Programmer Convention: Why i and j in Loops

🧠 I built my own programming language — GreyMatter! Inspired by Grey Matter from Ben 10 (yes, the alien genius 😄), I created a fully functional interpreted programming language built in Python using SLY. GreyMatter supports: ✅ Variables, loops, and conditionals ✅ User-defined functions with FEEDBACK (return values) ✅ String utilities like WAYBIG() (uppercase) and NANOMECH() (lowercase) ✅ Time utilities via PARADOX.SLEEP() and PARADOX.UNIDATE() ✅ A memory system called BRAINSTORM ✅ Even experimental @WEB_SEARCH and @AI query features! The whole interpreter works through Lexical Analysis → Parsing → AST Generation → Runtime Execution. This project taught me how real-world languages like Python and JavaScript actually work under the hood. If you're a CS student or a curious developer, building your own language is one of the most valuable things you can do. 🔗 GitHub: github.com/Abineshabee #Programming #Python #ComputerScience #OpenSource #LanguageDesign #StudentProject #Interpreter #Ben10 #GreyMatter #BuildInPublic

Parameters and Arguments are not the same thing. It's not really a big deal but then again you'll meet some people who would make it such, so better safe than sorry. In theory: Parameters are the variables defined in the function. Arguments are the values you pass into it. (You use arguments in the function call and parameters in the function signature) Example: def greet(name): # ← parameter print(f"Hello {name}") greet("Dan") # ← argument And once again… In the real world, most developers use the words interchangeably. And most of the time? It doesn’t break anything. What does break things is miscommunication. If your team understands what you mean, you’re fine. But knowing the correct distinction sharpens your thinking — especially when teaching, documenting, or reviewing code. Precision in language leads to precision in logic. I made a short video breaking it down clearly (with practical examples). If you’ve ever mixed them up — you’re not alone. Link in the comments #Python #SoftwareEngineering #Programming #CleanCode #TechEducation

An abstract programming language (something like Python compared to C) for AI agents could save a huge amount of token usage in coding agent workflows. Think of it as another layer on top of existing high-level programming languages that is optimized for minimal token usage rather than developer experience i.e., less syntactic sugar or even a complete rewrite of a high-level language. I don’t know how much this would help an individual developer to reduce cost, but at scale, where developers are burning through billions of tokens on extra syntax, it could massively impact the economics of token consumption and reduce waste. This thought came to me while I was listening to this amazing talk between Lex Fridman and Peter Steinberger (Openclaws developer). You can listen to the talk in the link below, there are amazing insights into how software development as a hobby is becoming a thing. https://lnkd.in/ewEn8KkW

How do you explain a “function” to a newbie?🤔 For me, the simplest way is this: a function is a reusable tool for a specific task. ✨ Think about it like this: when you want to sweep your floor, you don’t reinvent the broom every time, right? You grab the broom, sweep, and done. ✅ In programming, a function works the same way. Once you’ve written it, you can reuse it anywhere you need that task done. No need to repeat the steps from scratch. So the next time someone asks you what a function is, don’t get lost in technical jargon. Just say: “It’s your broom in the coding world. Once you have it, you just keep using it.” 😉 What everyday object would you compare a function to? #Programming #Python #BackendDevelopment #SoftwareEngineering #LearnigInPublic

A couple of years ago, I wrote that "The Builder pattern is a finite state machine!". A state machine consists of states and transitions between them. As a developer, I want to make illegal states unrepresentable, i.e., users of my API can’t create non-existent transitions. My hypothesis is that only a static typing system allows this at compile-time. Dynamic typing systems rely on runtime validation. In this blog post, I will show that it holds true, with a caveat. If your model has many combinations, you also need generics and other niceties to avoid too much boilerplate code. My exploration will use #Python, #Java, #Kotlin, #Rust, and #Gleam. With that background in mind, let’s move on to the model itself. #builderpattern #finiteStateMachine #illegalState #programming #coding (link in the comments)

AI assessment of the recent enterprise grade software project, consisting of 43.000 lines of python. ----- >>> Your .cursorrules explicitly mandates functional programming, pure functions, higher-order functions over classes, and RESTful routing. An AI left to its own devices defaults to messy object-oriented state management or monolithic scripts. You forced the structural discipline. ----- So yes, functional programming + Vibe Coding can yield something useful. But I don't know if the same is possible for OOP + Vibe Coding.

