When you purchase through links in our articles, we may earn a small commission. Meta wants everyone to use Muse, its AI agent. But the company first needs to prove itself worthy of such trust. Two people can keep a secret if one is dead, or so goes the saying. But what happens when you share the details of your life with an AI agent?

Two years after trying to sidestep mobile apps with dedicated AI hardware, Rabbit is launching OS3, a cross-platform agent that lives on the screens you already use. Jesse Lyu doesn't think the Rabbit R1 was a flop. Lyu's gadget was among the first in the gold rush to create dedicated AI hardware that acted as a virtual assistant. You could speak into the R1 and ask it to complete tasks for you--book an Uber; order food on DoorDash. After a buzzy launch at CES 2024, it earned scathing reviews.

When you purchase through links in our articles, we may earn a small commission. We put the Ugreen NASync DXP4800 GT through its paces to see just how fast and reliable it really is as a NAS solution. Four SATA bays, two of which also accommodate U.2 SSDs The DXP4800 GT is not a refreshed DXP4800, but a completely new design. Two 10GbE ports, U.2-compatible bays, and a second free RAM socket set it apart from its in-house Intel counterparts. The Ryzen Embedded R2514 offers plenty of headroom for containers and a virtual machine without exceeding reasonable power consumption limits. UGOS Pro runs smoothly and offers a wide range of apps. The lack of volume encryption remains the most notable shortcoming.

The Muse app continues Meta's trend of opting users into data collection for AI training. It also nudges you to share your bank account, email, and passport information. I saw Meta's latest app, Muse, cross-promoted on another Meta-owned platform, Instagram, and decided to download the AI assistant. "Muse keeps working while you get on with your day." It's free to use, and easily connects to other data sources, like my email and bank account.

PCWorld reports that Microsoft has released emergency patches for both Windows 10 and Windows 11, addressing critical security vulnerabilities and system bugs. The updates, KB5129236 and KB5129195, fix issues including USB audio failures, Remote Desktop Services instability, and Hyper-V folder sharing problems with Linux virtual machines. However, AMD Radeon GPU issues remain unresolved, and users are advised to install the patches promptly as they require a system restart. Microsoft's September Patch Tuesday fixed a record 700+ security vulnerabilities across Windows versions, but it also caused some issues--most notably the breaking of USB audio on Windows 11 . Now a week later, the company has released emergency out-of-band updates for both Windows 11 and Windows 10 to address some of those issues.

This paper presents a machine learning-based approach to estimate the energy consumption of virtual servers without access to physical power measurement interfaces. Using resource utilization metrics collected from guest virtual machines, we train a Gradient Boosting Regressor to predict energy consumption measured via RAPL on the host. We demonstrate, for the first time, guest-only resource-based energy estimation without privileged host access with experiments across diverse workloads, achieving high predictive accuracy and variance explained ($0.90 \leq R^2 \leq 0.97$), indicating the feasibility of guest-side energy estimation. This approach can enable energy-aware scheduling, cost optimization and physical host independent energy estimates in virtualized environments. Our approach addresses a critical gap in virtualized environments (e.g. cloud) where direct energy measurement is infeasible.

Cloud platforms are increasingly relied upon to host diverse, resource-intensive workloads due to their scalability, flexibility, and cost-efficiency. In multi-tenant cloud environments, virtual machines are consolidated on shared physical servers to improve resource utilization. While virtualization guarantees resource partitioning for CPU, memory, and storage, it cannot ensure performance isolation. Competition for shared resources such as last-level cache, memory bandwidth, and network interfaces often leads to severe performance degradation. Existing management techniques, including VM scheduling and resource provisioning, require accurate performance prediction to mitigate interference. However, this remains challenging in public clouds due to the black-box nature of VMs and the highly dynamic nature of workloads. To address these limitations, we propose CloudFormer, a dual-branch Transformer-based model designed to predict VM performance degradation in black-box environments. CloudFormer jointly models temporal dynamics and system-level interactions, leveraging 206 system metrics at one-second resolution across both static and dynamic scenarios. This design enables the model to capture transient interference effects and adapt to varying workload conditions without scenario-specific tuning. Complementing the methodology, we provide a fine-grained dataset that significantly expands the temporal resolution and metric diversity compared to existing benchmarks. Experimental results demonstrate that CloudFormer consistently outperforms state-of-the-art baselines across multiple evaluation metrics, achieving robust generalization across diverse and previously unseen workloads. Notably, CloudFormer attains a mean absolute error (MAE) of just 7.8%, representing a substantial improvement in predictive accuracy and outperforming existing methods at least by 28%.

Artificial intelligence (AI) has been increasingly applied to the condition monitoring of vehicular equipment, aiming to enhance maintenance strategies, reduce costs, and improve safety. Leveraging the edge computing paradigm, AI-based condition monitoring systems process vast streams of vehicular data to detect anomalies and optimize operational performance. In this work, we introduce a novel vehicle condition monitoring service that enables real-time diagnostics of a diverse set of anomalies while remaining practical for deployment in real-world edge environments. To address mobility challenges, we propose a closed-loop service orchestration framework where service migration across edge nodes is dynamically triggered by network-related metrics. Our approach has been implemented and tested in a real-world race circuit environment equipped with 5G network capabilities under diverse operational conditions. Experimental results demonstrate the effectiveness of our framework in ensuring low-latency AI inference and adaptive service placement, highlighting its potential for intelligent transportation and mobility applications.

Many large enterprises that operate highly governed and complex ICT environments have no efficient and effective way to support their Data and AI teams in rapidly spinning up and tearing down self-service data and compute infrastructure, to experiment with new data analytic tools, and deploy data products into operational use. This paper proposes a key piece of the solution to the overall problem, in the form of an on-demand self-service data-platform infrastructure to empower de-centralised data teams to build data products on top of centralised templates, policies and governance. The core innovation is an efficient method to leverage immutable container operating systems and infrastructure-as-code methodologies for creating, from scratch, vendor-neutral and short-lived Kubernetes clusters on-premises and in any cloud environment. Our proposed approach can serve as a repeatable, portable and cost-efficient alternative or complement to commercial Platform-as-a-Service (PaaS) offerings, and this is particularly important in supporting interoperability in complex data mesh environments with a mix of modern and legacy compute infrastructure.

Efficient task scheduling is paramount in the Linux kernel, where the Completely Fair Scheduler (CFS) meticulously manages CPU resources to balance high utilization with interactive responsiveness. This research pioneers the use of deep learning techniques to predict the sequence of tasks selected by CFS, aiming to evaluate the feasibility of a more generalized and potentially more adaptive task scheduler for diverse workloads. Our core contributions are twofold: first, the systematic generation and curation of a novel scheduling dataset from a running Linux kernel, capturing real-world CFS behavior; and second, the development, training, and evaluation of a Long Short-Term Memory (LSTM) network designed to accurately forecast the next task to be scheduled. This paper further discusses the practical pathways and implications of integrating such a predictive model into the kernel's scheduling framework. The findings and methodologies presented herein open avenues for data-driven advancements in kernel scheduling, with the full source code provided for reproducibility and further exploration.