Forwarded from AI Vibe Notes
Hands-on comparison against a deliberately broken cluster, with real outputs and failure-mode differences. Practical for deciding where AI Kubernetes tools fit: scanner, agent framework, or natural-language kubectl layer.
https://decodeops.substack.com/p/k8sgpt-vs-kagent-vs-kubectl-ai-what
https://decodeops.substack.com/p/k8sgpt-vs-kagent-vs-kubectl-ai-what
Substack
K8sGPT vs Kagent vs Kubectl-AI: What Each Actually Does
Install K8sGPT, Kagent, and kubectl-ai. Run all three against a broken Kubernetes cluster. Real output, real comparison, honest verdict on which one to keep.
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The article explores how integrating these two prominent tools can accelerate and enhance platform engineering initiatives.
https://platformengineering.org/blog/platform-engineering-with-crossplane-and-argocd
https://platformengineering.org/blog/platform-engineering-with-crossplane-and-argocd
platformengineering.org
Combining practical Platform Engineering with Crossplane and ArgoCD
A hands-on look into tools and their integrated usage can help kickstart your knowledge about Platform Engineering patterns. Therefore let's dive into two top frameworks when it comes to Platform Engineering today: Crossplane & ArgoCD. Both alone can alreadyโฆ
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Containerlab focuses on the containerized Network Operating Systems which are typically used to test network features and designs
https://github.com/srl-labs/containerlab
https://github.com/srl-labs/containerlab
GitHub
GitHub - srl-labs/containerlab: container-based networking labs
container-based networking labs. Contribute to srl-labs/containerlab development by creating an account on GitHub.
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Lessons from Moving a Live Production Database describes the complex process of migrating a massive dataset while keeping the service available to users.
The case study covers specific strategies for achieving zero downtime during a high-risk infrastructure change.
But is zero downtime always worth the engineering effort?
Imagine that you have two options:
1. Spend several weeks preparing and testing a zero-downtime migration.
2. Schedule 20 minutes of downtime during a low-traffic period.
Considering that 99.9% availability allows approximately 43 minutes of downtime per month, which option would you choose?
What factors would change your decision: revenue loss, SLA penalties, customer expectations, rollback complexity, or the size of the engineering team?
https://www.tines.com/blog/zero-downtime-database-migrations-lessons-from-moving-a-live-production/
The case study covers specific strategies for achieving zero downtime during a high-risk infrastructure change.
But is zero downtime always worth the engineering effort?
Imagine that you have two options:
1. Spend several weeks preparing and testing a zero-downtime migration.
2. Schedule 20 minutes of downtime during a low-traffic period.
Considering that 99.9% availability allows approximately 43 minutes of downtime per month, which option would you choose?
What factors would change your decision: revenue loss, SLA penalties, customer expectations, rollback complexity, or the size of the engineering team?
https://www.tines.com/blog/zero-downtime-database-migrations-lessons-from-moving-a-live-production/
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Forwarded from AI Vibe Notes
Hands-on vendor-neutral instrumentation for GenAI spans, tool calls, token metrics, and trace exploration. Worth reading if you want AI-agent debugging to fit existing OTel/Grafana/Loki-style observability rather than a separate black box.
https://opentelemetry.io/blog/2026/genai-observability/
https://opentelemetry.io/blog/2026/genai-observability/
OpenTelemetry
Inside the LLM Call: GenAI Observability with OpenTelemetry
Your AI agent just took 45 seconds to answer a simple question. Was it the model? A slow tool call? A retry loop? Every time an application calls an LLM, a chain of model calls, tool invocations, and token exchanges happens behind the scenes โ and withoutโฆ
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Kubernetes configuration tracking controller.
Wave watches Deployments, StatefulSets and DaemonSets within a Kubernetes cluster and ensures that their Pods always have up to date configuration.
By monitoring mounted ConfigMaps and Secrets, Wave can trigger a Rolling Update of the Deployment when the mounted configuration is changed.
https://github.com/wave-k8s/wave
Wave watches Deployments, StatefulSets and DaemonSets within a Kubernetes cluster and ensures that their Pods always have up to date configuration.
By monitoring mounted ConfigMaps and Secrets, Wave can trigger a Rolling Update of the Deployment when the mounted configuration is changed.
https://github.com/wave-k8s/wave
GitHub
GitHub - wave-k8s/wave: Kubernetes configuration tracking controller
Kubernetes configuration tracking controller. Contribute to wave-k8s/wave development by creating an account on GitHub.
