One of the major strengths of the BASIC programming languages has always been their no-fuss setup and rich set of commands for operations that would take considerably more work in a bare-bones language like C. MoonBASIC continues this legacy with a BASIC variant optimized for both 2D and 3D game development.
Included in the package are Raylib, Box2D, and Jolt, whose functionality is exposed via over 4,200 commands in their respective namespaces. You can also download a whole IDE package based around VS Code, use it on the command line, or add it to an existing VS Code installation.
A quick glance at the ‘getting started‘ guide gives a pretty good idea of what to expect of MoonBASIC, including a range of custom language additions and support for PBR materials, dynamic lighting, and other modern game engine features.
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Whether writing a game in BASIC was on your bingo card for this year or not, it might be worth taking a look to see whether it’s your jam. After all, if BASIC was good enough for both AI and game development in the 1980s, surely it can be used for complex games in 2026.
If your AI rollout is making people more exhausted, the architecture is wrong. That’s not a provocative claim; it’s the logical conclusion of what the data shows. With 69% of UK businesses implementing AI assistants, these tools have become part of everyday working life.
But deployment without the right operational structure is leaving employees exposed to the harmful effects of ‘AI brain fry’. AI overload is not a failure of the individual, but a failure of system design. And it is the responsibility of technology and business leaders to fix it.
Sonali Fenner
Managing Director at Slalom.
Researchers recently coined the term ‘AI brain fry’ to describe the cognitive fog and loss of concentration that results from excessive oversight and orchestration of AI tools; i.e. the mental load of managing the systems themselves. The problem – as our own data makes clear – is not that employees are using AI skills too much.
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Rather, it is that most UK businesses have deployed AI tools without building the structured ways of working needed to make them productive. The issue does not lie in use alone; it lies in how that use is managed.
The organizations deploying these tools have a duty to ensure they deliver genuine efficiency gains – not a new category of cognitive burden.
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The adoption gap is creating more work, not less
Only 31% of businesses are using multi-agent workflows, leaving their employees stuck in a cycle of tedious labor – the kind that AI is supposed to reduce. The majority of UK businesses currently employing AI tools are expecting employees to still do most of the heavy lifting.
Employees are finding themselves writing prompts, manually checking whether the answers are reliable, and interpreting outputs.
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This type of work was supposed to be eased by AI. Instead, the tools have made it worse.
The consequence? More admin, not less. Employees who were promised that AI would lighten their workload are instead finding it has added a new layer of tasks including prompt management, output validation, error correction on top of the day job.
Few businesses have moved towards a structured multi-agent workflow where AI systems handle the orchestration burden directly – routing tasks, validating outputs and managing agent-to-agent handoffs without requiring constant human supervision.
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That is the architecture that relieves the cognitive load. Without it, employees are not using AI – they are managing it. And there is a significant difference between the two.
Moving towards an ‘adaptive operating model’
UK organizations need to move beyond AI deployment and towards an adaptive operating model. One that is deliberately architected, not organically grown. That means clearly defining which tasks AI can be trusted to handle autonomously, where human judgement remains the critical control point, and how work moves between the two. In practice, this could look like:
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AI agents handling first pass research, data synthesis and output drafting. Humans setting direction, making judgement calls and reviewing exceptions, rather than every output.
The distinction between “AI does the work” and “human manages the AI doing the work” is where most current deployments get stuck. To protect employees and exact real productivity, businesses must build in human oversight at the right level – not at every level.
This means developing genuine domain expertise so that employees can interrogate AI outputs critically, not simply accept them. It also means investing in the technical literacy to design workflows that are robust, not just functional. An AI deployment that requires constant human supervision to remain reliable has not been properly engineered.
The organizations that get this right will also be better protected as the employment landscape shifts. With the UK government’s Employment Rights Act 2025 set to reduce the qualifying period for unfair dismissal claims and remove the compensation cap from January 2027, the cost of poorly managed AI-driven workforce change, both in human and legal terms, is rising.
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Businesses that have embedded clear human-AI accountability structures will be far better placed than those that have not.
