Technology

• A detailed specs leak suggests the Pixel 10 Pro Fold could have an IP68 rating
• It might also have a large 5,015mAh battery, among other upgrades
• However, its cameras might not be improved

The Samsung Galaxy Z Fold 7 is in many ways a very impressive foldable phone, but it has some weaknesses, and going by the latest leak, the Google Pixel 10 Pro Fold might not have these same issues.

Android Headlines has shared a detailed Pixel 10 Pro Fold specs list, and one of the most eye-catching aspects is the claim of a 5,015mAh battery. Not only is that significantly higher capacity than the 4,650mAh battery in the Google Pixel 9 Pro Fold, it’s also far bigger than the 4,400mAh battery in the Samsung Galaxy Z Fold 7.

Samsung actually didn’t increase the capacity at all for the Z Fold 7, so this is one of the more disappointing aspects of that phone.

According to this leak, the Google Pixel 10 Pro Fold will also be the first foldable phone to have an IP68 rating. That would mean it’s dust-tight and can be submerged up to 1.5 meters deep in water for up to 30 minutes.

Now, this is a rating that’s commonly found on high-end non-foldable handsets, but foldable phones have really struggled with dust resistance, with the Samsung Galaxy Z Fold 7 for example only having an IP48 rating, which is the same level of water resistance but means it can only resist dust particles that are greater than 1mm in size.

A better screen but no change to the cameras

The Google Pixel 9 Pro Fold (Image credit: Philip Berne / Future)

There are other specs listed too, and some of these are also improvements on the Pixel 9 Pro Fold. The upcoming phone is said for example to have a 6.4-inch cover screen (up from 6.3 inches on the current model), but due to smaller bezels, the actual size of the handset might not increase.

That screen has also apparently had a brightness boost, reaching up to 3,000 nits (compared to 2,700 nits on the current model), and of course the Pixel 10 Pro Fold is expected to have a new Tensor G5 chipset – though an early benchmark suggests this might not be overly powerful.

Other tipped details include 16GB of RAM once again, but with 256GB, 512GB, and 1TB storage capacities – the last of which would be new.

Sadly, the Pixel 10 Pro Fold’s cameras might not be upgraded, with the same source claiming to expect a 48MP main camera, a 10.5MP ultra-wide, a 10.8MP telephoto (with 5x optical zoom), and a pair of 10MP front-facing cameras, all of which would be a match for last year’s phone.

We would however take all of these specs with a pinch of salt, because with the exception of the chipset the majority of this has only come from one source. But we should know for sure what specs the Pixel 10 Pro Fold has soon, as it’s likely to land next month, with August 20 being tipped as the announcement date.

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What truly separates us from machines? Free will, creativity and intelligence? But think about it. Our brains aren't singular, monolithic processors. The magic isn't in one “thinking part,” but rather in countless specialized agents—neurons—that synchronize perfectly.

Some neurons catalog facts, others process logic or govern emotion, still more retrieve memories, orchestrate movement, or interpret visual signals. Individually, they perform simple tasks, yet collectively, they produce the complexity we call human intelligence.

Now, imagine replicating this orchestration digitally. Traditional AI was always narrow: specialized, isolated bots designed to automate mundane tasks. But the new frontier is Agentic AI—systems built from specialized, autonomous agents that interact, reason and cooperate, mirroring the interplay within our brains.

Large language models (LLMs) form the linguistic neurons, extracting meaning and context. Specialized task agents execute distinct functions like retrieving data, analyzing trends and even predicting outcomes. Emotion-like agents gauge user sentiment, while decision-making agents synthesize inputs and execute actions.

The result is digital intelligence and agency. But do we need machines to mimic human intelligence and autonomy?

Every domain has a choke point—Agentic AI unblocks them all

Ask the hospital chief who’s trying to fill a growing roster of vacant roles. The World Health Organization predicts a global shortfall of 10 million healthcare workers by 2030. Doctors and nurses pull 16-hour shifts like it’s the norm. Claims processors grind through endless policy reviews, while lab technicians wade through a forest of paperwork before they can even test a single sample.

