Sven Hodapp’s Post

Factory Method is one of the most misunderstood design patterns. It often gets mixed up with Simple Factory especially when we see if-else logic inside a “factory” class. The real difference only becomes clear when you understand extensibility, Open/Closed Principle, and framework-level architecture. So I wrote a detailed article explaining everything from scratch with proper Python examples and no jargon left unexplained. If you’re serious about improving your design thinking, give it a read: https://lnkd.in/gnrgbtgq

What is OOPS in C#?* OOPS (Object-Oriented Programming System) is a paradigm that uses *objects* and *classes* to design software. C# is a fully OOP language, so it supports the four core pillar Encapsulation - Bundling data (fields) and methods that operate on that data into a single unit – *class*. - Hides internal state using access modifiers (`private`, `protected`, `public`). - Example: public class BankAccount { private decimal balance; // Encapsulated field public void Deposit(decimal amount) { if (amount > 0) balance += amount; } public decimal GetBalance() { return balance; } } Inheritance - Allows a class to inherit members from another class. - Promotes code reuse. - Example: public class Animal { public void Eat() { Console.WriteLine("Eating..."); } } public class Dog : Animal { public void Bark() { Console.WriteLine("Barking..."); } } // Dog now has Eat() and Bark() Polymorphism - Ability to present the same interface for different underlying forms (data types). - Two types: - *Compile-time (Method Overloading)* - *Runtime (Method Overriding)* - Example (Overriding): public class Shape { public virtual void Draw() { Console.WriteLine("Drawing Shape"); } } public class Circle : Shape { public override void Draw() { Console.WriteLine("Drawing Circle"); } } Abstraction - Hiding complex implementation details and showing only essential features. - Achieved using *abstract classes* or *interfaces*. - Example (Interface): public interface IVehicle { void Start(); void Stop(); } public class Car : IVehicle { public void Start() { Console.WriteLine("Car Started"); } public void Stop() { Console.WriteLine("Car Stopped"); } } Why OOPS in C#? - Modularity:Breaks app into smaller, manageable pieces (classes). - Reusability:Inheritance & polymorphism enable code reuse. - Security:Encapsulation protects data integrity. - Flexibility:Abstraction allows easy changes without affecting external code.

deque vs list — Performance Matters ⚡ Many developers use list.insert(0, x) when building queues. But here’s what actually happens under the hood: list.insert(0, 99) → O(n) deque.appendleft(99) → O(1) I ran a simple benchmark with 50,000,000 elements: list.insert(0, 99) → 0.186789 seconds deque.appendleft(99) → 0.000076 seconds That’s not a small difference. When you insert at the beginning of a list, Python shifts every existing element one position to the right. That cost grows linearly with size. collections.deque, on the other hand, is designed for fast appends and pops from both ends — making it the right choice for queues and high-performance pipelines. Same code. Different data structure. Massive performance impact. Choose wisely.

OOP: Encapsulation 🔒 When people hear encapsulation, they often think: “Private fields + getters/setters.” That’s part of it… but the real idea is deeper: ✅ Encapsulation = protecting an object’s rules by controlling how its state can change. It’s not just hiding data, it’s exposing behavior and keeping the object valid. Why it matters - Prevents invalid states (keeps invariants true) - Reduces bugs by limiting what can be changed from outside - Makes code easier to refactor (you can change internals without breaking callers) The object stays consistent because all changes go through controlled operations. 👉 Don’t pull data out of an object to make decisions outside of it. ✅ Instead, tell the object what you want to achieve, and let it decide how to do it using its own rules. (Call methods that do the job instead of poking values) 📌 Encapsulation is how we make objects trustworthy. Next in the series: Abstraction (how to design clean interfaces without leaking details). What’s the best example of encapsulation you’ve seen in real projects? #OOP #Encapsulation #SoftwareEngineering #CleanCode #CSharp #Dotnet #DesignPrinciples #WebDeveloper #FullStackDeveloper #SoftwareEngineering

