Build CLI App with Python and Typer

If your Python scripts are making 50 API calls, synchronous code spends most of its time waiting around doing absolutely nothing. Suresh Vina has written an intro to async Python using a simple analogy: boiling a kettle and making toast at the same time. Sync code does one, then the other. Async runs both concurrently. Same two tasks: 5 seconds sync, 3 seconds async. It’s not a big deal when making breakfast. But scale that concept across dozens of API calls and the difference adds up. The Infrahub Python SDK supports both sync and async natively. Switching between them is simple. Suresh walks through the core concepts, shows side-by-side code examples, and builds up to running the same operation across multiple sites concurrently. If async Python has been sitting on your "I should learn that someday" list, now’s your chance to *get up to speed*. (See what we did there? 😉) Link in comments 👇

Announcing Shiny for Python 1.6: Toolbar Components and Production Observability 🔭 We are pleased to announce the release of Shiny for Python 1.6. This update focuses on making applications more compact and their underlying processes more transparent. • New Toolbar Components: Designed to optimize screen real estate, the new toolbar components allow for more compact and professional UIs. • Built-in OpenTelemetry Support: This release introduces native OpenTelemetry integration. You can now emit traces and spans to track app performance and health, providing much-needed observability for production environments. Create more robust, performant, and observable applications today! Learn more about Shiny for Python 1.6 and its new capabilities here: https://lnkd.in/gFeG_QTk

One thing I’ve learned while working with Python recently really stood out to me 👇🏾 In Python, capitalization matters a lot. If you define something in uppercase and later try to use it in lowercase, you’ll likely run into an error. But in HTML (and sometimes even in React), things are more flexible—especially with tags, where capitalization doesn’t usually break your code. This made me realize how important it is to pay attention to detail depending on the language you're using. Every technology has its own rules, and understanding them makes you a better developer. Small lessons like this are part of the journey 🚀 #Python #React #HTML #WebDevelopment #LearningJourney #100DaysOfCode

THE BEST WAY TO LEARN PYTHON ISN'T WATCHING VIDEOS. IT'S RUNNING THE CODE YOURSELF learn-python is a playground and cheatsheet in one repo every topic operators, data types, functions, classes, exceptions, file handling, standard libraries has real working scripts with code examples, inline comments, and assertions that show you the expected output right away you don't just read it. you run it, break it, change it, and test it https://lnkd.in/gZCT3EEK variables, loops, decorators, generators, lambda expressions, OOP all covered, all runnable

🐍📺Check if a Python String Contains a Substring [Video] In this video course, you'll learn to check whether a string contains a substring. You'll also learn about idiomatic ways to inspect a substring further and search for substrings in pandas #python

Python Practice: Split & Join Strings I recently solved a basic yet important problem from HackerRank using Python. Problem: Given a string of space-separated words, split the string and join the words using a hyphen (-). Solution: def split_and_join(line): words = line.split(" ") # split string into list result = "-".join(words) # join list with hyphen return result if __name__ == '__main__': line = input() result = split_and_join(line) print(result) Key Concepts: split() → Converts a string into a list join() → Combines list elements into a string Clean function design for reusability if __name__ == '__main__' for proper execution structure Example: Input: this is a string Output: this-is-a-string What I learned: Even simple problems help build strong fundamentals in string manipulation and writing clean, structured Python code. #Python #CodingPractice #HackerRank #Programming #100DaysOfCode #LearningJourney

Sometimes it is useful to go one layer lower than Python. A while ago Nikita showed a simplified print implemented through a direct syscall path to explain what actually happens under the hood: arguments, string representation, buffer length, registers, and then the call into the OS. This is not about rewriting Python in assembly. It is about something more practical: when you understand where the abstraction ends, you make better decisions higher up the stack too. In complex Python systems, many expensive problems start exactly there: - too many layers nobody questions anymore - hidden cost behind “convenient” abstractions - weak understanding of I/O boundaries - performance discussions without a clear model of what the system is actually doing You do not need assembly to build a good backend. But when someone can reason from Python code down to the runtime and OS boundary, they usually see architecture risks earlier than others do. That is one of the reasons architecture reviews are useful: they are not just about code style or framework choices. They are about understanding where the system becomes expensive to change, hard to trust, or harder to scale than it should be.

🐍 Codédex March Challenge 2026: My Python Gallery What is this project about? During the entire month of March, I took part in the Codédex Challenge, solving a different logic problem every day using Python. Instead of leaving all my solutions hidden in folders and text files that no one reads, I decided to build this website to showcase my work in a visual and engaging way. This application is literally an interactive gallery for my code. You enter one place and can browse day by day to see how I think and how I solve problems step by step. What can you see in each day of this gallery? - The Problem: What each daily challenge was about (from decoding secret messages to calculating time differences or finding unique words). - My Python Code: The exact script I wrote to solve it, displayed in a block that simulates a real code editor inside the web app. - My Explanation: A short summary where I explain how I solved it and why I chose certain loops, lists, or functions to reach the result. The Approach The main goal here is to let my code speak for itself. Instead of overloading it with boring technical descriptions, I built a super clean and easy-to-use interface (with both light and dark mode) whose only purpose is to let you read my Python code comfortably, understand my daily logic, and see the result of 31 days of consistency. 🔗 Explore the Project 🌐 Live Website: https://lnkd.in/emixPMTw 💻 GitHub Repository: https://lnkd.in/ek_-HUbh

🐍📺 In this video course, you'll learn Python's namedtuple and how to use it in your code. You'll also learn about the main differences between named tuples and other data structures, such as dictionaries, data classes, and typed named tuples #python

I used to marvel at how an LLM can generate a functional Python script that does a job. But you can also just rewrite it in Rust... no, in Zig... well I changed my mind, do it in C ! It doesn't care - it will produce it. It will also produce a benchmark that will compare all those implementations. Fun fact - Python is ~5 times slower at parsing json vs Rust / C