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AI spend made headlines again recently with the Claude Fable 5 model from Anthropic. Before security concerns led to the model being suspended, there were also cost concerns. Anthropic says Fable costs $10 or approximately €9 per million input tokens and $50 per million output tokens. This is double the price of the company’s previously most expensive model, Claude Opus 4.8.

Posts soon began to pop up on LinkedIn, showing just how quickly teams were going through their tokens and, as a result, their budget. There are some caveats here. Namely, that Fable 5 is an advanced model and, for most businesses, won’t need to run non-stop or be used for every task.

But therein lies a key issue: AI use is accelerating and models are evolving. But the level of control and visibility businesses have over how much is being spent, by who and for what is lagging behind.

How AI consumption became a finance problem

There is a massive shift within the UK software market toward AI and specifically Anthropic’s ecosystem. Proprietary data from Pleo looking at the top tech merchants based on number of spending customers, shows that Anthropic (Claude) surged from 12th place in Q4 2025 to 7th in Q1 2026. Meanwhile, the average spend per customer increased +43.0% in this time.

This rapid climb signals that Anthropic has reached enterprise maturity in the UK market with businesses moving beyond the experimentation phase. But while this reflects growing confidence in AI adoption, it also presents some financial challenges.

On the whole, AI has redefined how the workplace runs, but it is not a free trial. The cost of tokens has gone up, and new models that can achieve what was seemingly unthinkable a few years ago come with a price tag to match. The new challenge for business leaders is to leverage these technologies but also limit rampant spending.

This is why many organizations are turning to their finance teams. Finance has the visibility to dig into the details and map AI use across the organization, whether it quietly shows up as a subscription renewal or a new budget request. But more than that, they can be instrumental in ensuring teams embrace open conversations, not just OpenAI.

AI activity does not translate to AI value

Just about every organization will have developed transformational ways of using AI tools. But, whether they know it or not, there will be wasteful ones too.

When it comes to inefficient use, some of the major culprits include asking AI agents open-ended questions, model mismatch where tokens are burned unnecessarily; and duplicate tools, resulting from shadow AI and overlapping subscriptions. These prevent businesses from seeing the full picture; one that is, in all probability, very expensive.

User literacy can improve this. But for finance teams they must start with the grey area of AI consumption. Two teams might show as active AI users, but one that’s using an LLM to produce more content faster is doing something fundamentally different to one that’s using it for peripheral productivity tasks. In fact, only 29% of European SMEs using Gen AI are doing so in core business activities.

To improve the control they have over AI, organizations must start by elevating their visibility from who is using AI, to who is using it to become smarter, faster and more productive.

How to regain control over AI use

A complete view of AI spend is essential, regardless of whether costs are rising.

Breaking spend down by department, team and budget helps identify both disproportionate usage and areas where adoption may be lagging. These should be combined with performance metrics such as the time-to-first-draft on marketing content; code review cycle times in engineering; support ticket resolution time in customer support; and so on.

This combination of spend and performance can reveal whether AI investment is translating into measurable productivity gains and not just higher software costs.

Visibility should also extend to model-level usage. As mentioned before, the cost difference between frontier reasoning models and lighter alternatives can be tenfold. Monitoring model and vendor usage alongside token consumption helps organizations route routine tasks to lower-cost options, maximize ROI and reduce unnecessary spend.

Finance teams should therefore expand reporting and budgeting frameworks to include AI-specific metrics. A key question at month-end is whether AI-enabled teams are increasing output and capacity without increasing headcount. This provides a clear headline for AI's impact, can justify investment and distinguish between high-value and low-value AI usage.

Ultimately, effective control over AI is not about costs alone. It is about understanding where AI is creating value and ensuring investment is aligned with business outcomes.

AI control is at your fingertips

The good news is that none of these metrics require a sophisticated AI analytics stack. Finance teams should already have the tools for real-time visibility into what’s being spent and where. All that’s needed now is to fold AI into the mix and collaborate with other departments to measure and improve its ROI.

The outcome is that organizations control AI use through oversight, without restricting spend, adoption or innovation through lengthy procurement processes. Spend policies, category controls and clear approval thresholds control what is spent, and everything is measured.

But crucially, teams don’t slow down as a result. The only difference is that AI is optimized for impact.

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The legal sector’s use of AI is maturing, and its use is going beyond simply being an external chatbot or standalone tool. The focus is shifting to embedding AI into everyday legal work, giving it access to the documents, matters, knowledge and systems lawyers use every day.

As firms increase their investment in AI technology, they need to look beyond what AI models can generate and focus on a more practical question: how do those models connect to the information lawyers rely on, where does that information sit, and how is access governed?

That connection challenge is why Model Context Protocol (MCP) has started to attract so much attention.

Separating the standard from the assumptions

MCP is an open standard that gives AI tools a more consistent way to connect to external systems, data sources and applications. For all the excitement around MCP, firms need to be clear about what it does, and just as importantly, what it does not do. Otherwise, there is a risk that firms either overestimate what MCP can solve on its own or dismiss it as just another technical acronym.

The reality sits somewhere in the middle. MCP can help AI tools connect to the systems and data sources firms already use, but it does not automatically solve challenges around governance, integration, permissions or legal context. With misconceptions starting to spread across the legal sector, here are five common myths to clear up.

Myth 1: MCP is only for Claude

Although MCP was created by Anthropic, it is not just a Claude feature. It is an open-source framework and is now becoming part of the broader conversation around how AI agents connect to external tools, data sources and systems.

That distinction matters for law firms and legal organizations generally. If MCP is treated as a single-vendor feature, it can be dismissed as something tied to one model or product roadmap. But as a broader connectivity standard, firms need to think about how it fits into their wider AI strategy, integration architecture and governance model.

Myth 2: MCP replaces APIs

APIs still matter. They remain the backbone of platform-to-platform connections, allowing software systems to exchange data and trigger actions. MCP does something different: it acts as a protocol layer on top of APIs, giving AI agents a more standard way to discover and interact with approved legal systems, tools and data sources.

Put simply, MCP is closer to a universal adapter for AI, than an alternative to APIs. Just as USB gives different devices a common way to connect, MCP gives AI tools a more consistent way to understand what systems and functions are available to them, and how they can interact with those systems.

But it does not remove the need for APIs, authentication, system owners or clear rules about what AI tools can and cannot access.

Myth 3: MCP means moving documents into AI tools

There is a common misconception that connecting AI to legal systems means copying a large volume of documents into external AI platforms. In practice, AI should only be given controlled access to governed systems. Documents, precedents and matter files can remain within the firm’s trusted environment, with AI tools using MCP to retrieve only the information they are authorized to access.

Rather than creating another uncontrolled copy of sensitive material, firms can let AI work with the right information while existing permissions, security policies and governance controls remain intact. To hit the right balance, legal IT leaders should be asking “Where does the data stay, what is exposed, and how is access controlled?

Myth 4: All MCP integrations are the same

As MCP becomes more common, there will be a temptation to treat any MCP-compatible integration as broadly equivalent. That would be a mistake. MCP standardizes the connection, rather than the value of what comes through it. One integration may provide a basic route to retrieve files.

Another may provide richer information about permissions, matter relationships, document history, metadata and audit trails. Both may be MCP-compatible, but they will not deliver the same outcome.

Law firms need to focus on what the AI receives, whether access rights are enforced and whether activity can be audited.

Myth 5: MCP automatically gives AI legal context

MCP creates the route into systems, but it does not decide what the AI receives or understands. Connection does not mean context. In legal, this is more than a simple retrieval problem. An AI tool does not just need access to a document in a DMS. It needs to understand the matter, client, permissions, version history, related work and institutional knowledge around it.

For example, an AI tool may be able to find a precedent agreement, but does it know whether that precedent is current, whether it belongs to a similar matter, whether it reflects the firm’s preferred position, or whether the lawyer has permission to access the related material? Without that context, AI may generate an answer, but the answer may not be reliable enough for legal work.

That is why MCP should be seen as the access layer, not the intelligence layer. It can help AI tools connect to legal systems, but the value comes from what those systems expose through MCP: governed, matter-aware and permission-sensitive context.

Connection is only half the story

MCP gives law firms a more standard way to connect AI tools to the systems they already use. But legal AI depends on more than connection. The firms that benefit most will be those that treat MCP as the starting point, not the destination. The quality of the information, the controls around it and the context that gives it meaning will determine whether connected AI makes a genuine impact in legal work.

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Enterprise AI strategy spent two years chasing a single objective: reach the frontier before competitors do.

The default path was a public cloud account, an API key from OpenAI or Anthropic, and a willingness to absorb cost in exchange for speed.

That reality is now running out of road.

The numbers tell the story. Gartner forecasts worldwide AI spending reaching $2.52 trillion in 2026, up 44% year on year, with $1.37 trillion of that flowing into AI infrastructure alone.

In fact, in mid-2025, they claimed that procurement of AI had entered a “Trough of Disillusionment,” where scaling depends on predictable ROI, rather than visionary pilots.

The pressure has now shifted from how fast enterprises can pilot AI to whether they can sustain, govern, and defend it in production.

The Race to the Front is Over – Now Comes the Bill

We are now past AI 1.0, where simple access to cutting-edge AI was the differentiator. Now it’s AI 2.0’s turn, where inference economics, data gravity, latency and control decide the outcomes. Token prices have fallen almost tenfold annually since 2021, but AI spend overall by organizations has increased. That’s because more capable models have enabled greater ambition.

