Ravi Kumar Pal’s Post

Recently I published an open-source utility I built back in 2024: skeletonpy. TL;DR: If you do AI-assisted Python coding and want a simple way to include your project's code summary without bloating the context, with pretty much zero setup or installation, give it a try. Simply run this in your project's source root: uvx skeletonpy src And include the generated `summary.py.txt` file in your favorite LLM's context. The Backstory I built this because I had no luck with RAG. Granted, back then it was just naive RAG, and there were very few good reranking models. Maybe RAG is simply not a good fit for code, or maybe it was just me. Either way, I always ended up with a context half-full of irrelevant garbage. Furthermore filling up the prompt often leads to standard context rot or the "lost in the middle" phenomenon, where models just start ignoring or confusing data. Today top-tier models got better but some performance degradation is still there. I use skeletonpy occasionally when coding Python projects even today, especially when working with coding assistants like Cline and I want to have full control and insight into the generated code (no vibes :-) ). It gives the AI a focused, accurate map of the repo with class-level resolution to quickly find what it needs. How it works Skeletonpy is deliberately "simple and stupid": it does exactly one thing. It parses your source code offline using AST (no LLMs, no vector DBs, no complex local indexers) to generate a highly compressed skeleton of your repository. It squashes a few pages into a few lines while still providing references back to the original code so the LLM agent can "dig deeper" in the right location. At the same time, the output summary perfectly resembles Python. I've tested it against various LLMs over the last two years and they all had no problems navigating and understanding this structural pseudo-code. https://lnkd.in/dW7QYBkF

Mastering Python Classes and Inheritance for Scalable Code What is Python?  Python is one of the high-level interpreted programming languages. Its major aspect is the simplicity of writing and interpreting it. It is one of the fantastic languages worldwide, created by Guido van Rossum in 1991. Understanding Python Classes A class in Python is a sort of template for creating objects. It states what attributes or properties and what methods or behaviours its objects will support. Instead of repeating code for every given occasion, classes encapsulate related data and functions together under one roof. Let us consider a simple example of a Car class. We wouldn't have to define attributes like brand, model, and speed for each car separately; instead, the class encapsulates all instances together. Each car we create from this class is an object (or instance) with its own unique values. Merits of Using Classes Encapsulation: Groups related data and behaviour together. Reusability: Write once, use many times. Maintainability: Easier to understand, test, and extend. Scalability: Supports complex applications without losing clarity. The Power of Inheritance Inheritance is one core concept of OOP. By inheritance, one class (child class) can obtain properties and methods from another (parent class), thus preventing redundancies and allowing extensions of code without altering the original structure. For example, let us assume we have a Vehicle type; then we will have child classes Car and Bike that inherit common features (such as speed and fuel capacity) but have their unique behaviours. Bezier curve is one of the multiple curves that serve to interpolate a set of data points using polynomial functions or sometimes using analytic geometrical curves. Benefits of Inheritance: Code Reusability: It allows for the use of existing functionality without rewriting it. Extensibility: Child classes can add new functionality without modifying parent classes. Polymorphism: Multiple classes can have different behaviours while using the same name for a method. Scaling Applications with Classes and Inheritance Inheritance in classes acts as a means to manage complexity in real-world applications, such as e-commerce or even machine learning systems for that matter.  Let's take an e-commerce example: A base class User might define those common attributes (name and email), and specialised classes like Customer and Admin derive functionality from that base class with a few other additional features.  Example of Game Development: The Character class can be inherited to form Warrior, Archer, and Mage, in which each new class has its own set of skills but shares some core logic.  By This Example of Data Science Pipelines, Base models can be designed with common methods (train, predict), and special models such as LinearRegression or DecisionTree, can override and extend the version in that.

🐍 Your Python code is working… but is it efficient? Many beginners write code that: 👉 Works fine 👉 But becomes slow with multiple tasks That’s where async/await comes in. Let’s simplify it 👇 ⚡ Async programming = run tasks without blocking execution Instead of waiting for one task to finish: 👉 You can handle multiple tasks at the same time Example: 🕒 Normal code → wait → execute next 🚀 Async code → handle multiple operations concurrently ✨ async / await in Python ✔ Makes async code readable ✔ Improves performance for I/O tasks (APIs, databases, etc.) ✔ Essential for modern backend systems 💡 Real-world use cases: ✔ API calls ✔ Web scraping ✔ Real-time applications Reality check: If your app handles multiple users or requests, sync code alone won’t scale. I wrote a beginner-friendly guide covering: ✔ What async/await is ✔ How it works in Python ✔ When to use it (and when NOT to) 🔗 Read here: https://lnkd.in/gx-8sn-7 🚀 Pro tip: Use async only for I/O-bound tasks — not CPU-heavy work. Comment "PYTHON" and I’ll share async project ideas 👇 #Python #AsyncProgramming #BackendDevelopment #Developers #Coding #Tech #LearnToCode

