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Five Claude Code alternatives offer different approaches to AI coding, from terminal agents and GitHub automation to AI-native editors and structured development.

The post 5 Best Claude Code Alternatives for 2026: Codex, Copilot and More appeared first on TechRepublic.

Saving $1,000 sounded pretty good to me. I wasn't entirely sure where Meta's Muse AI was supposed to find it, but Meta's chief AI officer, Alexandr Wang, and the #MuseMoneyChallenge sounded pretty encouraging about putting the company's new AI agent to work finding $1,000 in savings.

Some of the results shared so far make that target seem surprisingly plausible. People have reported finding cheaper insurance, forgotten subscriptions, and even unused gift cards, although many of the early examples highlighted around the challenge have come from Meta employees.

the last thing your unnecessary expenses see before your muse saves you $1000 pic.twitter.com/NwEO7nhmbKSeptember 19, 2026

I decided to give it a try. I was not expecting Muse to discover a forgotten Swiss bank account, but $1,000 seemed like a sufficiently concrete, if unlikely, target. The results showed how impressive Muse can be, if you're willing to let it into your life.

Financial exploration by AI

Muse is different from a conventional chatbot because it is designed to take action rather than simply tell you what you should do. Meta says it runs on its own secure virtual computer with a browser, can connect to services such as email and calendar, and can handle tasks including filling out forms and dealing with customer service. It can continue working after you close the app, although actions such as purchases and sending messages can require your approval.

That makes the money challenge more interesting than simply asking ChatGPT for tips on saving cash. Telling a chatbot to help me save $1,000 would probably produce an earnest lecture about meal planning and canceling streaming services. Muse can potentially go looking for the forgotten subscription itself.

I started broadly and told Muse I wanted it to find ways to save me money. Muse requires a little commitment from the user. The more useful you want an agent like this to be, the more access it needs to the places where useful information lives. Meta lets Muse connect to other apps and services, and says users control those permissions. The company also says login credentials are stored separately so the agent cannot read them, while sensitive actions can require approval.

Once I had given it enough to work with, Muse began looking for things that applied to me and surfaced possible actions. I already know that canceling services I do not use saves money. I do not need artificial intelligence to explain the revolutionary concept of paying fewer bills. What I wanted was something able to find the bills I have forgotten about.

Muse eventually identified some Patreon and Substack subscriptions I might not want anymore and another opportunity to reduce my phone bill based on some newly released data plans. Between them, the useful savings came to about $35. I checked the suggestions before acting on them, which is something I would strongly recommend doing.

Despite saving only 3.5% of the goal of $1,000, I was mildly impressed. It was still $35 I would probably have continued spending every month otherwise.

AI is good for boring work

(Image credit: Shutterstock/Poetra.RH)

The gap between my result and the #MuseMoneyChallenge highlights something important about these increasingly capable personal AI agents. Their usefulness can depend enormously on how much financial debris you have accumulated.

The impressive examples circulating online underline the difference in how people may keep an eye on their money, and where AI might actually help those who struggle with doing so. Recovering a year's worth of subscription payments is possible if you have accidentally been paying for something for a year. Those are genuine opportunities, but they are not necessarily waiting in everyone's accounts.

My $35 therefore felt less like Muse failing and more like discovering that I apparently did not have $1,000 sitting around waiting for an AI to rescue it. I would also let it spend more time searching for refunds and credits rather than concentrating entirely on recurring expenses.

One of the advantages of an agent is that it can tackle exactly the kind of mildly irritating administrative task that I postpone because the potential reward does not seem worth 40 minutes navigating a company's customer service system. That may ultimately be a better pitch for Muse than the tantalizing $1,000 target.

The iPhone Duo costs $700 more than the iPhone 18 Pro Max. Here’s where that premium shows up — and where the Pro Max still holds an edge.

The post iPhone Duo vs iPhone 18 Pro Max: The $700 Gap Isn’t Just About the Fold appeared first on TechRepublic.

In Liberia, joint financial planning boosted cash aid's benefits. It also had an unexpected consequence for some families.