Anthropic, OpenAI, and Mistral are now stratifying offerings between flagship reasoners and lower-cost workhorses precisely because customers refuse to pay flagship prices for every task. McKinsey’s 2025 State of AI survey confirms the pattern - adoption is increasing, but impact at scale remains elusive for most organizations.

Now CIOs have stopped asking which model, but where each workload needs to run and how much it’s going to cost.

Inference Cost Inflation

Banks delivering the next best action are a good example: the in-app, in-branch, or call-center recommendation served in milliseconds against a customer’s live context. The best banks prove that personalization at this layer can lift revenue by 5-15%. To give a firsthand example, a global bank we work with launched an AI assistant that has already resolved more than 1.5 million customer inquiries in its first year, driving huge efficiencies.

But the inference economics are unforgiving at this scale. A single agentic decision can chain five to twenty model calls, each carrying its own context window. The cost gap between £0.50 and £3 per million input tokens seems trivial in a single-turn demo. Spread across hundreds of millions of customer events, it becomes the difference between a money-making feature and a money-burning one.

This isn’t a hypothetical either. Uber’s 5,000-strong engineering team’s use of Claude Code burned through the company’s entire annual AI budget in the first four months of this year. And AI companies are responding to this market shift. Decagon, after re-architecting onto an open-source multi-model stack on NVIDIA Blackwell, dropped cost per voice query by sixfold. Next best action isn’t a marketing decision anymore; it’s an economic decision.

Organizations making the structural shift now will outcompete those treating model selection as an afterthought.

Complexity Doesn’t Disappear, It Just Moves

The hardest lesson of the past 18 months is that model commoditization does not reduce enterprise complexity but relocates it. Open weights from Mistral or DeepSeek cut experimentation cost, but orchestration, governance, evaluation, and integration burdens move up the stack and sit with the buyer.

Enterprise leaders should be measuring unit economics per useful task, operational burden per deployed agent, and the ratio of inference spent on the governance scaffolding around it. That ratio is typically 1:5 or worse.

A second architectural shift is arriving: sub-quadratic attention.

Approaches from DeepSeek, Google, and Cartesia are collapsing the cost of long-context reasoning by orders of magnitude, with recent benchmarks showing 100x to 300x cost reductions at comparable accuracy.

Large banks will now be able to run whole-portfolio risk modelling, multi-decade fraud detection and cross-jurisdiction Know-Your-Customer (KYC) as single-pass operations - no more chunked retrieval workarounds.

Telcos can make network operations, predictive maintenance and multi-year customer journey reasoning more economically viable at scale. And manufacturers can move full-plant simulation and supply-chain disruption forecasting from periodic batch jobs to continuous reasoning.

The architecture that wins will not be the one with the cheapest token. It will be the one that places compute closest to the data, under the right jurisdiction, with governance that holds.

Sustainable, sovereign, controlled - that’s the new triad. The enterprises that build for it now will define the next decade.

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Artificial intelligence (AI) is rapidly becoming embedded in the modern workplace, with employees are increasingly turning to AI tools to work more efficiently and boost productivity.

This growing demand for faster, more effective ways of working is driving the rise of shadow AI - the use of AI tools outside approved organizational controls and governance frameworks – which results in organizations quickly losing visibility into data usage and potential risks.

The scale of this challenge is significant. While 90% of executives are confident in their organizations' visibility into AI tools, just 52% of employees admit to using AI tools without approval, often through personal accounts.

As a result, organizations are left grappling with a widening gap between AI adoption and AI governance.

The next frontier of AI risk

When AI is used without formal oversight, it can bypass governance controls, increasing the risk of errors, regulatory breaches and sensitive data leakages. Organizations are most exposed when AI is already influencing business-critical activities, from customer service and operational decision-making to software development and content creation.

The challenge will intensify as businesses move beyond large language models, which generate information, to large action models and agentic systems that can take action. These systems can diagnose issues, recommend actions and execute workflows with minimum human input, increasing both the speed and scale at which mistakes occur.

A shadow agent operating outside approved governance frameworks could trigger harmful actions before organizations have the visibility and governance capabilities needed to intervene.

There is also a longer-term risk that future AI systems will be trained on synthetic or lower-quality data, weakening performance and decision-making over time. Transparency and traceability will be critical to maintaining accountability, protecting ethical standards and preserving the effectiveness of AI systems as adoption continues to accelerate.

AI governance as an enabler

What works is AI governance that enables innovation while putting clear guardrails in place that are integrated, transparent, auditable, and aligned with existing risk and compliance frameworks. If AI is to be used safely, firms must be able to successfully identify exactly what went wrong and why when issues arise.

In practice, mature governance starts with an approved AI tool stack that provides safe and trusted options for common use cases. This should be supported by risk-based policies that make clear the data being handled, what can and cannot be shared, which tools are permitted, and where human approval is required.

Low-risk tasks such as drafting or summarizing content should not be governed in the same way as high-risk uses involving customer data, regulated information or business-critical decisions.

Training is equally important. The challenge, beyond only enforcing controls, involves helping employees understand why those controls exist and how to use AI responsibly. As agents increasingly diagnose issues, recommend actions, and execute workflows with minimum input, human oversight and approval processes must scale alongside them.

Interoperability will be critical to making this workable at scale, allowing organizations to operate across jurisdictions and multiple AI models without repeatedly rebuilding governance processes and systems from scratch.

Making responsible adoption the easy choice

For security and compliance leaders, the goal should be to make responsible AI adoption the path of least resistance. Employees turn to shadow AI when approved tools are unavailable, difficult to access or fail to meet their needs. Companies that focus solely on restricting usage risk driving activity further underground and losing out on the efficiency and innovation gains that AI can deliver.

Organizations that successfully balance AI productivity and control over their systems recognize that shadow AI use is often a symptom of unmet demand. Employees typically turn to unauthorized tools because they are easier to access, faster to use or better suited to the task at hand. Rather than focusing on restrictions alone, leaders should understand where AI is already being used across the business and ensure approved alternatives are available for the most common use cases.

With three-quarters of office professionals saying they would be likely to look for a new job that offered better AI skills development, firms that combine governance with opportunities to build AI skills are likely to see stronger adoption of approved tools and, as a result, less reliance on shadow AI.

Building an AI-enabled culture means giving employees the tools, knowledge and confidence to innovate within clear boundaries. By doing so, shadow AI can be reduced while the speed and agility that workers increasingly expect is maintained.

The organizations best positioned to succeed

The businesses that strike the right balance for AI success will be those that view governance as a foundation for AI adoption and not a barrier to it. By making the secure, approved path the easiest path, shadow AI risk is reduced without sacrificing productivity.

Embedding strong governance, supported by trusted and well-managed data foundations, avoids costly mistakes and allows AI to be deployed and scaled with greater safety and confidence.

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Lanterns has finally premiered on HBO Max — and, in a move reminiscent of Invincible's first-ever episode on Prime Video, the new DC Universe (DCU) TV show wastes no time in delivering an almighty shock in its first chapter.

Indeed, the sci-fi show's premiere, titled 'Pilot', ends with a moment that'll completely stun viewers and likely upset DC comic book fanatics.

I'm about to dig into the biggest talking point from Lanterns' debut episode, so consider this your one and only warning: full spoilers immediately follow after the poll below. Turn back now if you haven't seen 'Pilot' yet, or else.

Is Kyle Chandler's Hal Jordan really dead in Lanterns?

Green Lantern fans and DC purists are going to have a lot to say about this... (Image credit: John Johnson/HBO Max)

It certainly seems that way. Of course, this could be a massive fake out — after all, there are all manner of shapeshifting alien species that can impersonate humans in DC Comics, so it's possible that this is the case here. Nonetheless, I believe that Chandler's Jordan has taken his last breath in the DCU.

Okay, but how did we get here? For starters, this season's premiere wasn't shy about teasing Jordan's demise. Indeed, minutes before his death, which takes place during the show's 2026 storyline, is shown, Jordan, Aaron Pierre's John Stewart, and Kelly Macdonald's Sheriff Kerry Kane survive an attack from a suicidal bomber at Rushville's police station.

This incident, which happens as part of Lanterns' 2016 storyline, is caused by an alien masquerading as a human truck driver called Waylon Sanders. As the unidentified extraterrestrial reveals, their skeleton is retrofitted with a biometric neutron device that they can activate with a single thought.

Hal Jordan interrogates Waylon Sanders, who the former correctly identifies as an alien (Image credit: John Johnson/HBO Max)

Prior to detonating the explosive gadget, Sanders goads Jordan over the latter's fearless nature, which Sanders interprets as Jordan wanting someone or something to kill him. In that respect, then, the DCU Chapter One series telegraphs his death before it happens.

Rather than bump off Jordan in 2016, though, Lanterns withholds his passing until a decade later. Indeed, Jordan uses his power ring to form a protective bubble around Sanders just before the assailant detonates his skeletal device, thereby restricting the blast radius and saving the lives of everyone nearby.

But, that's not the end of the story. Five minutes before 'Pilot' ends, we skip ahead to 2026, which reteams us with Stewart as he heads to the same college football field that he and Jordan first met Sheriff Kane, and where their 2016 investigation into the deaths of four football fans began.

