Oleg A.’s Post

𝗧𝗵𝗲 𝗕𝗼𝗼𝗹𝗲𝗮𝗻 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 You spent hours debugging a loading spinner. The UI showed a spinner and an error message at the same time. This happened because of three booleans: - isLoading - isError - data These booleans created eight possible combinations. But only four of these combinations are valid. The problem is not specific to TypeScript or React. It's a programming issue. You can avoid this problem by using union types instead of booleans. Here's an example: - Instead of using booleans, you can use an enum to define the state of a widget. - You can use a union type to define the state of a loading process. Using union types makes it impossible to create invalid states. This approach is used in many programming languages, including Rust, Python, Swift, and Kotlin. It's not about being trendy, it's about writing correct code. So, what can you do? - Use union types to define states - Avoid using booleans as function parameters - Use enums to define valid states By following these principles, you can make your code more robust and less prone to errors. Source: https://lnkd.in/gBavVtF7

NestJS vs. FastAPI: Different Languages, Same Soul? 🤯 If you are moving from TypeScript to Python (or vice versa), you might be expecting a total culture shock. But if you look under the hood of NestJS and FastAPI, you’ll find they are actually long-lost twins. Here are the 4 ways they are practically the same: 1. The "Contract" (DTOs vs. Schemas) 🤝 In both worlds, we hate "guessing" what’s in a JSON body. NestJS uses DTOs (Class-validator). FastAPI uses Pydantic Schemas. Both ensure that if a user sends a string instead of a price, the API shouts "400 Bad Request" before your code even runs. 2. Dependency Injection (DI) 💉 Both frameworks move away from "Hardcoding" dependencies. In NestJS, you inject services into constructors. In FastAPI, you use the Depends() function. This makes swapping a "Mock Database" for a "Production Database" a breeze during testing. 3. Decorators are King 👑 Whether it’s @Get() in NestJS or @app.get() in FastAPI, both use decorators/annotations to handle the heavy lifting of routing and metadata. It keeps the code readable and declarative. 4. Built for Speed (Async/Await) ⚡ Both are "non-blocking" by nature. NestJS rides on the Node.js Event Loop, while FastAPI is built on Python’s anyio/asyncio. They both handle thousands of concurrent connections (like search queries) without breaking a sweat. The Real Difference? It's all about Freedom vs. Structure. NestJS is "Opinionated." It tells you exactly where to put your files (Modules, Controllers, Services). Great for big teams! FastAPI is "Unopinionated." It gives you the tools but lets you decide the structure. Great for speed and AI integration! The takeaway? If you master the concepts in one, you’ve already mastered 80% of the other. The syntax is just a detail. #WebDev #FastAPI #NestJS #Python #TypeScript #SoftwareArchitecture #Backend #CodingTips

𝗜 𝘂𝘀𝗲𝗱 𝘁𝗼 𝘁𝗵𝗶𝗻𝗸 𝗳𝗮𝘀𝘁𝗲𝗿 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝘄𝗮𝘀 𝗮𝗹𝘄𝗮𝘆𝘀 𝗯𝗲𝘁𝘁𝗲𝗿. Over time, I realised that speed without structure often leads to: • refactoring later • inconsistent logic • difficulty scaling • hidden bugs Taking a bit more time early to think about: • database structure • naming conventions • modular design often saves significantly more time later. Sometimes slower decisions produce faster systems. #softwareengineer #saasbuilder #leadsoftwaredeveloper #python #laravel #javascript #node.js

Python is often seen as just a “beginner-friendly” language, but in reality it’s widely used in building real, production level systems. In my own experience working with Python for backend development, I’ve seen how powerful it becomes when combined with frameworks like Django. It allows you to build complete web applications with authentication systems, APIs, database design, and structured business logic. What stands out most for me is how Python encourages clarity. Instead of focusing on complexity, it pushes you to build systems that are readable, maintainable, and easy to extend. On the frontend side, tools like Tailwind CSS complement this by helping structure clean and responsive interfaces without overcomplicating design. Overall, building with Python is not just about writing code it’s about understanding how real systems are structured and how different components work together in production environments.

Most people building React frontends with Python backends overcomplicate the connection. React and FastAPI is honestly one of the cleanest full-stack combos right now. Here's why it works so well FastAPI gives you automatic docs at /docs the moment you define a route. No extra setup. Your React dev knows exactly what endpoints exist and what they return before you've even written the fetch call. Pydantic schemas on the FastAPI side act as a contract. If the backend returns a User object, you know exactly what fields are coming. Pair that with TypeScript interfaces on the React side and you've eliminated an entire class of runtime bugs. CORS setup is two lines. Async endpoints mean your API doesn't choke when React fires multiple requests simultaneously. Response times stay fast without extra infrastructure. The pattern that works in prod: FastAPI handles all data logic, auth, and business rules React owns the UI state and user interactions entirely They talk only through clean typed API boundaries No shared state nightmares. No tightly coupled mess. If you're coming from a Django or Express background and haven't tried this stack yet, it's worth a weekend project. The developer experience gap is noticeable. What's your go-to Python backend when building React apps? #React #FastAPI #Python #FullStackDevelopment #WebDevelopment

