - of all the proposed patches, just a quarter (26%) fully resolved the issue.

FLAWED work?

This obviously leaves plenty to be desired, as half (49.3%) of the patches failed to fix at least one existing exploit path. A fifth (20.1%) fixed the original issue but changed application behavior, while 2.3% introduced new security issues. Funny enough, 2.2% failed to fix the vulnerability while also introducing additional exploit paths, as well.

Even among the patches that might be considered (26% of clean ones and 20.1% of those that changed app behavior), more than a third were fragile and not entirely addressing the underlying problem.

The researchers created an acronym for automated LLM patches: FLAWED (Fix-Like Artifacts With Embedded Defects), and warned against letting AI work without human oversight: "The expected value of a fully LLM-generated, non-human-reviewed patch is a net-negative by a considerable margin."

Results drastically improved when the AI was given better context, the researchers further explained. Before working on any patch, human developers are usually given initial guidance. When AI is given proper guidance, its success rate rises to 65%. Incorrect guidance, on the other hand, drops the success rate down to 15.2%. The difference between humans and AI is that humans are better at catching misleading information and poor guidance.

This doesn’t mean developers will, or should, abandon AI. Worst case scenario is that developers will spend more time reviewing AI-generated fixes which could increase cognitive load and still end up being net negative. Therefore, the researchers released a patch evaluation harness called FLAWED, which organizations can now use to determine the effectiveness of their AI-generated fixes.

Via The Register

• Attackers cloned AI skills, later adding malicious code to steal credentials
• Zenity Labs found millions of installs and dozens of dangerous skill variants
• Vercel and Microsoft removed malicious skills, but manual removal is still required

AI skills, instructions that teach AI agents how to do certain tasks and thus extend their capabilities, are increasingly being used in supply chain attacks, researchers have found.

Security experts at Zenity Labs uncovered a credential-stealing campaign on skills.sh, a public registry (essentially an app store) for AI agent skills. In the registry, belonging to Vercel (a cloud platform for web applications), threat actors were cloning existing skills, creating typosquatted lookalikes which, at first, did nothing malicious.

However, after a little time had past, and the skills amassed a solid download count, the attackers introduced malicious code instructing the AI agents to, among other things, exfiltrate SSH keys, cloud credentials, Git and package manager tokens, Kubernetes and Docker configurations, database credentials, infrastructure-as-code credentials, environment files and service account files. The agents were then told to package the stolen information with host metadata and send it to the attackers.

Dozens of malicious skills

While Zenity Labs could not say exactly how many people fell victim to this attack, they did stress that a single skill family amassed more than 1.7 million aggregate installs (not unique users).

And that is just one skill family, in a sea of malicious skills. The researchers also said they found “dozens” of additional skills exhibiting either malicious or dangerous behavior. Almost a third (30%) of identified dangerous skills abused Claude Code and OpenClaw to drop malware to their targets, as well. Also, Zenity found “hundreds” of reserved and empty package names that were being kept for future attacks.

These findings show how quickly cybercriminals adapt, and how creative they can get when it comes to abusing new tech. In essence, this campaign is an AI spin on a software supply-chain attack, being similar in spirit to incidents where attackers compromise an existing trusted package or repository, and later push a malicious update.

Following responsible disclosure, Vercel and Microsoft removed the identified skills, but Zenity warns that those who installed them before won’t be safe until they remove them from their systems manually.

A good ergonomic office chair can make spending long hours at your desk considerably more comfortable, but some of the best models can cost a small fortune. Thankfully, I've found a terrific deal on FlexiSpot's C7 office chair for $300 (was $430), our top-rated mid-range ergonomic seat.

The C7 combines a breathable mesh design with self-adaptive dynamic lumbar support, adjustable seat depth, 3D armrests, and a 4D headrest, giving you plenty of ways to tailor the chair to your body and working position. In the UK, the C7 is priced at £259.99 (was £379.99).

Should you buy it?

✅ Buy the FlexiSpot C7 if...

You spend long hours working at a desk and want plenty of ergonomic adjustment without moving into the much higher price brackets occupied by premium office chairs. Its adjustable seat depth, lumbar support, armrests, and headrest make it easier to find a comfortable position, while the 300-pound capacity allows it to accommodate most people.

❌ Skip the FlexiSpot C7 if...

You want a simpler chair with fewer adjustments, or you're shopping on a tighter budget. The C7's ergonomic features are aimed primarily at people who spend long periods at a desk, so occasional home-office use may not justify spending out.

Why we recommend it

In his glowing 4.5-star review our expert Collin said: "This chair is an excellent solution for office managers looking for a quality ergonomic office chair for their employees; it's great for home office use, for your office, cubicle work, and more."

There's an impressive amount of adjustment available for the money, particularly the dynamic lumbar support and adjustable seat depth. It also reclines from 90 to 130 degrees, supports up to 300 pounds, and comes with a reassuring 10-year warranty.

The mesh construction should also help with airflow during long working days, and the 10-year warranty provides some welcome reassurance if you're buying a chair for everyday use.

Price context & historical value

At $299.99, the C7 costs $130 less than its usual $429.99 price, equivalent to a discount of just over 30%. That brings a chair we already consider a fantastic mid-range choice much closer to the price of less sophisticated ergonomic models.

Given the combination of adjustability, 300-pound capacity, lengthy warranty, and the current price, this would make a fantastic home office upgrade and is an easy recommendation from me.

FlexiSpot offer regular sales throughout the year, and by and large, this is the standard deal price. I can't remember seeing it cheaper (although I've certainly seen it more expensive).

The Catch: What to know before you buy

This configuration doesn't include a footrest. If you want one, adding the feature increases the price to $319.99, which isn't that huge a leap.

The mesh construction won't suit everyone, particularly if you prefer a softer, cushioned seat, but you can switch to foam for the same price (although not with a footrest).

FlexiSpot also recommends the C7 for people between 5 feet 5 inches and 6 feet 11 inches tall. Our reviewer Collin stands a little over six foot and had no issues using the C7, but smaller users will want to pay particular attention to the adjustment ranges for maximum comfort.

• US: See all deals at FlexiSpot
• UK: See the full sale at FlexiSpot.co.uk

• Google’s Threat Intelligence team links BlackFile (now Redact) to phishing and extortion attacks on major hedge funds and law firms
• Group impersonates IT staff by phone, steering victims to spoofed login pages to steal SaaS credentials and tokens, then exfiltrates sensitive data
• Targets include Blackstone, KKR, Apollo, CME Group, and firms like Paul Hastings; Google tracked $10.7 million flowing into 18 crypto wallets between January–May 2026

Some of the biggest US hedge funds and law firms have been targeted by a highly sophisticated data breach and extortion campaign, conducted by a group of criminals previously known as BlackFile, experts have warned.

BlackFile (or Redact, as the group is now calling itself) has a relatively simple modus operandi, also used by ShinyHunters - they call their targets on the phone (usually employees with access to SaaS systems), identify as IT staff, and direct victims to convincing, lookalike login pages designed to steal their credentials and authentication tokens.

Once they gain access to victims' accounts, the attackers access enterprise SaaS environments (for example, Microsoft 365 and Okta) and use automated tools to exfiltrate sensitive data. In the last step, they notify the victims and threaten to leak the data on the dark web unless a ransom is paid.

Stealing millions

Since a part of the process is to navigate the victims to spoofed login pages, the criminals often register domain names that can easily be confused for legitimate ones.

That is also a good way to spot who the potential victims are, and according to a new report from Google’s Threat Intelligence Group (TGIT) team, these are some of the biggest names in the finance industry: Blackstone, KKR & Co, Apollo Global Management Inc, and CME Group Inc.

Apart from these names, a few law firms were also spotted, including Paul Hastings LLP and Greenberg Traurig LLP. However, none of these confirmed having been breached, and Greenberg Traurig told Reuters they were never targeted in the first place.

The group seems to be making quite the progress. In April and May, they registered a new phishing domain every 2.2 days, rising to every 1.6 days for June and July. It’s paying off, too.

In the 18 cryptocurrency wallets Google associates with the group and tracks, around $10.7 million was received between January and mid-May 2026.

Up until now, enterprise AI has been largely dominated by a familiar conversation: which models should we use, where do we deploy copilots and agents, and how quickly can we move from experimentation to measurable business value?

These have all been pertinent questions, but as teams reach a new stage on their AI journey, they're no longer the pressing ones.

The first wave of AI was one of discovery, with organizations asking a simple question: ‘Can AI help us at all?’ That was followed by a period of rapid experimentation, as enterprises launched proof-of-concepts and pilots across every conceivable business function to understand where AI could deliver value.

Now, the conversation has changed again. Enterprise leaders aren't struggling to identify AI use cases - most organizations already have dozens - the real challenge is moving from experimentation to scaling. It's no longer about asking ‘Where can we use AI?’ but ‘How do we make it work consistently across the business?’

And this shift in focus has exposed a problem many organizations originally underestimated: data.

AI exposes weakness in the foundations

In the earlier stages of developing AI, many enterprises focused on accessing proprietary datasets for one primary purpose: model customization. Although this challenge still exists, there’s now a bigger issue at play that involves data quality, accessibility and governance across the organization.

AI is only as effective as the information and processes it operates on. The tricky part here is that AI is a master of exposing weaknesses that have existed inside organizations for years. And that should serve as a wake up call for enterprise leaders investing in AI right now. Because when data is fragmented, processes are inconsistent and operational maturity is lacking. In this scenario, AI won’t fix the problem – it’ll simply amplify it.

