Understanding OOP: Classes, Methods, Encapsulation, Inheritance, Polymorphism, Abstraction

🌟 𝐑𝐞𝐯𝐢𝐬𝐢𝐭𝐢𝐧𝐠 𝐎𝐎𝐏: 𝐀 𝐖𝐞𝐞𝐤𝐞𝐧𝐝 𝐃𝐞𝐞𝐩 𝐃𝐢𝐯𝐞 𝐢𝐧𝐭𝐨 𝐎𝐛𝐣𝐞𝐜𝐭-𝐎𝐫𝐢𝐞𝐧𝐭𝐞𝐝 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 This weekend, I took a step back to revisit the fundamentals of Object-Oriented Programming (OOP). It was a refreshing dive into the core principles that power so much of modern software development! Here’s a quick recap of what I explored: 🔑 𝐓𝐡𝐞 𝐅𝐨𝐮𝐫 𝐏𝐢𝐥𝐥𝐚𝐫𝐬 𝐨𝐟 𝐎𝐎𝐏 𝑨𝒃𝒔𝒕𝒓𝒂𝒄𝒕𝒊𝒐𝒏 Abstraction means hiding the implementation details and providing only the necessary methods or APIs to the outside world. We don’t know how it works; we only know what to do. Example: In real life, you start a car by pressing the start button. You don’t know how the engine starts. 𝑬𝒏𝒄𝒂𝒑𝒔𝒖𝒍𝒂𝒕𝒊𝒐𝒏 Encapsulation is the act of enclosing something, like in a capsule. It means each object in your code should control its own state. A state is basically a snapshot of your object. Example: For medication, we get a capsule that contains all the necessary medicine, keeping everything enclosed. 𝑰𝒏𝒉𝒆𝒓𝒊𝒕𝒂𝒏𝒄𝒆 Inheritance is the ability to create a new class from an existing class. It’s when an object acquires the properties of another object. There are two types of relationships in OOP: i) Is-A Relationship: Inheritance (Example: A Teacher is a User) ii) Has-A Relationship: Composition and Aggregation (Example: A Department has Subjects, a Teacher has a Department) 𝑷𝒐𝒍𝒚𝒎𝒐𝒓𝒑𝒉𝒊𝒔𝒎 Polymorphism means “many forms,” derived from the Greek words “poly” (many) and “morph” (form). This concept allows objects or methods to behave differently under different circumstances. 📊 𝗠𝘆 𝗙𝗶𝗿𝘀𝘁 𝗨𝗠𝗟 𝗗𝗶𝗮𝗴𝗿𝗮𝗺! After learning these key concepts, I focused on a case study where I designed a UML diagram. This was my first time designing a UML diagram, and now I believe every developer or engineer should learn it. Tomorrow, I’m diving into another case study to keep the momentum going! If you’re curious about the resources I’m using to learn, comment “Resource” below, and I’ll share the details via DM! Let’s keep the conversation going. 😊 What’s your favorite OOP concept, or do you have any tips for mastering UML diagrams? Share your thoughts! 👇 #OOP #Programming #SoftwareDevelopment #UML #TechJourney

Object-Oriented Programming (OOP) Let's take a closer look at the core concepts of OOP: ➡️ Classes & Objects - Classes are blueprints. They define the properties and behaviors of the objects created from them. - Objects are instances of classes. They bring the blueprint to life with unique attributes. ➡️ Inheritance - This allows one class to inherit attributes and methods from another. - It promotes code reusability and creates a hierarchy. Think of it as building upon a solid foundation. ➡️ Encapsulation - Encapsulation restricts access to certain components of an object. - It helps in protecting data and creates a clear separation between an object's interface and implementation. This is your shield against chaos! ➡️ Abstraction - Abstraction simplifies complex systems by exposing only the necessary parts. - It allows you to focus on high-level functionalities without getting lost in the details. ➡️ Polymorphism - This concept allows methods to do different things based on the object that is calling them. - It enhances flexibility and facilitates efficient code management. By mastering these concepts, you can: - Write cleaner code - Enhance collaboration - Improve system design 💡 Remember, OOP isn't just about syntax; it's a way of thinking. Understanding these principles transforms you from a coder into an architect of solutions. I help technical professionals build impactful career brands on LinkedIn. : Check in Comment below 👇 Create LinkedIn Diagrams for FREE using AI! Join waitlist : Check in Comment below 👇 Follow Ashish Sahu for more tech content

