Marcel Koch’s Post

"Why thinking like a Rust programmer is different" Day 1 of Thinking like a Rust Programmer Most developers try to learn Rust the same way they learned other languages. That’s the mistake. In languages like C/C++, you constantly think about memory leaks and manually managing memory. In languages with garbage collectors, like Java or Python, you don’t think much about memory — the system handles it for you. But Rust is different. There is no garbage collector. And you’re not manually managing memory either. Instead, Rust forces you to write code in a way where memory is managed automatically through rules. And those rules are: → Ownership → Borrowing This is where everything changes. In Rust, a value has only one owner at a time. If you assign it to another variable, the ownership moves — and the previous variable becomes invalid. When the owner goes out of scope, the value is automatically dropped from memory. No garbage collector. No manual free. Just clear rules. Rust is not just a new language — it’s a new way of thinking. In most languages, you think like this: → Write code → Run it → Fix errors if they happen Rust flips this completely. In Rust, you think first: → Who owns this data? → Who is allowed to use it? → What happens when it goes out of scope? Before you even run the program. That’s why Rust feels “hard”. Not because of syntax — but because it forces you to think about memory, safety, and responsibility upfront. And once this mindset clicks… Rust stops feeling hard and starts feeling correct. Over the next 30 days, I’ll help you build this way of thinking step by step. As we Completed Day-1 of Thinking in Rust way. If you’ve struggled with Rust, you’re not alone — you’re just thinking in the old way. Let’s fix that 🤝 #RustLang #LearnRust #SystemsProgramming

🚨 The repo is now at 117k stars and climbing. All others have been DMCA'd but it's still standing. Frequently being updated. Codex is saying good things about the architecture. Either we will have a massive security event that will be remembered for decades or Open Source Claude Code... but like actually open source. (if you let codex read it, make sure you are in a safe container. beware of prompt injection whenever letting an agent interact with anything.)

Rewriting the same project in a new language isn't about the language, it took me two rewrites to figure that out. I rebuilt a task management app in three different languages over two years convinced each rewrite would result in clean code, but every version ended up with the same structural problems. The first version in Python had messy code with business logic scattered everywhere. I blamed Python, so I rewrote it in Java thinking static typing would force better structure. The Java version had the same problems. I rewrote it again in Typescript and this version had the same architectural mess because the language wasn't the problem, my approach to organizing code was. Rewriting in a new language feels productive, but you carry the same design decisions and bad habits into each version. If you don't understand why your code became messy, switching languages just produces the same mess with different syntax. Problems that make code hard to maintain rarely come from language limitations but from unclear boundaries between components, mixing different concerns, duplicating logic, and poor naming. I finally understood this on the third rewrite when I realized I was structuring the Typescript code exactly like the Python version. Fix your design approach, not your language choice.

Most developers are not slowed down by bad code. They are slowed down by bad thinking. Not syntax. Not framework choice. Not whether the project uses Java, Go, or Python. The real damage usually comes earlier: - no clear boundaries - too many dependencies in one request path - retries added without thinking - APIs designed around convenience instead of failure - teams optimizing for feature speed over system clarity That’s why some codebases feel heavy even before they get big. The problem is not always technical debt. Sometimes it’s decision debt. And that is much harder to fix. Debate: What does more long-term damage to software teams? A) bad code B) bad architecture C) bad product decisions D) bad debugging habits My vote: B first, C second. What’s yours? #Java #SoftwareArchitecture #Microservices #DistributedSystems #BackendEngineering

💡 The moment I started thinking like a backend developer: I stopped asking "Is my code correct?" And started asking 👉 "What could go wrong?" Now whenever I build something in Django, I think: → What if the user sends wrong data? → What if the API fails? → What if the database returns nothing? Earlier, I only focused on the happy path. Now I focus on edge cases. That one shift completely changed how I write backend code. Because real applications don't break on correct inputs… They break on the ones you didn't expect. If you're learning backend development, stop only building for perfect scenarios. Your users definitely won't cooperate. 😅 Are you thinking about edge cases yet? 👇 #Django #BackendDevelopment #Python #LearningInPublic #WebDev