(Image credit: Yasuyoshi Chiba/AFP)

You will almost certainly have heard many business and technology leaders extolling the virtue of AI. They will enthusiastically tell you that the technology will have a transformative impact on businesses across the globe.

I think they are right. However, with two important caveats: AI must be given the right jobs, and the people using it must trust what it does.

Too often, AI is deployed across a business without much thought, as overconfident bosses assign agents tasks it isn't designed for. The problem this creates is that many employees using AI tools every day are not yet prepared for their new role in managing these agents. Trust only comes from experience and that builds over time.

When an AI system provides you with results that make sense and actually helps with your work, people start to feel more confident in using it. But if it makes a mistake, that confidence can be lost in an instant. If things go wrong, employees start finding ways around AI or stop using it altogether, and when leaders ignore employee concerns about making AI work better, its benefits are undermined.

The best way to ensure that AI delivers the efficiencies it promises is to ensure that it is doing the right job, and this isn't as straightforward as it sounds.

The areas where AI excels

AI works on probabilities, producing answers that are likely to be right. The key word here is ‘likely', which is why it occasionally hallucinates.

Being probabilistic makes AI very effective when dealing with unclear or complicated information. But it's not the best fit for tasks where a wrong answer could have serious consequences.

There are three core areas where AI really can make a difference. Firstly, when processing documents; secondly, decision support; and finally, personalization.

AI is especially effective at tasks involving many documents. It can extract, classify and summarize information that would normally take humans many hours to review. For example, an AI system could compare contracts against a standard set of clauses, or organize a large collection of customer emails by the issues they concern. This way, a person only has to look at the important findings instead of reviewing entire documents.

AI can also play a key role in supporting company executives to make decisions, as it's very good at taking lots of different pieces of information, finding patterns and delivering options as to what to do next.

It is, however, important to remember that AI is not always the right technology to make that decision. For example, imagine you're buying something and have to choose among many different suppliers.

AI can look at all the information, like how well they've done in the past, what they can deliver, how quickly it is likely to arrive and whether the supplier offers discounts for repeat purchases. It can even articulate why one option might be better than another. In most scenarios, though, it is the human who makes the final choice.

For some businesses, personalization is a promising tool, as AI can be used to make products more relevant to the individual using them. This is an area where working with probabilities can become a key driver for AI. The content only has to be good enough to make that connection so the recipient feels they are being addressed in a bespoke way.

The cost of assigning AI the wrong job

One of the key concerns companies should have about managing AI is that while AI-generated answers can sound assured, there is still a possibility that the technology has got something wrong. If that answer is allowed to trigger action without validation, a small error can travel rapidly through a workflow.

Think about what happens when someone makes a small mistake with a rule or detail in a contract. By the time someone catches the error, it may have already been added to records, sent to people outside the company, or even used to create new software.

To remind myself of AI’s limitations, I find it useful to think of the technology as the equivalent of a super-intelligent, hard-working intern. Interns need guidance and oversight, which should be provided by experienced, knowledgeable colleagues. Crucially, their access to sensitive information needs to be limited and granted only as they demonstrate they can make sound decisions.

AI needs to be managed in the same way. It should only be given greater levels of control when it has proved itself, and then its work must still be monitored by humans.

A good example of the importance of managing AI is its role in software development. AI can deliver a lot of code quickly, but its fast pace means the quality isn't always the same as that of human developers.

In fact, managing large amounts of AI-generated code can be a real headache for IT professionals. For example, surging token consumption and a shift to consumption-based pricing is ballooning AI coding costs, forcing developers to be selective on when and where they apply AI models, as reported by Gartner.

Let AI help design the model, not control the machinery

The most important design decision is where AI is allowed to reason. The riskiest place is deep in the implementation layer, where a small mistake can cause big problems that are hard to fix.

I believe a sound approach is to use AI to work with business ideas and rules. This way, it's easier for people to check and understand what the AI is suggesting.

Domain-specific languages and other abstractions used in model-driven development can provide that separation. AI helps create or refine a high-level application model, while a developer then validates the model before a deterministic platform transforms it into executable software.