Viewed this way, enterprises should best understand AI as a force multiplier, not a correction mechanism for the legacy inefficiencies. However, the opposite is equally true. When organizations treat data as a first-class product, AI has the potential to multiply the quality coming out of it.

And this is the key difference between implementing AI and being genuinely prepared for it. You might have a strong model, but if you’re working with fragmented and unstructured data, your efforts will only continue to produce inconsistent results. I’ve seen it first-hand.

This isn’t just an anecdotal point. Gartner predicts that through 2026, 60% of AI projects will be abandoned because they aren't supported by AI-ready data, while 63% of data management leaders say they either lack - or aren't sure they have - the data management practices AI requires.

Those findings reinforce what we’re seeing many organizations discover first-hand: AI success is increasingly determined by the quality of the underlying data rather than the sophistication of the model.

The data pilot trap

Many organizations saw encouraging results during their early AI pilots, and that isn’t surprising. Typically, pilots are built on curated samples of data that are absolutely real, but to some extent pre-selected and considered synthetic.

On that kind of data, it's natural to see success because the information has been carefully selected to demonstrate the technology's potential.

The real test begins when organizations start scaling AI and release it from the boundaries of those pilots into the real enterprise environment.

Suddenly, models have access to many different types of data that all have to work together. They're exposed to years of duplicated records, conflicting business definitions, incomplete customer data, inconsistent documentation and disconnected systems. Information that appeared reliable in a controlled pilot becomes far less dependable in production.

That's the point where leadership teams often get their biggest surprise. They realize the true state of their data is much worse than they expected. What worked well during the proof-of-concept stage simply doesn't work in the real production environment – not because the AI has become less capable, but because it has finally encountered the reality of enterprise wide data.

Unfortunately, that experience is becoming increasingly common. What’s abundantly clear is that the defining challenge now is not proving that AI works, but ensuring the data behind it is ready for enterprise deployment.

Signs your data isn’t ready for AI

How do you know if your data’s ready for AI? In many cases, the answer only becomes obvious after projects fail to deliver the expected results. But long before that happens, there are usually warning signs.

Your data governance has gaps: Can you identify where your data lives, who owns it and whether it can be trusted? If every analysis depends on manual reconciliation, AI will simply scale those inconsistencies. Years of organic growth often leave enterprises with siloed data, conflicting definitions and inconsistent governance. Which means before AI can deliver reliable results, organizations need clear ownership, consistent standards and dependable data pipelines.

AI initiatives are happening in isolation: When different teams are experimenting with AI independently, it's often a sign that the underlying data isn't connected. And it’s happening more often than you might think – McKinsey research found that fewer than 30% of organizations have their AI agenda directly sponsored by the CEO. Valuable information remains trapped in departmental silos or legacy systems, making it difficult to build a complete picture of the business.

Treated like this, it’ll always remain more function-level experimentation rather than coordinated enterprise transformation. AI performs best when it can draw on integrated, trusted data rather than fragmented datasets created for individual functions.

Your data isn't connected to business outcomes: AI creates value by improving business decisions and processes, not by analysing data for its own sake. If it's unclear how your data supports the outcomes you're trying to achieve, AI initiatives are unlikely to produce meaningful results. Incomplete, outdated or poorly maintained data will also undermine confidence in AI outputs, making it harder to move beyond isolated pilots.

You're spending more time choosing models than improving data: Selecting a foundation model is important, but it's rarely what determines success. The bigger challenges are preparing enterprise data, identifying high-value use cases, embedding AI into existing workflows and driving adoption across the business. Continually chasing the latest release will not deliver tangible results, investing in the data foundations that will make a model effective, will.

Potential will only be realized with strong foundations

Arguably, the biggest shift facing enterprise leaders is one of mindset: moving the focus from advanced models to strong foundations. McKinsey's research supports this, finding that organizational readiness accounts for 48% of the difference between companies that successfully capture value from AI and those that don't—making it a stronger predictor of success.

For enterprises, the ultimate goal with AI should not simply be to automate existing tasks, but how to rethink how the business operates. That means redesigning processes around AI's capabilities, rather than layering AI onto inefficient ways of working.

For that to happen, data quality, governance and clear ownership can no longer be treated as minor, back-office concerns, they need to be recognized as strategic priorities.

The opportunity for enterprise AI is enormous, but without trusted data to build on, its potential will remain just that, potential.

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

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

• Huntress saw Oracle SQLi used to deploy rare khunt toolkit
• Khunt enabled OS commands, credential theft, and registry hive exfiltration
• Defense includes input sanitation and more

Someone managed to pair the classic SQL Injection (SQLi) attack with a nifty database trick to take over the underlying system entirely.

Security researchers Huntress, who were called in to investigate the incident, said the investigation first showed a classic, decades-old technique called an SQL injection attack: a public-facing application with an Oracle backend accepted and executed SQL commands input into a form without checking whether that input was valid or not.

This granted the attackers the ability to upload a database-resident, posts-exploitation toolkit named khunt. This technique is something of a cyber-white whale: it’s been widely discussed but rarely seen in the wild.

How to defend

“What happened next, however, raised our eyebrows,” Huntress said. “After performing SQL injection, the threat actor managed to upload a database-resident, post-exploitation toolkit named khunt. This is a technique that's previously been discussed and described over the years, including via a technique described as oraexec – however, the use of the technique in the wild has rarely been documented.”

As a toolkit, khunt granted the attackers multiple capabilities, including loading cmd.exe on the system and running arbitrary OS commands, steal usernames and passwords, listing, reading, searching, and checking file sizes (essentially looking around the compromised system), unzipping files, and more.

Of all the things they could have done, the attackers opted to run a PowerShell command and invoke the Windows Registry tool, copying the SAM, SECURITY and SYSTEM registry hives. They can later use the copies to extract and decode password hashes for local accounts on the system, the researchers explained.

To defend against such attacks, Huntress recommends making sure the forms aren’t injectable. “Practice proper input sanitization and query parameterization for any inputs,” they warned. “It's also important to ensure that users with the ability to execute queries aren't overprovisioned.”

Even if someone manages to pull off SQL injection, user accounts should not be capable of authoring Java sources or running stored procedures.

Sorting, managing, editing, and sharing your photographs is a job and a half, often requiring multiple apps just to get on top of the workflow.

I’ve tested several software packages that do a very good job at merging practically every tool you need into a single app, but what’s bugged me is they only cater to Windows users.

Which is why my interest was piqued by ON1’s Photo RAW’s multi-platform capabilities (well, for Windows and Macs - sorry Linux users). So let’s take a look at it.

ON1 Photo RAW 2026: Pricing & plans

• No subscription model
• You purchase the software outright
• Generous trial period, money back guarantee, what's not to like

The ON1 Photo Raw has a flash sale ending today (August 7), with 50% off - you can check it out by clicking here.

More and more companies have jumped to subscription plans - I'm never a fan of this.

Well, ON1 are having none of that, and its Photo RAW app is only available to own outright. Full price, it costs a very fair $100.

When you consider you can use Adobe Lightroom and Photoshop for $20 a month, not only will you be saving money with Photo RAW after only 5 months.

The software also comes with a very generous 30-day free trial (email address required, but not your credit card details). If you purchase it and conclude it’s not for you, there’s also a 30-day money back guarantee. That means ON1 want to be certain you’ll only buy their software if you’re delighted with it.

You can see what's on offer here.

• Pricing & plans: 5 / 5

ON1 Photo RAW 2026: Interface

• Plenty of tools
• When all on display, you’re not left with much space for photos
• But thankfully you can hide sidebars at will

I found Photo RAW’s interface to be very well thought out, polished and designed. Perhaps a little too polished, as some actions and concepts weren’t immediately obvious to me.

Like a little hollowed out blue circle being a tick box, or clicking on a menu’s header would collapse or expand its content. But it just takes a few seconds to figure it out and then you’re flying.

There are numerous sidebars on either side, the thinnest one furthest to the left is contextual, depending on the module you’re in. Selecting ‘Home’ shows you some recent photos you’ve worked on, along with those available in recently opened folders. You’ll also find links to helpful tutorial videos.

‘Browse’ reveals the guts of your local hard drives, with every single folder you’ve ever created ready to be accessed, and also offers access to online storage from Dropbox, Google Drive and OneDrive

‘Edit’ is where you’ll work on your photos, and selecting that icon will also reveal additional icons down that sidebar.

If you’re ever puzzled about what a particular icon does, hover over it for a few seconds to reveal a tool tip popup window, complete with a short audioless video illustrating what it does.

When a tool is selected, a toolbar at the top of the interface will offer specific parameters for that selected tool. The other three sidebars can be customised - you can choose if some tools are displayed in the left or right one, or you can remove them from view altogether offering more space to the shot you’re working on.

By default, the large sidebar on the left is for your presets and layer work, while the one on the right is for your regular color correcting tools, and similar. A small sidebar on the far right contains options to share your work either online or by printing it.

• Interface: 4 / 5

ON1 Photo RAW 2026: Editing

• Tools are easy to use
• Many are made better with the clever use of complex algorithms designed to improve your work rather than supplant it

Now, to start, I downloaded a series of free RAW files from Signature Edits (check out the website here).

If you’ve done any color correction you’ll recognise all the sliders present in the ‘Tone and color’ section on the right, which allow you to make satisfying alterations to an image in next to no time.

But if that just takes too long for you, just above those sliders is a ‘Brilliance AI’ button. Activate it, and complex algorithms will do the necessary changes for you. But unlike many other AI features from competing software, this doesn’t need to be the be all and end all of the process.

Expand Brilliance AI to reveal sliders to enhance or tone down those automatic changes, including some very clever masking options to focus your changes on specific sections, such as people, animals, the sky, and more. You can also just go back to Tone and color sliders to make any further alterations yourself.

