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Announcing up to 45% price reduction for Amazon EC2 NVIDIA GPU-accelerated instances

AWS chops up to45%from Amazon EC2 NVIDIA GPU prices. Now your AI training costs less even as GPUs play hard to get... read more  

Announcing up to 45% price reduction for Amazon EC2 NVIDIA GPU-accelerated instances
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Scaling Test Time Compute to Multi-Agent Civilizations

Turns out, Reasoning AIs use a single test compute unit to pack the punch of something 1,000 to 10,000 times its size—an acrobatics act impossible before the might of GPT-4.Noam Brown spilled the beans on Ilya's hush-hush 2021 GPT-Zero experiment, which flipped his views on how soon we'd see reasoni.. read more  

Scaling Test Time Compute to Multi-Agent Civilizations
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Mistral named most privacy-friendly AI, Google ranks low: report

Mistral AI’s “Le Chat” leads in privacy-focused AI, beating out OpenAI’s ChatGPT and xAI’s Grok.Consumer privacy concerns are reshaping the AI landscape, with 68% worried about online privacy.Regional regulations impact privacy practices, with Mistral AI benefiting from Europe’s strict GDPR rules... read more  

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Why AI Features Break Microservices Testing and How To Fix It

GenAIcomplexity confounds conventional testing. But savvy teams? They fast-track validation insandbox environments, slashing AI debug time from weeks down to mere hours... read more  

Why AI Features Break Microservices Testing and How To Fix It
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Run the Full DeepSeek-R1-0528 Model Locally

DeepSeek-R1-0528's nanized form chops space needs down to162GB. But here's the kicker—without a solid GPU, it's like waiting for paint to dry... read more  

Run the Full DeepSeek-R1-0528 Model Locally
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End to End Argo-Workflow for CI/CD

Argo Workflowsisn't just another tool; it sings for Kubernetes-native CI/CD. It juggles complex workflows as DAGs, brings dynamic execution to life with CRDs and parameters. Got a weekly CI? Automate it withCronWorkflows. Secure those Docker pushes using Kubernetes secrets, and let shared volumes ha.. read more  

End to End Argo-Workflow for CI/CD
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Kubernetes 1.33: Resizing Pods Without the Drama (Finally!)

Kubernetes 1.33brings in-place pod vertical scaling, allowing you to adjust CPU and memory without restarting pods, a game-changer for seamless resource management in production workloads. This feature simplifies vertical pod autoscaling especially for stateful workloads like databases... read more  

Kubernetes 1.33: Resizing Pods Without the Drama (Finally!)
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A Journey Through Kafkian SplitDNS in a Multitenant Kubernetes Offering

SCHIPfaced off with tenant demands for serverless Kafka. Their weapon of choice? A crafty DNS trick usingCoreDNSand a few clevernode-local DNSadjustments. They kept multitenancy alive and kicking without wearing out the ops team. Nice move... read more  

A Journey Through Kafkian SplitDNS in a Multitenant Kubernetes Offering
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GitOps for Kubernetes With Nixidy and ArgoCD

Nixidyturns Kubernetes YAMLs into sleek, declarative Nix setups. It offers a robust, repeatable config flow—even for those complex Helm charts. Spice up your deployment by pairingArgoCDwith encrypted secrets viasops-secrets-operator. Now you can wrangle sensitive data in Git with style—and security... read more  

GitOps for Kubernetes With Nixidy and ArgoCD
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The Ultimate Guide to Running Kubernetes in a Home Lab

K3sandMicroK8sshine in makeshift home labs with minimal hardware. Throw inLonghornfor storage andVelerofor backup bliss. Now that's a recipe for tech nirvana... read more  

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.