💻 Compiler vs Interpreter — The Battle Behind Your Code 🚀 Every program you write needs to be understood by a computer. But here’s the twist 👇 Computers don’t understand your code directly. They need a translator. And that’s where Compiler & Interpreter come in. ⚙️ Compiler: ✔ Converts entire code at once ✔ Faster execution ✔ Errors shown after compilation ⚙️ Interpreter: ✔ Executes code line by line ✔ Slower but easier to debug ✔ Errors shown instantly Simple analogy 👇 📘 Compiler = Translate full book, then read 🗣 Interpreter = Translate while speaking Languages using Compiler: 👉 C, C++ Languages using Interpreter: 👉 Python, JavaScript 💡 My takeaway: It’s not about which is better… It’s about where to use what. Understanding this changed how I see programming. If you're learning coding, this is a must-know concept 🔥 #Programming #Coding #ComputerScience #Students #Tech #Learning

We see it all the time. Someone learns Python from tutorials, builds a few projects, and still feels stuck. Not because they are not trying hard enough. But because there is a difference between learning syntax and learning to think like a programmer. Copying code that works is not the same as understanding why it works. Knowing what a function is is not the same as knowing how to design one well. And that gap? It shows up the moment things get complex. The fix is not more tutorials. It is going back to the ground up. How data types truly behave. How to write clean, reusable functions. Recursion, which trips up almost every beginner but becomes second nature with the right foundation. Then object-oriented programming. Then algorithms, sorting, searching, stacks, queues and symbol tables. That progression changes how you think, not just what you can type. You stop asking "does this work?" and start asking "why does this work and how will it hold up at scale?" That is the shift that turns a beginner into a real programmer. If your team or your learners are at the "it works but I don't know why" stage, the answer is foundation, not more frameworks. What does your organisation use to build strong programming foundations? Share it below 👇 #Python #Programming #LearnPython #TechEducation #SoftwareDevelopment

Writing code that works is one thing but designing something that’s easy to extend and doesn’t break as it grows is a different challenge. Lately, I’ve been thinking more about what actually makes object-oriented design effective not just functional. Especially when building systems in Python that need to handle complexity. I built a turn based card game system in Python to focus on that using object oriented programming to managing state, interactions, and edge cases through clean class design. What stood out to me was how much the structure of code impacts its ability to handle complexity. Designing components that interact cleanly and behave correctly across different scenarios made me realise how important good OOP design really is. Through this Python based project, I was able to: - Design a modular class structure to manage system state and interactions - Implement clear separation of responsibilities across components - Handle edge cases and ensure robustness - Build logic that consistently passes all test scenarios This has pushed me to explore object-oriented programming in Python more intentionally, focusing on building systems that are maintainable and scalable. I’ve shared the project on GitHub for anyone interested in trying out themselves: https://lnkd.in/gV2bmvMS #SoftwareEngineering #Python #ObjectOrientedProgramming #StudentProject #Tech

#Duck_Typing A traditional idea says: “The king’s son will become the next king.” A more modern interpretation would be: “The one who is capable will become the king.” This transition from identity-based selection to capability-based evaluation loosely resembles Python’s duck typing. In duck typing, an object’s suitability is determined not by its type, but by its behavior. If an object implements the required methods, it can be used — regardless of its class hierarchy. However, it is important to avoid overextending this analogy. Duck typing does not imply merit-based selection or optimization. It simply enables flexibility by accepting any object that satisfies the expected interface. This distinction is critical. #Python #SoftwareEngineering #Programming #Coding #TechConcepts #CleanCode #SoftwareDesign #LearnToCode #DeveloperMindset #TechLearning