Speed Up Python Type Checking with Pyrefly

Most implementations of the State pattern in Python look very “clean”. Lots of small classes. A base interface. One class per state. But if you’ve ever worked with one in a real project, you know the downside: transitions are scattered, behaviour is hard to see in one place, and adding new states often means touching multiple files. In today’s video, I rebuild the State pattern in a very different way. Instead of relying on inheritance, I make the state machine explicit as data and use decorators to define transitions. The result is a small, reusable engine where the entire flow becomes visible at a glance. If you’re interested in writing Python that’s easier to reason about and extend, this is a pattern worth understanding. 👉 Watch here: https://lnkd.in/eg22yEHR. #python #softwaredesign #designpatterns #statemachine #cleancode

Day 4 – Python Deep Dive 🚀 Topic: Mutable vs Immutable Immutable: int, string, tuple Mutable: list, dictionary Example: x = "hello" x[0] = "H" ❌ (error) 👉 Key takeaway: Immutable objects cannot be changed after creation. This impacts memory and performance. #Python #DeveloperThinking

Python Challenge – Can you solve this? Today was all about deep-diving into Lists vs. Sets and I came across a common mistake that we can sometimes overlook. Let’s test your Python understanding👇 numbers = [1, 2, 3] numbers.append([4, 5]) print(len(numbers)) A) 3 B) 4 C) 5 D) Error It’s a classic interview question that tests if you truly understand how Python handles memory and lists. Day 15/30 #30DaysOfCode #DataStructures #Day15 #PythonQuiz

Most implementations of the State pattern in Python look very “clean”. Lots of small classes. A base interface. One class per state. But if you’ve ever worked with one in a real project, you know the downside: transitions are scattered, behaviour is hard to see in one place, and adding new states often means touching multiple files. In today’s video, I rebuild the State pattern in a very different way. Instead of relying on inheritance, I make the state machine explicit as data and use decorators to define transitions. The result is a small, reusable engine where the entire flow becomes visible at a glance. If you’re interested in writing Python that’s easier to reason about and extend, this is a pattern worth understanding. 👉 Watch here: https://lnkd.in/e9Y3xGNF. #python #softwaredesign #designpatterns #statemachine #cleancode

Python Dunder Methods: Making code more "Pythonic" 🐍 I’ve been playing around with operator overloading today! Instead of writing a bulky function like add_packages(pkg1, pkg2), Python allows us to use the __add__ magic method. By defining __add__, I can simply use the + operator to combine two package objects. Cleaner syntax? Check. More readable? Absolutely. I also added __str__ to ensure that when I print the result, I get a clear, formatted summary of the dimensions and weight rather than a messy memory address. #Python #CodingTips #SoftwareDevelopment #ObjectOrientedProgramming #CleanCode

Learn how to create a chatbot with Python and Dialogflow, including step-by-step guide, advanced features, and real-world applications, with a focus on creating a chatbot with Python https://lnkd.in/g8GFzcvs #CreateChatbotWithPython Read the full article https://lnkd.in/g8GFzcvs

Learn how to create a chatbot with Python and Dialogflow, including step-by-step guide, advanced features, and real-world applications, with a focus on creating a chatbot with Python https://lnkd.in/g8GFzcvs #CreateChatbotWithPython Read the full article https://lnkd.in/g8GFzcvs

🚀 Built a simple Day/Night image classifier from scratch using Python. This project is a simple machine learning pipeline built from scratch that classifies images as day or night using handcrafted features and a custom linear SVM. 💾 Source code: https://lnkd.in/dp9kmRmN 💻 Tutorial: https://lnkd.in/d5epPRnt

There's a common myth in Python: "List Comprehensions are vectorized because they are faster than for loops." The truth: They aren't. While Comprehensions are slightly faster than .append() loop, they are still sequential. If the user has 1 million items, Python is still performing 1 million individual fetch calculate store cycle. Comprehensions are scalar and they process data one by one. Use Comprehensions for readability and small-to-medium data transformations. Just because Comprehension is in one line, it doesn't mean it's running in parallel. #Python #SoftwareEngineering #DataScience #CodingTips #PerformanceOptimization

Managing Python environments shouldn’t be a nightmare. But for many — it still is. Here’s how to keep things clean, fast, and production-ready in 2026: 🛠️ Use uv or poetry to manage environments & dependencies. Faster, safer, and simpler than old-school pip+venv. ⚡ Speed matters – use polars, numpy, and numba to vectorize heavy loops. Even small tweaks can give 10× performance wins. 🧼 Lint + Format + Type-check = non-negotiable Ruff for linting Black for formatting Pyright or Pyrefly for fast type-checks 💡 Bonus tip: Use Typer to build CLIs in minutes. So clean, it feels like magic. 💬 What’s one Python setup rule you wish you knew earlier? #Python #CodeQuality #Productivity #DevTools #DataEngineering