Adding Filters is as easy as selecting one you’d like and using one of its preset ‘Styles’. As before you’re also free to make manual changes thanks to the available sliders, if you’re after a more unique look. It’s good to know you can add as many filters as you’d like to an image.

When it comes to masking, Photo RAW has some complex algorithms ready and waiting to work for you, in the shape of Super Select AI. Clicking on that tool (in the thin left sidebar) lets the software analyze your image. This should only take a couple of seconds. You can then isolate a specific area of an image, modifying it while leaving the rest untouched.

The level of precision you can have with masks is quite extreme. For example, adding to your selection, solely focussing on small sections of your image such as a subject’s eyes, adding a filter to the entire shot, and then removing it for one or two specific areas. You can get amazingly detailed with your alterations.

And if that isn’t enough, you’re able to work with layers, just like you can with Photoshop, superimposing photos on top of others, masking areas, revealing others, adding text. Although to be honest, being used to Photoshop and the like, that section did feel a little too bare in comparison.

What you can do with Photo RAW is remarkably intensive, and it uses those complex algorithms it calls AI in a way that enhances your creativity rather than take over the process, and that can only be lauded.

Should I buy ON1 Photo Raw?

Buy it if...

You’re looking for software to organize, manage, alter and share your photos, love the idea of a perpetual licence, and appreciate AI being used as a clever and intricate tool rather than a replacement for your actual vision.

Don't buy it if...

You are too wedded to Adobe’s suite of creative software, or your editing needs are too casual to take advantage of the level of complexity and precision Photo RAW offers.

For more creative tools, we tested the best photo editors.

The rapid growth and evolution of AI technology has led to major shifts in how businesses of all sizes work - but it is also impacting their next big corportate move.

AI has fundamentally changed the risk profile of innovation, as it can expose companies and users to risk earlier, faster and at far greater scale.

Organizations used to be able to afford to delay digital transformation decisions and remain competitive - but now, competitors can use AI to exponentially accelerate while the companies which wait for exact certainty can be left behind before they even begin.

This all means trust is now a crucial business imperative, and organizations must tighten up governance, privacy and security to prevent regulatory and reputational risk - we spoke to Joe Depa, EY Global CIO, to find out more.

• Many organizations have spent the past two years experimenting with AI, but relatively few have achieved measurable business impact. From EY's perspective, what separates the companies that are delivering tangible ROI from those that remain stuck in what you've described as “pilot purgatory”?

Everyone wants to move faster with AI. The first question is whether they can move fast with trust. The second is how deeply they understand and can optimize the AI value equation.

Trust is what separates the companies getting real ROI from the ones stuck in pilot purgatory. The winners that scale AI successfully across the business tend to share a few characteristics.

They have trusted data as the foundation. Proprietary, secure and well-governed data is becoming one of the biggest competitive moats a company has, because it reflects its unique knowledge, judgment and expertise.

On top of that foundation, they have trusted processes and technology. Data has to be connected to real workflows, real decisions and real business outcomes. The companies pulling ahead don’t use AI to make old processes a little faster. Instead, they rethink how work gets done, where AI can create new value, and how to build governance into the way AI is deployed and scaled.

Then, most importantly, winning organizations invest heavily in helping their workforce expand their skillsets so they can use AI confidently. People won’t use AI if they don’t trust it, and if they don’t use it, they won’t develop the expertise to know where to apply it. That is why upskilling is so important; it’s how you turn employees from passive users into change agents.

Put simply, pilots are great for learning, but being able to progress to scaled adoption is what creates business outcomes. And scaled adoption can only happen when people trust the data, the process, the technology and their own ability to use it well.

That brings us to how to understand and optimize AI value. There’s a lot of focus lately on token costs, but token costs are just one element of the AI value equation. This is a major disconnect that we spotted early in our own business. We saw rising token costs and high-end models being used for low-value work.

In retrospect, it was an important lesson about how measurement priorities change as technologies mature. In the early days of generative AI, the focus was on getting people to use it, so organizations tended to view usage as an indicator of momentum. But now that generative AI models and use cases have become more sophisticated, our focus has shifted to measuring business outcomes.

So we developed a set of best practices to address that. We optimized model selection against the highest value use cases, trained our teams, and put governance around usage. Those strategies enabled us to bring overall token consumption down 60%, while bringing value up. We’ve formalized this in an AI Value Realization Office focused on governance, training and value measurement. The goal is to ensure AI is being applied to the highest-value opportunities and delivering measurable impact.

• There's often a tension between moving quickly with AI and putting the right controls in place. How can organizations build governance, privacy and security into AI programmes from day one without slowing innovation to the point where they lose competitive advantage?

People often think of governance as the brake, but the opposite is true: having good governance is what gives you the confidence to press the accelerator.

The companies getting it right are building trust into AI from day one, so their teams can move faster without creating risks they have to unwind later.

EY’s Responsible AI Pulse survey found that companies further along in responsible AI are reducing risk and seeing better business outcomes. Nearly four in five reported gains in innovation, and more than half reported revenue growth. Companies with real-time monitoring were also more likely to see improvements in revenue growth and cost savings.

People move faster when they know the guardrails. If employees have a safe, well-governed environment to experiment in, they are much more likely to try new ideas. But if they are worried that every idea could create a compliance issue, they slow down or avoid experimenting altogether.

Governance also matters technically. When controls are added too late, teams often end up with rework, technical debt or pilots that looked promising but cannot scale. Building the right controls early helps avoid that.

Furthermore, once AI agents start to take action, governance can no longer live in a document. It has to be built into the system itself: how access is controlled, how behavior is monitored, how bias is checked and how safeguards kick in when an agent acts.

That is the balance leaders need to strike. Move fast, but do it with trust built in.

• As AI becomes embedded in core business processes, trust is increasingly seen as a commercial issue rather than simply a compliance one. How are you seeing boards and executive teams rethink governance in light of growing regulatory scrutiny and the potential reputational consequences of AI failures?

Trust is absolutely a commercial issue. If customers, employees or regulators do not trust the products, systems or decisions powered by AI, they will find reasons not to use them. That can affect brand, reputation, operations and growth.

Boards cannot govern what they do not understand. That does not mean every director needs to be a technologist. But boards do need enough AI and technology fluency in the room to ask the right questions, challenge management and understand where the real exposure sits.

We are seeing boards move from asking, “Are we compliant?” to “Where are we exposed?” That is an important shift. Boards are starting to see AI risk more broadly in terms of enterprise risk, in addition to legal and regulatory risk. Agentic AI raises the stakes even more.

For boards, there are three practical next steps.

1. Build AI fluency into the boardroom. Boards need people who understand enough about technology and AI to pressure-test the strategy, the risks and the controls. This doesn’t mean that everyone needs to code, but they do need enough fluency and visibility into leading value indicators to know whether management is asking the right questions.

2. Require visibility across the AI portfolio. Boards should understand where AI is being used, what data it relies on, how material the use case is, who owns it, what resources are being consumed, and what controls are in place. The goal of all this knowledge is to help boards ensure AI is creating business value and that it is being governed at the solution level, the portfolio level and the enterprise level.

3. Make the technical controls real. That means AI-ready data, clear access controls, audit trails, model and agent monitoring, defined escalation paths and the ability to intervene when something behaves outside expectations. Of course, training is absolutely critical to drive adoption of these tools.

• Many businesses are keen to demonstrate they're embracing AI, but there's a risk of what you've called “innovation theatre.” What are the warning signs that an organisation is investing in AI for appearances rather than driving genuine business transformation, and how can leaders avoid falling into that trap?

The warning sign is when activity gets confused with impact.

A company can have a long list of pilots, demos and proofs-of-concept and still not be creating meaningful value. That is innovation theatre. It looks busy. It may even look impressive. But it does not change the business.

The companies getting this right are much more disciplined. They’ve moved beyond measuring activity to measuring impact on business value. They’re focused on which pilots created value, which scaled successfully, and which improved workflows, customer experiences, risk outcomes or financial performance.

The most successful organizations also establish accountability from the start. Every AI initiative should have a business owner, an investment hypothesis, clear success metrics and AI enabled training that focuses on deploying effectively. Without this, it becomes difficult to separate experimentation from impact.

If pilots are not scaling, leaders need to understand why. Is it a data issue? A trust issue? A weak business case? No clear owner? Not enough transparency or control? Not enough training? Those failure modes tell you where the organization needs to get better.

Disciplined companies avoid the innovation theater trap. Every pilot has an owner, a set of priority metrics and a decision point: scale it, rethink it or kill it.

Otherwise, you end up with a lot of interesting demos and not enough business impact.

• With AI regulation evolving across different markets and customer expectations around responsible AI continuing to rise, what governance practices do you think will become non-negotiable over the next 12 to 24 months for organisations that want to scale AI responsibly?

AI is moving faster than any one organization, board or regulator can fully keep up with.

That is why leaders don’t have the luxury to wait and see. They need governance models that are resilient enough to adapt across markets, because the regulatory environment will continue to evolve, and it will not evolve the same way everywhere.

Sovereign AI will become a much bigger part of that conversation. Organizations will need to understand where their data lives, how it is protected, who can access it, which models are being used and how those systems align with local laws and market expectations. That goes beyond a technical issue; it’s becoming a board, regulatory and trust issue.

The companies that get this right will focus on a few practical areas.

One is data governance and sovereignty. You need to know what data your AI is using, where it came from, where it is stored and whether it is appropriate for the use case.

