Python

Introduction Imagine a doorbell. You press it (something happens), the chime sounds (a reaction happens), and perhaps a camera starts recording (another reaction). You don’t call the chime function directly. You signal that “an event occurred,” and any number of listeners react. That’s the core of events in software: something happens, interested parties respond. Events are everywhere—GUI buttons, network sockets becoming readable, a file changing, a business action like “order_placed,” or a job finishing. In Python, you can use events via libraries (Tkinter, Qt, asyncio, Django signals), operating-system interfaces (selectors), or create your own event systems. ...

Introduction As artificial intelligence (AI) and machine learning (ML) models grow in size and complexity, the need for scalable and efficient computing frameworks becomes paramount. Ray, an open-source Python framework, has emerged as a powerful tool for distributed and parallel computing, enabling developers and researchers to scale their ML workloads seamlessly. This article explores Python Ray, its ecosystem, and how it specifically relates to the development, training, and deployment of Large Language Models (LLMs). ...

Design patterns are time-tested solutions to recurring problems in software design. In Python, patterns take on a uniquely “pythonic” flavor because the language emphasizes readability, duck typing, first-class functions, and batteries-included libraries. This guide takes you from beginner to advanced—covering the classic Gang of Four (GoF) patterns, Pythonic equivalents, concurrency and async patterns, architectural patterns, and metaprogramming techniques. You’ll learn when to use a pattern, the pitfalls to avoid, and how to apply patterns idiomatically in Python so you can ship maintainable, scalable systems and be more capable than 99% of your peers. ...

Object-Oriented Programming (OOP) in Python is a superpower when you learn to use the language’s data model and protocols to your advantage. This tutorial is a comprehensive, end-to-end guide—from the very basics of classes and objects to advanced features like descriptors, protocols, metaclasses, and performance optimizations. The goal: to make you more capable than 99% of your peers by the end. What makes Python’s OOP special isn’t just syntax—it’s the “data model” that lets your objects integrate naturally with the language (iteration, context managers, arithmetic, indexing, etc.). We’ll cover essentials, best practices, pitfalls, and real-world patterns, with concrete code examples throughout. ...

Introduction If you want to learn algorithmic crypto trading without risking real money, paper trading is the safest, fastest way to start. In this guide, you’ll build a minimal, efficient paper trading loop in Python that runs on your laptop, uses real-time market data, and simulates orders with fees and slippage—no exchange account or API keys required. We’ll use public market data (via CCXT) and a small “paper broker” to track positions, PnL, and trades. ...