Mehdi Sagar’s Post

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

What actually happens when you hit an API? 🤔 Most developers understand this at a high level. But very few understand what actually happens inside a real system. From the moment a request leaves your browser to the moment a response comes back, it passes through multiple layers: ⚖️ Load balancing ⚙️ Application processing 🗄️ Databases, cache, external services 🔍 And several invisible system concerns What looks like a simple API call is actually a coordinated system flow. Understanding this flow matters because: ✅ It helps you debug faster ✅ It improves how you design systems ✅ It changes how you think about performance & scalability I’ve broken this down step by step in the carousel. 👉 Start from the first slide. More deep dives coming. #backend #systemdesign #softwareengineering #api #programming #learndeep

What actually happens when you hit an API? ⚙️ A clear breakdown of how requests flow through real-world systems from client to backend to data layers. Understanding this flow is fundamental for backend and system design. More deep dives coming from LearnDeep.

“What if you don’t know System Design?” You don’t get errors… You get chaos 😅 API fails Queue gets full Database goes down And suddenly everyone is looking at you like… 👉 “Fix it” But you’re just there like: “Wait… where is this even coming from?” That’s the reality. Without system design knowledge: you fix symptoms, not the root cause you don’t know where the bottleneck is scaling feels like guesswork debugging becomes panic mode System Design is not about drawing diagrams. It’s about: 👉 understanding flow 👉 identifying failure points 👉 knowing what breaks first Because in real systems… Everything works fine until it doesn’t And when it breaks, that’s when System Design knowledge matters the most. So if you’re skipping System Design… You’re not avoiding it. You’re just postponing the chaos 😂 What was your “everything is on fire” moment? #SystemDesign #BackendDevelopment #SoftwareEngineering #Debugging #DeveloperLife #ProgrammingHumor #TechLife #Coding #Developers #Learning

Most Go developers think “dependency injection” means frameworks, magic, and unnecessary complexity. It doesn’t. In Go, DI is simply about passing what your code needs instead of creating it internally—nothing more. In my latest article, I break down how to: Use constructor-based patterns the idiomatic Go way Keep your code loosely coupled and testable without heavy abstractions Avoid over-engineering with DI containers and frameworks The key insight? 👉 In Go, DI isn’t a tool—it’s a mindset. You don’t need a framework to write clean, modular systems. You just need to design your dependencies intentionally. If you’ve been overcomplicating DI (or avoiding it altogether), this will change how you think about structuring Go services. Read: https://lnkd.in/d3i6zCMy #golang #softwarearchitecture #backend #cleanarchitecture #programming

I've spent hours debugging apps I build to make the stackpack-debug 🪲 better. Here're the patterns that kept showing up and what improved: 1. Server-side errors were invisible. When a Next.js API route calls console.error(), that output lives in the Node.js process stderr. Our toolkit was capturing framework access logs (GET /programs 200 in 45ms) but missing the actual application errors. In 2 out of 3 sessions, we had to resort to a workaround — surfacing error details in HTTP response bodies — because the tool couldn't see what the server was logging. 2. Configuration state was opaque. A provider setting kept resetting to "auto" on every server restart. The debug tools could see the downstream symptom (API errors from the wrong endpoint) but couldn't see the upstream cause (the setting wasn't persisted). 20 minutes spent chasing the wrong problem. v0.21.0 fixes both. The uncomfortable truth about building developer tools: you don't find the real gaps by writing tests. You find them by using the tool on a real project, with real time pressure, and writing down every minute you wasted. 🔮 Runtime error parsing — Server-side errors used to be invisible to the toolkit. Now it catches them as they happen and shows them in the live status report. The errors were always there — we just weren't reading them. 👁️ Configuration state visibility — The toolkit now reads your app's settings files and shows what's configured where. If a setting only lives in memory (not saved to a file), it warns you — because it'll disappear the next time you restart. stackpack-debug is open source — runtime DevTools for AI coding agents over MCP and a proxy, https://lnkd.in/gcaqf_eS

How to trace and debug an error. Most people debug by guessing. That’s the slowest way to fix anything. Real debugging starts when you stop touching code and start understanding it. First rule: Don’t change anything yet. Do this instead: Reproduce it If you can’t make it happen again, you don’t understand it yet. Find the boundary Where does it break? Frontend? Backend? API? Database? Don’t debug everything. Find where things go wrong. Follow the data Take one request: Input → Processing → Output Trace it step by step. Log with intention Not random logs. Log: What came in What changed What went out Now you can see the story. Challenge assumptions “It should work” is not debugging. Verify everything. Fix the root cause Not the symptom. Not the quick patch. The real issue. Debugging isn’t about being clever. It’s about being systematic. #SoftwareEngineering #Debugging #BackendDevelopment #Programming #TechCareers #CleanCode #DeveloperTips

🧩 SOLID Principles — The Foundation of Clean Code If you want to write code that scales, survives change, and stays maintainable, you’ll keep hearing one word: SOLID. It’s not about memorizing definitions — it’s about writing code that doesn’t break every time requirements change. Here’s the idea in a simple, practical way 👇 🔹 S — Single Responsibility Principle A class should have one reason to change. If your class handles business logic, logging, and validation… it’s doing too much. 🔹 O — Open/Closed Principle Code should be open for extension, but closed for modification. Instead of changing existing logic, extend it (think interfaces, inheritance, strategies). 🔹 L — Liskov Substitution Principle If you replace a base class with a derived class, nothing should break. If it does — your design is wrong. 🔹 I — Interface Segregation Principle Don’t force classes to implement things they don’t need. Small, focused interfaces > large, bloated ones. 🔹 D — Dependency Inversion Principle Depend on abstractions, not concrete implementations. High-level code shouldn’t care about low-level details. 💡 Why this matters SOLID isn’t just theory. It helps you: ✔ Write cleaner, more readable code ✔ Reduce bugs when requirements change ✔ Make testing easier ✔ Build systems that scale over time 🚀 The takeaway You don’t need to apply all principles perfectly. But the more you think in terms of SOLID, the less your code will fight you later. #dotnet #dotnet10 #csharp #linq #aspnetcore  #softwareengineering #backenddevelopment  #codingtips #developer #programming

🚀 Unlock High-Performance Backend Systems Your backend isn’t just code — it’s the engine that powers speed, scalability, and security. Want to build systems that actually perform under pressure? Focus on these 3 core principles 👇 ⚡ 1. Asynchronous Programming Stop blocking your system with slow operations. Use async workflows to handle multiple tasks efficiently. 📊 2. Smart Data Optimization Fast systems aren’t about more data — they’re about better queries. Use indexing and analyze query performance. 🔒 3. Security-First Approach Security is not optional. Implement authentication, validate inputs, and always use secure protocols. 💡 Build systems that are: ✔ FAST ✔ SCALABLE ✔ SECURE 👇 What’s your go-to backend optimization tip? #BackendDevelopment #mr_vepari #SoftwareEngineering #WebDevelopment #TechTips #Programming #Coding #SystemDesign