Devops Monk

By default, Claude generates its response token by token without any deliberate planning step. For most tasks — answering a question, writing a function, explaining a concept — this is fine. The response comes quickly and it is good. For some tasks, it is not enough. Complex multi-step reasoning problems, ambiguous architecture decisions, intricate security analyses — these benefit from Claude thinking through the problem before committing to an answer. That is what extended thinking and adaptive thinking provide.

Building an AI agent that runs autonomously — browses the web, executes code, reads and writes files, persists memory across sessions — requires infrastructure. You need a sandbox, a process that can run for hours without your web server timing out, and a way to resume from where you left off after a network hiccup. Claude Managed Agents, launched in public beta in April 2026, offloads all of that to Anthropic.

Picking the wrong Claude model is expensive. Opus on every task costs 5x more than Sonnet for comparable results on most work. Haiku on a complex reasoning task produces worse output than just asking Sonnet. And if you are still using models from early 2025, some of them are deprecated — or will be soon. This guide covers every current Claude model, what each is good at, how much they cost, and a concrete decision framework for choosing the right one.

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.

Publishing an event to Kafka after saving to the database looks simple. It has a subtle, dangerous flaw: if the Kafka publish fails after the DB commit, or the app crashes between the two, your event is lost and your data is inconsistent. The Transactional Outbox Pattern solves this by writing the event to the database in the same transaction as the business data, then publishing to Kafka separately. This guide covers the pattern, the implementation, and idempotent consumers.

Spring Boot applications running as GraalVM native images start in milliseconds, use a fraction of the memory, and fit in tiny containers. The tradeoff is a longer build time. In 2026, with Spring Boot 4 and GraalVM 24, native images are production-ready for most Spring applications. This guide covers everything: what Spring AOT does, how to build your first native image, how to fix the common issues, and how to add native builds to CI.

“Spring Boot is magic” is something you hear a lot. Add spring-boot-starter-data-jpa and suddenly you have a working DataSource, a JpaTransactionManager, and a LocalContainerEntityManagerFactoryBean — without writing a single @Bean method. Understanding how this actually works turns the magic into a tool you can control, debug, and extend. The Entry Point: @EnableAutoConfiguration @SpringBootApplication is a shorthand for three annotations: @Configuration @EnableAutoConfiguration // this is the one that matters here @ComponentScan public class MyApplication { public static void main(String[] args) { SpringApplication.

The OWASP Top 10 lists the most critical web application security risks. Spring Boot apps have their own common failure patterns: exposed Actuator endpoints, secrets in properties files, SQL built from string concatenation, and Spring Security misconfiguration. This guide covers the vulnerabilities that actually appear in Spring Boot applications and how to fix each one. 1. SQL Injection SQL injection remains one of the most critical vulnerabilities. It allows attackers to manipulate database queries.

Spring AI 1.0 GA shipped in May 2025. It brings the Spring programming model to AI development: a unified ChatClient API that works across Claude, OpenAI, Gemini, Ollama, and Azure OpenAI — switching AI providers is changing one dependency. This guide builds a complete RAG (Retrieval-Augmented Generation) application that answers questions about your documentation using any AI provider. What Is RAG? A large language model (LLM) knows everything in its training data but nothing about your specific documents, code, or business data.

Many teams are still running Spring Boot 2.7.x. Spring Boot 2.x reached end of life in November 2023, which means no more security patches. The jump to 4.0 is two generations, and the breaking changes are real — but they are also well-documented and mostly automatable. This guide walks through the migration in stages: 2.x → 3.0 first, then 3.x incremental updates, then 4.0. Each section lists what breaks and how to fix it.