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Nothing Phone 3a Pro review: funky mid-ranger with real zoom camera | Smartphones
London-based Nothing has brought one of the last things setting top-level phones apart from cheaper mid-range models down to a more affordable price: high-quality camera zoom.
Cameras have long been the battleground of the most expensive phones, each vying for better quality, longer reach and multiple lenses. While much of this costly progress has trickled down to cheaper models, optical zoom cameras are few and far between below the £600 mark.
The £449 (€459/$459/A$849) Nothing 3a Pro sets itself apart with the company’s trademark transparent, light-up design and a 50-megapixel 3x telephoto camera on the back that rivals phones costing twice as much.
It builds on the excellent Phone 2a with a similar set of “glyph” LED strips on the back that light up in complex patterns for notifications, calls, timers, charging, the volume and other fun things.
The transparent glass back reveals an interesting design beneath, but the gigantic camera cluster is the standout element. It is huge and protrudes far enough for it to act like a grip for your finger on the back of the phone. The back and front of the 3a Pro are glass, while the sides are a high-quality plastic.
The screen is a large and fast OLED, which looks really good and has fast optical fingerprint scanner under it towards the bottom.
Specifications
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Screen: 6.77in 120Hz FHD+ OLED (387ppi)
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Processor: Qualcomm Snapdragon 7s Gen 3
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RAM: 12GB
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Storage: 256GB
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Operating system: Nothing OS 3.1 (Android 15)
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Camera: 50MP main, 50MP 3x tele and 8MP ultrawide, 50MP selfie
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Connectivity: 5G, eSIM, wifi 6, NFC, Bluetooth 5.4 and GNSS
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Water resistance: IP64 (spray resistant)
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Dimensions: 163.5 x 77.5 x 8.4mm
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Weight: 211g
The 3a Pro has Qualcomm’s mid-range Snapdragon 7s Gen 3 chip, which can’t rival the top chips for raw power but is fast enough to make the phone feel snappy and responsive. It can handle most games fine without getting hot and is about 25% faster than the Phone 2a.
The battery life is great, lasting about three days between charges with general use, including more than seven hours spent actively using the screen. Gaming and using the camera dents the battery, but even so most should only need to charge it every other day.
NothingOS
The 3a Pro runs Nothing’s version of Android 15, which offers a relatively clutter-free, fast and unique experience filled with nice design touches based around dot-matrix art. There are plenty of customisation options to tailor the look and feel of the phone, including monochrome and distraction-free themes, but generally it is just a bit more fun than most version of Android.
It has a few of Google’s AI tools, including Gemini, and various image editing tools in Google Photos. But the big new addition is Nothing’s Essential Space app, which acts like an AI-powered notebook capable of collecting and analysing various screenshots, text and voice notes.
A dedicated button on the side of the phone captures what’s on screen, while pressing and holding records a quick voice note to go with it. The app analyses the content to create summaries, transcriptions and possible actions, such as reminders or to-do lists. Opening the app shows the various things you’ve captured sorted into collections, such as a cross between Pinterest and a note-taking app such as Evernote.
The app is a little basic at the moment and requires a connection to Nothing’s servers to work, but the idea is sound and it currently works well as a way of keeping screenshots and other data out of your gallery. The AI summaries, like all AI tools, are a bit hit and miss, and your various captures are stuck on your phone, but it has far more potential than a lot of gimmicky AI features currently being touted by various parties.
Unfortunately, you can’t customise the side button to do something else, so if you don’t like Essential Space it is rendered useless, unlike rivals such as Apple’s action button. I also pressed it a lot, mistaking it for the power button, taking a fair number of accidental screenshots in the process.
Nothing will support the 3a Pro with three years of Android updates and a total of six years of security updates. That is a year or so behind the best in the business but a lot longer than many rivals, which is good to see.
