Clayface review: Tom Rhys Harries shines in DCU body horror gamble that largely pays off

Light spoilers follow for Clayface.

Not many people would've predicted that a Clayface movie would be part of James Gunn and Peter Safran's DC Universe (DCU). Three years after Gunn unveiled the initial DCU slate, fewer still expected a shape-shifting villain to beat heavy hitters like Batman and Wonder Woman to the big screen.

And yet, here we are. Four months after Supergirl bombed at the box office, Clayface feels like a crucible moment for a cinematic universe that's still trying to find its footing and drop a few spontaneous surprises along the way. And, while the lovingly crafted origin story delivers on the latter front, it doesn't fully succeed in helping to shape the direction of DC's fledging comic book franchise.

Face/Off

Clayface is arguably the biggest role of Welsh actor Tom Rhys Harries' career so far (Image credit: DC Studios/Warner Bros. Pictures)

Clayface stars Tom Rhys Harries as Matt Hagen, a Gotham City native and an actor on the cusp of superstardom. However, while celebrating a career-defining Best Actor award nomination, Hagen makes the fatal error of sleeping with Mia Torrez (Joana Ribeiro), the girlfriend of high-profile mobster Markos Szabo (David Dencik). In retaliation, Szabo’s henchmen brutally attack Hagen, disfiguring his prized face and destroying his life overnight.

Clayface feels like a crucible moment for the DCU

Abandoned by his allies and, after he takes Szabo to court for his crimes, failed by the justice system, a desperate Hagen seeks out Doctor Caitlyn Corr (Naomi Ackie), a researcher testing an experimental, regenerative nanocell therapy. However, while the initial trial restores his handsome features, Hagen soon succumbs to the treatment's terrifying side effects, leading him down a self-destructive path of revenge.

Matt Hagen is left horrifically scarred by his run-in with Szabo's men (Image credit: DC Studios/Warner Bros. Pictures)

It might sound strange, but witnessing a full-blown origins story is a rarity in this nascent comic book universe.

Sure, Supergirl was peppered with flashbacks of Kara Zor-El's life on Krypton, while Lanterns on HBO Max spotlighted John Stewart's journey to becoming a Green Lantern. Generally, though, DC Studios has deliberately shied away from origin tropes, which makes Clayface a rare exception to the rule.

Given that Clayface remains a mystery to anyone outside hardcore comic book circles, this focus on Hagen's transformation into the monstrous Batman adversary isn't only justified, but ultimately necessary.

Acting on impulse

Hagen's emotional turbulence is captured in near-pitch perfect fashion by Harries (Image credit: DC Studios/Warner Bros. Pictures)

More importantly, though, Clayface gives Harries ample room to fully explore this deeply tragic individual. Hagen isn't just a monster in the making; he's a man drowning in grief, wrestling with the overwhelming despair of losing his face and career overnight, and, eventually, his humanity.

Having lost his sense of self post-attack, Hagen attempts to literally and figuratively rebuild himself with Corr's pioneering yet hazardous serum. But, as the treatment heightens his darkest impulses, his emotional turmoil curdles into unbridled rage.

Clayface gives Harries ample room to fully explore this deeply tragic individual

This descent — from heartbroken victim to full-blown villain — gives Harries complete freedom to examine and showcase Hagen's devastating psychological unraveling. Running the full emotional gamut, Harries delivers a multi-layered performance worth talking about, brilliantly veering from fear and fury, to dismay and infatuation with an ease that cements his leading man credentials.

Moreover, Harries will leave you conflicted over whether you should actually be rooting for Hagen. Yes, his personal and professional ruination will draw sympathy, but Clayface's major narrative pivot point — i.e. the moment Hagen starts enacting revenge on his betrayers — quickly sees that compassion dissipate as his killing spree devolves from tragic accidents to cold premeditation.

Hagen and Corr's complicated dynamic is exacerbated by the former's transformative unraveling (Image credit: DC Studios/Warner Bros. Pictures)

But, this isn't just the Harries show. A large part of Clayface's storytelling success stems from the co-dependent, emotional dynamic between Harries' Hagen and Ackie's Corr.

A large part of Clayface's storytelling success stems from the co-dependent, emotional dynamic between Hagen and Corr

Initially built on a shared idealism of proving Corr's treatment is safe to use on humans, there's a satisfying earnestness to Hagen and Corr's embryonic bond at the outset. However, as their toxic obsessions take over — particularly where Hagen's unrequited limerence toward Corr, who he romanticizes as his savior, is concerned — their connection morphs into a warped romance with shades of Fatal Attraction, Phantom of the Opera, and a dark, twisted inversion of (500) Days of Summer.

Max Minghella's Gotham police detective isn't given much to do in Clayface (Image credit: DC Studios/Warner Bros. Pictures)

While the exploration of this increasingly discomforting bond is worthy of praise, I was disappointed that the love triangle between Hagen, Corr, and her Gotham cop boyfriend John Borelli (played by the ever-watchable Max Minghella) wasn't examined further.

With a lean, sub-two hour runtime, there's only so much narrative runway afforded to this DCU Chapter One entry. Nonetheless, I would've liked to see more the burgeoning rivalry between Hagen and Borelli that's only lightly peppered throughout.

I'd level a similar criticism at the underuse of Hagen's new shape-shifting abilities, specifically with how his acting background influences his powers. Sure, he combines his craft and superhuman talents to good effect as his quest for vengeance takes over. Still, while the creativity displayed in the film's climactic, gruesomely camp set-piece is plenty of fun, I was left wanting more from a premise with so much theatrical potential.

Monster mayhem

Clayface has some issues it needs to face up to (Image credit: DC Studios/Warner Bros. Pictures)

Clayface doesn't fully lean into its skin-crawling body-horror potential, either. In a ironic twist for a movie about shape-shifting, the film suffers from its own identity crisis, never quite deciding whether it wants to be an uncompromising creature feature or a low-budget comic book flick.

I wish Clayface had leaned harder into viscerally repulsive territory

To be fair, there are some wonderfully grotesque moments that'll delight genre enthusiasts and leave more squeamish viewers wriggling uncomfortably in their seats. Moreover, while visual effects have an increasingly important role as Hagen's transformation progresses, it's the practical makeup that helps to really elevate Harries' performance, and add a real tactile viscosity and nauseating quality to his physical degradation.

