Google’s rumoured Googlebook laptop is leaking again, this time in what appears to be its first outing from Asus.

The leaked device, reportedly called the Asus Googlebook CX9406 (snappy, eh?), surfaced briefly on the iF Design website before the listing was removed. While the page described it as a Chromebook, several details suggest otherwise.

Most notably, the listing references an operating system called “Aluminium OS”, rather than ChromeOS. Previous leaks have pointed to Google’s upcoming laptop platform using the same name internally. However, it’s still unclear whether Aluminium OS will be the final branding when the devices launch.

The hardware itself also hints at something different. Images show an LED “Glowbar” positioned above the Asus logo on the lid. This is expected to work similarly to the HiLight notification strip rumoured for Google’s upcoming Pixel 11 phones. The keyboard also swaps the traditional Windows key for Google’s familiar “Go” logo.

After drifting aimlessly for more than a year and a half, a discarded SpaceX Falcon 9 upper stage finally ran out of empty space on Wednesday. The four-ton cylinder, roughly the size of a school bus and more than 40 feet long, slammed into the Moon at 5,400 miles per hour near Einstein Crater on the sunlit western limb.

SpaceX launched a rocket from pad 39A at the Kennedy Space Center on January 15, 2025. The aim was to launch two private lunar landers, Firefly Aerospace’s Blue Ghost and ispace’s Resilience, on a path to the Moon. Blue Ghost landed safely on the Moon’s surface, and then there was Resilience. Unfortunately, Resilience didn’t fare so well. Once the top stage had completed its burn and jettisoned the landers, it had no further instructions and insufficient fuel for any course correction. It was just lying there, floating, until the combined force of the sun’s gravity, the Earth’s gravity, and solar activity gradually began to alter its route, and the Moon became the inevitable destination.

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Bill Gray, a skilled amateur astronomer who first raised the alarm a few months ago with available orbital data, calculated the impact window. When the time arrived for astronomers all across the world to focus their telescopes at the anticipated impact zone, they were unable to see anything. That was due to the fact that the impact zone was on the edge of the moon’s near side during daylight hours, which was not optimal for terrestrial observers. Marco Langbroek, a Dutch astronomer, trained in the area but discovered nothing, believing it was a long shot even before. Still, he believes the stage made it.

Confirmation came from a Boston University team led by Carl Schmidt, who discovered traces of a large plume of sodium and lithium streaming from the crash site.You could see this plume stretching for miles. Unfortunately for ground-based telescopes, it was shortly after daylight, so you wouldn’t get a good look. The European Southern Observatory’s Very Large Telescope in Chile was able to capture some images of the debris cloud, which lingered for at least 5 to 10 minutes, after which it was still unclear whether the impact had occurred, but Schmidt is certain that what his team saw was the impact. Both NASA’s Lunar Reconnaissance Orbiter and the South Korean Danuri spacecraft will hopefully be able to take before and after photos of the new crater when they pass over, as it is predicted to be 60 feet broad and 12 feet deep.

The blast produced when the rocket stage collided with the Moon was very small in comparison to the larger lunar impacts that occur on occasion, weighing approximately three tons of TNT, but nonetheless noteworthy. It hurled lunar dust and debris in all directions. Though astronomers point out that impacts of nearly the same energy occur on the Moon on a regular basis, the fact that these objects are man-made raises questions. Not to mention how frequently they occur these days. A similar incident occurred in March 2022, when a Chinese rocket stage formed a double crater on the moon’s far side under similar conditions.
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It’s a little more than three months until Grand Theft Auto 6 rocks the video game industry, and other than two trailers, little of the game has been shown by developer Rockstar Games. The first real look at maybe the most hotly anticipated game ever is coming soon, but fans will need a Netflix account to watch it first.

GTA 6 will make its debut on the streaming service on Aug. 27, Rockstar Games announced Thursday, in what’s likely an extended preview of the game. Grand Theft Auto 6: An Extended Look will premiere first on the streaming platform at 12 p.m. PT (3 p.m. ET) and then on a new GTA 6 YouTube channel at 6 p.m. PT (9 p.m. ET) the same day.

