Systems | Development | Analytics | API | Testing

When developing web applications with Django, your interaction with the database impacts overall application performance. Django's Object-Relational Mapper (ORM) is a powerful ally that offers an intuitive way to work with your data through abstractions called QuerySets. These are your primary tools for fetching, filtering, creating, and managing data. In this article, we'll explore fundamental — yet highly effective — techniques to optimize your Django QuerySets.

Applications and systems using AI have exploded in popularity, with every company looking to integrate AI anywhere they can. This move toward AI-assisted and AI-powered products appears to be the future. However, early adoption is great, but gaps form quickly at scale. For example, in 2023 OWASP began to publish the OWASP Top 10 for LLM Applications (updated again in 2025), which outlined ten common security flaws found in LLM-based applications.

Integration testing is an essential part of development, ensuring applications can survive the rigors of deployment and function in the real world. Getting the most out of them is key. It’s about making sure you write meaningful tests that ensure your code works as expected. If you’re running integration tests in Python, you may appreciate better visibility and deeper insights into application errors.

A Product Requirements Document (PRD) articulates the purpose, features, and functionality of a product. It is a blueprint for development teams to understand: While the exhaustive PRDs of the past are less common in today's Agile landscape, their core function remains the same: to align all stakeholders so that everyone from product managers to developers and testers shares a common vision.

Before you can test software, you need to know what to test. That’s where many QA teams stall out. They don’t have the right software testing tools for mapping the app, identifying user paths, and determining testing priorities. So, building a test plan can take days (or more) of manual work. It’s often slow, frustrating, and error-prone.

API and AI services now sit at the heart of modern products. However, the more we use them, the harder it seems to become to account for the budget. Launching an AI product often leads to massive end-of-period bills. This requires attributing costs to the key internal power users and consumption drivers. The challenge is identifying the departments, products, or projects responsible for the consumption, and the extent to which they contribute.

You can make your API observability posture more powerful and beneficial by treating Moesif as an engineering implement. The platform automatically captures API traffic out-of-the-box and provides actionable analytics and visualizations. However, the degrees to which they precisely and empirically illustrate the data, depend on where and how you’ve integrated Moesif.

Today marks a monumental step in our commitment to fueling the growth, reach, and impact of our global partner network. We’re thrilled to announce the official launch of Confluent Marketplace (formerly Confluent Hub), a centralized resource designed to accelerate innovation, drive connectivity, and dramatically simplify the developer experience within the data streaming landscape. For years, integration engineers have been the quiet force behind the modern digital world.

Picture this: You’re creating test cases for a new feature. You have a Jira ticket with text requirements, a Figma mockup from design, a workflow diagram from the architect, and a screenshot from a stakeholder meeting. Traditionally, you’d manually translate all of this into test steps: describing the UI in words, interpreting the diagram, cross-referencing the mockup. But what if your testing tool could “see and “understand” all these artifacts directly, just like you do?

If you find that your team is struggling to get releases out the door, it could be inefficient testing practices. Oftentimes, software teams don’t know what their tests actually cover, or which tests are relevant after each code change — so they run everything. This means spending hours executing full test suites for minor updates or burning through CI/CD resources while bugs slip through untested paths. On top of this, software is always becoming more complex.