How much more horrifying would it have been to see this full scene for the first time on the big screen? (Image credit: DC Studios/Warner Bros. Pictures)

That said, I wish it had leaned harder into viscerally repulsive territory, especially given its most nightmarish moments are featured heavily in its first, second, and third trailers. A few more of the 'show and tell' body-horror beats that Clayface handles so well, rather than deliberately dodging them with cliched, shadow-draped discretion shots, would've gone a long way to appeasing me.

Thankfully, the DCU's take on Gotham City doesn't suffer a similar atmospheric fate.

As grimy and seedy as any live-action iteration of the fictional megalopolis, Clayface presents Gotham as a stylish, neon-drenched playground. Blending rain-slicked noir aesthetics with a vibrant, comic-book palette, it creates a city that feels both oppressively dangerous and visually intoxicating. Its striking combination of Batman: The Animated Series' heightened gothic style and the gritty realism of Matt Reeves' The Batman is particularly notable, especially when you consider Clayface was originally pitched as a spin-off of the latter. Such comparisons will do little to assuage so-called 'merger bros', who want nothing more than to see Robert Pattinson's Dark Knight grapple-hook his way from Reeves' Bat-Verse to the DCU.

My verdict

Part-monster movie, part-Shakespearean tragedy, Clayface is a film of captivating contradictions. Driven by Harries' powerhouse lead performance, it delivers a surprisingly intimate and tragic character-driven story that explores the artificiality of fame, and puts a devotedly modern day spin on 'Feat of Clay', the two-part Batman: The Animated Series tale, it's inspired by. Even so, its hesitation to fully embrace its primary genre underpinnings, coupled with a few underbaked subplots and underutilized shape-shifting gags, holds it back from true greatness.

As a litmus test for the DCU, Clayface raises more questions about the franchise's broader direction after Supergirl and Lanterns. More generally, it doesn't reshape the mold for comic book cinema.

What it accomplishes, though, is far more intriguing: it proves Gunn and Safran are willing to take genuinely weird, character-focused gambles. I crave the next Batman, Wonder Woman, or Justice League film as much as the next person but, if we get more of these smaller scale offerings alongside their blockbuster brethren, Clayface will prove to be a risk worth taking.

Clayface is out now in multiple international territories, and will be released in US theaters on Friday, October 23.

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DJI’s Power 1000 V2 delivers all-day drone charging with 2,600W of home backup — and Amazon dropped the price to $400

DJI might not be as well-known in the power supply space as Anker, Bluetti, EcoFlow, or Jackery, but its range of portable power stations have proved rather impressive in our tests. I find them particularly good for drone operators, since the units feature SDC ports for charging DJI drones. But that's not the only use for these power stations.

The DJI Power 1000 V2, currently $400 (was $600) at Amazon, is a prime example of that, with its 1,024Wh battery capacity, 2,600W output, and a port selection that makes it well-suited for home backup during emergency power outages.

• Best for: Drone operators charging multiple devices on the move, or home-owners wanting emergency backup power for appliances, electronics, and outdoor equipment.

• What we like: The DJI Power 1000 V2 is a portable battery station that can also come along on camping trips, RV adventures, and outdoor challenges.

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Why we recommend it

The DJI Power 1000 V2 combines a 1,024Wh battery with 2,600W of continuous AC output, making it capable of running a wide range of appliances.

DJI claims the battery can recharge from empty to 80% in 37 minutes, while a full charge takes approximately 56 minutes (under suitable conditions). The station uses lithium iron phosphate (LFP) battery chemistry for long-term durability, and it is rated for 4,000 charge cycles while retaining at least 80% of its original capacity.

You get two AC outlets, two USB-A ports, and two USB-C ports capable of delivering up to 140W each. There's also SDC and SDC Lite ports for charging DJI drones. Alongside this, the unit has a UPS function with an approximately 10ms switching time to allow compatible equipment to continue receiving power when the mains supply is interrupted. For overnight use, the company quotes noise levels as low as 26dB. DJI offers up to five years of warranty coverage.

For more picks, we've tested out the best portable power stations and the best DJI drones.

Price context & historical value

Usually priced at $699, the current deal brings the DJI Power 1000 V2 down to $399.98. That's a saving of $299.02, equivalent to approximately 43%.

It's the lowest price I've personally seen for this model and its combination of battery capacity, continuous output, and rapid recharging make it a great deal.

Should you buy it?

✅ Buy the DJI Power 1000 V2 if...

You want a portable source of electricity for camping, RV trips, outdoor work, or occasional power outages. The 2,600W output gives you flexibility and the fast recharge time reduces how long the battery needs to remain connected to mains power. The dual 140W USB-C ports are also useful.

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You need to run large household appliances for extended periods during an outage. Yes, the 2,600W output can handle substantial loads, but the 1,024Wh battery capacity limits how long it can sustain them.

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Russia drafted plans for AI that could block 95% of VPNs — but the idea has quietly vanished from its national roadmap

• A draft of Russia's national AI plan proposed using AI to analyze 70% of blocked VPN services and lift blocking effectiveness to 95%
• The Digital Development Ministry says the measure is absent from the current draft, but Roskomnadzor's separately budgeted and arguably crucial ML filtering project remains
• The 95% is an effectiveness target, not accuracy, and sources expect AI to raise false positives, with corporate VPN, cloud and voice traffic most exposed

Russia's Digital Development Ministry considered using artificial intelligence to push the effectiveness of its VPN blocking to 95%, according to a front-page report in Kommersant.

The measure appeared in a draft of the country's national plan for implementing AI through 2030; the ministry now says it is not in the current version.

This isn't the first time such a move has been reported, but some earlier coverage might have been overzealous, to say the least, when it reported that "Russia Eyes AI-Powered VPN Crackdown With 95% Blocking Accuracy." The problem with that headline is that not only was that proposal dropped, but the 95% accuracy figure was never actually in play.

A draft that proposed AI use

None of the above reporting should reassure an estimated 57 million Russians who rely on circumvention tools.

Roskomnadzor (the Federal agency tasked with "Supervision of Communications, Information Technology and Mass Media") has its own machine-learning filtering project budgeted outside the national AI plan, and the money for Russia's blocking infrastructure is set to double next year.

