The article explores the newly introduced CloudWatch Logs delivery feature for Amazon EKS Auto Mode.
https://shinyaz.com/en/blog/2026/03/19/eks-auto-mode-enhanced-logging
https://shinyaz.com/en/blog/2026/03/19/eks-auto-mode-enhanced-logging
Shinyaz
Visualizing Karpenter Internals with EKS Auto Mode Enhanced Logging
Set up CloudWatch Vended Logs for EKS Auto Mode's 4 components (Compute/Block Storage/Load Balancing/IPAM) and analyze scale-up to scale-down behavior with Logs Insights queries.
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Airbnb migrated its high-volume metrics infrastructure to adopt the OpenTelemetry Protocol (OTLP) and Prometheus. To do so without massive disruption, they implemented a dual-emit strategy in their shared metrics libraries. They encountered and solved specific performance bottlenecks regarding high-cardinality data and replaced their legacy Veneur aggregator with a custom-sharded vmagent setup. Crucially, they developed a "zero injection" technique to solve systemic undercounting issues when translating StatsD-style counters into Prometheus cumulative counters.
https://medium.com/airbnb-engineering/building-a-high-volume-metrics-pipeline-with-opentelemetry-and-vmagent-c714d6910b45
https://medium.com/airbnb-engineering/building-a-high-volume-metrics-pipeline-with-opentelemetry-and-vmagent-c714d6910b45
Medium
Building a high-volume metrics pipeline with OpenTelemetry and vmagent
A production-tested approach for moving a large-scale metrics pipeline from StatsD to OpenTelemetry and Prometheus.
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A utility for fetching Kubernetes Manifest documents from a running cluster. This utility can be run inside or outside a Kubernetes cluster, and utilizes a config file to determine what kind of objects to detect. Manifests files are stored in an output directory in the format:
https://github.com/grafana/k8s-manifest-tail
<outputDir>/<kind>/<namespace>/<name>.yamlhttps://github.com/grafana/k8s-manifest-tail
GitHub
GitHub - grafana/k8s-manifest-tail
Contribute to grafana/k8s-manifest-tail development by creating an account on GitHub.
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Shopify discovered that deeply nested, high-cardinality GraphQL queries were bottlenecking not on I/O, but on CPU-bound field resolver execution driven by GraphQL’s standard depth-first traversal model. To solve this, Shopify built "GraphQL Cardinal," a breadth-first execution engine that resolves each field once across all objects rather than recursively per object, vastly reducing platform overhead and resolving N+1 issues more efficiently.
https://shopify.engineering/faster-breadth-first-graphql-execution
https://shopify.engineering/faster-breadth-first-graphql-execution
Shopify
Shopify’s journey to faster breadth-first GraphQL execution (2026) - Shopify
We questioned why conventional GraphQL execution incurs hidden costs, and rewrote it in a faster breadth-first manner to avoid them.
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CloudGoat is Rhino Security Labs' "Vulnerable by Design" AWS deployment tool
https://github.com/RhinoSecurityLabs/cloudgoat
https://github.com/RhinoSecurityLabs/cloudgoat
GitHub
GitHub - RhinoSecurityLabs/cloudgoat: CloudGoat is Rhino Security Labs' "Vulnerable by Design" AWS deployment tool
CloudGoat is Rhino Security Labs' "Vulnerable by Design" AWS deployment tool - RhinoSecurityLabs/cloudgoat
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Technical postmortem detailing a sophisticated supply-chain compromise of the TanStack ecosystem on May 11, 2026
https://tanstack.com/blog/npm-supply-chain-compromise-postmortem
https://tanstack.com/blog/npm-supply-chain-compromise-postmortem
TanStack
Postmortem: TanStack npm supply-chain compromise | TanStack Blog
On 2026-05-11, an attacker chained a pull_request_target Pwn Request, GitHub Actions cache poisoning across the fork↔base trust boundary, and OIDC token extraction from runner memory to publish 84 malicious versions across 42 @tanstack/* packages on npm.…
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Forwarded from AI Vibe Notes
Practical walkthrough of running kAgent against a Kubernetes cluster, using MCP tools to investigate common workload failures. Useful for thinking about safe AI copilots for day-2 ops, not just chat-based kubectl wrappers.