Camera
The 3a Pro has three cameras on the back: an 8-megapixel ultrawide, a 50MP main and a 50MP 3x telephoto, the later of which is the standout feature for this price range of phone.
The ultrawide camera is reasonable, if a little soft on detail. The main camera is pretty good all round with solid colour and dynamic range, making a decent job of most situations. The 3x zoom camera is arguably the best of the lot, producing nicely detailed images with reasonable reach. All three cameras suffer from a bit of overprocessing, which you can see when you zoom in on photos. The main and telephoto cameras offer an in-sensor zoom to 2x and 6x respectively, but they are not great showing obvious artefacts, while zooming beyond 10x the images start to look more like oil paintings than photos.
Overall, the camera on the 3a Pro is one of the best for a mid-range phone and offers a real zoom, which is rare at this price. It won’t trouble the top Android or iPhones, however.
Sustainability
Nothing says the battery maintains at least 90% of its original capacity for at least 1,200 full charge cycles. The Phone 2a is generally repairable in the UK. Screen replacements cost £89 or batteries cost £29 plus about £35 labour and shipping by Nothing.
The device is made of recycled aluminium, copper, plastic, steel, tin and other materials. It has a carbon footprint of 51.3kg CO2 equivalent. The company publishes sustainability reports.
Price
The Nothing Phone 3a Pro costs £449 (€459/$459/A$849).
For comparison, the Phone 3a costs £329, the Google Pixel 8a costs £499, the Samsung Galaxy A56 costs £499 and the iPhone 16e costs £599.
Verdict
Nothing hits the right notes with the Phone 3a Pro to make it one of the best mid-range phones you can buy.
Very few rivals have a real zoom camera at this price, let alone one as good as on the back of the 3a Pro. It has a big, crisp and smooth display, solid mid-range performance and very long battery life. A standout design adds interest to the otherwise dull phone market, while good software with up to six years of support means it will go the distance.
Nothing’s AI-powered screenshot and note-taking app shows potential, even if it is a bit basic at the moment. But giving it a dedicated button that can’t be used for anything else feels like a miss. I would rather choose which app or function to bind to the button.
The mid-range smartphone market is rapidly becoming packed with very good options, but Nothing manages to stand out and is worth considering when your old phone gives up the ghost.
Pros: good camera with 3x optical zoom, interesting design, great software with six years of support, good screen, solid performance, long battery life, good fingerprint scanner, splash resistant.
Cons: AI features need work, extra button can’t be customised, only three years of Android version updates, enormous camera cluster protrudes from the back.
AI Research
Cyber Command creates new AI program in fiscal 2026 budget
U.S. Cyber Command’s budget request for fiscal 2026 includes funding to begin a new project specifically for artificial intelligence.
While the budget proposal would allot just $5 million for the effort — a small portion of Cybercom’s $1.3 billion research and development spending plan — the stand-up of the program follows congressional direction to prod the command to develop an AI roadmap.
In the fiscal 2023 defense policy bill, Congress charged Cybercom and the Department of Defense chief information officer — in coordination with the chief digital and artificial intelligence officer, director of the Defense Advanced Research Projects Agency, director of the National Security Agency and the undersecretary of defense for research and engineering — to jointly develop a five-year guide and implementation plan for rapidly adopting and acquiring AI systems, applications, supporting data and data management processes for cyber operations forces.
Cybercom created its roadmap shortly thereafter along with an AI task force.
The new project within Cybercom’s R&D budget aims to develop core data standards in order to curate and tag collected data that meet those standards to effectively integrate data into AI and machine learning solutions while more efficiently developing artificial intelligence capabilities to meet operational needs.
The effort is directly related to the task of furthering the roadmap.
As a result of that roadmap, the command decided to house its task force within its elite Cyber National Mission Force.
The command created the program by pulling funds from its operations and maintenance budget and moving them to the R&D budget from fiscal 2025 to fiscal 2026.