The developer didn’t provide any additional details about this GTA 6 preview or how long it will be. There was also no info provided on this partnership between the game company and Netflix, and whether it signalled an ongoing retlationship. It’s an unprecedented move to have the game first shown on a paywalled streaming service.

Rockstar Games didn’t immediately respond to a request for additional comment.

“Grand Theft Auto reveals have become cultural moments in their own right. The anticipation and fandom around Grand Theft Auto 6 is unprecedented, and we’re honored that Rockstar Games has partnered with us to debut the next part of the Grand Theft Auto story with Netflix members first,” Brandon Riegg, Netflix’s vice president of nonfiction series, said in a statement. “It’s a reflection of what we hope Netflix is becoming: a place where the most ambitious storytelling, from any medium, can find the biggest possible audience.”

Netflix and Rockstar Games have had a close working relationship in recent years. Back in 2023, Grand Theft Auto: The Trilogy made its debut on Netflix Games.

The streaming platform has had its ups and downs with gaming since it started offering mobile games in 2021. Back in April, the company introduced an ad-free gaming platform for kids called Netflix Playground, but in July, Netflix removed more than 20 games from its service. There are still more than 100 games on Netflix Games, according to its site, including Sonic Mania Plus, Red Dead Redemption and WWE 2K25 Netflix Edition.

GTA 6’s release date is set for Nov. 19, and it will launch on the PS5 and Xbox Series consoles for $80.

Oscar Gonzalez

Contributing writer

Oscar Gonzalez is a Texas native who covered video games, conspiracy theories, misinformation and cryptocurrency.

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For astronomy enthusiasts, August 2026 will be a month to remember. Some of the most spectacular celestial phenomena—including ones that are easy to observe without telescopes or other specialized equipment—will all occur during the same week.

It’s a great time to get your viewing plans together and start refreshing your weather app to see whether the skies will be clear where you are to catch all the happenings in the heavens.

Perseid Meteor Shower

The Perseids are among the most spectacular meteor showers of the year, along with the Geminids in December and the Quadrantids in January. Every August, Earth passes through the trail of particles left behind by Comet 109P/Swift-Tuttle. As the fragments enter the atmosphere at high speed, they produce an intense meteor shower.

In 2026, activity will peak during the night of Wednesday, August 12, and extend into the early morning hours of the following day. It will coincide with the new moon. That means the sky will be exceptionally dark, offering prime viewing conditions.

The Perseids appear to radiate from the constellation Perseus. In reality, the meteors can streak across any part of the sky, but locating that constellation with the help of a stargazing app can make it easier to know where to look for the best chance of catching streaks across the night sky.

Solar Eclipse

The main astronomical event of the month will be a total solar eclipse. Not everyone will get a glimpse, though, with the path of totality crossing sliver of Spain, Portugal, Iceland, and Greenland. Areas outside those locations will still be treated to a partial eclipse.

The timing will be particularly dramatic, with totality set to occur at sunset on August 12. The Sun’s low position above the horizon will offer an unusual—and photogenic—scene.

This will be the Iberian Peninsula’s first time seeing a total solar eclipse in more than a century. Madrid is in the path of totality as are regions that include Galicia, Asturias, Castile and León, Madrid, Aragon, Catalonia, Valencia, and the Balearic Islands. In the rest of the country, the eclipse will be partial.

Those outside the eclipse path will also be able to follow the event. Agencies such as NASA and the European Space Agency will broadcast the event live. The main piece of advice for those trying to observe it: Do not under any circumstances stare directly at the sun.

Planetary Alignment

As if the solar eclipse and the meteor shower weren’t enough, in the early morning hours of August 12, an alignment of six planets will also occur. Jupiter, Mercury, Mars, Uranus, Saturn, and Neptune will form an apparent line in the sky.