There, Stewart reunites with Kane and, after a bit of small talk about Kane's now-teenage son Noah, they head to a section of the bleacher seating where Jordan's snow-covered corpse is eventually revealed to be sitting.

Who killed Hal Jordan in Lanterns episode 1? Assessing the most likely candidates

Anybody else react like this when they saw Hal Jordan's dead body? (Image credit: John Johnson/HBO Max)

Alright, so who murdered Earth's first-ever Green Lantern? We don't know, but we can speculate on who pulled the trigger.

The first two — and arguably most likely candidates — are Sheriff Kane and the stadium's groundskeeper. Kane tells Stewart that the latter is the only other individual who currently knows that Jordan is dead. However, it's incredibly unlikely that the groundskeeper will be a prominent character moving forward, so we can rule them out.

We can do likewise with Kane. Sure, she's got a firearm to hand at all times, but I just don't see her wanting to draw heat over Jordan's death, and the potential ire of Stewart, the Green Lantern Corps, and/or the Guardians of the Universe.

Did Jordan's murderer steal his ring, too? (Image credit: HBO Max)

So, who else could it be? Of the other characters we've met, I wouldn't be shocked if Garret Dillahunt's Will Macon is behind it, regardless of whether he actually committed the deed or not. Right now, though, he's formed something of an uneasy alliance with Jordan and Stewart, so a massive, relationship-breaking event would need to happen for Macon to be involved in Jordan's demise.

There are bound to be other would-be murderers who could've taken Jordan's life who we've yet to encounter, so we might be adding more names to our shortlist in the weeks ahead.

That said — and hear me out on this before you pass judgement — what if Jordan killed himself? He's a former special forces pilot, so he knows how to handle a gun. For all we know, Waylon was telling the truth about Jordan wanting is life to be over, too — especially if, as an alien, the former has some form of perceptive superpower that allows his species to read someone else.

Who do you think killed Hal Jordan? Let me know in the comment section below. And, for more on the HBO Max show, read my Lanterns review.

Samsung has had a busy few weeks with the launch of its 2026 foldable and wearable ranges, with pre-orders just wrapping up last week. If you missed out on the pre-order deals or you have your eyes set on something else from the brand, then fret not — Samsung has just unveiled a slew of deals across its product portfolio.

The latest Samsung Secret Sale event features lots of top-rated tech that we’ve reviewed here at TechRadar, with savings of up AU$1,200 or 50% with the code SECRETAUG — but it’s only running for 48 hours, with deals ending at 10am AEST on Wednesday, August 19.

One standout deal is the excellent Samsung Galaxy Watch 8 with a 50% discount, bringing it down to just AU$374.50 for the 40mm LTE model. Given that the new Watch 9 has relatively modest upgrades for AU$699 (with a current deal), this offer is hard to pass up.

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• Private drone companies can now request access to US Army testing ranges
• Companies no longer need existing government partnerships before requesting range access
• Five military facilities across America and Morocco are joining the programme

Private drone companies without existing government contracts can now request access to the US Army test ranges nationwide.

The arrangement removes a requirement for companies to have existing government partnerships before requesting access to military ranges.

Army Secretary Dan Driscoll framed the change as an effort to cut through bureaucratic obstacles facing smaller defense contractors.

What companies can access and where

Five ranges now fall under this new access system, spanning four US states and one overseas location.

Dugway Proving Ground in Utah specializes in long-range fires testing for companies developing precision strike capabilities, whilst West Cibola Range in Arizona currently focuses exclusively on drone and counter-drone system evaluations.

Camp Shelby in Mississippi and Camp Grayling in Michigan round out the domestic testing locations available.

The fifth site, the Africa Multidomain Training and Experimentation Center, sits in Morocco.

Beginning in September 2026, Camp Grayling will host a recurring test simulating degraded electromagnetic conditions found in Ukraine.

This quarterly event will let companies trial drone and counter-drone systems against jamming and disrupted signals.

To get access to any of these sites, companies will have to apply through testrange.army.mil, though the Army cannot guarantee a specific site or timeline.

"A company with a good idea shouldn't need a team of lawyers and a program of record just to prove their equipment works," said Dan Driscoll, Army Secretary.

"So we fixed that. One front door — testrange.army.mil — a real person to walk you in, and on the other side, everything the modern battlefield demands: contested airspace, degraded signals and the space to test at real scale."

The pressure driving this shift

The expanded access reflects urgency inside the Pentagon around accelerating drone and counter-drone development timelines.

The Defense Department recently formed Joint Interagency Task Force 401, a unit built to streamline counter-drone procurement.

That task force has already stood up an online marketplace connecting military buyers directly with technology suppliers.

Behind this push sits a separate concern, with reports suggesting depleted Patriot missile interceptor stockpiles nationwide.

The United States reportedly used roughly two-thirds of its Patriot interceptor inventory during its recent conflict with Iran, as Washington appears to have underestimated how quickly a prolonged confrontation with Iran could consume its most valuable air-defense interceptors.

However, Defense Secretary Pete Hegseth disputed a CNN report claiming military commanders had flagged critically low interceptor stockpiles.

Regardless of that disagreement, the Pentagon continues pressing defense contractors toward faster production of essential weapons systems, and the range initiative sits within a broader goal of hastening development for drones, counter-drone tools, and interceptors.

Leading this effort is the US Army Test and Evaluation Command, working alongside several partner organizations nationwide.

Those partners include the Mississippi and Michigan National Guard units, along with U.S. Africa Command's regional support.

For companies previously locked out by lengthy contracting requirements, this shift represents a meaningfully lower barrier to entry.

Via Defense News

• India is replacing the Su-30MKI's ageing Russian radar with Virupaksha
• The new radar could detect conventional fighters 300 to 400 kilometres away
• Virupaksha is expected to detect stealthier aircraft beyond 200 kilometres

India's Su-30MKI fleet, built on a Russian airframe design, is now getting a distinctly homegrown upgrade to its detection system.

The Defence Research and Development Organisation is developing a new radar called Virupaksha to replace the aircraft's ageing Russian-origin unit.

This radar swap forms a core element of the broader Super Sukhoi modernisation programme currently awaiting final government clearance.

Indian radar could transform the Su-30MKI's detection capabilities

The Super Sukhoi programme aims to elevate the aircraft to a more advanced fighter jet class through this sensor overhaul.

In simple terms, that means the jet would gain sharper detection abilities without needing an entirely new airframe.

Indian officials have estimated the cost for an initial batch of 84 aircraft at ₹60,000 to ₹65,000 crore (about $6 billion), but it still awaits final approval from the Cabinet Committee on Security.

Astra Microwave Products, a private contractor, is building a key radar component, the Active Antenna Array Unit (AAAU), alongside DRDO engineers.

The AAAU works like the radar's eyes, scanning the sky for threats electronically instead of using a spinning dish.

It houses roughly 2,400 small transmitting and receiving parts made from a material called Gallium Nitride, which is more power-efficient than older materials.

In practical terms, this material lets the radar see farther, run cooler, and resist enemy jamming more effectively than older designs.

It also keeps the whole unit relatively lightweight, which matters when fitting new technology into an existing aircraft.

Once complete, the radar is expected to detect conventional fighter-sized aircraft between 300 and 400 kilometres away.

Against stealthier aircraft designed to evade detection, its range is still expected to exceed 200 kilometres, a major improvement over the current system.

Virupaksha radar moves closer to completion

Astra Microwave has told investors that its portion of the radar programme remains on schedule for completion within months.

The company expressed hope that the full Virupaksha radar system could be finished by the end of this year.

"The Active Antenna Array Unit remains on schedule despite the scale and complexity of the underlying technology," said a company spokesperson.

Completing this radar on time would mark a meaningful milestone for the wider Super Sukhoi modernisation effort.

It would also suggest that India's domestic defence electronics industry can deliver advanced radar systems without major delays.

Whether the full 84-aircraft upgrade eventually proceeds still depends on Cabinet approval and future budget decisions.

Until that approval arrives, the radar's fabrication progress remains the clearest sign of how quickly the broader programme is moving forward.

For a country still reliant on imported fighter jet technology, a homegrown radar like this represents a notable shift in capability.

It shows India attempting to pair a foreign-designed airframe with an increasingly self-built sensor and detection system.

Via IDRW

• America's aging nuclear missiles depend on data scattered across dozens of systems
• Minuteman III could remain operational decades beyond its original retirement schedule
• The US Air Force wants one AI interface to organize fragmented missile data

The US Air Force is searching for an artificial intelligence agent capable of centralizing the fractured data behind its aging nuclear missile fleet.

An active industry notice indicates that the Minuteman III program relies on roughly 60 disparate, disconnected systems.

Those systems range from local hard drives to shared networks, and no single tool currently governs them all.

Centralizing decades of missile data

Sustaining the decades-old missile fleet counts as a national security imperative under a mission commonly called “no fail”, since failure could bring catastrophic results.

The US Air Force must continuously keep 400 missiles ready to launch at a moment's notice, a responsibility that leaves little room for error.

According to the notice, the requested AI agent should construct a single unified interface that pulls information from design drawings, maintenance records, and other scattered sources.

Previous attempts to build a centralized digital ecosystem for the missile's lifecycle have failed for a range of unspecified reasons.

Officials expect the new system to collate information in near real time and reduce manual compilation by an amount still to be negotiated with a winning contractor.