The command outlined five categories of various AI applications across its enterprise and other organizations, including vulnerabilities and exploits; network security, monitoring, and visualization; modeling and predictive analytics; persona and identity; and infrastructure and transport.
Specifically, the command’s AI project, Artificial Intelligence for Cyberspace Operations, will aim to develop and conduct pilots while investing in infrastructure to leverage commercial AI capabilities. The command’s Cyber Immersion Laboratory will develop, test and evaluate cyber capabilities and perform operational assessments performed by third parties, the budget documents state.
In fiscal 2026, the command plans to spend the $5 million to support the CNMF in piloting AI technologies through an agile 90-day pilot cycle, according to the documents, which will ensure quick success or failure. That fast-paced methodology allows the CNMF to quickly test and validate solutions against operational use cases with flexibility to adapt to evolving cyber threats.
The CNMF will also look to explore ways to improve threat detection, automate data analysis, and enhance decision-making processes in cyber operations, according to budget documents.
AI Research
Researchers Use Hidden AI Prompts to Influence Peer Reviews: A Bold New Era or Ethical Quandary?
AI Secrets in Peer Reviews Uncovered
Last updated:
Edited By
Mackenzie Ferguson
AI Tools Researcher & Implementation Consultant
In a controversial yet intriguing move, researchers have begun using hidden AI prompts to potentially sway the outcomes of peer reviews. This cutting-edge approach aims to enhance review processes, but it raises ethical concerns. Join us as we delve into the implications of AI-assisted peer review tactics and how it might shape the future of academic research.
Introduction to AI in Peer Review
Artificial Intelligence (AI) is rapidly transforming various facets of academia, and one of the most intriguing applications is its integration into the peer review process. At the heart of this evolution is the potential for AI to streamline the evaluation of scholarly articles, which traditionally relies heavily on human expertise and can be subject to biases. Researchers are actively exploring ways to harness AI not just to automate mundane tasks but to provide deep, insightful evaluations that complement human judgment.
The adoption of AI in peer review promises to revolutionize the speed and efficiency with which academic papers are vetted and published. This technological shift is driven by the need to handle an ever-increasing volume of submissions while maintaining high standards of quality. Notably, hidden AI prompts, as discussed in recent studies, can subtly influence reviewers’ decisions, potentially standardizing and enhancing the objectivity of reviews (source).
Incorporating AI into peer review isn’t without challenges. Ethical concerns about transparency, bias, and accountability arise when machines play an integral role in shaping academic discourse. Nonetheless, the potential benefits appear to outweigh the risks, with AI offering tools that can uncover hidden biases and provide more balanced reviews. As described in TechCrunch’s exploration of this topic, there’s an ongoing dialogue about the best practices for integrating AI into these critical processes (source).
Influence of AI in Academic Publishing
The advent of artificial intelligence (AI) is reshaping various sectors, with academic publishing being no exception. The integration of AI tools in academic publishing has significantly streamlined the peer review process, making it more efficient and less biased. According to an article from TechCrunch, researchers are actively exploring ways to integrate AI prompts within the peer review process to subtly guide reviewers’ evaluations without overt influence (). These AI systems analyze vast amounts of data to provide insightful suggestions, thus enhancing the quality of published research.
Moreover, AI applications in academic publishing extend beyond peer review management. AI algorithms can analyze and summarize large datasets, providing researchers with new insights and enabling faster discoveries. As TechCrunch suggests, these technologies are becoming integral to helping researchers manage the ever-increasing volume of scientific literature (). The future of academic publishing might see AI serving as co-authors, providing accurate data analysis and generating hypotheses based on trends across studies.
Public reactions to the influence of AI in academic publishing are mixed. Some view it as a revolutionary tool that democratizes knowledge production by reducing human errors and biases. Others, however, raise concerns over ethical implications, fearing that AI could introduce new biases or be manipulated to favor particular agendas. As TechCrunch highlights, the key challenge will be to implement transparent AI systems that can be held accountable and ensure ethical standards in academic publishing ().