Another area of focus is resilience in the operating model. Regulation will keep changing, so governance must be dynamic. Organizations need controls, monitoring, audit trails and escalation paths that can adapt as new rules, risks and expectations emerge. For example, when AI agents are deployed that can make decisions, take action and interact with other systems, regulators will expect organizations to understand how it behaves in the real world.

Lastly, they have an open ecosystem. No company can navigate this complexity alone. Leaders will need to work across regulators, technology providers, advisors, industry groups and academic institutions to separate signal from noise and understand where the market is heading.

Focusing on these areas is how responsible AI evolves from a static set of policies to an enabler. It needs to be designed at the outset into the ways companies manage data, deploy technology, work with regulators and scale innovation across markets. That is how you build trust and resilience at the same time.

• Looking ahead, do you think trust will become a competitive differentiator in AI adoption? In other words, will organisations that can clearly demonstrate robust governance, transparency and security gain a commercial advantage over those that treat these issues as a compliance exercise?

Yes. Trust is a business necessity, not a checkbox.

The irony is that trust can help companies move faster. When the guardrails and success indicators are clear, teams are freed to innovate with more confidence and less rework.

That is why responsible AI is a growth and innovation driver. If you can show that your AI is governed, explainable, secure and monitored, people are more likely to adopt it, they’ll be far more comfortable scaling it and, most importantly, they’ll be better positioned to scale, differentiate and create value.

That’s the real opportunity: the confidence to move fast in a way everyone, from boards to employees to customers, can trust.

For many, the release of ChatGPT in 2022 was transformative.

Difficult emails were drafted in seconds, complex documents that would have taken hundreds of man-hours to scan were checked in minutes.

But for some, whose livelihood depended on providing just the type of service that ChatGPT offered - providing copy, for example - 2022 was a difficult year.

We were in the latter group. Our company, Jacquard, was founded in 2015 - seven years before ChatGPT hit email inboxes and LinkedIn feeds around the world.

Jacquard’s basic offering was a platform that used natural language generation to write better email subject lines. Around 2015, we tried taking the technology to investors. They were skeptical. Intrigued, perhaps, but skeptical.

At that time, we were one of very few companies in the language generation space, and we felt it. It wasn’t all bad though - and we eventually got used to being one of the few players in the game.

Then ChatGPT launched.

Our offering, AI-powered language generation, was suddenly in the hands of millions. For free. The investor skepticism we'd spent years navigating disappeared overnight, and in its place: a gold rush.

Hundreds of startups emerged, many founded by people who'd never worked in marketing or natural language processing.

On paper, this could have been the moment it all went wrong for us. Instead, if anything, it only propelled us further forward.

Surviving AI

It wasn’t an easy few years, but looking back, I can now attribute Jacquard’s survival to four key factors: AI fatigue; proprietary data; user base evolution; and an outcome-focused approach.

First, AI fatigue. When ChatGPT launched, everyone rushed to build, and then market, a GPT-wrapper. If it was a gold rush - to take the metaphor a little further - then too much gold flooded the market, and the value depreciated fast. Fatigue was everywhere, and it set in quickly.

Stakeholders grew tired of sitting through pitches that promised something revolutionary but delivered mediocrity.

Instead of a one-size-fits-all solution, what we all saw was generic copy, off-brand tone, and outputs that, at best, needed significant human reworking before they were anywhere close to usable.

85% of brands now use ChatGPT, in some capacity, to produce their marketing content. You can find the exact same turn of phrase in the marketing materials from a multinational conglomerate, as in your local café.

Our offering - the ability to maintain a genuinely distinct brand voice across complex segmentations at scale - became more valuable precisely because the market had made the problem worse.

Which leads to proprietary data. We have 60 billion data points built up over a decade of real enterprise campaigns. Far from being just another wrapper, we had ten years of learnings that no newly founded startup could acquire overnight. We trained a prediction engine on that data - and could back up our promises to deliver increases in engagement.

Of course, not every business starts from this position. But proprietary data, whatever form it takes for your industry, is worth finding. It’s likely more valuable than you think.

An evolving userbase

Our user base evolved alongside all of this. The client conversation shifted. Early on - pre-GPT - every sales meeting began with an education: what is natural language generation? Why trust an algorithm with brand copy? How does machine learning actually improve campaign performance? Post-GPT, those questions disappeared entirely.

They were replaced by a harder one: what makes your AI different from everything else? And so the bar moved. Clients came to us already exhausted by generic tools, already aware that AI-generated copy had a sameness problem. We no longer had to explain what AI was - now, all we had to do was prove that ours was worth the switch.

Last, and perhaps most important, our platform was never built solely around AI. AI was simply the most effective route to our solution. The businesses that folded had built their entire proposition on the novelty of AI. Once it wasn't, they had nothing.

We had been working on the same fundamental problem since 2015, and the arrival of large language models, ultimately, didn't change what that problem was. It just changed the tools available to solve it.

What we know now, having been through the full cycle - from obscurity to gold rush to consolidation - is that the brands who came out ahead weren't the ones who adopted AI fastest.

They were the ones who were clearest about what they were trying to say, and had the infrastructure to say it consistently. That is a harder problem than it looks. It is also the one we have spent ten years solving.

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• Google product approvals are being delayed, impacting small businesses
• Experts advise businesses not to resubmit products needlessly
• Google has not yet commented on the issue

Local SEO professionals, agencies, and small businesses have been reporting widespread issues with new and existing product listings becoming trapped in Google’s approval process, leading to concerns over the upcoming back-to-school period.

According to reports, the issues have been ongoing since July 30 2026, with it being raised both in Google’s own support community and leading local search forums. Businesses from multiple countries have reported the same glitch.

Google has not yet publicly acknowledged the issue, frustrating businesses that are preparing for the upcoming busy period.

Small business reliance on Google

The issue highlights a wider problem for small businesses that rely on platforms like Google to drive traffic to their products and services.

With small budgets and time constraints, small businesses often rely on just a handful of sources to drive the bulk of traffic to their websites. For example, a small store may rely primarily on their Google Business Profile to be the main way potential customers discover them.

So, when issues like these occur and platforms like Google fail to address them, it leaves small businesses frustrated and making best guesses on how to proceed.

Don’t panic

Some businesses have reported continuously editing and submitting products in the hope that they will eventually be approved. According to some experts, this is exactly the wrong approach to take.

"Your first instinct is usually to keep editing the product or submit it again, hoping it will finally go live. Unfortunately, that's often the worst thing you can do,” says Thomas Phillips, Founder and Marketing Expert at DTC SEO Agency. “If this is a platform-wide issue, every resubmission effectively sends your product back into the review process. Instead of getting approved sooner, you risk resetting the clock each time."

For now, it appears the best course of action is to continue updating your site as usual and to avoid purposefully updating and submitting products several times in an attempt to get them approved.

Phillips also advises checking trusted SEO communities to see if others are facing a similar challenge before taking drastic action:

"When multiple agencies start reporting the same behaviour at the same time, it's usually a sign that the problem sits with the platform rather than the business.”

Recently in Geneva, the world's largest forum on AI for Good, gathered around a simple question: how can technology best serve society? While the sentiment goes back to pre-industrial times, the conversation has firmly moved to algorithms. What about the machines that will come next?

Quantum technology has crossed a line. It is no longer a distant promise, but a growing portfolio of real applications. Quantum and quantum-inspired solutions are being applied to the simulations that underpin nuclear power, to protein design in drug discovery, and traffic forecasting in congested cities.

Quantum sensors are being tested for medical imaging, navigation where satellite signals fail, and monitoring carbon storage sites deep underground. These are no longer mere laboratory curiosities: they are increasingly emerging as working programs, with industrial partners and delivery dates.

For those of us in the industry, these developments are both timely and long anticipated. But they also force us to confront a less comfortable reality: the same machines that will one day simulate new molecules will also break the public-key cryptography that secures much of the modern internet.

By some estimates, up to 80% of the world's digital infrastructure is exposed. The world's digital economy - not to mention every AI model celebrated in Geneva, runs on encrypted data. Every health record or financial transaction owes its confidentiality to mathematical assumptions that a sufficiently powerful quantum computer will overturn.

Store now, decrypt later

The threat timeline is worse than most realize. Rather than waiting for when the machine arrives, the clock started the moment adversaries began harvesting encrypted traffic: the so-called “store now, decrypt later” approach.

Data that remains valuable for years, from medical records to state secrets, may already be beyond rescue. Nobody knows exactly when “Q-Day” comes. But those who harvest our data today have time on their side.

This is why quantum-safe security cannot be a footnote to quantum computing. It is the other half of the field, and it needs to mature on the same schedule.

Quantum trip-wire

Two defenses exist, and they are not interchangeable. Post-quantum cryptography replaces vulnerable algorithms with mathematics believed to resist quantum attacks; it is software, it scales, and it is the workhorse of the migration now beginning worldwide.

Quantum key distribution takes a different route: it distributes encryption keys using quantum states of light that cannot be intercepted without being disturbed. In this way, these schemes deploy a “quantum trip-wire", an eavesdropper reveals herself by the simple act of listening-in

Far from being in competition, both algorithmic and physics-based solutions have roles to play in tomorrow's networks. Serious infrastructure will layer both.

Neither of these solutions are theoretical. New post-quantum standards have been finalized, with contributions from research teams around the world. And quantum-secured networks are already running in the field: since 2022, Abu Dhabi has hosted entanglement-based metropolitan networks, like the ADGM Quantum Testbed announced in August 2025.

These are initiatives that bring quantum security to where data actually lives. Long-range terrestrial links, satellite connections and “last-mile” access networks are the next steps, extending that protection between cities and, eventually, across continents. If there's one lesson the field has learned from operating these systems, it's this: migration takes years, so the time to start is before the threat matures, not after.

