Anton Biletskiy-Volokh’s Post

How to Break Any Problem Into Small Logical Steps Most people don’t fail at coding because of syntax. They fail because the problem feels too big. The secret? Never solve the problem at once. Solve it in pieces. Here’s a simple mindset I follow 👇 1️⃣ Understand the goal clearly What should the final output look like? If you can’t explain it in one sentence, you’re not ready to code. 2️⃣ Break it into sub-problems Ask: What smaller tasks must happen to reach the goal? Each task should feel “easy” on its own. 3️⃣ Think in steps, not code Write the logic in plain English first. If a non-developer can understand it, you’re on the right track. 4️⃣ Solve one step at a time Don’t jump ahead. Finish one step → test it → then move forward. 5️⃣ Connect the steps Once individual parts work, combine them logically. This is where clean, maintainable code is born. Big problems are just small problems hiding together. If you can break problems down, you can learn any language, any framework, any tech. 💬 What’s the hardest part for you—understanding the problem or breaking it down? Let’s talk in the comments 👇 #ProblemSolving #ProgrammingLogic #DeveloperSkills #CodingTips #SoftwareEngineering #ThinkBeforeCode

It's time to stop fetishizing 'clean code' at the expense of 'shipping code'. We've all been there. You spend hours refactoring a working function, extracting helpers, renaming variables, applying patterns to make it perfect. Meanwhile, the feature that solves a real user problem sits waiting. Don't get me wrong: I love clean, maintainable code. It's a professional standard. But when the pursuit of theoretical purity becomes the primary goal, we've lost the plot. The hidden cost of premature optimization isn't just time, it's momentum. First, make it work correctly. Then, make it work fast. Only then, make it beautiful. Beauty is vital, but it must not be at the cost of optimization. Perfection is the enemy of shipped. This isn't a license for sloppy code. It's a call for pragmatic prioritization. Solve the user's problem, then refine the solution. Where do you draw the line? When does clean code become over-engineering in your projects? #SoftwareEngineering #CleanCode #Programming #Developer #Coding #Tech #WebDevelopment #Agile

This article is inspired by Clean Code by Robert C. Martin. In this series, we’ll walk through the core principles of the book, breaking them down into clear, practical points with very short examples to highlight the difference between clean code and bad code. 1: Readability Matters Clean Code is easy to read and understand at first glance. Bad Code forces you to pause, guess, and re-read. Clean: calculateTotalPrice() Bad: calc_tp2() If you can’t understand the code quickly, bugs will hide easily. 2: Focus: Do One Thing Clean Code components do one thing and do it well. Bad Code tries to handle multiple responsibilities at once. Clean: validateUser() Bad: validateAndSaveAndNotifyUser() Focus makes code safer to change. 3: Change Should Be Easy Clean Code is designed to evolve. Bad Code breaks when you touch it. Clean: Small, isolated changes Bad: One change → multiple unexpected failures Speed comes from cleanliness, not shortcuts. 4: Bugs Love Messy Code Clean Code makes intent obvious, leaving fewer places for bugs to hide. Bad Code creates tangled logic where defects survive longer. Clear intent = fewer defects. 5: Tests Are Not Optional Clean Code is backed by reliable tests. Bad Code has no safety net. Clean: Refactor with confidence Bad: Fear every change No tests → no clean code. 6: Maintenance Cost Tells the Truth Clean Code lowers long-term maintenance cost. Bad Code increases frustration, time, and cost exponentially. Mess slows you down. Cleanliness makes you faster. This is just the beginning. In the next posts of this series, we’ll dive deeper into: Meaningful names Clean functions Comments: when to use them (and when not) Code smells and refactoring mindset #CleanCode #SoftwareEngineering #CodeQuality #BestPractices #Programming #Refactoring #Developers #LinkedInTech

