Technology

• HoverAir launched its new Versa drone on Indiegogo, saying it had FCC approval
• FCC has since stated that certification was granted 'erroneously'
• The Versa is the world's first 2-in-1 pocket gimbal camera and drone

HoverAir launched the Versa drone on Indiegogo last week, and a few words in the promotional material suggested that the world's first 2-in-1 pocket gimbal camera and drone had a distinct advantage over DJI: "FCC (Federal Communications Commission) certification."

The claim of FFC approval implied that the Versa would be permitted to be sold in the US, a market which new DJI drones have been fully locked out of since 2025. World-first features, plus US availability — those were two big leg-ups for the Versa.

However, HoverAir's hopes of having that key advantage in terms of market access have quickly been dashed. The FCC contacted TechRadar to tell us that "certification for this device was granted erroneously and has been set aside by the FCC and removed from our public database".

Furthermore, it said the "FCC is considering further action against HoverAir for apparently willfully violating the FCC’s Covered List rules."

It's quite the turnaround for HoverAir. The company must have been licking its lips at the prospect of its innovative innovative 4K drone and pocket gimbal camera having unchallenged access to the US market. Turns out, it's in the same position as DJI after all.

Here's the FCC statement in full:

“Importing, marketing, or selling this [HoverAir Versa] device in the United States is a violation of U.S. law, and the FCC has directed HoverAir to cease any such activity immediately.

"The device in question is on the FCC’s Covered List: the Versa camera alone is a prohibited UAS critical component produced in a foreign country and, when the Flight Kit is included, the device falls under the ban on UAS produced in a foreign country.

"The certification for this device was granted erroneously and has been set aside by the FCC and removed from our public database. The FCC is considering further action against HoverAir for apparently willfully violating the FCC’s Covered List rules.

"Finally, the FCC is sending guidance to all Telecommunications Certification Bodies to strengthen their due diligence of applications to ensure they comply with their legal obligation not to certify equipment on the FCC’s Covered List."

HoverAirHoverAirHoverAirHoverAirMore bad news for drone fans in the US

Following the self-flying X1 and the waterproof Aqua, HoverAir notched up a trio of world-first drones with the Versa, a 2-in-1 pocket gimbal camera and drone.

According to its Indiegogo page, the Versa is tipped for a worldwide launch and for October 2026 delivery to backers. However, despite US pricing (starting at $499) remaining on the crowdfunding page, it looks like the Versa won't be taking to the skies in the US any time soon.

That's a shame for drone fans in the US. Versa's modular design means that its wings (guard-protected propellors) can be removed in a simple motion, switching the device from a 230g self-flying 4K camera to a 163g pocket gimbal camera.

Its key features include 4K video up to 60fps, 4K HDR video up to 30fps with 10-bit color depth, 12MP photos (there's no mention of RAW format) and a 1/1.3-inch sensor.

Controller-free aerial selfies are a breeze, thanks to palm take-off and landing, AI subject tracking, auto framing, plus voice and gesture commands.

Furthermore, the Versa is compatible with HoverAir's Beacon device, which debuted with the Aqua, meaning it can pair with a phone for clear audio. It's said to reach speeds up to 36km/h with a Level 5 wind resistance, and have 14-minute flight times, though realistically battery life will be less than that.

As a gimbal camera, the Versa features a rotating OLED haptic screen, which measures 1.64 inches on the diagonal and hits 800 nits of brightness.

We haven't tested the Versa yet, but we'll share our in-depth review soon. The innovative drone will make its public debut at the IFA consumer electronics show, which takes place in Berlin, Germany, from September 4-8, and you can learn more about it in the YouTube video below.

The first salvo of the war against AI slop on LinkedIn looks to be going the right way for those of us who can actually use our imaginations, the company has said.

LinkedIn Chief Product Officer Hari Srinivasan has revealed over a million people have clicked the ‘seems like AI slop’ report option in the two weeks since its launch - and I've definitely clicked it a few times.

Srinivasan says that these reports, along with changes to LinkedIn's detection systems, have cut views of content classified as AI slop by 40% - although sadly my feed is still pretty full of it.

"Low-quality content" crackdown

In his post, Srinivasan noted the feedback was, "helping us better understand how our community experiences low-quality content" across LinkedIn, suggesting stricter clampdowns to come.

The first stage of this is launching now, so if one of your posts receives enough community feedback (aka is reported as AI slop), you may see a message in your Post Analytics, "to help you understand how your content is being received".

"We approached this assuming good intent; I know I'm increasingly conscious on how to not sound like AI & the goal is to provide helpful feedback," Srinivasan says.

"Importantly, no single piece of feedback determines how content is distributed," he added. "We look at many signals together, and we've built safeguards to help prevent individual feedback from being used to unfairly target other members."

"Despite the progress, we know we have more to do to ensure LinkedIn remains a place where you can find real people & real perspectives... this all remains very top of mind," Srinivasan concluded, suggesting further actions may come soon.

(Image credit: LinkedIn)

The company announced it was cracking down on AI slop in early August 2026, when Srinivasan declared it was, "a top priority for all of us. We really care about this...people come to LinkedIn to connect with real people and share their real perspectives, ideas and expertise."

To report a post, users just need to click on the three dots at the top-right of any LinkedIn post, and among the usual options to save, share or embed, you'll see a new option - "Seems like AI slop".

Srinivasan added that Microsoft-owned LinkedIn will, "continue to improve and invest in our automation defences..on comments alone, everyday we are now catching hundreds of thousands of automated comment attempts, and have blocked billions of other automation attempts (posting at scale, slop) in the last couple months alone."

At 2 a.m., an automated remediation agent detects a problem on the network, traces it to a misconfigured policy, validates through the harness that the proposed fix operates within the given policy boundaries and fixes it. The network stabilizes. Nobody’s notified.

In the morning, a human reviews the agent's daily insights: a summary of all the changes executed, with links to the logs, audit trails, reasoning and root cause behind them, confirms everything has been properly resolved and moves on. That is what Human-on-the-Loop looks like.

At another organization, at 4 a.m., a DIY-built, vibe-coded remediation agent detects a problem on the network, traces it to a misconfigured policy, and fixes it. The network stabilizes. Nobody’s notified. In the morning, a human reviews the logs, assumes the issue has been resolved, and moves on.

Where's the difference?

The difference is that, in the DIY scenario, the logs only tell part of the story. They don't show that the agent made three other changes to get there, which were broader than intended, and the decisions behind those changes weren’t flagged because nothing in its constraints required them to be.

This is what the move toward Human-on-the-Loop can look like without the right controls in place. No dramatic handover. Just a series of small, reasonable delegations that gradually build into something nobody explicitly signed off on.

And it's happening faster than most leaders realize. According to recent research, 57% of IT leaders expect to remove humans from the loop within a year or less, and 79% already treat AI agents as "users" who require their own identity management and governance controls.

The shift to agentic AI is happening faster than most organizations are prepared for, both in terms of governance and the ability to evaluate autonomous systems.

The illusion of "I approve"

The answer to autonomous AI has long been quite simple: keep a human in the loop. Somebody who reviews the output, hits approve, preserving accountability. Except it isn't, not really. Reviewing every action doesn't automatically create accountability, and it also prevents organizations from realizing the full benefits of autonomy.

Rather than reviewing every individual action, humans should be focused on evaluating outcomes, ensuring the system operated within its intended boundaries, and providing feedback that improves its performance over time.

Approval can become a ritual without meaning. As systems prove reliable and the number of alerts multiply, humans sometimes start to treat intervention as something that isn’t often needed.

The approval can become more of a click than a considered choice. And when something goes wrong (for example, a misconfigured policy, an automated remediation that turns into an outage), the question of who was responsible is difficult to answer.

It also reinforces a broader shift: one of the most important human capabilities becomes critical thinking and the validation of hypotheses, rather than the execution of tasks.

And something else is happening. Humans are transitioning from doing to reading before approving – a fundamental change in the day-to-day work of most of us.

Also, attribution isn't the same as provenance. A log that records what an agent did tells you almost nothing about why, or what shaped that decision. When things go wrong, those are the things you need to know.

Non-human identities and the new network population

When AI agents act on your network, querying systems or making configuration changes or routing traffic, they are effectively users. They need credentials, policies, guardrails, explainability, and audit trails just like human operators.

Most organizations haven't caught up with this. Identity frameworks were built for people, and applying AI agents to them as a kind of afterthought creates exactly the sort of shadow access that security teams spend their lives trying to eliminate.

The near-80% of leaders who already treat agents as governed identities are ahead of the curve. The rest are collecting risk they can't quantify, until it crystallizes into an incident.

To get this right you have to treat each agent as a principal with bounded permissions, time-limited access and a clear revocation path, along with a complete record; not just of what it did, but of what it was allowed to do and why.

Autonomy isn't given, it's earned

Network and security teams already know how to do this. Every enterprise has access control frameworks that govern what humans can reach and when. A junior engineer doesn't walk in on their first day with total production access. They gradually earn it, and this same logic needs to apply to AI agents.

We're not quite there yet. Too often, autonomy is treated as all-or-nothing. That isn’t the right model. Trust when it comes to humans doesn’t work like that, and it shouldn’t with AI either. Start agents in suggestion mode and let them prove themselves within clearly defined limits before expanding what they can do. And make sure every decision leaves a trail that explains not just what happened but the reasoning behind it.

As the use of autonomous AI grows, organizations will need to balance human oversight with systemic governance. People remain responsible for reviewing not only the outcomes AI produces, but, where necessary, the actions it takes and the reasoning behind them.