In a well-orchestrated Agentic AI world, these professionals get some relief. Claim-processing bots can read policies, assess coverage and even detect anomalies in minutes—tasks that would normally take hours of mind-numbing, error-prone work. Lab automation agents could receive patient data directly from electronic health records, run initial tests and auto-generate reports, freeing up technicians for the more delicate tasks that truly need human skill.

The same dynamic plays out across industries. Take banking, where anti-money laundering (AML) and know-your-customer (KYC) processes remain the biggest administrative headaches. Corporate KYC demands endless verification steps, complex cross-checks, and reams of paperwork. An agentic system can orchestrate real-time data retrieval, conduct nuanced risk analysis and streamline compliance so that staff can focus on actual client relationships rather than wrestling with forms.

Insurance claims, telecom contract reviews, logistics scheduling—the list is endless. Each domain has repetitive tasks that bog down talented people.

AI is the flashlight in a dark basement

Yes, agentic AI is the flashlight in a dark basement: shining a bright light on hidden inefficiencies, letting specialized agents tackle the grunt work in parallel, and giving teams the bandwidth to focus on strategy, innovation and building deeper connections with customers.

But the true power agentic AI lies in its ability to solve not just for efficiency or one department but to scale seamlessly across multiple functions—even multiple geographies. This is an improvement of 100x scale.

1. Scalability: Agentic AI is modular at its core, allowing you to start small—like a single FAQ chatbot—then seamlessly expand. Need real-time order tracking or predictive analytics later? Add an agent without disrupting the rest. Each agent handles a specific slice of work, cutting development overhead and letting you deploy new capabilities without ripping apart your existing setup.

2. Anti-fragility: In a multi-agent system, one glitch won’t topple everything. If a diagnostic agent in healthcare goes offline, other agents—like patient records or scheduling—keep working. Failures stay contained within their respective agents, ensuring continuous service. That means your entire platform won’t crash because one piece needs a fix or an upgrade.

3. Adaptability: When regulations or consumer expectations shift, you can modify or replace individual agents—like a compliance bot—without forcing a system-wide overhaul. This piecemeal approach is akin to upgrading an app on your phone rather than reinstalling the entire operating system. The result? A future-proof framework that evolves alongside your business, eliminating massive downtimes or risky reboots.

You can’t predict the next AI craze, but you can be ready for it

Generative AI was the breakout star a couple of years ago; agentic AI is grabbing the spotlight now. Tomorrow, something else will emerge—because innovation never rests. How then, do we future-proof our architecture so each wave of new technology doesn’t trigger an IT apocalypse? According to a recent Forrester study, 70% of leaders who invested over 100 million dollars in digital initiatives credit one strategy for success: a platform approach.

Instead of ripping out and replacing old infrastructure each time a new AI paradigm hits, a platform integrates these emerging capabilities as specialized building blocks. When agentic AI arrives, you don’t toss your entire stack—you simply plug in the latest agent modules. This approach means fewer project overruns, quicker deployments, and more consistent outcomes.

Even better, a robust platform offers end-to-end visibility into each agent’s actions—so you can optimize costs and keep a tighter grip on compute usage. Low-code/no-code interfaces also lower the entry barrier for business users to create and deploy agents, while prebuilt tool and agent libraries accelerate cross-functional workflows, whether in HR, marketing, or any other department.

Platforms that support PolyAI architectures and a variety of orchestration frameworks allow you to swap different models, manage prompts and layer new capabilities without rewriting everything from scratch. Being cloud-agnostic, they also eliminate vendor lock-in, letting you tap the best AI services from any provider. In essence, a platform-based approach is your key to orchestrating multi-agent reasoning at scale—without drowning in technical debt or losing agility.

So, what are the core elements of this platform approach?

1. Data: Plugged into a common layer

Whether you’re implementing LLMs or agentic frameworks, your platform’s data layer remains the cornerstone. If it’s unified, each new AI agent can tap into a curated knowledge base without messy retrofitting.

2. Models: Swappable brains

A flexible platform lets you pick specialized models for each use case—financial risk analysis, customer service, healthcare diagnoses—then updates or replaces them without nuking everything else.

3. Agents: Modular workflows

Agents thrive as independent yet orchestrated mini-services. If you need a new marketing agent or a compliance agent, you spin it up alongside existing ones, leaving the rest of the system stable.

