Spring Boot 4 Simplifies AI, Microservices, and Java Development

I spent today building something I've wanted for a long time. An AI that automatically reviews Java code and posts inline comments directly on GitHub PRs. No manual triggering. No setup per repo. Just open a pull request and feedback appears automatically. Here's what the pipeline looks like: → Developer opens a PR on GitHub → Webhook fires to my service → AI reviews the changed Java files → Comments posted automatically on the PR with line numbers First real test on a Spring Boot service. The AI caught a hardcoded password and missing @Transactional on line 12. Automatically. On a real GitHub PR. What makes this different from tools like CodeRabbit: It's built specifically for Java and Spring Boot teams. It catches things generic tools miss — missing @Transactional rollback config, N+1 query patterns in JPA, Spring Security misconfigurations, swallowed exceptions. And it doesn't just flag issues. It explains why the issue matters and links to the relevant Spring docs or Baeldung article. Over time it tracks whether each developer is actually improving. Built with Spring Boot, GitHub Webhooks, PostgreSQL and an AI model. I'm turning this into a full SaaS product — ReviewCraft. Launching in 4 weeks If you work on a Java team and want early access completely free, drop a comment or DM me. Looking for 5 beta testers who will give honest feedback. #java #springboot #ai #github #softwaredevelopment #buildinpublic

Finally got around to open-sourcing a small tech demo - a Spring Boot WebFlux application that uses Spring AI, pragmatica-lite, Ollama, and pgvector to do semantic similarity search. Everything runs locally. No API keys, no external accounts, just Docker. The key idea: fetch a random quote, then find similar ones by meaning - not by keyword matching. Embeddings do the heavy lifting. - Lazy embedding - quotes come back instantly, embeddings happen in the background. Simplified for the demo, but the pattern is there. The more quotes you fetch, the richer the search gets. - Spring AI handles the embedding provider abstraction - swapping from Ollama to OpenAI or another provider is a configuration change, not a code change. That's Spring AI doing its job well. A reactive Spring AI would be nice though... - Hexagonal architecture with Use Case pattern, following Java Backend Coding Technology - a methodology built on monadic types and functional programming patterns. Even in a small demo, the clean separation between domain logic and infrastructure made the code easier to reason about and test. - Direct pgvector queries with cosine distance - for this specific use case it was a natural fit. - Open API Swagger UI at the root url as a cherry on top. This is a demo, not production code. I kept things simple on purpose. Shoutout to Sergiy Yevtushenko for his work on Java Backend Coding Technology (https://lnkd.in/eXuNhfAC) and pragmatica-lite (https://lnkd.in/eZrD8ZC3) - a remarkable contribution to the community. I used it in this project. The thing is - Ollama has made local AI surprisingly practical. No accounts, no keys, no vendor lock-in. That changes what's possible for dev setups and demos. One script, docker compose up, and you're in. https://lnkd.in/e5HXFjXt #Java #SpringBoot #SpringAI #WebFlux #pgvector #Ollama #SemanticSearch #OpenSource

From Writing Code to Building Systems Most beginners learn syntax. Few learn system thinking. When I started working with Spring Boot + Angular, I thought development was about making APIs work. But real learning started when I began solving problems like: ✅ Handling claim validation edge cases ✅ Managing fallback logic in backend systems ✅ Fixing SSO token validation issues ✅ Debugging production-level data mismatches ✅ Writing optimized SQL joins across multiple tables That’s when I understood: Software development is not about writing code. It’s about designing reliable systems. Working on real-time claim processing systems taught me: 🔹 Always validate external data 🔹 Never trust third-party APIs blindly 🔹 Logs > Assumptions 🔹 Clean architecture saves debugging time 🔹 Small backend mistakes = big business impact Now I focus less on “Does it work?” And more on “Will it survive production?” Still learning. Still improving. But thinking bigger every day. #Java #SpringBoot #Angular #BackendDevelopment #SystemDesign #SoftwareEngineering #LearningJourney #AI