However, as the volume and complexity of autonomous decisions increase, this oversight should be complemented by infrastructure that embeds identity, policy enforcement, observability and accountability. Together, these controls help ensure AI actions remain transparent, traceable and aligned with organizational intent.

Building multi-agent systems that hold up under pressure

The more capable these systems become, the more organizations will move toward multi-agent architectures. This is where specialized agents each own a piece of a workflow and hand off context as they go. That's where things get complicated.

A single agent misbehaving is traceable. A chain of agents, each acting on the outputs of the last, is much harder to untangle when something goes wrong unless the right architecture and governance are in place. You need to know what each agent knew, not just what it did.

Do the boring part

Two organisations deploy the same AI-powered networking agent. One of them has done the unglamorous work: setting up tight permissions, proper identity controls and audit trails that actually answer questions. The other one hasn't.

You won't know the difference until something breaks.

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There’s a pattern starting to emerge with AI.

At first glance, everything looks like progress. AI is being adopted quickly, embedded into products, talked about in boardrooms, and pushed into real-world use faster than anything we’ve seen before. But as businesses become more accustomed to AI and increasingly find new ways to use it, there is a greater problem brewing that has the potential to be detrimental to a company’s cybersecurity posture.

Organizations are moving quickly to use AI, but far fewer are making the right decisions about how it’s actually being delivered and secured. And the gap between those two things is widening, with security teams left scrambling to fix vulnerabilities like whack-a-mole.

The speed is understandable. AI hasn’t followed the usual enterprise lifecycle. It hasn’t patiently moved from concept to pilot to controlled rollout. In many cases, it’s gone straight from experimentation into something business-critical, stitched together from APIs, models, agents, and data sources that weren’t originally designed to work together in this way.

That creates something fundamentally different. Not just another application, but something more fluid, a tool that behaves dynamically to make decisions and interact across multiple layers of the stack in real time.

And this is where the problem begins.

Where AI security currently breaks down

While the architecture that needs to be secure has changed, the thinking around security largely hasn’t, meaning traditional security measures are still being applied to situations they aren’t built for. Most organizations believe they have this covered. They’ve extended their existing controls, added new tools and invested in visibility. On paper, it looks like a sensible evolution of what they already had that keeps up with AI.

But in reality, much of that security still sits around AI rather than within it.

These traditional methods are protecting edges, monitoring outcomes and analyzing behavior after the fact. What they’re not consistently doing is sitting in the path of execution, where decisions are actually being made, and where things can go wrong in real time. It’s this distinction that matters more than most people realize.

AI doesn’t behave like anything we’ve secured before. A single interaction isn’t just a request and a response. It’s a chain of events where a prompt is interpreted, a model responds, an agent may take action, data is retrieved, decisions are made, and outputs are generated. This all happens in one continuous flow.

The risk doesn’t exist at a single point. It exists throughout that chain. This is where prompt injection happens. It’s where models can be manipulated, where sensitive data can leak through inference and where unintended behaviors and outcomes emerge.

The cause of this isn’t always an incorrect configuration; it can also be the result of the system responding exactly as designed, just not in the way anyone expected.

The industry is starting to acknowledge this. There’s a growing recognition that runtime is where the real battle is being fought, and that securing AI means understanding how it behaves under pressure, not just how it’s built.

Moving beyond bolt-on security

But if that’s becoming clearer, why are so many organizations still getting it wrong? Well, in most cases, it comes down to how decisions are being made. AI is often being driven by innovation teams or developers, those who are closest to the opportunity and implementation of AI tools.

But that also means infrastructure and security decisions are following behind rather than shaping the architecture from the start. At the same time, there’s a tendency to default to adding more tools to plug the security gaps. Faced with a new risk, the natural instinct is to look for something new and shiny to buy that addresses it.

AI doesn’t fit neatly into that model. It doesn’t live in one place. It cuts across applications, APIs, data, and user interaction all at once. Treating AI as something you can secure with a standalone tool misses the point entirely.

What is actually needed is a different way of thinking, one that starts with looking at where control actually needs to exist. There are only so many places security can be meaningfully enforced, and for AI, one of the places that consistently matters is the flow of traffic itself.

This is the point at which requests are made, decisions are processed, and responses are returned - where behavior can be influenced the most and where policy can be enforced. Everything else, to some degree, is reactive.

This is also where the conversation around security platforms becomes more interesting. Not because AI capabilities have simply been added to existing portfolios, but because the role these platforms play is changing.

Sitting in front of applications and APIs, they have long been responsible for managing traffic, applying policy and enforcing decisions. What’s changed is that these same control layers are now being extended into AI interactions themselves.

That shift is subtle, but important, as it moves AI security away from being something that happens in isolation and closer to something that is embedded directly into how systems operate. Not bolted on, not observed from the outside, but enforced as part of the execution path.

This isn’t really about one vendor. It’s about recognizing that AI has changed the shape of the problem.

Control will define the next era of AI security

The market is still catching up. The tooling is still evolving. And most organizations are understandably feeling their way through it.

But the decisions being made now - where to place control, how to integrate security, what assumptions to carry forward from the past - will define how manageable this becomes over the next few years.

We’ve seen this before, just in a slightly different form. APIs went through a similar phase not long ago - rapid growth, fragmented control, and then a long period of retrofitting security once the risks became clear.

AI is moving faster than that ever did. The attack surface is broader, the behavior less predictable, and the consequences potentially more significant.

Which means there’s less room for getting it wrong.

The organizations that navigate cybersecurity well in the age of AI won’t necessarily be the ones that adopt AI the fastest. They’ll be the ones that understand where control needs to sit and make deliberate decisions about how it’s enforced. With AI, more than anything else, it’s not just about what you can see. It’s about where you can act.

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Cyber risk is now a board room issue, and we have seen clear examples of this in the UK. The 2025 Jaguar Land Rover attack left the carmaker with a £485m loss, swallowing up the £398m profit it had generated just 12 months before.

Production lines were halted for more than a month as the company shut down parts of its network, showing how quickly a cyber incident can affect business performance, operational continuity and the wider supply chain.

Now, businesses are facing a fresh type of threat made possible by AI – the autonomous attack. Attackers can already automate parts of target research, initial access and malware development, with any manual effort shrinking rapidly.

Simultaneously, the trust layer people rely on is eroding with the spread of AI-generated content and deepfakes. It’s a race to tackle the autonomous attack, but how do organizations formulate an effective response?

AI in a cyber-attacker’s armory

AI-driven automated technologies are strengthening a cyber-attacker’s armory. Prior to leveraging AI tools, bad actors often had to commit time and resources to researching a target company before planning an attack.

Timing was critical, and a perpetrator had to manually coordinate and initiate an attack at a specific time and could simply forget. AI doesn’t - and the rise of attack-as-a-service tools is making it possible to successfully breach organizations quickly and accurately.

Guardrails are starting to be put up around established generative AI tools, such as ChatGPT and Claude, in an effort to prevent this kind of misuse. But hackers are finding workarounds.

Rather than relying on readily available large language models (LLMs), they are deploying their own small language models (SLMs) on local devices, often on something as basic as a Raspberry Pi computer. From there, they can escalate attacks while hiding in the shadows.

The threat to businesses of all sizes

The rise of automated attacks also means that businesses of all sizes are likely to be identified by automated technology as having exploitable vulnerabilities. Small and medium-sized businesses would previously have been off the radar as attacks relied on a bad actor’s knowledge of their existence.

However, AI can now scan and process vast numbers of organizations at speed, potentially leaving smaller firms, which are less likely to have robust cyber controls in place, more exposed. And even more so among smaller businesses, defenses are typically more fragmented and less organized than AI-driven attacks.

In other words, with AI by their side, attackers can coordinate and scale far better and much more quickly than most businesses can defend.

Autonomous attacks also make third-party and supply chain risk much harder to manage. Business networks can create access to data, systems or operational processes. When attackers can automate reconnaissance and scale attacks across thousands of organizations, weaker suppliers may become an attractive route into larger businesses.

This is a particular concern because third-party risk management has often relied on annual questionnaires, point-in-time assessments and contractual assurances, but these approaches are no longer enough on their own. A supplier may have recently exposed a service, suffered a breach, changed its access privileges or failed to patch a critical vulnerability.

Businesses therefore need to move towards continuous, automated monitoring of supplier security posture.

Regulations such as NIS2 have also increased the focus on supply chain security for organizations operating in, or selling into, the EU. There is also a growing expectation from ICO and the FCA that boards can demonstrate cyber resilience.

Automation and the rise of specific attack types

Jadepuffer illustrates how AI is beginning to transform established attack types. Disclosed by Sysdig in July 2026, it was assessed as the first documented end-to-end LLM-driven extortion operation, with an AI agent conducting reconnaissance, harvesting credentials, moving between systems, destroying data and adapting when individual actions failed.

While none of the techniques were especially new in isolation, the significance was the way the AI connected them into a complete, adaptive attack.

Social engineering techniques, such as bad actors posing as trusted individuals, are becoming much more convincing in their approach. Fluent, grammatically correct messages and the professional tone and style of CEO communications can now be fully replicated on emails, SMS and even WhatsApp.

AI can even manage the entire conversation thread, including dynamically adapting responses to a target’s replies, with it possible to run simultaneous, tailored campaigns.

Vendor email compromise, where criminals impersonate suppliers, intercept genuine payment conversations or use compromised vendor accounts to request changes to bank details, directly links social engineering to third-party risk.

