Systems | Development | Analytics | API | Testing

The need for impeccable mobile applications is unequivocal. Users want intuitive interfaces, smooth functionality, and uniform performance across various devices and operating systems. Development teams have a considerable difficulty in satisfying these requirements while expediting release cycles. Conventional mobile app testing services, although fundamental, often fail to keep pace with the velocity and complexity of contemporary application development.

Agentic AI has the potential to revolutionize workplace processes in nearly every sector by improving business decision-making, workflow efficiency and customer interactions and experiences. While interest in agentic AI is widespread, the motivation to use it differs by industry. Cloudera surveyed 1,484 enterprise IT leaders across 14 countries to better understand their approach to agentic AI in 2025, including how specific industries plan to implement the technology.

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.

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.

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.

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.