Systems | Development | Analytics | API | Testing

In an age of unprecedented video game popularity, the digital realm has emerged as a pivotal centre for entertainment, social engagement, and immersive enjoyment. This trend is reflected on a massive scale, with the global gaming population projected to reach 3.32 billion players worldwide. The COVID-19 pandemic further expedited this growth.

We’ve all been there: you’re building a chat experience and end up spending way too much time on UI glue - message bubbles, room lists, typing indicators… all before you even get to the good part: real-time logic. So today, we’re launching a set of lightweight, flexible UI components built for React and powered by Ably Chat.

A joint post from the teams at NeuBird and Confluent For organizations leveraging Confluent, ensuring smooth operations is mission-critical. While Confluent Cloud eliminates the operational burden of managing Apache Kafka, application teams still need to monitor and troubleshoot client applications connecting to Kafka clusters.

Managing data across hybrid systems while meeting regulatory requirements is a growing challenge for businesses. Here's what you need to know.

Speed isn’t everything in software development, but getting high quality apps out fast is still crucial. That’s where DevOps comes in: its core purpose is to streamline the delivery of apps as quickly and reliably as possible, without sacrificing quality at any stage of the process. ‍ At the heart of DevOps automation are three essential concepts: CI, CD, and CT. ‍ CI and CD are often used interchangeably, but each of the three concepts serves a distinct purpose.

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

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?

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.

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 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.