Systems | Development | Analytics | API | Testing

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.

We’re entering a phase where AI can draft emails, resolve tickets, summarise complex information, and occasionally present fiction as fact with equal conviction. Generative AI has become incredibly powerful, but in enterprise environments, power without precision quickly becomes a risk rather than an advantage. This is exactly where the shift is happening.

Developers and Quality Assurance (QA) teams utilize many different API testing and validation tools to help them simplify the processes of testing, debugging, and validating APIs in the increasingly API-centric world of software development. Modern teams often combine End-to-End Testing with API-level testing to ensure full workflow reliability.

MLRun 1.10, the latest version of our open source AI orchestration framework, is available today to all users. Iguazio started out as a platform to operationalize enterprise machine learning projects. Though we’ve been through quite a few waves of AI in just a short time, the underlying challenges are the same: getting from experimentation to production remains a major blocker.

For a long time, we spoke about “AI agents” like they were a future concept, something that might eventually book flights, run workflows, or make payments on our behalf.

Modern development strategies rely on continuous integration and continuous delivery (CI/CD) to ship high quality software and updates. A well-designed CI/CD pipeline automates key parts of the software development lifecycle so teams can build, test, and deploy changes faster with fewer errors. To achieve this, development teams need the right CI/CD tools. Before you select your CI/CD solutions, you need to understand CI/CD pipelines and how they work.

In the agile development methodology, the velocity of testing holds the key to delivering the best quality software within the stipulated budget and time constraints. As such, this drives the need to automate the testing process with test orchestration. Most software development teams visualize test automation as a discreet step in the delivery lifecycle instead of viewing it as a designed sequence of steps.