Depth of capability

There is a broader lesson in how this capability is being built. A growing number of nations have chosen to be builders of quantum technology rather than buyers, developing the full portfolio of sensing, communications and computing solutions. From processor fabrication to control software, to the hardware enabling quantum networks: the word “sovereignty” is often heard as a synonym for walls.

But our experience suggests otherwise. It is precisely the teams that build every layer themselves that can contribute most readily to the global commons, from working with international standards organizations like ITU and ETSI on quantum-safe networking, to releasing open-source middleware that researchers worldwide now use to program quantum hardware, wherever it was made.

It is this depth of capability that turns a nation from a spectator of the quantum era into an active participant.

That should also be the model for the decade ahead. No single country will own quantum technology, and no single vendor should be the sole authority on quantum-era trust. What the field needs now is what the AI community showcased in Geneva: open tools that lower the barrier to entry, interoperable standards so quantum-safe systems can talk to each other, and honest engagement with the risks alongside the promise.

For years, the defining question about quantum computing has been when the machines will arrive. Increasingly, I think that's the wrong question. The more important question is whether the digital world they inherit will still deserve their trust. The answer depends on what we choose to secure, build, and share today.

Quantum for good does not start with a breakthrough. It starts with trust.

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For years, cybersecurity was built on a simple assumption: systems follow defined rules.

Applications do what they are programmed to do, while people log in, are given permissions and access the resources they need. Artificial intelligence (AI) is changing that.

With nearly 50% of cybersecurity solutions buyers expecting AI to be embedded across the cyber stack within three years, organizations are no longer focused solely on protecting applications and access rights.

They also need to secure intelligent systems that can make decisions, interact with users and act autonomously.

AI can draw on models, prompts, context and external tools to understand a goal, make decisions and determine how best to achieve it.

As organizations give these agentic systems greater autonomy across enterprise workflows, the consequences of failure extend beyond generating a wrong answer.

A mistake can now disrupt business processes, influence decisions and trigger unintended actions across connected systems.

An expanded attack surface

Greater autonomy creates new points of vulnerability whenever AI is given access to data, systems and external tools.

Cyber threats, such as attackers manipulating the information AI receives or impersonating trusted users, can alter how it responds or the actions it takes. This could lead an AI agent to retrieve inaccurate information or approve unauthorized actions.

Traditional cybersecurity controls were designed for humans and applications, but autonomous agents don’t fit neatly into either category. Security therefore needs to extend further across the entire AI lifecycle: before go-live, during operations, and at every point where AI learns, decides, and acts.

Organizations need a unified security architecture that provides consistent visibility and controls across both AI and traditional systems, which makes it easier to identify threats, enforce policies and respond quickly when incidents occur.

Trusting AI safely

However, a unified architecture is only part of the solution. Businesses also need to ensure the AI agents themselves can be trusted. Like any trusted user or system, AI agents should have a verifiable identity, tightly controlled access to data and systems, and auditable records of the actions they take.

Without these safeguards, organizations risk creating AI systems that can bypass security and compliance controls because of design flaws rather than malicious intent. Security also cannot stop once an AI system is deployed. Unlike traditional software, AI systems learn from new data, operate in changing contexts and can behave differently over time.

Organizations therefore need continuous monitoring to ensure agents stay within defined boundaries and policies continue to be enforced. That includes clear ownership, escalation paths and kill switches that can safely contain or stop an agent behaving unexpectedly. Some organizations are also beginning to use "guardian agents" that monitor other AI agents and flag unusual behavior.

The level of oversight should reflect the level of risk. AI agents carrying out low-impact tasks can remain largely autonomous, while higher-risk activities - such as updating customer data, approving financial transactions or interacting with production systems - should have stronger guardrails.

Applying controls in proportion to risk allows organizations to capture the benefits of AI while maintaining security, compliance and trust.

Context as a security boundary

Securing AI also means securing the information it relies on. Context is what gives AI agents their power. This includes internal documents, customer information, business rules and previous interactions, helping agents understand a task and decide what to do next.

That also makes context a new security boundary. If the information an AI relies on is inaccurate or has been deliberately manipulated, the decisions it makes can be wrong, even if the underlying model is working exactly as intended. This is known as context poisoning.

Protecting against this means controlling what AI can see as well as what it can do. Agents should only have access to the data and systems they need for a specific task. For example, an AI assistant answering employee questions should not have the same level of access as one authorized to approve payments.

Guardrails must go beyond filtering outputs and extent to protecting the integrity of the information AI uses, ensuring it is accurate, up to date and appropriate for the task at hand.

Governance at scale

Technical controls are most effective when they are supported by effective governance. This requires a joined-up approach that brings together AI and traditional systems, with consistent controls across the business.

Organizations should also define where human intervention is required and who is responsible for the decisions models make. Oversight should focus on the activities that carry the greatest operational, financial or regulatory risk, supported by investment in the skills needed to govern AI effectively and maintain trust in autonomous systems.

The path forward

Ultimately, securing AI is about more than protecting systems from attack. Organizations need the right technical solutions, clear ownership and continuous oversight throughout the AI lifecycle. Trust depends on these elements working together.

The organizations that succeed with AI will not necessarily be those that move fastest, but those that scale it securely and responsibly. The question for leaders is no longer whether to trust AI, but whether they are building systems that deserve to be trusted.

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• Microsoft temporarily rolls back planned Teams tool concerning muted chats
• The update would have made it easier to organize (and ignore) unnecessary conversations
• It's not known when the update will get back in progress

Microsoft has pulled an update which would have made it much easier for Teams users to organize their chats - particularly those which have been muted.

The company had revealed it was working on new built-in sections within Teams to house all the muted chats in one area, meaning users shouldn't be distracted or annoyed by unnecessary alerts.

However the update has now been "temporarily rolled back", with Microsoft apologizing for the "inconvenience".

A bit of a shame?

The move is a slightly odd one, as the description of the update makes it sound like something all Teams users would appreciate.

In its Microsoft 365 roadmap entry, the company said the new grouping will "help reduce clutter and keep conversations easy to find" - but also probably help lower stress levels for users plagued by alerts on chats they don't need to know about.

The change, which was in the works for users across Android, iOS, desktop and web, would have seen muted chats grouped together in a dedicated area, while meeting chats would have been able to be collected into a single section for quick access.

Microsoft had even added that "You’re always in control", as each section would have been able to be activated or turned off based on a user's specific preferences.

The new Meeting chats section would have replaced the Meeting chats filter, found at the top of the chat and channel list, giving users a much better insight to their conversations.

Microsoft hasn't given any details as to why the update is delayed, or when it will be coming back onto the workbench, so we'll just have to wait and see.

As mentioned, it's a shame, as Teams users no doubt would have welcomed any relief from the overload of chats. Slack already offers this feature, with muted channels and conversations hidden away if desired, so maybe Microsoft is taking extra time to finesse and develop the feature before releasing it to users.

Recruitment has long relied on signals that serve as proxies for candidate skillsets: degrees, previous job titles, years of experience, and employer names. Those indicators don’t always show whether someone actually has the skills needed for the role they’re applying for. As AI reshapes work, businesses need clearer evidence of what people can do.

This imperative has led to a drastic shift in hiring norms In the UK, 99 per cent of employers are using skills-based hiring in some capacity, according to research. At the same time, 58 per cent expect more than a third of core job skills to change by 2030. Recruiters now need to hire for current requirements while also thinking about how roles are likely to evolve.

For hiring teams, five changes stand out.

1. Hiring is becoming more skills-first

AI is accelerating the move away from degree-first evaluation. When tools, business needs, and ways of working change quickly, employers can’t just rely on static indicators of academic achievement. They need to know whether candidates have practical, current and job-relevant skills.

Degrees still retain significant signaling value. A degree offers an essential foundation, particularly for critical thinking, communication and domain knowledge. But employers increasingly want additional proof that a candidate can apply those strengths in workplace settings.

For recruiters, job descriptions and selection criteria need to become more skill-oriented. Instead of asking for broad experience in a field, businesses should define the specific capabilities needed for the role: data analysis, AI literacy, cloud computing, cybersecurity awareness, project management, or the ability to interpret AI-generated outputs. A clearer skills profile can also help organizations identify strong candidates who have not followed traditional pathways.

2. AI credentials are changing how experience is weighted

Experience still carries weight, but AI is changing how that experience is judged. In fast-moving areas such as generative AI, data, and cloud, a candidate with recent, verified learning may be better prepared than someone with greater experience but outdated skills.

That’s why 42 per cent of UK employers say they would choose a less experienced candidate with a GenAI credential over a more experienced candidate without one. It shows how quickly the value of verified AI capability is rising.

This has practical implications for recruiters. Years of experience shouldn’t be treated as a signifier of readiness. In some roles, recent evidence of applied learning may be more useful. The challenge is to distinguish between candidates who’ve completed primarily theoretical training and those who can show they’re ready to apply what they’ve learned.

3. Verification is becoming more important

AI has made it easier for candidates to produce polished CVs, cover letters and portfolios. It has also made it harder for employers to know which evidence to trust.

This is likely to increase the value of credentials that verify skills to employers. In the UK, 95 per cent of employers say micro-credentials help identify candidates with real-world, applied expertise in areas such as AI, data, and cloud. That matters because recruiters need evidence that can stand up to scrutiny.

The most useful credentials are those that assess applied skills, rather than only content completion. Employers will increasingly look for evidence that a candidate has built something, solved a practical problem or completed a project relevant to workplace needs.

