Systems | Development | Analytics | API | Testing

Most web applications start clean with a handful of route handlers, each focused on a single business logic. As the app moves to production, the audit team wants every request logged, and the security team wants API key validation on all routes. As the app grows, we might introduce rate limiting, CORS for a new frontend, and a request ID for distributed tracing.

If you use the Internet, you have surely come across the term Markdown. Markdown is a lightweight markup language that makes it very easy to write formatted content. It was created by John Gruber and Aaron Swartz in 2004. It uses very easy-to-remember syntax and is therefore used by many bloggers and content writers around the world. Even this blog that you are reading is written and formatted using Markdown.

In 2006, the TIOBE Index crowned Ruby its Language of the Year for posting the fastest growth rate of anything on the list. Two decades later, it's settled in around - respectable, but a long way down from where it once stood. Ruby went from "the future of programming" to "wait, people still use that?" within a single career. This isn't a eulogy, though - stick around, because Ruby is very much alive, just not where the headlines are looking.

Python is a robust and powerful programming language. In addition to machine learning, Python can be used for tasks such as web scraping, image processing, scientific computing, and much more. A framework such as Django, which is built on top of Python, enables you to build beautiful web applications—top websites such as Dropbox, Instagram, and YouTube use Django.

Errors in Python are issues in a program that cause incorrect results or prevent proper execution. Some Python errors are loud and obvious, and your code barely gets started before it throws an error that tells you exactly what went wrong. Other errors are more subtle, allowing your Python program to run without complaints while silently producing incorrect results that only become apparent later.

Whenever you place an order on Amazon (or any other e-commerce site for that matter), you get that “order placed successfully” notification almost instantly. But did you know that there’s much more to the whole experience than meets the eye? In Python applications, Celery is the major driver behind the whole thing. The tasks that take time are queued and sent to brokers.

Python dominates AI development. It's where teams build their agents, orchestration layers, and the backend systems that turn LLM calls into products people actually use. Over the past year, those systems have matured rapidly. What used to live in notebooks and prototypes is now running in production, serving real users with real expectations around reliability and performance. That maturity brings infrastructure requirements. Tokens need to stream in order.

High-Performance Computing (HPC) has traditionally been an exclusive club. If you wanted to run massive simulations or crunch petabytes of data, you had to leverage the predominant languages used on supercomputing hardware—usually C, C++, or Fortran. Although fast and efficient, these languages demand strict memory management and complex syntax that require strong software development skills. Without them, development time can slow down significantly. But the landscape is shifting.

If you have been writing tests for a while, you have probably encountered code coverage and test coverage. These concepts can be difficult to differentiate because they are somewhat intertwined. In this article, you will learn what code coverage vs test coverage means, and the basis of these concepts. You will also learn the key differences between code coverage and test coverage in Python. You would discover tools, techniques, and best practices to improve your testing strategy.