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Publicaciones del Canal
Repost from Kube Builders
This article explains how to control runaway GPU spend in Kubernetes by adding taints, quotas, labels, Prometheus rules, and admission controls so teams can see who is using expensive GPU workloads and why.
More: https://ku.bz/6cFxnhHGb
| 2 |  This week on Learn Kubernetes Weekly 193:
🔍 Which of our Containers are Chainguard?
💸 One Forgotten Notebook on an A100. $1,800 a Month.
🔄 How an Admin Cluster Keeps Application Clusters in Sync with GitOps
📉 Cost Optimization of Spark on Kubernetes Batch Workloads on Public Clouds
🚪 ingress-nginx Is Archived: How We Migrated to kgateway (and Didn't Break Prod)
Read it now: https://kube.today/issues/193
⭐️ This newsletter is brought to you by LearnKube — master Kubernetes with hands-on training designed for engineers who want to learn the smart way https://ku.bz/hypSbyc-V | 308 |
| 3 |  Ray for distributed ML, K8sGPT for cluster diagnostics, and KRO for simplifying Kubernetes abstractions — Phil Estes from AWS shares the tools he's watching most closely.
1. K8sGPT is a CNCF Sandbox project that scans your cluster and explains issues in plain language.
2. KRO is a cross-cloud collaboration between AWS, Google, and Azure that lets you define reusable Kubernetes abstractions.
3. Ray has become the go-to framework for running distributed AI workloads on Kubernetes.
The pattern across all three: reducing complexity so teams can focus on workloads, not infrastructure.
Watch the full interview: https://ku.bz/_ZLldHwVC | 181 |
| 4 |  k8s-ingress-gen is a visual diagram builder for Kubernetes resources with bidirectional YAML workflow.
More: https://ku.bz/v84YBJFK2 | 180 |
| 5 | The CNCF ecosystem is evolving fast — here are three tools worth watching.
Elamaran Shanmugam highlights:
1. Kagent (declarative agent creation on Kubernetes)
2. Agent Gateway (an API gateway for securing agent-to-agent communication via MCP and A2A)
3. Dapr (a CNCF graduated project bringing building blocks like memory management to AI agents).
As agentic AI grows, these tools are making Kubernetes the platform for running agents alongside traditional workloads.
Watch the full interview: https://ku.bz/DCxhgWQqS | 175 |
| 6 | Kubernetes currently manages CPU, memory, and volumes. But AI workloads need more — GPUs, InfiniBand networking, specialized drivers.
Dynamic resource allocation (DRA) is Kubernetes' answer. Nick Eberts explains how DRA extends the scheduler to handle specialized hardware, making GPU sharing and topology-aware scheduling more efficient. The benefit isn't limited to AI/ML either — any future technology that requires scheduling-time decisions will plug into the same framework.
Watch the full interview: https://ku.bz/G1QSYQTn2
This interview is a reaction to Fabián Sellés Rosa's episode https://ku.bz/NsBZ-FwcJ | 218 |
| 7 | Webernetes is a browser-based Kubernetes simulator that allows users to run a subset of Kubernetes features, including Pods, Services, and Deployments, entirely in the Browser without backend infrastructure.
More: https://ku.bz/9M7CfFK16 | 217 |
| 8 | 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 | 205 |
| 9 | 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 | 314 |
| 10 | 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 | 286 |
| 11 |  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 | 282 |
| 12 | 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 | 325 |
| 13 | 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 | 332 |
| 14 |  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- | 308 |
| 15 |  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 | 962 |
| 16 | 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 | 355 |
| 17 |  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 | 393 |
| 18 |  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 | 330 |
| 19 |  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 | 281 |
| 20 | 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_ | 301 |