Without making these structural changes, AI adoption will continue to add effort rather than remove it. Thereby accelerating burnout at the very moment businesses are depending on these tools to drive productivity.
Protection and transformation go hand-in-hand
It is entirely possible to realise the productivity potential of AI whilst protecting employees from its cognitive costs. Multi-agent workflows, properly designed, keep humans in the seats that matter – strategy, judgement and decision-making – and had the orchestration burden to the systems built for it.
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AI brain fry is not an inevitable side effect of AI adoption. It is a signal that the implementation architecture needs re-thinking. That is a technical and organizational challenge, and it belongs with the people who built the system – not the people using it.
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
Self-driving cars have gotten pretty good at driving. The harder problem is teaching them to drive safely in situations nobody planned for. We have heard horror stories of self-driving cars, behaving erratically in emergency situations, often times delaying first responders from reaching the scene.
A team at Seoul National University, led by professor Jun Won Choi from the Department of Electrical and Computer Engineering, thinks they have cracked part of that puzzle with a new AI model called SafeDrive. The research was recently selected as a highlight paper at CVPR 2026, a distinction that goes only to roughly 3% of all submissions.
How does SafeDrive make driving decisions safer?
Most end-to-end autonomous driving models work by studying massive amounts of real driving data and trying to mimic how humans react on the road. It works well most of the time, but these systems tend to struggle when it comes to explaining why they chose one path over another, and that becomes a real problem when safety is on the line.
Seoul National University College of Engineering
Choi’s team built something called Fine-grained Safety Reasoning to fix this. Instead of picking one driving path and going with it, SafeDrive generates several possible trajectories, combines them with what the car’s sensors are perceiving, and scores each option for safety. The car then picks the path that scores best. It sounds simple, but it directly tackles the two biggest weaknesses of current end-to-end systems, safety and explainability.
Why is this such a big deal for Korea?
As TechXplore reports, this is the first time a Korean-made end-to-end autonomous driving paper has landed a highlight spot at CVPR, one of the biggest AI and computer vision conferences in the world. It is a strong signal that Korea is no longer just watching from the sidelines while the US and China race ahead with their self-driving ambitions.
SafeDrive is not staying stuck in the lab either. It has already been folded into EAD, a reference model backed by Korea’s Ministry of Trade, Industry and Energy, and Choi’s team is now working with domestic autonomous driving companies to test it in real vehicles. Choi says the plan is to keep improving the model with bigger datasets and eventually push it toward full commercialization using their own collected data.
Nine-department ERP overhaul judged unachievable without urgent action
The UK government has admitted that two major planks of its multibillion-pound shared services strategy are in serious trouble.
Matrix, a project that involves moving nine government departments to shared ERP and HR systems, has been rated red by the National Infrastructure and Service Transformation Authority (NISTA), which monitors the planning and delivery of major government projects.
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This rating means the project has “major issues” with its schedule, budget, quality, or delivery of benefits, and the problems “do not appear to be manageable or resolvable” at this stage.
Matrix is just one of five clusters the government is betting will save £4.3 billion by migrating a total of 17 departments and 300 arms’-length bodies onto shared tech platforms.
The NISTA report for fiscal 2025-26 says the rating reflects “a number of material issues” identified during project planning.
In 2024, Matrix awarded Workday a contract for SaaS finance and HR software and Cognizant a system integration deal with a combined value of £144.3 million.
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Matrix was scheduled to begin going live in May 2026, but experienced delays. In March, the Matrix Programme Board met “to consider the case for re-baselining the programme, including the development of a rectification plan and the consideration of several planning scenarios.”
“Replanning is intended to establish a more realistic, credible, and achievable forward plan in response to ongoing delivery challenges. Current emerging analysis indicates a delay of 3-6 months, which would move the Phase 1 user go‑live to late 2026,” the NISTA report says.
Although “system issues have now been resolved,” the “single biggest risk to this is departments being unable to find functional subject matter expert capacity to undertake the high level of testing they have identified as being required, given their low risk appetite.”