#The_most_expensive_comma_I’ve_ever_typed. 💸 I recently spent way too long debugging a background task that was "failing successfully." No errors in the frontend, no crashes in the logs—just... silence. The culprit? A single trailing comma at the end of a string: #email_body = "Hello there...", In Python, that tiny comma turns your intended String into a Tuple. So, instead of sending a block of text to the email handler, I was sending ("Hello there...",). Because we had fail_silently=True enabled in our background thread (standard practice to keep the UI snappy), the system was quietly choking on the Tuple and dying without a word. #The Lesson: Syntax Matters: Even in a "readable" language like Python, one character changes the entire data structure. The Danger of Silence: fail_silently is a double-edged sword. It keeps the user experience smooth, but it can bury the "why" during development. Log Everything: If it’s failing silently, make sure your logger isn’t! Has a single character ever brought down your entire workflow? Let’s commiserate in the comments. 👇 #Python #SoftwareEngineering #CodingLife #Debugging #Django #WebDevelopment

Built a VS Code extension that lets you “see” a Python codebase instantly. I was testing it on a small fitness app project and within seconds it mapped: • API routes • Functions & classes • Cross-file dependencies • Execution paths • Impact of code changes Instead of opening 50 files and tracing imports manually. I call it Python Code Graph. It uses AST-based static analysis to understand Python structure and then visualizes the architecture in an interactive graph inside VS Code. A few things it can do: → Detect FastAPI / Flask / Django routes → Find Click / Typer CLI entry points → Detect Celery tasks → Show blast radius before changing code → Highlight critical paths to business logic → Generate AI-powered explanations Built with: Node.js + TypeScript VS Code Extension API Custom heuristic symbol resolution Glassmorphism-inspired UI Still improving it, tested on a smaller codebase but the first working version is live on GitHub. #Python #VSCode #BuildInPublic #DevTools #Programming #SoftwareEngineering

To understand the depth of Rust, you must look at how it manages memory at the hardware level. In JavaScript, every object is a heavy structure stored in the heap memory, requiring a Garbage Collector to constantly scan and clean it up. Rust replaces this with a much faster system that separates data from behavior. You define your data using an Enum, which is stored directly on the stack for instant access. You then use an impl block to define the logic separately. This means the computer only loads the instructions when they are specifically called, rather than carrying a bulky object around in the RAM at all times. When you write an implementation for fmt::Display, you are fulfilling a Trait. In systems programming, a Trait is a strict professional contract. The Display trait is a set of rules from the standard library that tells the compiler exactly how your data should be translated into text. Because this is a Compile Time guarantee, the Rust compiler checks every detail before the program even runs. If your function does not match the required signature perfectly, the code will not compile. This eliminates the common runtime errors and crashes found in higher level languages like JavaScript. The power of this system lies in the function arguments &self and &mut f. These are not just variables they are instructions for the Borrow Checker, which is the heart of Rust safety. The &self parameter is an Immutable Borrow, meaning you are looking at the data exactly where it lives in memory without making a copy. The f parameter is the Formatter, a specialized tool that streams text to the output. By using &mut, you are taking a Mutable Borrow, which gives you exclusive temporary permission to change the formatter. Rust laws forbid any other part of the program from touching that formatter while you are writing to it, which prevents memory corruption and data races. Finally, the function returns a fmt::Result. While JavaScript might allow a print command to fail silently, Rust requires a status report for every low level action. The Formatter does not build a giant string in the heap; instead, it performs String Streaming, pushing characters one by one to the destination. This is incredibly efficient because it uses almost zero extra RAM regardless of how large the message is. By returning a Result, the program knows immediately if the hardware successfully processed the output. This combination of zero cost abstractions and total memory control is why Rust can perform at the speed of C while remaining completely safe.

🚀 Starting my journey in web development! I’m happy to share my first project — a Flask CRUD Web Application 💻 🔹 Built using: Flask, MySQL, HTML, CSS 🔹 Features: Create, Read, Update, Delete operations 🔹 Deployed using Render 🔹 Source code available on GitHub 🌐 Live Demo: https://lnkd.in/gXdhjUYY 💻 GitHub Repo: https://lnkd.in/gRbe_2YB This is just the beginning — looking forward to building more advanced and impactful projects ahead! #Flask #Python #WebDevelopment #BeginnerProject #svhec Dr. Muralisankar Kumaresan Guide KABILESH RAMAR

I built a GUI server you can call from any program with zero GUI dependencies : You know what's genuinely painful? When your program needs to ask the user something. Just one thing. A filename. A number. A confirmation. And suddenly you either have to pull in a full GUI library as a dependency, or you ship a CLI that feels like shit, or you just hardcode it and pray. So I built fxgui. fxgui is a local runtime GUI server. You run it as a background process, and any program - in any language - can talk to it via pure HTTP calls. You ask it to show a window. It shows a window. You ask it to update a progress bar. It updates it. You ask it to prompt the user for a string. It blocks until the user types something and returns the value. No GUI lib linked to your program. No dependencies. No framework to wrestle with. Just HTTP calls. Think about where this becomes actually useful. Your CLI tool needs user input ? No need to pull in Qt for 3 buttons. You have a long loop and you want a progress bar without freezing anything ? Done, from any thread. You're writing a Blender plugin and you want a proper progress window ? Oh yes. That last one. If you've ever tried to show a progress bar from a background operation in Blender's Python API, you know what I'm talking about. Their modal operator system is a nightmare. With fxgui, you just fire an HTTP call and keep running. The API is pure HTTP under the hood, which means you can use it from C++, Python, JavaScript, Rust, Go, whatever. The C++ and Python wrappers are just thin layers on top. If you can make an HTTP request, you can use it. The server is the dependency, not your codebase. That's the whole point. Still in development, but it'll be open source and free once it's out. In the meantime, https://lnkd.in/gZWWQAqQ