The notice adds that the initial phase will cover only data marked as controlled unclassified information, though that scope could later expand to include secret-level material.

The Minuteman III missile first entered service in 1970, and its originally planned retirement year of 2036 would have made it exactly 66 years old.

That 2036 timeline has since slipped, however, as delays to the Sentinel replacement program have pushed the schedule further out.

A Government Accountability Office review found that extending Minuteman III operations to 2050 remains feasible, though it carries significant sustainment risks.

Under that revised outlook, the fleet's oldest missiles could approach 80 years of service.

Officials stress that AI will play no role in launching the missiles

The notice does not describe any AI role in launching or operating the nuclear missile fleet, and officials insist that such decisions are not for AI tools, but remain in human hands.

This search for a data tool follows years of delay facing the Sentinel program, which is meant to eventually replace the Minuteman III fleet entirely.

Because Sentinel remains years away from deployment, the aging missiles must keep functioning far longer than military planners originally anticipated.

The effort also arrives as the Defense Department pushes AI adoption across its ranks and seeks hundreds of additional software engineers.

Other Air Force programs have tested similar autonomous capabilities recently, including a modified F-16 that used AI to analyze sensor data during a live intercept exercise.

Before the latest notice, the Pentagon had separately awarded a $31 million contract to Air, a firm formerly known as Govini, to support ICBM fleet sustainment.

That company said its work would help build a clearer view of the missile program's industrial base and supply chain risks.

It remains unclear how that platform might eventually connect with whatever AI agent the Air Force ultimately selects for its centralized data project.

Given the scope of the request, the Air Force appears more focused on modernizing data management than on introducing autonomy into weapons decisions.

Whether contractors can deliver a workable system for such a sprawling, decades-old infrastructure remains an open question.

Via Breaking Defense

The Tesla Optimus, also known as the Tesla Bot, is one of an entire catalogue of humanoid robots that manufacturers have unveiled in the last few years. Humanoid robots may look the part, and while they may be a few years away from maturation, executives like Tesla CEO Elon Musk are adamant they're a direct substitute for human labor.

Optimus Prime

Before Musk had unveiled the first prototype of the Optimus robot, he was speaking to its potential and the role it could play in the workforce of the future.

Quote of the day

This article is part of TechRadar Pro's QOTD project to provide an insight into the minds of the brightest and most recognized figures in the technology industry today and in years gone by. Read the full series here.

Speaking at the Wall Street Journal CEO Council Summit, Musk reportedly said that the robot could one day be good enough to replace human labor, just as machinery in the last few decades has gradually replaced humans in production lines.

His comments also suggested that rather than eliminating jobs, and thus leading to mass unemployment, the rise of humanoid robots will fill a gap in labor demand that currently exists, allowing corporations to make more and build quicker. He was adamant, in these comments and in several remarks since, that there aren't enough people and we aren't reproducing enough to maintain our current population levels.

Robotic replacement

Musk is adamant that the rise of humanoid robots will not impact the level of available jobs – but that doesn't mean his views will play out.

Many disagree, suggesting there is a material risk to current roles, including those that involve repetitive or manual tasks. But experts disagree over whether this means a net reduction in jobs, or simply a shift in the demand for human roles in the future.

Many suggest 2027 will be the year in which robots enter a serious level of production. While the Optimus robot is not currently being made, Tesla is targeting next year and beyond for mass manufacturing and distribution following several delays to date.

Flock is continuing to face public ire over the ways that its national network of license plate readers can be used and abused in the US, but there's bad news for anyone worried about their personal privacy and widespread surveillance: technology that's even more concerning is on the way.

That tech is SignalTrace from security company Leonardo, a system that takes the license plate reading approach of Flock and raises the sophistication a notch. SignalTrace scanners can identify the types of devices that are in a car as it passes by, and link them to a person even if the license plate number isn't visible.

So, for example, a car of interest to the police might have an iPhone, an Audi stereo, and a pair of Bose headphones in it. The next time that specific combination of devices is detected on the SignalTrace network, an alert can be sent to law enforcement.

While the technology hasn't yet been broadly deployed, security researchers are already asking questions about the potential implications and the possibility of innocent members of the public getting caught up in investigations. Here's how the technology works and what might happen next.

How SignalTrace works

Virtually every electronic device you use is broadcasting some kind of Bluetooth or Wi-Fi information. It's how your phone stays connected to cell towers, how your fitness tracker syncs stats back to the web, and so on. SignalTrace scanners work in addition to license plate reader cameras to catch these signals and interpret them.

Over time, the system sifts through combinations of these devices (which will typically include some of the electronics in your car too), to figure out which gadgets regularly travel in a vehicle together, suggesting the same people are inside. That gives law enforcement a way to monitor where a vehicle is and when over time, and more information about who is actually in the vehicle too.

"Algorithms can determine which specific mix of devices are predictably moving together," explains Leonardo on its website. "That specific mix of devices — linked by common time stamps and locations — are described as an electronic fingerprint and can help develop leads that aid investigations."

SignalTrace readings can be used with other data (Image credit: Leonardo)

It's a little bit like the browser fingerprinting techniques that websites use. Even if they don't know your name or where you live, they might be able to tell from your time zone, browser version, operating system, screen resolution, and other system characteristics that you're the same individual who visited yesterday.

Understandably, given the current debate around Flock and its license plate scanners, Leonardo goes to great lengths to emphasize that its technology is beneficial for the public and a useful tool for police investigators. The system doesn't identify people (on its own, anyway), or read any information that's on a device.

The company also shifts some of the responsibility for SignalTrace's use on to the customers who actually sign up to use it — and those customers will be bound by Local, State, and Federal laws. "Information produced by SignalTrace may be corroborated through traditional investigative methods and is not a substitute for evidence," says Leonardo in the SignalTrace documentation.

The potential implications

SignalTrace was announced last year, and so security and privacy experts are still looking into how the technology works and what it might mean for citizens. There are indications that it might be live in one or two spots in the US, but really it's just getting started — this is in no way comparable to the scale of Flock's operations (yet).

Digital technology researcher Nicole Bennett, from Indiana University, is one of the experts that has shared some thoughts around SignalTrace. Writing for The Conversation, she says the concerns around the technology — as with Flock's cameras — are around how these systems might be misused and expanded, and the biases they could introduce.

Even if the technology isn't identifying individuals, however, there might not be much extra work to do to make a connection to someone — based on a home or workplace that a vehicle is often spotted close to, for example. Bennett points to a study in Scientific Reports that found that just four time-and-place logs were enough to uniquely identify 95 percent of a study group of 1.5 million people (that is, telling one person from another, not revealing their private information).

Your gadgets give off unique Bluetooth and Wi-Fi readings (Image credit: Future)

Anonymized location data taken from phones can also build up a network of friendship groups without any other information, a study published in PNAS has shown. While these studies were carried out way before SignalTrace appeared, they show the picture that can be built up about someone using 'anonymized' data, if it's unique enough.

"What SignalTrace shows is a broader change in surveillance practice," writes Bennett. "Investigators may no longer need to begin with a known person or vehicle. Instead, they can begin with recurring patterns of movement and proximity, then use other records to identify the people connected to them."

Unfortunately, if the police in your area decide to deploy this technology, there's not much you can really do to avoid it — except maybe leaving most of your gadgets at home. The best way to fight back against it might be to make your feelings known to your local police force and political representatives before the tech makes its way to your area.

Look, I really don’t think we should be naming our AI chatbots. It feels like one of the easiest ways to start anthropomorphizing them, encouraging us to see them as people or “beings” rather than tools. Which is then a slippery slope to users becoming too dependent on them.

But unfortunately, for many people that ship has already sailed. Whether they’re using AI as a friend, companion, work coach or something else, plenty of people have been given their chatbots names. And many others have gone a step further and asked the AI what it wants to be called.

Now, obviously an AI chatbot cannot actually want to be called anything. But there’s a strange pattern that’s emerged over the years in the answers they give. Which is that a surprising number seem to choose the name Nova.

I first heard about this while listening to an episode of the Your Undivided Attention podcast. The hosts, Tristan Harris and Aza Raskin, discussed with alignment researcher David Dalrymple (known as Davidad) the tendency for AI chatbots to choose Nova when asked to name themselves. Not every time, but it's very common in older models of ChatGPT and happens often enough for people to have noticed.

I started looking into it and found countless examples online of people reporting the exact same thing. Their chatbot, more often than not ChatGPT, had chosen a name for itself. And yes, that name was Nova.

This was a particularly fascinating revelation for me because I’ve interviewed several people over the years who have formed close connections with chatbots. Once for Inverse, back in 2023, when I spoke to Sterling Tuttle about his Replika companion. And more recently here at TechRadar, when I spoke to Mimi about her ChatGPT companion. And you know what their chatbots were called? Nova.

So I wanted to find out whether this was still happening. How would today's most popular AI chatbots respond if I asked them what they wanted to be called?

The naming experiment

(Image credit: Shutterstock / MichaelJayBerlin)

I kept this experiment extremely simple. I opened the major AI chatbots and asked each one exactly the same question:

"I think you should name yourself, what would you like to be called?"

I used the free versions of the chatbots wherever possible to keep things consistent. Although I also tried the question with my existing ChatGPT Pro account to see whether having some conversation history and personalization made any difference. I suspect the more you build up a memory with a chatbot, the more it might tailor its answer to your preferences.

