	Lenovo Idea Tab Plus
Geekbench 6 single core	757
Geekbench 6 multi core	2008
Geekbench 6 GPU	1227
1 hour video playback (Netflix, HDR)	7 %
3D Mark – Wild Life	1212
3D Mark – Wild Life Stress Test	99.3 %

	Lenovo Idea Tab Plus Review
UK RRP	£299.99
USA RRP	$298.99
Manufacturer	Lenovo
Screen Size	12.1 inches
Storage Capacity	256GB
Rear Camera	13MP
Front Camera	8MP
Video Recording	Yes
IP rating	Not Disclosed
Battery	10200 mAh
Size (Dimensions)	278.80 x 181.05 x 6.29 MM
Weight	530 G
Operating System	Android (Lenovo ZUI)
Release Date	2025
First Reviewed Date	18/06/2026
Resolution	2560 x 1600
HDR	Yes
Refresh Rate	90 Hz
Ports	USB-C
Chipset	MediaTek Dimensity 6400
RAM	8GB
Colours	Luna Grey, Sand Rose
Stated Power	45 W

Tech

JBL Live Beam 3 Earbuds Turn the Charging Case Into a Practical Tool with a Touchscreen

Published

4 minutes ago

on

6 July 2026

By

NewsAdmin

JBL Live Beam 3 Touchscreen Charging Case Earbuds
JBL packed a color touchscreen into the charging case of the Live Beam 3 earbuds, priced at $99.95 (was $149.95). That addition changes how people interact with their audio gear during a normal day. Someone heading to work or hitting the gym can open the case and swipe through options right there. Volume goes up or down with a touch. Playback pauses or skips without pulling out a phone. ANC modes switch from full noise blocking to letting in some surroundings. Even EQ presets become accessible on the go.

A tap wakes up the display, which shows the battery levels for both the buds and the casing at a glance. Users can pick a favorite photo as the background, and it rotates appropriately when the lid opens, allowing guests to see it right side up. A flashlight option brightens the screen for quick light in a pinch. Shortcuts remain adjustable in the companion app, so only the most often used tools appear. This setup addresses a major complaint about many wireless earphones. Touch controls on the buds handle basic commands but frequently need sacrifices. Adjusting one thing results in losing simple access to another. The case screen eliminates this limitation and keeps everything in one easy location that goes with the buds anyway.

The sound quality backs up the convenience, as these earbuds provide a dynamic presentation with plenty of detail and strong bass that sounds engaging across a variety of music styles. Listeners that prefer a little more vitality in their songs will find it enjoyable. Advanced codec compatibility enables compatible phones and players to send higher-quality audio when available. Those who wish to fine-tune their EQ can use an app.

The battery life is outstanding, with a single charge lasting nine to ten hours with noise canceling turned on. If you turn off ANC, it will last about twelve hours. The enclosure increases the total playback time to nearly two full days of use. When time is of the essence, a quick ten-minute charge via USB-C adds an extra four hours.

Silicone tips provide fit by forming a seal that is beneficial to both sound quality and noise reduction. The buds’ IP55 rating ensures that sweat from exercises or unexpected rain will not be an issue. Many individuals find the stem style comfortable for lengthy usage, however results vary depending on ear shape, as with most in-ear designs. Active noise canceling works nicely in this price range. It successfully minimizes traffic noise, train noise, and workplace chatter, making it suitable for everyday commutes or concentrated work. Adaptive modifications are made automatically based on the surroundings and fit. Calls come through clearly thanks to multiple beamforming microphones that eliminate wind and background interference. Multipoint Bluetooth allows the buds to connect to two devices at once, making it easy to swap between a phone and a laptop or tablet.

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Tech

Meta’s Kylie Jenner collab doesn’t make me feel any better about smart glasses

Published

18 minutes ago

on

6 July 2026

By

NewsAdmin

It’s not often we mention the Kardashian-Jenner clan here at Trusted Reviews, but Kylie Jenner’s surprise collaboration with Meta is all I’ve been thinking about.

In case you missed it, the youngest Jenner recently unveiled her own pair of Meta smart glasses. Coined Starfire, the oval-shaped specs are not only framed as a trendy choice but they’re fitted with Meta’s controversial features, including Meta AI and, most notably, the built-in 12MP camera.

Kylie’s collaboration with Meta is surprising and disappointing for so many reasons. Firstly, in a viral interview back in May, she recalled how scary and invasive growing up with paparazzi essentially stalking her for photos was.

