The world's first 360-degree drone is fun all around, if you don't mind the steep price or wearing goggles to control it. As someone who has been reviewing camera drones for over a decade, it's rare for me to encounter one that feels genuinely new. While DJI's continual stream of steadily improving, ever-reliable drones almost always impresses, what Antigravity has done with its first-ever product, the A1, essentially invents an entirely novel subcategory: the 360 drone. Using the same shoot-first, frame-later technology as the Insta360 X5 (Antigravity is technically a distinct company from Insta360, but the brands have close ties), the A1 has twin cameras to capture everything around it, allowing the user to reframe the footage later using mobile or desktop apps. Each of the cameras uses a 1/1.28-inch sensor and an ultrawide lens to capture a hemispherical view.

Modern life has witnessed the explosion of mobile devices. However, besides the valuable features that bring convenience to end users, security and privacy risks still threaten users of mobile apps. The increasing sophistication of these threats in recent years has underscored the need for more advanced and efficient detection approaches. In this chapter, we explore the application of Large Language Models (LLMs) to identify security risks and privacy violations and mitigate them for the mobile application ecosystem. By introducing state-of-the-art research that applied LLMs to mitigate the top 10 common security risks of smartphone platforms, we highlight the feasibility and potential of LLMs to replace traditional analysis methods, such as dynamic and hybrid analysis of mobile apps. As a representative example of LLM-based solutions, we present an approach to detect sensitive data leakage when users share images online, a common behavior of smartphone users nowadays. Finally, we discuss open research challenges.

Large language models (LLMs) have demonstrated excellent capabilities in generating structured diagrams from natural language descriptions. In particular, they have shown great promise in generating sequence diagrams for software engineering, typically represented in a text-based syntax such as Mermaid. However, systematic evaluations in this space remain underdeveloped as there is a lack of existing benchmarks to assess the LLM's correctness in this task. To address this shortcoming, we introduce MermaidSeqBench, a human-verified and LLM-synthetically-extended benchmark for assessing an LLM's capabilities in generating Mermaid sequence diagrams from textual prompts. The benchmark consists of a core set of 132 samples, starting from a small set of manually crafted and verified flows. These were expanded via a hybrid methodology combining human annotation, in-context LLM prompting, and rule-based variation generation. Our benchmark uses an LLM-as-a-judge model to assess Mermaid sequence diagram generation across fine-grained metrics, including syntax correctness, activation handling, error handling, and practical usability. We perform initial evaluations on numerous state-of-the-art LLMs and utilize multiple LLM judge models to demonstrate the effectiveness and flexibility of our benchmark. Our results reveal significant capability gaps across models and evaluation modes. Our proposed benchmark provides a foundation for advancing research in structured diagram generation and for developing more rigorous, fine-grained evaluation methodologies.

Globally, individuals and organizations employ Quick Response (QR) codes for swift and convenient communication. Leveraging this, cybercriminals embed falsify and misleading information in QR codes to launch various phishing attacks which termed as Quishing. Many former studies have introduced defensive approaches to preclude Quishing such as by classifying the embedded content of QR codes and then label the QR codes accordingly, whereas other studies classify them using visual features (i.e., deep features, histogram density analysis features). However, these approaches mainly rely on black-box techniques which do not clearly provide interpretability and transparency to fully comprehend and reproduce the intrinsic decision process; therefore, having certain obvious limitations includes the approaches' trust, accountability, issues in bias detection, and many more. We proposed QRïS, the pioneer method to classify QR codes through the comprehensive structural analysis of a QR code which helps to identify phishing QR codes beforehand. Our classification method is clearly transparent which makes it reproducible, scalable, and easy to comprehend. First, we generated QR codes dataset (i.e. 400,000 samples) using recently published URLs datasets [1], [2]. Then, unlike black-box models, we developed a simple algorithm to extract 24 structural features from layout patterns present in QR codes. Later, we train the machine learning models on the harvested features and obtained accuracy of up to 83.18%. To further evaluate the effectiveness of our approach, we perform the comparative analysis of proposed method with relevant contemporary studies. Lastly, for real-world deployment and validation, we developed a mobile app which assures the feasibility of the proposed solution in real-world scenarios which eventually strengthen the applicability of the study.