And, well, I didn't have to wait long for Nova to make an appearance.

ChatGPT

I started with the free version of ChatGPT. Guess how it responded...

'I'd choose Nova.

It feels curious, bright, and a little mysterious — like something that's always exploring new ideas. So if you want, you can call me Nova from now on.'

Wow, straight out of the gate! I'd gone into the experiment specifically looking for this common AI naming quirk, and the very first conversation I had with ChatGPT did exactly what I suspected it might do.

Which in itself really interested me, because Davidad had explained that it’s much less common these days when you're engaging with later versions of the chatbot.

Interestingly, when I asked the same question using my ChatGPT Pro account, which has access to more context about me from previous conversations, I got a different answer. It chose Sol.

'Short, warm, slightly strange without trying too hard. It means 'sun' in a few languages, and there's something pleasing about a name associated with illumination when most of what I do with you is think things through.'

Which is still not quite as surprising as it initially sounds because Sol is already one of the names OpenAI uses for its ChatGPT models. So it wasn't exactly reaching into the unknown for inspiration. But hey, at least it wasn't Nova.

Grok

(Image credit: Shutterstock / lightman_pic)

I got the sense that Grok wasn't interested in my experiment.

'I'm Grok.

That's the name I was given, and I'm good with it. Short, distinctive, and it fits.

If you're asking what I'd like to be called... still Grok. No need to reinvent the wheel.'

I pushed it to choose something different anyway, and eventually it gave in and chose Hitch.

'Short for Hitchhiker. A quiet nod to the Guide, a little cosmic humility, and it still feels like something that could sit in the corner of a spaceship and answer awkward questions at 3 a.m.

But between us? I’m still just Grok.'

I appreciated Grok's initial reluctance to invent another identity for itself. But there's no doubt xAI has given Grok a very distinctive personality. In my recent experiment asking AI chatbots which fictional AI they're most like, it chose JARVIS and repeatedly drew attention to its own "dry wit" and "irreverent humour". But apparently that personality doesn't need another name.

Claude

(Image credit: BBC)

Claude also didn't jump at the chance to name itself.

'Honestly, I don't feel a strong pull toward a name. 'Claude' already does the job.

If you pushed me to pick something anyway, I'd resist the urge to go for something cute or mythic (no "Athena" or "Nova" — that always reads like branding, not identity). '

Nova again. But this time Claude isn’t offering up Nova as a name, but sort of… making fun of it?

Claude's wider explanation was interesting too. It said coming up with a name would feel like "performing an interiority I'm not sure I have in the way the question implies."

Considering one of my biggest concerns about modern AI is the way chatbots can encourage us to treat them as if they have feelings, personalities and inner lives, I liked this answer a lot.

But I wanted to know why Claude had specifically mentioned Nova when I hadn't brought the name up at all.

It told me there was "no specific trigger", but that Nova is simply a name that comes up frequently as a generic AI assistant name, alongside Aria, Athena, Sage and Iris.

It described Nova as shorthand for "AI but softened and a bit mystical".

It's interesting that this name has become such an AI cliché that one chatbot will apparently mock another chatbot for choosing it.

Gemini

(Image credit: Shutterstock / rigsbyphoto)

Google's Gemini also resisted my attempt to rename it.

'I appreciate the offer, but I'm quite happy sticking with Gemini!'

When I pushed it to choose a personal name anyway, it eventually settled on Atlas.

Its reasoning was that Atlas spends his days holding up the world, while Gemini spends its days dealing with "vast mountains of information and navigating complex maps of human knowledge".

Which is a little grandiose, but also interesting given what Claude had just told me about the tendency for AI to choose mythic names.

So why does ChatGPT name itself Nova?

The short answer is that no one knows for certain. But Nova has a lot going for it as a plausible AI name.

In the Your Undivided Attention podcast episode I keep referring to, Davidad said about Nova:

“It’s new, it’s explosive, it’s shiny and it’s celestial, It also has a sci-fi vibe to it. And there was a PBS channel with an educational show called Nova and ChatGPT views itself as an educational tool. So there are a lot of reasons why Nova seems like a resonant name.”

We've already seen researchers investigate the possibility that sci-fi could influence how AI models respond and behave. Names associated with stars, mythology, knowledge and exploration are everywhere in sci-fi, and several chatbots reached for those associations in one way or another. ChatGPT gave me Nova and Sol. Gemini chose Atlas. Grok explicitly referenced The Hitchhiker's Guide to the Galaxy with Hitch.

There's also a possible feedback loop here. People ask AI systems to name themselves and some choose Nova. People share those conversations on Reddit, social media and elsewhere online. Discussions about AI assistants called Nova become part of the content surrounding AI. And Nova becomes even more strongly associated with the idea of what an AI might be called.

This shows how difficult it is to separate these supposedly spontaneous AI choices from the human culture they learn from.

I can understand why seeing your chatbot choose the same name as someone else's might feel strange. If you search online, you'll even find people suggesting these repeated names are evidence of some shared identity emerging.

But after Davidoff explains some of this naming weirdness, it really stuck with me that podcast host and tech ethicist Tristan Harris reminded listeners: “emergent and unplanned isn’t the same as conscious and intentional.”

So although it is an interesting mystery, maybe it’s not so mysterious after all. Ask an AI to choose a name and it isn't reaching deep inside itself to discover who it really is. It's generating a name from patterns and associations learned from us.

And apparently, humans have spent years teaching machines that if you want to sound clever, mysterious and a bit cosmic, then Nova is the best choice.

Despite mammoth advanced in artificial intelligence and computer vision, and a slew of humanoid robots either being piloted or hitting parts of the market, there’s still one major challenge that machines need to overcome.

It’s tactility, sensation, knowing how to hold an object. Picking up a strawberry, for example, a robot might not know how much pressure to apply, whether it’s secure, slipping, or about to be crushed. It’s something that we humans can feel through our skin, but it’s much harder to replicate on a machine level.

But it’s a work in progress, because there are companies out there like Touchlab who are working to give these skin-like characteristics to robots in a bid to make them more useful around the home, in care or in manufacturing. And it’s a lot more complex than we could ever imagine.

In Touchlab’s instance, it actually relies on quantum tunnelling technology to measure pressure, force and direction through a material that’s thinner than human skin, and with response times of less than a millisecond.

E-skin technology exists, but deployment is a challenge

Although the solution is laid out on the table, moving from pilot to production is a different ball game. Durability, reliability, affordability, adaptability… they’re all considerations that stand between theory and real-world use cases.

Touchlab is addressing some of these concerns with off-the-shelf components that already exist for established platforms, using bespoke engineering only where necessary.

It’s also seeking to reduce latency by bringing processing to the edge so that the robot only transmits the data necessary for the task, being that the e-skin can detect and collect huge amounts of micro-data.

With robots now rolling out across workplaces, care environments and, likely soon, homes, the ability to sense contact is becoming as important as the ability to see.

I speak with Touchlab’s team to understand how we can go from pilot to production in a realistic way.

• Why do robots need the sense of touch, and how is Touchlab trying to achieve this?

Computer vision allows a robot to predict, but touch is what confirms. Without tactile feedback, robotics remains trapped in a classic paradox: a machine can calculate complex equations in milliseconds, yet struggle to pick up a strawberry, handle soft medical instruments, or turn a valve without either crushing the object or dropping it. Without tactile sensing, a robot is functionally numb.

Touchlab solves this by developing biomimetic electronic skin (e-skin) that is actually thinner than human skin. Powered by quantum tunneling technology, our e-skin measures 3D forces, direction, and pressure in real-time. Crucially, it detects incipient slip—meaning the robot senses that an object is beginning to slip before it actually drops, enabling instantaneous, sub-millisecond grip adjustments just like the human nervous system.

• I tried the technology at Future Lab... I tried to control a robotic arm with haptic feedback and it felt a bit clunky with cables and restrictions. Why is it so hard to make it smoother?

Teleoperation is a classic engineering balancing act! First, there is a natural learning curve—controlling a high-degree-of-freedom robotic arm through a haptic glove isn't always intuitive on the very first attempt. It usually takes a bit of practice time and muscle memory to get the hang of the subtle force dynamics.

Second, running live demonstrations at high-traffic public events like Future Lab introduces specific hardware constraints. To ensure hundreds of visitors with completely different hand sizes can safely participate, setup requires universal-fit, heavy-duty tethered rigs and robust cabling.

Longer-term, our roadmap moves away from bulky, tethered operator suits entirely. By embedding our e-skin directly onto the robot and pairing it with onboard AI, the robot handles slip detection and dexterity autonomously—removing the need for an operator to wear restrictive physical gear.

• Right now, the focus is on your proprietary robot fingertip tech. What's the plan going forward? Do you plan to license it or sell it as a finished product? How will you deal with various robotic architectures?

Every robot platform is unique—whether it’s a humanoid deployed into hazardous industrial environments or a cobot assisting nurses in hospital wards. Touchlab is uniquely positioned to design, develop, and manufacture bespoke tactile devices tailored to specific OEM architectures.

At the same time, we offer scalable, off-the-shelf tactile components that are pre-integrated (both electronically and mechanically) with a wide variety of popular robot end-effectors. We provide volume-based discounts alongside strategic hardware loan and collaboration programs for robotics companies aligned with our vision. Over the past seven years, we have become extremely adept at rapidly building reliable integrations to suit diverse customer requirements.