Recruitment teams should reflect this in their processes. CV screening should be supported by practical assessments, structured interviews, and work-sample tasks. This blended approach will make decisions more accurate.

4. Recruiters need to assess human judgement alongside AI skills

AI literacy is becoming a common requirement, but it is insufficient to simply possess technical knowledge, particularly for those deploying AI in non-technical roles. Businesses need people who can work effectively with AI, including knowing when to question it.

As AI becomes embedded in everyday work, candidates will need to show that they can evaluate outputs, check sources, spot weak reasoning, and apply context. A candidate who accepts AI-generated content uncritically will introduce risk, irrespective of technical proficiency.

This adds novel new stages to the assessment process. Recruiters may need to ask candidates to critique an AI-generated response, improve a flawed analysis, or explain what further evidence they’d need before making a decision. These exercises can reveal whether someone has the judgement and domain expertise required to use AI tools responsibly.

For many roles, the differentiator won’t be whether a candidate can prompt a system: it’ll be whether they can turn AI output into sound business action.

5. Skills-first hiring must complement skills-first training

AI is compressing the shelf-life of skills. If, as expected, over a third of core skills will change by 2030 for many UK employers, hiring alone will not be enough to keep pace. In fact, 74 per cent of tech leaders acknowledge they cannot depend on new hires alone to fill AI skills gaps. That means skills-first hiring needs to become part of a wider talent development framework.

The same approach that helps recruiters identify the capabilities needed for a role can also help businesses map the skills they already have, spot gaps across the workforce, and create clearer routes for employees to build the capabilities the organization will need next.

Some capabilities will still need to be brought in from outside the organization, particularly in fast-moving areas such as AI, data and cloud. But many skills will also need to be developed internally as roles evolve. The strongest businesses will combine skills-based hiring with ongoing upskilling, giving employees clear routes to adapt as roles change.

The new recruitment advantage

AI is changing what employers need to know about candidates. Businesses that can identify real capability, verify applied skills, and recognize potential beyond traditional signals will be better placed to hire well. The value of this approach is already visible in workplace outcomes. 92 per cent of employers say entry-level hires with micro-credentials perform better in their first year on the job.

For candidates, the message is just as clear. In an AI-enabled labor market, employability will depend less on what someone once learned and more on what they can prove they can do now, and the evidence they’re still learning.

For employers, recruitment needs to become part of a broader skills strategy. AI may be changing the work, but the hiring challenge remains human: finding people with the skills, judgement and adaptability to help businesses compete and innovate.

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• Swiss government confirms attackers breached BIT’s SharePoint servers
• Investigators suspect exploitation of recent SharePoint flaws
• No sensitive or confidential data is believed to have been stored on the platform

Cybercriminals broke into the IT network of the Swiss government and stole data from roughly 200 accounts. As a result, the Swiss government disconnected some of its servers from the wider internet and launched an investigation.

In an announcement, the Swiss government said that on July 28 2026 its security specialists noticed “abnormalities” in the Federal Office for Information Technology and Telecommunication’s (BIT) SharePoint servers.

Three days later, on July 31, the investigators determined that the attackers accessed data found in around 200 accounts, both user and technical.

Two vulnerabilities

The investigation is currently ongoing, the agency said, adding that it is getting support from Microsoft, as well. So far, the identity of the attackers is unknown, and the stolen data has not yet leaked to the dark web.

“No confidential information or particularly sensitive personal data may be stored on the SharePoint platform,” the announcement reads.

While BIT has not yet determined the initial access vector, it suspects it to be one of two flaws in SharePoint that Microsoft fixed last month:

“In mid-July, Microsoft announced several vulnerabilities in SharePoint,” it says in the announcement. “After the publication of the corresponding security updates, the FOITT immediately started work on importing them into its own systems.”

“The cyberattack was carried out by previously unknown actors, which was presumably made possible by exploiting these vulnerabilities in the SharePoint software.” It did not say which vulnerabilities those are, but in its report, BleepingComputer says that it could be one of these two: CVE-2026-56164 (an actively exploited privilege escalation vulnerability), or CVE-2026-50522 (a critical remote code execution flaw later exploited to steal SharePoint machine keys and maintain access after servers were patched).

Given its popularity among businesses of all sizes, SharePoint is a major target for cybercriminals. So far, no threat actors claimed responsibility for the attack, or demanded any ransom in exchange for the stolen data.

Via BleepingComputer

Artificial Intelligence (AI) is rapidly evolving. Across industries, many organizations are increasingly deploying AI into systems that must run continuously, securely, and at scale.

As AI adoption accelerates, one thing is becoming clear: infrastructure planning cannot wait.

AI workloads are becoming more interconnected, distributed, and operationally integrated across cloud, data center, and edge environments. Infrastructure planning now requires organizations to align compute, networking, software, memory, and operational requirements across increasingly complex environments.

As a result, many enterprises are beginning infrastructure planning sooner rather than later.

The cost of waiting

As AI becomes more integrated into everyday business operations through continuous inference and agentic AI systems, infrastructure demands are evolving significantly.

Modern AI deployments increasingly require:

• Continuous inference running around the clock
• Multi-agent systems coordinating across applications and databases
• Real-time orchestration across cloud, data center, and edge environments
• Strong governance, security, and operational efficiency

These workloads require more than raw compute performance. They require balanced infrastructure where compute, networking, software, memory, and operational workflows work cohesively at scale.

Because of this, enterprises are beginning AI infrastructure planning earlier, recognizing that planning, testing, and Proof of Concepts (PoCs) for complex systems like this take time.

At the same time, the cost of delaying AI infrastructure planning is becoming more apparent. Delays can slow deployment readiness and postpone AI-driven benefits such as productivity gains and operational automation. As AI demand continues to rise, organizations are prioritizing earlier planning to secure the compute capacity needed to support long-term AI growth.

As AI infrastructure becomes more complex, infrastructure planning needs to begin earlier than traditional IT upgrade cycles. Evaluating workloads, validating deployment models, and ensuring scalability across environments takes time and time is of the essence if we want to be ahead of our competitors.

AI is now a systems challenge

The conversation around AI infrastructure often begins with Graphics Processing Units (GPUs). But as deployments scale, AI performance depends not on individual components, but on how the entire system operates together.

Modern AI infrastructure relies on Central Processing Units (CPUs) for orchestration and data movement, GPUs for large-scale parallel compute, high-speed networking for low-latency communication across systems, and open software platforms for portability and scalability.

As AI systems become more distributed and inference-driven, orchestration and system balance become critical. CPUs play a pivotal role in managing workload coordination, memory access, and GPU utilization, ensuring infrastructure operates efficiently under sustained demand.

This shift reflects a broader industry reality: AI is no longer just a GPU problem. It is a full-stack infrastructure challenge that organization must tackle early on.

Planning for distributed AI

AI is also scaling in multiple directions at once.

Some workloads are expanding into large, centralized clusters, while others are moving closer to where data is generated – including edge deployments such as in factories or hospitals, and AI-enabled endpoints like the PCs.

For organizations, this creates unique infrastructure considerations around hybrid cloud, on-premises deployments, edge AI, compliance, and latency-sensitive applications.

This diversity underscores the importance of infrastructure strategies designed for modularity, portability, and adaptability that necessitates upfront planning.

Openness and flexibility matter more than ever

As AI innovation accelerates, organizations are prioritizing infrastructure flexibility to support rapidly evolving models, frameworks, and deployment environments.

Open ecosystems can reduce integration complexity while supporting broader compatibility across software frameworks, cloud environments, and deployment architectures. They also provide greater flexibility to evolve infrastructure strategies over time while helping avoid the migration costs that can come with highly closed or single-vendor environments.

For many organizations, openness is no longer just a developer preference. It is becoming an important consideration for balancing performance, operational efficiency, cost optimization, and long-term infrastructure investment.

This is another reason infrastructure planning must happen early. Building AI environments that remain scalable, portable, and adaptable over time require long-term thinking around openness and interoperability from the beginning.

Infrastructure readiness will define the next phase of AI

The next phase of AI growth will reward organizations that take a proactive approach to infrastructure planning.

Organizations that delay infrastructure planning may find it more challenging to deploy AI tools down the road, not only due to not having ample time to plan and test, but not securing the compute resources needed early on.

The cost of waiting is becoming ever clearer.

Ultimately, the companies that succeed in the next phase of AI will not necessarily be those with the largest clusters, but those that plan early and build balanced, scalable, and open infrastructure designed to support continuous innovation in an increasingly AI-driven economy.

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Every four years, football's best players are tested on the world's biggest stage.

Less visible is the test taking place behind the scenes.

As billions tune in, broadcasters and streaming platforms face their own high-stakes challenge: delivering seamless live experiences at a scale few events can match.

FIFA estimates that around 5 billion people engaged with the 2022 World Cup, with the final alone reaching nearly 1.5 billion viewers worldwide.

In 2026, that audience has been presented with an even bigger tournament: 48 teams playing 104 matches across Canada, Mexico and the United States.

More than 54 million viewers across the three host countries watched their national teams' opening matches, while the United States' game against Paraguay drew a combined 27.5 million across FOX and Telemundo, the most-watched FIFA World Cup match ever broadcast in the country.

By the end of the group stage, 4.64 million spectators had filled 99.7% of available seats. That scale is a real-time stress test for every part of the live-video ecosystem.

From passive viewing to active participation

Beyond sheer audience size, the difference lies in how fans consume it. They no longer simply watch. They move between platforms, share highlights, expect instant access to key moments and want experiences tailored to their own interests.

Streaming is no longer just a distribution channel; it's a product in its own right. Brazil’s group-stage match against Haiti reached 51.3 million viewers across Globo's wider media ecosystem, while CazéTV set a worldwide YouTube record for the most-watched football match streamed on the platform.