Like her eldest sister, she’s known for keeping certain parts of her life private. For example, she hid her first pregnancy entirely from the media, and then later was reluctant to share photos of her second child online. This is completely understandable, as everyone has a right to privacy and absolutely shouldn’t feel any need to share images online.

With the above in mind, why on earth is Kylie therefore promoting smart glasses that have the power to take privacy away from pretty much anyone who has the bad luck of walking past a desperate aspiring content-creator-slash-creep?

Meta glasses have a terrible reputation for being a complete privacy nightmare, especially when it comes to women and girls’ safety. Back in May, the BBC reported that a woman was going about her day when a man approached her, without a camera or phone in hand. Instead, he was wearing smart glasses, and she had “no idea she was being filmed”.

The video was then posted online and viewed thousands of times, with the woman only finding out when a friend sent it to her.

While we don’t know the exact brand of smart glasses the man was wearing, Meta glasses all have a light that comes on when you’re filming, which technically should show people that they’re being filmed. However, and I’ve seen this for myself, that light literally couldn’t be any smaller. I would totally understand if someone passed off the light as a simple reflection or maybe even just a large scratch on the glasses.

Meta Starfire Kylie Edition — Meta Starfire as shown on Kylie Jenner. Image Credit (Meta)

That’s not the only worrying story. As uncovered by Wired last month, Meta has recently embedded face-recognition technology into the Meta AI app. While it’s not currently accessible by users, it will identify people captured by the glasses’ camera and alert the wearer when it recognises someone.

This has, unsurprisingly, caused concern. Experts who spoke to The Independent earlier this year feared this technology could pose a “direct and serious risk” to domestic abuse survivors as it could enable their abusers to locate and track (in other words, stalk) them, without them even knowing.

Plus, the fact that anyone who walks past a Meta glasses wearer’s image will be “cropped, indexed, and saved to a folder marked ‘pending’” is incredibly unnerving. What if your neighbour or the fellow commuter who always gets the same train as you is wearing Metas? Will your image be consistently stored for them to see? Will Meta actually note that you’re a frequent passer-by and attempt to identify you?

What are the merits of smart glasses?

I’ve had hands-on experience with both the Meta Ray-Bans 2 and the Oakley Meta Vanguard too. The latter I somewhat understand the purpose of more, as they’re used as sports glasses and enable you to capture your surroundings, get real-time stats and more without needing to reach for your phone. The Ray-Bans 2 and similar glasses, on the other hand, are a different story.

Ray-Ban Meta 2. Image Credit (Trusted Reviews)

Yes, Meta glasses allow you to do interesting things like translate live, but so do AirPods and many of the best Android phones. And yes, the glasses also give you real-time answers with Meta AI without you needing to reach for your phone, but is anything really that urgent?

I admit, I just can’t get on board with smart glasses, and maybe it’s because I’m not the target audience. But once you factor in the high price, the limited style options and, most notably, the serious privacy concerns, the cons surely vastly outweigh the pros.

However, Kylie Jenner’s influence is undeniable, and Meta clearly knows this as she’s one of the most followed users on Instagram. Her collaboration with Meta is not only hypocritical from someone who publicly states how much she favours privacy, but it will undoubtedly attract a new demographic of younger users who grow to think it’s simply fine to film people without their knowledge.

Obviously (and very unfortunately) it’s not as easy to say “let’s just ban smart glasses”, but there undoubtedly needs to be more regulation of filming and sharing content online.

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Tech

Quordle hints and answers for Monday, July 6 (game #1624)

Published

28 minutes ago

on

6 July 2026

By

NewsAdmin

Looking for a different day?

A new Quordle puzzle appears at midnight each day for your time zone – which means that some people are always playing ‘today’s game’ while others are playing ‘yesterday’s’. If you’re looking for Sunday’s puzzle instead then click here: Quordle hints and answers for Sunday, July 5 (game #1623).

Quordle was one of the original Wordle alternatives and is still going strong now more than 1,500 games later. It offers a genuine challenge, though, so read on if you need some Quordle hints today — or scroll down further for the answers.

Enjoy playing word games? You can also check out my NYT Connections today and NYT Strands today pages for hints and answers for those puzzles, while Marc’s Wordle today column covers the original viral word game.

