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Modern cloud-native architecture is distributed by design — code repos, CI/CD pipelines, metrics, logs, traces, all spread across different tools and layers. Greg Eppel explains how AWS DevOps Agent fits into this: it connects across the whole toolchain,…

Modern cloud-native architecture is distributed by design — code repos, CI/CD pipelines, metrics, logs, traces, all spread across different tools and layers. Greg Eppel explains how AWS DevOps Agent fits into this: it connects across the whole toolchain, stitches all those signals together, and simplifies what is typically an overwhelming amount of context into something an agent can reason about. Watch the full interview: https://ku.bz/KZRgrbmwt

Kubernetes at the edge, AI inference, and better UX — that's the next decade. Peter Kelly sees three big shifts ahead: Kubernetes expanding to edge deployments, supporting AI model inference through projects like llm-d, and UX improvements with tools like…

Kubernetes at the edge, AI inference, and better UX — that's the next decade. Peter Kelly sees three big shifts ahead: Kubernetes expanding to edge deployments, supporting AI model inference through projects like llm-d, and UX improvements with tools like Headlamp. The platform is maturing. What comes next is about reach and usability. Full interview: https://ku.bz/xgqZJhdyn Watch the full interview: https://ku.bz/xgqZJhdyn

Owumi Festus explains his philosophy of building complex systems from scratch to understand their core mechanisms. He describes how constructing a simplified version of Kubernetes in Go revealed that the orchestration platform is much simpler than commonly…

Owumi Festus explains his philosophy of building complex systems from scratch to understand their core mechanisms. He describes how constructing a simplified version of Kubernetes in Go revealed that the orchestration platform is much simpler than commonly perceived when broken down to its fundamental components. Watch the full episode: https://ku.bz/pf5kK9lQF

AI models are expensive, but the bigger problem is who can access them once they run on Kubernetes. Glen Messenger explains that frontier AI customers require more than standard cluster security. They need provable isolation for model weights, platform admins…

AI models are expensive, but the bigger problem is who can access them once they run on Kubernetes. Glen Messenger explains that frontier AI customers require more than standard cluster security. They need provable isolation for model weights, platform admins, and cloud providers, which pushes GKE toward attestation and a much smaller trusted computing base. Watch the full interview: https://ku.bz/N5njxPHdY

Molly Sheets, Director of Engineering, Kubernetes at Zynga, challenges conventional approaches to measuring Kubernetes reliability in multi-tenant environments. She explains why traditional metrics like control plane uptime and node availability aren't useful…

Molly Sheets, Director of Engineering, Kubernetes at Zynga, challenges conventional approaches to measuring Kubernetes reliability in multi-tenant environments. She explains why traditional metrics like control plane uptime and node availability aren't useful when running managed Kubernetes services like Amazon EKS, where the cloud provider handles much of the infrastructure layer. Instead, Molly advocates for focusing on SLOs for individual applications and add-ons. She provides specific examples from Zynga's infrastructure, emphasizing the importance of monitoring critical networking components like Cilium agents and external DNS, since failures in these areas cascade to affect all workloads in the cluster. Watch the full episode: https://ku.bz/Rmpl8948_

A practical place to start with AI in Kubernetes is not replacing engineers. It is improving the developer experience. Christopher Tineo argues that teams should use AI and platform tooling to simplify deployments, standardize secure container builds, and…

A practical place to start with AI in Kubernetes is not replacing engineers. It is improving the developer experience. Christopher Tineo argues that teams should use AI and platform tooling to simplify deployments, standardize secure container builds, and make CI/CD easier to use so developers spend less time fighting delivery workflows. Watch the full interview: https://ku.bz/5f9sFC6CG

Most teams are excited about AI generating YAML. David Parry says the safer first step is using AI to assist developers and platform teams, not handing it autonomous control of production cluster operations. He argues that AI is useful for drafting configs…

Most teams are excited about AI generating YAML. David Parry says the safer first step is using AI to assist developers and platform teams, not handing it autonomous control of production cluster operations. He argues that AI is useful for drafting configs, reviewing logs, and adding checks, but human accountability still has to stay in the loop. Watch the full interview: https://ku.bz/c5J05syX3

Not every organization can move to the cloud immediately — data residency, sovereignty, and compliance requirements are real constraints. Raglin Anthony walks through the options: self-managed clusters with Kubeadm, vendor-managed solutions, AWS Outposts…

Not every organization can move to the cloud immediately — data residency, sovereignty, and compliance requirements are real constraints. Raglin Anthony walks through the options: self-managed clusters with Kubeadm, vendor-managed solutions, AWS Outposts, EKS Hybrid Nodes, and EKS Anywhere for fully air-gapped environments. Each trade controls for operational burden differently. Watch the full interview: https://ku.bz/2XqMJnLVx

Ben Poland, Senior Staff Platform Engineer @ Faire, breaks down why their massive monolithic CI pipeline became a major bottleneck for developer productivity. He explains how 4-5 thousand lines of Groovy code with complex pipeline libraries created a system…

Ben Poland, Senior Staff Platform Engineer @ Faire, breaks down why their massive monolithic CI pipeline became a major bottleneck for developer productivity. He explains how 4-5 thousand lines of Groovy code with complex pipeline libraries created a system so difficult to understand that only a few platform engineers could make changes, forcing teams to rely on centralized support instead of self-service. Watch the full episode: https://ku.bz/klBmzMY5-

This week on Learn Kubernetes Weekly 196: 🛠️ How I Rebuilt YouTube’s Load Balancing Algorithm in Go 💾 When ETCD Crashes, Check Your Disks First: A Pod CrashLoopBack Debugging Story 🧩 We Blamed CoreDNS for Weeks. The Real Culprit Was a Default We Never Questioned 🚚 Zero-Downtime EKS Migration: Production-Grade Kubernetes at Scale ⚡ Deterministic Routing: The Hidden Key to Low Latency Read it now: https://kube.today/issues/196 ⭐️ 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

Rolling your worker nodes during a Kubernetes upgrade sounds straightforward… until your PDBs get ignored. Jason Deal explains why PDB-aware tooling matters: if your infrastructure provider doesn't respect Pod Disruption Budgets, those constraints won't protect…