Systems | Development | Analytics | API | Testing

In this post, we'll run through testing a Node-MongoDB app, step by step. You can test MongoDB using mongodb-memory-server, an in-memory version of MongoDB that runs independently of a persistent database. A freshly spun-up mongod process starts at roughly 7 MB of memory, providing a lightweight, self-contained environment for running tests. Let's get going!

Do you ever feel like you need a superpower to get the information you need? Especially when you’re really into Python? APIs are pretty much that superpower! APIs (Application Programming Interfaces) let your code "talk" to other systems and get exactly what you need. They can help you come up with a new app, find the next big market trend, or even automate your morning weather report. This guide?

To be honest, most Python developers are not using list comprehensions. Even I, who is writing this blog, never used list comprehensions before. But when I saw some examples, I felt I had to try and use them in my Python code. The reason for this change of mind is that there are a few advantages we get if we implement list comprehensions. Let’s see what these are in this blog today.

When the testing volume increases without effective control, maintenance is more time-consuming, feedback is slower, and risks are bigger, especially in environments where continuous integration and release cycles are accelerated. In this article, you’ll explore smart automation strategies that can help you reduce test duplication and improve QA team efficiency.

Engineering teams now move faster than ever. In 2025, they build smarter, ship quicker, and rely heavily on AI tools and continuous delivery pipelines.

Software release cycles are accelerating. In fact, 85% of organizations now release at least once per month, with a 51% increase in automated testing spend last year alone. Yet API quality still breaks down. Why? APIs sit at the center of this acceleration, but as velocity increases, many organizations face a persistent challenge.

Another day, another API. Fueled by AI-assisted coding and agile workflows, the speed of innovation has never been higher. But for the compliance team? It’s panic mode. Every new API must follow a minefield of internal rules: security protocols, naming conventions, reuse policies, documentation standards. And while the dev team is flying forward, compliance is stuck doing manual reviews, chasing specs, and untangling inconsistencies often after the code is already written.

AI failures rarely look like crashes. More often, they appear as confident but incorrect answers, subtle bias, or culturally inappropriate responses.

Agentic coding tools like Cursor, GitHub Copilot, and OpenAI’s Codex are reshaping how software is developed. They enable developers to offload routine tasks and accelerate feature delivery. However, these tools also introduce new challenges – particularly in how we test and validate the code they produce.