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Tech

AI in Mathematics Is Forcing Big Questions

Published

39 minutes ago

on

6 July 2026

By

NewsAdmin

In the mid-noughties, when music by the Killers and Franz Ferdinand blared out of every pub and nightclub I passed, I spent my days and nights struggling through a Ph.D. in applied mathematics. My research focused on simulating how special light waves interact in liquid crystals and using simple equations to approximate and understand those interactions. When I look back at my thesis now, liquid crystal technology is old hat, and I imagine my work could be completed with AI assistance in a matter of days—maybe hours.

But the same cannot be said for the work of the pure mathematics Ph.D. students with whom I shared a cramped office at the University of Edinburgh. At the time, I felt sorry for these colleagues, who day after day sat at their desks, seemingly tearing their hair out and making no progress. (Though I was struggling too, I was at least always making some headway.) When we finished and went our separate ways, some hadn’t even published a paper.

Now, in hindsight, I finally understand why they toiled for years on abstract mathematical problems that only a handful of people in the world care about. It wasn’t arrogance, as I thought at the time; they weren’t trying to prove their superior intelligence by being the first to solve a seemingly intractable mathematical problem. It wasn’t even a form of masochism (which was my second guess)—penance for some imagined inadequacy. I realized they derived joy, satisfaction, and meaning from the long journey toward understanding.

“Sometimes, understanding just strikes you as being very beautiful. Sometimes it’s a feeling of accomplishment, like completing a marathon,” muses Carnegie Mellon University mathematician Jeremy Avigad. “But it’s not quite either of those: It’s just a wonderful feeling when you’ve been thinking long and hard about something complex, difficult, and then—all of a sudden—it just comes together.”

This feeling has driven mathematicians throughout history. Likewise, the way mathematicians pursue that feeling has changed little over the centuries. They notice or imagine links, patterns, or properties in numbers, shapes, or logical structures. From this, they write conjectures—unproven statements of their speculation. They or other mathematicians then use logical reasoning and the tools of mathematics in often creative ways to prove or disprove those conjectures. Finally, yet other mathematicians verify (or challenge) the proofs.

Invariably, this process requires a whole heap of thinking time. “I went to a pure maths camp with classes where we would sit with hard maths problems for half an hour and no one would say anything—everyone was just thinking,” says Krystal Maughan, a mathematician and computer scientist about to get her Ph.D. at the University of Vermont. “But then we would work together and kind of tease out the problem.”

This is the age-old joy of math in action. But today’s AI systems are starting to make inroads into bypassing this slow, deliberative process. Taking this trend to its logical conclusion, what happens if AI makes the mathematician’s struggle completely unnecessary? Might AI even sideline humanity completely?

AI’s Growing Role in Mathematics

For decades, computation has accelerated mathematical progress. This began 50 years ago, when mathematicians used a computer to prove the four-color theorem, which asks whether any map can be colored using no more than four colors, with no adjacent regions sharing the same color. The answer is yes, and the computer proved it, controversially, by checking 1,936 cases in a way no human could realistically verify.

Yet throughout this computational era, even in proofs relying on massive computational resources, the role of the human mathematician has remained central. Humans propose conjectures, guided by intuition. They devise strategies to prove them, guided by creativity and experience. And humans verify whether those proofs are correct.

Now AI is challenging the status quo. In just a few years, large language models (LLMs) have evolved from “stochastic parrots,” capable of little more than regurgitating basic mathematics scraped from the internet, into advanced mathematical reasoning machines.

Last summer, systems from Google DeepMind and OpenAI reached a level equivalent to the world’s most mathematically gifted high school students, achieving gold-medal status at the International Mathematical Olympiad. In this annual competition, contestants must solve six notoriously difficult problems from various areas of mathematics.

Earlier this year, Google DeepMind’s experimental AI system Aletheia achieved an even more significant milestone when it autonomously produced publishable Ph.D.-level research results. While the work itself is obscure mathematically—calculating structure constants in arithmetic geometry—the significance lies in the complex reasoning it displayed in tackling an unsolved mathematical problem. And more recently, a new general-purpose AI system from OpenAI disproved an important conjecture in combinatorial geometry. This result would have been worthy of publication in a major mathematics journal if humans had been the authors, and top mathematicians hailed the feat as a milestone for AI in mathematics, demonstrating independent, original, and sophisticated thinking.

Another shift has come from combining LLMs with mathematical tools known as proof assistants, which have been around for more than a decade. These systems—such as Isabelle, Lean, and Rocq—are specialized programming languages that check mathematical proofs step-by-step, verifying their logical correctness. Traditionally, mathematicians have had to translate their theorems and proofs into this machine-readable format by hand, a laborious process known as formalization. Now, LLMs are starting to remove this bottleneck, automating the translation of informal proofs into formal code that proof assistants can verify.