4. Governance: Guardrails at scale

When your governance structure is baked into the platform—covering bias checks, audit trails, and regulatory compliance—you remain proactive, not reactive, regardless of which AI “new kid on the block” you adopt next.

A platform approach is your strategic hedge against technology’s ceaseless evolution—ensuring that no matter which AI trend takes center stage, you’re ready to integrate, iterate, and innovate.

Start small and orchestrate your way up

Agentic AI isn’t entirely new—Tesla’s self-driving cars employs multiple autonomous modules. The difference is that new orchestration frameworks make such multi-agent intelligence widely accessible. No longer confined to specialized hardware or industries, Agentic AI can now be applied to everything from finance to healthcare, fueling renewed mainstream interest and momentum. Design for platform-based readiness.

Start with a single agent addressing a concrete pain point and expand iteratively. Treat data as a strategic asset, select your models methodically, and bake in transparent governance. That way, each new AI wave integrates seamlessly into your existing infrastructure—boosting agility without constant overhauls.

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This article was produced as part of TechRadarPro's Expert Insights channel where we 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/news/submit-your-story-to-techradar-pro

An "MP3" or digital audio player can help you take the tunes offline or at the very least off your phone.

Europe has begun to actively test the waters of cloud sovereignty, with 72% of European businesses prioritizing data control when selecting technology vendors. However, despite the growing desire to protect data integrity and security within European borders, over 70% of European businesses use US hyperscalers from their cloud computing provider.

While Google is doubling down on its commitment to data sovereignty, there is a growing concern over hyperscalers' dominance over the European market, as it leaves critical infrastructure in the hands of dominant foreign providers.

As sweeping tariffs continue to escalate tensions between Europe and the US's big tech, many are questioning whether Google's commitment is enough to protect Europe’s data from the Big Three.

US influence on Europe’s tech ecosystem

US Policies, like the 2018 Clarifying Lawful Overseas Use of Data (CLOUD) Act, give US hyperscalers massive influence in Europe. The act grants US authorities and federal agencies access to data stored by US cloud service providers, even when hosted in Europe. This raises concerns about whether European data stored with US-based providers can ever truly be sovereign, even if housed within European borders.

Another concern in Europe: being cut off from US services. If Europe were to suddenly lose access to US cloud services or face rising costs, businesses would experience immediate setbacks, from service disruptions to escalating operational expenses. These concerns, along with a push for more leadership, independence, and economic competitiveness, have led to Europe steadily building its own cloud ecosystem – fostering a network of regional providers and implementing policies that reinforce data independence.

The question has now become, do these changes signal a true tipping point for Europe? Or are they merely the first steps in a much longer transformation?

Who is driving the adoption of sovereignty?

Both the public and private sectors play pivotal roles in advancing cloud sovereignty across Europe. Governments have established regulatory frameworks to enhance standards and avoid fragmentation. However, policymaking is often slow and subject to political negotiation, making private sector initiatives crucial in accelerating the shift toward true sovereignty.

The private sector has emerged as a driving force behind the practical implementation of sovereignty ideals. According to Dominique Tessier, Head of Cybersecurity Focus Group at the European Champions Alliance (ECA), “the move to make sure the EU Cloud Certification Scheme will finally include an “upper security layer” is mainly driven by private European companies, as AIRBUS, EDF, Telecom Italia and others, whose efforts are gaining momentum”.

While companies like Microsoft have invested heavily in EU infrastructure to comply with local regulations, concerns remain about whether this represents true sovereignty or just a regulatory workaround. In contrast, European companies and partnerships, such as the joint venture between OVHcloud and Capgemini, are working to offer services independent of US control, aiming to create fully sovereign cloud solutions.

These initiatives reflect a growing acknowledgement of the strategic importance of cloud sovereignty. This is supported by Rahiel Nasir, Research Director, IDC Europe, who states that “interest in sovereignty has moved from governments and regulated sectors to all industry sectors, especially in Europe, and everywhere else where cloud is just beginning to pick up”. These efforts are becoming more widespread, indicating a collaborative push towards achieving European digital independence, but more needs to be done to make this achievable.