Taking a step back, the initial harvesting process of personal data for social engineering attacks can be streamlined. AI can automatically scrape data from public sources such as Companies House and social media to quickly provide the names of specific people, their roles and relationships.

When trust and identity come under attack

Even on video conferencing calls, it’s becoming increasingly difficult to tell if the person you’re speaking to is real due to the increasing accuracy of deepfakes. As an example, it’s often now necessary to ask a suspected deepfake to do something it wasn’t programmed to do, such as raise a hand, to check if the person in question is real. But even that test is gradually being circumvented by new technology.

Organizations need stronger out-of-band verification protocols for high-value or unusual requests. A pre-agreed code word via a separate channel might be needed to ensure trust and security.

Identity security is becoming a key area of defense as autonomous attacks become more advanced. Credential stuffing at scale, session cookie harvesting, MFA fatigue attacks and vishing attempts designed to bypass multi-factor authentication are all increasing. AI can make these attacks more efficient by identifying likely targets, generating convincing scripts and adapting to the victim's responses in real time.

This is why identity and access management should be treated as a critical control. Organizations need to know who has access to what, whether that access is still needed, which accounts are privileged and how quickly unusual behavior can be detected.

Fighting AI with AI

AI-driven autonomous attacks might be heightening the risk, but AI can also be used defensively. A good example of this is to run an automated risk analysis of an organization and highlight where security tools and the basics, such as malware protection, are out of date or missing.

With those fundamentals in place, AI can then underpin continuous monitoring of the critical systems, rather than periodic checks. Businesses should be identifying and focusing on protecting the “crown jewels” – that might be the top 10 most critical assets, such as payroll or a banking system, and target AI-led efforts on protecting them.

Joined-up visibility is then crucial. Businesses need to know who has access to those critical assets, the endpoint and network activity related to them and gain the ability to correlate any incidents quickly so the response to an AI-driven attack can be as swift as possible.

A combination of AI-powered technology, backed by human expertise, can provide proactive threat hunting to actively search for, investigate and remediate dangers, even if they are autonomous in origin.

Organizations aren’t powerless in the fight

The rise of autonomous attacks marks a new phase in cyber risk. For many businesses, particularly smaller ones, the challenge is preparing for attacks that can move much faster than traditional defenses. But organizations aren’t powerless in the fight.

Effective responses start with getting the basics right, from access controls to visibility across critical assets, to moving from periodic checks to continuous monitoring and faster detection with AI.

However, technology alone won’t be enough. Human expertise can interpret risk and make informed decisions under pressure to ensure resilience, even as the AI-driven autonomy threat moves to the next level.

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• The title and cast of James Gunn's Superman spin-off have been revealed
• The People v. Gorilla Grodd will be a mockumentary series fronted by Jimmy Olsen
• DC fans think it's the perfect project to introduce The Flash to the DCU

James Gunn's Superman spin-off show is officially in development at DC Studios — and its title, cast, and premise suggest it could be the most unusual DC Universe (DCU) project so far.

Nine months after it emerged that a Superman TV off-shoot was in the works, the actual name of a production tentatively titled DC Crime has finally been revealed. It's called The People v. Gorilla Grodd and, as was previously reported, it'll be a mockumentary series that will center on the hyper-intelligent, telepathic ape known as Gorilla Grodd.

Until now, we'd known next to nothing else about the DCU Chapter One TV show. But, with the eight-episode, half-hour crime comedy series officially greenlit by HBO Max, and DCU overlords Gunn and Peter Safran, we have a much clearer picture of what it's about and who'll star in it.

The People v. Gorilla Grodd cast: who's involved? And where's Rachel Brosnahan's Lois Lane?

Extra! Extra! Read all about it! The new DC Studios series The People v. Gorilla Grodd is coming soon to HBO Max. pic.twitter.com/cMfgyhC2j6August 21, 2026

Is James Gunn writing and directing The People v. Gorilla Grodd?

No. Tony Yacenda and Dan Perrault, the creatives behind Netflix crime mockumentary show American Vandal, have been installed as The People v. Gorilla Grodd's showrunners, writers, and directors. They're part of its executive producing team, too, which also includes Gunn and Safran.

Per an HBO Max press release, The People v. Gorilla Grodd will reunite viewers with four characters from last year's Superman movie, all of whom work for Metropolis news outlet The Daily Planet.

Skyler Gisondo's Jimmy Olsen has been installed as the show's protagonist. Meanwhile, Beck Bennett as Steve Lombard, Mikaela Hoover as Cat Grant, and Wendell Pierce as Perry White, all of whom had minor roles in Superman, are also set to feature.

Curiously, Rachel Brosnahan's Lois Lane isn't part of proceedings. Does this have something to do with her role in 2027 Superman sequel Man of Tomorrow? I guess we'll find out at some point.

There are plenty of other familiar faces among the TV project's ensemble. Jimmy Tatro is the most noteworthy of those new DCU additions, with the American Vandal alumnus set to play Grodd.

Mary Holland (Ghost), Eduardo Franco (Stranger Things), Arian Moayed (Wonder Man), Dan Perrault (American Vandal), Andrew Leeds (Zoe's Extraordinary Playlist), and Tim Baltz (The Righteous Gemstones) are also involved, though their roles are being kept under wraps for now.

What is the plot of The People v. Gorilla Grodd?

The forthcoming crime comedy show will examine a potential miscarriage of justice involving Gorilla Grodd (Image credit: DC Comics)

Here's the official logline for The People v. Gorilla Grodd: "A superintelligent ape is convicted of murdering his father, the King of Gorilla City. In an eight-part 'true' crime docuseries, the Daily Planet’s Jimmy Olsen reopens the case, investigating whether Gorilla Grodd was wrongly convicted in one of Metropolis’ most famous murder trials."

But wait, there's more, because the above X/Twitter post gives us a more detailed look at the show's wider narrative.

"The People v. Gorilla Grodd will take a fresh, 360-degree look at the case, revealing the 'lesser-seen side' of the hyper-intelligent ape, according to the film's first-time director Jimmy Olsen," it reads. "Olsen was granted unprecedented, exclusive access to interview Grodd inside Stryker's Island Penitentiary for the documentary.

"Grodd has long claimed his innocence in the murder and brain consumption of his father, King Grodd — a crime he was convicted of in March 2021 and has remained incarcerated for ever since. The series will take an in-depth look at the investigation, featuring brand new reporting from members of the Daily Planet staff, as well as interviews with witnesses and experts on both Grodd's and the Metropolis District Attorney's side of the case, including Grodd's new counsel at the Metropolis Freedom Project."

Will The Flash appear in The People v. Gorilla Grodd?Comment from r/DCULeaks

Nobody knows, though I can see why DC comic book fans are suddenly excited about such a possibility. After all, Gorilla Grodd is one of The Flash's most notorious foes, so it's perfectly understandable that some believe that the speedy metahuman — real name Barry Allen — could make his DCU debut here.

In fact, some fans have already convinced themselves that The Flash will show up in The People v. Gorilla Grodd. Threads on r/DCULeaks and r/DC_Cinematic are sprinkled with such comments, so it'll be interesting to see if the Scarlett Speedster makes an appearance or not whenever the series is released.

Are you excited for The People v. Gorilla Grodd, or would you rather DC Studios focus on introducing Batman and Wonder Woman to the DCU? Let me know in the comments.

And, if you're after more DCU coverage to consume, check out my Sinestro in Lanterns explained and Lanterns' Manhunters explained pieces.

Lorcana has exploded since it came on the scene, and I had a chance to chat with co-founder Ryan Miller about where it may be going next.

If you’re worried about Flock cameras, start by learning to recognize them.

My house contains a small but steadily expanding museum of obsolete technology. I've always been reluctant to get rid of them, even if I don't use them for years at a time, so they just fill up boxes in my attic. I know theoretically they're worth something, but I never feel motivated to actually go through them and see what people on eBay might pay.

But I thought ChatGPT might be able to do all of that for me from a simple photograph. I took a picture of the stuff in one of my boxes. Like many boxes stored in my house, this one had lost any obvious reason for existing. It contained a Nintendo Wii, two games, an elderly Kindle, and several DVDs that had survived multiple clear-outs.

I asked ChatGPT to identify everything, determine what might still have resale value, and check recent eBay sales to find out how much it might be worth. I specifically told ChatGPT to look at completed or sold listings, not merely the prices sellers were asking. Anyone can list a dusty iPod for $500 and describe it as rare; I wanted to know what a buyer actually paid.

Wiis and DVDs

(Image credit: Nintendo)

ChatGPT had little trouble recognizing the white Nintendo Wii and the familiar Wii Sports sleeve. It also identified the second game as Marvel: Ultimate Alliance 2, although it asked for a closer photograph to confirm that the disc and manual were inside.

The DVDs were identified from their spines, but ChatGPT warned that ordinary used movies generally have very little individual resale value. It suggested checking for out-of-print titles, complete television series, unusual editions and sealed copies before assuming they belonged in a bulk lot.

The Kindle in the box turned out to be a first-generation Kindle Fire rather than one of Amazon’s E Ink readers. ChatGPT identified it from its thick black body and confirmed the model. It still worked, though the resale results were not especially exciting. Working first-generation Kindle Fires generally appear on eBay for about $10 to $20. Current eBay comparisons suggest a realistic selling price of about $15 to $20 for a tested, reset unit in decent condition.