Observing this alignment is more challenging than watching the meteor shower given the faintness of some of the planets. However, if atmospheric conditions are favorable, it will be possible to see Mercury, Mars, and Saturn with the naked eye near the horizon shortly before sunrise. To observe Uranus and Neptune, you’ll need a telescope.

Lunar Eclipse

August 27 will close out the month with a partial lunar eclipse that will cover up to 96 percent of the moon’s disk. Unlike solar eclipses, which only cuts a very narrow path across the Earth’s surface, lunar eclipses can be seen from much larger regions of the planet. On this occasion, it will be visible across virtually the entirety of the Americas, from Canada to Argentina.

Also unlike this month’s solar eclipse, observing this one will require no special eye protection. Simply find a dark spot, away from light pollution and, if possible, with a clear horizon. The only protection you might need is from the cold, particularly if you’re in the southern hemisphere given the season.

This story originally appeared on WIRED en Español and has been translated from Spanish.

Bottom line: Uber is moving quickly to put robotaxis on its main app, backed by a large cash pile and new approvals for real-world testing. The company plans to spend about $10 billion over the next few years to deploy 120,000 autonomous vehicles. It expects to run robotaxi services in at least 15 cities this year, putting it in direct competition with Alphabet’s Waymo and Tesla as they all push to turn self-driving technology into a commercial service.

Chief executive Dara Khosrowshahi said the spending is possible because Uber is generating strong cash flow from its core business. In the second quarter, the company produced a record $2.8 billion in free cash flow. Over the past 12 months, free cash flow has reached about $10.1 billion as bookings continue to grow. “We’re investing from a position of strength, as we accelerate our cross-platform strategy at a global scale and build the world’s largest platform for autonomous vehicles,” he said. “Our ambition is clear: to become the world’s leading commercialization platform for autonomous mobility.”

Instead of building a single in-house autonomous driving system, Uber is trying to become the main platform that connects multiple AV technologies to riders. The company shut down its own autonomous vehicle program in 2020 during a cost-cutting push. Over the past year, it has shifted to working as an intermediary for AV startups around the world.

That includes agreeing to invest in vehicle fleets and taking equity stakes in companies such as Zoox, Rivian, and Lucid, a change from its earlier asset-light approach. Uber is also sending out hundreds of sensor-equipped vehicles to collect driving data for its partners, who use that information to train and refine their self-driving systems.

One of the key partnerships is with Wayve, a UK-based autonomous driving company focused on software that can handle complex city streets. On Wednesday, Uber said Wayve had received permits from Transport for London to launch a commercial robotaxi service with a supervising driver behind the wheel. “I believe that it’s the UK’s first license for AV technology to operate and a big step forward for the UK,” Alex Kendall, Wayve’s chief executive, told the Financial Times, adding that the approval should allow the two companies to offer rides to Londoners in “a matter of weeks.” Transport for London said it had cleared 15 of Wayve’s modified Ford Mustang Mach-E vehicles to operate with a safety driver while the technology is tested on public roads.

Uber’s push into robotaxis comes as investors are still debating how autonomous vehicles will affect its core ride-hailing business. The stock is down 13% in 2026 as some shareholders worry that robotaxis could disrupt the existing model that relies on human drivers. Uber is arguing that its base of more than 200 million customers and its ability to route trips across different services will give it an advantage as AVs become more common.

Balaji Krishnamurthy, Uber’s chief financial officer, said the company will keep using its balance sheet to back growth projects, including AVs and international expansion. He pointed to Uber’s €13 billion offer for German delivery group Delivery Hero as part of that strategy. Uber has already paid about $4 billion for a 37% stake ahead of a formal bid, a move that would open access to faster-growing markets in Europe and the Middle East if the deal goes through.

The next test for Uber will be whether its platform-based AV strategy can turn technical progress into dependable services on busy city streets. The Wayve permits in London and robotaxi launches in other cities will bring Uber’s autonomous vehicle partners to more riders and regulators. The industry must still prove that driverless transport can be safe, scalable, and profitable.