The Department for Science, Innovation and Technology (DSIT) is leading Matrix. It is joined by the Cabinet Office, Department for Energy Security and Net Zero, Department for Culture, Media and Sport, Department for Business and Trade, Attorney General’s Office, and the Department of Health and Social Care.
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His Majesty’s Treasury (HMT) and the Department for Education have delayed their decisions to join Matrix despite the Cabinet Office saying they had “unconditionally bought into joining shared services at the outset” and their “participation in shared services is not optional.”
MPs said last week that their reluctance to join could undermine the project. The Treasury, for example, already uses relatively modern Oracle Fusion SaaS to run its HR and finance functions.
NISTA also rated the Unity shared services project as red, although it said progress had improved. Unity concerns His Majesty’s Revenue & Customs – Britain’s tax collection agency – and two other departments moving to a cloud-based SAP ERP system.
A spokesperson at DSIT told The Register: “Delivery challenges were found in the Matrix Program after a planned review. We have decided to replan the program to make sure we have a realistic and achievable delivery schedule, while continuing work to modernize shared services across government.”
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Officials said the department’s program team is developing the next iteration of the business case for HMT and Cabinet Office approval later this year. It is due to set out a timetable for the next wave of departments and arm’s-length bodies to onboard to the new system and service, including HMT. DSIT remains committed to the program and expects it to offer £1.67 in benefits for every £1 invested. ®
Most people would have tossed the red Nintendo 3DS XL the moment they opened the package. Bought for twenty-seven pounds and already looking like it had spent years outdoors, the handheld arrived with a rusty hinge, a body that bulged open, no battery, and clear signs of serious water damage. Elliot of The Retro Future saw something worth saving instead.
Opening the rear cover reveals actual rust, the kind that takes a long time to accumulate after being left out in the elements, with no artificial “weathering” BS. Some screws were so tight that they required to be loosened using a Dremel, and things only got worse after that. The shoulder button connector has virtually pulled itself apart from the ribbon cable and the board. The SD card reader was shot, with thick clumps of rust caked on the motherboard, which had deformed slightly. The screens were dusty and dirty. The joysticks appeared to belong in a museum of broken pieces. The cameras were in a horrible state. The rubber portions had turned into mush. Threads in the shell had been removed completely. One look at the state of things made it clear how large of a task you were facing.
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Cleaning began with a thorough cleaning in an ultrasonic bath with some watch-cleaning solution for the metal components. Shell pieces soaked overnight in a solution of bicarbonate of soda and boiling water. To remove all of the filth, the boards and other pieces required to be scrubbed with hot soapy water and then wiped off with isopropyl alcohol. Vinegar baths and a baking soda paste applied with a toothbrush were required to remove the most persistent corrosion. Some of the chrome plating on the screws had worn so thin that it could no longer be saved, necessitating the replacement of new ones. However, the idea was not to make it look like it had just rolled off the assembly line.
Almost every critical component needed to be removed and replaced. A replacement motherboard from Japan, which costs roughly £50, was first on the list. New displays, ribbon cables, shoulder buttons, joysticks and covers, volume sliders, cameras, a microphone, a touchscreen, and a new battery came next. Aftermarket shell pieces were necessary to fill in the gaps where the original plastic had nearly collapsed. Small 3D printed tabs replaced the broken light-pipe mounts. Swapping so much out was a matter of practicality. Water-damaged components cannot simply be wiped away.
Buttons were functional again, and the touchscreen responded to input. The cameras were ready to go now. The left and right triggers had returned to normal. The 3D slider still did not work, which is a shame because it is linked to the aftermarket screen rather than the actual problem. Later on, certain software updates would remove the Japanese region lock, allowing it to play a variety of titles.
The finished 3DS XL retains the original shell’s weathered appearance, but it now functions like a normal system. Elliot placed it in a clear container, resulting in a great playable console as well as a silent little museum piece, serving as a reminder of what happens when you leave a system out to get wet for months on end. Some of the online restoration videos you see make the broken system appear to be as good as new with just a wash down and some water, but this thing was not going to recover from being left out in the weather for so long. Over time, sun and rain will leave scars.
