If you are calling the Claude API repeatedly with a large system prompt, a big document, or a long codebase context — and you are not using prompt caching — you are paying full price every time for content that has not changed. Prompt caching stores a prefix of your prompt server-side and charges 90% less to read it back on every subsequent request. For applications that repeatedly process the same context, this is the single highest-impact API optimisation available.

Spring Boot 4.0 was released on November 20, 2025. It is built on Spring Framework 7 and represents the most significant shift in the Spring ecosystem since the Jakarta EE migration in Spring Boot 3. The headline change is full modularisation — the single spring-boot-autoconfigure JAR has been split into 70+ granular modules. But that is just the start. This guide covers every change that matters, what breaks on upgrade, and what is genuinely new and useful.

There are three ways to containerise a Spring Boot application: a naive single-stage Dockerfile, a proper multi-stage Dockerfile with layered JARs, and Cloud Native Buildpacks. Each has different tradeoffs in build speed, image size, and maintenance overhead. This guide covers all three approaches, explains why layered JARs matter for CI/CD speed, and shows how to produce small, secure, production-ready images. The Problem with the Naive Dockerfile Most tutorials show this:

Running Spring Boot on Kubernetes is not just packaging the app in a container and deploying it. You need to configure health probes correctly, handle graceful shutdown so in-flight requests don’t get dropped, manage configuration without baking secrets into images, and make sure the JVM respects container memory limits. This guide covers the production-critical Kubernetes configuration for Spring Boot applications. Health Probes Kubernetes uses three probe types to manage pod lifecycle:

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 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.