Scala: A Mindset Shift from Instructions to Outcomes

🚀 AI is helping me write code faster… but here’s what actually made me a better developer 💭 Earlier, I used to focus only on writing code. ✨ Now I’m learning how to write better, production-ready code. Here’s what I explored recently: 🧠 Caching (Redis vs Caffeine) → Learned when to use in-memory vs distributed caching based on system design 🧪 Unit Testing & Code Coverage → Writing test cases using JUnit → Measuring code quality with JaCoCo 🤖 AI in Development (Cursor) → Used AI to write cleaner and faster code → Helpful in refactoring and improving code quality 🔥 Biggest Learning: Writing code is easy. Writing scalable, testable, and maintainable code is the real skill. 💬 Do you use AI while coding, or do you prefer writing everything manually? #Java #SpringBoot #AI #BackendDevelopment #CleanCode #LearningInPublic

I use AI tools every day. They help me write faster, explore ideas faster, and get unstuck faster. That part is real. But I think many senior developers feel the other side of it too: the work did not disappear. It moved. In enterprise Java, AI often gives you code that looks clean, plausible, and production-ready. It compiles. It boots. It even passes more checks than it probably should. And that is exactly why the human cost is still there. Maybe even more than before. Less typing. More supervision. More semantic review. More mental context switching. More carrying half-finished judgment into the evening. I wrote about that here, from a Java and Main Thread perspective: https://lnkd.in/duhPeeft #Java #EnterpriseJava #SoftwareEngineering #ArtificialIntelligence #DeveloperProductivity #Leadership

🚨 GitHub hosts a program that rewrites itself… across 128 languages 🤯 And yes. It comes back to where it started. Because this isn't just code. This is programming eating itself. Ruby → Rust → Scala → … 128 languages. One chain. One loop. It starts as Ruby. Ends as Ruby. Let me say that again. Same code. After 128 transformations. This project—Quine Relay—is pure madness. No APIs. No AI. Just logic. Brutal. Precise. Beautiful. And my personal favorite… It proves something insane: Code can survive translation. Across ecosystems. Across paradigms. I tried understanding even 5 steps. Got lost instantly. This? 128 steps deep. Because this isn’t just a project. This is a statement. Languages don’t matter. Logic does. Here’s what chills me most… If code can evolve and return unchanged… what happens when AI starts doing this automatically? Self-writing systems. Self-translating stacks. Self-evolving software. What used to need developers… might soon need just intent. So where does that leave programming? Craft… or automation? Art… or abstraction? Are we building tools… or replacing ourselves? Greatest intellectual flex ever… or a preview of what’s coming next? #ArtificialIntelligence #AI #Programming #SoftwareEngineering #MachineLearning #FutureOfWork #Coding #TechTrends #Developers

AI-assisted coding has made it incredibly easy to write code. But writing code was never the hard part. The real engineering begins when you ask: - How does this behave under load? - What do JVM metrics say when traffic spikes? - Should we go full concurrency, or controlled parallelism? - Do we scale horizontally or vertically? - What’s the cost trade-off of each decision? That’s where judgment matters. AI can generate threads, but it won’t decide the right level of concurrency for your system. It can suggest patterns, but it won’t own your production incidents. It can optimize snippets, but it won’t balance performance vs cost vs reliability. That’s still on us.(At least for now… unless someone builds an incredibly complex agent architecture that can debug production issues, tune performance, scale systems, and run cost analysis better than us. In which case, we might need to have a different conversation.) I’ve always enjoyed working with Java, especially its multithreading capabilities. But in real systems, even something as “simple” as threading becomes a series of trade-offs: - Throughput vs stability - Latency vs resource usage - Speed vs predictability The difference between a good engineer and a great one isn’t how fast they write code anymore. It’s how well they think through trade-offs. Code is easy. Decisions are hard. And that’s exactly where engineering lives. #SoftwareEngineering #AI #AICoding #SystemDesign #BackendEngineering #Java #JVM #Multithreading #Concurrency #Scalability #DistributedSystems Read “Multithreading in Java: Implementing Multithreading with Spring Annotations“ by Yash Paliwal on Medium: https://lnkd.in/g2qyD3JK

I didn’t just complete Claude Code 101 — I changed how I approach building software. Here’s what stood out 👇 This wasn’t about “using AI to generate code.” It was about: → Structuring problems clearly → Writing better prompts like specifications → Thinking in terms of systems, not just functions What I learned: • AI works best when your thinking is clear Not when your prompt is long • Good developers won’t be replaced But developers who don’t adapt will struggle • Prompting is not magic It’s closer to requirement engineering As someone working on Java backend systems, this unlocked something important: I can now: → Design APIs faster → Debug with better reasoning → Break down complex features into structured steps This is just the beginning. Next step: Applying this to real projects (Finance Tracker + Backend Systems) Curious— How are you integrating AI into your development workflow? #AI #BackendDevelopment #Java #ClaudeAI #LearningInPublic