🚀 From legacy Java Spring monolith to full microservices — I didn't write a single line of code. Just me, my command prompt and Claude Code CLI. Watch the video 👇 What Claude did automatically: ✅ Read and understood my entire codebase ✅ Migrated to Spring Boot 3.2 microservices ✅ OAuth2 + JWT security ✅ Docker + docker-compose for everything ✅ All tests written and passing ✅ Postman collection generated My only job? Review each phase and type "ok". ⚠️ Note: This is just a small experiment I tried on my local system — still exploring how far Claude Code CLI can go. But the results genuinely surprised me. AI won't replace developers. Developers who use AI will. #Java #SpringBoot #AI #ClaudeCode #Microservices #Docker

🚀 New Spring Boot Auditing Tutorial — Live on YouTube! 👉 https://lnkd.in/g9dBRdTw In this lecture, I walked through implementing Auditing in Spring Boot using Spring Data JPA — complete with real code and a working demo. No more manually updating timestamps! Now you can automatically track: ✔️ createdAt ✔️ lastModifiedAt ✔️ (Optional) createdBy / modifiedBy I covered: ✨ Enabling JPA Auditing ✨ Using @CreatedDate & @LastModifiedDate ✨ Implementing AuditorAware ✨ Full coding demo This is part of my Spring Boot backend learning series — focused on practical implementation, best practices, and real-world readiness. Whether you’re: 🔹 Preparing for Backend Developer roles 🔹 Learning Spring Boot from the ground up 🔹 Building scalable Java APIs This lecture will give you clarity and confidence 🚀 I’d love to hear your thoughts — drop a comment after watching! #SpringBoot #Java #BackendDevelopment #SpringDataJPA #Hibernate #SoftwareEngineering #YouTubeLecture

Cursor-Based Pagination in Backend Systems (Java | Spring Boot) 🔹 Cursor-based pagination retrieves the next set of results using a unique reference (cursor) — usually the last fetched record’s ID or timestamp — instead of page numbers. Unlike offset-based pagination (page=2&size=10), cursor pagination uses something like: /api/orders?cursor=105&limit=10 Why Cursor-Based Pagination? Better performance for large datasets No skipping or duplicate records Ideal for real-time feeds & event-driven systems Scales efficiently in microservices architecture In Spring Boot with JPA, this is commonly implemented using queries like: WHERE id > :cursor ORDER BY id ASC LIMIT :size Perfect for high-volume systems, Kafka-driven services, and infinite scroll APIs. Efficient data retrieval = Scalable systems #Java #SpringBoot #Microservices #BackendDevelopment #SystemDesign #Pagination #APIDesign

Is your Spring Boot app a "Lean Machine" or a 200MB "Hello World"? 🛑 We’ve all seen it. A simple microservice that somehow ends up with a JAR size larger than a high-def movie. As Backend Developers, our job isn't just to "make it work." Our job is to make it efficient. In 2026, "Performance" isn't just about execution speed—it’s about footprint, security, and maintainability. Here is my 4-step "Spring Clean" for Java developers: 1️⃣ The Dependency Audit 🔍 If you’re pulling in a 5MB library just for one String utility method, you’re doing it wrong. Check your pom.xml. If it's unused or redundant, exclude it. Your build server (and your security scanner) will thank you. 2️⃣ Kill the Boilerplate 🪓 If I see manual Getters, Setters, and Constructors in a modern project, my soul hurts. Use Lombok or, better yet, Java Records. Less code = fewer places for bugs to hide. 3️⃣ You Ain't Gonna Need It Rule 🚫 Redundant logic and "future-proofing" are the biggest sources of technical debt. If the requirement doesn't exist today, don't write the code for it. Over-engineering is just performance optimization for your ego, not your app. 4️⃣ Streamline the JAR 📦 Use mvn dependency:tree to find those transitive dependencies that are just hitching a ride. A smaller JAR means faster startup, lower memory footprints, and cheaper cloud costs. 5️⃣ The "AI Hallucination" Debt 🤖 ⚠️ Here is the hard truth: Blindly using AI on big projects is a recipe for bloat. AI is great at solving a single function, but it lacks "Contextual Efficiency." I've seen AI-generated PRs that: • Import entire heavy libraries for a task Java handles natively. • Suggest deprecated Spring Security patterns. • Add redundant "safety checks" that are already handled by the framework. • Result: You get code that "works" but a codebase that "suffers." Don't let your LLM be your Architect atleast for now. The Goal: Every line of code should earn its right to exist. The Big Debate: 💬 When it comes to cleaning up boilerplate, are you Team Lombok or Team Java Records? Or do you still prefer writing your own POJOs? Let’s hear the horror stories of the biggest "bloat" you've ever found in a legacy project! 👇 #Java #SpringBoot #BackendDevelopment #CleanCode #SoftwareEngineering #Microservices #Java21 #CodingTips #PerformanceOptimization