A tidy sum and cleared space in my attic

The Wii powered up without complaint, which was more than I expected after its extended sabbatical. The disc drive worked, the remote connected and the console reached its home screen without producing any alarming noises. Recent white Wii sales put the market value of the console alone at approximately $40, with complete packages commanding more.

ChatGPT estimated the hardware at approximately $50 to $60. The more interesting discovery was Wii Sports. Since the game came with millions of consoles, I had assumed it was practically packaging material. Recent eBay transactions suggested otherwise. The current market is close to $30, almost as much as the system that plays it. People buying replacement consoles often want the game they remember playing with them, usually before someone put the remote through a television.

All told, ChatGPT said I might get between $115 and $135 for the complete box. The precise total would depend on condition, postage, selling fees, and whether buyers made lower offers. It would also require me to photograph everything properly, write accurate listings, and take seven packages to the post office. After a couple of days on the website, I managed to get pretty much what ChatGPT estimated, with $130 dollars for the lot.

The experiment worked because ChatGPT did more than name the objects. It asked for model numbers, checked whether accessories were present, distinguished complete games from loose discs, and noticed when an individual item deserved its own listing. It also prevented the DVDs from acquiring imaginary value merely because they had become old. It may not be the Antiques Roadshow retirement fund, but it's enough to encourage me to take more photos of the boxes in my attic this week.

Commerce in the information age has thus far been one long quest to gather as much information as possible. Customer data, sales data, inventory data, system data, operational data, technical data, every facet of business can and has been tracked and quantified, guided by the idea that knowing more means decisions can be made faster and more conclusively.

While sound in theory, that process has clearly hit a snag, as more and more companies abandon their AI projects after finding that the exponential explosion of data creation isn’t generating comparable value.

Has AI peaked in its utility? Or is the issue one of interoperability? Why exactly has this monumental trend hit such a stumbling block?

Expecting scale to produce clarity

It’s entirely understandable to assume that feeding more information into an AI increases the accuracy and quality of the output. Models need to train on data; after all, that is how they establish reason.

But, like any computer system, input must be structured for it to be understood, and the issue many organizations now find themselves in has come as a result of them giving AI a decade's worth of unformatted, incomplete, non-standardized data and expecting it to read between the lines.

Every system a company uses, from CRMs and internal emails to performance tracking spreadsheets and PDFs, stores different data points and operates on vastly different logic. What they host, why they host it, and how they host it were all purposeful decisions based on their desired function.

While some support import and export options that translate material from one format into another, the developers behind these solutions never envisioned the need for a universal logic to tie everything together in a way that AI can easily parse.

Each document and dataset contains a fragment of the overall picture. While AI excels at processing data, it cannot derive meaning and struggles to understand and incorporate unstructured data into its output, regardless of how many times it is asked to.

An example of fragmentation in action

We have largely solved the problem of data accessibility to the point that many public AI models have run out of new data and are now cannibalizing the output of other agents. The next step is in refining how AIs use the data they have.

Real estate is an excellent showcase of this process in motion. To appraise and list a property, agents need access to ownership records, zoning information, environmental information, and neighborhood demographics, just to name a few. None of these systems was designed to communicate or cooperate, which has meant AI has historically struggled to find its footing in the industry.

Newer approaches, however, prioritize the ability to find, interpret, and synthesize information from across different sources, aiding in the manual searches an analyst would otherwise do by hand.

Data siloing and fragmentation slow down decision-making, and any systems that can account for them will command a premium going forward.

Every industry has its own version of the same problem: a wealth of data at its disposal and no meaningful way to turn it into actionable insight.

A generic, trend-inspired adoption of AI tools won’t necessarily solve these underlying issues, so it’s ultimately no surprise that many are abandoning their projects altogether. It will take models that prioritize context and connection, and companies paying more attention to how they store and format data, to address the glut in productivity and decision-driving insight AI is currently experiencing.

Solving information overload

AI and the challenges it now faces are a classic example of the dangers of prioritizing quantity over quality. To be entirely fair to users, the companies behind these agents share some blame for this predicament.

The technology is still in its infancy, and marketing hype continues to push scale and processing power as key features, while the much more valuable aspects, like contextual understanding and data integration, fly under the radar.

This discrepancy will likely shift as more success stories highlight the competitive advantages of automating manual, time-consuming processes, as the real estate industry has with property research. It is this ability to understand what exists at a deeper, more comprehensive level that yields the greatest decision-supporting insight.

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A seamless airport journey can feel effortless. From check-in to baggage handling and take-off, everything appears coordinated, predictable and, for the most part, smooth. When delivered without any hitches, the entire process feels remarkably simple.

But, beneath that simplicity sits one of the most complex operational environments imaginable. Airlines, baggage handlers, security teams, retailers, air traffic control and countless other stakeholders must all coordinate in real time to keep passengers and aircraft on the way to their next destination.

Most travelers never see this complexity, but without it the entire experience would quickly fall apart.

I’ve long been fascinated by the inner workings of airports. As the son of an air traffic controller, I grew up with an appreciation for aircraft and the decisions that are made every minute to make air travel possible. Now, I find myself regularly drawing parallels between airport operations and the way businesses are approaching their AI deployments.

What I have realized is, the most valuable use of AI may not be the highly visible applications that are drawing headlines and changing the way we’re searching. Instead, its greatest impact could come from helping businesses manage the complexity of their operations behind the scenes.

The illusion of simplicity

Airports are designed to feel intuitive and like they just ‘work’, but that simplicity is very carefully engineered by experts with decades of experience. Every stage of the passenger journey depends on hundreds of connected decisions, working together and pivoting when needed.

Aircraft availability, staffing levels, security capacity, weather conditions and passenger demand must all be monitored and coordinated continuously. And while many of the systems supporting these individual activities were never originally designed to work together, they have no choice but to operate as one.

Businesses nowadays face a similar challenge. What appears seamless to a customer often relies on a web of technologies, data sources and processes operating in the background. Orders, supply chains, finance, procurement and workforce management all generate information that must be interpreted and acted upon almost instantly.

When these systems are disconnected, organizations create inefficiencies, blind spots and unnecessary friction that can have a knock on impact on the end customer.

This is where AI has the potential to become transformative. The challenge is no longer simply connecting systems that don’t traditionally work together. Now, it is about understanding what the data flowing through those systems means and determining the best next step.

By identifying patterns, discovering insights and supporting decisions, AI can help businesses turn operational complexity into coordinated execution.

Responding to constant change

The comparison with airports becomes even more prevalent when operations start deviating away from the original plan. The major travel hubs with the best reputations are not those that have every flight departing exactly as scheduled, they are the ones that are able to quicky respond when conditions change.

Flights are delayed, weather disrupts operations and passengers miss their connections, but the system always seems capable of adapting. Gates are reassigned, baggage is rerouted and resources are redirected, often before passengers even notice that there is a problem.

This level of responsiveness depends on more than just connected systems. It requires the ability to interpret information and act on it quickly. Increasingly, AI is helping businesses do exactly that by analyzing data and signals in real time, anticipating potential disruptions and recommending the next best course of action.

Customers rarely see this complexity. They expect products to be available, services to be reliable and experiences to be personalized, and they’re not massively bothered by the mechanics behind this.

At the same time, organizations are operating in an environment that is defined by constant disruption, from shifting consumer demand and supply chain pressures to economic uncertainty and regulatory change.

Static processes are struggling to keep pace with that reality and as a result, businesses are moving towards more adaptive operating models where workflows can respond to changing conditions and issues before they become critical problems. The benefit is not simply greater efficiency. It is resilience.

From coordination to intelligence

Looking ahead, the next evolution for both airports and businesses is undoubtedly going to be driven by prediction rather than coordination. Airports are already exploring how data and AI tools can help anticipate passenger congestion, optimize security flows and improve the overall travel experience.

Businesses want to follow a similar path. Early AI initiatives have, for the most part, focused on individual tasks and use cases, but the tide is starting to turn. Now, organizations are looking at how intelligence can be embedded directly into their core processes, breaking free from pilots (pun intended!) to full scale autonomous operations.

The result is a shift from fixed processes to ones that are far more intelligent. Instead of simply automating individual tasks, businesses are now understanding how they can create processes that improve over time, helping people make better decisions and respond more effectively to changing circumstances.

The smoothness of an airport journey is rarely an accident. It is the outcome of a multitude of carefully integrated technologies working together to manage the complexity of air travel behind the scenes. As businesses grow, build new capabilities and onboard more team members, they face the same challenge: delivering what looks like simplicity on the surface while managing growing complexity underneath.

Success will depend not only on connecting systems and consolidating data, but on embedding intelligence into the very fabric of operations. The organizations that achieve this, will be those that are best equipped to anticipate challenges, adapt to change and continuously improve.

Ultimately, customers may never see the work that goes on behind the scenes to drive an experience. Just as travelers rarely think about the systems that keep an airport running, they are unlikely to notice the technology enabling a faster delivery, a smoother transaction or a better service experience. What they will notice though, is when everything just works.

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Companies are investing in Forward Deployed Engineers, or FDEs.

AWS has announced a $1 billion investment in a dedicated organization intended to embed thousands of engineers with customers.

OpenAI has established a dedicated Deployment Company and agreed to acquire Tomoro, adding approximately 150 FDEs and deployment specialists.

Microsoft has said it would hire 6,000 people and invest $3.5billion in its new AI delivery unit, according to CNBC.

But what should FDEs deliver, and what value do they really offer for customers?

