Java JShell for Rapid Prototyping and Agile Development

SOLID Principles – Explained Simply 👇 As software engineers, writing code that works is not enough. We must write code that is easy to maintain, extend, and scale. That’s where SOLID principles help: 🔹 S – Single Responsibility Principle A class should have only one reason to change. 🔹 O – Open/Closed Principle Open for extension, closed for modification. 🔹 L – Liskov Substitution Principle Subclasses should be usable without breaking the parent behavior. 🔹 I – Interface Segregation Principle Don’t force clients to depend on methods they don’t use. 🔹 D – Dependency Inversion Principle Depend on abstractions, not concrete implementations. 👉 Following SOLID leads to cleaner code, fewer bugs, and easier refactoring. #SOLID #CleanCode #Java #SoftwareEngineering #BestPractices

The Java inference engine deliverance (https://lnkd.in/en-BAHPS) has its 3rd release. New features: stop_tokens support (save computer to stop at user defined keywords maven vibe plugin https://lnkd.in/e_AxVwDB support for semantic search with the #mongo leaf model other stuff I forgot! #java #ai

Hitting a 0ms runtime is the developer equivalent of hitting a game-winning shot at the buzzer. I just optimized my solution for finding the Intersection of Two Linked Lists and reached that "Beats 100%" milestone. The beauty of Linked List problems isn't just in solving them—it’s in the pointer magic that makes them run instantly. For this one, the two-pointer approach kept the logic clean and the execution lightning-fast. The Stats: * Runtime: 0ms (Beats 100.00% of Java users). * Complexity: Pure O(m + n) efficiency. Coding is often about trade-offs, but when you find that sweet spot where simplicity meets peak performance, you have to celebrate it. Keep grinding, keep optimizing. #LeetCode #Java #SoftwareEngineering #DataStructures #CodingLife #Optimization

💻 Crash vs 🧩 Exception 💻 Errors / Crashes OS/JVM/Hardware level serious issues 🔴 Serious system-level issues 🔴 Usually unrecoverable Examples: OutOfMemoryError StackOverflowError 🧩 Exceptions 🟡 Program logic or runtime issues 🟡 Can be handled Examples: Divide by zero Null pointer File not found 💥Exception An exception is an unexpected problem during program execution that breaks the normal flow of a program 🚧 📌 Key clarity: 👉 An exception is NOT the user’s mistake itself 👉 It is the error event created because of that mistake 🧠 Example: User enters 0 ➝ Code tries 15 / 0 ➝ 💥 ArithmeticException ➗ Division by Zero — Important Clarity ⚖️ ✅ Integer division: 15 / 0 💥 Throws ArithmeticException ❌ Program stops if not handled ⚠️ Floating-point division: 15.0 / 0 ♾️ Result = Infinity ✅ No exception thrown 👉 So yes, “it gives infinity” is correct, but only for float/double, not int ✔️ 🚨 What Happens When an Exception Is NOT Handled When an exception occurs: ✔️ Execution stops from that exact line ✔️ Java creates an exception object ✔️ JVM searches for a handler ✔️ No handler found ➝ 💀 program terminates ✔️ Stack trace printed 📄 📌 This behavior is called: 👉 Abnormal termination of the program ⏱️ Compile-Time vs Runtime Exceptions ✅ Checked Exceptions (Compile-time checked) 🔹 Compiler forces handling 🔹 Example: File handling (IOException) ✅ Unchecked Exceptions (Runtime) 🔹 Occur during execution 🔹 Compiler does NOT force handling Examples: ArithmeticException NullPointerException 🧠 Throwable Hierarchy Object ↓ Throwable ↓ Exception ↓ ↓ CTE RTE 🔖Frontlines EduTech (FLM) #Java #ExceptionHandling #ProgrammingConcepts #CoreJava #DeveloperMindset #TechAustralia #AustraliaJobs #SydneyTech #MelbourneTech #BrisbaneTech #AustraliaIT #LearningInPublic #SoftwareEngineering #CodingLife #TechExplained

