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Red Hat's AI blog series part 3 details how llm-d routes model inference traffic on Amazon EKS within their managed Kubernetes platform, using Kubernetes resources and Envoy integration for real-time decisions.
Turbopuffer describes their method for deploying database upgrades daily using Kubernetes CRDs and a control plane that supports public SaaS, single-tenant SaaS, and BYOC deployments without requiring direct access.
A GitHub repository providing hands-on projects to learn Kubernetes, covering basic to advanced topics like deployments, microservices, and cloud integration.
Oxide's Solutions Software Engineer describes how customer feedback shaped the Kubernetes integrations they built for the Oxide cloud platform, including Rancher, Omni, and Cluster API support.
Batch processing remains significant, and Netflix has adopted Kueue, an open-source Kubernetes job queueing controller originally introduced by Google, to replace an in-house solution.
A GitHub repo curating real DevOps interview questions, broken pipelines, and Kubernetes challenges for hands-on interview preparation.
A GitHub repository featuring 151 real DevOps/SRE interview records from 85 companies. Questions were recorded verbatim by candidates and cover Kubernetes, Docker, Terraform, AWS, CI/CD, and more, making it convenient for job seekers to prepare by company or topic.
Agent Substrate is a Google open-source runtime that enables high-density lifecycle management for large-scale AI agent deployments, multiplexing many stateful agents onto fewer physical workers via Kubernetes and microVM/gVisor sandboxes with sub-second suspend/resume.
Olares is an open-source personal cloud OS from the beclab team, built on Kubernetes, specifically designed for 7x24 always-on AI Agents. It can convert old computers or NAS into a self-hosted AI platform, supporting local data storage and natural language operation.
A technical blog post exploring why Karpenter's node consolidation appears counter-intuitive, with cluster capacity staying constant while nodes constantly churn, and explaining the underlying mechanics.
The article explains that BYOC (Bring Your Own Cloud) is a spectrum of deployment models, including BYOC-Account, BYOC-VPC, BYOC-K8s, and air-gapped environments, driven by customer needs for data residency, security, cost alignment, and data gravity.
Microsoft's Orchard is an open-source framework for agentic modeling, providing a Kubernetes-native environment substrate, RL training stack, and datasets for software engineering, GUI, and computer use agents.
Pinterest Engineering shares a detailed investigation into CPU bottlenecks and network driver issues causing Ray-based training jobs to crash on their Kubernetes platform, offering lessons in profiling performance problems.
A pre-registered ablation on a 4B model classification task shows that harness design (rule placement, evidence order, turn structure) swings accuracy from 60% to 82%, demonstrating that poor harness design is often mistaken for poor model capability.
FreeCodeCamp published a full book teaching how to build a production-ready DevSecOps platform from homelab to AWS, covering Kubernetes, CI/CD, GitOps, security scanning, and observability.
The author shares a runbook for deploying in-house LLM inference on Kubernetes, based on their experience building the infrastructure at their organization.
The CNCF and Linux Foundation Research have opened this year's annual survey on cloud native adoption, covering containers, Kubernetes, and challenges users face. Respondents receive a 35% discount on a Linux Foundation Education course.
Infrawrench is a unified cloud management tool that connects to 25+ providers, offering SSH, Kubernetes management, SQL editing, object storage browsing, and custom dashboards in a single interface.
The article draws parallels between the rise of open-weight AI models and Kubernetes, arguing that open-weight models are becoming a foundational platform for AI innovation. It warns against US isolation and encourages competition in the open-weight AI ecosystem.
This paper introduces CRAWO, a framework for adaptive workload orchestration of AI pipelines across heterogeneous edge infrastructures. It uses a control-loop model and Kubernetes-based implementation to improve workload distribution and reduce reliance on centralized cloud processing, demonstrated in a vehicle surveillance scenario.