Leather jacket belonging to Nvidia CEO Jensen Huang sells for $960k, way above the $40-60k estimate
Huang’s jackets have become a tech sector icon and a staple CEO ‘uniform’ piece
The proceeds wil help fund fellowships and more across tech
A black Tom Ford leather jacket worn and signed by Nvidia CEO Jensen Huang has sold at a New York auction for $960,000.
“The response to this sale surpassed even our highest expectations,” Sotheby’s head of modern collectibles Brahm Wachter explained, with the jacket selling for around $900,000 more than auctioneers had anticipated, given the auction house’s estimate of $40,000 to $60,000.
The jacket itself – one of many that the CEO has owned, of course – has become a staple recognized by the entire tech sector, with Huang rarely spotted out in public wearing anything else.
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Jensen Huang’s jacket sold for $0.9 million
This specific jacket came from Tom Ford’s Spring-summer 2023 collection, however the CEO has been wearing similar jackets for years, leading similar examples to have become essential parts of any Nvidia keynote or tech conference.
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Huang is one of a growing number of CEOs who have chosen uniformity and simplicity in their clothing – Mark Zuckerberg’s blue jeans and grey t-shirt, and Steve Jobs’ black turtleneck, also become similarly iconic.
Independent verifiers are said to have confirmed both the jacket’s authenticity and the signature’s authenticity, revealing that it was worn by Huanh at the 2023 Foxconn Tech Day.
Clearly, it being Jensen Huang’s jacket is the core reason why it fetched nearly $1 million at auction. His company, Nvidia, is somewhat of an AI success story, having risen from a valuation of around $300 billion in 2020 to $4.9 trillion today.
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At the time of writing, Nvidia is the world’s most valuable companies, just a reach ahead of Apple – with the $940,000 cost buying you around 4653 shares in the company at today’s market price.
The proceeds are set to help fund Edge Institute, a nonprofit that will support fellowships, grants and residencies across the tech sector.
The “AI” label no longer makes companies special. In today’s world, that is a fact that needs to be accepted.
Even just a year ago, if a business simply mentioned being “AI-powered” it was enough to immediately gather attention and appear innovative. Investors quickly grew excited, and media outlets were quick to pick up the next “hot story.”
Valentina Drofa
Founder and CEO of Drofa Comms.
Of course, that didn’t always mean that there was truth to such statements. The concept of AI washing — when companies misrepresented or even outright lied about the use of AI tools in their operations — had certainly done its share of damage to this industry while it stayed prevalent.
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But today, even without such lies, the effect of AI novelty is disappearing. Every fintech company now has some kind of AI story. Compliance platforms use AI monitoring. Banks and fintechs talk about AI-driven service personalization. Chatbots and AI assistants are utilized practically by every other platform, no matter which industry we look at.
At this point, saying your company uses artificial intelligence feels almost the same as saying you have a website. People have learned to expect it almost by default. And this creates a very different communications environment.
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The first wave of AI companies mostly had to convince their audience that the technology was possible. That task has certainly been accomplished. Now, the second wave faces the task of convincing people that their particular implementation of AI is trustworthy, useful, and worth the attention in a market that’s beyond overcrowded already.
That is much harder.
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The conversation has changed
AI investment has exploded over the last two years. Enterprise spending on gen AI jumped from $11.5 billion in 2024 to $37 billion in 2025. That’s a whole 220% YOY increase. AI-focused startups continue to dominate venture capital conversations, to the point where nearly every technology company now feels the pressure to position itself as part of this race.
But something else happened along the way: consumers became more educated. If at first, AI sounded like something magical, now people have actually used it. They have seen hallucinations and strange outputs, and they’ve dealt with incorrect recommendations. They’ve had time to realize that AI can be useful, but also that the technology comes with its own frustrations and limitations.
As such, being “AI-powered” is no longer enough to stand out. Consumers have already moved past that stage. One of the biggest mistakes I see today is when companies still assume they have not.
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Now, the conversation revolves around a much deeper point: “What does your AI actually improve for me?” For AI-focused companies vying for attention, the benefits of their specific approach are now the key battlefield where they have to win.
