How to Name Variables, Functions, and Classes in Programming

20 Common Coding Abbreviations Every Developer Should Know 1. DRY – Don’t Repeat Yourself → Write reusable code instead of duplicating logic everywhere. 2. KISS – Keep It Simple, Stupid → Simplicity beats complexity — avoid overengineering. 3. YAGNI – You Aren’t Gonna Need It → Don’t add features until they’re actually needed. 4. SOLID – Set of 5 OOP principles → Stands for Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, Dependency Inversion. 5. OOP – Object-Oriented Programming → Organize code around objects and data rather than functions. 6. API – Application Programming Interface → A way for programs to communicate with each other. 7. REST – Representational State Transfer → A common standard for designing networked APIs. 8. CRUD – Create, Read, Update, Delete → Basic operations for working with data in apps and databases. 9. SQL – Structured Query Language → The language used to manage and query databases. 10. JSON – JavaScript Object Notation → A lightweight format for exchanging data between systems. 11. HTML – HyperText Markup Language → The structure of web pages. 12. CSS – Cascading Style Sheets → Defines the style and layout of web pages. 13. HTTP – HyperText Transfer Protocol → The foundation of data communication on the web. 14. CI/CD – Continuous Integration / Continuous Deployment → Automating the testing and delivery of code. 15. MVC – Model–View–Controller → A design pattern for separating app logic, UI, and control flow. 16. TDD – Test-Driven Development → Write tests before writing the actual code. 17. BEM – Block, Element, Modifier → A CSS naming convention for cleaner, modular code. 18. SDK – Software Development Kit → Tools and libraries for building software on a platform. 19. IDE – Integrated Development Environment → A software suite for coding, debugging, and testing. 20. WYSIWYG – What You See Is What You Get → Editors where the visual output matches the final result (e.g., website builders). Pro Tip: Mastering principles like DRY, KISS, and YAGNI will level up your code quality faster than learning any new framework. From Coding Tips. #coding #programming

🧩 𝗪𝗿𝗶𝘁𝗲 𝗖𝗼𝗱𝗲 𝗧𝗵𝗮𝘁 𝗕𝗿𝗲𝗮𝘁𝗵𝗲𝘀: 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝗙𝗼𝗿𝗺𝗮𝘁𝘁𝗶𝗻𝗴 As developers, we often try to write less code. But sometimes, it’s not about fewer lines. It’s about writing clearer ones. That's where the vertical coding style helps. Instead of cramming multiple arguments or chained methods into one long line, break them down vertically. It’s a small habit that makes a big difference in how readable and maintainable your code becomes. 📌 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 ✅ Easier to read at a glance ✅ Cleaner code reviews ✅ Simpler to extend or modify logic later Whenever you’re working with complex filters, method chains, or multiple parameters, try going vertical. It makes your intent clear without extra comments. Writing vertical code isn’t just about formatting. It’s about being kind to the next developer who reads your code, even if that developer is you, six months later. 💬 Do you use vertical formatting in your projects? I'd love to hear how it helps you. 👇 #CleanCode #CodeQuality #ProgrammingTips #Developers #SoftwareEngineering #Readability

Linking to my post yesterday and over-engineering code, maybe the goal drive the complexity? If I'm writing code for a project, and the _goal itself_ is to write the code, I want to maximize the time I spend writing code, hence I'll start introducing abstractions and moving parts, *but* if the goal is to ship code that solves a problem, I want to minimize the time I spend writing code to achieve my goals faster, hence the final code would inevitably be simpler than the former. Obviously, it's a balance between the two and context dependent but I think the general idea could explain why some people to tend to over-engineer stuff; they just want to maximize the time they spend coding.