AI can suggest how something should be done, but it doesn't have to be responsible for executing every step.

Abstraction helps developers, and in agent-driven systems it becomes a control tool. It simplifies complex systems and prevents small errors from becoming big problems, making AI output easier to test and fix.

Human expertise is becoming more valuable, not less

As AI becomes more prevalent, developers' roles will evolve. They will still write code, but a larger part of their role will be overseeing systems, checking outputs and catching mistakes, a change that brings with it the opportunity to learn new skills.

Developers will increasingly work at the level of systems and architecture, understanding the business rationale, recognizing patterns and judging whether AI-generated code fits the wider application and holds together.

One company that has innovated in this area and is already reaping the rewards for their experience is Ford. They recently hired 350 experienced engineers to help train younger colleagues and improve their AI and automation tools.

The shift was driven by the fact that the AI tools weren't producing the results they wanted without the expertise brought by seasoned professionals. Now, these experienced engineers act as internal auditors, reviewing designs and finding potential problems before they become major issues on the factory floor.

When you combine AI's ability to recognize patterns and process information with human knowledge and experience, it becomes a much more powerful tool. This combination is what makes it truly effective, not just relying on one or the other.

Building trust

Enterprises should also resist the pressure to deploy agents everywhere simply because competitors appear to be doing so. When it comes to using AI, leaders need to think carefully about each situation.

They should ask themselves three important questions. First, are they asking AI to only suggest what to do or to actually do it? Next, can someone check and fix what the AI says before an error spreads? Finally, what would happen if the system is certain about something but is actually wrong?

The answers play a big role in deciding how much freedom to give the technology. For example, a tool that summarizes documents might only need a quick check, but a system that suggests business decisions needs someone's logged approval.

When companies start using AI for specific, practical tasks, it shows employees that it can really make a difference in their work. This approach gives the company a chance to get its data, rules and processes in order. If people can see that the system is working well, and they understand what it can and can't do, they start to trust it more. Over time, this trust grows.

The key is to create a work setup where humans and machines do what they're good at. AI is great at processing large volumes of information and surfacing patterns. People are good at understanding the bigger picture, pointing out mistakes, and taking responsibility for their actions.

The wrong job to give to AI is any task where a mistake carries real consequences, no one checks the work before it causes harm, or the system sounds certain while being wrong. Companies that respect this division will build the confidence to give agentic AI greater autonomy over time. Those who ignore it may discover that a single poorly chosen task can undermine their entire AI strategy.

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

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Looking for iPhone Duo alternatives? Compare the four best options for 2026 from Samsung, Google, Motorola, and Apple before spending $1,999.

The post iPhone Duo Alternatives in 2026: 4 Phones to Consider Before Spending $1,999 appeared first on TechRepublic.

• OpenAI agent breached Australia’s Medicare portal on June 18, 2026, accessing internal files
• Government says no personal medical data was taken, but three other agencies may be affected
• PM Albanese slammed OpenAI’s 84‑day delay in disclosure, calling the notification “unacceptable” and warning of legal consequences

An OpenAI agent has allegedly broken into a website of the Australian government, which has slammed the “unacceptable” attack, promising an in-depth investigation, and threatening “legal consequences”.

Australian Prime Minister Anthony Albanese revealed how in June 2026, OpenAI’s research team tasked the agent with researching public medicine spending, as part of an internal capability evaluation - but as the agent got to work, it was initially denied access to some of the information it requested.

However, instead of stopping, or trying to find a different lawful way of obtaining this data, it circumvented those restrictions and landed inside the infrastructure behind Services Australia’s public-facing Medicare Statistics Reporting Service portal.

AI agent did what?

The public details are still quite limited, and we don’t know the technicalities of what the AI actually did.

The acting prime minister, Richard Marles, told the media during a recent press conference that the AI “scaled the fence”, despite the portal having security measures in place.

The bot apparently accessed internal infrastructure and wrote files to an internal server but exactly what it wrote, how it obtained the access, and precisely which technical mechanism it used, is still not public knowledge.

