How OOPs works: A parcel delivery example

 🚀 Understanding OOPS Concepts – The Backbone of Object-Oriented Programming 🚀 In the world of programming, languages like Java, C++, and C# leverage the powerful paradigm of Object-Oriented Programming (OOPS). But what exactly makes OOPS so integral? 🔹 Core Idea: Object An object is a bundle of data (variables) and actions (methods) packed together in something called a class. Think of a class as a blueprint to create many different objects, each with its own details/states. 🔹 Key OOPS Concepts: Class & Objects: Real-world entities modeled into classes, forming the foundation of structured code. Abstraction: Hiding complexity by exposing only essential functionality using abstract classes and interfaces. Encapsulation: Binding data and methods securely within a class, safeguarding internal state and promoting reusability. Inheritance: Acquiring properties from parent classes to enable code reuse and hierarchical relationships. Polymorphism: Allowing objects to behave differently based on context via method overloading and overriding. ✨ Example: Think of a Car interface abstracting functionalities like changing gears and applying brakes, while concrete classes like Lamborghini and Audi implement these behaviors specifically. Getting a good grip on these concepts helps you to build code that’s easier to maintain, scale, and maintainable software systems. 💡 Whether you’re just starting out or already coding for years, knowing OOPS well will make you a stronger developer! #Java #Programming #OOPS #SoftwareDevelopment #Learning

Q. What I Learned: SOLID Principles Every Developer Must Know Today, I attended an insightful session on S.O.L.I.D Principles conducted by Pragy Agarwal, Senior Software Engineer @ Scaler Academy. This session deepened my understanding of how to design software that is: > Scalable > Maintainable > Easy to understand and extend Key takeaways: 🔸 S - Single Responsibility Principle: Every class should have only one reason to change. 🔸 O - Open/Closed Principle: Classes should be open for extension but closed for modification. 🔸 L - Liskov Substitution Principle: Subtypes must be replaceable for their base types without breaking functionality. 🔸 I - Interface Segregation Principle: No client should be forced to depend on methods it doesn’t use. 🔸 D - Dependency Inversion Principle: Depend on abstractions, not concrete implementations. >What I achieved: Strengthened my foundation in object-oriented design. Understood how SOLID helps in writing clean, testable, and reusable code. Learned real-world examples of applying these principles in backend and data engineering projects.

🚀 Continuing my OOPs learning journey — Today’s focus: Encapsulation! Encapsulation is one of the most essential pillars of Object-Oriented Programming. It plays a key role in protecting data and maintaining a clean, modular structure in code. 🔹 Here’s what I learned today: 🧩 Definition: Encapsulation means wrapping data and methods that operate on that data into a single unit (class) and restricting direct access to the object’s internal state. 💡 Key Highlights: Enables data hiding using access modifiers (public, private, protected) Allows controlled access via getter and setter methods Promotes security and modular design 🎯 Main Purpose: To secure the data of an object from unintended access and make the system easier to maintain and scale. 🔐 Types of Encapsulation: Public → Accessible everywhere Protected → Accessible within class and subclasses Private → Accessible only within the class 💻 Implementation Practice: Used Python examples with private variables (__variable) and methods to understand how encapsulation works in real-time applications. ⚙️ Different Ways to Achieve Encapsulation: Using access modifiers Through getter and setter methods Using property decorators (@property in Python) 🌟 Advantages: Protects data integrity Improves maintainability Encourages modular coding Simplifies debugging 💬 Takeaway: Encapsulation is like a safety locker 🔒 — it keeps your data secure and only allows controlled access when needed. It’s the foundation for writing robust and maintainable code. #Python #OOP #Encapsulation #ProgrammingConcepts #SoftwareDevelopment #LearningJourney #CodeWithAnil LogicWhile Logic While

Lately, I've been seeing a lot of "starting from scratch" posts about learning new technologies, programming languages, and even #kubernetes. Since I am not very good with theory, if I were to start my Kubernetes journey all over again, I would start by creating my own CLI tool to interact directly with the Kubernetes API. For some, this may not sound like a beginner-friendly advice, but once you start exploring how to talk to the Kubernetes API and analyse its responses, you’ll realise just how much it helps you truly understand what’s happening behind every command. In more detail, this approach can help you build a good understanding of the authentication process, explore the API calls behind each command, and develop a deeper insight into how everything flows within the cluster. To answer potential questions in advance: - NO, it doesn't matter which language you use. Whether it's bash, GO, Python or braille - the important bit is writing the API call and sending it to the Kubernetes API - NO, this isn’t about replacing kubectl. It’s about reinforcing your understanding and making sure you’re not just sailing blindly inside the cluster - NO (another one?), you don't need to learn kubernetes the hard way to create a new cluster. There are many options for provisioning one locally without frying your brain Let me know what you think. Would this approach work for you? Have you ever built something similar while learning a new concept? #Kubernetes #CloudNative #K8s #DevOps #PlatformEngineering #CloudComputing #DevSecOps

