Systems | Development | Analytics | API | Testing

By Erez Schnaider, Technical Product Marketing Manager, ClearML Slurm has powered HPC environments for years. It is battle tested, widely adopted, and deeply embedded in research and engineering workflows. Over 60% of the TOP500 supercomputers use it to manage their large infrastructure, orchestrate workloads and schedule jobs, as it is powerful and versatile with over 20 years of engineering behind it.

Ably AI Transport is now in early access. It gives your AI the continuity, awareness, and multi-device reliability users actually expect. Drop it in, keep your existing stack, and start shipping Gen-2 AI right away.

The meeting started with a question I wasn't expecting. "Dragan, if AI can write tests now, what exactly will your team be doing next year?" Q1 2025, six months before I founded Quantum Quality Engineering. The executive wasn't hostile, just genuinely curious. Behind that curiosity lurked a more complex question: Do we still need QA leaders in an AI-driven world?

As the lead sustainability officer at Qlik, I've had the privilege of witnessing firsthand the collaborative spirit and escalating urgency driving climate action today. Events like Climate Week NYC, the C40 World Mayors Summit, and COP30, highlight a clear reality: commitments are essential, but implementation and accountability matter more — within private and public sectors alike. And at the heart of this transition lies a powerful trio: data, collaboration, and AI.

Applications and systems using AI have exploded in popularity, with every company looking to integrate AI anywhere they can. This move toward AI-assisted and AI-powered products appears to be the future. However, early adoption is great, but gaps form quickly at scale. For example, in 2023 OWASP began to publish the OWASP Top 10 for LLM Applications (updated again in 2025), which outlined ten common security flaws found in LLM-based applications.