Why I’m Diving Deep into OOP with Python 🐍 | Building for Scalability I’m excited to share that I am currently leveling up my software engineering toolkit by mastering Object-Oriented Programming (OOP) in Python at Camerinfolks. 🚀 As I progress through this program, I’ve realized that OOP isn't just a technical requirement—it’s a mindset shift. It’s the difference between writing "scripts that work" and building "systems that last." For my fellow learners and tech enthusiasts, here is how I’ve been refining my understanding of the 4 Pillars of OOP: 🛡️ 1. Encapsulation: The "Private Vault" In Python, we use encapsulation to bundle data and methods, keeping the internal state of an object safe from unintended interference. The Value: Security & Integrity. It ensures that our objects are "self-contained," making the code much easier to debug and maintain. 🔍 2. Abstraction: The "Dashboard" Abstraction allows us to hide complex background logic and only show the essential "interface" to the user. The Value: Clarity. Just like you don't need to know how an engine works to drive a car, abstraction lets developers use complex tools without getting lost in the "how." 🧬 3. Inheritance: The "Blueprint" This is the ultimate tool for efficiency. We can create a base class and have other classes "inherit" its features. The Value: Efficiency. Why write the same code twice? Inheritance allows us to build a hierarchy where specialized objects reuse the logic of general ones. 🎭 4. Polymorphism: The "Master Key" Polymorphism allows different objects to be treated as instances of the same general class through a unified interface. The Value: Flexibility. It allows our systems to stay "open for extension but closed for modification," a core principle of high-quality software architecture. 💡 My Takeaway Learning these concepts Camerin - Indian Institute Of Upskill has completely changed how I approach problem-solving. Instead of thinking in "steps," I’m learning to think in "systems." By focusing on these values—Security, Clarity, Efficiency, and Flexibility—we create software that isn't just functional, but professional and future-proof. To my network: For those who made the switch from procedural to object-oriented programming, what was your "aha!" moment? I’d love to hear your insights! 👇 #Python #OOP #SoftwareDevelopment #Camerinfolks #CodingJourney #WebDevelopment #ContinuousLearning #TechCommunity #camerinfolks

🚀 Python Series – Day 15: Exception Handling (Handle Errors Like a Pro!) Yesterday, we learned how to work with files in Python 📂 Today, let’s learn how to handle errors smartly without crashing your program ⚠️ 🧠 What is Exception Handling? Exception handling is a way to manage runtime errors so your program continues running smoothly. 👉 Without it → program crashes ❌ 👉 With it → program handles error gracefully ✅ 💻 Understanding try and except try: # risky code (may cause error) except: # runs if error occurs 🔍 How it Works: ✔️ Python first executes code inside try ✔️ If NO error → except is skipped ✔️ If error occurs → Python jumps to except ⚡ Example 1 (Basic) try: num = int(input("Enter number: ")) print(10 / num) except: print("Something went wrong!") 👉 If user enters 0 or text, error is handled. 🔥 Why Avoid Only except? Using only except is not a good practice ❌ 👉 It hides the real error. ✅ Best Practice: Handle Specific Errors try: num = int(input("Enter number: ")) print(10 / num) except ZeroDivisionError: print("Cannot divide by zero!") except ValueError: print("Please enter a valid number!") ⚡ Multiple Exceptions in One Line except (ZeroDivisionError, ValueError): print("Error occurred!") 🧩 else Block (Less Known 🔥) try: num = int(input("Enter number: ")) except ValueError: print("Invalid input") else: print("No error, result:", num) 👉 else runs only if no error occurs 🔒 finally Block (Very Important) try: print("Trying...") except: print("Error") finally: print("This always runs ✅") 👉 Used for cleanup (closing files, database, etc.) 🎯 Why This is Important? ✔️ Prevents crashes ✔️ Makes programs professional ✔️ Used in real-world apps, APIs, ML projects ⚠️ Pro Tips: 👉 Always use specific exceptions 👉 Use finally for cleanup 👉 Don’t hide errors blindly 📌 Tomorrow: Modules & Packages (Organize Your Code Like a Pro) Follow me to master Python step-by-step 🚀 #Python #Coding #Programming #DataScience #LearnPython #100DaysOfCode #Tech #MustaqeemSiddiqui