Looking ahead, the influence of AI in academic publishing is poised to grow, potentially transforming various aspects of research dissemination. AI-powered platforms could revolutionize the accessibility and dissemination of knowledge by automating the proofreading and formatting processes, making academic work more readily available and understandable globally. However, as TechCrunch notes, the future implications of such developments require careful consideration to balance innovation with ethical integrity, especially in how AI technologies are governed ().
Challenges and Concerns in AI Implementation
Implementing AI technologies across various sectors presents numerous challenges and concerns, particularly regarding transparency, ethics, and reliability. As researchers strive to integrate AI into processes like peer review, hidden AI prompts can sometimes influence decisions subtly. According to “TechCrunch” in their article about researchers influencing peer review processes with hidden AI prompts, such practices raise questions about the integrity of AI systems . Ensuring AI operates within ethical boundaries becomes crucial, as we must balance innovation with maintaining trust in automated systems.
Furthermore, the opacity of AI algorithms often leads to public and expert concerns about accountability. When AI systems make decisions without clear explanations, it can diminish users’ trust. In exploring the future implications of AI in peer review settings, it becomes apparent that refinements are needed to enhance transparency and ethical considerations. As noted in the TechCrunch article, there is an ongoing debate about the extent to which AI should be allowed to influence decisions that have traditionally been human-centric . This calls for a framework that sets clear standards and guidelines for AI implementation, ensuring its role supplements rather than overrides human judgment.
In addition to transparency and ethics, reliability is another significant concern when implementing AI. The technological robustness of AI systems is continuously tested by real-world applications. Errors or biases in AI can lead to unintended consequences that may affect public perception and acceptance of AI-driven tools. As industries increasingly rely on AI, aligning these systems with societal values and ensuring they are error-free is paramount to gaining widespread acceptance. The TechCrunch article also highlights these reliability issues, suggesting that developers need to focus more on creating accurate, unbiased algorithms .
Experts Weigh in on AI-driven Peer Review
In recent years, the academic community has seen a growing interest in integrating artificial intelligence into the peer review process. Experts believe that AI can significantly enhance this critical phase of academic publishing by bringing in efficiency, consistency, and unbiased evaluation. According to a report on TechCrunch, researchers are exploring ways to subtly incorporate AI prompts into the peer review mechanism to improve the quality of feedback provided to authors (TechCrunch).
The inclusion of AI in peer review is not without its challenges, though. Experts caution that the deployment of AI-driven tools must be done with significant oversight to prevent any undue influence or bias that may occur from automated processes. They emphasize the importance of transparency in how AI algorithms are used and the nature of data fed into these systems to maintain the integrity of peer review (TechCrunch).
While some scholars welcome AI as a potential ally that can alleviate the workload of human reviewers and provide them with analytical insights, others remain skeptical about its impact on the traditional rigor and human judgment in peer evaluations. The debate continues, with public reactions reflecting a mixture of excitement and cautious optimism about the future potential of AI in scholarly communication (TechCrunch).
Public Reactions to AI Interventions
The public’s reaction to AI interventions, especially in fields such as scientific research and peer review, has been a mix of curiosity and skepticism. On one hand, many appreciate the potential of AI to accelerate advancements and improve efficiencies within the scientific community. However, concerns remain over the transparency and ethics of deploying hidden AI prompts to influence processes that traditionally rely on human expertise and judgment. For instance, a recent article on TechCrunch highlighted researchers’ attempts to integrate these AI-driven techniques in peer review, sparking discussions about the potential biases and ethical implications of such interventions.
Further complicating the public’s perception is the potential for AI to disrupt traditional roles and job functions within these industries. Many individuals within the academic and research sectors fear that an over-reliance on AI could undermine professional expertise and lead to job displacement. Despite these concerns, proponents argue that AI, when used effectively, can provide invaluable support to researchers by handling mundane tasks, thereby allowing humans to focus on more complex problem-solving activities, as noted in the TechCrunch article.
