First deploy pipeline
The project started with a simple deployment path: build the site, ship it, and keep the production service predictable.
SteadyOps is not only a website about DevOps/SRE work. It is a working example of that approach: clear environments, infrastructure as code, automated deployment, content workflows, API integrations, smoke checks, and documented guardrails.
The first version was intentionally simple. It gave the project a working production baseline. Then I added the first deployment pipeline. At that moment, some nginx and runtime behavior still lived close to the server. It worked, but the next natural step was to bring the operational model into the repository.
From there, the project kept growing in small practical steps: infrastructure became code, nginx became modular, stage and production were separated, content publishing became automated, and API workflows were added where they created leverage.
git push stage→ deploy preview→ visual check→ promote sync→ deploy production→ smoke checksEach step made the system easier to operate, review, and extend.
The project started with a simple deployment path: build the site, ship it, and keep the production service predictable.
nginx, systemd, environment examples, deployment rules, and smoke checks moved closer to the repository.
As the configuration grew, it was split into smaller files: server blocks, locations, ACME handling, routing, and security headers.
Stage became the safe review area. Production became stable. Promotion became controlled instead of casual copying.
The content workflow gained a plan, publication state, Python rendering, validation, commits, and production deployment after publishing.
The platform gained server-side API workflows for LLM-powered content and DevOps Copilot features, while credentials stayed outside the browser.
The platform uses a content plan, publication state, Python scripts, validation, rendering, generated article covers, repository commits, and production deployment after publishing.
Server-side API routes support LLM-powered workflows while keeping provider logic and credentials out of frontend code.
The repository documents how future agents and developers should work with the project so automation stays predictable.
SteadyOps shows the same engineering habits I use in client infrastructure: start simple, automate repeatable work, move infrastructure into code, separate preview from production, validate before deployment, and keep the system understandable.