Systems | Development | Analytics | API | Testing

Before I ever ran Claude in my terminal, I thought I already understood AI tools pretty well. Like most people, I had used ChatGPT, Google Gemini, and Perplexity for everyday tasks. Such as helping with schoolwork, organizing ideas, summarizing information, or getting through something faster when time was tight. They were useful, but they still felt separate from how real work happened.

At Ably we recently shipped AI Transport, our drop-in transport layer for streaming LLM output over Ably channels, with all the resumability, multi-device continuity, and handover guarantees that implies.

Play Quality People - Huy Tieu: The Evolution of Agentic QA: From scripts to Harnesses 17: 18 A conversation with Huy Tieu, Senior Product Manager at Katalon, on why the scripted testing model broke, what replaces it, and the one experiment every QA leader should run this week. The term "Agentic QA" is everywhere in 2026. Gartner predicts 40% of enterprise applications will feature task-specific AI agents by year's end, up from under 5% in 2025.

Resource governance is primarily an operational discipline, but it has direct security implications that are usually overlooked. This post covers what those implications are, what Kubernetes provides natively, where it falls short for AI workloads, and how ClearML addresses both dimensions. This is the third post in our four-part series on Kubernetes Security for Enterprise AI Environments.

New executive appointment strengthens the managed crowdsourced testing leader's commitment to advancing digital quality by pairing human expertise with AI efficiencies.

Enterprises are pouring millions into agentic AI. New findings from Fivetran's “Agentic AI readiness index 2026” reveal why most won’t see the return.

Conversational analytics leveraging Fivetran, Jira, and Claude saves analytical effort and boosts productivity.

In my experience working with enterprise leaders, the journey to the cloud rarely follows a straight line. Many organizations set ambitious goals to move all operations to the cloud. They quickly find that certain legacy systems must remain on-premises. This reality results in a complex, hybrid multicloud environment. That means they need to adopt a new strategy for managing test data.

Adding large language models (LLMs) and artificial intelligence (AI) to real-time event streams comes down to one thing: picking the right boundary between data transport and model compute. Where you run inference determines your system's resilience, latency, and cost. This article is for data engineers, streaming architects, and developers who want to add AI capabilities to their Apache Kafka event backbone without destabilizing production consumer groups or blowing through API rate limits.

AI has evolved fast. We've gone from static, predictive models to dynamic, interactive agents. But most organizations still run data pipelines that haven't kept up. Consider what’s happening in modern AI architecture. Teams deploy high-performance engines like large language models (LLMs) and real-time fraud detectors, then feed them data that's hours or days old.