CamelCase vs Snake_Case: Why Consistent Naming Matters in Coding

Vibe Coding Tip #3: I’ve spent a lot of time experimenting with agents and trying to understand where they actually help versus where they quietly sabotage progress. This has mostly come from working across larger codebases where mistakes compound quickly. One pattern shows up over and over. Agents do fine on small, well-scoped tasks, but they struggle badly when you hand them too much responsibility at once. Large tasks inflate context windows, important details get compacted away, and you end up with partial implementations that look plausible but fail in subtle ways. A few practices that have helped me keep things under control: Break work into very small tasks Even if you use a strong LLM to plan the work, execution should happen in tiny steps. Treat each step as its own agent run. When context stays small, agents are far more reliable. I often spawn a new agent for each task rather than letting one carry on for too long. Be deliberate about free vs paid tools Free agents are perfectly reasonable for debugging small issues or making incremental changes. Once you are doing architectural work, large refactors, or operating in complex domains like simulation engines or multi-physics code, the cheaper tools start to fall apart. At that point, the more capable paid models are worth it. Give agents a sandbox inside the repo I usually create a /dev or /sandbox directory where agents can place test scripts and intermediate files. This keeps the main codebase clean, especially since agents tend to generate a lot of exploratory artifacts while figuring things out. When the task is done, I either archive or delete that folder. Write down what the agent actually did For each task, I have the agent end by writing a short, timestamped implementation report describing what changed, what broke, and what still needs work as their last task. These reports become inputs for the next planning session and prevent you from losing context over long, multi-prompt efforts. Markdown files under /docs/<topic>/ have worked well for me. Let the LLM write the agent prompts Rather than manually crafting every agent instruction, I use a separate planning session to generate agent prompts from the plan and prior reports. That lets me focus on intent and correctness. When an agent goes off the rails, I explicitly describe the behavior I want to avoid and feed that back into the prompt generation step. None of this is groundbreaking in any way, but it has made agent-based workflows far more predictable for me on larger projects. How are you structuring agent-heavy workflows? Happy vibe coding!

🚀 Normal Coding vs SOLID Coding We all write code that “just works”, but how easy is it to change, maintain, or scale later? That’s where SOLID principles make a huge difference. 🔹 Normal Coding ➡️ Classes that do everything ➡️ Functions that are hard to test ➡️ Adding new features often breaks existing code ➡️ Debugging is a nightmare 😓 🔹 SOLID Coding SOLID helps us write clean, smart, and maintainable code: 1️⃣ S – Single Responsibility: Each class has one job 2️⃣ O – Open/Closed: Code can grow without breaking existing parts 3️⃣ L – Liskov Substitution: Replace parts without breaking things 4️⃣ I – Interface Segregation: Use small, focused interfaces instead of one giant one 5️⃣ D – Dependency Inversion: Depend on abstractions, not concrete implementations ✅ Benefits of SOLID: ✔️ Easier to maintain and scale ✔️ Reusable & modular ✔️ Unit-test friendly ✔️ Fewer bugs, fewer headaches ✔️ Makes your code future-proof 🛡️ 💡 Example: Instead of a giant AuthManager handling login, registration, OTP, and notifications, SOLID breaks it into: 🔹 LoginService 🔹 RegistrationService 🔹 OtpService 🔹 NotificationService Each part is focused, testable, and easy to change. Writing SOLID code isn’t about being “perfect”, it’s about making your life easier as a developer ✨ #CleanCode #SOLID #SoftwareDevelopment #CodingTips #Flutter #Dart #Programming

