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| Date | Subscriber Growth | Mentions | Channels | |
| 01 July | +2 |
Channel Posts
Repost from LearnKube news
New on LearnKube: Server-side apply: what happens when you run kubectl apply
Server-side apply changes how Kubernetes handles field ownership.
Kubernetes objects are shared state. Manifests, controllers, release tools, autoscalers, webhooks, and operators can all shape the same object.
With client-side apply, stale intent can overwrite live changes.
With server-side apply, ownership moves into the API server. Kubernetes tracks which manager owns each field and surfaces conflicts when ownership is contested.
Chiara put serious work into this guide, and you can read it in full here: https://learnkube.com/server-side-apply-kubernetes
| 2 | Luxury Yacht is a cross-platform desktop app for managing Kubernetes clusters, available for Linux, macOS, and Windows, built with Go and Wails.
More: https://ku.bz/fxHb6Vcsf | 321 |
| 3 |  Alessandro Pomponio, Research Software Engineer @ IBM Research, explains how his team implemented GitOps to eliminate manual cluster interventions and ensure consistent project configurations across their research computing environment.
Alessandro demonstrates how they leveraged ArgoCD Application Sets with Git directory generators to automatically provision Argo applications for each project folder, ensuring every project inherits their standardized base configuration.
Watch the full episode: https://ku.bz/5sK7BFZ-8 | 396 |
| 4 | Custom metrics adapters are great. The Prometheus adapter is great. But there's a significant lift to get them running.
Nicholas Eberts explains why KEDA changes the game: when you install it, you get everything out of the box β no separate metrics adapter, no manual exports. It gives Kubernetes more of a platform feel with intelligent routing based on what your proxies are exporting.
Watch the full interview: https://ku.bz/jlDL5XzCd | 532 |
| 5 | Thibault Jamet, DevOps and Technical Product Owner @ Adevinta, introduces a critical production incident where their Vertical Pod Autoscaler caused widespread pod evictions across their Kubernetes platform.
Thibault explains their multi-tenant architecture with clusters of varying sizes serving European marketplaces, setting the stage for understanding how autoscaling failures can cascade across complex, distributed environments. This incident analysis reveals the real-world challenges of running VPA in production and the potential for seemingly helpful automation to become destructive at enterprise scale.
Watch the full episode: https://ku.bz/rf1pbWXdN | 487 |
| 6 | Kube Binpacking Exporter exposes Prometheus metrics that show how efficiently your cluster packs requested CPU and memory across nodes, groups, and DaemonSet overhead so you can measure fragmentation over time.
More: https://ku.bz/VpnFlkz-0 | 488 |
| 7 | Marc Campora explains the scenarios where containers are more effective than AWS Lambda for consistent, high-volume traffic.
He covers how computational requirements like GPU and CPU-intensive workloads favor containers due to better control over underlying instances. Marc also discusses Lambda's cold start problem - where execution environments starting on demand create variable latency, especially with complex applications loading many libraries.
Watch the full episode: https://ku.bz/5gMTkzLhV | 586 |
| 8 | This week on Learn Kubernetes Weekly 189:
π₯ Zero-Downtime Kubernetes Ingress Controllers on GCP
ποΈ Architecting GPUaaS for Enterprise AI On-Prem
π Conditions, Phases, and Declarative Phase Rules in Kubernetes Operators
βοΈ Container-Aware GOMAXPROCS
π Why Your Kubernetes Pod Was OOM Killed and Who Really Killed It
Read it now: https://kube.today/issues/189
βοΈ This issue is brought to you by Solanica - replace RDS with a self-hosted DBaaS on your own Kubernetes clusters with automated Day 2 operations and zero vendor lock-in https://ku.bz/NTszfwH40 | 432 |
| 9 | Platform teams face a dilemma: standardize everything and frustrate developers, or give full autonomy and create chaos.
Zain Malik from ExoStellar proposes a clear boundary: high-level APIs (pod specs, deployments) should be fully accessible to users, while low-level components (device plugins, scheduling plugins) must be standardized by the platform team.
This approach gives developers the freedom they need while preventing teams from implementing conflicting topologies and scheduling policies.
Watch the full interview: https://ku.bz/rwttMCncv | 522 |
| 10 | Stephan Schwarz, DevOps Engineer at iits-consulting, explains the performance implications of Kubernetes scaling delays and how to test for them effectively. He discusses how Horizontal Pod Autoscaler scaling isn't instantaneous - there are built-in delays between scaling events, and in larger environments, node scaling adds additional time.