https://andamp.io/insights/blog/hands-on-with-kagent-ai-assisted-kubernetes-troubleshooting-with-mcp
https://andamp.io/insights/blog/hands-on-with-kagent-ai-assisted-kubernetes-troubleshooting-with-mcp
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🤦♂️ Tech is fundamentally broken. Microsoft's brilliant new fix to make the Windows 11 Start menu feel snappy is just spiking your CPU to maximum frequency for three seconds every time you click it, rather than optimizing the UI. What a world.
https://www.windowslatest.com/2026/06/10/windows-11s-performance-boost-released-today-enable-it-using-these-steps/
https://www.windowslatest.com/2026/06/10/windows-11s-performance-boost-released-today-enable-it-using-these-steps/
Windows Latest
Windows 11's CPU performance boost released today, enable it using these steps
Learn how to check and enable Windows 11’s new Low Latency Profile (June 2026 update) to speed up your Start menu, Search, and Action Center.
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Operator to streamline renovate executions in Kubernetes
https://github.com/mogenius/renovate-operator
https://github.com/mogenius/renovate-operator
GitHub
GitHub - mogenius/renovate-operator: Operator to streamline renovate executions in Kubernetes
Operator to streamline renovate executions in Kubernetes - mogenius/renovate-operator
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This article provides an insightful, framework-driven overview of automated post-mortem generation, defining how AI transforms incident retrospectives from manual reconstruction into automated drafting based on existing artifacts. It introduces a structural model for evaluating tools rather than just summarizing vendor features.
https://www.arvoai.ca/blog/automated-post-mortem-generation
https://www.arvoai.ca/blog/automated-post-mortem-generation
www.aurorasre.ai
Automated Post-Mortem Generation: 2026 Guide
Automated post-mortem generation turns chat transcripts, observability data, and investigation traces into retrospectives. 2026 tools and architectures.
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Pull Request-like Review/Approval flow for database queries. For compliant but smooth Engineering access to production.
https://github.com/kviklet/kviklet
https://github.com/kviklet/kviklet
GitHub
GitHub - kviklet/kviklet: Pull Request-like Review/Approval flow for database queries. For compliant but smooth Engineering access…
Pull Request-like Review/Approval flow for database queries. For compliant but smooth Engineering access to production. - kviklet/kviklet
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kubectl debugging plugin to collect full or partial cluster state and serve via an api server. Kubernetes time machine
https://github.com/crust-gather/crust-gather
https://github.com/crust-gather/crust-gather
GitHub
GitHub - crust-gather/crust-gather: kubectl debugging plugin to collect full or partial cluster state and serve via an api server.…
kubectl debugging plugin to collect full or partial cluster state and serve via an api server. Kubernetes time machine - crust-gather/crust-gather
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Deep-dive technical guide into the exact mechanics of pod restarts and configuration updates in Kubernetes
https://www.cncf.io/blog/2026/03/17/when-kubernetes-restarts-your-pod-and-when-it-doesnt/
https://www.cncf.io/blog/2026/03/17/when-kubernetes-restarts-your-pod-and-when-it-doesnt/
CNCF
When Kubernetes restarts your pod — And when it doesn’t
A production internals guide verified against Kubernetes 1.35 GA Companion repository: github.com/opscart/k8s-pod-restart-mechanics Engineers say “the pod restarted” when they mean four different…
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The 10-step playbook to reduce K8s bills by 40-60%, focusing heavily on bridging the gap between requested and actually used resources.
https://leanopstech.com/blog/kubernetes-cost-optimization-guide-2026/
https://leanopstech.com/blog/kubernetes-cost-optimization-guide-2026/
Leanopstech
We Profiled 200 K8s Clusters: 65% Is Pure Waste | LeanOps
200 production clusters audited. Median utilization: 28%. Three specific changes save $14K-40K/month. The first takes 10 minutes and nobody does it...
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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 already…
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