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Publicaciones del Canal
Repost from N/a
Zain Malik, Software @ Exostellar, explains how his team doubled the number of managed Kubernetes clusters without growing headcount.
He describes how automation through Cluster API and GitOps transformed their operations by reducing firefighting and manual node pool upgrades. The improvements allowed them to reduce from multiple dedicated engineers to just one person handling cluster upgrades, with the ability to rotate different team members through standardized processes. Zain highlights how removing human intervention from critical operations and implementing defined rules for workload disruption significantly reduced user-impacting incidents during maintenance windows.
Watch the full episode: https://ku.bz/5PLksqVlk
| 2 |  Tsahi Duek from AWS is watching three different Kubernetes layers: composition, scheduling, and autoscaling.
- KRO handles resource composition while ACK bridges Kubernetes to AWS APIs.
- Kueue manages gang scheduling for GPU workloads — keeping nodes physically close for better network topology.
- Karpenter handles node provisioning, including capacity reservations for AI workloads.
Watch the full interview: https://ku.bz/2r41YKBZb | 229 |
| 3 | This article explains how kube-gpu-top helps Kubernetes teams find idle or compute-idle GPUs by mapping NVIDIA GPU metrics to owning pods and estimating monthly waste without Prometheus or Grafana.
More: https://ku.bz/RztgvXMZZ | 222 |
| 4 |  Migratowl is an AI-powered dependency migration analyzer that upgrades dependencies in isolated sandboxes, runs tests, reads changelogs, explains breakages, and suggests fixes.
More: https://ku.bz/5Ddt6Pjj1 | 221 |
| 5 | kubectl-mcp-server lets AI assistants use natural language to inspect and manage Kubernetes clusters through kubectl operations, Docker support, kubeconfig mounting, and MCP-compatible tooling.
More: https://ku.bz/r2PJ8Y4zs | 277 |
| 6 | Olawale Olaleye breaks down the three Kubernetes tools he is watching closely for AI workloads: KRO, KServe, and Karpenter.
He explains why each one matters, from reusable Kubernetes abstractions to serving model inference and provisioning the right compute for AI.
Watch the full interview: https://ku.bz/LKc3mB0lp | 282 |
| 7 |  This article explains how to detect which running Kubernetes containers use Chainguard or other base images by reading runtime OS data through node-level proc inspection.
More: https://ku.bz/_hgKPc3L- | 281 |
| 8 |  This week on Learn Kubernetes Weekly 192:
🔧 Our Kubernetes Operator Didn't Scale, So We Rebuilt It
🔀 ClickHouse Shard Rebalancing on Kubernetes: From Talk to Operator
💥 Invisible OOMkill: Java Pods Crashing in Kubernetes
🗂️ Using Kubernetes ConfigMaps as a Real-Time State Store
🚨 From Container Escape to Cloud Takeover: A Real-World Cloud Security Assessment
Read it now: https://kube.today/issues/192
⭐️ This newsletter is brought to you by Buoyant, the creators of Linkerd https://ku.bz/BB-RtVFWs | 826 |
| 9 | LFK is a keyboard-first terminal UI for navigating Kubernetes clusters with a Miller-column layout, owner-based resource hierarchy, logs, Helm, ArgoCD, Trivy, RBAC, and crash investigation views.
More: https://ku.bz/-GycMtgx6 | 316 |
| 10 |  Amine Hilaly, Software Development Engineer at Amazon Web Services (AWS), shares his biggest takeaway from KubeCon: gang scheduling.
This Kubernetes concept involves scheduling multiple pods simultaneously, rather than individually.
Watch the full interview: https://ku.bz/DVM_j_Qjw | 339 |
| 11 |  This article shows how a probe-driven Go load balancer was tested against Kubernetes workloads and why benchmark discipline mattered more than the algorithm.