COCOTB 2.0 → Python Co-simulator that becomes more Pythonic compared to 1.0 cocotb 2.0, released in Sep 2025, removes most deprecated features to make the API cleaner and consistent with Python 3+. Python has the yield keyword that return values on demand. cocotb 1.0 exploited this behavior to pause coroutine execution and resume it whenever an event occurred in the hdl simulator. However, this approach did not align well with native Python IDEs and tooling. cocotb 2.0 removed generators completely and switched to native async/await, which naturally supports the pause and resume capabilities required for RTL verification where we wait for simulator events and then perform actions. second major change is replacing fork with start_soon. With fork, if the scheduler is currently executing a coroutine and fork is called, the new coroutine may start executing immediately without waiting for the current coroutine to finish. This could lead to inconsistent trigger ordering issues. start_soon instead queues the coroutine and allows the current coroutine to complete before the new coroutine begins execution. cocotb 2.0 also allows awaiting tasks directly. A coroutine can be awaited to wait for its completion, and tasks can be cancelled using cancel(), which raises a CancelledError exception inside the coroutine which allows coroutine to perform proper cleanup. BinaryValue was the default data type used when accessing signals of the DUT. BinaryValue stored values as binary strings, which meant the HDL bit order and Python indexing were effectively reversed hence we need to convert value to a string and reverse it before using. Accessing individual bits also required converting to a string, reversing and then indexing. This process was error-prone. cocotb 2.0 removed BinaryValue and introduced Logic and LogicArray types. These provide consistent indexing with HDL and allow direct access to individual bits without string conversion. BinaryValue supported only binary strings and therefore did not fully support all 9 logic levels used in HDL. Values other than 0 and 1 are silently converted to 0, which sometimes caused mismatches between DUT and testbench values. Logic and LogicArray support all 9 logic levels. The Clock class now supports period_high, allowing variable duty cycles. In cocotb 1.0, TestFactory was used to generate multiple tests and failures were reported by raising special cocotb exceptions. cocotb 2.0 introduces decorators similar to pytest and uses normal Python assertions for test failures, aligning test writing with standard Python testing practices. IPC mechanisms has also been simplified, and Event objects no longer require name or data attributes. With these changes, cocotb 2.0 becomes more powerful and easier to use compared to cocotb 1.0. We are happy to launch COCOTB 2.0 foundation course to help you get familiar with the newer cocotb 2.0 and learn how to write cleaner, more Pythonic tb. Explore here : https://lnkd.in/dFvsAM_n

🐍 Master Python OOP – Advanced Interview Q&A (Save This!) Preparing for Python interviews? Here are advanced OOP questions & answers + study resources you must know 🚀 🧠 1. What is OOP in Python? ✔ Object-Oriented Programming is a paradigm based on objects, classes, and methods ✔ Helps in writing reusable, scalable, and structured code 🔐 2. What is Encapsulation? ✔ Binding data + methods together ✔ Restrict access using private/protected variables 👉 Example: _protected, __private 🧬 3. What is Inheritance? ✔ One class inherits properties of another ✔ Promotes code reusability Types: ✔ Single ✔ Multiple ✔ Multilevel 🎭 4. What is Polymorphism? ✔ Same function name, different behavior 👉 Example: Method Overriding 🧩 5. What is Abstraction? ✔ Hiding implementation details ✔ Showing only essential features 👉 Achieved using abstract classes (abc module) ⚙️ 6. What are Magic (Dunder) Methods? ✔ __init__, __str__, __len__ ✔ Define object behavior 🔄 7. What is Method Overriding? ✔ Child class modifies parent class method ➕ 8. Method Overloading in Python? ❌ Not directly supported ✔ Use default arguments 🧵 9. What is Multiple Inheritance? ✔ A class inherits from multiple classes 👉 Watch out for MRO 📌 10. What is MRO? ✔ Method Resolution Order defines search path ✔ Use ClassName.mro() 🧠 11. Class vs Instance Variables ✔ Class → Shared ✔ Instance → Unique 🧠 12. Static vs Class Methods ✔ @staticmethod → No class/instance access ✔ @classmethod → Uses cls 🚨 13. Constructor in Python ✔ __init__() initializes objects ⚡ 14. __str__ vs __repr__ ✔ __str__ → User-readable ✔ __repr__ → Debug-focused 🌐 Study Resources • freeCodeCamp https://lnkd.in/gMqHidXr • W3Schools Python OOP https://lnkd.in/gQyfuh7X • GeeksforGeeks OOP in Python https://lnkd.in/gtAfT3ig • Real Python https://realpython.com • Programiz Python OOP https://lnkd.in/gmTJC6n3 • Coursera (Python Courses) https://www.coursera.org 🎯 Pro Tips ✔ Practice real coding examples ✔ Focus on concepts, not memorization ✔ Build mini-projects using OOP ✔ Prepare with mock interviews 🔥 Mastering OOP = Strong coding foundation + Interview success ✍️ About Me Susmitha Chakrala | Professional Resume Writer & LinkedIn Branding Expert Helping students & professionals with: 📄 ATS-Optimized Resumes 🔗 LinkedIn Profile Optimization 💬 Interview Preparation Guidance 📩 DM me for resume & career support #Python #OOP #CodingInterview #Programming #Developers #TechCareers #CareerGrowth 🚀

