Edge computing 101: Deploying containers closer to users — what the new global-edge container platforms mean for you
Edge computing has always promised one simple benefit: put compute where the user is, and shave milliseconds off every request. For years that meant serverless functions and tiny runtime isolates....
Practical Docker Compose Patterns for local microservices: profiles, overrides, and reliable startup
Docker Compose is a great tool for running a multi-service application on your laptop. But when your local microservices stack grows (API, DB, cache, dev tools, migration tasks), a single...
Hands-on with Helm: package and distribute your charts as OCI artifacts
Kubernetes packaging has been moving fast: Helm charts are no longer confined to index.yaml-hosted repositories. Today, storing charts as OCI artifacts in container registries is a practical, well-supported pattern that...
Faster Docker Builds: Practical Guide to BuildKit cache, buildx, and image optimization
Slow Docker builds in CI waste developer time and CI minutes. BuildKit (via docker buildx) gives you powerful ways to persist and share build cache across runs and machines —...
Getting started with GitHub Actions for continuous integration: a practical Node.js pipeline
Continuous integration (CI) catches bugs earlier, gives teams fast feedback, and makes releases less stressful. GitHub Actions makes it easy to run CI inside your repository using YAML workflows that...
Intro to observability as code: managing dashboards with GitOps
Dashboards are the musical score of your observability system — they arrange metrics, logs and traces into something your team can “hear” and act on. But like any score, if...
Automating Carbon-Aware DevOps: Practical Patterns to Reduce Cloud Carbon Footprint
Cloud computing brought huge operational flexibility — but it also shifted a lot of energy use (and carbon) into providers’ data centers. Sustainable DevOps focuses on shrinking that footprint without...
Hybrid Executors, Datasets, and KEDA: a practical path from DevOps to MLOps with Airflow on Kubernetes
Moving ML from notebooks to production feels a bit like taking a garage band on tour. You need the right stage (Kubernetes), a reliable manager (Airflow), and a crew that...