Secretless deployments from GitHub Actions to Kubernetes with OIDC and Argo Rollouts
Deploying to Kubernetes from CI used to mean stuffing long-lived cloud credentials into secrets, then hoping they never leaked. Today you can avoid that risk by using GitHub Actions’ OpenID...
When logs sing before systems scream: using RAG and embeddings to spot infra problems early
Logs are the noisy, honest heartbeat of modern infrastructure. They record everything from a failed API call to a slow database query, but the sheer volume and variety make them...
Picking the Right Serverless Use Case: when (and when not) to use serverless for your backend
Serverless computing is no longer a cutting‑edge experiment — it’s a mature set of options that includes classic FaaS (AWS Lambda, Google Cloud Functions, Azure Functions), serverless containers (Cloud Run,...
Managing dashboards with GitOps: an intro to observability as code
Observability as Code (OaC) applies the same engineering practices we use for infrastructure and application code—source control, code review, and automated pipelines—to monitoring assets like dashboards, alerts, and data sources....
Containers, WebAssembly, and the edge: choosing the right runtime to deploy closer to users
Edge computing is often framed as “put the code where the users are” — closer network hops, less jitter, faster responses. But “put the code” hides a choice: do you...
Faster, smaller Docker images: practical patterns with BuildKit, Buildx cache and minimal base images
Building Docker images is part craft, part engineering: you want fast, reproducible builds in CI and tiny, secure images in production. Over the last few years the tooling around Docker...
Bringing AI to Logs: How embeddings, LLMs, and modern observability tools help detect infrastructure issues earlier
Infrastructure teams face an ever-growing firehose of logs: application traces, system events, kernel messages, load balancer access logs, and agent telemetry. That volume and variety make it hard to surface...
Ephemeral GPU workloads: running scalable ML training with Airflow on Kubernetes
Bridging DevOps and MLOps means giving model training the same repeatable, observable, and automated workflows we expect from data pipelines. A practical, widely-adopted pattern is to run ML training and...