🚀 Day 312 of solving 365 medium questions on LeetCode! 🔥 Today’s challenge: “622. Design Circular Queue” ✅ Problem: Design a circular queue (also called a "Ring Buffer"). It uses a fixed-size array and follows the FIFO (First In, First Out) principle, but connects the end of the array back to the front to reuse empty spaces left behind by dequeued elements! ✅ Approach (Array + Modulo Arithmetic) Instead of relying on built-in queue libraries, I implemented it from scratch using a fixed-size list and two running counters: Initialization: Create an array of size k and set absolute start and end counters to 0. Enqueue / Dequeue: Instead of manually resetting the pointers to 0 when they hit the end of the array, I just let them grow indefinitely! I used modulo arithmetic (index % k) to figure out exactly where that absolute counter maps to within the fixed circular array. Status Checks: isEmpty() is beautifully simple: start == end. isFull() is just: end == start + k. ✅ Key Insight The biggest headache when building a circular queue is distinguishing between a "Full" state and an "Empty" state, since traditional read/write pointers overlap in both scenarios. By letting the pointers act as absolute operation counters (and only applying modulo during array access), the capacity math becomes incredibly elegant. It completely eliminates the need for messy if/else wrap-around logic! ✅ Complexity Time: O(1) — Every single operation (enqueue, dequeue, front, rear, checks) executes in constant time. Space: O(K) — Where K is the predefined maximum capacity of the queue. 🔍 Python solution attached! 🔥 Flexing my coding skills until recruiters notice! #LeetCode365 #DataStructures #Queue #Python #ProblemSolving #DSA #Coding #SoftwareEngineering

From 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥 𝐭𝐨 𝐖𝐞𝐛-𝐁𝐚𝐬𝐞𝐝 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐓𝐨𝐨𝐥 𝐑𝐞𝐜𝐞𝐧𝐭𝐥𝐲 , I built a Laptop Recommendation System using Python and cosine similarity. Now I implemented the same 𝐛𝐫𝐨𝐰𝐬𝐞𝐫-𝐛𝐚𝐬𝐞𝐝 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐭𝐨𝐨𝐥 𝐮𝐬𝐢𝐧𝐠 𝐇𝐓𝐌𝐋, 𝐂𝐒𝐒, 𝐚𝐧𝐝 𝐉𝐚𝐯𝐚𝐒𝐜𝐫𝐢𝐩𝐭 𝐓𝐡𝐢𝐬 𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐟𝐨𝐜𝐮𝐬𝐞𝐬 𝐨𝐧 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠: • Takes user inputs (budget, RAM, storage, processor level) • Calculates a dynamic match score • Applies weighted scoring logic • Filters laptops within a flexible budget range • Sorts results manually based on score • Displays top recommendations instantly 𝐖𝐡𝐚𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐌𝐋 𝐯𝐞𝐫𝐬𝐢𝐨𝐧? 𝐓𝐡𝐞 𝐌𝐋 𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐮𝐬𝐞𝐝: – Feature scaling – Cosine similarity – Structured numeric modeling 𝐓𝐡𝐞 𝐖𝐞𝐛 𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐮𝐬𝐞𝐬: – Scoring logic instead of similarity – Manual ranking (custom sorting logic) – Real-time frontend interaction 𝐁𝐢𝐠 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐭𝐡𝐢𝐬 𝐭𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧: Building a model is one skill. Turning logic into a usable interface is another. 𝐓𝐡𝐢𝐬 𝐡𝐞𝐥𝐩𝐞𝐝 𝐦𝐞 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧: Algorithm thinking vs Product thinking. 𝐍𝐨𝐰 𝐈 𝐜𝐚𝐧 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐟𝐫𝐨𝐦 𝐛𝐨𝐭𝐡 𝐚𝐧𝐠𝐥𝐞𝐬: Data Science + Frontend Implementation. 𝐆𝐢𝐭𝐇𝐮𝐛 𝐑𝐞𝐩𝐨𝐬𝐢𝐭𝐨𝐫𝐲: https://lnkd.in/gKmkaaFm #WebDevelopment #MachineLearning #JavaScript #Frontend #ProductThinking #LearningInPublic #MCA

─ Decorators | ├── Function Decorators | ├── Class Decorators | ├── functools.wraps | └── Chaining Decorators | |── Context Managers | ├── with Statement | ├── enter / exit | └── contextlib Module | |── Concurrency & Parallelism | ├── threading | ├── multiprocessing | ├── concurrent.futures | ├── asyncio | ├── async / await | └── GIL Impact | |── Testing | ├── unittest | ├── pytest | ├── doctest | └── Mocking (unittest.mock) | |── Packaging & Distribution | ├── pip | ├── setup.py | ├── pyproject.toml | ├── Wheels | └── Publishing to PyPI | |── Debugging & Profiling | ├── pdb | ├── Logging Module | ├── timeit | ├── cProfile | └── tracemalloc | |── Memory Management | ├── Reference Counting | ├── Garbage Collection | ├── Weak References | └── Memory Profiling | |── Python 3.x Advanced Features | ├── f-Strings | ├── Walrus Operator (:=) | ├── Pattern Matching | ├── Type Hinting Improvements | ├── Async Enhancements | └── Structural Pattern Matching | |── Popular Libraries & Ecosystem | ├── Web Development | | ├── Django | | ├── Flask | | └── FastAPI | ├── Data Science | | ├── NumPy | | ├── Pandas | | ├── Matplotlib | | ├── Seaborn | | └── Scikit-learn | ├── Machine Learning | | ├── TensorFlow | | └── PyTorch | ├── Automation | | ├── Selenium | | └── BeautifulSoup | └── API Development | └── Requests | |── Security | ├── hashlib | ├── hmac | ├── secrets | └── ssl | |── Best Practices | ├── PEP 8 | ├── PEP 20 (Zen of Python) | ├── Code Organization | ├── Virtual Environments | └── Documentation Standards | |── Advanced Topics | ├── Metaclasses | ├── C Extensions | ├── Cython | ├── Internals (Bytecode, AST) | └── Performance Optimization | |____________ END __________________