• Microsoft is eating humble pie over its unrealistic RAM recommendations
• It has deleted articles that pushed 32GB as an ideal or ‘no-worries’ loadout
• Apple is also feeling the heat in the RAM crisis, with rumors that it’s struggling to secure an alternative source of memory supply from China

There are some fresh twists with the RAM crisis hitting some big tech companies, as Microsoft has backtracked on its previous memory recommendations, and even Apple is apparently finding it difficult to cope with the scarcity of memory.

Let’s discuss Microsoft first, and as Windows Latest pointed out, the company has been busy backpedalling on previous memory recommendations now that the RAM crisis – which just keeps getting worse – has made those suggestions look foolish.

Presented by Tata Communications

Continuous inference, agent-to-agent communication, and real-time data pipelines are generating unpredictable, always-on traffic that legacy architectures were never built to support. As AI moves from pilot project to operational backbone, the network is emerging as a critical control layer that determines performance, reliability, and cost.

The shift is forcing organizations to question assumptions that have held for decades. Legacy systems were static and rigid, and lacked the ability to manage network demand efficiently or dynamically, while AI-ready networks need to adapt in real time. A study by Cisco notes that 80% of executives believe their company’s competitive survival will depend on agentic AI, and consumer usage of AI is already prevalent and accelerating. This is driving a fundamental shift in how traffic is generated, distributed, and experienced, with implications for service providers and enterprises that manage large-scale networks.

This infrastructure gap is a global concern. A recent Bloomberg study, “The Future-Ready Enterprise,” commissioned by Tata Communications, found that while 3 in 4 leaders consider AI a board-level priority, nearly two-thirds (65%) of enterprises continue to operate on transitional or legacy infrastructure. This disconnect between ambition and reality is a primary obstacle to realizing value from AI investments.

The performance bar has also moved by an order of magnitude. Traditional business applications could tolerate 100 to 500 milliseconds of latency, while mission-critical AI workloads now require latency below 10 milliseconds.

“This isn’t just an incremental improvement,” says Kapil, Vice President, Global Network Services at Tata Communications. “It’s a completely different performance paradigm that breaks traditional network design assumptions, where such extreme low latency was never a primary consideration.”

How network performance affects AI reliability and cost

That gap between what legacy infrastructure can deliver and what AI demands turns network performance into a direct driver of AI reliability and cost. Treating the network as a best-effort transport layer introduces risk that many organizations only discover once a deployment underperforms in production. A model built for real-time fraud detection or supply chain optimization becomes worthless the moment network congestion delays the data it depends on, and Kapil notes that every millisecond of that delay can carry a direct financial or operational cost.

“Relying on a ‘best-effort’ network turns multi-million-dollar AI stack investments into a high-stakes gamble, where performance is left to chance,” Kapil says.

He adds that businesses often underestimate the complexity of using the public internet as a global enterprise network. Performance may look acceptable within a single country, but once data starts crossing borders or connecting to international cloud platforms, the lack of end-to-end control becomes an operational barrier.

Distributed AI across cloud, edge, and enterprise increases complexity

Complexity compounds as AI components spread across cloud, edge, and enterprise environments. Organizations often focus on compute power and data infrastructure while overlooking the network fabric that connects them. That blind spot often surfaces as a performance bottleneck created by high-frequency east-west traffic moving between GPUs.

Distribution also widens the surface enterprises have to defend. Applications, users, and partner ecosystems are now spread across cloud, SaaS, edge, and device environments, and Kapil notes that AI-driven malicious bots account for roughly 37 percent of online traffic, making it increasingly difficult to distinguish legitimate users from automated threats. Many enterprises have responded by layering on siloed tools, which has produced fragmentation, inconsistent security, and a lack of unified visibility rather than a coherent defense.

“SASE helps mitigate these risks by converging networking and security into a unified, cloud-delivered architecture,” Kapil says. “This convergence is enabling consistent policy enforcement across cloud, on-premises, and edge environments, while supplying the scalability and proximity needed to secure real-time AI-driven interactions.”