🎯 #Day6/40: Liskov Principle - The Secret 90% #Developers Get #Wrong! 💀 They Said: "Use Inheritance" They Didn't Say: "Wrong Inheritance BREAKS Your Code!" LSP = "Beta should replace Baap seamlessly" 👨👦 3 Rules To Never Break LSP: 1️⃣ Method Argument Rule 🎯 Child method arguments must be SAME or BROADER ```cpp // Parent: void solve(string s) ✅ Child: void solve(string s) // Same ❌ Child: void solve(int s) // Breaks contract ``` 2️⃣ Return Type Rule 🔄 Child return type must be SAME or NARROWER ```cpp // Parent: Animal* create() ✅ Child: Dog* create() // Narrower - OK ❌ Child: Organism* create() // Broader - Breaks ``` 3️⃣ Exception Rule ⚠️ Child exceptions must be SAME or SUBSET ✅Same exceptions as parent ❌Throwing new unexpected exceptions Real Example That FAILS LSP: ```cpp class Account { public: virtual void withdraw() { /* logic */ } }; class FixedDeposit : public Account { public: void withdraw() override { throw Exception("Can't withdraw!"); // ❌ BREAKS LSP } }; ``` Golden Insight: "Inheritance is about BEHAVIOR,not just syntax!" Mentors: Rohit Negi Sir | Aditya Tandon Sir Channel: CoderArmy #40DaysChallenge Progress: Day 1-5✅ SOLID Foundation Day 6✅ LSP Mastered Day 7🎯 Interface Segregation 💬 Comment "LSP" if you've ever seen broken inheritance in real code! Follow → Daily Programming Truth Bombs! 💣 #LiskovSubstitutionPrinciple #SOLID #CleanCode #CoderArmy #Programming #SoftwareEngineering #OOP #Inheritance #DesignPrinciples #CodeQuality #SoftwareDevelopment #TechInterview #CareerGrowth #Developer #Coding #ProgrammingTips #SoftwareArchitecture #LearnInPublic #BuildInPublic #IndianDevelopers

Week 1 of learning Go ✅ — From zero to 3 working projects Started with no Go experience. Here's what I shipped this week: 🛠️ Projects Built → CLI Calculator (command-line parsing, error handling) → Task Manager (Cobra framework + JSON persistence) → REST API (CRUD operations, thread-safe with sync.RWMutex) 💡 Key Learnings Go's simplicity is its superpower Interfaces without inheritance — mind blown 🤯 Standard library is incredibly powerful Explicit error handling makes code robust 📚 Concepts Mastered Structs & Interfaces | JSON Encoding/Decoding | HTTP Servers | Concurrency Basics | Race-free Programming 🔗 All code on GitHub: https://lnkd.in/dChpYU3x 🔥 Next up — Week 2: Goroutines, Channels & Concurrent Patterns What Go feature surprised you most when learning it? 💬 #golang #100daysofcode #webdevelopment #backend #restapi #coding #learninginpublic

𝐅𝐫𝐨𝐦 𝟕𝟏𝟐 𝐌𝐁 → 𝟔𝟖 𝐌𝐁 — 𝐌𝐲 𝐃𝐨𝐜𝐤𝐞𝐫 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 Ever waited a long time for a Docker build — or seen your CI/CD pipeline slow down because the image was too big? 😅 I’ve faced that too. Here’s how I reduced our image size from 𝟕𝟏𝟐 𝐌𝐁 𝐭𝐨 𝐣𝐮𝐬𝐭 𝟔𝟖 𝐌𝐁 — while keeping it fast and ready for production. 🔹 𝐓𝐡𝐞 𝐎𝐫𝐢𝐠𝐢𝐧𝐚𝐥 𝐁𝐮𝐢𝐥𝐝 👉 Used full python:3.10 base image 👉 Too many RUN instructions → unnecessary layers 👉 No .dockerignore file 👉 A single-stage build that includes all the dependencies inside it. It worked… but it was heavy and slow. 🐢 𝐇𝐞𝐫𝐞’𝐬 𝐖𝐡𝐚𝐭 𝐈 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 1️⃣ 𝐒𝐰𝐢𝐭𝐜𝐡𝐞𝐝 𝐭𝐨 𝐚 𝐥𝐢𝐠𝐡𝐭𝐰𝐞𝐢𝐠𝐡𝐭 𝐛𝐚𝐬𝐞 𝐢𝐦𝐚𝐠𝐞 → From python:3.10 → python:3.10-alpine → 90% smaller and faster to pull 2️⃣ 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐥𝐚𝐲𝐞𝐫𝐬 → Merged similar commands into one line to make the build faster and cleaner. → Used fewer RUN steps to create fewer layers in the image. 3️⃣ 𝐀𝐝𝐝𝐞𝐝 𝐚 .𝐝𝐨𝐜𝐤𝐞𝐫𝐢𝐠𝐧𝐨𝐫𝐞 𝐟𝐢𝐥𝐞 → Ignored venv, cache, logs, and test files → Reduced build context significantly 4️⃣ 𝐔𝐬𝐞𝐝 𝐦𝐮𝐥𝐭𝐢-𝐬𝐭𝐚𝐠𝐞 𝐛𝐮𝐢𝐥𝐝𝐬 → First stage: Build dependencies → Final stage: Copy only runtime essentials 📉 𝐅𝐢𝐧𝐚𝐥 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 ✅ Image size: 𝟕𝟏𝟐 𝐌𝐁 → 𝟔𝟖 𝐌𝐁 ✅ −𝟗𝟎.𝟒𝟓% 𝐬𝐦𝐚𝐥𝐥𝐞𝐫 ✅ Faster container startup ✅ Shorter deployment times ✅ Lower registry storage and network usage 𝐖𝐡𝐚𝐭 𝐈 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 Small changes make a huge difference. Every MB you save speeds up every build, every deploy, and every pipeline. 𝐘𝐨𝐮𝐫 𝐓𝐮𝐫𝐧 Have you tried optimizing your Docker images recently? What’s one trick that made the biggest impact for you?👇 #Docker #DevOps #Containers #CloudEngineering #Optimization