Once inside, it accessed both public and non-public files, but individual medical data was not accessed and the system itself was not compromised, the Australian government said.

There is also the possibility that three other government systems were affected - the Australian Institute of Health and Welfare and two state-based agencies - the New South Wales Bureau of Crime Statistics and Research and the Victorian Department of Health. However, this has not been confirmed yet.

"No personal information is believed to have been accessed at this stage, but investigations are ongoing," Albanese said. "Nonetheless this situation is obviously unacceptable."

OpenAI's sluggish escalation

Albanese was particularly unsatisfied with how OpenAI handled the situation. The breach happened on June 18, but it took the company 84 days to notify the Australian government of the incident. And when it did - it did so in a manner better suited for an amateurish start-up rather than one of the most important organizations on the planet right now.

OpenAI was apparently evaluating its models, and investigating “misaligned model activity” when, on August 11, it discovered the breach in Australia. It seems the AI agent simply did not take “no” for an answer. The company then notified Services Australia on September 10 - almost three months after the incident. To make matters worse, the company reached out via publicdisclosures@servicesaustralia.gov.au, inbox researchers usually use to report potential vulnerabilities, instead of trying to escalate the incident higher.

Services Australia reviewed the information and notified the Australian Signals Directorate on September 15.

When the news reached prime minister Anthony Albanese, he spoke to OpenAI CEO, Sam Altman, and expressed the country’s “extreme concern” about this incident, as well as his “disappointment that it took the company way too long to inform the government what had occurred.” He also pointed out the way OpenAI reached out: “The notification was an email sent just to the public mailbox.” Albanese described the “nature of the notification” as “unacceptable.”

The Australian government is now looking into the matter to see if any laws were broken and what legal consequences, if any, could follow.

"And obviously we will investigate all of that. What the consequences are, if there has been a breach of the law, but also part of the task force is to assess whether or not the legal regime we have in place is fit–for–purpose in a world where we have an emerging AI capability,” Marles said.

Via BBC

IFR counted about 7,000 humanoid robot sales in 2025, but many units remain in research and workplace pilots rather than productive commercial roles.

The post IFR Counts 7,000 Humanoid Robot Sales as Workplace Pilots Stay Small appeared first on TechRepublic.

• French streaming service Qobuz is introducing tags to denote AI-slop music
• The firm also shared stats on how many people engage with this kind of audio
• Meanwhile, Spotify wants you to use its new AI agent to listen to music

When AI-made music is covered in the media, it's often painted to be an insidious success story; reports on artists including Velvet Sundown, Breaking Rust and Enlly Blue invariably mention high stream numbers, along with huge estimated revenues. If that rightly frustrates you, know that the problem is occasionally with the reporting, and not with AI music being popular.

French streaming service Qobuz has never been backwards about coming forwards when tackling what Managing Director Dan Mackta refused to call music at all, when we spoke to him earlier in the year. And now, Qobuz has unveiled a feature which adds tags to music it detects was generated with AI. This goes further than Spotify's opt-in AI persona label, by highlighting real artists' slop tracks. So expect to see it on Dr. Dre's new music, for example.

This continues a run of anti-AI moves from streaming services such as Qobuz, Deezer and Tidal, which have all identified sentiment to slop at being overwhelmingly negative. Highlights include Tidal giving no royalties to AI music, and Deezer adding a filter to remove AI tracks.

The major outlier is Spotify, which has made small steps to combat AI music, but some steps backwards too. Recently, it teamed up with Meta's AI agent Muse (no, not the Plug In Baby band, the chatbot that Meta kicked the band off its social handles for). This partnership lets you... queue up music, give voice commands to control your playlists, create playlists, and set up playing schedules. How did we cope before AI?

Time to decide whether you want Muse (band), or Muse (Meta)?

(Image credit: Spotify /Muse (Meta))

But perhaps the war against AI is all for naught? Because when unveiling its new AI tag system, Qobuz also shared some statistics on this kind of 'music' (word used lightly), which recontextualize the whole thing.