Quality Vs. Quantity of Attempts in Coding ------------------------------------------------ Hi, everyone! I would like to share that I've recently been up to this course I newly found called Exercism, with Co-founder & Director, Katrina Owen and Co-founder & CEO, Jeremy Walker. It's super neat! And it made me strategize as to how I could solve the numerous number, string and list puzzles in a more effective way, rather than just tweaking my code every few times and hitting the 'Check Code' button, hoping to be notified that my output was correct. So, here it is, a few tips and tricks on how to get your program working within the first few attempts: 1. BREAK DOWN COMPLEXITY INTO TINY FUNCTIONS... It's simple but to reap the most out of this principle, break down your requirements into the tiniest of functions in which you can handle the complexity of the functions you are about to code. 2. USE PSEUDO-CODE OR STEPS AS PART OF YOUR COMMENTS... So, an example would be: def is_valid_isbn(isbn):   # 1. convert the string version of isbn to numeric version of isbn.   # 2...   # 3...   # testing_status: ?   return is_valid From the pseudo-code, you'll figure out what sub-functions to include: def convert_isbn_to_numeric_version(isbn):   # only take numbers and letter "X".   # testing_status: ?       # no hyphens.   # testing_status: ?       # no alphabets, except for "X".   # testing_status: ? 3. TEST, TEST, AND TEST UPFRONT! Try NOT to do testing only after you've written a huge chunk of code, instead for every tiny function, make sure you test it until it works, and then proceed with the next, to slowly build upon what you've previously confirmed working. Only then, you'll be able to identify trouble spots easily to work on them in isolation. There you have it, a little bit of what I wish to share from my Exercism experience thus far! Highly recommend the course!! And happy coding 😊 Cheers,  Prasanna.

𝐈𝐬 𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐜𝐨𝐝𝐞 𝐢𝐧 𝐨𝐧𝐞 𝐥𝐢𝐧𝐞 𝐚 𝐬𝐢𝐠𝐧 𝐨𝐟 𝐩𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐢𝐬𝐦? 🤔 Many students seem to think so. But here’s the truth: you're not writing code only for the machine — you're writing it for another developer too. Someone like you, who will need to read, understand, and possibly debug it under pressure. Clean code isn't about being clever. It's about being clear. Readable > Short Descriptive > Cryptic Helpful > Smart-looking Even if your variable name is 20 characters long — if it makes the code easier to understand, it's worth it. 𝐖𝐫𝐢𝐭𝐞 𝐜𝐨𝐝𝐞 𝐲𝐨𝐮𝐫 𝐟𝐮𝐭𝐮𝐫𝐞 𝐭𝐞𝐚𝐦𝐦𝐚𝐭𝐞𝐬 𝐰𝐢𝐥𝐥 𝐭𝐡𝐚𝐧𝐤 𝐲𝐨𝐮 𝐟𝐨𝐫. What do you think — where’s the balance between clean and concise? #CleanCode #CodeReadability #ProgrammingTips #SoftwareEngineering #DeveloperMindset #BestPractices #CodingStandards #WriteForHumans #CodeQuality #MaintainableCode #Developers #ProgrammingPhilosophy #SoftwareDevelopment

Modular Programming vs Over-Abstraction Writing software as a set of clear, self-contained modules is powerful: • Easy to read — you open a module and know immediately what it’s for. • Easy to use — others just need the interface, not your internal logic. • Easy to modify — change one module without fear of breaking everything. • Easy to develop — parallel work, less merge conflict, clearer responsibilities. But here’s the catch: Modularity is not an excuse to over-abstract. Creating dozens of layers, interfaces, factories, and indirections for something simple turns your codebase into a maze. Over-abstraction kills readability, slows development, and makes changes painful. ⚠️build modules that encapsulate complexity where it matters, but don’t wrap every little detail in another layer. #ModularProgramming #CleanCode #SoftwareArchitecture #CodingBestPractices #KeepItSimple #DevLife #CodeMaintainability #AvoidOverAbstraction #EngineeringExcellence #SoftwareDesign

Context management with coding agents (I mostly use Claude Code) is still pretty painful. There's a sweet spot where you've got all the right information in there and the model just cranks through your tasks. Too little and it doesn't work, too much (especially if it's relating to multiple tasks) and it just gets confused. Unfortunately, it seems to take about 20 or 30 minutes of back and forth to prime it for a given problem, after which it can go literally all day on that one context, as long as you stay focused on the same problem. This is a lot of friction, and context files only go so far. You can have certain standard things written down, but often you're working on something new, so by definition it won't be in a context file yet. Also, I suspect that taken to the extreme, context files have the same problem as the source code. It's too much for context and so you have to spend ages being specific about which context files to load anyway. I think the real fix is true memory, and I'm not convinced that looks like a context file or a tool call.

