Code that works everywhere is engineering

Excited to share my first open-source Claude Code skill — Expert Builder 🛠️ Expert Builder gives Claude a structured engineering workflow for real software development tasks: → Build projects from scratch → Debug and fix broken code → Design system architecture → Generate professional documentation → Deploy to Streamlit, Docker, and GitHub Pages The skill is structured into workflows, domain modules, engines, and templates — so Claude always knows exactly how to approach your request. 📦 Install: /skill install Not-muzzyy/expert-builder 🔗 GitHub: https://lnkd.in/dUqZw8DM Feedback and contributions welcome! #OpenSource #AI #ClaudeAI #SoftwareEngineering #DevTools #BuildInPublic

Static or Shared? A Guide to Choosing the Right Library When you're ready to package your software into a library, you have a fundamental choice to make: static or shared? Both are compiled. Both contain machine code. The difference is in how that code reaches the final executable. 📦 Static (.a / .lib) The linker copies the library's object code directly into your binary. The result is self-contained and no runtime dependencies. Delete the .a file afterward and your app still runs. 🔗 Shared (.so / .dll / .dylib) Your binary stores a reference. The OS loads the shared library into memory at runtime. Multiple programs can share a single copy in memory, but if the .so goes missing, your app won't start. When to reach for static: → Single, self-contained deployables (CLI tools, containers, embedded) → Maximum link-time optimisation (LTO can inline across the boundary) → Reproducibility is critical, no wrong .so version loaded at runtime When to reach for shared: → Multiple executables share the same library (one copy in memory) → Hot-patching / in-place upgrades without rebuilding consumers → Licensed libs that mandate dynamic linking And header-only? Just #include and go. Zero build friction. Perfect for template-heavy and constexpr code. The trade-off: every translation unit recompiles it, which can slow builds on large projects. What's your default choice when packaging a library? I'd like to hear your reasoning. #cpp #cmake #softwareengineering #libraries #programming

I used to think performance issues only show up at scale. Turns out, they often start much earlier just hidden. Recently, while working on a backend feature, everything looked fine on the surface. APIs were responding correctly. No errors. No obvious issues. But something felt slightly off. Response times weren’t bad, but they weren’t great either. So instead of ignoring it, I decided to dig a little deeper. What I found was interesting: The endpoint was making multiple database queries inside a loop. Each query was fast individually, but together they were adding up. It wasn’t noticeable with small data. But as soon as the dataset grew, the impact became clear. Instead of jumping into a big rewrite, I made a few focused changes: Combined queries where possible Reduced unnecessary data fetching Simplified part of the response structure Nothing dramatic. But the result was a noticeable improvement in response time and a much cleaner flow. That experience changed how I look at performance. It’s not always about scale. Sometimes it’s about small inefficiencies quietly stacking up over time. Now I try to pay attention to these early signals before they turn into real problems. Curious have you ever found a performance issue hiding in something that “looked fine”? #softwareengineering #backenddevelopment #performance #systemdesign #programming #webdevelopment

"Array vs Linked List vs Stack vs Queue – How to Choose?" Early in my coding journey, I used Arrays for everything. Then I built a real project and learned the hard way: Choosing the wrong data structure costs performance. Let's break them down simply: 1) Array (The Apartment Building) 🏢 Rooms next to each other. You know exactly which one is which. -Pros: .Fast access O(1) – direct indexing .Cache friendly -Cons: .Slow insert/delete O(n) – need to shift elements .Fixed size (unless dynamic array) 📱 Use when: .You need fast access by index .Data size is known and stable .You mostly add/remove at the end 2) Linked List (The Scattered Houses) 🏡 Each house knows the address of the next one. No index, just follow the pointers. -Pros: .Fast insert/delete O(1) – just update pointers .Dynamic size – grows/shrinks as needed -Cons: .Slow access O(n) – must traverse from head .More memory overhead (stores pointers) 📱 Use when: .You frequently add/remove at beginning or middle .Random access isn't important .Data size is unpredictable 3) Stack (The Pringles Can) 🥔 Last In, First Out (LIFO) 📱 Use cases: .Undo/Redo in apps .Browser back button .Function call stack 4) Queue (The Cafeteria Line) 🍽️ First In, First Out (FIFO) 📱 Use cases: .Print queue .Mobile notifications .Request processing (API calls) 🎯 How to Choose? 1) Need fast index access? → Array 2) Frequent insert/delete? → Linked List 3) Last in, first out logic? → Stack 4) Fair processing order? → Queue 💡 From My Experience: In one of my small projects, I initially used Arrays for task storage. As the number of tasks grew, insertions and deletions became slower. Switching to a Doubly Linked List made these operations much smoother. 💬 Which one do you use most in your projects? And why? #DataStructures  #Programming  #SoftwareEngineering  #Coding  #Tech  #CSStudents

