Software Engineering

Inside Claude Code: How Sub-Agents and Parallel Execution Define Next-Gen Coding Agents Introduction: The Evolution of Coding Agents Coding agents represent a fundamental shift in how developers interact with their codebases. Unlike traditional autocomplete tools or simple code generation models, modern coding agents operate autonomously across multiple files, maintain context over extended sessions, and can break down complex tasks into manageable subtasks. These systems leverage Large Language Models (LLMs) in sophisticated agentic loops where the model can call tools, observe results, and iteratively work toward task completion. ...

Many of us in Data Science come from math, biology, chemistry or engineering or other non-Computer Science backgrounds, which may mean that we don’t have much experience writing and maintaining large code bases. Recently, I found myself getting frustrated with the structure of some of my code and searching for a better way to structure my code base. Let’s explore some ways that we can improve our Software Engineering skills as Data Scientists! ...

In Python, decorators allow Data Scientists to extend and modify callables, such as functions, methods and classes, without explicitly changing the callable. Using decorators can improve the readability of your code as well code flexibility and modularity. In this article, we’ll discuss why we would use decorators, how to implement decorators and give a few examples. Use Cases for Decorators in Python Decorators are powerful because they enable “wrapping” of functions with a master function. Some common use cases for decorators in Python include: ...