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Google Is Winning on Every AI Front

Google's Gemini 2.5 Probulldozes through benchmarks like LMArena and GPQA Diamond. With its gargantuan1 million token context windowand zero-cost access, it leavesOpenAIeating its dust. Google’s sprawling ecosystem welcomes Gemini with open arms. They're not just ruling AI text models; they command .. read more  

Google Is Winning on Every AI Front
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Exploring GPU Sharing in Kubernetes with NVIDIA KAI Scheduler and SDG

NVIDIA's KAI SchedulerandExostellar's SDGshowcase the nerd ballet of fractional GPU scheduling. KAI slices GPU time like a master chef carving a roast, yet can't keep its focus solo—leading to app skirmishes. In contrast,Exostellar SDGnails resource control, quarantines workloads like a germaphobe, .. read more  

Exploring GPU Sharing in Kubernetes with NVIDIA KAI Scheduler and SDG
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Google announces Sec-Gemini v1, a new experimental cybersecurity model

Sec-Gemini v1steamrolls cybersecurity benchmarks, leaving rivals eating digital dust. It’s 11% better on CTI-MCQ and 10.5% sharper on CTI-Root Cause Mapping, thanks to cutting-edge threat intelligence and vulnerability insights. With a little help fromGoogle Threat Intelligenceand OSV, it decodes co.. read more  

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AI code suggestions sabotage software supply chain

Look sharp!LLM-driven toolsare fabricating package names out of thin air. In commercial models, it's5.2%. For open models, a staggering21.7%. Ideal for those up to no good and into "slopsquatting.".. read more  

AI code suggestions sabotage software supply chain
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Microsoft Copilot in Azure is now generally available

CopilotinAzurereaches general availability, choppingresponse times by 30%and saving over30,000 developer hours a month. Now free with a rock-solid>99.9% uptime. Tuned up for accessibility, real-time AI chat, andTerraformsupport—all with a keen eye on responsible AI and localization! 🚀.. read more  

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The best AI for coding in 2025 (and what not to use - including DeepSeek R1)

ChatGPT Plusaces coding tests. Meanwhile,Microsoft's CopilotandMeta AItrip over their virtual feet. These AIs can patch bugs like pros, but crafting full-fledged apps? Not in their current skill set... read more  

The best AI for coding in 2025 (and what not to use - including DeepSeek R1)
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Announcing the Agent2Agent Protocol (A2A)- Google Developers Blog

A2A Protocoltosses AI agents from different vendors into a communal sandbox. Over 50 tech behemoths likeGoogle, Salesforce, and PayPalrally behind it. Here, silos crumble. Built on solid tech standards, it lets agents dance through vibrant, multi-agent workflows. Think of it as a revolutionary leap .. read more  

Announcing the Agent2Agent Protocol (A2A)- Google Developers Blog
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Computer Use Agents (CUAs) for Enhanced Automation

Azure OpenAI Service's Responses APIhas rolled out theComputer Use Agent (CUA)—an AI that actually uses a computer like a human, and no, you're not dreaming. These CUAs harnessmultimodal visionand AI frameworks to navigate tasks with nimble reasoning. Forget your one-trick-pony RPAs; these guys brea.. read more  

Computer Use Agents (CUAs) for Enhanced Automation
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Building A Virtual Machine inside ChatGPT

ChatGPTmoonlights as a virtual Linux machine, performing calculations faster than some actual hardware. Impressive, right? But don't get too excited—it can't juggle real-time tasks or tap into a GPU. A digital superhero with a glaring Achilles' heel... read more  

Building A Virtual Machine inside ChatGPT
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Benchmarking a 65,000-node GKE cluster with AI workloads

GKE’s now flexes with a colossal 65,000-node cluster—a boon for AI workloads that feast on mega infrastructure. Building on their 50,000+ TPU cluster saga, GKE tackles AI workload quirks like resource juggling and node chatter. In CPU stress tests, they whipped up 65,000 StatefulSet Pods, flaunting .. read more  

Slurm Workload Manager is an open-source, fault-tolerant, and highly scalable cluster management and scheduling system widely used in high-performance computing (HPC). Designed to operate without kernel modifications, Slurm coordinates thousands of compute nodes by allocating resources, launching and monitoring jobs, and managing contention through its flexible scheduling queue.

At its core, Slurm uses a centralized controller (slurmctld) to track cluster state and assign work, while lightweight daemons (slurmd) on each node execute tasks and communicate hierarchically for fault tolerance. Optional components like slurmdbd and slurmrestd extend Slurm with accounting and REST APIs. A rich set of commands—such as srun, squeue, scancel, and sinfo—gives users and administrators full visibility and control.

Slurm’s modular plugin architecture supports nearly every aspect of cluster operation, including authentication, MPI integration, container runtimes, resource limits, energy accounting, topology-aware scheduling, preemption, and GPU management via Generic Resources (GRES). Nodes are organized into partitions, enabling sophisticated policies for job size, priority, fairness, oversubscription, reservation, and resource exclusivity.

Widely adopted across academia, research labs, and enterprise HPC environments, Slurm serves as the backbone for many of the world’s top supercomputers, offering a battle-tested, flexible, and highly configurable framework for large-scale distributed computing.