How can Europe achieve ‘true sovereignty?’

Achieving true cloud sovereignty requires more than just localized data storage, it demands complete independence from hyperscalers. To achieve this, Europe must go beyond compliance and foster a robust ecosystem of local providers that can match and work alongside hyperscalers.

While hyperscalers play a role in the broader cloud landscape, they should not be relied upon for sovereign data. According to Tessier, “the new US Administration has shown that it won’t hesitate to resort either to sudden price increases or even to stiffening delivery policy. It’s time to reduce our dependencies, not to consider that there is no alternative”.

For Nasir, the key is striking a balance. “In an ideal scenario, local providers and global providers should partner for sovereignty to work at scale”. Leveraging their capabilities where appropriate while ensuring critical data and workloads remain within truly sovereign infrastructure.

By shifting away from hyperscaler dependency and building a diverse, sovereign infrastructure, organizations can move beyond regulatory compliance and achieve operational independence within their own jurisdictions.

The path to sovereignty

While Europe is leading the way in advocating for cloud and digital sovereignty, achieving true independence requires a strategic balance. Reducing reliance on US hyperscalers while developing competitive local alternatives is crucial. This balance involves leveraging public and private sector initiatives to create an environment where local providers can thrive and compete on a global scale.

Ultimately, sovereignty is not just about regulatory compliance; it's about a strategic vision for independence. Empowering local providers and creating interconnected networks will allow Europe to set its own digital agenda and drive long-term economic and technological growth, helping to achieve “true” sovereignty.

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This article was produced as part of TechRadarPro's Expert Insights channel where we 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/news/submit-your-story-to-techradar-pro

We consulted dentists to discover the potential benefits and side effects of fluoride-free, nano-hydroxyapatite toothpaste.

AI is democratizing access to software development in new and innovative ways, with 'vibe coding' emerging as the latest buzzword for budding developers. For the uninitiated, vibe coding makes it easy for anyone to get a head start on projects, by merely describing what they want AI tools such as Cursor, GitHub’s Copilot or Replit to accomplish.

This is no small thing, especially for those who before had not been able to create software on their own. However, vibe coding requires a high level of trust in the AI’s output, and there are potential trade-offs in confidence and security for a faster turnaround and expanding capabilities to those who would not otherwise be able to code.

Exploring the latest AI technologies

Exploring the latest AI technologies can undoubtedly help developers experiment with new ways in which to do things better and faster, and vibe coding is no exception. However, even Andrej Karpathy, the former AI director at Tesla who coined the term, advises that the methodology is better suited for “weekend projects” than for more complex projects.

For smaller, proof-of-concept style projects, the speed of vibe coding can shine, but as things grow so does the need for important context and knowledge of edge cases. Vibe coding practicalities aside - recognising security exploits, lack of contextual understanding, bug fixing and software life cycle/maintenance considerations – there’s also a larger issue at play.

For AI-powered coding grunt work to improve in any format or scale, socially responsible AI must be the foundation on which technology solutions are built and delivered. The more trusted and accurate the data that large language models train on, the higher quality the outcome - for the code and most importantly, for the larger tech community.

There is a need for a symbiotic relationship to form: data helps create and improve AI experiences, and AI experiences help guide new, human verified information.

Understanding the opportunities and risks associated with vibe coding

AI continues to democratize access to software development in new and innovative ways for aspiring developers. Developers can ask AI tools such as Cursor, GitHub’s Copilot or Replit to help initially scaffold what they would like to see at the beginning of a new project. This is where AI generated code can be very powerful, and I use it on my own homegrown projects.

There is little need for context, edge cases are much fewer, and security concerns are more “standard” than other larger projects with bespoke needs. However, the trade-off for improved speed can be a sacrifice in potential security concerns as those code bases grow.

For budding programmers, vibe coding has the potential to provide the necessary support during the early stages of a project, and anything that gets more people into our field and shortens the learning curve to coding is always a good thing. But caution should also be exercised given the risks associated with the method for things outside of some early, base use cases.

Handing over the reins to AI

Andrej Karpathy describes vibe coding as interacting with AI to assist developers shift away from manual programming activities through LLMs (large language models), prompting developers through intuitive decisions to make it easier to create software as LLMs continue to improve code writing skills.