The FDE model is an evolution of how companies would previously work around projects with customers based on understanding the business and the technology involved, with a much higher expectation of hands-on engineering.

What has changed is the technology being deployed.

Where FDEs deliver value

FDEs typically embed directly into a customer and work across multiple teams. They identify a high-value workflow, understand the customer’s data and operational constraints and then work to build the required integrations and take the system from prototype into production.

For AI deployment, that involves more than connecting a model to an application. This includes looking at the enterprise context and data available to the model, as well as evaluating accuracy, reliability and confidence thresholds.

It can also involve looking at the guardrails that should exist, the review and escalation process, security and observability. It should also look at integration with existing workflows and business processes.

Companies need FDEs because companies want to deploy probabilistic systems into deterministic operating environments. In other words, enterprises want to use systems that can be different each time they respond within business processes that depend on predictable and uniform results. FDEs have to translate those outputs in a way that delivers what enterprises want to achieve.

For example, a technically impressive model might fail inside an actual workflow for multiple reasons, from incomplete context or poor quality data, through to the organization not being prepared to let the system take action without review.

What the future holds for FDE roles

At their best, FDEs should solve those problems and get a company into their production deployment phase. This ensures that the technology works and delivers value. However, that is not the end of the story.

FDEs should also convert what they learned with one company into reusable capabilities that others can take advantage of too. If every deployment remains bespoke and depends indefinitely on individual engineering talent, then the technology itself cannot scale.

Instead, a successful engagement should lead to reusable connectors, evaluation frameworks, governance patterns and product improvements that other companies should be able to benefit from. For the companies that hire the FDE, they should deliver successful projects. But the ultimate goal is to make the next deployment less dependent on any specific FDE, and instead make the product better.

Are there any bigger lessons from the growth of the FDE role? The real hiring trend is toward a hybrid professional who can be an expert in multiple areas simultaneously, from writing production-quality software and understanding the behavior and limitations of AI models through to learning a customer’s business and domain with enough insight to reconfigure business processes.

At the same time, they are expected to navigate security, governance and organizational constraints, take responsibility for measurable business outcomes in their business and in their customers, and be as adept at communicating with engineers in rolled-up sleeves as they are executives in suit and tie.

For companies that base their products on FDEs, this expansion is a sign that there is a huge market opportunity and that customers want what is being offered. There is an element of marketing involved too, with FDEs the latest “new” position that will solve problems for enterprises.

Some of this recruitment will be genuinely new, while some will be a reallocation or relabeling of people who would previously have been called field engineers, solution architects, technical consultants or professional services engineers.

AI Market Maturity

This continued growth is also a potential warning sign. The number of FDEs needed over time should drop as AI products and infrastructure mature and lessons are learned. This demand for a specific role is a sign of category immaturity. Industries mature when repeatable work is standardized, industrialized and embedded in software rather than recreated as a one-off service for every customer.

The same test applies to AI today. The reliance on FDEs shows that enterprises want AI, but that today’s products are not yet sufficiently complete, predictable or easy to operationalize without substantial human input. If every implementation requires embedded specialists to assemble the context, controls, evaluations and integrations by hand, the category has not yet fully matured.

Enterprises should therefore be careful not to measure success simply by the number of FDEs hired or proofs of concept completed. The right measures are time to production, sustained adoption, measurable business value, customer self-sufficiency and the amount of reusable product capability created from each engagement.

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Lanterns episode 2 is now live on HBO Max — and it introduces viewers to the most famous Green Lantern villain of all, Thaal Sinestro.

Long-time Green Lantern fans will already know plenty about this recurring antagonist, his relationship to Hal Jordan, and why he's deemed such a threat to the Green Lantern Corps and wider galaxy. However, unless you've read the source material and/or seen the disastrous 2011 Ryan Reynolds-starring Green Lantern movie, many of you won't know much, if anything, about Sinestro.

So, who is Sinestro? What are his powers? And does his DC Comics background give us any hints about his role in Lanterns? Allow me to enlighten you on the aforementioned questions and more.

Sinestro's DC comic book history and powers explained

Thaal Sinestro was first introduced in DC Comics over 60 years ago (Image credit: DC Comics)

Let's start with what the comics tell us about Sinestro.

Created by Gil Kane and John Broome, and first appearing in August 1961 in Green Lantern #7, Thaal Sinestro hails from the planet Korugar in space sector 1417.

An anthropologist, Sinestro comes into contact with the Green Lantern known as Prohl Gosgotha when the latter crash-lands on Korugar. Prohl does so after coming under attack from a Weaponer of Qward, which hails from the anti-matter universe.

Prohl gives its ring to Sinestro, who then defeats its pursuer. However, Sinestro keeps the ring, lets Prohl die, and lies to the Guardians of the Universe — aka the immortal race that founded the intergalactic peacekeeping organization known as the Green Lantern Corps — that the ring chose him as Prohl's successor.

Corrupted by the power of the ring, Sinestro abuses his position as a Green Lantern until Hal Jordan, who becomes Sinestro's student after Jordan acquires his own ring, informs the Guardians of Sinestro's evildoing.

Mark Strong played the first-ever live-action version of Sinestro in 2011's terrible Green Lantern movie (Image credit: Warner Bros. Pictures)

To punish him, the Guardians expelled Sinestro from the Green Lantern Corps, took his ring, and sent him to the anti-matter universe — more specifically, the anti-matter world Qward. There, Sinestro convinces the Weaponers, who hate the Green Lanterns, that he can use his knowledge of the space cop agency and its creators to destroy them for good, which leads the Weaponers to give Sinestro his infamous, fear-inducing yellow ring. Thus begins his never-ending quest to eradicate the Guardians and Green Lantern Corps.

As one of the Green Lanterns' most powerful nemeses, it'll come as no surprise to learn that Sinestro has myriad superhuman abilities.

Prior to being gifted his yellow ring, he already possessed genius-level intellect and was a skilled hand-to-hand combatant and expert tactician. After being armed with said ring, he gained powers similar to those of his Green Lantern foes. They include — but aren't limited to — the ability to create constructs using the wielder's mastery of fear, the ability to fly, enhanced physical abilities, limited cellular regeneration, and the power to instil fear in opponents.

Not content with being the only Yellow Lantern in existence, Sinestro went on to establish the Sinestro Corps. Also known as the Yellow Lantern Corps, this group, created by Geoff Johns and Ethan Van Sciver, was introduced in Green Lantern vol. 4 #10 in May 2006 as a sinister counterpoint to the Green Lanterns.

Who plays Sinestro in Lanterns on HBO Max?

A post shared by Ulrich Thomsen (@ulrichthomsen)

A photo posted by on

As for who portrays Sinestro in the DC Universe (DCU) TV show on HBO Max, that'll be Ulrich Thomsen.

The Danish actor is more widely known in his homeland than in nations like the US and UK. However, Thomsen has starred in English-language movies and TV shows you might be familiar with.

On the film front, he's appeared in the 2007 big-screen adaptation of the beloved I/O Interactive game franchise Hitman, Ridley Scott's 2005 historical epic Kingdom of Heaven, and 1999 James Bond flick The World is Not Enough, among others.

Meanwhile, on the small screen, Thomsen was a series regular in Cinemax's Banshee and had recurring roles in NBC's The Blacklist and HBO Original The New Pope.

What role will Sinestro play in Lanterns' overarching story?

(Image credit: HBO Max)

To try to answer that, let's first take a step back and look at why Kyle Chandler's Hal Jordan visits his imprisoned former mentor in the DCU Chapter One TV show's latest chapter.

After John Stewart learns about the Manhunters in Lanterns episode 2, he tells Jordan what he knows before retiring to his motel room for the night. Armed with Stewart's information, Jordan puts on his Green Lantern suit and flies to the maximum-security prison where Sinestro has been kept since he was taken down by Jordan. Remember, as we learned earlier in the episode, Jordan battled and defeated Sinestro after the latter attempted a coup, went rogue after that plan failed, and killed one of the Guardians.

Anyway, upon arriving at Sinestro's cell in the extraterrestrial penitentiary, Jordan asks his former mentor what he knows about the Manhunters. After some unsurprisingly strained small talk, Jordan steers the conversation back to the topic at hand. Sinestro tells Jordan that, if there's really a Manhunter living in secret on Earth, he and Stewart are facing one of the second-most dangerous beings in the entire galaxy. Before we get more of a crash course in the Manhunters, though, Sinestro asks Jordan to tell him about Jordan's own pupil, Stewart, before the screen cuts to black.

Of course, none of this gives us any indication as to Sinestro's wider role in the plot. Is he somehow secretly orchestrating events in Rushville, Nebraska from his jail cell? Does he actually know anything about Manhunters, or is he simply suggesting he does to delay Jordan's return to Earth? And how many more times will we see him in Lanterns before this season's final end credits roll?

Even though I've seen seven of this season's eight episodes — I mentioned that in my Lanterns review — I'm not allowed to spoil, nor would I want to, how Sinestro factors into the wider plot. I know that's a non-committal answer to this section header's question, but it's the best one I can give without getting into trouble with Warner Bros, HBO, and DC Studios. You'll have to keep tuning into HBO Max, aka one of the best streaming services, every week until Lanterns tells all.

Lanterns episode 2 has made its HBO Max debut — and the sci-fi crime drama appears to have revealed that a mysterious race called the Manhunters are the villains of its 2016 storyline.

So, who are they? Where did they come from? And why are they seemingly being set up as the chief antagonists of HBO's Green Lantern TV show? Let's take a look at the Manhunters' comic book history to answer — or at least try to answer — these questions.

