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: Layer What it does When to reach for it Hooks Shell or HTTP calls that fire on lifecycle events “This must happen every time, without exception” Custom Commands Reusable slash commands for repeatable prompts “I type the same prompt repeatedly” Skills Context-aware instructions Claude loads automatically “Claude should always do X when working on Y” Subagents Separate Claude instances for isolated, parallel work “This task is noisy and the main session only needs a summary” This post covers how to create each one and when to use them.

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.

Every CLAUDE.md file gets loaded into context on every session. Most teams treat it like documentation — a place to describe the project, list the tech stack, explain what the tests do. That is the wrong mental model and it is why most CLAUDE.md files are both too long and too ineffective. CLAUDE.md is behavioral programming. Its job is to change how Claude makes decisions, not to describe facts that Claude can read from the codebase itself.

There is a tendency in the AI tooling space to reach for frameworks — LangChain, AutoGen, CrewAI, OpenClaw — the moment you want an AI that does more than answer one question at a time. Most of the time, that is the wrong move. The framework adds complexity, dependencies, and debugging surface area for problems that a few shell scripts and cron jobs solve perfectly well. Claude Code’s headless mode (-p flag) plus a markdown file for personality plus cron scheduling is a complete AI assistant stack.

If you run a home server — a Raspberry Pi, a NAS, a Kubernetes cluster in your garage — you have probably hit the same annoying wall: your internet provider gives you a different public IP address every few days, and suddenly nobody can reach your server anymore. This post explains how I solved that problem by building ddns.devops-monk.com, a fully self-hosted Dynamic DNS platform. I will walk through the idea from scratch, explain every moving part in plain English, and include full architecture diagrams for those who want the deep technical picture.