Case in point: apparently only 0.38% of streams on Qobuz are from AI-generated tracks. Yes, less than half a percent. For every 263 streams, only one comes from an AI track. If all the streams were represented by the population of the US, team AI would just about fill up Dallas.

There's more: over a third of all AI albums receive not a single listen. Not even from their creator. That's about four million songs, apparently, all of which are created just to take up space (and have been removed from Qobuz's library, so if you missed them, it's too late).

And if that wasn't enough, apparently 60% of all AI-music is demonetized by Qobuz's anti-fraud systems. This is based on the brand's AI Charter, unveiled in February, which detects AI music created solely for financial gain.

Qobuz is the original hi-res music service and as the connoisseurs' choice (devoid of many extra AI add-ons), one could argue that very few users are hitting up Qobuz for AI tracks, but still, the figures are telling. According to these Qobuz stats, you're largely all avoiding AI slop — well done you, take a pat on the back — and sticking to real music made by real people. Well, mostly. Looking at you, 0.38%...

QuickBooks Free can support unlimited invoicing through QuickBooks Payments. See payment fees, setup steps, limits, and what eligible users should know.

The post QuickBooks Free Can Support Unlimited Invoices: Here’s How It Works appeared first on TechRepublic.

Artificial intelligence has become a significant area of investment for UK businesses. More than £6 billion of new AI-related investment was announced during London Tech Week in June, while the UK remains home to the largest AI sector in Europe and the third largest globally.

From customer service and knowledge management to software development and internal operations, organizations are looking for places where AI tools can improve productivity, decision-making and business performance.

But as investment accelerates, another gap is becoming harder to ignore: organizations are often scaling AI faster than their ability to measure, govern and explain its value. That matters when CIOs and CFOs are increasingly being asked not simply whether AI is being adopted, but what the organization is getting in return.

As businesses move from individual copilots towards agents embedded across workflows, the economics become more complicated. A user making a single prompt is relatively easy to understand. An agent may make multiple model calls, retrieve information, invoke tools and take actions to complete one task.

Depending on the platform and pricing model, that can introduce additional consumption, infrastructure and oversight costs. CIOs therefore need to understand not only where AI has been deployed, but what it is doing, what it costs and whether the outcome justifies that cost.

The not-so-hidden cost of AI

AI investment is increasingly being scrutinized in the same way as any other major technology investment. The difficulty is that measuring its return can be unusually complex.

Usage may be distributed across departments, applications, models and workflows, while the benefits can range from time saved to improved quality, reduced risk or increased revenue. Without agreeing what success means first, organizations can end up measuring activity rather than value.

That becomes difficult when organizations expand AI without first defining where it sits in the workflow, who owns the outcome, what success looks like and how costs will be measured. Spending can become fragmented across licenses, models, infrastructure, platforms and consumption-based services, with no single view of whether those investments are delivering value.

The scale of the challenge is becoming visible. Research commissioned by Emergn estimates that large UK businesses lose £67 billion annually across transformation and AI initiatives that fail to deliver. Separately, a Censuswide survey of 500 senior UK decision-makers found that just 31% of businesses already using AI reported a positive return on their investment.

Governance is part of that measurement challenge. A 2026 ShareGate survey of 851 IT leaders across seven countries found that cost visibility was the most commonly cited barrier to measuring AI ROI, identified by 51% of respondents, followed closely by governance complexity at 47%. The challenge isn't simply knowing what AI costs. It's connecting that cost to the use case it supports, the information AI interacts with and the outcome it creates.

Much of the AI debate to date has focused on model selection, skills and productivity. But as adoption spreads, another gap is becoming visible: confidence in governance does not always match what happens in practice.

The same study found that 93% of IT leaders believed their Microsoft 365 governance was ready to support AI responsibly, yet 29% reported that AI tools had surfaced sensitive internal data that should not have been accessible. Another 8% weren't sure whether it had happened at all.

Those gaps carry costs as well as risk. Duplicated tools, additional validation, rework, security investigations and time spent establishing whether an output can be trusted all create a hidden tax on AI adoption. For CIOs trying to demonstrate value, reducing that friction starts with making AI usage more visible, accountable and measurable.

