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.

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.

Recursive Neural Networks have recently obtained state of the art performance on several natural language processing tasks. However, because of their feedforward architecture they cannot correctly predict phrase or word labels that are determined by context. This is a problem in tasks such as aspect-specific sentiment classification which tries to, for instance, predict that the word Android is positive in the sentence Android beats iOS. We introduce global belief recursive neural networks (GB-RNNs) which are based on the idea of extending purely feedforward neural networks to include one feedbackward step during inference. This allows phrase level predictions and representations to give feedback to words. We show the effectiveness of this model on the task of contextual sentiment analysis. We also show that dropout can improve RNN training and that a combination of unsupervised and supervised word vector representations performs better than either alone. The feedbackward step improves F1 performance by 3% over the standard RNN on this task, obtains state-of-the-art performance on the SemEval 2013 challenge and can accurately predict the sentiment of specific entities.