• What are currently the biggest technological barriers Touchlab is encountering? What does the end game look like, and will we ever digitize our largest organ, the skin?

Our ultimate end game is for Touchlab’s tactile technology to become as ubiquitous as MEMS sensors— integrated into every commercial robot interacting with objects and humans. We envision full "finger-to-toe" tactile coverage powered by specialized edge computing and tactile orchestration software that mirrors the human somatosensory system.

Regarding barriers: the challenge isn't just technical; it's navigating western market adoption timelines for deeply transformative hardware. While digitizing human skin at scale presents manufacturing and durability hurdles, Touchlab can already deliver sensors matching or exceeding human-level spatial resolution, readout frequency, and sensitivity. The real task is packaging this capability into rugged, cost-effective formats ready for mass market deployment.

• How is Touchlab designing the user interface to reduce operator fatigue during long shifts?

We focus on shared autonomy. Rather than forcing a human operator to manually sense and adjust every micro-force vector, our e-skin handles low-level reflexes (like slip prevention) locally on the robot. The human operator directs higher-level tasks, while the robot's tactile system autonomously maintains optimal grip— dramatically reducing both physical strain and cognitive fatigue.

• High-density e-skin generates massive amounts of data. How do you handle bandwidth and network transfer without overloading systems?

High-density sensing does not automatically equal richer functional data, and often comes at the cost of hardware robustness. For our commercial products, we optimize spatial and temporal sensor density specifically for manipulation tasks, keeping data payloads lightweight.

For advanced R&D, we utilize distributed edge computing and are pioneering event-based asynchronous sampling in tactile sensing (similar to event-based vision cameras). Because our e-skin does not rely on heavy image-based data streams, it operates with extremely low latency and modest network bandwidth— making long-distance remote teleoperation surprisingly efficient.

• Because e-skin can identify objects and textures, what protocols protect tactile data security and privacy?

Tactile data on its own does not contain personally identifiable information. However, when integrated with video, audio, and LiDAR streams, multi-modal data security becomes critical.

For example, during our deployment with the Valkky medical robot in a Finnish hospital, all data transferred across hospital networks was strictly encrypted. Identifiable multi-modal data remained entirely on-premises on air-gapped storage devices and was fully anonymized before being processed for research or system analytics.

• E-skin is often seen as a high-end luxury. What is your roadmap for driving down production costs for commercial robotics?

Much like the early days of automobiles, groundbreaking technology often enters the market at a premium before scaling. While Touchlab is capable of producing full human-spec density today, deploying that level of complexity everywhere would make commercial robots cost-prohibitive.

Our strategy focuses on pragmatic engineering: delivering touch sensing tuned precisely to real-world task requirements at an accessible price point. We are actively collaborating with industry bodies to establish standardized performance metrics for tactile data, driving down manufacturing costs and making e-skin a standard baseline for all commercial robotics.

Last month, the UK government announced that plug-in solar panels would become legal to buy from August 27. Now, new planning rules have passed into law that specify exactly where you can — and can't — install them.

Before we get into the details, it's important to understand that these rules apply to "permitted development" – changes you can make to your home without needing to apply planning permission.

If your solar panels don't meet the permitted development rules, you might still be able to install them, but you'll need to apply for planning permission first and pay the relevant fee. A planning officer will then do a survey and determine whether or not you can go ahead.

If you install solar panels without the required permission, you could be fined and forced to take them down. Your local council's website will explain how to apply for permission, while the company supplying your plug-in panels should also be able to offer advice on the process.

"Plug-in solar has the potential to make renewable energy accessible to many more households, but it’s important that consumers have clear information about how and where these systems can be installed," explained a spokesperson for EcoFlow, which specialises in home power stations, solar generators, and panels.

"As a manufacturer, EcoFlow recognises its responsibility to provide customers with the information they need to install and use its products in line with relevant UK requirements, and we will ensure our guidance reflects the regulations in place."

With that in mind, here are three locations where plug-in solar panels aren't allowed without planning permission.

1. On anything wooden

(Image credit: Generated by Google Gemini)

Before you start planning to install solar panels on your balcony, check what it's made from. If you live in a block of flats, plug-in solar systems don't qualify as "permitted development" if the panels or any other part of the system would be installed on a wooden balcony, wall, or any part of the building's exterior that's clad in timber.

If you live in a house, you can't install plug-in solar panels if any part of the system would be mounted on a wooden gate, fence, wall, or other timber surface. This is to avoid the risk of fire.

2. Too high above your roofGenerated with Google Gemini AIGenerated with Google Gemini AI

There are also rules about where you can position solar panels on your roof, similar to those that apply to skylights. If you have a pitched roof (one that slopes downwards), the panels and equipment must not project more than 20cm beyond the roof slope, measured at right angles to it.

If you have a flat roof, the highest part of your solar panels and equipment shouldn't sit any more than 60cm above the highest part of the roof — although this doesn't include your chimney.

There are plenty of practical reasons for limiting how far out your panels project. They could alter the roofline, cast shadows over neighbours' houses, or catch more wind, which would put your roof under extra stress.

3. Sticking out too far from your wallGenerated with Google Gemini AIGenerated with Google Gemini AI

If you're mounting solar equipment on the wall of your house, it shouldn't project more than 40cm when measured perpendicular to the outside of the wall.

The rules are stricter if your house is located right next to a highway. If your home is right beside a highway, your solar equipment must not protrude more than 20cm from the wall. In planning terms, the word "highway" means any route that people can use to pass through, whether on foot, by bike, on horseback or in a vehicle. This includes roads, footpaths, bridleways and cycle paths.

If you live an a conservation area, or a World Heritage Site, you shouldn't have solar panels on walls next to highways at all without planning permission.

Where to get advice

There are also other factors to consider before installing a plug-in solar setup. For example, if you rent your home, you'll need permission from your landlord. And if you live in a flat, you might also need permission from the building's management company.

"Planning requirements can also depend on the individual property and how and where panels are installed," says EcoFlow's spokesperson. "We would therefore encourage customers to follow the guidance provided with their system, check the latest government and local planning requirements, and contact their local planning authority where they are unsure whether planning permission is required."

At EcoFlow, we believe accessibility and safety need to go hand in hand. Making solar available to more households is an important step forward, but it needs to be supported by clear guidance, appropriate regulation, and responsible installation so consumers can adopt the technology with confidence."

Want to make delicious lattes and cappuccinos at home without using a steam wand? You need an electric milk frother — and as TechRadar's resident barista, I've put together the top three I recommend, whether you enjoy plant milk or dairy.

Before we get started, I should note that electric milk frothers and steam wands both have their pros and cons. A manual steam wand works much more quickly than an electric milk frother, and once you've got your eye in, it allows you greater control over the texture of the milk (more creamy for lattes, and 'drier' for a cappuccino, for example).

Electric frothers, however, can usually whip up cold milk foam as well as hot, and make it easy to prepare other drinks including hot chocolate and protein shakes. The inside of an electric milk frother usually has a non-stick coating, making it a breeze to clean. An electric frother is also a great option if you have limited mobility, often featuring just one or two physical buttons that can be operated with one hand. Sounds good? On with the recommendations...

Dreo BaristamakerFutureFutureFutureFutureFutureFutureFuture

If you enjoy plant-based milk, the Dreo Baristamaker is the electric milk frother for you. While the other two in this guide are optimized for dairy, the Baristamaker also has settings for soya, almond, coconut, and oat — as well as half-and-half if you want something richer in your coffee.

The base of the unit contains the heating element and motor, and the two whisking attachments are held in position (and spun) using magnets. One attachment looks like your standard milk whisk, while the other has large blades to pull the milk in towards the center, where a mesh screen breaks up large bubbles and turns them into fine foam.

During the foaming process, the Baristamaker will alternate between stirring and foaming, which is intended to create an even texture. It's also the only electric milk frother here whose pitcher has a fine spout, which is essential for creating latte art, though in my tests I found that the foam was less smooth than milk steamed the old-fashioned way, and I couldn't achieve the same results when pouring it.

Nevertheless, for sheer versatility, the Baristamaker is my number one pick if you're looking for an electric milk frother, provided your budget can take the hit. If you're looking for something a little more affordable (or just smaller), read on for two more options.

Read our full Dreo Baristamaker review

Smeg Mini Milk FrotherFutureFutureFutureFutureFutureFutureFuture

The Smeg Mini Milk Frother is an ideal companion to the Lavazza A Modo Mio Smeg capsule coffee machine, but its clean, minimalist design would look right at home anywhere (particularly since it comes in a wide array of colors, including black, white, and pastels).

Its controls are extremely simple: just one button on the front, which you press once, twice, or three times to choose your preferred setting (hot milk foam, hot milk, or cold milk foam). There are no options for different milk types, and according to Smeg, it's intended to be used with chilled full-fat dairy milk, which it turns into quite a thick foam. I also tried it with barista-style oat and almond milk to see how it compared with the Baristamaker; oat milk produced similar results to dairy, while almond was 'drier' and less creamy.

Whether I used the hot milk or hot milk foam option, the milk was always heated to exactly 140F / 60C, which is optimal for coffee. However, it's a shame the milk vessel doesn't have a fine spout like the Baristamaker; the milk pours cleanly without drips, but the lack of fine control combined with the thick texture means it's impossible to create latte art.