More: https://ku.bz/4RK0ZTM26 | 294 |
| 12 |  k8s-d2 generates D2 diagram files from Kubernetes cluster topology, visualizing namespaces, workloads, services, and their relationships with customizable grid layouts and filtering options.
More: https://ku.bz/xQh3dWh5q | 229 |
| 13 | Molly Sheets, Director of Engineering, Kubernetes at Zynga, explains the strategic decision points for when teams should create separate clusters instead of continuing with multi-tenant approaches. She outlines two primary scenarios: geographic and latency requirements for global gaming deployments, and critical workload isolation during major incidents.
Using Battle Royale games as an example, Molly demonstrates how different game components have varying latency needs - competitive gameplay requires ultra-low latency through regional clusters, while transactional features like purchasing can tolerate higher latency. She also discusses how separate clusters enable prioritized incident response, allowing teams to handle critical workloads first when cloud providers experience outages or availability zone failures.
Watch the full episode: https://ku.bz/Rmpl8948_ | 248 |
| 14 | Sai Vennam, Principal Solutions Architect at Amazon Web Services (AWS), discusses the evolution of Kubernetes over its first decade and what the next 10 years hold.
He reflects on how the community has matured from learning basic concepts to implementing complex patterns and best practices at scale.
Watch the full interview: https://ku.bz/MgHpbXg4Y | 345 |
| 15 | Ariadne turns Kubernetes cluster state into a Memgraph property graph so agents can answer relationship-heavy questions with read-only Cypher instead of raw YAML dumps.
More: https://ku.bz/s3Pyv-5M9 | 341 |
| 16 | Three tools Raglin Anthony is keeping an eye on: KRO (Kube Resource Orchestrator), Kueue/Multi-Kueue, and KubeVirt.
- KRO simplifies the creation of custom APIs in Kubernetes clusters.
- Multi-Kueue solves GPU job scheduling across regions for AI/ML workloads.
- KubeVirt lets you run virtual machines inside Kubernetes now that EC2 supports nested virtualization.
Watch the full interview: https://ku.bz/2XqMJnLVx | 356 |
| 17 | Tanat Lokejaroenlarb, Staff Site Reliability Engineer at Adevinta, explains their wave-based approach to Kubernetes upgrades despite thorough preparation with node rebuilds and API deprecation checks. He shares a valuable lesson from a Reddit post-mortem where an upgrade from Kubernetes 1.23 to 1.24 caused a major outage despite comprehensive testing in development environments.
Tanat's team implements a gradual upgrade strategy by categorizing clusters based on workload criticality. They start with less critical clusters, monitor for 1-2 days to ensure stability, then progressively move to higher-impact systems. This methodical approach acknowledges that "nothing is like production" and builds confidence by validating changes in real production environments with minimal business risk first.
Watch the full episode: https://kube.fmhttps://ku.bz/VVHFfXGl_ | 283 |
| 18 | Most teams reach for custom operators when they need to automate Kubernetes workflows. Jason Deal from AWS thinks KRO (Kubernetes Resource Orchestrator) changes that equation.
KRO lets you define complex automations using declarative custom resources — no operator code required. Paired with ACK (AWS Controllers for Kubernetes), you can manage AWS primitives directly from Kubernetes. And for node lifecycle, Karpenter continues to evolve: v1 shipped over a year ago, and work on accelerated hardware support and reserved capacity is ongoing.
Three tools worth watching if you want to reduce the operational surface of your cluster.
Watch the full interview: https://ku.bz/1_-DTgLsg | 306 |
| 19 | Infisical's case study explains why its Kubernetes operator hit memory and authentication scaling limits and how a reference-based CRD design fixed secret sync.
More: https://ku.bz/-V6qjC7h- | 942 |
| 20 |  zeropod is a tool that automatically checkpoints containers to disk after a certain amount of time of the last TCP connection, allowing for fast and seamless scaling down to zero.
More: https://ku.bz/DXHBX4qqQ | 265 |