The latest update for #AppSignal includes "#Monitoring #Django Query Performance with AppSignal" and "Tracking Celery Task Failures in #Python". https://lnkd.in/drUMytXV

Most developers coming to Elixir from languages like JavaScript or Python see [1, 2, 3] and assume it is an array. This is a mistake that makes functional programming feel much harder than it really is. An Elixir list is not a row of boxes you can jump into by position; it is a linked structure where each element only knows its value and what comes next. If you find yourself reaching for an index or appending to the end of a list, you are likely fighting the data structure. In Elixir, the only cheap operation is at the front. Understanding this head-first nature changes how you write algorithms, moving you away from expensive appends and toward idiomatic patterns like prepending and reversing. This week's post breaks down the mental model of lists as nested structures and why the [head | tail] syntax is about exposing shape rather than just memorizing syntax. https://lnkd.in/ewSsdkzw Paulo Valim & Bruce Tate at Groxio

Week one with Rust. Nobody told me this would happen. For years, I've been coding in JavaScript. I've got my understanding of memory all clear, or so I thought. Enter the borrower checker. The ideas themselves aren't complex. Ownership, borrowing, lifetimes all these things look reasonable on paper. The second you try to implement them in code, the compiler just stares at you, as though programming has never been your forte. First error: tried to use a variable after moving it. Second error: tried to create two mutable references at once. Third error: thought I solved the problem. I was dead wrong. I couldn't argue with any of these errors; the compiler had been absolutely correct. And there's something else people don't tell you about working in Rust. Each error comes to you as a reminder: "This would've been a bug in any other language but you wouldn't find out until release." Eventually, something changed. Looking at the borrow checker, I didn't see a restriction anymore but a code reviewer with an eye for making sure your program won't break at 3 in the morning. I'm not yet fluent in Rust. Far from it. But now I approach problems differently, and that's precisely the reason why I'm using this language

To make your first post truly engaging, you want to hook the reader with a "problem" they didn’t know they had, and then present your page as the "solution." Here is a short, punchy, and high-energy post description for your LinkedIn launch: The Ghost in the Machine 👻 Ever wonder why your Java code behaves differently under heavy load? Or why your Python backend hits a performance wall despite "clean" logic? The truth is: The code you write isn't always the code that runs. Between your syntax and the hardware lies a world of bytecode, memory management, and invisible execution layers. That is where the real bugs—and the real optimizations—hide. Welcome to The Bytecode Phantom. We’re here to deconstruct the "tricky" side of backend engineering: * 🔍 Java Internals: JVM secrets and bytecode-level logic. * 🐍 Python Mastery: Beyond the basics into the runtime core. * 🏗️ System Design: Architectural patterns that actually scale. Stop following the documentation. Start mastering the mechanics. [Follow to Decrypt the Complex] #TheBytecodePhantom #Java #Python #BackendEngineering #SystemDesign #SoftwareArchitecture #CodingSecrets

Exploring FastAPI: A Full-Stack Developer's First Impressions After 8+ years working primarily with Java/Spring Boot and Angular, I decided to expand my toolkit and learn Python. Started with Corey Schafer's excellent tutorial series, and just began exploring FastAPI. Initial Thoughts: Coming from strongly-typed languages, I expected Python to feel loose and unpredictable. FastAPI changed that perception immediately. The type hints with Pydantic, the intuitive async/await syntax, and the automatic API documentation generation - these aren't just conveniences, they're thoughtfully designed developer experiences. What Resonated: The dependency injection pattern feels familiar from Spring, but cleaner. The middleware approach mirrors Express.js, but more elegant. The async capabilities remind me of Node.js, but more readable. It's not about FastAPI being "better" than what I know - it's about recognizing the right tool for specific problems. For AI/ML integration and rapid API development, Python's ecosystem is genuinely compelling. Grateful for Quality Education: Corey Schafer's tutorials made this transition smooth. Clear explanations, practical examples, and proper fundamentals - exactly what experienced developers need when learning a new stack. Looking forward to building more with this technology and seeing where it fits in my full-stack toolkit. #Python #FastAPI #ContinuousLearning #SoftwareEngineering #FullStackDevelopment