Devops Monk

These are the questions that actually come up in Spring Boot interviews — at startups, scale-ups, and large enterprises. Each answer explains the concept clearly, with the level of depth an interviewer expects from a mid-level or senior developer. Questions are grouped by topic. For junior roles, focus on sections 1–3. For senior roles, everything here is fair game. Section 1: Core Fundamentals Q1. What is the difference between Spring and Spring Boot?

You’ve been coding for three hours straight. Your fingers are tired. Your wrists are starting to complain. You have a complex prompt to type out—explaining the bug, the context, what you’ve already tried, what you want next. You’re tempted to just write “Fix this” and hit enter. But Claude needs real context to help you effectively. Here’s the problem: Humans speak about 3x faster than they type. You can describe a problem in 30 seconds of talking that would take 2 minutes to type properly.

When Claude Code works on your project, it can touch dozens of files in a single prompt—refactoring a module, renaming methods across the codebase, restructuring folders. What happens when it goes wrong? Traditional developers rely on keyboard undo or Git commits, but neither fits an AI that operates at speed. Claude Code’s checkpoints are your safety net. They automatically capture the state of your code before each edit, letting you rewind in seconds if anything goes sideways.

Most AI assistants are chatbots. You ask, they answer, the interaction ends. The interesting shift happening right now is treating AI as an autonomous worker — something that runs on a schedule, produces real artifacts, and delivers results without you being in the loop. This post walks through building that kind of assistant: a background agent that runs weekly, researches a set of topics relevant to your work, and delivers a structured briefing via Telegram or email.

Most stock screeners cost $30–$200 per month. Bloomberg Terminal costs $24,000 per year. I built something that does a meaningful fraction of what those tools do — analysing 220+ UK and US stocks every hour, scoring them across six dimensions, detecting bearish warning signals, running insider trading checks via SEC EDGAR, and presenting everything in a React PWA — at zero ongoing cost. The platform is live at share.devops-monk.com. The full source is at github.

Tools like ESLint, Semgrep, and Bandit catch what they are programmed to find: known patterns, common injection strings, deprecated API calls. They are fast, reliable, and deterministic. They are also blind to anything that requires understanding what your code is supposed to do. Claude Code operates differently. It reads code the way a human security researcher would — tracing data flows across files, understanding business logic, and reasoning about what could go wrong given the specific context of your application.

Most teams use Claude Code reactively — they type a prompt, Claude responds, they type another. That is fine, but it leaves significant value on the table. Claude Code has four automation layers that let you turn it from a reactive assistant into an active workflow participant. Consider the core problem each layer solves: you tell Claude never to touch a file, it touches it anyway. You ask it to skip secrets, it commits an API key.

Claude’s computer use capability — the ability to see your screen and interact with applications via mouse clicks, keyboard input, and scrolling — is one of the most discussed features in the AI space. It is also one of the most misapplied. The correct mental model is not “Claude can now automate everything on my screen.” It is “Claude now has a flexible fallback layer for tasks that do not have a structured API integration.

If you have been using Claude in a browser tab to help with code, you are leaving most of its capability on the table. Claude Code CLI brings the full power of Claude directly into your terminal — it reads your actual codebase, runs real commands, edits files, commits code, and integrates with every tool in your DevOps stack. This guide covers everything from installation to advanced patterns that most engineers never discover.

The MCP (Model Context Protocol) ecosystem now has over 14,000 servers. Most of them you do not need. Installing too many slows responses, inflates your context window with tool definitions that never get used, and turns debugging into a guessing game across a dozen integrations. This guide applies a simple filter: only install a server if it replaces a daily copy-paste workflow. It is organised by role — developers, QA testers, and DevOps/platform engineers — because the right stack is different for each.