Versions of such systems, sometimes called reasoning agents, are becoming highly sophisticated. In February, for example, the AI company Math, Inc. used its aspirationally named reasoning agent Gauss to formalize a proof that had earned the mathematician Maryna Viazovska, of EPFL, in Switzerland, a Fields Medal in 2022. Gauss first helped human mathematicians complete the formalization of Viazovska’s solution to the 8-dimensional sphere-packing problem in a matter of days, and then autonomously formalized the more complicated 24-dimensional case in just two weeks.

Such achievements suggest that AI is already capable of handling some mathematical tasks long considered uniquely human. As the technology advances, more of the day-to-day work of human mathematicians is likely to become fair game for AI.

Mathematicians Debate AI’s Role in Discovery

Person in a dark blazer with blurred face against a blue background

Gluekit

Human mathematicians could become “priests to oracles.” —Yang-Hui He, London Institute for Mathematical Sciences

In September 2025, I attended the 12th Heidelberg Laureate Forum—an annual conference that brings hundreds of young mathematicians and computer scientists together with their intellectual idols. AI dominated the conversation and, from the get-go, tension was in the air.

Speakers described a future in which superhuman AI mathematicians transcend human knowledge and capabilities: forming conjectures, searching solution spaces, proving conjectures, and finally verifying the proofs and generalizing the results, all without human involvement. If this future comes to pass, Yang-Hui He of the London Institute for Mathematical Sciences memorably declared, human mathematicians could become “priests to oracles.”

While such startling predictions were being voiced on stage, my gaze was drawn to the audience. Frowning, fidgeting, and exchanging furtive glances—the crowd’s unease was palpable. Trill White, a student at Australia’s Deakin University, later recalled sitting in that hall and thinking: “ ‘That’s devastating. What will people have to contribute to mathematics? Will it become something that no one understands?’ I did get a sense that this is going to change everything.”

Portrait of a long-haired person with blurred face on an orange background

Gluekit

“We certainly started realizing AI has the potential to replace us.” —Jessica Randall, Google Developer Groups

Jessica Randall, a South African mathematician for Google Developer Groups, says she sensed a collective existential dread rising among the young mathematicians. “I could feel everyone was worried, because they hadn’t thought that far ahead,” she says. “It was like a big bombshell that hit us, and we certainly started realizing AI has the potential to replace us.”

Some established mathematicians, including He, seem comfortable with AI taking on tasks that are currently the preserve of human mathematicians. That’s because they just want to know the answers to the biggest questions in mathematics—such as the six remaining Millennium Prize Problems—even if AI does it all. “A lot of mathematicians are pragmatic and just want to understand. They would sell their soul for the solution to a problem,” jokes Avigad. “Whatever it takes, right?”

But this “just want to know” camp is by no means the only faction: Most mathematicians do not hope or expect AI to replace them entirely. Instead, two broad alternatives are emerging. The first is a human-centric aspiration that prioritizes human understanding of mathematics and treats AI as a tool, much like a calculator. The second is a collaborative “teamwork makes the dream work” vision, where humans and AI work together to tackle problems neither could solve alone.

The Human Role in Mathematics

Portrait of a person with blurred face on pink background

Gluekit

Numbers are “a way of bringing us to agreement.” —Akshay Venkatesh, Princeton University

Fields Medalist and Princeton mathematician Akshay Venkatesh has been thinking about this topic from the human-centric viewpoint for years. In 2022, he used his Fields Medal Symposium to implore the mathematics community to deeply consider what AI might mean for the practice of mathematics. At the time, the idea that AI could replace mathematicians seemed far-fetched. Now, he says, “we’re reaching the point where, for at least some tasks with abstract mathematical reasoning, computers are becoming competitive with humans.”

For Venkatesh, the question is not just what computers can do, but what mathematics is for. “Sometimes I think when we use numbers, it’s not so much that we are describing phenomena that are intrinsically numerical, but that we can all agree exactly what the numbers mean,” he says. “It’s a way of bringing us to agreement.”

A photo shows a woman standing in front of a chalkboard filled with mathematical formulas.

Maia Fraser of the University of Ottawa argues that mathematics is more than finding answers. For her, the struggle to understand a problem is one of the discipline’s greatest rewards.