Stephan recommends a practical testing approach: if your deployment can handle 1,000 requests per second, deliberately overload it with 3,000 requests and measure how long Kubernetes takes to recover. This helps teams understand whether the scaling response time aligns with their SLA requirements and error budgets.
He emphasizes that teams often overlook these timing constraints when planning capacity, noting that you may need more spare capacity if the scaling delays don't meet your availability requirements.
Watch the full episode: https://ku.bz/yY-FnmGfH | 506 |
| 11 | Kogaro continuously validates Kubernetes config with 60+ checks across reference, resource, security, image, and network domains, catching silent failures before they impact production.
More: https://ku.bz/_DdDJ5wzj | 492 |
| 12 | Calin Florescu discusses preventing a centralized Helm chart from becoming a single point of failure.
He outlines a two-part strategy: using semantic versioning to control how services consume chart updates (patch, minor, major versions) and implementing thorough testing before releasing new versions. This approach helps teams manage dependencies safely across multiple microservices.
Watch the full episode: https://kube.fmhttps://ku.bz/mcPtH5395 | 561 |
| 13 | Hortator lets AI agents spawn sub-agents at runtime, with each agent running in its own pod with budget caps, network policies, PII redaction, and capability inheritance so children can never escalate beyond their parent's permissions.
More: https://ku.bz/kh47Xb28t | 595 |
| 14 | "Never upgrade in production" - always use a test environment and run Pluto to detect deprecated APIs.
Salman Iqbal on cluster upgrade best practices for Kubernetes platforms.
Watch the full interview: https://ku.bz/vsVhFMNZN
This interview is a reaction to Pierre Mavro's episode https://ku.bz/cVYyDRLqQ | 733 |
| 15 |  This week on Learn Kubernetes Weekly 188:
π₯ When ipBlock Breaks HTTPS in Kubernetes: Debugging NetworkPolicy, Traefik, and Hairpin Routing
π§ How Nginx's New resolve Directive Finally Fixed Our Kubernetes 502s
β‘ Before You Implement KEDA, Do This First
π€ Why Your CI/CD Pipeline Failures Still Need a Human β And How We're Changing That
π Your SLOs Should Be Kubernetes Resources, Not Grafana Dashboards
Read it now: https://kube.today/issues/188
βοΈ This newsletter is brought to you by LearnKube β master Kubernetes with hands-on training designed for engineers who want to learn the smart way https://ku.bz/7py0zX-ct | 608 |
| 16 | This repo contains 20+ tools that auto-generate Kubernetes architecture diagrams from manifests, Helm charts, or cluster state.
More: https://ku.bz/VrpBRx5MF | 598 |
| 17 | Amine Hilaly, Software Development Engineer at Amazon Web Services (AWS), discusses practical strategies for maintaining operational consistency when scaling from a few Kubernetes clusters to dozens across multiple regions.
He identifies 10 clusters in production as a critical threshold where organizations need to fundamentally change their approach from manual cluster management to process-driven automation.
Watch the full interview: https://ku.bz/DVM_j_Qjw | 1 028 |
| 18 | FrΓ©dΓ©ric Gaudet, Senior SRE @ BlaBlaCar, breaks down the technical mechanics behind why Java applications experience slow startup times in Kubernetes environments.
He quantifies the real-world impact: 1-1.5 minutes of degraded performance before applications reach optimal speed. This technical deep-dive provides essential context for understanding why Java workloads need special consideration in containerized environments and sets the foundation for discussing infrastructure solutions like service mesh-based pod warming strategies.
Watch the full episode: https://ku.bz/grxcypt9j | 733 |
| 19 | Review quality tends to drop when changes move from code to configuration.
Shivay Lamba argues that YAML and Helm updates often avoid the same level of automated testing and repeated execution paths that application code gets, which makes configuration defects easier to miss until the later deployment stages approach.
Watch the full interview: https://ku.bz/lzl3pF_Ft | 602 |
| 20 | This tutorial shows how to secure east-west traffic in GKE using an Internal Regional Gateway with Envoy proxies, certificates, HTTP Routes with path rewriting, and a zero-trust architecture for service-to-service communication.
More: https://ku.bz/VqqYrclKm | 569 |
