Systems | Development | Analytics | API | Testing

In today’s rapidly evolving tech world, coding isn’t just about writing lines of code — it’s about crafting them smarter, faster, and with greater precision. This is where AI coding assistants come in: intelligent tools designed to streamline development workflows, catch errors early, suggest enhancements, and even generate complete blocks of code, empowering developers to work more efficiently than ever before.

Artificial intelligence is one of the most impactful innovations the financial services industry has ever seen. From streamlining financial operations to enhancing customer experiences, artificial intelligence capabilities help financial sector organizations stay competitive in a marketplace that never stops shifting. The benefits of AI also extend to payment processes. Here’s a real-life example.

Everyone is taking part in the AI scramble, some more successfully than others, as we all know. But many enterprise companies are still just getting their feet wet. Here at Qlik we saw an opportunity to try to capture what it is that these successful companies did to get value from AI for their organizations. So, we partnered with IDC to get those answers and discovered that preparation is a major difference maker for success with AI.

Between the relentless pressure to ship new features and updates, and the increasing adoption rates of DevOps, AI’s influence in the future of the testing landscape is palpable, and the focus in the coming years will be applying that to QA teams everywhere.

When ChatGPT hit headlines, many equated artificial intelligence with simple chatbots. Useful? Sure. But limited to isolated tasks and virtual assistants, they fell short of their full potential. That’s changing. Businesses are now entrusting AI agents with real decision-making power on complex tasks. These agents reason, adapt, and act autonomously—without waiting for human intervention. When they’re deployed directly into processes, they provide real value at enterprise scale.

By Erez Schnaider, Technical Product Marketing Manager, ClearML In a previous blog post, we laid the foundations for understanding multi-tenancy in GPU-access infrastructure and highlighted its critical importance. In this post, we’ll dive into ClearML’s approach to achieving secure and efficient multi-tenancy. At a high level, multi-tenancy refers to the ability to share a single resource pool, typically GPU or CPU clusters, across multiple, logically isolated entities known as tenants.

As organizations continue to adopt AI-driven applications, managing usage and costs becomes more critical. Large language models (LLMs), such as those provided by OpenAI, Google, Anthropic, and Mistral, can incur significant expenses when overused. This blog will explore how you can streamline your AI workloads by leveraging Kong’s token rate-limiting and tiered access features.