Kommersant, which reviewed the draft, reports that the ministry wanted AI to analyze the characteristics of 70% of all the VPN services Russia blocks. The drafters reckoned that would lift blocking effectiveness to 95%.

Asked about it, the ministry said the current draft of the plan "contains no such information," in Meduza's translation. A source familiar with the discussions told Kommersant that agencies had pitched a range of measures for the plan, some of which may never be implemented, and that the traffic-analysis algorithms already in service are a form of intelligent analysis but do not count as AI.

An AI layer would behave fundamentally differently; rather than matching known signatures, models would weigh packet sizes and timing, session length, and the balance of inbound to outbound traffic, then flag likely VPN servers from indirect clues: how a node behaves, which host it sits on, and what its neighboring IP addresses look like. The payoff is speed, catching new servers before they reach any blocklist.

Kommersant uses the word effectiveness, and the distinction matters: a blocking-effectiveness figure describes how much targeted traffic it stops. It says nothing about how much legitimate traffic gets stopped with it, and that is what the catch is; the solution proposed does not account for any mistakes, just how effective it would be at its job of blocking out VPN traffic.

The 95% target is also less of a leap than some headlines suggest, because Russia already has 92% on paper, at least by its own estimates. A Roskomnadzor subsidy decision for its Main Radio Frequency Center (GRChTs), dated January 2025 and surfaced in May 2026 by journalist Maria Kolomychenko, sets an "average level of effectiveness" of 92% for restricting VPNs via signatures by the end of 2030. Verstka reports that the AI proposal was meant to lift that same indicator to 95%.

The more consequential point is that striking a line from a draft strategy does not strike the capability. Forbes Russia reported in January 2026 that Roskomnadzor planned to spend 2.27 billion rubles (around $29 million) in 2026 on a machine-learning-based traffic-filtering mechanism, a project we flagged when Windscribe saw a roughly 90% drop in its Russian traffic that same month.

The false-positive risk is not hypothetical in Russia, because the government has already learned how hard it is to separate good VPNs from bad ones. When the ministry ordered major platforms in April to block users with VPNs switched on, its guidance promised a whitelist for corporate VPNs.

Telegram founder Pavel Durov claimed the crackdown had "triggered a massive banking failure," and outage-tracking data showed bank disruptions on April 3 2026. A source told RBC at the time that no reliable technical method existed to tell permitted corporate VPN traffic from the rest.

For now the picture on the ground is unchanged: blocking is intensifying with or without an AI mandate. At least 10 VPN providers reported tighter restrictions in the week after September's State Duma elections, and VPN Guild chairman Alexey Kozlyuk described it to Meduza as "a continuation and gradual intensification" of a campaign that began in August.

The Kommersant report also landed a day after RBC revealed a draft ministry order requiring phones on Russia's 5G networks to support the FSB-developed Kuznyechik cipher from March 1, 2032.

Providers keep adapting. "In Russia, VPNs face pressure almost every month; it is a new norm," an Amnezia VPN spokeswoman told TechRadar Pro. But Sarkis Darbinyan of RKS Global and the VPN Guild warned TechRadar that for users, the end result is "an ever-shrinking number of services that actually work."

The 95% target may be gone from one document. The infrastructure, the money, and the intent behind it are not.

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Millions of California students have sensitive data stolen — names, SSIDs, home addresses and more taken in attack on nonprofit

• California Department of Education third-party partner falls victim to a data breach attack
• An estimated three years' worth of students (5.7 million per year) likely affected
• It seems access was obtained by a simple password theft

Malwarebytes has reported that millions of current and former California public school students have had their sensitive personal information stole following a massive data breach.

The breach occurred in a Secure File Transfer Protocol (SFTP) system used by Educational Testing Service (ETS) – a third party used by the California Department of Education for managing student information.

According to the report, the attackers were able to regain entry repeatedly over the course of four months, accessing student files filled with names, addresses, dates of birth and more.

California student system breach likely impacting millions

Though ETS hasn't officially disclosed how many students have been impacted, Malwarebytes notes that the state's public schools counted more than 5.7 million students in the 2025-2026 year alone. The state's Department of Education believes students who enrolled in the previous three years could be impacted – or around 17 million students if all three years were equal.

The sensitive files were accessed and copied multiple times between May 22 and September 18, with the activity finally being discovered on September 25, and it all comes down to human vulnerability.

Rather than a software vulnerability, the attackers are believed to have gained access via compromised credentials, though the intricacies around the specific attack vector haven't been shared yet.

ETS and the California Department of Education are both working to notify impacted individuals, who are being given 24 months of identity protection software for free. No evidence seems to have been found to prove that the stolen information has been publicly released, and it doesn't seem that a ransom threat has been made.

"We have implemented additional safeguards, including enhanced monitoring, strengthened access controls, and further third-party review of our security practices," ETS added in a statement (via Reuters).

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Russian bot activity plunged 84% after Ukraine hit Yandex data centers, but the bots were back at full strength within two days

• Ukraine's CCD says suspected Russian bot posts in its sample fell 83.6% on October 8 after a strike shut Yandex's Sasovo data center
• The same data shows volume back above pre-strike levels by October 10, with a smaller 42.6% dip after the Vladimir shutdown
• Cloudflare and Graphika found no comparable drop in Western political communities, though Russian-language Telegram output fell 5% to 15%

Ukraine's Center for Countering Disinformation (CCD) says suspected Russian bot accounts went almost silent after drones knocked out Yandex data centers earlier this month, with message volume across a monitored network falling 83.6% in a single day.

The findings come from an analytical report the CCD dated October 15 2026. Using Osavul monitoring software, analysts tracked 22,664 messages from 5,201 accounts showing signs of inauthentic behavior across 26 topics in Ukraine's Facebook and Telegram space between October 6 and 12.

The 83.6% figure is real, but the same report shows those accounts posting more than before within 48 hours, and independent data from Cloudflare shows that at least some of the malicious automation running on Yandex's cloud did not die so much as move house.

A collapse in automated traffic after the Yandex Cloud took a beating

The first collapse came on October 8, hours after a drone strike and fire shut down Yandex's Sasovo data center in Ryazan region, home to two of the company's three supercomputers.