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Ministack: Free, open-source local AWS emulator - 55+ services, Terraform compatible, real databases. Free forever. MIT licensed.
https://github.com/ministackorg/ministack
https://github.com/ministackorg/ministack
GitHub
GitHub - ministackorg/ministack: Ministack: Free, open-source local AWS emulator - 60+ services, Terraform compatible, real databases.โฆ
Ministack: Free, open-source local AWS emulator - 60+ services, Terraform compatible, real databases. Free forever. MIT licensed. - ministackorg/ministack
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Deep dive into Prometheusโs
https://prometheus.io/blog/2026/03/05/uncached-io/
use-uncached-io work and why page cache behavior can make Kubernetes container memory metrics misleading. Useful for anyone running Prometheus at scale: covers memory predictability, compaction writes, OOM risk, and tradeoffs around direct I/O.https://prometheus.io/blog/2026/03/05/uncached-io/
prometheus.io
Uncached I/O in Prometheus | Prometheus
An open-source monitoring system with a dimensional data model, flexible query language, efficient time series database and modern alerting approach.
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A lightweight tool for deploying and managing containerised applications across a network of Docker hosts. Bridging the gap between Docker and Kubernetes โจ
https://github.com/psviderski/uncloud
https://github.com/psviderski/uncloud
GitHub
GitHub - psviderski/uncloud: A lightweight tool for deploying and managing containerised applications across a network of Dockerโฆ
A lightweight tool for deploying and managing containerised applications across a network of Docker hosts. Bridging the gap between Docker and Kubernetes โจ - psviderski/uncloud
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Trusted builds made easy! A cloud-native software factory for building, testing, and releasing trusted software artifacts
Konflux is an open-source, Kubernetes-native CI/CD platform that manages the full software delivery lifecycle for software artifacts โ with supply chain trust built in from the start. Built on Tekton and the Conforma policy framework, it brings together best-in-class open source projects into a single, integrated software factory. Managed by a Kubernetes operator, Konflux runs on Kind, OpenShift, and any conformant Kubernetes cluster.
https://github.com/konflux-ci/konflux-ci
Konflux is an open-source, Kubernetes-native CI/CD platform that manages the full software delivery lifecycle for software artifacts โ with supply chain trust built in from the start. Built on Tekton and the Conforma policy framework, it brings together best-in-class open source projects into a single, integrated software factory. Managed by a Kubernetes operator, Konflux runs on Kind, OpenShift, and any conformant Kubernetes cluster.
https://github.com/konflux-ci/konflux-ci
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Morgan Stanley presented their journey scaling Flux to manage over 500 clusters and tens of thousands of resources. They detailed their transition from push-based CI/CD pipelines to a pull-based, continuous reconciliation model using Flux to eliminate configuration drift. The article outlines their strategy for handling enterprise-grade security, performance tuning for scale, and adapting architecture (like moving from Git to S3) to meet high availability and compliance needs.
https://fluxcd.io/blog/2026/03/stairway-to-gitops-morgan-stanley/
https://fluxcd.io/blog/2026/03/stairway-to-gitops-morgan-stanley/
fluxcd.io
Stairway to GitOps: Scaling Flux at Morgan Stanley
Reflecting on Morgan Stanley's journey to production GitOps with Flux, presented at FluxCon NA 2025.
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Forwarded from AI Vibe Notes
https://www.cncf.io/blog/2026/06/25/building-a-cluster-aware-ai-agent-with-kubernetes-argo-cd-and-gitops/
Practical walkthrough for a self-hosted, read-only cluster AI agent with GitHub Actions, Argo CD Image Updater, and no cluster data leaving the network. Good pattern material for safe โAI assistant inside the clusterโ experiments.
Practical walkthrough for a self-hosted, read-only cluster AI agent with GitHub Actions, Argo CD Image Updater, and no cluster data leaving the network. Good pattern material for safe โAI assistant inside the clusterโ experiments.
CNCF
Building a Cluster-Aware AI Agent with Kubernetes, Argo CD, and GitOps
A practical walkthrough of running a self-hosted, read-only AI agent inside a Kubernetes cluster, with the full CI/CD chain handled by GitHub Actions and Argo CD Image Updater. No data leaves theโฆ
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How GitHub uses eBPF to improve deployment safety
https://github.blog/engineering/infrastructure/how-github-uses-ebpf-to-improve-deployment-safety/
https://github.blog/engineering/infrastructure/how-github-uses-ebpf-to-improve-deployment-safety/
The GitHub Blog
How GitHub uses eBPF to improve deployment safety
Learn how Github uses eBPF to detect and prevent circular dependencies in its deployment tooling.
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From Silos to Service Topology: Why Netflix Built a Real-Time Service Map
https://netflixtechblog.com/from-silos-to-service-topology-why-netflix-built-a-real-time-service-map-0165ba13a7bc
https://netflixtechblog.com/from-silos-to-service-topology-why-netflix-built-a-real-time-service-map-0165ba13a7bc
Medium
From Silos to Service Topology: Why Netflix Built a Real-Time Service Map
By Parth Jain, Rakesh Sukumar, Yingwu Zhao, Renzo Sanchez-Silva & Nathan Fisher How we built a living map of our distributed infrastructureโฆ
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