Markian Lozowchuk

Mathematician and machine learning expert Maia Fraser, of the University of Ottawa, shares this sentiment. She says the joy she derives from mathematics is something distinctly human that integrates the subconscious and conscious mind. She describes starting with an intuitive sense that a certain thing should be true and gradually bringing out something that she can express in a rigorous proof. Communicating and sharing these deep-born thoughts is “a form of collective intelligence that is something beautiful about the human spirit,” she says.

By these arguments, an AI proof of a mathematical conjecture that has stubbornly resisted human efforts would be useful only if comprehensible to humans. “That the statement can be proved by AI is already useful information,” concedes Fraser. “But then it’s still an open problem to come up with an elegant, beautiful human proof.” Even if no such proof exists, she says, searching for it “is still a valuable endeavor.”

AI and the Future of Mathematical Collaboration

A more collaborative approach to AI in mathematics comes from Terence Tao, who first competed in the math Olympiad at the age of 10. In 1986, 1987, and 1988, he won bronze, silver, and gold medals, respectively, making him the youngest winner of each of the three medals in Olympiad history. Now a Fields Medalist and professor at the University of California, Los Angeles, he has earned a reputation as one of the most gifted mathematicians alive.

Unlike some of his peers, Tao is neither dismissive of AI nor fearful. Instead, he sees it as the catalyst for a fundamental shift in the discipline—a transition toward what he calls “big mathematics.” He envisions a future of large-scale, decentralized collaborations between humans and machines, where complex mathematical tasks can be diced and sliced, with humans claiming the creative parts and AI doing the lion’s share of the technical grunt work.

Already, Tao is experimenting with this concept, working on problems alongside scores of online collaborators, some using AI tools. “A hundred years ago, almost every mathematics paper was single author,” he says. “But now I collaborate with people I’ve never met—and maybe in the future, I won’t even know if they are AI or real people.”

The key to Tao’s vision is uniquely mathematical: formalization. When a proof is translated into code and checked step-by-step by proof assistants, it removes any chance of human error or dishonesty. This approach changes how collaboration works, because trust is established through verification rather than reputation or rapport. An idea from an unknown researcher or even an amateur can be taken seriously if it has a formal proof.

“If it wasn’t for this formal verification layer, opening projects up without any safeguards would just be a disaster,” adds Tao. “But in math, we can completely check and verify outputs, and this really filters out a lot of the rubbish.”

The Risks of AI in Mathematics

From the young researchers at the Heidelberg Laureate Forum to some of the biggest names in the field, mathematicians all seem to agree on one point: AI has the potential to transform their discipline. But there’s far less consensus on what that transformation will mean in practice.

Some worry about the accessibility of AI tools. Traditionally, mathematicians have required little more than intuition, training, and a pen and paper to advance their field. If this slow, deliberative process is no longer valued by society, and particularly by research funders, then mathematics could become an elitist activity, only practiced by select organizations that can afford to work with proprietary AI models.

Another concern is motivation. As AI systems take on more of the work, the incentive to engage deeply with difficult problems may weaken. Princeton’s Venkatesh says that the long human process of formulating and understanding a proof may be hard to justify, not just to funders, but even to mathematicians themselves. “There have been times where I’ve spent years thinking about something, and I’ve slowly struggled to understand it,” he says. “If your computer can do large chunks of that for you, will you have the motivation to spend that time?”

That concern extends to the next generation. If students can use AI to jump straight to answers, they most likely will. But every time they skip the struggle, they miss an opportunity to build the foundations of their own unique intuition. Over time, some worry, the next generation of mathematicians may suffer from a form of intellectual atrophy, unable to think outside the AI box that trained them.

In response to such fears, the mathematics community is taking action. Individuals are writing essays, organizing workshops, and debating in journals, while institutions and community groups are developing guidelines for how AI should be used in research and publication. Indeed, mathematicians are applying the same rigor and curiosity that they use every day to reckon with the challenges of AI. Taken together, these efforts reflect a broad effort to try to retain control over the direction of mathematics in the era of AI.

So, is AI sucking the soul out of math? In one way, it is doing the opposite. It is forcing mathematicians to confront deep questions about what mathematics is, why they have devoted their lives to it, and the purpose math serves in society. At the same time, though, it is reshaping the practice of mathematics in a way that may be difficult to reverse.

“Mathematics makes me a better problem solver at normal problems, because it frames my mind to think in a very logical, rational way,” says Randall, who noted the existential dread at the Heidelberg Forum. “It helps with every aspect of my life.” As AI transforms mathematics, many researchers wonder whether future mathematicians will be able to say the same.

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