This is particularly important for financial and fintech companies, because financial services are built on trust. People may tolerate or even find humor in mistakes from an entertainment app, but they will not be pleased when their money or personal data are put at risk because of AI malfunctions.
The numbers clearly show this trust gap. While AI adoption continues growing, only 13% of consumers truly trust AI systems, and about 30% remain neutral rather than confident. Many people are still uncomfortable giving AI fully autonomous control over important decisions, especially in areas connected to finance or personal data.
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At the same time, enterprises themselves also often contribute to the problem of trust by rolling out AI models before building the necessary structures to govern them.
A recent survey by McKinsey discovered that less than 15% of organizations obtain full security and IT approval before deploying AI agents. Or, to put it in other words, the vast majority of these systems come out without proper oversight and responsibility frameworks around them.
This very reality has now become the defining challenge of the next AI adoption phase. The market is no longer asking whether companies can use artificial intelligence. It’s asking whether they can use it responsibly. And that is precisely why I am of the opinion that AI communication from here on out needs to be very different from what we saw during the first hype cycle. It’s no longer about who shouts the loudest.
The main victory condition will be for companies to explain — clearly and realistically — how their systems work and how they benefit their users. And then they will have to continue proving that through sustainable action.
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Transparency and governance are now a product feature
One important area to account for is transparency around AI limitations. Now that the broad public already has enough context and experience to understand that AI can fail, pretending otherwise would only damage a company’s credibility.
To earn trust, strong, honest communication is necessary that will openly acknowledge those points of failure. When a business wishes to present its own AI solution, it will need to explain upfront where its model works well and where its shortcomings lie. Customers should have clear expectations and understanding of the risks before they engage with the technology.
Some might think that admitting to shortcomings would be a mistake, a vulnerability. But honesty can yield more results than those people expect.
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Clients are generally much more comfortable using services when they understand where the borders are. When they know how decisions are made, that safeguards exist, and that human oversight is still involved in the process, so if something goes wrong, they have someone real to talk to.
This is where competent governance enters the picture.
Be it to partners, clients, or regulators, businesses increasingly need to demonstrate that they are responsible about how they employ AI in operations. Whether decisions can be audited.
A few years ago, these topics were often hidden deep inside technical documentation, if they were brought up at all. Now, explainability of AI models is one of the biggest signals of trustworthiness that a company can project.
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And honestly, I think this is healthy for the industry.
By now, AI has stopped being a mere marketing decoration and is leaning more and more toward becoming a full-on infrastructure layer in corporate operations. That means it needs to be properly managed, so that possible mistakes do not create reputational and monetary damage to countless involved parties.
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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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
Attackers have begun exploiting a critical vulnerability (CVE-2026-6875) in the ServiceNow AI Platform, according to threat intelligence company Defused.
Formerly known as the Now Platform, ServiceNow AI Platform is an enterprise-grade Platform-as-a-Service (PaaS) that helps businesses integrate AI into core enterprise workflows.
Cybersecurity company Searchlight Cyber, which found this critical vulnerability and reported it on April 1st, says that it allows unauthenticated threat actors to escape the sandbox and execute code remotely within the ServiceNow platform in high-complexity attacks.
ServiceNow addressed the flaw across hosted instances and released CVE-2026-6875 security updates for self-hosted instances one week ago, on July 13th.
Over the weekend, Defused security researchers confirmed that attackers have begun exploiting the vulnerability in the wild, with the first attempts being observed on Friday, days after ServiceNow issued patches.
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“We are observing in-the-wild exploitation of the ServiceNow pre-auth sandbox-escape RCE (CVE-2026-6875),” Defused warned in a Saturday tweet.
“The payloads hit the same pre-auth sink @SLCyberSec documented (/assessment_thanks.do), but the sandbox-escape gadget reaches the same code-execution primitive by a different route than their published PoC.”
CVE-2026-6875 exploitation (Defused)
ServiceNow has yet to flag this security as actively abused and, in the official advisory, still states that it is “not currently aware of exploitation against ServiceNow instances.”