The Most Common Error in the Art of Programming The most common (and fundamental) error in the art of programming is: The Lack of Abstract Thinking and Upfront Design In other words: starting to write code without having understood or designed the solution first. This manifests in several ways: Direct Consequences: Spaghetti Code: Massive functions, tangled logic, excessive coupling. Lack of Modularity: Code that cannot be easily reused, tested, or maintained. Fragile Solutions: Code that breaks with every change or new requirement. Wasted Time: Constantly rewriting because the first implementation was incorrect. Specific Technical Errors That Derive From This: Not Validating Input Data: Assuming the user or system will always send what's expected. Poor Error Handling: Using generic try-catch blocks or, worse, silently ignoring errors. Not Considering Edge Cases: Thinking only about the "happy path." Ambiguous Naming: Variables like data, temp, x that don't reveal intent. Not Writing Tests: Believing it "works" after superficial manual testing. Premature Optimization: Perfecting performance details before having a solid, functional design. The Root of the Problem: Many programmers, especially beginners, confuse "knowing a language's syntax" with "knowing how to solve problems with code." They jump straight to implementation without: Decomposing the problem into smaller sub-problems. Thinking about the most suitable data structures. Defining clear contracts between modules or functions. Considering long-term maintainability. The Solution (More Art Than Science): Pseudocode or Diagrams Before Coding: Clarifies the logic. KISS Principle (Keep It Simple, Stupid): The simplest solution is usually the best. DRY Principle (Don't Repeat Yourself): Identify and abstract repeated patterns. Read Others' Code: Learn from well-designed patterns. Refactor Constantly: Improve the design as you better understand the problem. Essentially, the biggest mistake is treating programming as writing instructions rather than designing solutions. Code is merely the final manifestation of a structured thought process. What do you think is the most costly error you've made (or seen) in a software project? #Programming #SoftwareDevelopment #SoftwareArchitecture #BestPractices #Coding #Tech #Learning

The Most Common Error in the Art of Programming The most common (and fundamental) error in the art of programming is: The Lack of Abstract Thinking and Upfront Design In other words: starting to write code without having understood or designed the solution first. This manifests in several ways: Direct Consequences: Spaghetti Code: Massive functions, tangled logic, excessive coupling. Lack of Modularity: Code that cannot be easily reused, tested, or maintained. Fragile Solutions: Code that breaks with every change or new requirement. Wasted Time: Constantly rewriting because the first implementation was incorrect. Specific Technical Errors That Derive From This: Not Validating Input Data: Assuming the user or system will always send what's expected. Poor Error Handling: Using generic try-catch blocks or, worse, silently ignoring errors. Not Considering Edge Cases: Thinking only about the "happy path." Ambiguous Naming: Variables like data, temp, x that don't reveal intent. Not Writing Tests: Believing it "works" after superficial manual testing. Premature Optimization: Perfecting performance details before having a solid, functional design. The Root of the Problem: Many programmers, especially beginners, confuse "knowing a language's syntax" with "knowing how to solve problems with code." They jump straight to implementation without: Decomposing the problem into smaller sub-problems. Thinking about the most suitable data structures. Defining clear contracts between modules or functions. Considering long-term maintainability. The Solution (More Art Than Science): Pseudocode or Diagrams Before Coding: Clarifies the logic. KISS Principle (Keep It Simple, Stupid): The simplest solution is usually the best. DRY Principle (Don't Repeat Yourself): Identify and abstract repeated patterns. Read Others' Code: Learn from well-designed patterns. Refactor Constantly: Improve the design as you better understand the problem. Essentially, the biggest mistake is treating programming as writing instructions rather than designing solutions. Code is merely the final manifestation of a structured thought process. What do you think is the most costly error you've made (or seen) in a software project? #Programming #SoftwareDevelopment #SoftwareArchitecture #BestPractices #Coding #Tech #Learning

Most teams fail with coding agents for the same reason. They give more autonomy and do less thinking. What actually works is the opposite. Agents ship faster when you put them in a tight harness. Clear goals, small scope, and something objective that defines “done”. The biggest unlock we see is simple: Plan before code. A useful agent plan answers three things only: ↳ What will change. Files, routes, data. ↳ Where it will change. Be explicit. ↳ How success is verified. Tests, commands, checks. If you cannot describe your definition of done in two sentences, an agent cannot infer it. The second rule is context hygiene. Let the agent search. Do not dump half the repo. Context is a budget and noise always wins. And the reliability multiplier is verifiable goals. Tests that must fail first. Linters that must pass. Diffs reviewed like a real PR. Compiles is not a signal. The real takeaway: Agents do not replace judgment. They reward structure. If you treat planning, tests, and review as non negotiable, agents stop feeling magical and start feeling dependable. Worth a read if you are shipping with agents and want fewer rewrites and surprises. 👉 https://lnkd.in/dq5XfHFR #softwareengineering #aidevelopment #indiehackers #devtools

