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News and links on infrastructure and building Kubernetes clusters curated by the @Learnk8s team
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Channel Posts
Repost from LearnKube news
hanoi-cli analyzes pod distribution across nodes, detects CPU/memory hotspots, generates safe redistribution plans, and simulates node failures — all without touching the cluster.
More: https://ku.bz/7jV8-XGg8
| 2 | node-address-labeler is a controller that watches a node's interface and dynamically labels Kubernetes nodes with node.ip/<ip> based on real-time interface changes.
More: https://ku.bz/WdmR1LGQh | 61 |
| 3 |  Jorrick Stempher explains his team's 10-week research project on predictive Kubernetes scaling.
The project involved two parallel research streams: cloud compute optimization for selecting optimal VM configurations, and scaling automation focused on predictive models and node startup times.
Watch the full episode: https://ku.bz/clbDWqPYp | 66 |
| 4 |  Registries fail. Images disappear. Pulls break.
Enix is hosting a free live Kubernetes session on surviving registry failures with kuik and Jérôme Petazzoni!
The session covers how kuik, the open-source kube-image-keeper operator from Enix, helps keep images available when Docker Hub rate limits hit, tags disappear, registries go down, or workloads start failing with ErrImagePull and ImagePullBackOff.
📆 June 18
🕗 8am PT / 5pm CEST
⏱️ 45 min + Q&A
🌐 Online
Register: https://ku.bz/mqL0HP8-p | 56 |
| 5 |  This article explains how building a k3s media server with Claude Code exposed both the speed and the limits of AI-first engineering across GitOps, observability, storage tuning, and Kubernetes debugging.
More: https://ku.bz/94Y_G5wtb | 104 |
| 6 | Shyam Jeedigunta, Principal Engineer at Amazon Web Services (AWS), shares his approach to building production-ready AI/ML platforms on Kubernetes.
Shyam discusses specific AWS technologies like EKS Auto for GPU and accelerator support (including Trainium and Inferentia), highlighting how teams can balance elastic and static capacity based on workload predictability. He covers performance optimizations, including fast image pulls with Containerd using Seekable OCI and multi-ENI pods that allow AI/ML workloads to leverage multiple network cards for high-bandwidth processing.
Watch the full interview: https://ku.bz/SG50vplJs | 111 |
| 7 | Sai Vennam, Principal Solutions Architect at Amazon Web Services (AWS), addresses a critical production challenge: clusters that scale up easily but struggle with efficient scale-down and rebalancing.
Sai positions Karpenter as the primary solution for intelligent node provisioning and cost optimization. He walks through a practical example where peak traffic utilizes three nodes at 80% capacity, but as demand drops, those same workloads spread across three nodes at only 20% utilization each. With Karpenter's bin packing capabilities, these workloads can be consolidated onto a single node at 60% utilization.
Watch the full interview: https://ku.bz/MgHpbXg4Y | 98 |
| 8 |  PostgreSQL on Kubernetes is no longer “can we?” but “how?”
This guide covers the architecture choices that matter: operators, HA, PgBouncer, storage, monitoring, backups, and PITR.
https://ku.bz/LvMcNf6KT | 74 |
| 9 |  With k-inv, you can stress a Kubernetes cluster in a fun way and check its resilience by playing space invaders.
More: https://ku.bz/chMMB0vF_ | 434 |
| 10 |  This week on Learn Kubernetes Weekly 187:
🧠 Applying Kubernetes Patterns to LLM Workloads
🐢 Why Your Grafana is Slow on Kubernetes (and 3 Replicas Won't Fix It)
📊 Observability at Albert Heijn
🎬 Vibe Coding a Kubernetes Media Server: What I Learned About AI-First Engineering
🔌 Installing Kong Gateway Custom Plugins on Kubernetes using Helm Charts
Read it now: https://kube.today/issues/187
⭐️ This newsletter is brought to you by WeAreDevelopers World Congress — The World’s Largest Event for Developers, AI Builders & Tech Leaders https://ku.bz/cwnthSpPK | 49 |
| 11 | This case study walks through a real debugging story on EKS Fargate where missing a DHCP option set caused silent DNS failures and pods stuck in pending — and how to find and fix it.
More: https://ku.bz/vHt_h5qKy | 108 |
| 12 | Kubee automates the setup of a K3s cluster on a single VPS and installs tightly integrated Helm charts (ArgoCD, Vault, Prometheus, etc.) with zero manual configuration.
More: https://ku.bz/1sWyY2M2w | 129 |
| 13 | @miamorecadenza CEO at Techaro shares a pragmatic approach to building Kubernetes homelabs, emphasizing the principle of "plan for today, not tomorrow".
The discussion focuses on the three fundamental building blocks: compute, network, and storage. Xe explains how to build incrementally and know when to stop adding features.
Watch the full episode: https://ku.bz/2kzj2MgfH | 131 |
| 14 |  🚀 New on LearnKube: Microservice authentication with Kubernetes Service Accounts.
Service Accounts are usually described as identities used to call the Kubernetes API.
But you can also use them to authenticate requests between services inside the cluster.
The article walks through:
- how an API service can pass its Service Account token to a data store
- how the data store can validate the token with the TokenReview API
- why accepting any valid token is not enough
- how projected Service Account tokens let you bind a token to a specific audience
Thanks to Gulcan for putting together the full walkthrough with diagrams, manifests, Go snippets, TokenReview examples, and projected Service Account tokens.
Read the full guide:
https://learnkube.com/microservices-authentication-kubernetes | 82 |
| 15 | SREs are drowning in logs. The real error is buried under thousands of noise messages — and AI tools are starting to fix that.
Mario Fahlandt sees AI's biggest value in reducing white noise: filtering log floods so engineers can focus on the actual issue instead of sifting through patterns manually. But he draws a clear line — automated AI debugging of clusters is still a step too far. The human component matters.
AI should handle the groundwork. Humans should make the calls.
Watch the full interview: https://ku.bz/36wSy63cC
This interview is a reaction to Isala Piyarisi's episode https://ku.bz/kJjXQlmTw | 155 |
| 16 |  This blog post tells how the Render team:
- tracked down Kubernetes memory waste caused by many daemonset namespace watches,
- fixed config issues,
- and freed over 7 TiB of memory across clusters by reducing unnecessary listwatch overhead.
More: https://ku.bz/2vS0QsvjY | 875 |
| 17 | Kubectl OpenAI plugin is a kubectl plugin to generate and apply Kubernetes manifests using OpenAI GPT.
More: https://ku.bz/fxBdsk7Kf | 162 |
| 18 | Pumba lets you kill, pause, and stress containers while injecting network delays, packet loss, and corruption.
You can deploy it as a DaemonSet for cluster-wide chaos engineering.
More: https://ku.bz/K7_RB9tSq | 148 |
| 19 | Billy Thompson, DevOps Platform Engineering - Office of the CTO @ Akamai, discusses the strategic decision between building custom Kubernetes tools versus adopting existing CNCF projects.
The discussion provides a practical framework for evaluating time investment, maintenance capacity, and the broader impact of tooling decisions in Kubernetes environments.
Watch the full interview: https://ku.bz/Jk2xSwXHp
This interview is a reaction to Alessandro Pomponio's episode https://ku.bz/5sK7BFZ-8 | 169 |
| 20 | This article shows why Grafana becomes slow on Kubernetes when multiple replicas share SQLite over EFS, and explains why a single replica on block storage or a real external database is the correct fix.
More: https://ku.bz/JGj7gl5wt | 129 |