From Doctest to Runnable Markdown: Modernizing Python Documentation Python documentation has always aimed to do more than explain code—it strives to demonstrate it in action. Over the last two decades, the concept of executable documentation has evolved dramatically. From the early days of doctest, where examples doubled as tests, to modern approaches using runnable Markdown blocks, Python projects now have tools that keep documentation accurate, readable, and trustworthy. Hugging Face’s recent work on the doc-builder project exemplifies this evolution, allowing Markdown code snippets to remain both instructive and automatically testable, ensuring that examples never quietly break as libraries evolve....

🚀 Day 8 to10 — Python Full Stack Training | Conditional statements 🐍 Condition statements in Python are fundamental constructs used to control the flow of execution in a program. They enable decision-making by executing specific blocks of code based on whether given conditions evaluate to True. 1️⃣ if Statement The simplest form, used to execute a block of code only when a condition is satisfied. Example: x = 10 if x > 5: print("x is greater than 5") 2️⃣ if-else Statement Provides an alternative path of execution when the condition is not satisfied. Example: x = 2 if x > 5: print("Condition is True") else: print("Condition is False") 3️⃣ if-elif-else Structure Used when multiple conditions need to be evaluated in sequence. Python executes the first block where the condition is True. Example: score = 78 if score >= 90: print("Excellent") elif score >= 75: print("Good") elif score >= 50: print("Average") else: print("Needs Improvement") 4️⃣ Multiple if Statements In some scenarios, conditions need to be evaluated independently rather than exclusively. Using multiple if statements ensures each condition is checked regardless of others. Example: x = 15 if x > 10: print("Greater than 10") if x % 5 == 0: print("Divisible by 5") 5️⃣ Nested if Statements A nested structure allows you to place one condition inside another, enabling more granular decision-making. Example: x = 18 if x > 10: if x < 20: print("x is between 10 and 20") else: print("x is 20 or more") else: print("x is 10 or less")

🚀 Python Full Stack Development Journey – Day 6 Day 6 of my training at Codegnan was focused on understanding Lists and Tuples in Python — two fundamental data structures that are widely used in real-world applications 📊🐍 🔹 Lists – Mutable and Dynamic Today, I explored how lists allow us to store and manipulate collections of data. Practiced key methods such as: ✔️ append() – adding elements to the list ✔️ insert() – inserting elements at specific positions ✔️ remove() and pop() – deleting elements ✔️ sort() and reverse() – organizing data efficiently Also learned how lists are flexible and can be modified anytime, making them ideal for dynamic data handling. 🔹 Tuples – Immutable and Efficient Understood that tuples are similar to lists but immutable, meaning their values cannot be changed once created. This makes them faster and more secure for fixed data. 🔹 Key Learnings ✔️ Difference between lists and tuples ✔️ When to use mutable vs immutable data structures ✔️ How these structures are used in data storage and processing 💡 This session gave me a clear understanding of how to manage collections of data effectively in Python. Knowing when to use lists vs tuples is an important step toward writing optimized and efficient programs. 🔹 What are Sets? Sets are unordered collections of unique elements. They are particularly useful when we need to eliminate duplicates and perform mathematical operations like unions and intersections. 🔹 Key Set Methods I Learned ✔️ add() – Adds a single element to the set ✔️ update() – Adds multiple elements at once ✔️ remove() vs discard() – Removes elements (with different error handling) ✔️ pop() – Removes a random element ✔️ clear() – Empties the set completely 🔹 Mathematical Operations on Sets ✔️ union() – Combines elements from two sets ✔️ intersection() – Finds common elements ✔️ difference() – Returns elements present in one set but not the other ✔️ symmetric_difference() – Elements present in either set but not both