DevOps & SRE notes

The article challenges the traditional "three pillars" of observability (metrics, logs, traces), arguing that they are insufficient for modern, distributed architectures. Through three real-world incident examples at a large financial institution, the author demonstrates that application-level telemetry often fails to reveal actual customer impact. Instead of siloed data, the author advocates for "single threads"—clear, end-to-end distributed traces of individual customer requests—enriched with business context and measured by customer-facing Service Level Objectives (SLOs). https://www.honeycomb.io/blog/you-dont-need-three-pillars-you-need-single-threads

A command-line tool for Jira workflow management. Quickly select issues, track your current work, and generate consistent branch names. https://github.com/tutunak/jcli

Agentic CI/CD: Kubernetes Deployment Gates with Elastic MCP Server — Elastic Observability Labs https://www.elastic.co/observability-labs/blog/agentic-cicd-kubernetes-mcp-server

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

How GitHub uses eBPF to improve deployment safety https://github.blog/engineering/infrastructure/how-github-uses-ebpf-to-improve-deployment-safety/

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 “AI assistant inside the cluster” experiments.

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/

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 — 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

A lightweight tool for deploying and managing containerised applications across a network of Docker hosts. Bridging the gap between Docker and Kubernetes ✨ https://github.com/psviderski/uncloud

Deep dive into Prometheus’s 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/

Ministack: Free, open-source local AWS emulator - 55+ services, Terraform compatible, real databases. Free forever. MIT licensed. https://github.com/ministackorg/ministack

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

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/

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/

Containerlab focuses on the containerized Network Operating Systems which are typically used to test network features and designs https://github.com/srl-labs/containerlab

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

Open, Multi-Cloud, Multi-Cluster Kubernetes Orchestration https://github.com/karmada-io/karmada