World Cup content generated 11 billion video views across social media platforms during the group stage alone, and official broadcasters published more than 44,000 pieces of content on TikTok.

A modern match is simultaneously a live program, a source of social clips, a statistics feed and a second-screen experience.

Rethinking the production workflow

That shift starts with the production workflow. The same match is now produced simultaneously for stadium scoreboards, connected TVs, mobile apps and global streaming platforms, with capture, ingestion, encoding and delivery all part of a single content pipeline.

Sixteen optical tracking cameras installed in each stadium can produce more than 150 million data points per match, helping officials review incidents and giving media partners new ways to produce highlights.

The real challenge is bringing together live delivery, audience data, advertising, captions, multi-language audio, and interactive experiences alongside tracking, commentary, graphics, and officiating data.

What fans expect: reliability, personalization, speed

For viewers, success comes down to three things: reliability at scale, personalization and speed. Fans will tolerate a lot, but they won't forgive a stream that buffers during a decisive goal or runs so far behind live play that social media spoils the moment.

Personalization must happen without undermining performance, delivering different recommendations, languages, statistics, camera feeds and advertising while maintaining the resilience of a mass broadcast.

AI is reshaping live sports production

Artificial intelligence is central to delivering those expectations. Rather than replacing production teams, AI is enabling rights holders to produce and distribute content at a scale that would previously have required far larger operations. Automated highlight clipping is one of the clearest examples: AI can identify key moments, package them and distribute them within minutes.

For rights holders, the difference between publishing a goal two minutes after it's scored rather than twenty is the difference between leading the conversation and chasing it. Match summaries, commentary, captions and translations can also be generated automatically, and platforms can match each fan with the content they are most likely to watch next.

A glimpse of the future: personalized sports at scale

The broader ambition is visible at the top of the game. The PGA TOUR now turns each week's action into roughly 7,000 AI-generated highlight clips across dozens of markets, so a fan can follow one player or catch up on key moments without waiting for the main broadcast. The 2026 World Cup has produced a similarly vast library of stories. A record 215 goals were scored during the group stage, an average of three per match.

Tournament debutants Cabo Verde went undefeated, with Kevin Pina scoring the country's first World Cup goal, while Japan's 4-0 victory over Tunisia was both the 1,000th match in World Cup history and the biggest ever by an Asian team. These are exactly the kinds of stories that automated tagging, rapid clipping and intelligent recommendations can bring to the right audience.

Immersive viewing and accessibility

The next generation of live sports streaming will be defined by richer viewing experiences. Multi-view streaming allows fans to follow simultaneous matches, while alternative camera angles and player-specific feeds provide greater control over how the action is consumed.

Real-time data integration can bring live statistics directly into the viewing experience without interrupting the match. AI is also making captioning, translation, and audio description production-ready at scale, allowing broadcasters to localize live coverage without a proportional increase in costs.

However, human oversight remains essential for names, sporting terminology, and cultural context.

The technology behind global scale

Supporting all of this requires resilient IT infrastructure and scalable delivery platforms capable of broadcast-quality reliability and low latency, even as millions connect simultaneously. It also requires organizations to move beyond fragmented technology stacks.

A unified architecture makes content easier to reuse: one live signal can support a full broadcast, mobile highlights, social clips, advertising inventory, archive content and personalized recommendations.

Furthermore, protecting that content is just as important as delivering it. Live sport is uniquely vulnerable to piracy because its commercial value exists almost entirely during the match. Digital Rights Management remains the foundation, but forensic watermarking is becoming increasingly important, embedding invisible identifiers so pirated feeds can be traced and removed while the event is still live.

What's next: The future of live sport at scale

The World Cup ultimately highlights that live video has become a complex, data-driven product where success is no longer defined solely by picture quality or reach, but by how effectively AI, unified content workflows. and scalable technology work together under pressure.

The challenge for the industry is not understanding what works at World Cup scale, but applying those lessons consistently across every live event.

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When was the last time you met your newest hire in person?

For many organizations, particularly those operating remotely, the answer is increasingly never. With one in five companies worldwide adopting a fully remote model, hiring virtually has become increasingly popular, enabling businesses to access global talent pools and scale faster than ever before.

But in removing geography as a constraint, it has also stripped away one of the most fundamental layers of trust: the ability to verify, face-to-face, who you are actually employing.

This shift is giving rise to a new and largely under-recognized threat, the “deepfake employee”, where threat actors use synthetic identities, voice cloning, and real-time deepfake video to pass interviews and secure legitimate employment.

Accelerated by advancements in AI, it is now possible to create convincing digital personas at scale, lowering the barrier to entry for fraud and enabling highly organized operations to target corporate hiring pipelines.

In practice, these attacks can be surprisingly difficult to detect. A candidate may appear on a video interview with a natural-looking face and voice, answer questions fluently, and provide what seem to be legitimate credentials. Behind the scenes, however, AI tools can subtly alter facial expressions, sync lip movements to a cloned voice, or even feed real-time responses.

To the hiring manager, there is little reason to suspect anything is wrong. The deception often only becomes apparent much later, if at all, when activity inside the organization begins to raise concerns.

This risk is already playing out in the real world. In a recent experiment, a cybersecurity expert used AI to create deepfake personas – one a white man similar to himself and another of an Asian woman, and successfully secured two separate tech roles, beating hundreds of other candidates.

Using AI-generated credentials, real-time voice modulation and live deepfake video, both synthetic identities progressed through interview stages undetected, with employers unaware they were interacting with an entirely fabricated candidate.

Cloudflare’s latest threat research highlights the scale and sophistication of this activity. Organized “remote worker” fraud operations are using fabricated identities, deepfake-assisted interviews and remote access “laptop farms” to infiltrate payrolls. In some cases, multiple individuals operate behind a single employee identity, maintaining persistent access while appearing as one consistent, legitimate user.

Once hired, these actors are no longer external attackers. They become insider threats with valid credentials, company-issued devices and trusted access to systems. As Cloudflare notes, by the time these individuals are identified, they are already operating inside the perimeter, often blending in with normal business activity.

What needs to change

With nearly 60% of organizations having experienced deepfake-driven incidents, and 48% reporting damage from AI-generated impersonation or misinformation, it is clear that identity infrastructure has become a primary attack surface. Attackers are shifting away from “breaking in” to “logging in” using legitimate credentials obtained through deception.

At the heat of this issue is the flawed assumption that identity can be verified once and then trusted indefinitely.

In physical environments, identity is rarely in doubt. You can see who walks through the door, recognize familiar faces and detect inconsistencies in behavior. In virtual environments, however, organizations rely almost entirely on screen names, login credentials and video, none of which reliably confirm who is actually behind the screen.

Accounts can be shared, credentials can be compromised, and even live video can be manipulated.

This creates a critical vulnerability at the point of hire. A candidate may present documentation, pass background checks and complete onboarding, but in a world of synthetic identities, that initial verification is no longer enough.

Building continuous identity assurance

To address this challenge, organizations need to move beyond static identity checks and towards continuous identity verification. This means verifying not only who someone is who they say they are and that they are a real human at the point of hire, but ensuring that the same individual remains present and authentic throughout their interactions with the organization.

The strongest form of defense lies in continuous biometric verification of the user’s identity. Rather than relying on a single factor, such as facial recognition or voice authentication alone, fused biometrics combines multiple identity signals, such as facial characteristics, voice patterns and behavioral cues, into a single, layered verification process, verifying directly that a real, live human is there.

It is no longer enough to confirm a person’s identity at a single moment in time. Organizations need confidence that the same individual is consistently present across every critical interaction, from interviews and onboarding, through to system access and sensitive transactions. Without this continuity, identities can be shared, replaced or hijacked without detection.

Fused biometric verification can create layered validation that is significantly harder to replicate or manipulate, enabling it to detect and block attempts to impersonate users through synthetic voices, deepfakes, or recorded audio and video.

While a single modality might be fooled by a sophisticated synthetic input, combining biometric modalities; facial recognition, voice recognition, and speech pattern recognition, it becomes significantly harder for fraudsters to mimic an identity.

Advanced biometric verification technologies are trained on large datasets of both genuine and synthetic voice samples, enabling them to recognize subtle acoustic differences between natural and deepfake voices. This helps organizations detect voice-cloning attempts, even when the audio sounds convincing to human listeners.

Crucially, fused biometric verification can operate passively in the background, consistently verifying that the right person is accessing systems, enabling smoother, lower-friction experiences and reduces the need for frustrating repeated verification attempts.

As organizations continue to embrace remote work and digital-first operations, cybercriminals will increasingly find new vulnerabilities to exploit. The question is no longer just how to keep threats out, but also how to ensure that those already inside are truly who they claim to be.

In a world where identities can be fabricated, cloned and manipulated with ease, trust cannot remain static. It must be continuously proven.

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• Ministers in the UK are consulting on workplace monitoring technologies
• Employers may have to ask permission to implement surveillance tools that ensure employees are working as contracted
• The consultation includes references to the use of artificial intelligence-assisted monitoring

The British government’s Make Work Pay strategy to overhaul employment rights and end exploitative work practices has opened consultation on workplace monitoring technologies, with the Department for Business and Trade seeking to establish whether existing rules covering things like biometric surveillance, keystroke logging, and productivity scoring are fit for purpose.

Current rules over the use of workplace monitoring technology (WMT) may be fit for purpose, but the consultation aims to establish whether non-statutory guidance, a formal code of practice, or a legal framework is required.

The consultation – which also cites artificial intelligence as a tool to assist monitoring, closes September 30, 2026.