Variable Naming Convention: Writing Code That Communicates Clearly A variable naming convention is a standardized approach to assigning meaningful, readable, and maintainable names to variables. Strong naming improves clarity, team collaboration, and long-term scalability of codebases. General Rules • Use descriptive names that reflect stored data. • Avoid single letters except loop counters (i, j). • Do not use spaces or special characters. • Do not start with numbers. • Avoid reserved keywords. • Maintain consistency across the project. Example student_age → clear sa → unclear Common Naming Styles snake_case (Common in Python) total_marks user_name is_logged_in camelCase (Common in JavaScript) totalMarks userName isLoggedIn PascalCase (Typically for Classes) StudentRecord UserProfile UPPER_CASE (Constants) MAX_SIZE API_KEY PI_VALUE Python Naming Convention — PEP 8 Variables student_name total_price file_path Boolean Variables is_active has_permission can_edit Private/Internal Variables _user_id _temp_data Meaningful Prefix Patterns Counter → count_students Flag/Boolean → is_valid List → students Dictionary → user_dict Good Naming total_students average_score customer_address Poor Naming t data1 valueTempX Domain-Focused Naming (Full-Stack / UI Development) api_response db_connection form_input_value grid_column_width Advanced Guidelines • Avoid redundant type hints in names. • Prefer semantic clarity over brevity. • Use singular for single items and plural for collections. • Maintain consistent terminology across modules. Clean naming is not style preference; it is architecture discipline. #Python #CodingStandards #CleanCode #Programming #SoftwareDevelopment

Following up on my previous post about why architects should learn programming, let’s talk about how to move forward. The "best" language depends entirely on what you want to build and how far you want to push the boundaries of the AECO industry. Here are the top 3 contenders to start your journey: C# - Automation / Plugins If your goal is deep integration and building heavy-duty automation tools, this is your go-to. Complexity: (3/5) – It takes longer to master the .NET/.NET Core frameworks compared to others, but the control you get is unmatched. Python - Data Intensive Python is the versatile all-rounder. It has caught up significantly in geometry automation (Grasshopper/Dynamo) but shines brightest when you step outside the canvas. The Scope: Working within existing frameworks but focusing more on design, data, and analytics. Complexity: (2/5) – Highly readable and beginner-friendly. JavaScript - Cloud and web based The outlier that is becoming essential. While C# and Python live mostly inside your desktop software, JavaScript lives on the web. The Scope: Creating custom cloud products, testing standalone validity, and bridging desktop tools to the cloud. Complexity: (3/5) – Requires understanding the web ecosystem, but offers the freedom to build independent products. Use cases and brief directions that one can take from starting with these languages here in my medium post. 🔗 https://lnkd.in/gfz7sJXg #Architecture #ComputationalDesign #AEC #Programming #CSharp #Python #JavaScript #RevitAPI #WebDevelopment #ConstructionTech

─ Object-Oriented Programming (OOP) | ├── Class | ├── Object | ├── Constructor | | ├── Default Constructor | | ├── Parameterized Constructor | | └── Constructor Overloading | ├── Method | ├── this keyword | ├── static keyword | ├── final keyword | ├── OOP Principles | | ├── Encapsulation | | ├── Abstraction | | ├── Inheritance | | | ├── Single | | | ├── Multilevel | | | └── Hierarchical | | └── Polymorphism | | ├── Compile-time (Method Overloading) | | └── Runtime (Method Overriding) | ├── super keyword | ├── instanceof | └── Object Class Methods |── Packages | ├── Built-in Packages | ├── User-defined Packages | ├── Access Specifiers | | ├── private | | ├── default | | ├── protected | | └── public | └── Import Statement | |── Strings | ├── String Class | ├── String Pool | ├── String Methods | ├── StringBuffer | ├── StringBuilder | └── Immutable vs Mutable | |── Arrays | ├── Single Dimensional | ├── Multidimensional | ├── Jagged Arrays | └── Arrays Utility Class | |── Exception Handling | ├── try | ├── catch | ├── finally | ├── throw | ├── throws | ├── Checked Exceptions | ├── Unchecked Exceptions | ├── Custom Exceptions | └── try-with-resources