Gmail Is Killing POP and Gmailify Access. If you have multiple email accounts, your Gmail setup may soon need some reorganizing. Google giveth, and Google taketh away. Two long-standing features are being removed from Gmail, and they both relate to how you access messages from other, non-Google email accounts through the Gmail interface. The features we're talking about are Gmailify and POP access, and if you rely on them to consolidate multiple email accounts into your Gmail inbox, you're going to have to find a different approach.

You'll get the best VPN experience by paying, but prices don't have to be steep. When talking about the best VPNs, I frequently warn about the dangers of trusting free VPNs without verifying them. Although there are a few free VPNs worth recommending, many other free providers are ineffective, malicious or looking to profit off their users (or sometimes all three). Even the best free VPNs work a lot better once you subscribe and access their full service. This can be frustrating if you want to enjoy the benefits of a VPN but don't have the budget for yet another subscription.

Plus: Asus exits the smartphone market, and Sony partners with TCL on TVs. After delaying its Siri improvements to 2026, Apple's artificial intelligence plans are starting to take shape, at least according to the rumor mill. Bloomberg reports that Apple is turning Siri into a chatbot that will replace the voice assistant's existing interface, akin to OpenAI's ChatGPT. Codenamed Campos, the chatbot will be powered by Google's Gemini models and will be integrated into the iPhone, Mac, and iPad in their respective operating system updates later this fall. We'll likely learn more about Campos at Apple's developer event, WWDC, which usually takes place in June.

There is a growing interest in device-control systems that can interpret human natural language instructions and execute them on a digital device by directly controlling its user interface. We present a dataset for device-control research, Android in the Wild (AitW), which is orders of magnitude larger than current datasets. The dataset contains human demonstrations of device interactions, including the screens and actions, and corresponding natural language instructions. It consists of 715k episodes spanning 30k unique instructions, four versions of Android (v10-13), and eight device types (Pixel 2 XL to Pixel 6) with varying screen resolutions. It contains multi-step tasks that require semantic understanding of language and visual context. This dataset poses a new challenge: actions available through the user interface must be inferred from their visual appearance, and, instead of simple UI element-based actions, the action space consists of precise gestures (e.g., horizontal scrolls to operate carousel widgets). We organize our dataset to encourage robustness analysis of device-control systems, i.e., how well a system performs in the presence of new task descriptions, new applications, or new platform versions. We develop two agents and report performance across the dataset.

Google's Chromebook Successor Is Coming. Here's Everything We Know So Far Google has officially acknowledged the upcoming merger of Android and Chromebooks, and it may be coming in 2026. It's never fun to be in last place. Google has been coasting along with its Android tablets and Chromebooks for years, playing second fiddle to the bigger players in the game. But the company has a new card up its sleeve: the upcoming merger of its two platforms into something entirely new.

Deep learning research for binary analysis faces a critical infrastructure gap. Today, existing datasets target single platforms, require specialized tooling, or provide only hand-engineered features incompatible with modern neural architectures; no single dataset supports accessible research and pedagogy on realistic use cases. To solve this, we introduce Binary-30K, the first heterogeneous binary dataset designed for sequence-based models like transformers. Critically, Binary-30K covers Windows, Linux, macOS, and Android across 15+ CPU architectures. With 29,793 binaries and approximately 26.93% malware representation, Binary-30K enables research on platform-invariant detection, cross-target transfer learning, and long-context binary understanding. The dataset provides pre-computed byte-level BPE tokenization alongside comprehensive structural metadata, supporting both sequence modeling and structure-aware approaches. Platform-first stratified sampling ensures representative coverage across operating systems and architectures, while distribution via Hugging Face with official train/validation/test splits enables reproducible benchmarking. The dataset is publicly available at https://huggingface.co/datasets/mjbommar/binary-30k, providing an accessible resource for researchers, practitioners, and students alike.

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W), which is orders of magnitude larger than current datasets.