Clean Code vs Messy Code — Why It Really Matters As developers, we don’t just write code for machines — we write it for humans too. Clean Code: - Easy to read and understand - Meaningful variable and function names - Follows consistent structure and standards - Easier to debug, test, and scale - Future-you (and teammates) will thank you Example of Clean Code: function calculateTotalPrice(float $price, int $quantity): float { return $price * $quantity; } Messy Code: - Hard to read and confusing - Poor naming and magic numbers - Difficult to maintain - Small changes can break everything Example of Messy Code: function c($p, $q){ return $p*$q; } Why Clean Code Wins: - Reduces bugs - Improves collaboration - Saves time in the long run - Makes code reviews smoother - Shows professionalism as an engineer Clean code isn’t about perfection — it’s about clarity, simplicity, and responsibility. “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” — Martin Fowler #CleanCode #SoftwareDevelopment #Programming #WebDevelopment #BestPractices #CodeQuality #DeveloperLife

Coding isn’t just building features—it’s problem solving, logical thinking, and improving user experiences. Every bug teaches a better approach, every project strengthens your skill. #DotNetDeveloper #CodingJourney #PersonalBranding #TechProfessional #CSharp #WebDesigner

Just published a beginner-friendly guide on Medium: "Understanding Design Patterns: A Simple, No-Jargon Guide to the 23 Classic Design Patterns Every Developer Should Know." This guide addresses key questions such as why design patterns exist, when to use them appropriately, and how the three main categories—Creational, Structural, and Behavioral—contribute to writing cleaner, more maintainable code. It presents the information in plain English, complete with real-world use cases and quick links to Refactoring.Guru for each pattern. No fluff, just practical insights to enhance your coding skills. Check it out here: https://lnkd.in/gxD6v2jF What’s your go-to design pattern right now? Drop it in the comments; I enjoy hearing what fellow developers are using. #DesignPatterns #SoftwareArchitecture #OOP #SoftwareEngineering #Refactoring #CleanCode #SystemDesign #Programming #SoftwareDevelopment #Coding

"Designers should learn to code, because it will teach them about capabilities and constraints." Sigh. Anyone who insists on this nonsense is someone who doesn't understand what "coding" actually entails. Nor has learned what actually determines capabilities and constraints. (hint: it has nothing to do with code) If a designer wants to learn code because they're interested, then hell yeah, they should learn to code. But in my 35 years of software, I've never once witnessed a designer's value, impact, and skill have any relation to whether or not they "knew how to code." What I have seen is countless occasions where designers restricted themselves, or built tension with engineering partners, because they were too heavily focused on what they felt were "constraints" of code. .. Background, for those who don't know me: Throughout my career I developed fluency in at least 10 different coding languages, from client-side to server-side to databases. I've even written a few applications in assembly. I've worked with innumerable servers, platforms, version managers, compilers, deployment services. That's what was necessary for a designer in the 90s and 00s, particularly those of us working for agencies and hustling on our own. Our concepts, ideas and solutions were only accepted because we could also execute and deliver the technical side as well. Nonetheless, like many of my colleagues from that area, we always led with the needs of the user and the business first. Realizing a great design solution was always the priority; figuring out how we could technically accomplish it was secondary. I don't recall a single design I proposed that was ever accepted without concern, to put it mildly, about how it would be built. But we always found a way.

Okay, I admit it: "The age of writing your own code is dead." I didn't think it was possible to say this 2 months ago, but the recent changes to AI and Agents has made it so. Computer source code is following the same path as Postscript, the page definition programming language developed by Adobe and released in 1985 and what powers every PDF file and document you've ever printed. Back then you could actually write Postscript code to do graphics and wonderful bezier transformations on text. But when Adobe released Illustrator, and Page Maker, and then MS Word and other programs adopted it, the age, and need, to write Postscript programs to make documents went away. Today we don't even think about it. But that's where we are in terms of programming applications today. Agents are the "Adobe Illustrator" of our time, except that instead of using a graphical interface to draw lines, we're using Prompts to tell the Agent what our software should be able to do. The code is simply the glue behind the scene, and no longer the interface to computation, the Agent is. While this seems perfectly strange to most, it is the world we have created, and a much better one than we had. Having developers being the human interface to computers for domain experts to design applications was a terrible idea. Developers have little to no expertise in the domains they programmed for resulting in over 80% of all software projects being late and overbudget. I've spent the better part of my professional and academic career building visual programming languages as a solution to this problem. It works, and we call it Low Code. But Agents and domain experts talking to them in English is an even better solution. If your company develops software, you need to stop, you need to invert your teams, and get the Agents to write the code, and be the best domain expert you can, because your very existence as a company depends on it... read the article below to learn what I'm talking about. If you want to talk to me about this and other AI related coding issues send me and DM or follow me. I'll be posting more about this in the coming months, including how Founders should be using this to build MVPs and launch....

