Systems | Development | Analytics | API | Testing

We’re excited to announce the beta support for Mesh Manager in the Konnect Terraform Provider — a new tool that brings the power of infrastructure-as-code to Kong’s Service Mesh management platform. This provider enables engineering teams to declaratively manage Konnect Mesh resources using HashiCorp Terraform.

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.