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✓ Human-authored analysis; AI used for formatting and proofreading. Delivery teams have DORA. Four metrics — deployment frequency, lead time for changes, mean time to restore, change failure rate that predict whether a team is shipping well. Thoughtworks recently added a fifth: rework rate, measuring how much of the pipeline is consumed by fixing work previously considered complete. These metrics changed how delivery organizations operate. Because they're leading indicators. They tell you the trajectory before the outcome arrives. A team with increasing lead times is heading for trouble. A team with rising rework rate is accumulating debt. You see it in the metrics before you see it in the incidents. Security teams have no equivalent. What security teams measure today Finding counts. "We found 247 misconfigurations this quarter." More scanning produces more findings. A team that scans more frequently or adds a new tool sees the number go up which looks worse even if posture is improving. Finding counts measure scanning effort, not security health. Compliance percentages. "We're 94% compliant with CIS Benchmarks." This measures the last audit, not the current trajectory. A team at 94% today might be at 87% next week if three Terraform changes introduced misconfigurations. The percentage is a snapshot, not a trend. It rewards breadth of coverage over depth. 94% across 200 checks sounds better than 100% across 50 checks, even if the 50 are the ones that matter. Incident counts. "We had two security incidents this quarter." This is a trailing indicator. It measures failures that already happened. A team with zero incidents might have excellent posture or might have excellent luck. You can't tell. By the time the count goes up, the damage is done. None of these answer the question delivery teams answer with DORA: are we getting better, and how fast? The mapping The five DORA metrics adapt directly to security posture. The definitions are concrete and measurable from eval

For 4 years, I lived in the world of IT Operations. My days were spent handling incident response, managing data lifecycles, and making sure systems stayed online. I learned how to troubleshoot under pressure, talk to frustrated users, and keep the business running. But I had a lingering frustration: I was always fixing other people's code. I never got to build it. And more importantly, I was fixing problems manually that I knew could be automated. So, I decided to make a massive pivot. I went back to university (VILNIUS TECH) and recently started a Java Engineering internship at Coherent Solutions. My goal isn't just to become a Java developer. My goal is to bridge the gap between Development and Operations- DevOps . In my first few weeks at Coherent, we started learning about enterprise architecture. But the moment that truly clicked for me was when I built my first Docker image for our project. In my past IT life, deploying an app was a nightmare. "It works on my machine!" was a constant joke (and a constant headache for the Ops team). Setting up environments, installing the right Java version, configuring databases—it was manual, error-prone, and boring. Then I wrote a Dockerfile . I packaged our Java application and its dependencies into a single, isolated container. Suddenly, I realized: This is how you solve the "works on my machine" problem forever. As someone who used to be the guy manually fixing those environment issues, writing a few lines of code to completely automate that process felt like a superpower. I'm starting this blog to document my journey in real-time. I'm currently diving deep into: 🔹 Java 21 (the newest LTS—highly recommend checking out Virtual Threads!) 🔹 Spring Boot & enterprise backend architecture 🔹 Docker & containerization 🔹 Next up: CI/CD pipelines and Infrastructure as Code (Terraform) If you are currently stuck in IT Support or SysAdmin roles and dreaming of becoming a DevOps or Software Engineer—you aren't alone. Let's learn toge

Intro An MCP server can look like another API layer: expose existing REST endpoints as tools and call it a day. Both receive input, execute backend logic, and return a result. But they operate under different assumptions. This two-part series explains why directly wrapping REST APIs is a bad default. This first article covers the differences in their runtime environments. The second will discuss how those differences should affect MCP design (you already know how to design a good REST API ). We can see those differences more clearly by comparing the two across several dimensions. Dimensions The consumer With REST, developers encode control in application logic. The application knows when to call an endpoint, what arguments to send, and how to handle the response. Those decisions are made during development. With MCP tools, much of that control moves to the AI agent. The model interprets the request, chooses a tool, constructs its arguments, evaluates the result, and decides what to do next. The harness can restrict it, but the model is still part of the control flow. A REST client already knows why it is making a call. An agent must first decide whether a tool is relevant at all. MCP tools The context A REST application can draw from application state, cookies, memory, and user input. Code written by a developer determines which parts become request parameters. An agent can draw from the current request, conversation history, and previous tool results. The MCP server does not see this context automatically, but the model may turn parts of it into tool arguments at runtime. The difference is who selects what reaches the backend: predetermined code or a model reasoning over a changing conversation. The action model REST APIs tend to expose focused, fine-grained operations that application code can compose. Keeping endpoints simple and stable limits regressions because a developer has already written and tested the workflow that connects them. With MCP, the agent often

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This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on the data center buildout, follow Emma Roth. The Stepback arrives in our subscribers' inboxes on Sunday at 8AM ET. Opt in for The Stepback here. How it started Years before the AI boom threatened local power […]

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