Systems | Development | Analytics | API | Testing

If you’re part of a software testing team using Jira, you know how crucial it is to keep track of all your tests, their statuses, and how they relate to requirements. But let’s be honest - sometimes getting real-time test insights in Jira isn’t always easy. That’s exactly why the latest update from Xray Cloud is a game changer for test management in Jira. This release introduces 29 new advanced JQL (Jira Query Language) functions designed specifically for testing.

In software development and automated testing, consistency really matters. One of the most frustrating barriers that developers and QA engineers encounter is a little something we call flaky tests: tests that pass or fail at random times with no changes to the code. These googly eyed tests tend to do the most damage and can produce unreliable results which erodes trust in the testing function and can even cause release cycles to slow down especially in the architectural context of CI/CD pipelines.

The key to securing large language models (LLMs) lies in adopting a Zero‑Trust framework. This approach ensures that every interaction - whether from users, devices, or applications - is verified, authenticated, and authorized. With the rise of LLMs in enterprise environments, traditional security models no longer suffice. Here's how DreamFactory's Gateway helps implement Zero‑Trust principles effectively.

In my last blog post, I looked at the essential first part of building any data pipeline—exploring the raw source data to understand its characteristics and relationships. The data is information about river levels, rainfall, and other weather information provided by the UK Environment Agency on a REST API. I used the HTTP Source connector to stream this into Apache Kafka topics (one per REST endpoint), and then Tableflow to expose these as Apache Iceberg tables.

By Erez Schnaider, Technical Product Marketing Manager, ClearML AI engineering today goes far beyond simply training a model. Teams are fine-tuning large language models on high-end GPUs, running massive, distributed experiments, and orchestrating hybrid workflows spanning on-premises clusters, private and public clouds. With great power comes great responsibility, and with powerful hardware comes complexity. Without robust controls, things can quickly descend into costly chaos: Who’s using what?

The real barrier to AI isn’t talent or tools — it’s broken data infrastructure.

Multiplayer is the only session recorder that combines frontend replays with unsampled backend traces, stitched together automatically. You don’t have to choose between drowning in noise or missing the critical data. Backend tracing is the backbone of understanding how modern distributed systems behave. Each request generates a chain of spans as it travels through your services and components: what happened, how long it took, and whether it failed.

Are you still running Node.js 12, 14, or even older versions in production? If so, you’re facing a serious challenge: these versions have reached End-of-Life (EOL) and no longer receive official updates or security patches. For many organizations, especially those operating on legacy environments like RHEL 7 or Ubuntu 18.04, upgrading to the latest Node.js version isn’t always feasible.

Choosing the appropriate development tool can either improve or inhibit your coding efficiency. Two tools that can confuse developers are Cline and Cursor because they are intended for different use cases in the software development lifecycle. Developer’s must understand the difference between Cline vs Cursor so that they can improve their workflow and select the appropriate tool for their project requirements.

I recently connected with a data engineering manager leading engineering efforts at an energy analytics company. His team processes complex data from hundreds of public sources, each with its own format, errors, and quirks. During our conversation about data quality challenges, he shared an insight that resonated deeply: This principle applies directly to QA. Every day, we generate test results that teams rely on for critical decisions.