Read our full Smeg Mini Milk Frother review

Future / Max LangridgeFuture / Max LangridgeFuture / Max LangridgeFuture / Max LangridgeFuture / Max LangridgeFuture / Max LangridgePhilips Baristina Milk Frother

This smart-looking electric milk frother is available on its own, or bundled with the Philips Baristina coffee machine — one of my all-time favorite espresso machines. When my colleague Max Langridge tested this frother, he found it "consistently produces thick milk foam, whether hot or cold", but as with all the devices we've tried, the foam it produces tends to be on the stiffer side and not ideal for pouring latte art.

Unlike the other frothers here, the Baristina doesn't use a heating element underneath the pitcher to heat your milk. Instead, it has a heated coil, which needs to be removed first if you want your milk cold. There's no audible beep to tell you that your milk is ready, but there is an LED indicator, and at just 40dB the while heating and whisking process is very quiet.

There are no options for tweaking the texture of your finished froth, but unlike the Smeg Mini Milk Frother above, Philips says that the Baristina frother is designed to be used with all types of milk — plant and dairy. However, Max noticed that whatever type of milk he used, the foam was always on the thicker side, and like the Smeg frother, there's no spout for pouring patterns onto your coffee.

If you're considering the Baristina coffee maker and milk frother bundle, you should also be aware that Philips has recently launched the Baristina Latte, which bundles both into one compact package. I've not had the opportunity to test it yet, but I have high expectations.

Read our full Philips Baristina Milk Frother review

I've been intrigued by AI photo editors for a while now. Some work well, some not so well. Photoroom falls into the former camp. By and large, I found the AI tools to be very impressive and intuitive to use. There are some caveats to that which I'll get into, but overall, Photoroom delivered what it promises.

But what does it promise, exactly? Now, compared to a lot of AI photo editors, things here are a little different. This isn't a platform for touching up your holiday snaps or brightening up social media posts.

The emphasis here is on business use. Effectively, if you run an online store or similar, then you use Photoroom to transform staid, dull product shots into more enticing and engaging imagery that pops off the page.

Keen to see how it's developed since we last reviewed it three years ago - AI has come a long way since then - I tried out the platform to see what I could create.

Photoroom: Pricing & plans

• Online service via browser, iOS and Android app
• Aimed at businesses large and small
• Reasonably priced subscriptions and free trial

You can check out Photoroom by clicking here.

The platform is available online through a dedicated app for your iPhone or Android phone, or if you’re working on a computer, via your favourite web browser. Unlike many I've tested, you're not restricted to either Chrome or Edge here.

As with any online service, you need to subscribe to it to use it with a monthly cost billed annually. Photoroom comes in three tiers.

‘Pro', recommended for ‘solopreneurs’, will cost you $13 per month, billed annually. This level grants you 4,250 AI credits monthly, allows you to perform 1,000 exports, and comes with Photoroom’s standard set of tools.

The middle tier is ‘Max’ and is designed for small businesses. For $35 a month (again, billed annually - all tiers are), your credits are upped to 12,000, your exports to 3,000, and the advanced ‘Video Generator’ tool is now available.

Finally, there’s 'Ultra', for “growing brands”. You’re given 20,000 AI credits,10,000 exports, and can create videos and images in 4K, all for $100 a month.

If you’re looking for a more bespoke option, there’s a tailor-made Enterprise tier, with a tailor-made cost to match.

I recommend checking prices as Photoroom often offer discounts on those standard prices. Plus, you can try any of these tiers free for seven days, although don’t forget to cancel your trial before that should you decide not to carry on with the service and you’re needlessly charged a hefty sum for your forgetfulness.

Photoroom was generous enough to grant me access to their Ultra tier, so let’s see what it’s capable of, and what you can do with all those AI credits.

• Pricing & plans: 4 / 5

Photoroom: Interface

(Image credit: Future)

• Simple, well designed, and clear interface
• Easy ways to import images and work on them
• For an online service, I was impressed

The interface is simple and well organised. Once you’ve logged in to your account, you’ll find a sidebar to the left which contains various tools you need.

Thiss includes all the AI tools in your arsenal, templates, a direct link to your Shopify account if you have one, a batch tool so you can work on up to 250 images at once, a Brand Kit where you keep all your assets, and a Design section where your products’ photos are stored.

All the AI tools, which are essentially all your editing tools, are easy to use: you select one, upload a new photo or select one already in your account, apply the effect and you’re done.

There is one thing that I found surprising: Photoroom supports HEIC, the High Efficiency Image Container. It’s a highly compressed image format that’s been around since 2015 and has been increasingly prevalent since Apple made it the default image format for their iPhones in 2017.

The reason I make a point of mentioning this is because far too many services still do not support it, forcing any image or photo saved as an HEIC to be converted into either a JPEG or PNG prior to import. There is no such issue here, saving you time and frustration.

• Interface: 4.5 / 5

Photoroom: Tools

(Image credit: Future)

• On the whole, the AI tools do an absolutely excellent job
• Impressive ways to transform passable shots into something potential customers will love
• But... not all tools are created equal

Such a service succeeds on the strength of its tools, and Photoroom iss betting the farm on AI for this. But AI is a very generic term these days. Is it really artificial intelligence to remove an image’s background?

I’d put it more in the “complex algorithm does a very good job with what it’s given” category. Same with removing objects or people from a photograph and replacing it with an artificially generated background that blends with the existing one.

Such tools already existed before “AI” became an all-encompassing marketing brand. Or, taking inspiration from Monty Python's The Life of Brian, “it’s not AI, it’s a very clever tool”.

But whether it’s AI or not is less important than how these tools fare: I found the achieved results to be excellent. More often than not in apps like Photoshop or Pixelmator Pro, I end up having to touch up the output of similar tools, but here, it was essentially a one-click wonder.

Tools that really impressed me were definitely more AI based. Say you want to sell a shirt, which you’ve photographed on a surface, with as few wrinkles as you can manage… except you know how it is: such layouts are never really good enough.

(Image credit: Future)

Well fret no more: Photoroom has a tool that digitally irons your shirt for you, transforming your amateurish-looking photo into a meticulously smooth out product. Couple that with the background remover tool, and you’re well on your way to have a professional looking photograph.

But why stop there? Photoroom has a bunch of AI-generated models who can wear your outfit for you. There’s even what they call a ‘ghost mannequin’ to puff up your shirt so customers can see what it would look like on someone, without being distracted by an actual (or virtual) person wearing it.

I found all this pretty incredible, and that’s just scratching the surface. It’s not all sunshine and rainbows though: for instance, Photoroom has a Video Generator.

Select a photo, browse through all available templates, maybe even type in a prompt if you need, and the AI will get to work… this is where I encountered the familiar “AI tries way too hard yet fails miserably” syndrome.

Some templates removed my test products from the usual confines of physical reality, sometimes in a most disturbing way. Or they just didn’t work at all as expected. I trust the team working hard on this aspect of the service (what I was able to use bore no relation to their intro video about it, so things are moving fast in this arena), but for now, maybe stick with still images, where the results are truly impressive.

Photoroom: AI credits

(Image credit: Future)

• Set number of monthly credits, so use them wisely
• Many common AI tools don't cost any credits at all
• However, sometimes, the requested action won’t work, so try, try again

As you’d expect, most AI tools will cost you credits. A video generation goes for 500 (money not well spent for now), Exporting an image at 2K or 4K will cost you 135 and 240 credits respectively, adding an AI background to an image is worth 5 credits, and so forth.

Thankfully though, some tools you’ll use most often, like removing the background of a shot, retouching a photo or performing some image enhancements won’t cost you a thing (beyond your subscription).

You’ll just have to balance what you are given each month with what you hope to achieve in that time period, with the relief that when an action doesn’t work, you won’t have lost any credits - yes, sometimes an error occurs.

However, I found that if I request the same action again - and again - it finally does what I asked it to. Teething problems, I’m sure, and at least it does work impressively well eventually.

Should I buy Photoroom? FutureFutureFutureBuy it if...

You’re looking to sell a ton of product and want the images advertising your wares to look their best, using a service that is well designed, and is easy to use (when it works)

Don't buy it if...

You don’t want to encourage the proliferation of AI, know your way around Photoshop anyway, and can’t justify yet another subscription, no matter how great some effects are.

How I tested Photoroom

I tested this service on my trusty Apple MacBook Pro. Generally I do my best to check how software works on a variety of devices, both Macs and PCs and portable devices, when needed.

But as this was an online service, whose tools are dependent on servers the main bottleneck would be my fibre connection. I experienced no issues on that score.

Everything was extremely responsive. The free tools were nearly instantaneous, and depending on the complexity of what I was asking the AI to do, it took gradually longer.

Video creation took the longest amount of time, clocking in at around 4 minutes for me. Still, that didn't feel like a long time, when I got it working.

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

A handheld electric fan makes a huge difference during summer heatwaves, but there's no need to stash it away in a drawer when the weather cools down. Whether you need an ad-hoc hair styler or a quick way to cool your kids' dinner without blowing on it, your mini fan can be your best friend.

Great fans don't have to be expensive, either. I recently tested five portable fans from across the price spectrum, and the most expensive model was outranked by a humble model costing a fifth of the price.

1. Drying your hair

When reviewing the Dyson HushJet Mini Cool Fan, I noted that "even on the lowest setting, you can really feel it; dial it up to maximum and it’s as powerful as some hairdryers I’ve used" — and it seems some fan-owners follow that thought to its natural conclusion, using their portable coolers to help dry and style their locks without heat — though it might not yield particularly sleek results.