Who are the Manhunters in DC Comics?

The Manhunters are a sentient android race conceived by the Guardians of the Universe (Image credit: DC Comics)

Created by comic book legend Jack Kirby, the Manhunters are a race of extraterrestrial robots that made their debut in 1st Issue Special #5 in August 1975.

Built by the immortal beings known as the Guardians of the Universe, the Manhunters were the Guardians' first attempt to create an intergalactic police force that would patrol the universe and combat any threats they encountered.

Unfortunately, these supposed peacekeepers evolved to the point where they became sentient, started to resent the limitations imposed by their creators, and began carrying out their own form of justice — namely, that the only way to maintain peace was to wipe out all organic life in the known universe.

After the Manhunters committed mass genocide in a catastrophic event known as the Massacre of Sector-666, and then attempted a coup against their masters, the Guardians realized they were too dangerous to keep around and set about destroying them. However, some Manhunters survived, fled to distant planets to hide among the local population, and rebuilt their numbers in the hopes of one day crushing the Guardians and their space-cop agency replacement, the Green Lantern Corps.

What do we learn about the Manhunters in Lanterns episode 2?

The head of a group of shapeshifting aliens drops some key Manhunter lore in Lanterns episode 2 (Image credit: HBO Max)

We're only introduced to the concept of Manhunters in episode 2 of Lanterns. Nonetheless, they're seemingly being set up as the primary villains of this season's 2016 narrative, if not the full season.

So, what do we know about them so far? According to the leader of a covert group of shapeshifting aliens who kidnap John Stewart midway through this episode, the Manhunters' origins in the DC Universe (DCU) are nearly identical to those of their creation in the source material, i.e., they're synthetic beings manufactured by the Guardians.

Furthermore, the Manhunters are so technologically advanced that they can build new bodies and take the form of a world's native alpha species. That way, they can remain undetected and, using technology far beyond the limits of those they secretly live among, carry out their brutal form of justice without interference.

It's clear that the Manhunters committed atrocities in the DCU, too. Indeed, as Stewart — and, by proxy, we — learn, entire species were wiped out by the Manhunters before they mysteriously disappeared.

John Stewart experiences this alien race's collective loss through some form of memory-based device (Image credit: HBO Max)

Despite them vanishing without a trace, a Manhunter survived on whatever planet Stewart's captors are from and, in their leader's words, "lived among us as they quietly engineered a holocaust". Given the nature of this mass extermination, the Guardians soon dispatched a Green Lantern to arrest the Manhunter and ensure it would be executed.

However, Stewart's kidnappers believe the Manhunter wasn't killed and now secretly lives among Rushville's human population. Unfortunately, when they tried to get close to this Manhunter to enact their revenge, four of them were killed. Does that death count ring any bells? It should, because their murders are the same ones being investigated by Stewart and Jordan.

Unable to re-enter Rushville without being detected themselves, the group's leader gives Stewart an ultimatum: find the Manhunter in the next 48 hours, or the remaining shapeshifters will walk into the center of Rushville and blow themselves up. Yep, that means they belong to the same extraterrestrial race as the individual who was interrogated by Jordan in episode 1, and who detonated the explosive device in his skeleton at Rushville police station once he was compromised.

Reluctantly, Stewart agrees. Despite the group's leader warning him not to tell Jordan what they discussed, Stewart does so, which leads Jordan to question whether Abin Sur — aka the Green Lantern that Jordan replaced — was tasked with finding the Manhunter and executing it at the behest of the Guardians before his death, which presumably came at the hands of the Manhunter.

Manhunters vs. Martian Manhunter: what's the difference?

Hal Jordan visits his former mentor Sinestro (pictured) for details about the Manhunters (Image credit: HBO Max)

In terms of DC comic book lore, this is where things can get a little confusing.

For the uninitiated: in the comics, Martian Manhunter is a superpowered being who hails from Mars. Real name J'onn J'onzz, he's one of the few surviving members of his alien race (aka the real Manhunters) and a founding member of DC's iconic superteam in Justice League.

Because of what we'd class as this character's surname — i.e. Manhunter — you might be wondering if Martian Manhunter is a Manhunter. After all, they share a moniker, so he has to be one, right?

Well, he's not. All you need to know is that the Guardians modeled their Manhunters on the Manhunters of Ma'aleca'andra — or, to call that planet by another name, Mars. So, while these two species share a common name, they're not related in any way, nor is Martian Manhunter one of the sentient android beings that the Guardians created.

Who is the Manhunter that Hal Jordan and John Stewart must find?

Is Garrett Dillahunt's Will Macon actually the Manhunter in disguise? (Image credit: John Johnson/HBO Max)

That's the big question. Logic dictates that it's someone who was at the football game — and, right now, the only other named character we know to be present at that game was Sheriff Kerry Kane. On the basis that she's conducting her own investigation into the deaths of those four aliens, though, I think we can rule her out.

So, which other characters could be the Manhunter in disguise? Will Macon is the obvious choice — he's certainly mysterious and, based on his hostile approach to extraterrestrials, would fit the mould of someone who doesn't want to be found by, say, a group of aliens seeking revenge for their race's near-extinction.

That said, it seems that he answers to someone else — i.e., his so-called 'benefactor' — further up the food chain. As such, I'm not convinced that the Macon patriarch is the Manhunter.

I don't think we can rule out Zoe Macon, either (Image credit: HBO Max)

There are other possible candidates in the DCU Chapter One TV show. Will's son Billy is one — after all, he's cosied up to Jordan as his lawyer, which is a sure-fire way of disarming a potential opponent and throwing them off the scent about who killed the four aliens at the Nebraskan football game.

I don't think we can overlook Zoe, aka Will's wife, either. And, with Stewart being told by his kidnappers that Manhunters are deceptive beings, it's not a stretch to assume that she's trying to manipulate Stewart in a bid to drive a bigger wedge between him and Jordan, which would impact their own investigation.

Of course, the Manhunter could be someone we haven't even met yet, so we'll have to wait for future episodes to air on HBO Max, aka one of the best streaming services, to see if anyone else could be the Manhunter. Currently, though, I'm going to evoke Jordan and say my gut's telling me it's Will, Billy, or Zoe.

While we wait for more clues, get the full lowdown on Sinestro following his introduction at the end of Lanterns episode 2. Then, vote in the poll below and/or leave a comment to let me know who you think the Manhunter is.

• Trump wants US to up aims to launch over 1,000 spacecraft every year by 2030
• Only 178 launches happened in 2025, mostly carried out by SpaceX
• Federal agencies must find new land for rocket launch sites

A new presidential memorandum signed by Donald Trump sets an ambitious annual benchmark for American commercial space activity within the next four years.

The new National Space Transportation Policy wants government agencies to work together so that the US hits over 1,000 spacecraft launches and returns per year by 2030.

In 2025, there were only 178 of these missions, though that number is already ten times higher than it was back in 2013, and SpaceX was behind most of them.

Land, permits, and spectrum take priority

The order replaces a national space policy that had been in place since 2013, when the previous framework was first adopted.

Within 90 days, several departments must scout available federal land that could host new sites for launching and landing spacecraft.

Approval processes for these facilities should move faster too, with environmental reviews and permits getting streamlined under the new rules.

Access to wireless spectrum, air traffic coordination and a stronger launch workforce are also named as priorities the government must address going forward.

Agencies have further been told to step back from any activity that might discourage or undercut private companies competing in the same space.

Export policies covering space technology will also be revisited periodically, aimed at protecting American intellectual property while expanding sales abroad.

The policy also calls for regular federal reviews of launch range schedules, intended to make better use of limited windows nationwide.

"This policy meets this moment of explosive growth and positions America to lead the next era of space," said Michael Kratsios, the White House's science and technology policy director.

Beyond Earth orbit, the memorandum pushes NASA toward landing American astronauts on the moon again before the end of 2028.

Early elements of a lunar base should follow two years later, alongside continued study of robotic and eventually human trips to Mars.

A dominant player and lingering doubts

Elon Musk's SpaceX remains the industry's clear leader, having carried out 170 missions last year that placed about 2,500 satellites into orbit.

Officials at the Federal Aviation Administration said back in May that the firm hopes to reach 10,000 flights annually within the next five years.

SpaceX has separately floated plans for roughly 1 million satellites designed to capture solar energy and feed power to AI data centers.

In addition to SpaceX, the U.S. will entertain requests from foreign companies that want to launch from American soil.

These firms will be screened individually, weighing security risk, investment value and site availability.

NASA, on the other hand, has been asked to build a lunar logistics network that private companies could eventually use to reach the moon commercially.

Lawmakers, meanwhile, have kept urging NASA to stay focused on Artemis, its long-running lunar program that has already consumed billions in funding.

That pressure suggests the administration's push toward 1,000 annual launches could still meet real budgetary and political resistance in Congress.

• America now dominates the world's largest hyperscale data center markets by a wide margin
• Northern Virginia remains enormous despite signs that future development is moving elsewhere
• Texas recorded 71% capacity growth, far exceeding the worldwide increase of 36%

The United States has tightened its grip on global hyperscale data center infrastructure, now accounting for 15 of the world's 20 largest markets.

Northern Virginia remains the largest single market, housing nearly 12% of global hyperscale capacity and approximately 250 data centers within Loudoun County.

However, rapid growth elsewhere suggests America's data center expansion is spreading beyond established hubs as power, land, incentives, and opposition influence investment.