🚨 Missed This Terminal Shortcut? Boost Your Productivity Instantly Every developer reaches for the same set of commands, yet a hidden feature in most shells can cut repetitive typing in half. The command “Ctrl R” launches reverse i search, letting you replay any previously entered line by typing a few characters. Imagine needing to run a long docker build you used last week; type the first few letters, press Ctrl R, hit Enter, and you’re back in action without retyping the whole string. But many terminals hide an even more powerful variant. Press “Ctrl R” twice quickly, and the search mode switches to incremental mode, showing matches as you type each character. This tiny double tap turns a vague memory into a precise command instantly. It works in Bash, Zsh, and even PowerShell when you enable PSReadLine. A quick test: open your terminal, hit Ctrl R twice, type “npm run”, and watch the most recent npm script appear. Accept it with the right arrow key, edit if needed, then press Enter. You’ve just saved seconds that add up over weeks of development. The Tech Tips, Tricks and Tutorials YouTube playlist currently holds 282 videos and has gathered 16 views, proving that even niche tips can reach an audience eager for efficiency. 💡 Try this double tap today, notice the speed boost, and share the trick with your team. ✅ Check if your workflow includes this shortcut and start reclaiming time. #TechnicalTips #TechTips #TechInsights #TechKnowledge #Technology #TechTrends #ITTips #WebDevelopment #SoftwareDevelopment #CodingTips #Developers #TechInnovation #DigitalSkills #Programming #LearnTech #DeveloperTips #TechCommunity #FutureOfTech #TechLearning #TechEducation

🚨 If your method has 8+ parameters… it’s not flexible — it’s fragile. It usually doesn’t start that way. We begin with 2–3 arguments. Then a new feature comes in, we add one more parameter. Another edge case appears, add one more. Before we know it, we're staring at a constructor or method with 10–15 arguments…and no one wants to touch it. This is how complexity quietly creeps into codebases. As a Senior Engineer, I've seen this pattern lead to: i) hard-to-read code ii) bugs due to incorrect argument order iii) painful refactoring Here are 3 ways to keep argument lists under control: 🔧 1. Prefer objects over primitives createUser(name, age, email, isActive, role) ❌ createUser({ name, age, email, isActive, role }) ✅ This improves readability and avoids order-related bugs. 🧩 2. Group related data into value objects If parameters belong together, they probably belong in a model/class. This makes your API cleaner and more expressive. 🔄 3. Refactor when arguments start growing A growing parameter list is a signal. Consider: i) splitting the method ii) using builder/factory patterns iii) rethinking responsibilities Good APIs are easy to use and hard to misuse. 💬 What's your rule of thumb — how many arguments are 'too many' for a method? #SoftwareEngineering #CleanCode #Programming #TechLeadership #DeveloperExperience

𝐒𝐭𝐨𝐩 𝐦𝐞 𝐢𝐟 𝐲𝐨𝐮’𝐯𝐞 𝐡𝐞𝐚𝐫𝐝 𝐭𝐡𝐢𝐬 𝐨𝐧𝐞: "𝐈𝐭 𝐰𝐨𝐫𝐤𝐞𝐝 𝐨𝐧 𝐦𝐲 𝐦𝐚𝐜𝐡𝐢𝐧𝐞, 𝐛𝐮𝐭 𝐧𝐨𝐰 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧 𝐢𝐬 𝐜𝐥𝐨𝐬𝐞𝐝." Once you understand Dependency Injection, the next step is using it correctly. Because DI doesn’t automatically make your code clean how you use it determines everything. In .NET, lifetimes (Transient, Scoped, Singleton) define how your application behaves at runtime. Get them right: ✅ predictable behavior ✅ better performance ✅ easier debugging Get them wrong: ❌ subtle bugs ❌ shared state issues ❌ hard-to-trace failures I’ve personally run into issues where: - A Singleton held onto state it shouldn’t - A Scoped service was used incorrectly and caused unexpected behavior These aren’t syntax problems they’re system design problems. And this is where modern development is heading: 👉 Less about writing code 👉 More about designing how everything works together AI can generate services and classes… but it won’t always choose the correct lifetime or structure. That’s still the developer’s responsibility. #DotNet #SoftwareEngineering #CleanCode #SystemDesign #Developers #Programming

Exit code 0… Exit code 1… You’ve seen these flash by. Most developers treat them like background noise. If it fails → retry If it crashes → restart But these aren’t random numbers. They’re how every program tells you what just happened. Here’s what they actually mean 👇 0 → Success. Everything worked ✅ 1 → General error. Something went wrong ❌ 2 → Misuse of command (check syntax) ⚠️ 126 → Permission denied 🔒 127 → Command not found 🚫 130 → Stopped manually (Ctrl + C) ⛔ 137 → Killed by system (memory issue) 💀 143 → Graceful shutdown (SIGTERM) 🛑 Rule of thumb: 0 → Clean exit 1–125 → Program error 126–127 → Command issue 128+ → Killed by system signal This applies everywhere — scripts, apps, containers, pipelines. Every tool speaks this language. Most errors aren’t mysterious… The exit code already told you. You just weren’t reading it 👀 #Programming #Developers #Coding #Debugging