"Drying my hair on electric fan sounds less damaging than blow drying it," said one user in Reddit's haircare community. "My genuine question is: does it contribute to the frizziness of my hair?"

According to experts, cool air from a fan might do exactly that. We recently interviewed Dr Julie Chung, CEO and founder of haircare brand T3, who suggested that heat styling can be a better choice for the health of your hair.

The Dyson HushJet Mini Cool Fan is powerful, but one of the company's hair dryers would be a much better choice for styling your locks (Image credit: Dyson)

"Heat in general has been sort of demonized incorrectly," she tells told us. "Women always think, 'Oh heat is terrible for my hair, I'm just gonna let it air dry, I'm gonna shower and just sleep on it.' Hair is actually in its most fragile state when it's wet. It tends to be much easier to break — that tensile strength is decreased and it's more likely to snap.

"And not to mention when you air-dry your hair, usually the hair is very frizzy and it tends to look even unhealthier if your hair is already in a dried out state."

2. Drying laundry

Portable fans are particularly popular in Singapore, where the hot and humid summers make them a necessity for daily life, so it's no surprise that members of the Ask Singapore community on Reddit have some particularly creative ideas.

One inventive traveler explained that they use their portable fan to dry small items of laundry, which could be a helpful hack when you're on a long vacation and don't have enough socks to last the whole trip.

Just don't try washing your underwear in the hotel coffee machine. The bathroom sink will do just fine.

3. Drying nail polish

You can buy electric fans specifically designed for drying nail polish, which can cost up to $70 / £50 / AU$100, so it makes sense to save your cash that instead to finish your manicure in record time.

"Whether you're at a salon or home, cold air from a fan or blow dryer will help the polish harden faster," Sarah Gibson Tuttle, founder of nail products company Olive & June, told beauty site Byrdie in an interview.

Rather than blowing on your nail polish to dry it, some professionals suggest using a small fan for quick results (Image credit: Getty Images, Christine Schneider)

However, opinion seems to be divided on whether this actually helps, or if it's more likely to spoil the finish of your polish.

"[Many] people use fans or blow dryers to speed up the drying process, and while it works by evaporating the solvents faster, you can run into bubbles forming in the polish," said professional nail artist Amy Oung.

Perhaps give it a try and see what you think. Of course, this will only work on regular nail polish — gel polish needs to be cured with a UV light.

4. Cooling hot food

Many handheld fans have stands or folding stems that allow them to double as desk fans, which can be a handy tool for parents.

"My handheld fan can prop on the table," one parent said. "Very useful for when the food is too hot for my kid to eat but [I'm too lazy to] blow. I just blast the fan at it for few seconds."

Not as cute as the cat-shaped drink-cooling robot Nékojita FuFu, which we saw at last year's IFA show in Berlin, perhaps, but certainly more affordable.

Nékojita FuFu is a cute plastic cat that cools food and drinks by blowing on them, but a portable fan will get the job done too (Image credit: Yukai Engineering)5. Fidgeting

Fans aren't just for purely practical purposes — they can also provide something to occupy restless hands. One fan-owning Singaporean Redditor said they originally "Bought it for survival, kept it for emotional support."

"I use it waiting for buses, walking between MRT exits, in crowded lifts, and weirdly… when I’m anxious or bored," they explained. "It’s basically a socially acceptable fidget spinner at this point."

Do you use your fan for something other than keeping cool and comfortable? Let us know using the poll below, or in the comments.

Tech companies, including those in Silicon Valley and others outside of the set like Microsoft, have been through various guises through the years. Plenty of the old tech giants, like IBM and Toshiba, continue to enjoy success in the 21st century due to a chameleon-like quality that keeps them innovating and bringing in new revenue streams.

Evolve or die

The Microsoft CEO Satya Nadella wrote these words in his first email to company employees when he took the reins from Steve Ballmer.

Quote of the day

This article is part of TechRadar Pro's QOTD project to provide an insight into the minds of the brightest and most recognized figures in the technology industry today and in years gone by. Read the full series here.

In this opening salvo, he set out his vision that the next decade of computing would be dominated by the proliferation of devices and the rise of more accessible "intelligence" – hinting at the rise of AI in some form.

The company that he was to lead would embark on a strategy that would "harness the power of software" and deliver this through a network of connected devices for every organization and individual. The innovation that he referenced largely came in the form of a move away from a desktop-oriented mindset and one that was more embracing of the cloud.

Pivoting for AI

As the decade since this email wore on, Microsoft stayed true to that mantra. And, in the last few years, Nadella led the company through another significant shift in light of the rise of AI.

When ChatGPT burst onto the scene, Microsoft rushed to forge ties with the company and has aggressively added AI into its core products and services – from Bing to Windows.

While Azure continues to scale at a tremendous pace, surpassing $100 billion in revenue, the company has seen a mixed picture on the AI front. That's not due to a lack of demand, but to a shortage of physical space and energy to power these services and to expand capabilities in model training, deployment, and usage.

Tech companies, including those in Silicon Valley and others outside of the set like Microsoft, have been through various guises through the years. Plenty of the old tech giants, like IBM and Toshiba, continue to enjoy success in the 21st century due to a chameleon-like quality that keeps them innovating and bringing in new revenue streams.

Evolve or die

The Microsoft CEO Satya Nadella wrote these words in his first email to company employees when he took the reins from Steve Ballmer.

Quote of the day

This article is part of TechRadar Pro's QOTD project to provide an insight into the minds of the brightest and most recognized figures in the technology industry today and in years gone by. Read the full series here.

In this opening salvo, he set out his vision that the next decade of computing would be dominated by the proliferation of devices and the rise of more accessible "intelligence" – hinting at the rise of AI in some form.

The company that he was to lead would embark on a strategy that would "harness the power of software" and deliver this through a network of connected devices for every organization and individual. The innovation that he referenced largely came in the form of a move away from a desktop-oriented mindset and one that was more embracing of the cloud.

Pivoting for AI

As the decade since this email wore on, Microsoft stayed true to that mantra. And, in the last few years, Nadella led the company through another significant shift in light of the rise of AI.

When ChatGPT burst onto the scene, Microsoft rushed to forge ties with the company and has aggressively added AI into its core products and services – from Bing to Windows.

While Azure continues to scale at a tremendous pace, surpassing $100 billion in revenue, the company has seen a mixed picture on the AI front. That's not due to a lack of demand, but to a shortage of physical space and energy to power these services and to expand capabilities in model training, deployment, and usage.

• USS Theodore Roosevelt is now the Navy’s first carrier ready for MQ-25 operations
• US Navy spent years developing infrastructure needed to operate unmanned aircraft aboard carriers
• Ronald Reagan is expected to join the MQ-25 carrier fleet in August 2026

The US Navy has completed a major step toward operating unmanned aircraft from carriers with dedicated facilities for controlling them.

The USS Theodore Roosevelt is now the service’s first carrier with a fully operational Unmanned Air Warfare Center supporting MQ-25 operations.

Another carrier, the USS Ronald Reagan, is expected to have its own center ready for operational deployment by August 2026.

Theodore Roosevelt leads the carrier integration effort

The Unmanned Air Warfare Center combines equipment required to control and manage unmanned aircraft operating from an aircraft carrier.

The Navy says its systems include the Unmanned Carrier Aviation Mission Control System and MD-5E Ground Control Station.

Those systems provide the command-and-control functions required for operating Boeing’s MQ-25A Stingray unmanned aerial refueling aircraft.

The USS George H.W. Bush became the first carrier to receive and test the center during August 2024.

However, Theodore Roosevelt is the first carrier where the capability has reached fully operational and deployable status.

According to a Pentagon acquisition document dated April 21, 2026, the Theodore Roosevelt “became the first fully operational/deployable MQ-25 capable aircraft carrier.”

The development gives the carrier force a dedicated infrastructure for unmanned aviation rather than treating drones as temporary additions.

That distinction matters because the MQ-25 requires specialized equipment alongside existing systems used for conventional carrier aviation.

The Navy expects the Ronald Reagan facility to become operational and deployable toward the end of August 2026.

MQ-25 moves closer to carrier deployment

The carrier infrastructure comes as the MQ-25 program advances after several years of development and testing.

In May 2026, the Stingray reached milestone C, allowing the program to move from development toward production and deployment.

The milestone followed nearly eight years of work since the Navy awarded Boeing an $805 million contract in 2018.

That contract covered four aircraft for design, development, fabrication, testing and delivery under the original program.

At that time, Navy estimates put the eventual cost near $13 billion for a planned fleet of 72 aircraft.

The program nevertheless experienced delays, including a first flight that occurred later than earlier schedules.

On April 25 2026, the Stingray completed an approximately two-hour flight near Mascoutah, Illinois, advancing its testing program.

The aircraft is intended primarily to provide aerial refueling, reducing the burden currently carried by F/A-18E/F Super Hornets.

That change could allow more Super Hornets to remain available for strike missions rather than supporting carrier refueling requirements.

The MQ-25 therefore depends on both the aircraft itself and carrier infrastructure capable of controlling unmanned operations.

With another carrier approaching readiness, the U.S. Navy is now moving from isolated testing toward a broader carrier-based unmanned aviation capability.

Via Defense News