Systems | Development | Analytics | API | Testing

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.

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.

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.

Today, we’re excited to announce the general availability of Serverless Postgres — a fully managed, fault-tolerant, and effortlessly scalable Postgres database service purpose-built for large scale and AI applications. Since the public preview, over 50,000 databases have been created for use cases ranging from multi-tenant SaaS to AI agent memory, RAG pipelines, and ephemeral dev environments.

Thanks to artificial intelligence’s increasing influence in everyday life, many previously uncommon terms have become part of the zeitgeist, much like AI itself. Chances are, you’ve already come across the terms ‘AI agent’ and ‘AI assistant’. You might even have seen them being used interchangeably. While the two terms sound similar, what each of them represents is very different.