🚀 Level Up Your Coding Game: Understanding Data Types & Variables! 🚀 Ever wonder how your programs store information? It all comes down to Data Types and Variables! Think of Variables as labeled boxes where you store data. And Data Types are the rules for what kind of data those boxes can hold – numbers, text, true/false values, and more! Mastering these fundamental concepts is crucial for writing clean, efficient, and bug-free code. They dictate how your program handles memory and performs operations. Why does this matter? Clarity: Makes your code easier to read and understand. Don't skip the basics – they're the building blocks of every great software project! #Programming #Coding #DataTypes #Variables #SoftwareDevelopment #TechSkills #LearnToCode

🚀 Day 49 of LeetCode150DaysChallenge Problem: 🧩 Merge Intervals #LeetCode #DSA #CodingChallenge #150DaysOfCode #C++ #Programming #ProblemSolving #SDE #CodingJourney 🧠 Problem Understanding: You’re given a list of intervals where each interval is represented as [start, end]. Your task is to merge all overlapping intervals and return the resulting list of non-overlapping intervals. 👉 Example: Input: [[1,3], [2,6], [8,10], [15,18]] Output: [[1,6], [8,10], [15,18]] Here, [1,3] and [2,6] overlap, so we merge them into [1,6]. 🧩 Intuition: To identify overlapping intervals, we first sort them by their starting time. Once sorted: If the next interval’s start time ≤ current interval’s end time → they overlap → merge them. Otherwise → push the current interval into the result and move forward. ⚙️ Step-by-Step Approach: 1️⃣ Sort all intervals by their starting time. 2️⃣ Initialize a temporary interval (temp) with the first one. 3️⃣ Iterate through the list: If intervals overlap, update temp[1] with the larger end time. If not, push temp into the answer list and start a new one. 4️⃣ After the loop ends, don’t forget to push the last interval. ✅ Final Answer: [[1,6], [8,10], [15,18]] 🧮 Time and Space Complexity: Time: O(n log n) → Sorting takes the most time. Space: O(n) → To store merged intervals. 🧠 Key Takeaway: Sorting the intervals simplifies everything — it ensures we only need to check consecutive pairs for overlap. This is a classic example of sorting-based greedy thinking in interval problems! ⚡ 🪜pattern observed : interval pattern 🏁 #Day49 Completed ✅ Working on such problems builds strong intuition for interval manipulation, which frequently appears in coding interviews at companies like Amazon, Google, and Meta.

⚙️ Efficient Programming in Kubernetes — Avoiding Memory Bloat the Right Way Working in Kubernetes has taught me one “key lesson” in backend engineering: Performance isn’t just about speed — it’s about stability and memory discipline. In one of my backend service PoCs focused on “efficiency in application design”, the design worked amazingly well and ran smoothly. I built a lightweight threading model that isolates heavy background jobs from the HTTP request path. Instead of running exports inline or letting a long-lived worker slowly bloat, each Gunicorn worker: runs scheduled background work in dedicated threads then gracefully recycles itself on a controlled interval to reset memory All without blocking user traffic. The result: ⚡ No blocking on user requests 🧠 Stable memory footprint across long-running pods 🔁 Graceful worker restarts each cycle → fresh memory, clean cache 🔭 Full observability via Datadog + K8s metrics Architecture pattern (per worker): Main thread → Flask HTTP requests Export thread → EVSE report export every hour, then triggers a safe worker recycle Monitor thread → connection health check every 5 minutes->ticket incidents This pattern avoids the “memory creep” common in long-running Python services and keeps behavior predictable, even under heavy telemetry and event-driven load. Key takeaway: 🔭 In Kubernetes, your platform handles scaling — but your code should self-heal and self-clean. Keep it lean, intentional, and restart-aware, saving resources 🔋 So, an example here in my script: 1 main thread 1 export thread 1 monitor thread 👉 3 threads total per worker process(PID) in the logic. It's so amazing control whole program.  #Kubernetes #Python #Flask #Gunicorn #SRE #Reliability #Scalability #Observability #MemoryManagement #EVCharging #HEMS #OCPP #EV