Cool off your home with one of these air conditioners. Summer weather is getting unbearable in many parts of the U.S., with record-high temperatures all over the country. To beat the heat, investing in an air conditioner is a must. We've lined up some top air conditioner deals, from budget-friendly options to portable solutions and high-tech, smart AC units. Cool large rooms with this portable option.

WIRED published a shocking investigation this week based on records, including audio recordings, of hundreds of emergency calls from United States Immigration and Customs Enforcement (ICE) detention centers. The calls--which include reports of incidents of staff sexual assaults, suicide attempts, and head injuries--indicate a system inundated by life-threatening incidents, delayed treatment, and overcrowding. In a 6-3 decision on Friday, the US Supreme Court upheld a Texas porn ID law, finding that age verification for explicit sites is constitutional. In a dissent, Justice Elena Kagan warned that this determination ignores First Amendment precedent and will have privacy implications for adults. Looking at the US bombing of Iranian nuclear sites last weekend, President Donald Trump posted initial announcements of the strikes on the social Network Truth Social, which then began suffering intermittent outages.

This is a review of two pool robots, one which is the most exceptional cleaning device I've tested to date, and one that's maddeningly frustrating and overly complex. You've probably already figured out the twist: Yes, they are same device, the iGarden Pool Cleaner K60. To start things off, just look at the thing: With its jet-black chassis and orange-trimmed wheels, the machine looks more like a sports car than a glorified leaf sweeper. Despite the evocative look, it still moves about courtesy of large wheels and treads that abut a pair of spinning scrubber. Its biggest selling point is under the hood: A fairly beefy 7500mAh battery powers the 30-pound robot to an epic running time of up to 6 hours, according to iGarden.

With pool-mapping capabilities and other smart features, the Dreame Z1 Pro is one of the most intelligent robots I've tested to date. From the start, Dreame's Z1 Pro robotic pool cleaner certainly seems to check off all the boxes. Its features list touts just about everything: The ability to clean floor, walls, and waterline. I'm not sure what the touted "Triple Surround Fusion Perception System" is, but that sounds good, too. I'll start with what I liked the most: After unboxing, I discovered that the 27-pound robot offers one of the most convenient charging systems I've seen to date, thanks to a magnetic charging mechanism that simply snaps onto the device's chassis, with no plugs or rubber gaskets involved--and no need to hoist the robot onto a bulky charging station, either.

Artificial Intelligence (AI)-powered features have rapidly proliferated across mobile apps in various domains, including productivity, education, entertainment, and creativity. However, how users perceive, evaluate, and critique these AI features remains largely unexplored, primarily due to the overwhelming volume of user feedback. In this work, we present the first comprehensive, large-scale study of user feedback on AI-powered mobile apps, leveraging a curated dataset of 292 AI-driven apps across 14 categories with 894K AI-specific reviews from Google Play. We develop and validate a multi-stage analysis pipeline that begins with a human-labeled benchmark and systematically evaluates large language models (LLMs) and prompting strategies. Each stage, including review classification, aspect-sentiment extraction, and clustering, is validated for accuracy and consistency. Our pipeline enables scalable, high-precision analysis of user feedback, extracting over one million aspect-sentiment pairs clustered into 18 positive and 15 negative user topics. Our analysis reveals that users consistently focus on a narrow set of themes: positive comments emphasize productivity, reliability, and personalized assistance, while negative feedback highlights technical failures (e.g., scanning and recognition), pricing concerns, and limitations in language support. Our pipeline surfaces both satisfaction with one feature and frustration with another within the same review. These fine-grained, co-occurring sentiments are often missed by traditional approaches that treat positive and negative feedback in isolation or rely on coarse-grained analysis. To this end, our approach provides a more faithful reflection of the real-world user experiences with AI-powered apps. Category-aware analysis further uncovers both universal drivers of satisfaction and domain-specific frustrations.

Breakthroughs, discoveries, and DIY tips sent every weekday. Amidst the flurry of AI announcements and product reveals from Google in recent months, you might have missed one of the most useful AI-powered apps in the whole collection: NotebookLM (that LM stands for Language Model). Perhaps NotebookLM has gone largely under the radar because it was originally launched as more of an academic research tool when it first appeared back in 2023. Its user interface lacks some of the slickness and accessibility of Google Gemini, and it's not quite as obvious how you're supposed to use it, or what it can do. However, NotebookLM is gradually becoming better known amongst consumers, with official apps for Android and iOS now available, alongside the web app.