When you purchase through links in our articles, we may earn a small commission. Microsoft's Edge team has posted a timeline for migrating away from Manifest Version 2, the extensions foundation upon which uBlock's superb uBlock Origin adblocker lives. Remember how one of the best adblockers, uBlock Origin, was effectively killed by Google Chrome? Throughout 2024 and 2025, Google said that it was migrating its extension support from what is called Manifest, from version 2 to version 3. Now Microsoft says that it is following Google's lead, and beginning that transition as well. Ads stink, and anyone who says that they don't affect productivity has never turned off an adblocker and tried to surf a major media site, including our own.

At the end of last year, Adobe leveraged OpenAI's Apps SDK to bring Photoshop, Acrobat and Adobe Express to ChatGPT. Now, the company is bringing nearly its entire suite of creative apps to ChatGPT with the introduction of the Adobe plugin. All told, once you've added the extension to your account, you'll have access to more than 70 of Adobe's tools, including Photoshop, Premiere, Acrobat, Lightroom, Illustrator and InDesign. To add the extension, open ChatGPT's settings menu and navigate to the plugins section, then select the Adobe one. You can then invoke it by typing @Adobe into ChatGPT's prompt bar, followed by your request.

PCWorld reports that'free' World Cup streaming sites are often scams delivering malicious ads, malware, and unwanted subscriptions instead of legitimate soccer content. Cybercriminals exploit these fake streams to trick users into clicking hidden malicious links, while legitimate alternatives include official streaming trials and reputable international services. Users should verify free services by checking for clear business models, established company histories, and proper terms of service before accessing any content.

Self-Supervised Learning (SSL) for Combinatorial Optimization (CO) is an emerging paradigm for solving combinatorial problems using neural networks. In this paper, we address a central challenge of SSL for CO: solving problems with discrete constraints. We design an end-to-end differentiable framework that enables us to solve discrete constrained optimization problems with neural networks. Concretely, we leverage algorithmic techniques from the literature on convex geometry and Carathéodory's theorem to decompose neural network outputs into convex combinations of polytope corners that correspond to feasible sets. This decomposition-based approach enables self-supervised training but also ensures efficient quality-preserving rounding of the neural net output into feasible solutions. Extensive experiments in cardinality-constrained optimization show that our approach can consistently outperform neural baselines. We further provide workedout examples of how our method can be applied beyond cardinality-constrained problems to a diverse set of combinatorial optimization tasks, including finding independent sets in graphs, and solving matroid-constrained problems.

The first one, MA-SPL, not only can achieve the optimal (1 ce)-approximation guarantee for the MA-OC problem with submodular objectives but also can handle the unexplored α-weakly DR-submodular and (γ,β)-weakly submodular scenarios, where c is the curvature of the investigated submodular functions, α denotes the diminishing-return(DR) ratio and the tuple (γ,β) represents the submodularity ratios. Subsequently, in order to reduce the reliance on the unknown parameters α,γ,β inherent in the MA-SPLalgorithm, we further introduce the second online algorithm named MA-MPL. This MA-MPL algorithm is entirely parameter-free and simultaneously can maintain the same approximation ratio as the first MA-SPL algorithm. The core of our MA-SPL and MA-MPL algorithms is a novel continuous-relaxation technique termed as policybased continuous extension. Compared with the well-established multi-linear extension, a notable advantage of this new policy-based continuous extension is its ability to provide a lossless rounding scheme for any set function, thereby enabling us to tackle the challenging weakly submodular objectives. Finally, extensive simulations are conducted to validate the effectiveness of our proposed algorithms.

Vision-language models have demonstrated impressive capabilities as computer-use agents (CUAs) capable of automating diverse computer tasks. As their commercial potential grows, critical details of the most capable CUA systems remain closed. As these agents will increasingly mediate digital interactions and execute consequential decisions on our behalf, the research community needs access to open CUA frameworks to study their capabilities, limitations, and risks. To bridge this gap, we propose OPENCUA, a comprehensive open-source framework for scaling CUA data and foundation models. Our framework consists of: (1) an annotation infrastructure that seamlessly captures human computer-use demonstrations; (2) AGENTNET, the first large-scale computer-use task dataset spanning 3 operating systems and 200+ applications and websites; (3) a scalable pipeline that transforms demonstrations into state-action pairs with reflective long Chain-of-Thought reasoning that sustain robust performance gains as data scales.

Continuously extending combinatorial optimization objectives is a powerful technique commonly applied to the optimization of set functions. However, few such methods exist for extending functions on permutations, despite the fact that many combinatorial optimization problems, such as the quadratic assignment problem (QAP) and the traveling salesperson problem (TSP), are inherently optimization over permutations.

Extensive research has produced robust methods for unsupervised data visualization. Yet supervised visualization--where expert labels guide representations--remains underexplored, as most supervised approaches prioritize classification over visualization. Recently, RF-PHATE, a diffusion-based manifold learning method leveraging random forests and information geometry, marked significant progress in supervised visualization.

PCWorld examines Chrome extension security risks, highlighting how these browser add-ons can potentially compromise user data and system safety. Security experts warn that malicious extensions may access browsing history, passwords, and personal information without users realizing the extent of permissions granted. The analysis emphasizes careful vetting of extensions, checking developer credibility, reviewing permissions, and regularly auditing installed add-ons to maintain browser security. If you're a PC user of a certain age, you probably remember when security focused on apps. What you downloaded and installed was the biggest danger.

Federated clustering (FC) aims to discover global cluster structures across decentralized clients without sharing raw data, making privacy preservation a fundamental requirement. There are two critical challenges: (1) privacy leakage during collaboration, and (2) robustness degradation due to aggregation of proxy information from non-independent and identically distributed (Non-IID) local data, leading to inaccurate or inconsistent global clustering. Existing solutions typically rely on model-specific local proxies, which are sensitive to data heterogeneity and inherit inductive biases from their centralized counterparts, thus limiting robustness and generality. We propose Omni Federated Clustering (OmniFC), a unified and modelagnostic framework. Leveraging Lagrange coded computing, our method enables clients to share only encoded data, allowing exact reconstruction of the global distance matrix--a fundamental representation of sample relationships--without leaking private information, even under client collusion. This construction is naturally resilient to Non-IID data distributions. This approach decouples FC from model-specific proxies, providing a unified extension mechanism applicable to diverse centralized clustering methods. Theoretical analysis confirms both reconstruction fidelity and privacy guarantees, while comprehensive experiments demonstrate OmniFC's superior robustness, effectiveness, and generality across various benchmarks compared to state-of-the-art methods.