I've been using GitHub Copilot and Claude AI daily for the past year as a Senior Java engineer. Honest take? They're genuinely useful. But not for the reasons most people think. What AI tools are actually good at: ✅ Boilerplate — Spring Boot service scaffolding, DTOs, test skeletons ✅ Explaining unfamiliar code fast ✅ Writing unit tests (especially edge cases I'd have missed) ✅ Debugging error messages at 11pm when no one's online ✅ First drafts of SQL queries and regex patterns What they're still bad at: ❌ Understanding your actual system context ❌ Architectural decisions — it'll give you an answer, but not necessarily the RIGHT one for your constraints ❌ Anything involving internal business logic it's never seen ❌ Knowing when NOT to use a pattern The engineers getting the most out of AI aren't the ones who trust it blindly. They're the ones who know enough to catch when it's wrong. AI made me faster. It didn't replace the judgment that comes from 8 years of building production systems. #AI #SoftwareEngineering #GitHubCopilot #Java #DeveloperProductivity

Every Java dev knows the feeling of trying to learn faster… and somehow ending up even more overwhelmed. AI was supposed to fix that, right? This video goes straight into that strange moment when AI gives you more answers, more options, more roadmaps… and way less clarity. It shows the hidden problem many developers don’t realize they’re stuck in. If you’ve been jumping between topics, tutorials, and AI prompts and still not moving toward better opportunities, this will make you pause. Weekly Live → https://bit.ly/48ophNZ

🚀 Building a Coding Agent with Spring AI Recently, I came across a great YouTube tutorial by Dan Vega titled: “Build a Coding Agent Like Claude Code with Spring AI” It was one of those demos that looks simple on the surface but teaches a very powerful pattern. 🤖 What did I learn? In just ~60 lines of Java, you can build a CLI-based AI coding assistant using Spring Boot + Spring AI that can: ✅ Read files from a codebase ✅ Search code using grep & glob ✅ Run shell commands ✅ Maintain conversational memory ✅ Reason autonomously about which tool to use and when You point the agent at a project and ask questions like: “What does the main application class do?” “Find all TODOs in the codebase” “Run the tests and tell me what failed” The LLM (Claude) decides which tools to call, chains them together, and responds intelligently — all from your terminal. 🧠 Why this is interesting (beyond the demo) This project isn’t just about a “coding assistant”. It demonstrates a powerful architectural pattern: An LLM + Tools + Memory + Conversational Loop And this same pattern can be applied to: 🔧 DevOps & Infrastructure assistants 📊 Log analysis & debugging 🗄️ Database exploration via JDBC tools 📚 Internal documentation search 🔐 Security auditing (grep + tools like semgrep/trivy) 🧪 Test generation and execution 🧭 Exploring large or legacy codebases 🧩 Tech Stack Used Spring Boot 4.0.2 Spring AI 2.0.0-M2 Spring AI Agent Utils Claude (Anthropic) Java 25 What I loved most 👉 model‑agnostic design. Thanks to Spring AI abstractions, switching from Anthropic to OpenAI, Ollama, or another provider requires config changes only, not code changes. 🌱 Key takeaway AI agents don’t need massive frameworks or thousands of lines of code. With the right abstractions, you can build useful, tool‑aware agents that work directly with real systems — not just chat windows. Big thanks to Dan Vega and the Spring AI community for such a clear and practical example 🙌 📌 I’m sharing this as part of my learning spring ai journey. If you’re experimenting with Spring AI, AI agents, or developer productivity tools, I’d love to hear your thoughts! #SpringAI #Java #AIEngineering #LLM #Claude #DeveloperTools #LearnInPublic #SpringBoot #Agents

Exploring how Java + AI can shape the future of enterprise applications. My key takeaway: AI isn’t replacing Java — it’s making applications smarter. Sharing a simple visual that captures this thinking 👇 Curious how others see this space evolving. #Java #AI #Innovation #SoftwareDevelopment #Curiosity

Something as trivial as 'getOrDefault' cost me hours of debugging 🤦♂️ I wanted a simple thing - “use a default value if the key is missing.” AI generated the code using `getOrDefault`. It looked correct. I reviewed it. Didn’t think twice. But the issue wasn’t a missing key. The key existed… with a null value. And getOrDefault doesn’t apply the default in that case. So the code was “right”… but the behavior was wrong. What’s uncomfortable is: I knew that a key can have a null value. Still, I didn’t catch it during review. Not because the code was complex but because I didn’t fully account for this nuance. That’s the real risk 😅 It’s not about AI making mistakes. It’s about us assuming the code does what we intend. What you ask for, what gets generated, and how it actually behaves - can all be different. And these gaps are not easy to catch with just tests or lower environments. Fundamentals and language-specific nuances matter more than ever. Because in the end, correctness is still our responsibility. #SoftwareEngineering #CleanCode #Java #Coding