Object-Oriented Programming (OOP) Let's take a closer look at the core concepts of OOP: ➡️ Classes & Objects - Classes are blueprints. They define the properties and behaviors of the objects created from them. - Objects are instances of classes. They bring the blueprint to life with unique attributes. ➡️ Inheritance - This allows one class to inherit attributes and methods from another. - It promotes code reusability and creates a hierarchy. Think of it as building upon a solid foundation. ➡️ Encapsulation - Encapsulation restricts access to certain components of an object. - It helps in protecting data and creates a clear separation between an object's interface and implementation. This is your shield against chaos! ➡️ Abstraction - Abstraction simplifies complex systems by exposing only the necessary parts. - It allows you to focus on high-level functionalities without getting lost in the details. ➡️ Polymorphism - This concept allows methods to do different things based on the object that is calling them. - It enhances flexibility and facilitates efficient code management. By mastering these concepts, you can: - Write cleaner code - Enhance collaboration - Improve system design 💡 Remember, OOP isn't just about syntax; it's a way of thinking. Understanding these principles transforms you from a coder into an architect of solutions. I help technical professionals build impactful career brands on LinkedIn. : Check in Comment below 👇 Create LinkedIn Diagrams for FREE using AI! Join waitlist : Check in Comment below 👇 Follow Ashish Sahu for more tech content

Object-Oriented Programming (OOP) Let's take a closer look at the core concepts of OOP: ➡️ Classes & Objects - Classes are blueprints. They define the properties and behaviors of the objects created from them. - Objects are instances of classes. They bring the blueprint to life with unique attributes. ➡️ Inheritance - This allows one class to inherit attributes and methods from another. - It promotes code reusability and creates a hierarchy. Think of it as building upon a solid foundation. ➡️ Encapsulation - Encapsulation restricts access to certain components of an object. - It helps in protecting data and creates a clear separation between an object's interface and implementation. This is your shield against chaos! ➡️ Abstraction - Abstraction simplifies complex systems by exposing only the necessary parts. - It allows you to focus on high-level functionalities without getting lost in the details. ➡️ Polymorphism - This concept allows methods to do different things based on the object that is calling them. - It enhances flexibility and facilitates efficient code management. By mastering these concepts, you can: - Write cleaner code - Enhance collaboration - Improve system design 💡 Remember, OOP isn't just about syntax; it's a way of thinking. Understanding these principles transforms you from a coder into an architect of solutions. I help technical professionals build impactful career brands on LinkedIn. : Check in Comment below 👇 Create LinkedIn Diagrams for FREE using AI! Join waitlist : Check in Comment below 👇 Follow Ashish Sahu for more tech content

𝗪𝗵𝘆 𝗢𝗢𝗣 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘄𝗲 𝘁𝗵𝗶𝗻𝗸 🚀 Lately I’ve been studying Object-Oriented Programming (OOP) again, and honestly — it reminded me how much easier coding becomes when your project starts getting bigger. 🔍 So, what is OOP? At its core, OOP is a way of writing code by thinking in objects — like little “mini-characters” in your program that hold: 📌 Data (properties) ⚙️ Actions (methods) It makes code feel more structured and less chaotic. 🚀 Why OOP actually helps Here are the parts that stood out to me: 🧱 Classes - Classes define the blueprint for creating objects. They group related data and functionality together, helping you keep your code organized and reusable. 🧬 Inheritance - Inheritance allows one class to extend another and reuse existing logic. This reduces repetition and creates cleaner, more consistent code structures. 🔒 Encapsulation - Encapsulation protects an object’s internal data by exposing only what’s necessary. It creates safer, more predictable components and prevents accidental misuse. 🎭 Polymorphism - Polymorphism enables different objects to use the same method name but behave in their own way. It improves flexibility and keeps your code easy to work with. ✨ Abstraction - Abstraction hides implementation details and highlights only the essential features. It reduces complexity and makes large systems easier to understand. 🧩 Composition - Composition offers a flexible alternative to inheritance by adding specific behaviors to objects as needed. It helps avoid rigid class hierarchies and keeps code adaptable.. Understanding OOP isn’t just “nice to know” — it actually helps you write cleaner, scalable code and think like an engineer. No matter what stack you work with, these concepts make your life easier.