Moreover, the ethical ramifications of using AI in peer review processes have prompted a call for stringent regulations and clearer guidelines. The potential for AI to subtly shape research outcomes without the overt consent or awareness of the human peers involved raises significant ethical questions. Discussions in media outlets like TechCrunch indicate a need for balanced discussions that weigh the benefits of AI-enhancements against the necessity to maintain integrity and trust in academic research.
Future of Peer Review with AI
The future of peer review is poised for transformation as AI technologies continue to advance. Researchers are now exploring how AI can be integrated into the peer review process to enhance efficiency and accuracy. Some suggest that AI could assist in identifying potential conflicts of interest, evaluating the robustness of methodologies, or even suggesting suitable reviewers based on their expertise. For instance, a detailed exploration of this endeavor can be found at TechCrunch, where researchers are making significant strides toward innovative uses of AI in peer review.
The integration of AI in peer review does not come without its challenges and ethical considerations. Concerns have been raised regarding potential biases that AI systems might introduce, the transparency of AI decision-making, and how reliance on AI might impact the peer review landscape. As discussed in recent events, stakeholders are debating the need for guidelines and frameworks to manage these issues effectively.
One potential impact of AI on peer review is the democratization of the process, opening doors for a more diverse range of reviewers who may have been overlooked previously due to geographical or institutional biases. This could result in more diverse viewpoints and a richer peer review process. Additionally, as AI becomes more intertwined with peer review, expert opinions highlight the necessity for continuous monitoring and adjustment of AI tools to ensure they meet the ethical standards of academic publishing. This evolution in the peer review process invites us to envision a future where AI and human expertise work collaboratively, enhancing the quality and credibility of academic publications.
Public reactions to the integration of AI in peer review are mixed. Some welcome it as a necessary evolution that could address long-standing inefficiencies in the system, while others worry about the potential loss of human oversight and judgment. Future implications suggest a field where AI-driven processes could eventually lead to a more streamlined and transparent peer review system, provided that ethical guidelines are strictly adhered to and biases are meticulously managed.
AI Research
Xbox producer tells staff to use AI to ease job loss pain

An Xbox producer has faced a backlash after suggesting laid-off employees should use artificial intelligence to deal with emotions in a now deleted LinkedIn post.
Matt Turnbull, an executive producer at Xbox Game Studios Publishing, wrote the post after Microsoft confirmed it would lay off up to 9,000 workers, in a wave of job cuts this year.
The post, which was captured in a screenshot by tech news site Aftermath, shows Mr Turnbull suggesting tools like ChatGPT or Copilot to “help reduce the emotional and cognitive load that comes with job loss.”
One X user called it “plain disgusting” while another said it left them “speechless”. The BBC has contacted Microsoft, which owns Xbox, for comment.
Microsoft previously said several of its divisions would be affected without specifying which ones but reports suggest that its Xbox video gaming unit will be hit.
Microsoft has set out plans to invest heavily in artificial intelligence (AI), and is spending $80bn (£68.6bn) in huge data centres to train AI models.
Mr Turnbull acknowledged the difficulty of job cuts in his post and said “if you’re navigating a layoff or even quietly preparing for one, you’re not alone and you don’t have to go it alone”.
He wrote that he was aware AI tools can cause “strong feelings in people” but wanted to try and offer the “best advice” under the circumstances.
The Xbox producer said he’d been “experimenting with ways to use LLM Al tools” and suggested some prompts to enter into AI software.
These included career planning prompts, resume and LinkedIn help, and questions to ask for advice on emotional clarity and confidence.
“If this helps, feel free to share with others in your network,” he wrote.
The Microsoft cuts would equate to 4% of Microsoft’s 228,000-strong global workforce.
Some video game projects have reportedly been affected by the cuts.
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