Daily messages fell from 4,032 to 660, and active accounts dropped from 1,526 to 258. Telegram took the harder hit, down 90%, while Facebook fell 79.5%. A second, shallower dip followed the October 11 strike that Yandex said had completely halted its Vladimir site, with messages falling 42.6% on October 12, from 4,711 to 2,704. A subgroup of 98 accounts the CCD flagged for justifying the invasion or inciting hatred followed the same curve, down 77.8% on October 8 and 49.2% on October 12.

The most telling detail sits in the middle. The October 9 strike at Yandex's Kaluga campus, which the company describes as its largest, only partially took the site offline, and the CCD recorded no fall in activity that day. The two dips line up with the two complete shutdowns, as one would expect if these accounts depended on that infrastructure, though that's not proof on its own.

A surprisingly fast turnaround time?

What the 84% figure that the CCD touts leaves out is how briefly the silence held. Message volume recovered to 2,231 on October 9, then reached 4,585 on October 10 and 4,711 on October 11, both above the pre-strike level. The monitoring window in the report closes on October 12, so it cannot show whether the second dip recovered the same way.

The agency has also been downplaying the event on other forums; two days before the report, the CCD said posting in some clusters of the Pravda, InfoDefense and Nod of Time networks roughly halved for about 12 hours, and stressed that it had not recorded a significant overall decline across Russian bot farms.

On October 16, the CCD's summary of remarks by media literacy head Ivan Slychko described the effect as "a disruption of automation, not the complete disappearance of bots."

It also makes no mention of a similar comparison for ordinary accounts. On the afternoon of October 8, banks, retailers, mobile operators and news sites across Russia suffered outages. Without said information, it is hard to separate bot farms losing their servers from operators inside Russia simply losing connectivity.

The CCD is hardly an impartial player here; it is a body attached to Ukraine's National Security and Defense Council, and it has an incentive to exaggerate victories even when the data doesn't always support them.

President Volodymyr Zelenskyy had already jumped the gun, telling the Warsaw Security Forum that after the strikes "it suddenly became a little easier to breathe on many social media platforms" and that might have been a deciding factor for how the CCD built its narrative.

Beyond Ukraine, the "bots went silent" story mostly rested on a Cloudflare Radar chart showing automated traffic from Yandex's AS13238 network falling to well under 1% of its recent peak on October 11. As we noted at the time, that traffic was far more likely to be crawlers than fake social media accounts, and "bot" is a loose word when automated requests make up around half of all web traffic.

Cloudflare's own post-mortem confirms it. Marina Elmore and Lai Yi Ohlsen found 99.5% of requests from AS13238 were automated, mostly verified Yandex search crawlers, and that the network supplied only about 7% of Russia-origin bot requests. Bot traffic from Russian IP addresses hitting the post, reply, and like endpoints of one social platform dipped just 5.7% across the event period.

Graphika reached a similar verdict from the other direction. Founder John Kelly's analysis of the AfD, MAGA and Reform UK communities that viral posts claimed had gone quiet found none lost their active accounts or heaviest posters.

AfD-linked accounts posted 40,365 times on October 11, 4% below the week before and well within normal swings, while MAGA mentions on X rose 25% in the eight hours after the Vladimir strike. Georgetown's Renée DiResta, writing in Lawfare, was blunt about the viral version: "It's a great story. But it looks like fiction rather than fact."

For now, the most defensible reading of the CCD's numbers is narrower than its headline. Knocking out the right data center can briefly cut the output of Russia-based accounts aimed at Ukraine. Keeping them quiet would mean hitting the servers they move to next, and on the evidence so far, that move takes a day or two.

As one analysis of battlefield computing puts it particularly well, resilience comes from designing for disconnection, and the bot operators appear to have learned that lesson faster than Yandex did.

Via UNITED24 Media

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'AI is no longer just supporting the business — it has become the business model': IBM VP tells us how your business can balance AI excitement with realism as governance becomes more important than ever

As excitement around AI continues to build among businesses of all sizes, many leading voices are urging caution for those who might scale too quickly.

At its recent Think EMEA event in London, IBM revealed research which claimed efforts to stay aware of potential risks and challenges posed by AI deployment and scaling are falling somewhat short of expectations.

So what can companies do in order to balance the hype and excitement around what AI can offer, with the need for sensible and thorough governance.

AI as the enabler

"What we are seeing is that if you don't provide an answer to security, to accountability, to responsibility, it is going to be very hard to scale AI in the corporate environment - particularly when we're talking about highly regulated industries - so for us, it's more of an enabler, than something that is going to slow down the adoption,” Ana Paula Assis, Senior Vice President and Chair for IBM EMEA and APAC told us in response to that very question during a media Q&A at the event.

“The question here is really, how do you balance the speed of innovation with the implementation of governance?”

IBM recently released research on the current state of AI and governance, finding that 80% of the companies surveyed claimed that an AI incident had been escalated to board level. However only 40% of those had then been able to use the incident to secure new investment in governance, or cement a new policy.

“I think the reason for that is because it's very hard to balance that innovation with the application of tools, processes, methodologies, to ensure that you are deploying AI at scale in a safe way,” Assis continued.

IBM is reportedly well placed to discuss such growth and progress with AI, with the company’s earlier keynote at Think EMEA claiming it had seen savings of around $5.5 billion in terms of improved productivity and efficiency utilizing AI to improve core processes - money that it was able to redirect to areas including R&D and innovation.

“But we are seeing this as an enabler, and as a key component of the architecture for agentic AI going forward, because these companies have tremendous responsibility - they need to ensure that they have trust from their clients, and they will only be able to do that if they have AI that they can observe and that they can govern.”

“AI is no longer just supporting the business,” Assis said. “It has become the business model. The organizations pulling ahead are not just adding AI, but rethinking how systems, decisions and processes work together with AI.”

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How to watch Snapped: Killer Moms online — stream the true crime series for FREE

• Watch Snapped: Killer Moms live on YouTube TV (free trial)
• Use NordVPN to watch your YouTube when outside US
• Airs on Oxygen on Sunday, October 18, 2026

If you've ever watched any of the other Snapped true crime TV shows, you'll know exactly what you're letting yourself in for here. The latest spin-off, Snapped: Killer Moms, details the experiences and shocking true stories of a range of different women who have been pushed to breaking point while defending their offspring.

The series opens with the story of Kimberly Warth, a woman in Las Vegas whose maternal devotion ended up in her partner being shot in 2022, and Warth spending time in prison ever since.