should I follow OOP or ORM? 🤔 I always get confused when starting a full-stack project Here’s a quick breakdown that might help you too 👇 💡 Understanding OOP vs ORM — Simplifying Modern Software Design In today’s development world, two powerful paradigms often come together: OOP (Object-Oriented Programming) and ORM (Object-Relational Mapping). While both sound similar, they solve very different challenges in software design. ⚙️ Object-Oriented Programming (OOP) OOP focuses on structuring your code using objects and classes — it’s about organizing logic. ✅ Merits: Improves code reusability and scalability Easier maintenance and modularity Better abstraction for complex systems ⚠️ Demerits: Can be overly complex for small projects Requires good design discipline 🗃️ Object-Relational Mapping (ORM) ORM bridges the gap between code and database, allowing developers to interact with data using objects instead of writing SQL queries manually. ✅ Merits: Reduces repetitive SQL code Provides database independence Improves productivity and readability ⚠️ Demerits: May reduce performance for complex queries Can hide inefficient SQL under the hood 🔄 OOP + ORM = Productivity + Structure When combined, OOP provides the design framework, and ORM provides the data handling efficiency, helping teams build faster, scalable, and maintainable applications. 📊 In short: OOP defines how you think and structure, while ORM defines how you connect and interact.

🤖 Part 2. AI in Programming: Which is the Best Assistant for Complex Code Now and in the Future? After months of testing and exhaustive analysis, I share my findings about this revolution that's transforming how we develop software👇: 🏆 Top 3 Generalists 1️⃣ GitHub Copilot ✅Perfect integration with VS Code and main IDEs ✅Excellent in JavaScript, Python, C#, Java ✅Ideal for daily productivity 💰~$10-20/month 2️⃣ Claude 4.5 Sonnet ✅Leader in complex code and system architecture ✅Excels in C++, multi-stage reasoning ✅200K token context (~150K lines of code) 📊92% on HumanEval benchmark 3️⃣ DeepSeek ✅Enterprise-grade capabilities FREE ✅Excellent in C# (complex and extensive codes), Python ✅128K token context ✅Excellent for legacy code analysis 📊 Technical Comparative Benchmark • Longest context: Claude (200K) vs DeepSeek (128K) vs Copilot (~8K) •Code accuracy: Claude 92% vs GPT-4 90% on HumanEval •Response speed: Copilot leads in instant suggestions •Cost-benefit: DeepSeek wins for being free with premium capabilities 🎯 By Programming Language C# (.NET) → GitHub Copilot + Claude 4.5 + DeepSeek C++/Rust→ Claude 4.5 Sonnet (absolute leader) Java/Spring→ GitHub Copilot Python/ML→ Cursor + DeepSeek 💡 Recommendations by Project Type 🏢 Large enterprises: GitHub Copilot + Claude ($50/dev/month) 🚀Startups: Cursor + DeepSeek ($0-20/month) 🔬Data Science: Cursor (optimized for Jupyter) ☁️Cloud AWS: Amazon CodeWhisperer 💼 Documented Success Cases Companies report after 6 months the use: 🏢FinTech: 60% fewer bugs in production with Claude + code review 🚀SaaS Startup: 3x development speed with Copilot + DeepSeek 🔬Research Lab: 80% reduction in data analysis time with Cursor 📈 Measured Real Impact ✔️ 40-55% reduction in coding time ✔️30% improvement in code quality ✔️Immediate ROI in development teams ⚠️ Things to Consider 🔸 No "absolute best" exists - it depends on context 🔸Risk of "hallucinations" (5-30% depending on the case) 🔸Important: maintain human code review 🔸Complement, NOT substitute for fundamental skills, debugger always necessary 🛡️ Best Implementation Practices ✅ Strategic combination: Use 2-3 complementary assistants ✅Adaptation phase: 2-4 weeks to adjust workflows ✅Governance: Establish review and security policies ✅Training: Train team in specific prompt engineering 🔮 The Future 🌟 Multimodal models (UML diagrams) 🌟Autonomous agents (Devin, AutoGPT) 🌟Private corporate fine-tuning 🌟Visual debugging with AI 🎓 My Personal Verdict For serious and complex work: Claude 4.5 Sonnet is currently the most underestimated. Its architectural reasoning is superior followed by DeepSeek. For daily productivity: GitHub Copilot (speed and inline completions). For limited budget: DeepSeek offers surprising capabilities at no cost. Important: no assistant is perfect and they never replace programmer experience. 🔗 Final recommendation: test various AI assistant algorithms and link their solutions to get the best implementation.