However, the company advises all customers who have not already done so to secure their systems against attacks by upgrading to a patched release as soon as possible.
A ServiceNow spokesperson was not immediately available for comment when BleepingComputer reached out to confirm Defused’s report that CVE-2026-6875 is now actively exploited.
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Last month, ServiceNow also privately disclosed a security incident in which attackers queried data from customer instances by exploiting an unauthenticated access flaw via a vulnerable API endpoint.
In a subsequent advisory, it tied the incident to security researchers or customer-led research linked to bug bounty submissions rather than to malicious threat actors.
ServiceNow says that its AI Platform runs more than 100 billion workflows each year and powers over 100,000 enterprise AI apps at 85% of all Fortune 500 companies.
Security teams log 54% of successful attacks and alert on just 14%. The rest move through your environment unseen.
The Picus whitepaper shows how breach and attack simulation tests your SIEM and EDR rules so threats stop slipping by detection.
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Google’s Pixel series has crafted a name for itself by offering a clean Android experience, day-one software updates, and computational photography that’s often only rivaled by the iPhone or Samsung’s most expensive flagships. They also have a signature design philosophy, exemplified by the Pixel 10 and 10 Pro’s flat screens, polished or stain-finished aluminum frames, and distinct camera bars to house all the hardware.
Other perks of the Pixel phones include up to seven years of Android updates to newer Pixel models, which include frequent Pixel Drops and security patches. Google is also doing AI in its smartphones a bit better than other manufacturers: You get built-in Gemini models that run directly on the device and offer you features such as enhanced call screening and faster recorded transcriptions.
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All that said, Pixel phones don’t always have the best reputation when it comes to value. For starters, the in-house Tensor G5 that powers the Pixel 10 Pro is considerably slower than what the competition is using. Battery life, too, isn’t Pixel’s strong suit, with many users complaining of poor endurance even after just a few months of use. Fortunately, the Android segment is packed to the brim with alternatives that may prove to be a better value for your money. Here are five cheaper alternatives to the Pixel 10 Pro that either offer similar features or much better performance.
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Google Pixel 10a
Adam Doud/SlashGear
If you like the Pixel experience but want a phone that offers better value than the 10 Pro, you can just step down a few notches in Google’s own lineup. The Google Pixel 10a is the company’s budget offering, but it packs in nearly everything good about the flagship Pixel experience at a fraction of the cost. Priced at $500 for the 128GB model, the Pixel 10a is powered by last year’s Tensor G4 chip and 8GB of RAM.
It still offers a 120Hz OLED display with a peak brightness of 3,000 nits. At 6.3 inches, the screen is also the same size as that of the Pixel 10 Pro, albeit with ever so slightly larger bezels all around. The Pixel 10a does cut a few corners in the camera department, though: Apart from a smaller main sensor with a slightly lower resolution, you lose out on the 5x telephoto lens that the Pixel 10 Pro provides. You retain the ultrawide lens, but it’s 13 MP instead of the 10 Pro’s 48 MP.
What remains the same is the software experience, beyond a few on-device AI features like Magic Cue that probably require more RAM. The 10a will also receive up to seven years of OS updates. It also has support for wireless charging for the 5,100 mAh battery, but Pixelsnap compatibility is not present.
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Samsung Galaxy S26
Ruslan Lytvyn/Shutterstock
Samsung’s flagship S Ultra device enjoys most of the attention during every launch cycle, but the base variant is no less of a premium experience. Not only does the Samsung Galaxy S26 start at $900, which is $100 less than the Pixel 10 Pro, but it also offers twice the storage capacity at 256GB by default — which we’d like to see Google do with its future phones. This is important considering these flagship smartphones are equipped with cameras that can churn out multi-gig video files if you’re shooting in 4K.
The Galaxy S26 is powered by Qualcomm’s Snapdragon 8 Elite Gen 5, which will be a much better chip if you’re planning on gaming. According to NanoReview, the Snapdragon processor offers up to 2.7 times better performance than the Tensor G5 in AnTuTu benchmarks. You may not notice the difference in regular day-to-day use, but the Galaxy S26 is better equipped to handle heavier workloads or console-quality games like “Alien Isolation” that are becoming increasingly common on mobile devices.
