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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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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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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  

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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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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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Introducing the Llama 4 herd in Azure AI Foundry and Azure Databricks

Llama 4 Scouton Azure AI Foundry doesn’t just sit around; it dives into its massive 10 million token context like it's born for deep dives and endless document wrangling. Meanwhile,Llama 4 Mavericktakes multilingual, multimodal chat conversations where few dare to go. Its Mixture of Experts architec.. read more  

Introducing the Llama 4 herd in Azure AI Foundry and Azure Databricks
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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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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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Python: Get YouTube Video Transcript From URL For Use In Generative AI And RAG Summarization

Ever tried wrangling YouTube transcripts with Python? Do it. Then crank your Generative AI’s IQ by tossing those transcripts straight intoLLMs. Voilà—you’ve got a brainier machine, serving up insight like a pro... read more  

Python: Get YouTube Video Transcript From URL For Use In Generative AI And RAG Summarization
Kueue is a Kubernetes-native job queueing and workload management system designed for large-scale, mixed compute environments such as AI/ML training, batch workloads, and HPC workflows. Instead of scheduling individual Pods, Kueue operates at the job level, deciding when a job should run based on resource quotas, fair-sharing policies, cluster availability, and workload priorities.

Kueue integrates tightly with Kubernetes, working alongside the default scheduler rather than replacing it. It provides features such as all-or-nothing (gang) admission, workload preemption, quota-based sharing across teams or tenants, and support for advanced frameworks like JobSet and Ray. Its goal is to help Kubernetes clusters run efficiently under heavy load while ensuring that critical, latency-sensitive, or large training jobs receive the resources they need without starving lower-priority workloads.