Object-Oriented Programming (OOP) was a trillion-dollar mistake that has overcomplicated modern development. 🤯 That's a bold claim, but hear me out. For decades, we've been taught to model the world with classes, inheritance, and complex design patterns, often leading to rigid, tightly-coupled systems. The core problem? The industry-scale challenge of OOP: * Boilerplate Hell: So much code dedicated just to setting up objects and interfaces. * Deep Inheritance: Makes codebases difficult to refactor and debug—a nightmare for maintenance. * Conceptual Overhead: The mental load for new developers to grasp complex hierarchies slows down onboarding and velocity. Why Functional Programming (FP) is winning The modern trend toward simpler, declarative, and immutable code isn't just a fad; it's a response to OOP's complexity. * Clarity: Focuses on what a program should do, not how an object's state changes. * Testability: Pure functions are inherently easier to test and reason about. * Scalability: Fits perfectly with distributed systems and parallel processing (think microservices). 💡 Actionable Insight: Don't abandon all your OOP knowledge, but start integrating FP principles today. Tools like React hooks, Go's composition over inheritance, and modern Python type hinting all lean toward a functional mindset. Embrace composition and immutability for systems that are faster to build and easier to maintain. What's your take? Did the massive shift to OOP unnecessarily complicate software, or is it still essential for large-scale system design? Share your perspective below! 👇 #SystemDesign #DeveloperTools #Tech #FunctionalProgramming #SoftwareArchitecture

