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Дописи каналу
Repost from N/a
Kubernetes abstracts away Linux primitives — but understanding them makes you better at your job.
Mauro Morales explains why knowing cgroups, namespaces, and how Linux processes work gives you a real advantage. You can architect systems deliberately instead of guessing, debug problems below the Kubernetes layer, and use that knowledge as a safety net when the abstractions don't give you answers.
You don't need to be an expert. But fluency in the layer beneath pays off.
Watch the full interview: https://ku.bz/8cpgjFfjn
This interview is a reaction to Eric Jalal's episode https://ku.bz/-jCTfgqRC
| 2 |  Federico Iezzi, Customer Engineer at Google Cloud, explains how his team achieved 1 million output tokens per second using Qwen 3.5 27B, vLLM, GKE Autopilot, and NVIDIA B200 GPUs.
You will learn:
- Why memory bandwidth limits decode performance
- How Federico chose between tensor and data parallelism
- What changed after enabling multi-token prediction and reducing the KV cache footprint with FP8 quantization
Watch (or listen to) it here: https://ku.bz/1xD9Md0mb
🌟 This episode is brought to you by LearnKube. Download the free book, The Technical Guide to Kubernetes Rightsizing, to understand what Prometheus and Grafana cannot tell you about safely reducing requests and limits: https://learnkube.com/kubernetes-rightsizing
With @Birthmarkb | 53 |
| 3 | Grafana Beyla is an open source eBPF-based tool that auto-instruments HTTP/gRPC apps to collect essential traces and RED metrics and exports them in OpenTelemetry or Prometheus formats.
More: https://ku.bz/61N3-25F4 | 230 |
| 4 | "Kubernetes 2.0. Let's go."
Nicholas Eberts on where Kubernetes is heading: AI/ML workloads are pushing the platform to adapt in ways it hasn't had to historically. Expect major movement in the scheduler — look at DRA (Dynamic Resource Allocation) and how it handles disparate hardware classes within deployments.
These changes are shaping what Kubernetes 2.0 will look like. The question is how far it can keep evolving with the industry.
Watch the full interview: https://ku.bz/jlDL5XzCd | 208 |
| 5 | This tutorial explains how to sign and verify Docker images in Amazon ECR using Cosign and AWS KMS.
It also shows how trusted image enforcement can fit into EKS and Kyverno-based supply chain security.
More: https://ku.bz/NG8185Rvq | 248 |
| 6 |  This case study shows how a 3-person team built a Claude-based AI SRE that triages SigNoz alerts, checks Kubernetes, GitLab, logs, traces, and Slack, then ignores noise, escalates, or runs safe fixes.
More: https://ku.bz/X_PPQ-4lR | 660 |
| 7 | Custom compute classes and in-place pod resize change how teams manage Kubernetes workloads.
Roman Arcea explains how both features reduce manual tuning and make cluster operations easier to control.
Watch the full interview: https://ku.bz/sFvFF3Kvs | 258 |
| 8 | This case study explains how one private container registry was shared across six AKS clusters in three regions.
It covers ACR geo-replication, image pull reliability, network access, permissions, and operational breakages.
More: https://ku.bz/CjrRY3Jp8 | 205 |
| 9 | Stephan Schwarz, DevOps Engineer at iits-consulting, explains the difference between load testing and observability when diagnosing performance issues in production Kubernetes environments. He addresses a common challenge: what to do when requests are slow in production and you can't run disruptive load tests to identify the root cause.
Stephan introduces OpenTelemetry tracing as the solution for distributed systems running in Kubernetes.
This approach enables teams to investigate specific customer requests that are performing poorly while other requests work normally, providing granular visibility into distributed application performance without impacting production workloads. The discussion highlights how observability complements traditional performance testing by addressing real-time production debugging scenarios.
Watch the full episode: https://ku.bz/yY-FnmGfH | 227 |
| 10 |  "I just want Kubernetes to disappear."
Zain Malik from ExoStellar shares his vision for the next decade: a world where Kubernetes becomes so automated and self-reliant that developers don't even know they're running on it.
The goal isn't to learn Kubernetes—it's to move to the next layer where everyone can reliably build applications without thinking about the underlying platform.
Watch the full interview: https://ku.bz/rwttMCncv | 244 |
| 11 |  Fulling automatically sets up a complete development environment with Next.js, PostgreSQL, and Claude Code so you can build full-stack apps through natural language conversation.
More: https://ku.bz/NWz4hg2d6 | 256 |
| 12 |  This tutorial shows how to deploy a 5-agent ADK system on Amazon EKS and test Agent-to-Agent workflows with Gemini CLI.
More: https://ku.bz/NwN8mpHb7 | 282 |
| 13 |  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 | 255 |
| 14 |  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 | 876 |
| 15 | 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 | 282 |
| 16 |  k8s-ingress-gen is a visual diagram builder for Kubernetes resources with bidirectional YAML workflow.
More: https://ku.bz/v84YBJFK2 | 277 |
| 17 | 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 | 289 |
| 18 | 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 | 279 |
| 19 | 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 | 278 |
| 20 | 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 | 280 |
