David Pech, Staff Cloud Ops Engineer, shares hard-learned lessons about introducing Kubernetes and cloud-native practices to organizations with legacy systems. Drawing from a real-world experience where his technically superior Kubernetes solution was ultimately rejected in favor of FTP deployment, David identifies three critical organizational factors that determine success:

1. Team readiness is fundamental - the development team must first recognize there's a problem worth solving.
2. Leadership must actively champion the change, not just passively approve it.
3. Understanding whether customers can actually operate the new solution is a go/no-go decision that many platform engineers overlook.

Watch the full episode: https://ku.bz/_MWX5m6G_

This episode is sponsored by LearnKube - get started on your Kubernetes journey through comprehensive online, in-person or remote training https://learnkube.com/training

When one pull request spans app code, config, and CI, the weakest point is usually not the app.

Artem Lajko explains that ownership is split between developers and platform teams, so config changes lack true end-to-end accountability. That makes breakage harder to spot before merge and easier to discover only when CI or infrastructure behavior changes.

When AI writes Kubernetes, who catches the risk? Qodo explores this in a series on wahy faster infrastructure code needs smarter review, not just more automation → https://ku.bz/wy4kwdFqg

Oleksii Kolodiazhnyi, Senior Architect @ Mirantis, explains the motivation behind his structured approach to Kubernetes workload assessment and his article "Graphs in Your Head."

Oleksii shares how he transformed his real-world assessment work on Mirantis Source Registry 4 (MSR4) into actionable guidance, bridging the gap between academic theory and practical application for architects and engineers who need to assess existing systems or build new ones.

Watch the full episode: https://ku.bz/zDThxGQsP

This episode is brought to you by StormForge — automate Kubernetes rightsizing with machine learning. Smarter limits, less waste, better performance. https://ku.bz/X7ls6SKmr

New infrastructure often gets treated like a shortcut to better delivery. It rarely works that way.

Nathen Harvey explains why tools are necessary but not sufficient: every infrastructure shift also changes how teams work, collaborate, and approach delivery. Kubernetes, containers, and observability all shape engineering practice, not just the technical stack.



Watch the full interview: https://ku.bz/LvXfZXzcN

This interview is brought to you by Google Cloud — build on GKE, the open platform for the AI era https://ku.bz/kKZQySLRV

Kubernetes tooling is shifting from deployment pipelines to AI request routing.

Joel Vasallo walks through the tools he is watching now: Argo and Kargo for software delivery, then Kagent and Agent Gateway for the next wave of platform work. His point is simple: platform teams now need standards for how AI and agent traffic gets routed, observed, and deployed.



Watch the full interview: https://ku.bz/zTPZwj-__

This interview is brought to you by Google Cloud — build on GKE, the open platform for the AI era https://ku.bz/kKZQySLRV

Karpenter can consolidate nodes and reduce cloud costs. But without Pod Disruption Budgets, those savings can expose workloads to avoidable downtime.

Ahmad Asmar from Zencity explains how they use Kyverno to generate PDBs automatically instead of relying on every developer to remember the required configuration.

You will learn:

- Why Karpenter consolidation and Spot instances make PDB coverage critical
- How Kyverno detects existing PDBs through workload labels
- Why percentage-based budgets work better for scaling workloads
- How ClusterRole aggregation extends Kyverno without modifying its Helm chart

Watch: https://ku.bz/xrlPJg54D

🌟 This episode is sponsored 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

Karpenter can consolidate nodes and reduce cloud costs. But without Pod Disruption Budgets, those savings can expose workloads to avoidable downtime.

Ahmad Asmar from Zencity explains how they use Kyverno to generate PDBs automatically instead of relying on every developer to remember the required configuration.

You will learn:

- Why Karpenter consolidation and Spot instances make PDB coverage critical
- How Kyverno detects existing PDBs through workload labels
- Why percentage-based budgets work better for scaling workloads
- How ClusterRole aggregation extends Kyverno without modifying its Helm chart

Watch: https://ku.bz/xrlPJg54D

🌟 This episode is sponsored 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

Brian Donelan, VP Cloud Platform Engineering @ JPMorganChase, explains the technical architecture behind his screen lock-based autoscaling solution. He walks through how a macOS daemon continuously monitors screen lock events and sends boolean metrics to CloudWatch, creating a time series that tracks whether his laptop is locked or unlocked.