🚀 YAGNI, KISS & DRY — 3 Rules Every Developer Should Follow Writing code is easy. Writing clean code is different. If you follow just these 3 principles, your code will improve a lot 👇 1️⃣ 𝗬𝗔𝗚𝗡𝗜 — 𝗬𝗼𝘂 𝗔𝗿𝗲𝗻’𝘁 𝗚𝗼𝗻𝗻𝗮 𝗡𝗲𝗲𝗱 𝗜𝘁 Do not build features that are not needed right now. ❌ “Maybe we will need this later…” ❌ Extra classes and layers ❌ Unused code ✅ Build only what is required today. Too much planning can make your code heavy and complex. 2️⃣ 𝗞𝗜𝗦𝗦 — 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗦𝗶𝗺𝗽𝗹𝗲 Do not make things complicated. ❌ Very complex logic ❌ Too many design patterns for small problems ❌ Hard-to-read code ✅ Clear and simple logic ✅ Easy to understand ✅ Easy to debug Simple code is not weak. Simple code is strong. 3️⃣ 𝗗𝗥𝗬 — 𝗗𝗼𝗻’𝘁 𝗥𝗲𝗽𝗲𝗮𝘁 𝗬𝗼𝘂𝗿𝘀𝗲𝗹𝗳 Avoid writing the same logic again and again. ❌ Copy-paste code ❌ Duplicate validation ❌ Same logic in many places ✅ Reusable methods ✅ Common utility functions ✅ One source of truth Good developers don’t write more code. They write better code. Which principle do you find hardest to follow? 👇 #Java #CleanCode #SoftwareDevelopment #Programming #YAGNI #KISS #DRY

Me: Writes 5,000+ lines of backend code confidently. Also me: Opens Excel to write test cases… “I’ll do this later.” 😅 After working 2 months on a project, I realized something — Coding is the fun part. Testing and documentation are the responsibility part. And real engineering requires both. Working on improving that balance every day. #DeveloperLife #BackendEngineer #BuildInPublic #SoftwareDevelopment #Java #backend

SRP fails when we apply it mechanically instead of observing real change in the code. Read the full article: Tip #26: Single Responsibility Principle ▸ https://lttr.ai/Anqgs #SoftwareDesign #java #SOLIDPrinciples #SingleResponsibilityPrinciple

Day 38/100 | Building Consistency 😃 Showing up every day. Learning, growing, and improving. #TightCoupling vs #LooseCoupling in #Java Some code works today. Some code survives change. That difference often comes down to coupling. Tight coupling happens when classes depend directly on each other. A small change in one class can force changes across the system. It’s quick to write — but hard to maintain. Loose coupling is when classes depend on abstractions (interfaces) instead of concrete implementations. This allows behavior to change without breaking existing code. Simple rule to remember: - Depend on what it does, not how it does it. Why this matters: - Makes code easier to maintain - Helps scale features without rewriting logic - Reduces risk during refactoring 🌍 Real-world analogy: - A phone with a non-removable battery → tight coupling - A phone with replaceable components → loose coupling This is why modern frameworks like Spring emphasize interfaces and dependency injection. #Day38 #OOPS #LooseCoupling #TightCoupling #Java #SoftwareDesign #BackendEngineer #FullstackDeveloper

0ms. 100% Beats. Thinking outside the box. There is nothing quite like the feeling of seeing your code hit a 0ms runtime. Today’s win was the "Delete Node in a Linked List" problem. At first glance, it feels impossible—how do you delete a node when you don't have access to the head of the list? The Solution: Instead of trying to "delete" the node in the traditional sense, I just copied the data from the next node into the current one and skipped the next node entirely. The Result: * Runtime: 0ms (Beats 100.00% of Java users). * Logic: O(1) Time and Space complexity. * Efficiency: 41/41 test cases passed instantly. In software engineering, we often get stuck trying to solve a problem the "standard" way. This was a great reminder that sometimes, the best solution is to reframe the problem itself. One optimization at a time. Do you prefer clever "hacks" like this, or do you stick to standard procedural logic? Let’s discuss in the comments! 👇 #Java #LeetCode #SoftwareEngineering #CodingLife #DataStructures #Optimization #TechCommunity

🤔 Still confused between Dependency Injection and Dependency Lookup in Spring? Let’s simplify it! Many developers use Spring daily… but not everyone clearly understands how objects actually get created and managed behind the scenes. And trust me — this concept is a game changer in writing scalable and maintainable applications. ✅ Dependency Injection (DI) ➡️ The framework automatically provides required objects ➡️ Promotes loose coupling & better testability ➡️ Cleaner and more maintainable code ✅ Dependency Lookup (DL) ➡️ Developer manually fetches objects from the container ➡️ More control but increases tight coupling ➡️ Harder to test and maintain 💡 Simple Rule to Remember: 👉 DI = Framework gives you objects 👉 DL = You ask framework for objects In modern enterprise applications, Dependency Injection is preferred because it improves flexibility, readability, and scalability. 🚀 Understanding this difference can significantly improve your Spring design decisions and interview confidence. 💬 Let’s Discuss: Which approach have you used more in your projects — DI or Lookup? And why? #Java #Spring #BackendDevelopment #DependencyInjection #SoftwareDesign #Programming #JavaDevelopers #CodingJourney