Each episode will focus on a different case and a different mother who went above and beyond, and then some, to protect her children. Naturally, there will be harrowing moments and violent content here, but if you're into true crime docs like the ones Netflix pump out, you'll be on the edge of your seat with this series.

Here's how to watch Snapped: Killer Moms online and on TV – from anywhere.

Watch Snapped: Killer Moms in the US (without cable)

In the US, you can watch Snapped: Killer Moms live on Oxygen on Sunday, October 18 at 9 pm ET.

No cable? Stream Oxygen through YouTube TV , Sling or Fubo – some of our favorite cable replacement services.

Can I watch Oxygen for free? Yes but only with a free trial – YouTube TV offers a free 21-day trial

Outside the US? If you're currently traveling outside the States, you can use this VPN (try risk-free) to watch your usual shows. We recommend NordVPN.View Deal

Watch Snapped: Killer Moms from anywhere

If you're keen to watch Snapped: Killer Moms but you're away from the US and access to the show is geo-blocked, then you could use a VPN to access it (assuming you're not breaching any broadcaster T&Cs, of course). You may be surprised by how simple it is to do. Plus, we have a great 75% discount on our #1 VPN...

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Can you watch Snapped: Killer Moms in the UK?

(Image credit: Other)

There is currently no broadcast or streaming plan for Snapped: Killer Moms in the UK.

If you're across the pond but dying to watch, you can use this VPN (try risk-free) to watch the show from anywhere. We recommend NordVPN.

Can you watch Snapped: Killer Moms in Canada?

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Oxygen is a US-only network, so you won't be able to catch a linear broadcast or stream of Snapped: Killer Moms in Canada.

If you are traveling at the time of the broadcast, you can use a VPN to access your usual streaming services from anywhere. We use NordVPN (try for 30 days).

Can you watch Snapped: Killer Moms in Australia?

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There is no broadcast or streaming option for Snapped: Killer Moms in Australia.

Moving around or working Down Under? You can use a VPN to access your usual streaming services from anywhere. We use NordVPN (try for 30 days).

Snapped: Killer Moms FAQs

What other Snapped shows are there?

The main Snapped series has now been running for 38 seasons, having first aired in 2004.

During that time, we've seen the following spin-offs:

• Snapped: Killer Couples - About romantic partners who attempt, or indeed, pull off a murder together
• Snapped: She Made Me Do It - About people who claim women manipulated them into committing serious crimes
• Snapped: Behind Bars - Follows convicted criminals as they spend time in prison for their crimes
• Snapped: Notorious - Follows the most high-profile, dangerous, shocking crimes
• Snapped: Rich B*tch Rampage - Hosted by Lisa Vanderpump, details upper class scandals and privileged women who turn to murder (coming in 2027)

You may also like to watch...

• How to watch 9-1-1 season 10 online from anywhere – stream LA first responders series now
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• Best streaming service 2026: Netflix, Apple TV Plus, Max, Disney Plus and more

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'The GPU never reads the same text twice': Belgian startup Corbenic wants to turn your server's SSD into AI memory, but its 14x speedup owes more to reading less

• Corbenic AI's Galahad beta saves a model's KV cache to encrypted local NVMe so servers can skip rereading text after GPU memory is cleared or after a restart
• Founder Sietse Schelpe tells TechRadar Pro no special hardware is needed, but the paper credits most of the 14x speedup to Blaise, a retrieval component, not SSD loading
• LMCache, Nvidia Dynamo and CMX, WEKA and Phison already persist KV cache to storage; Galahad's edge is exact, fail-closed reuse in one small library

Belgian startup Corbenic AI says it can stop GPUs from rereading documents they have already processed, and its founder told TechRadar Pro the trick needs nothing more exotic than the NVMe drive already sitting in the server.

Galahad, which entered beta on October 1 2026, saves a model's key-value (KV) cache, the attention state built while a model reads a prompt, to encrypted local storage and reloads it the next time the same text appears.

In an email to TechRadar Pro, Corbenic introduced Galahad with the line "the GPU never reads the same text twice," and founder and CEO Sietse Schelpe summed it up as "the SSD a server already has becomes part of the AI's working memory."

Catering to a market with plenty of alternatives in tow

The timing of Corbenic's announcement is impeccable; with data centers on course to absorb more than 70% of high-end memory chips in 2026, getting more work out of existing hardware is an easy sell. But Corbenic's own paper shows its headline 14x speedup owes more to reading less text than to loading saved work, and the company is walking into a field where Nvidia, Phison, and a well-funded open source project already offer something similar.

Galahad's approach is slightly different: In standard serving, a model answering a second question about a document recomputes that document's attention state from the first token, a step called prefill. Engines such as vLLM and SGLang already reuse that state while it sits in GPU memory, but Corbenic's paper notes it is evicted under memory pressure and lost whenever the process restarts.

Galahad's KV component, Taliesin, writes each block of text's state to disk under a fingerprint of the exact input bytes, the model, and the tenant, then loads it when those bytes reappear.

Schelpe stressed that the stored copy only comes into play once GPU memory has been cleared or the server has restarted. "So the choice is not SSD or RAM. It is SSD or computing it all again on the GPU," he told TechRadar Pro, adding that the load "happens once, before the first word of the answer."

He volunteered two limits: short texts are not stored because recomputing them is already fast, and network storage is much slower than a local NVMe drive. The paper sets a fail-closed rule on top: "serve without memory, never with wrong memory," so any record that fails a check is simply recomputed.

No special hardware is required, according to Schelpe, just Linux, an Nvidia GPU and a standard NVMe SSD. Asked about CXL, he said, "we tried faster storage, and it didn't make Galahad faster," which matches the paper's H100 test, where a RAM disk and a local disk delivered near-identical throughput.

On enterprise drives such as Phison's Pascari line, whose maker has been pitching flash as the answer to AI's memory bottleneck, he was refreshingly careful: "we haven't measured them, so I won't quote numbers."

Schelpe told TechRadar Pro that loading stored state from a fast SSD "is much quicker" than rereading, and that "is where our 14× comes from: 0.59 seconds per question on vLLM." The paper's own tables tell a more complicated story.

The 0.59-second result comes from a recall test with 100 facts hidden in a 96,726-token list, run on Gemma 4 31B, and it used Taliesin plus Blaise. Blaise is a second component that keeps the documents as text and hands the model only the section a question needs, about 668 tokens instead of the whole corpus. The paper itself credits Blaise with lifting the gain on llama.cpp from 3.1x to 14.5x.

