CI/CD pipelines, Docker, GitHub Actions, SRE and platform engineering.
Everyone in the cloud community acts like Kubernetes is the obvious default. It isn't. For most projects, K8s adds complexity that doesn't pay for itself until you reach a specific scale you probably haven't hit yet.
I was a serverless evangelist for two years. Then I ran a real production workload on Lambda and learned some expensive lessons about cold starts, timeout limits, and vendor lock-in.
We decided to migrate from our managed hosting to AWS in Q4. Three months of downtime incidents, one near-catastrophic data access issue, and one extremely uncomfortable conversation with investors later, here's what we learned.
I've been running Kubernetes in production since 2022. Here's what the blog posts don't tell you: the operational surprises, the 2am incidents, and the things I wish I'd known before I started.
We ran Jenkins for four years. Migration to GitHub Actions took three months. Here's the honest comparison: what got better, what we miss, and whether we'd do it again.
Last November our traffic spiked 12x in 90 minutes. We caught a cascade failure in under four minutes and resolved it before 95% of users noticed. Here's the monitoring setup that made that possible.
When people started calling it 'platform engineering' instead of DevOps, a lot of engineers rolled their eyes at another industry rebrand. They were wrong to. Here's what actually changed.
We built a fully automated CI/CD pipeline: commit to deploy in under eight minutes, zero manual steps. It was a genuine improvement. It also created a class of problems I didn't anticipate.
I used n8n and Claude to build an AI agent that triages, drafts responses to, and files my emails. Here's what it got right, what it got hilariously wrong, and whether I'm still running it.
Two of the most powerful AI code review tools are now Claude Code and OpenAI Codex. We compare their strengths, weaknesses, costs, and ideal use cases to help you pick the right one for your team.