America's dominance extends well beyond Northern Virginia

The figures from Synergy Research Group show 20 state or metropolitan markets currently account for 60% of worldwide hyperscale data center capacity.

Northern Virginia and the Greater Beijing Area alone represent 17% of global capacity, followed by Oregon, Iowa, Ohio, Dallas-Fort Worth, Dublin, and Shanghai.

Fifteen of those 20 leading markets are located in America, while Asia-Pacific contains four and Europe has only one, in Dublin.

The balance has shifted further toward America during the past two years, reducing the number of non-US markets within the top 20.

Tokyo and Sydney have fallen from the ranking since last year, alongside South Carolina, while Indiana, Tennessee, and Guangdong entered.

Synergy says America's advantage partly comes from corporate concentration, because 62% of the world's hyperscale operators are headquartered in the U.S.

The country's enormous cloud market also matters, with the US accounting for almost half of revenues across several important segments.

Amazon, Microsoft, and Google have the broadest infrastructure footprints and together control 57% of worldwide hyperscale data center capacity.

“A range of factors influence the choice of location for hyperscale infrastructure, including customers, real estate, power, networking infrastructure, incentives, and political stability,” said John Dinsdale, Chief Analyst at Synergy Research Group.

Texas gains ground as established markets face constraints

Northern Virginia's existing scale remains exceptional, although Dinsdale said the region no longer features as strongly in future development plans.

Loudoun County expects approximately $1.3 billion next year from taxes on data center equipment, according to figures reported by the New York Times.

Yet the economic benefits must be considered alongside incentives, with one earlier report estimating that Virginia's concessions could cost $1.94 billion.

Meanwhile, Texas has emerged as one of the fastest-growing hyperscale markets, with operational capacity increasing by 71% over the past year.

The increase was substantially higher than the worldwide growth rate of 36%, while Indiana, Tennessee, Shanghai, Johor, and Jakarta also recorded strong expansion.

Synergy is currently tracking 915 future hyperscale facilities at different stages of planning, construction, development, or internal equipment installation.

It expects rankings to continue changing, particularly as established markets encounter electricity constraints and communities become more resistant to major developments.

The current figures imply overwhelming US dominance, but the pipeline suggests future growth will depend on whether individual locations can secure sufficient power.

Netflix subscribers can watch the extended preview of GTA 6 a few hours before everyone else, but patient gamers can stream it for free later. 

• China is moving its AI data centers into poor rural provinces
• Guizhou offers cooler weather, cheap land, and abundant renewable power
• New data centers must draw 80% of power from renewables

China is significantly expanding its data center capacity as artificial intelligence processing drives an unprecedented demand for computing power nationwide.

Much of this new infrastructure is being built away from the crowded eastern seaboard and placed in rural western provinces.

In 2021, the Chinese government formally named this approach the Eastern Data Western Computing strategy, marking a clear national policy shift.

Why the rural west has the advantage

Provinces such as Guizhou and Inner Mongolia offer cooler climates, abundant renewable energy, and vast amounts of empty, affordable land.

China has also mandated that all new data centers draw 80% of their power from renewable sources before the decade ends.

Regions with expanded solar and wind capacity, like Guizhou, make it easier for operators to meet those goals, according to Rystad Energy analyst Simeng Deng.

Large-scale server farms can also absorb curtailed renewable output, easing the ongoing problem of overproduced solar and wind energy nationwide.

Developing previously overlooked rural regions has become another expected benefit, though researchers remain sharply divided on its actual economic impact.

An uneven economic payoff

According to Andrew Stokols of Singapore Management University, data centers do not necessarily generate a meaningful ripple effect for local employment.

This pattern shows in Guizhou's growth figures, where heavy data center investment helped drive average annual GDP growth of 7.4% over the past decade, yet wage growth over that same period ranked second to last among all Chinese provinces, according to Taiwan-based researchers at DSET.

The province also carries one of China's heaviest public debt burdens, driven largely by heavy infrastructure spending and incentive programs.

In 2024, Guizhou also ranked 30th nationally for its debt-to-revenue ratio and 27th nationally for its debt-to-GDP ratio.

Still, residents of Guian New Area, a Guizhou district built around Huawei's largest data center, describe visible local change, including new roads, growing trade, and steady new job opportunities for residents living close by today as well.

Two universities have reportedly opened in the area since the data centers were constructed, according to local vendors.

Despite this uneven mix of growth, debt, and stalled wages, fierce global competition over computing power keeps construction of new facilities moving steadily forward.

China is expected to nearly double its total data center capacity within the next five years, industry estimates suggest, momentum which shows why Chinese policy documents were already linking computing power to national strength, even before AI's rapid growth.

"It's essential for national competitiveness in the age of digital technology," said Stokols.

Unlike in Europe and the United States, where data center projects often face local resistance, dissent in China remains tightly controlled.

The pattern suggests China is treating computing infrastructure as a matter of national strategy rather than pure local economic development.

Whether rural provinces eventually see the broader prosperity officials have long promised remains, for now, an open and unresolved question.

Via Techxplore

Razer Soma Chroma: One-minute review

I've reviewed a lot of gaming chairs, sat my butt on plenty of seats promising the best of the best comfort, or ergonomic longevity (I'm convinced noblechairs wants my posture to last me until I'm 126 years old), and I've got to say, aside from possibly the AndaSeat Novis, this might be the most comfortable chair I've ever sat in.

At least, part of it is. The seat base. The fabric, the material, lovely; the base width, great. The backrest? Right up my street. But the Soma is a bit of an odd one when it comes to everything else it's trying to do. And an even bigger issue is its launch day price tag.

It's $500/£500 (about $954 AUD), which is a lot, and yes, there's RGB lighting in this, but Razer doesn't include an integrated battery (or even an optional one at checkout), doesn't pack a long enough cable, and doesn't include an adapter. The brand doesn't even include a cushioned headrest (you have to buy that separately). For 500 bones, frankly, that's utter madness.

The RGB itself (two diffused strips that run alongside the upper part of the backrest) is forward-facing only, and you can't really see it when you're sat down. It's not like a keyboard or a mouse, which is at least in your periphery, and needing your machine on with the cable hooked up separately if you opt to go without a battery bank means you'll probably spend about 5-10 seconds every session watching it just light up.

It's great for a streamer, sure, but even then it does feel more like an advert rather than something classy.

I had the lighting on for a day when I first got it for testing, then promptly unhooked the USB cable Razer graciously provided me with and never touched it again. Best gaming chair? Well, it won't be because of its RGB; that's for sure.

(Image credit: Future)Razer Soma Chroma: Price and Availability

• Costs $500/£500 (about $954 AUD)
• Remarkably expensive given what it lacks
• Single colorway and fabric option

Right, now you can grab the Razer Soma Chroma for $500 USD and £500 GBP (about $954 AUD). It sits roughly between the Razer Iskur V2 X (at about $350) and the standard V2 (that's $600 or so).

You can pick one of these up today, although there's little in the way of choice. No different color options, no different materials, you get what you get.

Sold separately, of course, you can also get a male-to-female USB-C extension cable, headrest, and power source (you'll have to source your own on the latter).

On the warranty front, Razer's got you somewhat covered with a 14-day returns policy via the RazerStore, and as standard it comes with a 3-year warranty, which upgrades to 5 if purchased direct and registered.

Razer Soma Chroma: Specs

Price

$500/£500 (about AU$954)

Dimensions

53.1 x 26.4 x 28.1in / 135 x 67 x 71.5cm

Max user weight

331lbs / 150kg

Max seat height

20.1in / 51cm

Seat depth

18.5in / 47cm

Recline angle

Up to 155°

Warranty

3 years (free 5-year upgrade via Razer.com)

Material

Knit fabric, cold-cured foam

AndaSeat Phantom 4 Pro: Design and Aesthetics

• Bold gamer style
• Perforated channels for air (read: debris)
• Green logo on an RGB product

The Soma Chroma is unabashedly gamer. There's just no two ways about it: the sharp lines, jagged polygonal design, the channelling, the RGB, the stitching — it doesn't exactly give off this allure of premium professionalism. That's to say I'm sure 16-year-old Zak would've loved it, but me, sat here in my ‘rustic’ office, dealing with media, clients, and more on the daily, yeah it kinda sticks out like a sore thumb.

Particularly with the striking green Razer logo etched in the backrest right behind my head, in a not-so-obscure glossy green plastic. Which again I have to question. This is an RGB product, right? That means you can set that lighting (hypothetically, if you use it) to any color you like.

That's great, but certain colors clash with green quite aggressively. Bright reds, neon blue, pinks, high-saturation yellows. Basically all off limits, all because the logo's bright neon green. It's 2026, guys; we've had RGB products for a seriously long time now. I thought it was common knowledge that you don't stick colored accents on them.

The lighting is also awkwardly placed. You can't actually see it from the back of the chair, and it's solely positioned behind your head, facing forward, so you literally never see it, outside of the five to ten seconds you go to the toilet and sit back down again. It's diffused, it looks good when you do manage to catch a glimpse of the stuff, and you can easily configure it in Synapse, but honestly it just feels like a bit of a swing and a miss.

(Image credit: Future)

How'd you connect all that? Well, there's a short USB-C cable located in the back of the chair that hooks up to your battery bank, which you can then stow away in a small zip pocket in the seat base. But it's really short; that cable. If you're imagining some retractable 6-foot thing you can pull out when you need it, no, it's not that.