With Taliesin alone, vLLM took 15.56 seconds per question, slower than its 8.11-second baseline, and SGLang took 22.02 seconds against 10.29. Only llama.cpp got faster on KV reuse alone, at 3.01 seconds against 9.25. Those comparisons are lopsided in both directions: the baseline used a 12,000-token window that saw only the last 12% of the corpus and answered 10 of 100 questions, while Taliesin let the model attend to everything and answered 98 of 100.

Schelpe positions Galahad against retrieval-augmented generation (RAG) pipelines such as RAGFlow, which split documents into chunks and pass the model whichever pieces a search ranks highest. "When the search misses, the model never sees the answer," he said, citing a benchmark in which Galahad answered 100 of 100 questions and RAGFlow 77.

Asked by TechRadar Pro why nobody else had come up with the strategy, Schelpe said rivals had chased "bigger is better" and that "there is no real glory" in building something like this, adding that he was lucky not to carry the same mental baggage. Corbenic's homepage makes the same point with the slogan "Stop building bigger models. Build the layer below."

The premise does not hold up well. LMCache, an Apache 2.0 project with around 10,900 GitHub stars, already moves KV cache into CPU memory, local SSDs and remote storage, and a 2024 post on its blog pitched it as "letting LLMs read each text only once." Nine days before Galahad launched, Samsung engineers detailed a restart-recoverable NVMe tier for the project, and its commercial backer, Tensormesh, raised $20 million in May from investors including AMD Ventures, CoreWeave, and Nvidia's NVentures.

Nvidia's Dynamo framework tiers KV cache from GPU memory through host DRAM to local NVMe and networked storage, and at CES 2026 the company unveiled a BlueField-4 context memory platform, now called CMX, built to push KV cache onto NVMe SSDs. Nvidia has even argued that storage is the next frontier of AI. WEKA sells its Augmented Memory Grid for the same job, Huawei pitches a Unified Cache Manager to avoid repeat calculations, and Phison's aiDAPTIV+ already parks KV cache on flash in consumer laptops such as Thunderobot's AI Master M7000.

A security-centric play that still needs independent testing

Galahad's real distinction from most of its prolificly backed competition is narrower. Its paper cites LMCache and Mooncake directly and sets itself apart on exactness, with bit-identical logits as the acceptance test and any mismatch treated as a miss. "Exact, not approximate," Schelpe said, though that contrast lands hardest on techniques such as CacheBlend, which LMCache uses to reuse non-prefix chunks with selective recomputation, rather than on plain offloading.

Corbenic's launch release actually leads with security rather than speed, calling Galahad "a secure vault for working memory" that encrypts stored state, keeps each customer's data separate, and can record activity so developers can roll a server's saved memory back to an earlier point.

The concern behind the pitch is real; researchers showed at NDSS 2025 that KV cache sharing in vLLM and SGLang could let one user reconstruct another's prompts through a timing side channel, and Galahad's library counters that by refusing to start without a tenant identity and model fingerprint. Schelpe also stressed that data stays put: "There's no shared cache network, and nothing is sent to us."

On the flip side, Corbenic is a small Belgium-based team registered as a Delaware corporation, and every number in its main Galahad paper comes from experiments the founder ran himself on rented cloud GPUs, as the paper discloses. Raw logs are promised but not yet published, Blaise's selection method is withheld pending patents, and the papers are self-published arXiv preprints rather than peer-reviewed work.

Its competition includes both proprietary and open-source solutions, most of which have undergone far more testing and will likely see much broader adoption unless Galahad finds industry partners willing to integrate it.

None of that makes the idea wrong. Paying a GPU to reread the same contracts, manuals, and code on every request is a genuine waste, and Schelpe frames the contest sensibly when he calls "the real competitor: buying more GPUs." With memory and storage prices where they are, software that makes existing servers do more will find buyers, and he said early reactions have been positive, with "some nice requests and some learnings already."

What Galahad needs now is someone else's benchmark: a head-to-head with LMCache on the same Nvidia hardware, with each runtime's own prefix cache switched on, and Blaise's retrieval tested on documents it was not built around. Until then, the safest reading is that Galahad is a careful, exact take on KV persistence, and that its most eye-catching numbers come from reading less rather than remembering more.

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Behold, Intel plans a 480GB GPU, a video card with more memory than the average laptop SSD has storage — but of course it's for AI, and curiously, it will use LPPDR5X, not even GDDR7

• Intel's GPU D-series sets a new GPU memory record with 480GB of LPDDR5X
• Intel chose LPDDR5X instead of GDDR7 for its high-capacity AI accelerator
• The D-series supports models containing up to 300 billion parameters

At the recent Open Compute Project Global Summit, Intel unveiled a graphics processor with up to 480GB of memory, an unusually large capacity designed for demanding AI inference workloads.

The GPU, called Intel GPU D-series and previously known by its Crescent Island codename, also hosts specialized computing hardware.

The Intel D-series has now dethroned Nvidia's Blackwell Ultra B300, which offers 288GB of HBM3e memory, as the data center GPU with the highest memory capacity.

Intel prioritizes memory capacity

Rather than using GDDR7 memory associated with modern graphics cards, Intel has chosen LPDDR5X, a memory technology commonly found in laptops and other power-conscious devices.

The configuration suggests that Intel is prioritizing memory capacity and power efficiency for AI inference instead of designing a conventional gaming graphics card.

Intel says the accelerator can support large language models containing up to 300 billion parameters on one card, while an eight-GPU system can accommodate models exceeding one trillion parameters.

These capabilities are intended for lengthy prompts, simultaneous inference requests, enterprise AI services, and robotics applications requiring responsive processing.

“Inference is becoming the defining AI workload, and enterprises need infrastructure that delivers the right performance at the right cost,” said Anil Nanduri, vice president, AI Products & GTM, Intel Data Center Group.

“With Intel GPU D-series, we're focused on making high-capacity inference practical to deploy and economical to scale.”

The 480GB capacity is particularly relevant when AI systems must keep substantial model weights and associated data accessible during inference.

However, memory capacity alone does not establish how quickly a GPU will serve requests, since performance also depends on bandwidth, computing resources, software, and workload characteristics.