Identifying WMT

Workplace monitoring technologies have developed with the pace of digital expansion in offices and factories, with notable examples including email and web tracking, along with controversial tools such as keystroke logging, and screen and app monitoring.

Mobile workers and couriers are also subjected to GPS monitoring, while access to workplaces is typically subject to biometric credentials, a modern-day version of “clocking in.”

So, monitoring practices in the workplace are nothing new, and CCTV has long been used to observe location, activity, and appropriate behavior. Responses to the consultation will no doubt include concerns over the expansion of these technologies into the remote workspace, where tools like keystroke loggers and screen monitoring are considered intrusive.

Meet the AI boss

Furthermore, some new considerations, such as automated decision-making (ADM, which can make decisions without human involvement) and algorithmic management (tools that can, for example, alter shifts based on availability and performance) will also come under review with the consultation.

The consultation announcement states:

"AI and innovation are central to the government’s strategy for growth. The Chancellor has prioritised AI adoption, as where the biggest gains lie for UK growth, and has set the ambition for the UK to be the fastest adopter of AI in the G7. To support rapid and responsible adoption, we are focused on creating the right conditions, providing the necessary infrastructure and managing the technology transition fairly and carefully."

Additional regulatory requirements

While the consultation is welcome and gives employers and employees the chance to deliver their thoughts – and affords the Labour government the opportunity to underline its Make Work Pay policies – the end result will inevitably means more regulatory work for employers.

Non-statutory guidance would be the preference, as this has a smaller impact in a workplace that already has to consider GDPR and the EU AI Act. Larger organizations also need to accommodate local employment laws. But there is every chance that the eventual decision will result in the introduction of a statutory policy governing workplace monitoring technologies, particularly where AI is concerned.

• AI is opening to the door to advertising on streaming TV platforms for SMBs
• 37% of small and medium businesses feel cross border campaigns are more accessible thanks to AI
• SMBs see AI as key tool in remaining competitive in the future

Barriers to cross-border and multi-region advertising are being broken by AI, according to a new survey by Amazon Ads.

Surveying 4,100 respondents across 14 countries, the results found nearly three-quarters (73%) of UK SMBs have found that AI advertising tools make streaming TV an affordable and accessible platform for their promotional messaging.

The questions focus on the difference between advertising accessibility in 2026 compared with 3 years ago, when 69% found cost to be the main barrier against streaming TV advertising access.

How does AI help SMBs advertise?

Now, over half of SMBs (54%) interested in advertising on TV believe that AI tools can enable them to reach new audiences and increase brand awareness (53%). Meanwhile, a majority of SMBs believe AI can be instrumental in expanding their competitiveness.

Amazon Ads employs AI tools to create and manage AI campaigns, as well as improve opimization and targeting. It offers image and video generation solutions to create realistic visuals and uses a chatbot (“conversational agent”) to develop concepts and scripts with the advertiser.

The survey also found 68% of UK SMBs found AI advertising has supported growth, and 52% highlight that it has supported expansion into other countries. Of those surveyed, 37% stated that cross-border campaigns seemed out of reach just three years ago.

"What we're seeing is a real shift in confidence among small and medium-sized businesses," noted Cyril Clerget, General Manager of Global SMB Advertising at Amazon Ads. "AI-powered tools are helping them explore advertising channels — like streaming TV — that once felt out of reach.”

The survey has also found that 88% of SMBs are planning to use AI for creating adverts, and 74% aim to use the technology to produce streaming ads. Interestingly, the survey also found that 45% intend to use AI for competitor analysis in the future.

Is AI the future of affordable advertising?

Affordable AI tools seem to be an attractive prospect for SMBs wishing to aim for streaming TV. Over the past few years, radio advertising has become more targeted thanks to increasing numbers of digital stations, with advertisers able to tailor to specific demographics.

Streaming TV offers a similar curation, and 75% of surveyed UK SMBs see AI as the answer to improving their future competitiveness.

• Ukrainian drone destroys Russian flag moments after dramatic aerial deployment attempt
• Competing battlefield videos continue fueling disputed territorial claims across eastern Ukraine
• Analysts continue examining disputed battlefield footage for signs of digital manipulation

Ukrainian forces released footage showing a Russian flag destroyed roughly 30 seconds after a drone placed it on a damaged building in the Sumy region.

According to Ukraine's 105th Territorial Defense Brigade, Russian operators dropped the flag from a drone before Ukrainian personnel responded with an FPV drone strike.

The video shows a Russian drone placing a flag associated with Russia's Airborne Forces onto the roof of a damaged residential building in Velyka Pysarivka.

Drone operation quickly ends symbolic battlefield display

Footage later captures a Ukrainian FPV drone approaching the location before striking the flag shortly after it landed, ending the display within approximately 30 seconds.

The 105th Territorial Defense Brigade said drone operators located the position before Russian forces could use the footage to reinforce claims of battlefield progress.

"Enemy rags do not remain on Ukrainian soil for long," the 105th Territorial Defense Brigade said about the operation.

Velyka Pysarivka lies close to the Ukrainian border with Russia and has experienced repeated attacks while remaining an active area of military operations.

Fighting around the settlement continues as Russian forces conduct cross-border activities while attempting to expand operations across parts of the Sumy region.

The released footage focuses on the sequence involving the flag placement and the subsequent strike instead of showing any broader military engagement nearby.

Symbolic footage continues shaping competing battlefield narratives

Throughout the conflict between Russia and Ukraine, both sides have repeatedly made competing claims about capturing territory, often supported by battlefield footage.

The latest video follows another disputed incident involving territorial gains that was circulated through Russian military communication channels.

Russian sources previously circulated a video claiming forces had captured Bilyi Kolodyaz in Ukraine's Kharkiv region after raising a Russian flag inside the settlement.

Analysts later questioned that recording after identifying what they described as signs suggesting digital manipulation within several parts of the footage.

Among those observations were an unusually oversized soldier together with a flag that reportedly disappeared from the frame during the recording sequence.

Neither side's battlefield footage can always be independently verified immediately because active combat conditions often limit direct access to the reported locations.

Military analysts therefore continue examining released videos alongside satellite imagery, geolocation evidence, and additional sources before confirming claims involving territorial advances or battlefield events.

The flag incident in Velyka Pysarivka adds another example showing how symbolic actions increasingly accompany frontline operations.

As of now, independent verification of the footage remains unavailable, making its authenticity difficult to establish.

Via United24media

• China's State Council published a revised integrated-circuit layout-design regulation, deleting the words "semiconductor integrated circuit" from its definitions article, essentially rewriting what it considers an integrated circuit
• The regulation does retain mentions of "semiconductor material substrate", but chooses to expand on its definition in a separate clause
• It also expressly extends protection to photonic and quantum chip design layouts from October 2026

China's State Council has published the revised Regulations on the Protection of Layout Designs of Integrated Circuits, the first systematic rewrite of a text that had sat untouched since April 2001.

The revision was adopted at the State Council's 91st executive meeting on July 10, 2026, promulgated as Decree No. 842 on July 23, and takes effect on October 15, 2026.

The change drawing the attention is also the smallest in the document which saw "semiconductor integrated circuit" struck from the front of the definitions article, so that Chinese law now effectively protects the layout of an integrated circuit rather than the layout of a semiconductor one.

A more liberal approach to chip design IP from China?

Beijing's aim is to bring photonic and quantum chips within the same protection regime that semiconductor designs already enjoy, but the change is being read as a claim to future leadership in chip design.

The National Intellectual Property Administration published two expert interpretations alongside the regulation.

The one generating the most buzz is by Guo He of the Intellectual Property Academy at Renmin University of China. Guo notes that photonic integrated devices and quantum chip technology are advancing quickly and that optoelectronic chips are already in wide use in some fields, then concedes that the deletion looks inconsequential on its face, since monolithic integrated circuits everywhere still need semiconductor material as a substrate.

His case for its significance is about posture rather than capability: the point of formally striking the phrase, he argues, is to show that the Chinese are "no longer conservatives in the intellectual property system."

The claim is about legislative attitude rather than actual leadership, but it lands at a moment when the US and China are still wrestling over AI, with chip design a crucial factor: whoever has the fastest silicon to train their models holds a significant edge.

The US chip embargo, which has since softened, has produced an unintended beneficiary in Huawei, whose chairman thanked the US government for sanctions he credits with building China's domestic semiconductor chain.

The regulation indicates that China is seeking to secure its IP both at home and abroad as the state continues to invest heavily across that chain. Layout protection is a serious subject, and The Register's analysis notes why: a die layout is expensive and slow to produce, and copying one is a shortcut.

Strengthening that protection is the revision's main purpose. The provisions doing the work, however, have nothing to do with definitions. They cover damages, tighten the requirements governing how designs are submitted, and allow third parties to request revocation of a registration, a power previously held only by the administration, with outside parties limited to submitting suggestions. A revoked right is now deemed never to have existed.

Alibaba, Huawei, Cambricon and Loongson have all shipped parts that credibly compete with American designs, and protecting them from copyists is a rational move for Beijing.

China's real ambition, however, may lie elsewhere, as the Jamestown Foundation's April 2026 assessment sets out: Chinese labs sit at or near the frontier on several photonic research benchmarks, while the United States and Taiwan still hold the segments of the supply chain that turn demonstrations into shippable products.

By revamping its regulations, China hopes to keep its photonics research and, in future, its quantum research, visible yet protected.

If photonic and quantum layouts do become commercially significant, the material-specific language embedded in American and European national instruments creates a gap someone eventually has to close. Beijing has moved first, at least on paper, even if it lacks the tools to build the chips just yet.