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Pinterest Uncovers Rare Search Failure During Migration to Kubernetes

Pinterest hit a weird one-in-a-million query mismatch during its search infra move to Kubernetes. The culprit? A slippery timing bug. To catch it, engineers pulled out every trick—live traffic replays, their own diff tools, hybrid rollouts layered on both the legacy and K8s stacks. Painful, but it .. read more  

Pinterest Uncovers Rare Search Failure During Migration to Kubernetes
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Terraform Validate Disagrees with Terraform Docs

Terraform’s CLI will throw errors on configs that match the docs—because your local provider schema might be stale or out of sync. Docs follow the latest release. Your machine might not. So even supported fields can break validation. Love that for us... read more  

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Who does the unsexy but essential work for open source?

Oracle led the line-count race in the Linux 6.1 kernel release—beating out flashier open source names. Most of its work isn’t headline material. It’s deep-core stuff: memory management tweaks, block device updates, the quiet machinery real systems run on... read more  

Who does the unsexy but essential work for open source?
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Estimate Your K8s Deployment Costs (Portainer Calculator)

A new TCO calculator breaks down what it really costs to run Kubernetes—DIY CNCF stacks, COSS platforms, and Portainer Business Edition. It crunches infra, labor, and software spend, then maps out staffing needs. It shows exactly where Portainer cuts Kubernetes bloat: itmaybe biased but it's worth t.. read more  

Estimate Your K8s Deployment Costs (Portainer Calculator)
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How I Cut AWS Compute Costs by 70% with a Multi-Arch EKS Cluster and Karpenter

Swapping out Kubernetes Cluster Autoscaler forKarpentercut node launch times to under 20 seconds and dropped compute bills by 70%. The secret sauce? Smarter, faster spot instance scaling. Bonus perks: architecture-aware scheduling formulti-CPU (ARM64/x86)workloads—more performance, better utilizati.. read more  

How I Cut AWS Compute Costs by 70% with a Multi-Arch EKS Cluster and Karpenter
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SUSE Adds Arm Support to HCI Platform for Running Monolithic Apps on Kubernetes

SUSE Virtualization 1.5 lands with64-bit Arm and Intelsupport,CSIstorage compatibility, and a tighter4-month release loopsynced with Kubernetes. Built on Harvester and KubeVirt, the update pushes harder on a clear trend: legacy VMs and cloud-native apps sharing the same Kubernetes real estate. Sys.. read more  

SUSE Adds Arm Support to HCI Platform for Running Monolithic Apps on Kubernetes
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Building a RAG chat-based assistant on Amazon EKS Auto Mode and NVIDIA NIMs

AWS and NVIDIA just dropped a full-stack recipe for running Retrieval-Augmented Generation (RAG) onAmazon EKS Auto Mode—built on top ofNVIDIA NIM microservices. It's LLMs on Kubernetes, but without the hair-pulling. Inference? GPU-accelerated. Embeddings? Covered. Vector search? Handled byAmazon Op.. read more  

Building a RAG chat-based assistant on Amazon EKS Auto Mode and NVIDIA NIMs
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Scale AI/ML Workloads with Amazon EKS: Up to 100K Nodes

Amazon EKS just leveled up—clusters can now run withup to 100,000 nodeswith support ofKubernetes 1.30and up. That's not just big—it’s AI-and-ML-scale big. Cluster setup got a lot less manual, too. The AWS Console’s"auto mode"auto-builds your VPC and IAM configs.eksctlplugs right into the flow... read more  

Scale AI/ML Workloads with Amazon EKS: Up to 100K Nodes
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Kubernetes 1.34 Debuts KYAML to Resolve YAML Challenges

Kubernetes 1.34 drops on August 27, 2025, and it’s bringingKYAML—a smarter, stricter take on YAML. No more surprise type coercion or “why is this indented wrong?” bugs. Think of it as YAML that behaves. kubectlgets a new trick too:-o kyaml. Use it to spit out manifests in KYAML format—easier to deb.. read more  

Kubernetes 1.34 Debuts KYAML to Resolve YAML Challenges
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AI is changing Kubernetes faster than most teams can keep up

AI workloads are taking over Kubernetes. Fastest-growing use case on the block. 90% of orgs expect that growth to keep climbing. 92% are betting on AI-driven ops tools to keep up. Edge Kubernetes? Up from 38% to 50% in a year. Real-time inference is pushing workloads closer to the source.System shif.. read more  

AI is changing Kubernetes faster than most teams can keep up
GPT-5.3-Codex is OpenAI’s advanced agentic coding model, designed to go beyond writing code and operate as a general-purpose collaborator on a computer. It builds on GPT-5.2-Codex by combining stronger coding performance with improved reasoning and professional knowledge, while running about 25% faster. The model is optimized for long-running tasks that involve research, tool use, and complex execution, and it performs at the top of industry benchmarks such as SWE-Bench Pro and Terminal-Bench.

Unlike earlier Codex models that focused primarily on code generation and review, GPT-5.3-Codex can reason, plan, and act across the full software lifecycle. It supports activities such as debugging, deploying, monitoring, writing product requirement documents, creating tests, and analyzing metrics. It can also autonomously build and iterate on complex applications and better interpret underspecified prompts, producing more complete and production-ready results by default.

A defining feature of GPT-5.3-Codex is its interactive, agentic workflow. Users can steer the model while it is working, receive progress updates, and adjust direction without losing context, making it feel more like a teammate than a batch automation tool. The model was even used internally to help debug its own training and deployment processes. GPT-5.3-Codex is available through paid ChatGPT plans in the Codex app, CLI, IDE extension, and web, with API access planned for the future.