One thing many developers overlook is that most bugs aren’t code problems.... they’re flow problems. When something breaks, it’s rarely about syntax or a missing semicolon. It usually happens because: • data arrives later than expected • state updates happen out of order • an edge case wasn’t considered • different parts of the system don’t agree with each other Before writing code, it helps to slow down and ask: • Where does the data originate? • How does it move through the system? • Who owns and updates this state? • What should happen when something fails or returns empty? Thinking in flows instead of isolated functions changes how you build: • architecture becomes clearer • debugging becomes easier • features become more predictable and stable This mindset applies everywhere — frontend state, backend APIs, database interactions, and integrations between systems. Good software is less about writing more code, and more about designing how things move. #webdevelopment #softwareengineering #programming #developers #fullstackdeveloper #systemdesign #coding #learninginpublic

Nobody tells you how much thinking happens outside the code. Before the editor opens, there’s already a lot going on: understanding the problem, questioning assumptions, choosing trade-offs. Writing code is visible. Thinking is not. But most issues I’ve seen weren’t caused by syntax they came from unclear thinking earlier on. The more experience I gain, the more I value clarity over speed and decisions over activity. I wrote about this invisible part of the work here  https://lnkd.in/gq6Y44fk #softwaredevelopment #programming #careergrowth #thinking #developers

10 Coding Tips Every Developer Should Keep in Mind 🚀 Whether you're just starting out or have been coding for years, refining your craft is a continuous journey. Here are 10 timeless coding tips to help you write cleaner, more efficient, and maintainable code: 1️⃣ Write Readable Code             Use meaningful variable names, consistent formatting, and a clear structure. Readability is the first step to maintainability. 2️⃣ Master Data Structures & Algorithms             Understanding arrays, trees, graphs, sorting, and searching is foundational to crafting efficient solutions. 3️⃣ Follow the DRY Principle      Don’t Repeat Yourself. Avoid duplication by creating reusable functions and modules. 4️⃣ Test Thoroughly        Write unit tests, integration tests, and edge-case tests. Automated testing saves time and ensures reliability in the long run. 5️⃣ Handle Errors Gracefully      Use try-catch blocks, implement proper logging, and provide meaningful error messages. 6️⃣ Optimize Later          Focus on correctness first, then improve performance using profiling tools. Premature optimization can complicate code. 7️⃣ Learn Debugging Tools          Master IDE debuggers, breakpoints, and logging libraries to resolve issues faster. 8️⃣ Understand Time & Space Complexity           Know your Big O notation. This helps in writing scalable and efficient code. 9️⃣ Follow Coding Standards      Adopt team conventions or language-specific standards to improve collaboration and consistency. 🔟 Keep Learning & Refactoring              Revisit old code and apply new patterns. Continuous learning is key to growth. What’s a coding tip that has made a significant impact on your work? #CodingTips #SoftwareDevelopment #Programming #DeveloperTips #CleanCode #Tech #Coding #DRY #Debugging #BestPractices

Coding agents are very good at coming up with syntactically valid working code. But does this mean we can just focus on design? Not really. Understanding someone else’s code isn’t always straightforward. Having that mental model takes time to build up. No one will know the code base better than the guy that wrote it from scratch. Even with the best structure and documentation. Coding agents abstract some of that work. I often find working with Claude that I could have refactored the code faster than my prompting skills allow with the tool. “Any fool can write code a computer can understand. Good programmers write code that humans can understand”. - Martin Fowler An agent will only optimise not replace existing skills. If compilers and build pipelines are our software assembly factories then source code is our blueprint. Getting the best outcomes out of these tools still demands the same fundamental skills. So if source code is also design, humans still need to form the mental model of the design. Taking that tacit knowledge cannot be automated out. #llms #softwareengineering