𝗪𝗵𝗲𝗻 “𝗖𝗹𝗲𝗮𝗻 𝗖𝗼𝗱𝗲” 𝗛𝘂𝗿𝘁𝘀 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 (𝗮𝗻𝗱 𝗪𝗵𝗲𝗻 𝗜𝘁 𝗗𝗼𝗲𝘀𝗻’𝘁) We often talk about clean code as if it were an absolute good. Clear abstractions, small functions, expressive names — all great. But at some point, I’ve learned that clean code and performant code don’t always pull in the same direction. Extra abstractions, excessive indirection, and “perfect” separation can introduce unnecessary allocations, re-renders, or expensive loops — especially in hot paths. In these cases, code that looks less elegant on the surface can actually be easier for the runtime to optimize and for humans to reason about in context. That said, clean code rarely hurts performance by default. It becomes a problem only when we optimize for readability without understanding execution cost. The real skill is knowing when clarity helps the system — and when it needs to step aside. 💬 Have you ever simplified code for performance reasons? How do you balance readability and efficiency in your projects? #SoftwareEngineering #CleanCode #Performance #CodeQuality #SystemDesign #Programming #DeveloperExperience #EngineeringMindset #pdsdev #React #Reactjs

Clean code isn’t about impressing other developers. It’s not about fancy patterns or trendy architectures. It’s about survival. Survival during debugging. Survival when scaling. Survival when a new developer joins the team. Survival when you revisit your own code after 3 months. Spaghetti code creates stress. Clean code creates clarity. And in tech, clarity is power. What’s one clean coding habit you never compromise on? #CleanCode #SoftwareDevelopment #Programming #TechLeadership #Developers #CodeQuality #SystemDesign

As a developer, naming conventions are extremely important. As a project grows, clear and consistent names for functions, methods, variables, and files help track updates and fix bugs more easily. Good naming also improves code readability, makes collaboration smoother, and helps the entire development team understand the codebase faster. There are proven ways to create effective naming conventions that support long-term maintenance, depending on your project structure and preferences. Commonly Used Naming Conventions 1. camelCase Used for variables, functions, and methods. Example: getUserData, totalPrice, isLoggedIn 2. PascalCase Used for classes, constructors, and components. Example: UserService, LoginForm, TaskManager 3. snake_case Commonly used in backend development and databases. Example: user_name, created_at, total_count 4. kebab-case Used for file names, URLs, and CSS class names. Example: user-profile.component.ts, main-header, /api/get-user 5. UPPER_SNAKE_CASE Used for constants and environment variables. Example: API_BASE_URL, MAX_RETRY_LIMIT 6. Boolean Naming Convention Boolean variables should clearly express a true/false state and usually start with is, has, can, or should. Example: isActive, hasAccess, canEdit 7. Function Naming (Verb-Based) Function names should describe an action. Example: fetchUsers(), createTask(), updateProfile(), deleteItem() 8. Plural Naming for Collections Arrays and lists should use plural nouns. Example: users, orders, tasks 9. Consistent Prefixes and Suffixes Use predictable naming patterns to improve traceability. Example: getUser, setUser, updateUser, deleteUser 10. Clarity Over Brevity Names should be descriptive and meaningful rather than short and unclear. Example: calculateTotalAmount() instead of calc() I hope this helps you grow and become a better developer. #CleanCode #CodingBestPractices #DeveloperTips #Programming #CodeQuality