Intel rates the D-series at 1.3 petaflops for FP8 computation, a numerical format commonly used to accelerate AI operations while reducing memory requirements compared with higher-precision formats.

Its 1.5TB/s memory bandwidth provides a separate measure of how quickly data can move between memory and the processor.

Intel wants the accelerator to fit existing data centers

The company specifies a 350W power envelope and a standard PCIe form factor, alongside air cooling and software compatibility across its GPU platforms.

These characteristics are intended to simplify deployment in existing data centers without requiring specialized cooling arrangements or entirely new rack infrastructure.

Intel also plans to connect the accelerator with its Xeon processors, allowing CPUs to coordinate tasks, manage data movement, and handle workloads that do not require GPU acceleration.

This approach addresses AI systems increasingly built around multiple stages, including retrieval, reasoning, orchestration, external tools, and execution.

“Open models are expanding the options enterprises have for deploying AI,” said Nicolas Dubé, corporate vice president, Data Center Systems and Solutions Group, Intel.

“To realize their full potential, organizations need infrastructure that can match different workloads with the right intelligence, right compute resources and right economics. That's what we mean by 'put every prompt in its place.”

As of the time of writing, Intel has not revealed the public retail price or a specific general-availability date for the GPU D-series.

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World's smallest PC with a 16GB GDDR7 desktop RTX 5080 GPU goes on sale for $2,647, but you will have to buy your own SSD, RAM, and OS — Zotac Magnus One Ultra has an 850W PSU and can drive 6 4K monitors

• Zotac Magnus One Ultra’s Core Ultra 7 265 delivers 20 cores and speeds of up to 5.3 GHz
• Nvidia rates the desktop RTX 5080 at 1,801 AI TOPS for artificial intelligence workloads
• The desktop GPU features 10,752 CUDA cores, 336 Tensor cores, and 84 RT cores

Zotac has released a compact desktop computer built around Nvidia's GeForce RTX 5080, with barebones pricing starting at approximately ~ $2,640 in Japan.

The Magnus One Ultra EU275080C combines a desktop graphics card with Intel's Core Ultra 7 265 processor inside an 11.46L enclosure.

The 20-core processor runs at 2.4GHz and boosts up to 5.3GHz, and Zotac says the device is one of the smallest PC to pair a desktop RTX 5080 with a desktop Intel Core Ultra chip.

Desktop RTX 5080 power comes in a compact enclosure

The system uses Nvidia’s Blackwell architecture and supports DLSS 4.5 with Multi-Frame Generation, ray tracing, 4K gaming and local artificial intelligence workloads.

Its desktop RTX 5080 includes 16GB of GDDR7 memory, distinguishing it from compact computers that rely on integrated graphics or mobile GPUs.

The memory runs on a 256-bit bus with 960GB/s of bandwidth, alongside 10,752 CUDA cores, 336 Tensor cores and 84 RT cores.

Nvidia rates the GPU at 1,801 AI TOPS, and it is considered to be about 33% ahead of the RTX 4080 in games.

Nvidia also sells laptop RTX 5080 graphics hardware, but those mobile components are not equivalent to the desktop version.

Zotac fits the hardware inside a chassis measuring 126 x 365.5 x 249 mm, with a total weight of approximately 6.34 kg.

An 850W power supply supports the configuration, while the system offers two memory slots accommodating compatible DDR5 modules and a maximum capacity of 96GB.

The GPU’s 360W rating takes about 42% of that supply, and memory is rated at DDR5-6400 for CSODIMM and 5600 for SODIMM.

Storage expansion comes through two M.2 slots, supporting PCIe 5.0 x4 and PCIe 4.0 x4 connections respectively.

The connectivity options include Thunderbolt 4, Wi-Fi 7, Bluetooth 5.4 and eight USB connections (four USB 3.2 Gen 2, four USB 3.2 Gen 1) for peripherals and external devices.

Display outputs comprise three DisplayPort 2.1b connectors, one HDMI 2.1b port, one HDMI 2.0 connection, and a DisplayPort-compatible Thunderbolt 4 port.

Together, these connectors support up to six total displays — or four simultaneous 4K displays driven directly by the dedicated graphics card.

With compatible displays and Display Stream Compression, DisplayPort 2.1b supports configurations reaching 4K at 480Hz or 8K at 165Hz.

The HDMI 2.1b output supports up to 4K at 480Hz or 8K at 120Hz, depending on the connected display and configuration.

Network connectivity includes one 5Gbps Ethernet port and one 1Gbps Ethernet connection, alongside a UHS-II SD card reader and audio inputs.

Barebones pricing leaves buyers to configure the system

Zotac lists several configurations, with Japanese direct-sale prices ranging from ¥418,000 (~$2,647) to ¥750,200 (~$4,750) depending on memory, storage, and operating system.

The entry-level package excludes all three, which means that users interested in this model will have to separately purchase the memory, storage and OS.

A version with 16GB of memory and a 1TB SSD costs ¥522,500 (~$3,309) with Windows 11 Pro installed.

The model with 32GB or 64GB of RAM and a 1TB drive costs ¥561,000 (~$3,552) and ¥654,500 (~$4,144), respectively, while the highest-priced option includes 96GB of memory for ¥750,200 (~$4,750).

There is also a Windows version of the Zotac Magnus One Ultra that comes with 32GB of RAM and two 1TB SSDs and costs ¥588,500 (~$3,727).

Via PC Watch (originally in Japanese)

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Microsoft's Maia 300 swaps raw compute for a 2.5x memory bandwidth gain as it moves to make in-house silicon its main AI engine while claiming a 40% efficiency bump — could going the Google route be the pin that pops the Nvidia AI bubble?

• Microsoft will make Maia its main inference engine at multi-gigawatt scale, following Google and Amazon into in-house silicon
• Microsoft's upcoming Maia 300 trades a modest 36% compute gain for 2.5x Maia 200's memory bandwidth, betting on memory-bound decode, with production in 2027
• Hyperscalers supply just over half of Nvidia's data center revenue at a 75% gross margin, so each move to in-house chips affects not only its pricing power but the chip industry's sustainability as a whole

In 2024, Microsoft told the European Commission that Google was the only company vertically integrated at every AI layer, from chips to a mobile app store, and that "everybody else must rely on partnerships to innovate and compete."

