Systems | Development | Analytics | API | 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.