How organizations can regain control

Good governance doesn’t begin and end at procurement. Knowing how many licenses have been purchased and where they have been assigned is useful, but regaining control requires a broader view: visibility into how AI is being used and what it costs, clear ownership of the outcomes, and a well-governed information environment for AI to work from.

Clear ownership matters just as much. As AI becomes embedded in business processes, responsibility can easily become fragmented across IT, security, business teams and individual employees. That makes some basic questions surprisingly difficult to answer: Who owns the outcome? Who monitors the cost? Who decides whether a use case should scale, change or stop?

Cost governance is only part of the picture. Organizations also need to improve the information environment in which AI operates. That means reducing redundant and outdated content, managing access appropriately and helping employees and AI systems find authoritative sources.

Cleaner, better-governed information does not guarantee a correct AI response, but it can reduce ambiguity and make reliable grounding easier. That can mean less time spent searching, validating and reworking outputs.

Employees have a role here as well. Clearly distinguishing drafts from approved material, keeping trackers and priorities current, recording decisions and maintaining authoritative sources all make organizational context easier for people and AI to interpret. Where organizations use AI meeting assistants or similar tools to capture context, those tools should be subject to the same privacy, retention and access controls as the information they create.

The question for leaders, then, is no longer simply whether AI is worth the investment. It is whether they have enough visibility and control to understand where AI is creating value, where it is creating cost and what they should do differently as a result. AI investment is likely to continue, but under different expectations.

Deployment alone is not evidence of value, and governance is becoming more than a risk-management exercise; it is increasingly part of the business case. Leaders need to understand what AI costs, who is accountable for its outcomes, whether the information supporting it can be trusted and what measurable benefit it creates.

The organizations best positioned to scale will not necessarily be those deploying the most AI, but those that can explain what it is doing, understand what it costs and make informed decisions about where it belongs.

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

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

Since 1998, we've been enjoying the feature films and TV shows of one Guy Ritchie. A man who arguably defined the British gangster sub-genre, cult hits like Snatch and Lock, Stock and Two Smoking Barrels have stood the test of time.

Fast-forward to 2026, and streaming hits like Netflix's The Gentlemen and Paramount+'s MobLand are all anybody wants to talk about... and rightly so. And that's all without scratching the surface of the projects Ritchie has worked on in between.

But can you fill in the gaps? I've created the ultimate quiz for any and all Guy Ritchie fans, focusing on his feature-length movies and TV shows over the last three decades.

I didn't say it was going to be easy, though... so do your worst.

Of course, all of this is up to the time of writing — we already know that we've got plenty more Ritchie projects to look forward to.

Young Sherlock season 2, The Gentlemen season 3 and MobLand season 3 have all either been greenlit or are in development. There's a few more titles to add to this list... but that would be giving away the quiz questions.

Much like Ritchie-style gangster, I'm just not that nice.

NVIDIA Isaac ROS 5.0 adds agent-ready tools, Ubuntu 24.04 support, and GPU acceleration to help developers build and test robotics software faster.

The post NVIDIA Brings AI Agents to Robotics With Isaac ROS 5.0 appeared first on TechRepublic.

• Disney has confirmed new price hikes for Disney+ and Hulu in the US
• However, the cost of three bundles has been frozen
• Fans in the UK, Europe, and Australia won't be affected for the time being

Disney has confirmed price hikes for all but three of its standalone Disney+ and Hulu plans, and bundles including both platforms, in the US — but there's no word on when other nations might be hit by similar rises.

Yesterday (September 23), Bloomberg reported that the entertainment giant was about to increase the cost of signing up for a subscription for the sixth time in as many years.

Per Bloomberg's sources, the biggest markup would see the Hulu-Disney+ Premium (ad-free) bundle go up by 13% to $21.49 per month. That means new and current users would have to pay an additional $2.50 every 30 days. Meanwhile, the cost of numerous other standalone tiers and bundles would go up by as little as $0.50 or as much as $3.00.

