Rate limiting shouldn't come with a side of dependency hell. 🐍 Most Python rate limiters force a trade-off: either use a basic "fixed-window" script or pull in a heavy framework with a dozen sub-dependencies. This library was built to provide production-grade primitives with an emphasis on architectural flexibility and a zero-dependency footprint. The Technical Breakdown: ✅ 6 Algorithms: Beyond the standard Token Bucket. Includes Fixed/Sliding Windows and ADAPTIVE logic for dynamic scaling. ✅ 3 Backends: Native support for Memory and Redis, supporting both local-first and distributed environments. ✅ Zero Dependencies: Designed for high-security environments and lean builds. No requirements.txt bloat. ✅ Implementation: Clean integration via decorators for functions or middleware for web frameworks. If you are managing API quotas or protecting services from traffic spikes, the implementation details and performance focus are worth a look. https://lnkd.in/gnikRUHa #Python #SoftwareEngineering #SystemDesign #OpenSource #Backend #DistributedSystems

I’ve just wrapped up a major milestone in my backend journey — implementing asynchronous processing in my Task Manager project, and the results are What I built: Sync vs Async API comparison endpoints Concurrent request handling using async routes External API integration with parallel calls Clean UI dashboard to visualize performance differences Results: Sync execution: 2160 ms Async execution: 1586 ms ~574 ms faster with async! This clearly shows how asynchronous programming can significantly improve performance when dealing with multiple I/O operations. Key Takeaways: Async = better scalability & responsiveness Perfect for external API calls & high-load systems Clean architecture makes debugging & scaling easier Tech Stack: FastAPI | Python | Async/Await | HTTPX | SQLite | Custom UI This phase really helped me understand how modern backend systems handle concurrency efficiently. #BackendDevelopment #Python #FastAPI #AsyncProgramming #WebDevelopment #SoftwareEngineering #LearningInPublic #100DaysOfCode

Your tests aren’t flaky. They’re fragile. If a test: • passes locally • fails in CI • works… then suddenly doesn’t That’s not randomness. That’s temporal coupling — hidden order dependencies between steps. page.goto(url) page.click("#login") page.wait_for_selector("#dashboard") Looks fine… Until someone removes or reorders one step: 💥 CI fails 💥 You rerun tests hoping they pass 💥 Debugging takes hours I’ve seen this repeatedly in Playwright-based UI tests. Because the test depends on when things happen—not just what happens. We call it: • flaky tests • timing issues • CI instability But the real problem? 👉 Fragile design. If your tests only pass in a certain order, they’re already broken. Seen this in your test suite? #SDET #TestAutomation #Playwright #Python #FlakyTests #QualityEngineering #AutomationTesting

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

Stop wasting time on manual tasks. 🐍⚡ I’ve put together a 12-slide Python Automation Roadmap to help you turn repetitive work into clean, scalable scripts. No fluff—just the core libraries and patterns you actually need. What’s inside: ✅ File Ops: Mastering os, shutil, and pathlib. ✅ Web & APIs: Scraping with Selenium and robust requests handling. ✅ Production Ready: Scheduling, Logging, and CLI design. ✅ Advanced: Asyncio, decorators, and a full real-world project. Whether you're looking to clean up your filesystem or build a headless web bot, this guide has you covered. 👇 Download the PDF below and start automating. #Python #Automation #Programming #SoftwareEngineering #PythonRoadmap

Task 2 CodSoft Built a To-Do List GUI Application using Python and CustomTkinter ✅ This project is a simple yet practical task manager with a modern dark-themed interface. Key features include: • Add, update, and delete tasks. • Persistent storage using JSON (tasks are saved and loaded automatically) • Interactive list display for better task management • Input validation with user-friendly warning messages • Clean and responsive UI using CustomTkinter Through this project, I strengthened my understanding of GUI development, file handling, and state management in Python applications. Planning to enhance it further with features like task deadlines, priority levels, and reminders! #Python #CustomTkinter #GUI #Productivity #Coding #Projects

🧠 Python Concept: functools.partial Pre-fill function arguments like a pro 😎 ❌ Without partial def power(base, exp): return base ** exp def square(x): return power(x, 2) def cube(x): return power(x, 3) 👉 Repeating logic 👉 Extra functions ✅ With partial from functools import partial def power(base, exp): return base ** exp square = partial(power, exp=2) cube = partial(power, exp=3) print(square(5)) # 25 print(cube(5)) # 125 🧒 Simple Explanation Think of it like preset settings 🎛️ ➡️ Fix some arguments ➡️ Reuse the function easily 💡 Why This Matters ✔ Reduces duplication ✔ Cleaner code ✔ Functional programming style ✔ Useful in callbacks & configs ⚡ Real-World Use ✨ API parameter presets ✨ Event handlers ✨ Reusable utilities 🐍 Don’t repeat functions 🐍 Pre-configure them #Python #AdvancedPython #CleanCode #SoftwareEngineering #BackendDevelopment #Programming #DeveloperLife