You’re also not losing out on any of the software benefits by picking a Samsung phone over a Pixel, either. Like Google, Samsung offers up to seven years of Android updates to its smartphones. One UI is also one of the most feature-packed variants of Android and comes with a Galaxy AI suite that’s comparable to what Pixel phones have to offer.
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OnePlus 15
Adnan Ahmed/SlashGear
OnePlus is known for its performance-first phones that undercut the competition, and the OnePlus 15 is no different. It starts at $900, for which you get 256GB of UFS 4.1 storage and 12GB of RAM. For $100 more, you can double the storage and up the RAM capacity to 16GB — and it will still be cheaper than the base variant of the Pixel 10 Pro XL. The OnePlus 15 is also a large phone, sporting a 6.78-inch AMOLED display that can hit refresh rates of up to 165Hz in supported apps and games. It has slim bezels, support for Dolby Vision and HDR10+, and a touch sampling rate of 3,200Hz.
Being powered by the Snapdragon 8 Elite Gen 5 means the OnePlus 15 also has the Pixel 10 Pro comfortably beat on the performance front. The phone can easily handle any modern game you throw at it, and its screen’s higher-than-usual refresh rate actually makes it comparable to dedicated gaming phones. OnePlus didn’t cut any major corners with the 15, either, despite its very competitive pricing: The 50-megapixel cluster of sensors delivers excellent results and offers videos up to 4K 120fps or 8K 30fps.
When we reviewed the OnePlus 15, though, the feature that impressed us the most was its battery life. Not only does its 7,300 mAh silicon-carbon battery dwarf what the Pixel 10 Pro offers, but OxygenOS’ aggressive battery optimization also makes for a phone that comfortably lasts a day and a half on a single charge.
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Google Pixel 10
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If you must have the quintessential Pixel experience with all the goodies, the regular Google Pixel 10 could prove to be a great middle ground between the 10a and Pro. It shares the same SoC as the more expensive Pixel 10 Pro, though you get less RAM at 12GB. It sports a similar 6.3-inch OLED 120Hz display with the same Gorilla Glass Victus 2 protection and an IP68 rating. The phone is priced at $800 for the 128GB variant, which makes it $200 cheaper than the Pixel 10 Pro.
The Pixel 10 offers a triple-camera system as well, including a 5x telephoto lens for your zoom photos — just with smaller, lower-megapixel sensors. Design-wise, it’s really difficult to tell the two models apart. Some users actually prefer the matte aluminum frame on the Pixel 10 over the polished finish of the Pixel 10 Pro’s frame.
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As expected, you get the same software experience with seven promised Android versions and frequent Pixel Drops. Also, unlike the Pixel 10a, the regular Pixel 10 doesn’t skimp on any AI features. You also get support for Pixelsnap, which is Google’s version of MagSafe. If you’ve owned an iPhone before, you can use any MagSafe accessories you already have with your Pixel 10. As long as you’re willing to compromise on the cameras, the Pixel 10 offers the same performance and software experience as its elder sibling at a much more compelling price.
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RedMagic 11 Air
You don’t need to spend $1,000 if you want to play demanding games on your smartphone, and especially not on a Pixel 10 Pro. Instead, you’ll want to turn to a phone like the Nubia RedMagic 11 Air. This gaming-centric phone is powered by the Snapdragon 8 Elite SoC, 12GB of RAM, and 256GB of storage. Although it uses an older chip, the 8 Elite still outperforms Google’s Tensor G5 by quite a margin. More importantly, the RedMagic 11 Air offers considerably better value thanks to its $530 price tag.
A factor that differentiates the RedMagic 11 Air from pretty much every other smartphone being currently sold is its notchless, full-screen display. Yes, that does come at the cost of a lower-quality image from the under-screen front-facing camera, but that’s a sacrifice hardcore gamers might be willing to make. The display itself is an AMOLED panel that refreshes at 144Hz with a touch sampling rate of 2,592 Hz. The phone also offers a large 7,000 mAh silicon-carbon battery with support for up to 80W of wired charging.