Now, it has revealed plans to follow the same route without breaking a sweat.

In an Official Microsoft Blog post, Scott Guthrie, executive vice president of Cloud + AI, said Microsoft will scale its Azure Maia accelerators to multi-gigawatt deployments starting in 2027 and make Maia the primary inference system for Microsoft's products and services.

The vehicle of its choice is Maia 300, which Rani Borkar, president of Azure Hardware Systems and Infrastructure, and Saurabh Dighe, corporate vice president of systems planning and architecture, detail in an architecture deep dive.

Feeding the beast, not growing it, as it takes the Google route

The move is a pointed statement from one of Nvidia's biggest customers, and it lands as bubble worries grow louder. Gartner forecast this week that AI providers will spend nearly $1 trillion on infrastructure in 2026 alone, and that AI revenue won't cover the build-out profitably until at least 2032.

If the largest buyers of AI hardware stop paying Nvidia's margins for the work they do most, the most richly valued part of that build-out starts to look exposed. Whether Maia 300 is the final catalyst for such a move, though, is a harder case to make.

Maia 300 moves from Maia 200's single die to a multi-chiplet package, with compute on TSMC's N2P process and memory-controller and I/O functions split onto separate dies. Eight HBM4 stacks deliver 288GB at 18TB/s per accelerator, against 216GB at 7.1TB/s for Maia 200, according to StorageReview's breakdown of Microsoft's spec sheet.

Microsoft hasn't abandoned compute so much as rebalanced around memory. In decode, the token-by-token phase that dominates long reasoning chains and agent loops, accelerators spend much of their time waiting on data rather than doing arithmetic.

The Maia 300 tries to close that gap by keeping data near the compute that uses it, through a non-uniform memory layout across chiplets, software-managed on-die SRAM, and narrower number formats, including an enhanced MXFP4 and sub-3-bit storage formats, with hardware conversion between them. The Maia 300 announces something the rest of the industry already knew, albeit more subtly: Custom silicon is no longer a side project for hyperscalers.

Google, the company Microsoft singled out two years ago, began recognizing revenue from TPU system sales delivered to customer data centers in the second quarter, CFO Anat Ashkenazi said on Alphabet's July earnings call, while CEO Sundar Pichai said the company is "seeing strong demand for TPUs as standalone systems from other customers."

It isn't the only one either; Anthropic has lined up 3.5GW of TPU capacity through Broadcom for 2027, on top of an earlier deal for up to a million TPUs, and Meta was reported in 2025 to be in talks to rent-cum-purchase them. Amazon has gone the same way. CEO Andy Jassy said in April 2026 that Amazon's chip business, spanning Trainium, Graviton and Nitro, had passed a $20 billion annual revenue run rate and was now "one of the top three data center chip businesses in the world." OpenAI, meanwhile, has lifted the lid on its own Jalapeño accelerator.

Nvidia's margins: the AI holy grail?

The incentive is plain from Nvidia's own results. In the quarter to July 2026, the company reported $96.2 billion in revenue, $89 billion of it from data centers, at a 75% gross margin. CFO Colette Kress put hyperscale revenue at $49 billion on the earnings call, meaning a little over half of Nvidia's data center business comes from a small group of buyers, the biggest of which now design their own accelerators.

Every inference workload that moves in-house is one where that buyer pays closer to manufacturing cost instead of Nvidia's margin. Microsoft told investors in July that Maia 200 was up to 40% cheaper to operate than Nvidia's latest chips on OpenAI and Microsoft models.

It did, however, fail to name a particular GPU it called a "leading GPU-based system," an important distinction given Nvidia's focus on efficiency via Vera Rubin, which OpenAI even refused to compare against outright.

The reveal didn't move stocks much; on Monday, Nvidia shares closed down 1.29% after the reveal, while Microsoft's rose 1.14%, and Microsoft's opaqueness about efficiency gains may be one reason.

The other reason is equally important: Evercore ISI analysts estimate Maia will account for only about 10% of Microsoft's AI chip spending by the first quarter of 2027, CNBC reported, and Nvidia has guided to $108 billion in revenue for the current quarter.

Scale is arguably an ongoing constraint, however; at 167 kW per rack, a gigawatt of rack power is roughly 6,000 Maia 300 racks, or about 575,000 accelerators, before cooling and facility overheads.

Going the Google route hasn't stopped anyone buying from Nvidia either. Amazon, with its "top three" chip business, is still set to take more than 3 million Nvidia GPUs by mid-2028.

Alphabet, which now sells its own TPUs, raised its 2026 capex guidance to between $195 billion and $205 billion in July, lists Vera Rubin alongside its own TPUs, and Ashkenazi said it is "still in a supply constraint environment."

Microsoft keeps Nvidia and AMD for training, and Satya Nadella said earlier in 2026 that "because we can vertically integrate doesn't mean we just only vertically integrate."

Jensen Huang's answer to the custom silicon question on Nvidia's August call could have been written about Maia: "Whereas many of these XPUs are inference-specific chips for one cloud or one service, NVIDIA is a platform." Microsoft's counter-bet is somewhat different: one cloud is big enough.

The deeper problem for the pin theory is that custom silicon changes who collects the margin, not how much gets spent. Gartner warns that the industry needs about $8.5 trillion in revenue between 2026 and 2030 to earn a reasonable return on its build-out, versus no more than $3.6 trillion it expects AI to generate. Microsoft isn't responding by building less; it is planning gigawatts more, and cheaper tokens arguably help close that revenue gap rather than widen it.

Some of what Microsoft saves will also flow elsewhere in the supply chain. Maia 300's pitch rests on HBM4, the same constrained memory Nvidia's Rubin uses at the same 288GB per package, and its compute dies come from TSMC, which supplies everyone.

Google's TPUs are developed and supplied under a long-term agreement with Broadcom, whose CEO Hock Tan said that "many investors in AI don't understand what they're putting their money into," and who expects chip shortages to ease only around 2028 or 2029. Nvidia isn't sitting idle either; it already paid $20 billion to license Groq's SRAM decode technology, a sign it sees the same inference threat coming.

Until production silicon ships and someone outside Microsoft can measure it, Maia 300 is a well-argued design whose most important numbers are ones that only Microsoft has seen, and a reminder that the biggest threat to Nvidia's margins may be its best customers rather than its rivals.

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