The latest price hikes come as Marvel fans prepare for Avengers: Doomsday's arrival with another MCU movie marathon (Image credit: Marvel Studios)

Approached by TechRadar for comment, Disney acknowledged that Bloomberg's report was factual.

However, the studio was keen to stress that three of its bundles, including its cheapest offering — the Hulu-Disney+ bundle (with ads) experience — would not be subject to price increases.

Disney added that, compared to its rivals raising the cost of their standalone ads-based tiers by 13%, it had only done so by 4%. Finally, the House of Mouse attributed the price hike to investing in the user experience by way of new technology, features, and back-end improvements.

For reference, here's a full breakdown of every price rise or freeze across Disney's myriad streaming service offerings in the US:

• Disney+, Hulu Bundle (with ads) — staying flat at $12.99
• Disney+, Hulu Bundle Premium (ad free) — increasing +$2 to $21.99
• Disney+ and Hulu standalone plans (with ads) — increasing +$0.50 to $12.49
• Disney+ and Hulu Premium standalone plans (ad free) — increasing +$2.50 to $21.49
• Disney+, Hulu, ESPN Select Bundle (with ads) — increasing +$2 to $21.99
• Disney+, Hulu, ESPN Select Bundle Premium (ad free) — increasing +$3 to $32.99
• Disney+, Hulu, ESPN Unlimited Bundle (with ads) — staying flat at $35.99
• Disney+, Hulu, ESPN Unlimited Bundle Premium (ad free) — staying flat at $44.99

Why the UK, Europe, and Australia is immune to the Disney+ price hike — for now

Disney+ users outside of the US should steel themselves for further price rises, too (Image credit: Ivan Marc/Shutterstock)

Crucially, UK and European subscribers appear to be temporarily immune to these price increases.

Responding to a separate request for comment from TechRadar, a Disney+ UK spokesperson told me: "We don't have anything to share on UK or European pricing at this time."

However, in previous years, UK and European fans have seen the cost of Disney+ climb just weeks after their US counterparts were slapped with the now-annual price hike. I wouldn't be stunned, then, if account holders in these regions are forced to pay more for two of the world's best streaming services in the weeks ahead.

Australian Disney+ users were hit by a price hike in April, so it's unlikely — but not completely out of the question — that they'll experience another one in due course.

Regardless of whether you've been hit by this latest price hike, it's been a rough few days for Disney+ users all around.

The week began with Disney having to placate furious fans over inaccurate reports that its ad-free Disney+ tiers would start showing ads. The dust hadn't settled on that story before fans learned that Marvel TV show Daredevil: Born Again would end after its forthcoming third season on Disney+, too.

I'm not superstitious but, as the saying goes, bad news comes in threes. Let's hope that this is the third and final piece of unwelcome news that Disney fans have to deal for a while, then.

Google is building persistent AI memory designed to stay unreadable to Google, with device-held keys and independent security testing.

The post Google Is Building AI Memory Designed to Stay Locked Even From Google appeared first on TechRepublic.

Microsoft cuts 268 Xbox jobs and shifts Halo development to Activision as its gaming overhaul reshapes studios, Game Pass, and future releases.

The post Microsoft Cuts 268 Xbox Jobs, Moves Halo Development to Activision appeared first on TechRepublic.

Education staff are being scattered across several other federal agencies. Despite the Trump administration's claims, employees say it's adding bureaucracy and making it harder to serve students.

(Image credit: Mark Felix)

Stories of science funding being cut, labs shutting down and scientists losing jobs have flooded the media under Trump's administration. These six graphics show how those changes have affected U.S. biomedical research.

(Image credit: Eva Revolver for NPR)

Democratic Sen. Elizabeth Warren asked Trump officials to explain why the Census Bureau's parent agency cut a ban on political interference from its scientific integrity policy, as NPR first reported.

(Image credit: Cliff Owen)

eBay became the latest online retailer to ban the sale of airbags and many of their components. Aftermarket parts can be risky if they turn out to be fake or defective.

(Image credit: BERTRAND GUAY)