To fully embrace vibe coding, developers must cede much of the control to the AI assistant during the entire process, rather than becoming aware and having an understanding of the code as it is put into the codebase.

As LLM assistants continue to improve their corpus of knowledge on developing code, real-time decisions and predictions are made around what you would like to happen next to successfully complete a project, but these are still assumptions and educated guesses based on the experiences of others.

Vibe code with caution

It should always be remembered that every coding process cannot be overseen exclusively with the help of AI assistance. Small, low-risk side projects are ideal for vibe coding. When it comes to larger, more complex projects – a human should always be a first-class part of that loop.

AI coding tools powered by LLMs can and do produce mistakes. Developing larger datasets of information and considering other factors such as quality control and security requires an expert eye to monitor flaws or weaknesses that could be new or unexpected to an AI who is only thinking of other use cases, or the most common ones.

Knowledgeable developers are able to identify and test vulnerable code themselves – LLMs are sometimes simply unable to even register any mistakes they may produce.

In short, what the AI assistant doesn’t know, it will not flag as an issue – be that a bug or security vulnerability, AI can be quite confidently wrong. Vibe coding is a new trend for the industry but cannot be seen as a silver bullet or license for cutting corners in the development process when quality and stability matter.

Human expertise must always contribute to the process along the way as code bases grow, either vertically or horizontally.

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This article was produced as part of TechRadarPro's Expert Insights channel where we 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/news/submit-your-story-to-techradar-pro

Here are the answers for The New York Times Mini Crossword for July 15.

• Claude can now connect to apps like Notion, Canva, and Stripe
• The AI can understand and assist with tasks using your actual work data
• Claude's secure access reduces the need to constantly explain context to the AI

Anthropic has upgraded Claude with a major new set of tools that let the AI assistant integrate directly with several popular software tools, including Notion, Canva, Stripe, Figma, Socket, and Prisma. The new Claude tool directory means you don't need to explain what you want to Claude every time you want to employ those tools; Claude can now look at the same information as you to help.

Until now, most AI interactions have required copying and pasting every detail from your project management tool, explaining what’s important, clarifying what each task means, and double-checking that the AI understood it. Now you can just ask it to do the task, and Claude will pull the information directly from the relevant tool to handle things.

That might not seem groundbreaking at first glance, but that context gap is where things usually fall apart when asking AI chatbots to help you. For instance, if you're working on a product launch in Notion and have a list of things to do, you'd normally have to retype or upload all the information to Claude. Now, once you connect Notion to Claude, the AI can read your project documents directly and start putting together timelines and presentation materials that fit the product because it sees what you see.

Or imagine a small business owner using Stripe to manage payments who wants a summary of which customers paid last week and which still owe you for your services. Claude can now pull that data directly from Stripe with your permission. And with Canva, a blank social media post template can now be filled in with a design and copy from Claude based on your brief. You describe what you need in plain language, and Claude will make something usable.

Claude connected

These integrations are powered by something called the Model Context Protocol, or MCP. That basically means Claude can understand and act on the tools you use without needing a whole tutorial. You just connect an app once, and Claude gets secure, limited access to the relevant information inside it. It doesn’t read your entire inbox or download your bank history, just what’s necessary to help you with the task at hand.

You can go to Claude’s tool directory and connect whatever apps you already use. If you’re on a paid Claude plan, you’ll get access to remote app connections like Stripe and Notion. Desktop integrations, like Figma and Socket, are available through the Claude desktop app.

Other AI tools are trying something similar. Google’s Gemini shows up in Docs and Gmail. Microsoft’s Copilot is baked into Word and Excel. But Anthropic’s take is more about linking what you already do with the AI, as opposed to baking the AI into those apps directly.

Of course, this doesn't make Claude autonomous. It can’t pay your bills or fully run your job. And while Anthropic says it’s designed everything with privacy and security in mind, some are likely to be wary, even if you can choose what Claude can access. But for most regular users, this update represents something potentially very useful in staying on top of things. If, as Anthropic claims, it will save time and mean you don't have to redo a lot of tedious paperwork, it will likely be a very popular feature.

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