Brian details how KEDA (Kubernetes Event-Driven Autoscaler) polls these CloudWatch metrics every 60 seconds and scales pods based on the boolean values - scaling up when unlocked (1) and down to zero when locked (0) or offline. He explains the KEDA scaled object manifest configuration, including minReplicaCount: 0 and maxReplicaCount: 1 settings that enable scale-to-zero capabilities.

Watch the full episode: https://ku.bz/sFd8TL1cS

This episode is brought to you by Testkube—the ultimate Continuous Testing Platform for Cloud Native applications. Scale fast, test continuously, and ship confidently https://ku.bz/lnxYK3s0L

Kubernetes turned 10 last year. Where does it go from here?

Zach Aller wants the declarative API to keep growing. Specifically, he's watching aggregated API services — CRDs that aren't stored in etcd — as a way to power entirely new types of APIs within the Kubernetes ecosystem. His vision: Kubernetes evolves from "just an autoscaler" into a full API platform where the declarative model extends far beyond container orchestration.

If you think Kubernetes is just for running pods, this might change your mind.



Watch the full interview: https://ku.bz/7Bf_w3bN_

This interview is brought to you by StormForge — automate Kubernetes rightsizing with machine learning. Smarter limits, less waste, better performance. https://ku.bz/X7ls6SKmr

New from LearnKube: Setting the right requests and limits in Kubernetes.

CPU and memory requests and limits appear together in a Pod manifest, but Kubernetes and Linux use them at different stages.

You will learn:

• How the scheduler compares requests with node allocatable capacity
• Why actual usage can exceed a request
• How CPU quotas cause throttling even when average CPU looks low
• Why an OOM kill is different from a node-pressure eviction
• How requests and limits determine Kubernetes QoS classes

The article is the first chapter of The Technical Guide to Kubernetes Rightsizing, co-authored by Gulcan and Daniele Polencic.

Read the chapter:
https://learnkube.com/setting-cpu-memory-limits-requests

Download the free technical guide:
https://learnkube.com/kubernetes-rightsizing

This book was made possible by CloudBolt Software.

How do you know when your control plane is struggling?

Alex Kestner shares three signals: API server request latency is the most critical — if it's growing, pay attention. Next, watch the API priority and fairness metrics to see if requests are failing to complete. Finally, keep an eye on etcd database size — if it's filling up, you're heading for trouble.



Watch the full interview: https://ku.bz/blMzG_XM7

This interview is brought to you by AWS — catch the talks and workshops from KubeCon Amsterdam 2026 on EKS, Karpenter, and GenAI on Kubernetes https://ku.bz/XM0_gPzX1

Danyl Novhorodov, Software Engineer & Architect @ Eneco, examines the organizational patterns behind Kubernetes adoption decisions.

He argues that Kubernetes often enters organizations through aspiration rather than clearly defined technical problems, with teams wanting to "level up" or match what they perceive big tech companies are doing.

The discussion highlights how political and emotional drivers often override technical considerations in infrastructure decisions, and provides a framework for distinguishing between legitimate use cases (companies with millions of customers and unpredictable traffic patterns) versus premature adoption scenarios.

Watch the full episode: https://ku.bz/BYhFw8RwW

Danyl Novhorodov, Software Engineer & Architect @ Eneco, examines the organizational patterns behind Kubernetes adoption decisions.

He argues that Kubernetes often enters organizations through aspiration rather than clearly defined technical problems, with teams wanting to "level up" or match what they perceive big tech companies are doing.

The discussion highlights how political and emotional drivers often override technical considerations in infrastructure decisions, and provides a framework for distinguishing between legitimate use cases (companies with millions of customers and unpredictable traffic patterns) versus premature adoption scenarios.

Watch the full episode: https://ku.bz/BYhFw8RwW