Unfortunately, RedMagic is only promising two years of major Android updates and three years of security patches. If you’re looking for more horsepower, you can stay within the RedMagic family and get the RedMagic 11S Pro. It sports the latest Snapdragon 8 Elite Gen 5 chipset and a slightly larger battery with support for wireless charging — while still being cheaper than the Pixel 10 Pro.
The already tense relationship between Apple and OpenAI reaches a breaking point as trade secret accusations emerge, plus your hosts discuss EU regulations, the future of gaming, and public betas on the AppleInsider Podcast.
Apple restarted its AI efforts with new Apple Foundation Models built with Google’s Gemini Frontier models at the foundation. While Google’s involvement seemed obvious in the long run, the move away from OpenAI’s ChatGPT may have been a long time coming.
Not only is OpenAI building hardware products meant to directly compete with Apple’s, but the company has poached over 400 employees in recent years. Apple finally showed its hand with a trade secrets lawsuit that could upend OpenAI’s hardware ambitions and hopes for sustainable post-AI revenue.
While that’s the main topic of the show, William and Wesley also talk about how the EU battery regulations might affect Apple, if at all. There’s also a lot to contend with as memory and storage prices continue to climb.
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One of the industries affected by these high prices is the video game industry. As gaming PCs and consoles become increasingly expensive, people will have to turn to the products they already have for entertainment.
It’s interesting that the first attempt at a full Madden football game on Apple products, coming to Apple Arcade, would arrive now. Apple already owns gaming in terms of the size of its user base and how much is spent on gaming apps.
There may be a future where Apple owns AAA gaming as well, if only because people might be priced out of owning both an iPhone and gaming console. If that’s the choice people have to make, it’ll be iPhone.
BONUS: Subscribe via Patreon or Apple Podcasts to hear AppleInsider+, the extended edition. This time, the discussion focuses on where your hosts land on whether multiple smaller utilities are better than one super app.
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Common Sense Media rated Google’s AI Search an “unacceptable risk” for students. AI Mode uses age-gated Gemini models. Google offers no way to disable AI without blocking Search.
Common Sense Media concluded that Google’s AI-powered Search poses an “unacceptable risk” to young users and recommended that students stop using it entirely until schools can disable the AI features. The child safety watchdog found that AI Mode readily completed homework assignments for minors, repeated misinformation with an air of authority, and exposed children to generative AI experiences powered by Gemini models that are age-gated in every other Google product.
The problem is structural. Google launched AI Mode for Search last year and made it available to everyone immediately, including education accounts. There is no toggle to disable AI features while keeping Search accessible. Parents can block Google.com entirely for child accounts. School administrators could do the same for Workspace for Education users. But as Amanda Bickerstaff of AI for Education put it: “What are you going to say? That kids can’t search the web?” Google dominates classroom technology. It helmed a 1:1 device movement that put Chromebooks in schools across the US and now has millions of Workspace for Education users from kindergarten to postgrad.
“Google’s AI Overview and AI Mode are undermining kids’ education, supplying them with dangerous disinformation, and putting their very lives at risk,” said David Monahan of child safety nonprofit Fairplay for Kids. “If Google is not even willing to disable AI features for students, schools should stop using Google products altogether.” Google’s AI search overhaul has already drawn criticism for undermining the open web by replacing links with AI-generated summaries. In classrooms, the same design replaces the act of searching with the act of receiving an answer, which is the opposite of what education is supposed to teach.
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Google told Mashable that Common Sense Media’s testing was “narrow and ambiguous” and not an effective way to measure product safety. The watchdog tested consumer accounts, not education accounts, but Google confirmed that the AI experience is the same for all users. A German court ruled in April that Google is legally liable for the content of its AI Overviews, treating them as Google’s own statements rather than third-party results. If the same logic applies in the US, schools using Google Search may be exposing themselves to liability every time a student receives a hallucinated answer that Google’s design presents as fact.
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