The Adobe Premiere mobile app is now available for Android devices, over a year after it first rolled out on iOS. Like the iOS version, the Android app is free with in-app purchases for its generative AI features. Its core editing capabilities are available at no cost, including the ability to split, trim, crop and merge footage, to adjust video speeds, as well as to add text, effects and audio layers like voiceovers. You'll also be able to export an unlimited number of 4K videos with no watermarks in the size you want for any platform without having to pay a cent. If you do need its AI capabilities, such as the ability to create sound effects and access to AI-generated assets on Adobe Firefly, you can buy generative AI credits you can spend.

You can allow up to five connections at the same time -- but keep in mind the more simultaneous connections, the more bandwidth you may end up using. Also: Privacy in the AI era is possible, says Proton's CEO, but one thing keeps him up at night

Googlebook Is Google's New AI-Powered Laptop Platform Built on Android They won't replace Chromebooks, but Googlebooks have an Android-centered operating system, AI-first features like the Magic Pointer, and a promise of desktop-grade apps. Almost exactly 15 years since Google introduced Chromebooks and ChromeOS --which ushered a wave of cheap, functional, web-based laptops that would come to dominate the US education market--the company has announced a new laptop platform called Googlebook. It's built around artificial intelligence and Android, and while it isn't replacing Chromebooks, it could give the company a more meaningful foothold in the premium computer market. Google announced the platform on The Android Show on YouTube, where it also detailed new features coming in Android 17 and Gemini Intelligence (you can read more about that here). Google is purposefully not sharing the operating system's name yet (it was codenamed Aluminium OS internally); Googlebook is the platform, and Dell, Acer, Asus, HP, and Lenovo have all signed up to produce Googlebooks coming later this fall.

Autonomous agents that control user interfaces to accomplish human tasks are emerging. Leveraging LLMs to power such agents has been of special interest, but unless fine-tuned on human-collected task demonstrations, performance is still relatively low. In this work we study whether fine-tuning alone is a viable approach for building real-world UI control agents. To this end we collect and release a new dataset, AndroidControl, consisting of 15,283 demonstrations of everyday tasks with Android apps. Compared to existing datasets, each AndroidControl task instance includes both high and low-level human-generated instructions, allowing us to explore the level of task complexity an agent can handle. Moreover, AndroidControl is the most diverse computer control dataset to date, including 14,548 unique tasks over 833 Android apps, thus allowing us to conduct in-depth analysis of the model performance in and out of the domain of the training data. Using the dataset, we find that when tested in domain fine-tuned models outperform zero and few-shot baselines and scale in such a way that robust performance might feasibly be obtained simply by collecting more data. Out of domain, performance scales significantly more slowly and suggests that in particular for high-level tasks, fine-tuning on more data alone may be insufficient for achieving robust out-of-domain performance.

PCWorld reports that over 1 million Android apps exposed 700 TB of sensitive user data through hardcoded API keys and security vulnerabilities. Research found 72% of AI apps contained dangerous "secrets" in their code, with 81% linked to Google Cloud projects enabling unauthorized third-party access. Users should exercise extreme caution when installing new apps, particularly AI applications that request sensitive financial or personal information. Towards the end of January, security researchers at Cybernews published a study on AI apps in the Google Play Store. The study revealed that numerous AI apps had inadequate security, leading them to inadvertently leak data from Google's cloud servers.

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In recent years, consumer-grade UAVs have been widely adopted by first responders. In general, they are operated manually, which requires trained pilots, especially in unknown GNSS-denied environments and in the vicinity of structures. Autonomous flight can facilitate the application of UAVs and reduce operator strain. However, autonomous systems usually require special programming interfaces, custom sensor setups, and strong onboard computers, which limits a broader deployment. We present a system for autonomous flight using lightweight consumer-grade DJI drones. They are controlled by an Android app for state estimation and obstacle avoidance directly running on the UAV's remote control. Our ground control station enables a single operator to configure and supervise multiple heterogeneous UAVs at once. Furthermore, it combines the observations of all UAVs into a joint 3D environment model for improved situational awareness.

Large language models (LLMs) have demonstrated remarkable capability in function-level code generation tasks. Unlike isolated functions, real-world applications demand reasoning over the entire software system: developers must orchestrate how different components interact, maintain consistency across states over time, and ensure the application behaves correctly within the lifecycle and framework constraints. Yet, no existing benchmark adequately evaluates whether LLMs can bridge this gap and construct entire software systems from scratch. To address this gap, we propose APPFORGE, a benchmark consisting of 101 software development problems drawn from real-world Android apps. Given a natural language specification detailing the app functionality, a language model is tasked with implementing the functionality into an Android app from scratch. Developing an Android app from scratch requires understanding and coordinating app states, lifecycle management, and asynchronous operations, calling for LLMs to generate context-aware, robust, and maintainable code. To construct APPFORGE, we design a multi-agent system to automatically summarize the main functionalities from app documents and navigate the app to synthesize test cases validating the functional correctness of app implementation. Following rigorous manual verification by Android development experts, APPFORGE incorporates the test cases within an automated evaluation framework that enables reproducible assessment without human intervention, making it easily adoptable for future research. Our evaluation on 12 flagship LLMs show that all evaluated models achieve low effectiveness, with the best-performing model (GPT-5) developing only 18.8% functionally correct applications, highlighting fundamental limitations in current models' ability to handle complex, multi-component software engineering challenges.

Fox News host Greg Gutfeld and guests discuss the reportedly woke answers from Google's AI chatbot Gemini on'Gutfeld!' Google is making a push to ensure its AI, Gemini, is tightly integrated with Android systems by granting it access to core apps like WhatsApp, Messages, and Phone. The rollout of this change started on July 7, 2025, and it may override older privacy configurations unless you know how to disable Gemini on Android. Here's what you need to know. Sign up for my FREE CyberGuy Report Get my best tech tips, urgent security alerts, and exclusive deals delivered straight to your inbox. Plus, you'll get instant access to my Ultimate Scam Survival Guide - free when you join my CYBERGUY.COM/NEWSLETTER.

Autonomous agents that control user interfaces to accomplish human tasks are emerging. Leveraging LLMs to power such agents has been of special interest, but unless fine-tuned on human-collected task demonstrations, performance is still relatively low. In this work we study whether fine-tuning alone is a viable approach for building real-world UI control agents. To this end we collect and release a new dataset, AndroidControl, consisting of 15,283 demonstrations of everyday tasks with Android apps. Compared to existing datasets, each AndroidControl task instance includes both high and low-level human-generated instructions, allowing us to explore the level of task complexity an agent can handle.