The network must evolve from passive transport to an intelligent layer

Closing that gap requires organizations to gain far greater visibility into how AI traffic moves across distributed environments and the ability to direct workloads accordingly. Kapil says that demands a different approach to network management.

“Leaders must realize that the network is no longer passive ‘plumbing.’ It must be managed as an active, intelligent platform foundational to the entire AI stack,” he says. “That platform requires real-time observability into how and where AI traffic flows, paired with the control to orchestrate workloads across the most efficient and secure path available.”

It’s the difference between merely connecting systems and unlocking new capability, for instance a seamless shopping experience during a peak sales period or a global sports broadcast streamed without buffering.

This intelligence also changes how infrastructure teams spend their day. The network itself is now software-defined and API-driven rather than fixed by hardware configuration, which Kapil says shifts infrastructure teams away from reacting to outages and toward designing the systems that prevent them.

“Instead of manually re-routing traffic during an outage, the team must define the rules, policies, and business outcomes for an intelligent fabric,” Kapil says. “The network itself then executes those policies automatically and autonomously.”

Tata Communications is putting this principle into practice with its recently launched IZO Data Centre Dynamic Connectivity. The software-defined platform creates a “self-healing, intelligent network” using deterministic multi-path routing to reroute traffic automatically in seconds during a disruption.

The company says the platform transforms resilience from a reactive process into an autonomous capability, providing the predictable, low-latency performance mission-critical AI applications require while reducing operational costs by up to 30%.

Real-time AI requires predictable, low-latency connectivity

Delivering on that intelligence in practice means giving mission-critical workloads dedicated capacity rather than having them compete for it. Reaching that level of consistency also requires enterprises to define performance far more precisely than they have in the past. It’s the shift from vague goals like “high performance” toward deterministic performance criteria where an organization commits to a guaranteed service level, such as latency for a specific workload not exceeding 10 milliseconds 99.999% of the time, for instance.

That same demand for predictability extends into capacity planning. As AI workloads become larger and more dynamic, networking infrastructure must be able to absorb rapid shifts in demand without sacrificing performance or efficiency.

“Without dynamic scalability, enterprises are forced into a false choice: either risk performance-killing congestion or engage in massive, inefficient overprovisioning of their network ‘just in case.’ This is incredibly expensive and unsustainable,” Kapil says.

Building this foundation for the world’s most demanding AI workloads is already underway. For example, Tata Communications is collaborating with Amazon Web Services (AWS) to build one of India’s largestAI-ready networks. This high-capacity, resilient network will connect major AWS infrastructure locations in Mumbai, Hyderabad, and Chennai, providing the ultra-low latency backbone needed to accelerate generative AI adoption and cloud innovation across the country.

He points to a consumption-based model, where software allows bandwidth and network functions to scale instantly with demand, as the operational alternative, since it lets organizations pay only for what they use while still protecting performance during spikes.

CIOs should treat the network as a strategic investment

CIOs and infrastructure leaders need to reframe the network, not thinking of it as a cost center but as something closer to an insurance policy for an organization’s broader AI investment portfolio. An intelligent network de-risks those investments in three ways:

enabling dynamic scalability that removes the need for overprovisioning

strengthening security and governance through the visibility needed to protect data and models

and providing a flexible, programmable foundation that can absorb future compute demands without a full architectural overhaul.

Getting there does not require enterprises to start from scratch.

Choosing a partner with a proven track record is critical. Tata Communications was recently named a Leader in the Gartner Magic Quadrant for Global WAN Services for the 13th consecutive year, reflecting its completeness of vision and ability to execute. That recognition reflects continued investment in areas such as SASE capabilities for AI-driven security and high-capacity 800G services designed for AI-scale infrastructure.

“We recommend a phased approach that begins with assessing the current state of the network and identifying inefficiencies, then prioritizing upgrades in areas such as AI-ready technologies, seamless data exchange, and advanced security solutions,” Kapil says. “Treating the network as a business enabler rather than overhead gives organizations the scalable, secure, and resilient infrastructure the AI economy will continue to demand.”

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