✅ Why OOP is important OOP helps us structure code the same way we think about the world: with things that have properties and actions. It turns messy logic into organized building blocks. That leads to: • Cleaner code that doesn’t look like spaghetti • Reusability so you don’t rewrite the same stuff again and again • Faster debugging and upgrades • Bigger, more complex apps without losing your mind When code grows from 100 lines to millions, OOP keeps everything sane. ✅ What is the use of OOP OOP gives you a blueprint for writing programs that are: • Modular: break a problem into small parts • Secure: protect data through encapsulation • Flexible: change features easily through inheritance and polymorphism • Scalable: perfect for apps that keep evolving That’s why almost every modern language uses OOP concepts. Java, C++, Python, C#, Swift…all rely on it for real-world software like: • Banking systems • Games and graphics • Mobile apps • Operating systems • Web applications Quick one-liner to remember 📌 OOP is used to build software that is organized, reusable, secure, and easy to maintain. Also, enhance your skills with these free courses: 𝗥𝗘𝗘 (𝗚𝗼𝗼𝗴𝗹𝗲) 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗿𝗲𝗴𝗿𝗲𝘁 𝗻𝗼𝘁 𝘁𝗮𝗸𝗶𝗻𝗴 𝗶𝗻 𝟮𝟬𝟮𝟱. 📌🔃10000+ free courses free access: https://lnkd.in/g9qssg4T Google Data Analytics: https://lnkd.in/g-tjpeqe Google Project Management https://lnkd.in/gNAapb_x Foundations of Project Management https://lnkd.in/gP-h_Qpq Google Introduction to Generative AI https://lnkd.in/ggbGgmGP Google Cybersecurity https://lnkd.in/g6qBj_in Google UX Design https://lnkd.in/gcpVd5ng Google Digital Marketing & E-commerce: https://lnkd.in/g2i68zzz Google IT Support: https://lnkd.in/gzvpYfwG Web Applications for Everybody Specialization: https://lnkd.in/gHQY-yWm Microsoft Power BI Data Analyst https://lnkd.in/gEvJ4zSN Microsoft Cybersecurity Analyst https://lnkd.in/gKKfbd8v Excel Skills for Business Specialization https://lnkd.in/gwHMJUqu The Structured Query Language (SQL) https://lnkd.in/ge6hewyd 🧠 𝐓𝐡𝐞 𝐌𝐨𝐬𝐭 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐀𝐈 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐘𝐨𝐮 𝐂𝐚𝐧 𝐓𝐚𝐤𝐞 𝐢𝐧 2025 Programming for Everybody https://lnkd.in/gV3ArxMj AI for Everyone by DeepLearning AI https://lnkd.in/gdgeuvFR Python for Data Science, AI and Development by IBM https://lnkd.in/ggG5suCz Introduction of AI by IBM https://lnkd.in/gD5iUUi4 IBM Data Science Professional Certificate https://lnkd.in/g-HWyEFk Google Cybersecurity Professional Certificate https://lnkd.in/g6qBj_in Data Science: Foundations using R Specialization https://lnkd.in/gNbx_tfU

Reactive Programming in 2025: Three Generations and One Big Lesson. Part 2. 3 New Wave Reactive — When the Runtime Thinks for You 🟢 3a. The Mature Runtime Reactive — Go (goroutines) (The absolute winner in terms of simplicity!) Core idea: Let the runtime handle suspension and scheduling automatically. Each goroutine runs as if it’s blocking — but the runtime transparently parks it when waiting for I/O. Strengths: Simplicity — write linear, synchronous code No backpressure boilerplate, no async keywords Predictable performance and excellent scaling Built-in cancellation (context.Context) and deadlines Mature ecosystem and stable tooling Weaknesses: Explicit context.Context passing may feel verbose (though IDEs and AI make it trivial) Shared memory concurrency (locks, channels) still requires discipline Context / OpenTelemetry: Trace context lives in context.Context — explicit but universal Full compatibility with OpenTelemetry SDKs and middleware Tracing, metrics, and cancellation all use the same mechanism Bottom line: Go delivers runtime-level reactivity with the lowest cognitive overhead. The simplest model that still scales — “reactive without trying to be reactive.” 🟡 3b. The Emerging Runtime Reactive — Java Loom (virtual threads) Core idea: Virtual threads bring Go-like scheduling to the JVM. The code looks fully synchronous, but virtual threads are parked and resumed by the runtime. Strengths: Fully transparent — no need for async, await, or suspend Works with most existing JVM frameworks Dramatically reduces thread management complexity Weaknesses: Pinning: blocking native calls (e.g., JDBC) prevent virtual threads from parking synchronized blocks still “pin” the carrier thread Tooling (profilers, MDC, logging) is catching up Limited production experience — still maturing Context / OpenTelemetry: New API: ScopedValue — safer alternative to ThreadLocal for context propagation Existing libraries (SLF4J, OpenTelemetry) still rely on ThreadLocal, so bridging adapters are needed Works well with StructuredTaskScope for request-scoped tracing once integrated Bottom line: Loom brings Go-style ergonomics to the JVM, but until the ecosystem catches up, you’ll still hit rough edges — especially around blocking I/O and context management. First part: https://lnkd.in/e6kY5eJ5