📚 Why good documentation is part of good engineering (lessons from 5 years of code reviews) I've reviewed over 1000 pull requests. The pattern is clear: Great engineers write great documentation. 🐍 Bad documentation I see daily: ```python # This function does stuff def process_data(x): return x * 2 + 1 ``` ✨ Good documentation: ```python def calculate_adjusted_score(raw_score: int) -> int: """ Applies our proprietary scoring algorithm. Args: raw_score: User's base score (0-100) Returns: Adjusted score using formula: (raw * 2) + 1 Example: >>> calculate_adjusted_score(50) 101 """ return raw_score * 2 + 1 ``` 📊 Impact on my teams: • Code review time: 60% reduction • Onboarding new developers: 3 days → 1 day • Bug reports: 40% fewer "how does this work?" questions • Technical debt: Much easier to refactor documented code 🔧 Documentation types that actually matter: 1️⃣ Code Comments • Explain WHY, not WHAT • Document edge cases and assumptions 2️⃣ API Documentation • Clear examples for every endpoint • Error codes and responses 3️⃣ Architecture Decisions (ADRs) • Why we chose React over Vue • Database schema decisions 4️⃣ Runbooks • How to deploy • How to debug common issues 💡 My documentation rules: • Write docs WHILE coding, not after • Update docs in the same PR as code changes • Include examples and edge cases • Make it searchable 🚀 Tools that changed my game: • Notion for team docs • Swagger for API docs • Mermaid for diagrams • GitHub wikis for project docs 🔑 The business case: Good documentation is a force multiplier: • Faster feature development • Easier maintenance • Better team collaboration • Reduced support tickets "Code is written once, read hundreds of times." Make those hundreds of reads count. What's your biggest documentation pet peeve? #SoftwareEngineering #Documentation #CodeQuality #TeamWork #BestPractices #TechnicalWriting

Mastering the Art of Professional Coding Here's my step-by-step approach to writing clean, maintainable, and efficient code: 1. Understand the Problem - Take time to fully comprehend requirements before diving into code 2. Plan Your Approach - Sketch out your solution architecture and data flow 3. Write Clean Code - Follow naming conventions, keep functions focused, and maintain readability 4. Test Thoroughly - Write unit tests and edge case scenarios 5. Refactor & Optimize - Review your code for improvements and performance 6. Document Well - Add meaningful comments and maintain proper documentation 7. Collaborate & Review - Embrace code reviews and learn from peers Remember: Great code is not just about making it work, it's about making it maintainable, scalable, and elegant! #CodingBestPractices #SoftwareDevelopment #Programming #CleanCode #TechTips #DeveloperLife

The Documentation Zone Imagine, if you will, a world not unlike our own... You're scrolling through LinkedIn at 2 AM, debugging production for the third night in a row. The code works. The docs say something else entirely. The Confluence page was last updated in 2019. The README is a lie. Now imagine something different. Imagine a world where your documentation IS the programming. Not "living documentation." Not "docs as code." The actual inverse. You open the README. You write in plain English: "When a user submits a form, validate the email, check if they exist in the database, and send a welcome email if they're new." You save the file. The system reads it. Understands it. Compiles it. Deploys it. It just... works. Imagine a world where production is never out of sync with knowledge. Because they're the same thing. The documentation is the system. Change the docs, change the behavior. Instantly. Atomically. Your README is your codebase. Your architecture diagrams are executable. Your flowcharts literally flow. That Pull Request review? You're reading English. Actual English. "Should we rate limit at 100 requests per minute or 1000?" Not buried in config. Right there in the docs. Change the number, change the system. Imagine a world where Python has no problem running from a README. No more # TODO: Update this docstring. No more comments that lie about what the function does. No more "the code is the documentation" cope. Because the documentation became the code. You write: "Calculate the Fibonacci sequence up to n." The system generates the optimal implementation for your context. Memoized if you need speed. Recursive if you need elegance. Iterative if you need memory efficiency. You don't choose. The documentation layer chooses. Based on your production metrics. Your usage patterns. Your constraints. In this world, there are no more "senior engineers who understand the legacy system." Because the system documents itself. Completely. Accurately. Always. That job security you had from being the only one who understood the ancient message queue? Gone. That tribal knowledge about why the cache invalidation works the way it does? Unnecessary. Everyone can read the docs. Because the docs are the truth. The whole truth. The executable truth. We're building this world right now. Large language models that read specifications and write code. Formal verification systems that prove docs match implementation. No-code platforms where natural language becomes logic. The question isn't if documentation becomes executable. Now Imagine, that's where I've been living.