Day 06 – @RestController vs @Controller (Important for REST APIs) While learning Spring Boot, see two annotations in the web layer: @Controller and @RestController 01. @Controller @Controller is mainly used in Spring MVC applications that return views (HTML pages). Example: @Controller public class StudentController { @GetMapping("/home") public String home() { return "home"; } } • The method returns a view name • Spring looks for a HTML/JSP page called home So @Controller is mostly used in web applications with UI. 02. @RestController @RestController is used when building REST APIs. Instead of returning HTML pages, it returns data (JSON or XML). Example: @RestController public class StudentController { @GetMapping("/students") public String getStudents() { return "Student Data"; } } • The response is sent directly to the client • Usually returned as JSON This is the common approach in microservices and backend APIs. Key Difference @Controller → Returns View (HTML page) @RestController → Returns Data (JSON/XML) Important Fact @RestController is actually a combination of: @Controller + @ResponseBody This means every method automatically returns response data instead of views. Real Backend Development In most Spring Boot microservices projects: • We use @RestController • APIs return JSON responses • Frontend applications consume these APIs Today’s Learning Understanding when to use @Controller and @RestController helps design proper backend APIs. Small annotation, but very important for REST-based systems. Tomorrow: Spring Boot Auto Configuration – How Spring Boot Reduces Boilerplate Code #Java #SpringBoot #SpringFramework #BackendDevelopment #Microservices #LearningInPublic.

Learning Spring Boot has reshaped how I think about backend development in Java. Early on, I built REST controllers the manual way, defining endpoints, handling requests and responses explicitly, wiring services together myself. It worked, but it made the boilerplate and tight coupling very visible. Then Spring MVC entered the picture and simplified how web APIs are structured. Controllers became focused on intent rather than mechanics. Request mapping, serialization, and status handling stopped being repetitive concerns and became conventions. That shift became even clearer when I moved from manually handling persistence logic to using data repositories. CRUD operations that once required explicit queries were now expressed declaratively. What felt like “magic” at first only made sense after understanding what Spring was actually doing underneath. The real turning point was dependency injection. Instead of creating objects directly, Spring’s Inversion of Control container manages object creation, wiring, and lifecycle. Classes declare what they depend on, not how to build it. Diving into the bean lifecycle clarified how applications are structured internally. Hooks like @PostConstruct and @PreDestroy showed where initialization and cleanup logic belongs. Understanding bean scopes — singleton, prototype, request, and session — revealed how state and lifetime are managed across an application. Annotations such as @Component, @Service, @Repository, and @Configuration made intent explicit, while creating custom beans offered flexibility beyond defaults. Features like @Lazy highlighted that even performance considerations are baked into the framework’s design. With these foundations in place, higher-level abstractions stopped feeling abstract. Spring Boot doesn’t eliminate Java’s complexity — it organizes it, enforcing structure, consistency, and scalability. Still revisiting concepts, still connecting dots, but steadily building a clearer mental model of how production-grade backend systems are designed. This is all thanks to connecting with my boss Elikem Asem who nudged me the right way, giving me just the push I needed to work on developing new skills and thriving in the ever evolving industry. #SpringBoot #Java #BackendEngineering #DependencyInjection #SoftwareArchitecture