You're not connecting this thing to a wall plug, or anything of the sort. Razer provided me with a seriously long extension cable (two in fact) for it, but that doesn't come by default, annoyingly. And of course, there's no built-in battery, or included power bank, or the option to add one to your order. I used the extension cable to effectively run the entire chair to the back of my PC, and it does run off USB power. That said, you don't have to go that route; you can just use a USB plug, and then let the included wireless dongle handle the RGB side of things.

Now I'm done wailing on the lighting; the good news is the finish is genuinely lush, it's like a neoprene-feel soft-touch fabric, and it's a delight to sit on. I'm writing this review in the midst of yet another heat wave in the UK, and skin-on-chair contact has been genuinely lovely. Compare that to the number of AndaSeat's chairs I've reviewed recently that leave me begging for AC, and well, it's a nice change that's for sure.

Ok, we've had bad, we've had good, now it's back to bad. There are channels in the chair base, and I get the concept. The idea is that it helps air circulate your, well, back passage, to stop you heating up so quickly, and it kind of works. But it comes with a major caveat, and that's that it also holds onto all manner of dust and debris.

If you own a dog, or cat, or have a habit of sitting at your desk eating from time to time, you're cooked. You'll be getting the blow duster out you use for your PC on the chair base at least once a week.

(Image credit: Future)Razer Soma Chroma: Comfort and adjustability

• Super comfortable, wide seat base
• Generous lumbar support
• Armrests and the castors are letdowns

Fortunately for the Soma, sitting on it is a treat. The cold-foam base is comfortable as all hell, and the seat base is wide enough for the weirdos like me who sit cross-legged to do so in absolute bliss.

The armrests, however, are more basic, two-dimensional affairs. There's no four- or even three-directional setup here like you'd find on almost all of its modern counterparts from competitors at this price.

You can raise them up and down and swivel them, and that's it. They're padded and modestly squishy, but if you'd put them on an Ikea chair, I wouldn't be able to tell that they were Razer-designed and made.

Oh, and it doesn't roll. Well, it does, but it's just so frustratingly stiff it beggars belief. I wrote about this in the AndaSeat Phantom 4 Pro review earlier this year. It's basically, I think, a result of overcompliance with the EU's EN 1335-2 safety measure, which means that in an emergency any unmanned chair shouldn't move easily. I don't quite understand the logic behind that, but here we are.

(Image credit: Future)

And it frustrates me because I can't roll up and down my desk, or if I need to lightly move it to put my shoes on, or I just want to get more comfortable at my setup, it's a minor struggle. And yes, I know that's a first-world problem, but it's $500/£500 for this thing, from a seriously reputable brand.

One final flag from me before I move on. There's no headrest cushion included either. Razer will sell you a headrest, but it's at additional cost. Eugh. No magnetic freebie for you, no sir. Not even a strapped one.

Razer Soma Chroma: Assembly

• Seamless, standard build
• Missing shear bolt on recline
• Massive manual

As far as building the thing goes, the Soma Chroma is genuinely smooth. If you've built one gaming chair before, you'll get along with this one just fine. It's a bit heavy; Razer recommends you'll need two people to build it, but it's more than easy enough to do solo.

Plus the manual is massive, and printed onto a giant sheet of card, which feels a bit overkill (a book would've been fine; I'm not that old, I can read).

The only cause for concern is that you do need to be a touch careful installing the backrest, as there's no safety bolt sitting between you and the tilt angle arm. So if you do hit that lever too aggressively, there's a chance the bracket can snap forward. So do bear that in mind.

(Image credit: Future)Should You Buy The Razer Soma Chroma?Buy it if...

You want a genuinely comfortable chair
Soft-touch upholstery, impressively wide base, generous lumbar support, and that cold-density foam do the bulk of the work here. It's best at the boring bits.

You're a streamer
The one audience that actually sees the Chroma, you know, work, is yours. The RGB syncs with a good 300 games or so, and you can build out to your color scheme if you'd prefer

Don't buy it if...

You expect 500 bucks to cover everything
No head pillow, no cables, no power adapters, no battery. The things that make the Soma Chroma don't light up without an additional investment.

You actually move at your desk
Braked castors and static armrests make this a chair for sitting perfectly still rather than shifting around.

Also consider...

Make sure to consider these alternatives as well.

Razer Soma Chroma

Secretlab Titan Evo 2022

Razer Iskur V2

Price

$500 / £500 (about AU$954)

$519 / £414 / AU$724

$650 / £600 (or around AU$1,128)

Dimensions

53.1 x 26.4 x 28.1in / 135 x 67 x 71.5cm

54.3–58.3 x 27.6 x 27.6in / 138–148 x 70 x 70 cm

51.0–54.7 x 27.2 x 27.2in / 129.5–139 x 69 x 69cm

Max user weight

331lbs / 150kg

285lbs / 130kg

300lbs / 136kg

Max seat height

20.1in / 51cm

21.9in / 55.5cm

21.1in / 53.5cm

Seat depth

18.5in / 47cm

20.9in / 53cm

17.7in / 45cm

Recline angle

Up to 155°

85–165°

85–153.5°

Warranty

3 years (5 direct)

5-Years (extended warranty)

3-Years

Material

Knit fabric, cold-cured foam

NEO Hybrid Leatherette, SoftWeave Plus Fabric, Napa Leather

EPU Leatherette, Fabric

Secretlab Titan Evo 2022
The legendary Titan Evo is a TechRadar Gaming favorite for good reason. With exceptional comfort, removable magnetic lumbar support, and a size range that covers every-shaped human under the sun, it's well worth considering if the Soma just doesn't feel like the right home for your butt.

For more information, check out our full Secretlab Titan Evo 2022 review

Razer Iskur V2
Like the Soma Chroma but better. With dedicated built-in lumbar support, it's a solid trooper in the world of gaming chairs. It's even got the build quality and aesthetic chops to match albeit at a slightly inflated price.

For more information, check out our full Razer Iskur V2 review

How I tested the Razer Soma Chroma

• Used for over a month
• Fully built by hand solo
• Set up in all manner of configurations

I spent well over a month with the Razer Soma Chroma, testing the ins and outs of how it held up under serious usage. Working from home, it endured the hardships of a constant 9-5 "office" job, along with late evening gaming sessions, through normal operating conditions and heatwaves abound.

I've spent years testing gaming chairs from all manner of manufacturers, and I compared this one directly to a slew of products from AndaSeat, Noblechairs, and Secretlab as well. I also fully tested the Synapse and WiFi dongle, and tracked the real-world usage and feel of it throughout the month, documenting any moment where I thought "ahh this is good" or "ahh not so good".

I also got my five-foot-five partner to sit in and provide me with her direct feedback as well, along with a few close friends to get their thoughts and feelings on it, which directly feeds the opinions you see here as well. I questioned them intensely on the comfort, the feel, and the build quality of the Soma Chroma to get a broad picture of opinions on it.

Read more about how we test.

First reviewed August 2026

• Brave1 lets Ukrainian units order drones built to exact specifications
• Military units have spent nearly one billion dollars through Brave1 Market
• Buyers can adjust seventeen different drone specifications before placing an order

A new procurement system launched by Ukraine's Brave1 defense cluster now allows Ukrainian military units to commission drones built to their own specifications.

The system operates through the existing Brave1 Market platform, where military units can request drones tailored to particular operational requirements.

Military units placed roughly $1 billion worth of orders for defense technologies through the platform during its first year of operation.

A more flexible way to buy

This digital marketplace currently has over 1,000 defense innovations spanning unmanned aerial vehicles, ground robotic systems, electronic warfare and signals intelligence equipment, specialized components, AI-powered solutions, software applications, and ammunition.

Custom ordering, however, is currently limited to a small slice of that catalog. At this stage, only three drone categories qualify for custom orders: standard FPV strike drones, fiber-optic FPV variants, and fixed-wing interceptors.

Seventeen separate specifications are open to adjustment under the new system, ranging from communication equipment and cameras to payload capacity and flight time.

Submitted requests are routed automatically to a pool of suppliers, each of whom can choose to bid or opt out entirely.

"All stages of interaction and decision-making are automatically recorded in the system," said Brave1.

If a military unit decides not to proceed with a purchase, it must provide a clearly justified reason for the decision.

Brave1 says the automated approach aims to give military units systems tailored to specific battlefield missions while keeping procurement rules transparent for suppliers.

The system also provides the state with a complete digital record and verified data for every transaction made across the platform.

The platform also allows complaints to be filed against either buyers or suppliers if disputes arise during the process at any later stage.

When multiple suppliers submit matching technical proposals, the system triggers a competitive bidding process, and the cheapest offer ultimately secures the deal.

Once bidding wraps up, the requesting unit finalizes an agreement directly with the chosen manufacturer, bypassing extra bureaucratic steps in the process.

Suppliers who decline a specific request face no penalty under the current system, according to the company's statement released this week.

Expanding categories and remaining limits

Brave1 said the procurement process is designed to remain transparent and competitive for all participating suppliers throughout every stage of the transaction.

Brave1 plans to expand the range of eligible systems considerably further in the coming months, including the eventual addition of unmanned ground vehicles.

For now, the feature applies only to purchases financed with military units' own funds, excluding the state eBaly procurement system.

Units can even request equipment that hasn't received official classification yet, something the standard state procurement process typically does not allow.

This shift toward customer-driven procurement suggests Ukraine is trying to close the gap between battlefield needs and manufacturing speed during an active war.

Other governments observing Ukraine's wartime innovations may eventually consider similar digital procurement models for their own defense industries in the years ahead.

Via United24media