𝗨𝗡𝗟𝗘𝗔𝗦𝗛𝗜𝗡𝗚 𝗧𝗛𝗘 𝗨𝗟𝗧𝗜𝗠𝗔𝗧𝗘 𝗣𝗬𝗧𝗛𝗢𝗡 𝗣𝗢𝗪𝗘𝗥𝗛𝗢𝗨𝗦𝗘: 𝗧𝗛𝗘 𝗦𝗘𝗖𝗥𝗘𝗧 𝗧𝗢 𝗕𝗨𝗜𝗟𝗗𝗜𝗡𝗚 𝗟𝗜𝗚𝗛𝗧𝗡𝗜𝗡𝗚 𝗙𝗔𝗦𝗧 𝗔𝗣𝗜𝗦 𝗥𝗘𝗩𝗘𝗔𝗟𝗘𝗗 As we head into 2026, building scalable microservices requires a rock-solid understanding of how your code is organized from the first line. This guide demystifies the structural foundations of FastAPI to ensure your backend remains maintainable as your codebase grows. THE FASTAPI INSTANCE The core of every application begins with initializing the FastAPI class. This instance acts as the central router and configuration hub, serving as the bridge between your incoming HTTP requests and your internal application logic. Understanding how to instantiate this object correctly is the first step toward managing middleware, dependencies, and route decorators effectively. ROUTING AND PATH OPERATIONS Path operations are the fundamental building blocks of your API. By using decorators linked to your FastAPI instance, you define how the server responds to specific HTTP methods like GET or POST. This video breaks down how these functions map directly to URL paths, allowing for clean, readable code that handles client communication without unnecessary complexity. PARAMETER HANDLING AND TYPE HINTS FastAPI leverages Python type hints to perform automatic data validation and documentation. By defining expected types for path parameters, query parameters, and request bodies, you enable the framework to enforce data integrity before your logic even executes. This approach significantly reduces the surface area for bugs while providing built-in Swagger UI documentation for your endpoints. As a Senior Engineer, I cannot overstate that the structure of your application in 2026 is just as important as the logic inside it. FastAPI enforces good habits early by requiring explicit type definitions and clear route mapping, which saves hundreds of hours in debugging and refactoring down the line. Treat your project structure as your primary documentation. Tags: #FastAPI #Python #API #BackendDevelopment 📺 Watch the full breakdown here: https://lnkd.in/d7YeSp75

Day 27/100: Building My First Graphical User Interface (GUI)! Today was all about moving from the terminal to the desktop. I dived deep into Tkinter, Python’s built-in library for creating windowed applications. Key Technical Takeaways: Widget Management: Learning how to create and configure Labels, Buttons, and Entry fields. Layout Managers: Mastering the difference between pack(), place(), and grid() to position elements precisely. Event Listening: Connecting buttons to Python functions to make the app interactive. Project: Mile to Kilometers Converter: Developed a clean, functional desktop app that performs real-time unit conversions. It’s incredibly satisfying to build something that has a "Window" and a "Button" you can actually click. My Python scripts are starting to look like real software now! Check out my GUI app here: https://lnkd.in/gtREXkTG #Python #GUI #Tkinter #100DaysOfCode #SoftwareDevelopment #VSCode #DesktopApps

🚀 New Release: LightningChart Python v2.2 is here The latest update brings a major step forward in interactive data visualization and dashboard development. Key highlights: 🎛 Built-in UI controls (CheckBox & ButtonBox) → Add interactivity directly into your dashboards 🎯 Fully customizable cursors → From styling to programmable multi-cursor setups 💬 Pointable annotations → Visually connect insights to data points 🧠 Smarter data interaction → Improved precision with features like nearest-point detection 🔁 Dynamic workflows → Flexible layouts with real-time axis swapping This release clearly focuses on one thing: 👉 Making dashboards more interactive, responsive, and user-driven If you're building real-time analytics or high-performance data apps, this is worth checking out. https://hubs.la/Q049-2vK0 #DataVisualization #Python #Analytics #Dashboard #TechRelease