Systems | Development | Analytics | API | Testing

The right AI tools make all the difference in QA testing AI has officially entered every corner of software testifng. The hard part now is figuring out which tools and features actually save time, speed releases, and (most importantly) improve quality outcomes. According to a recent survey from Test Guild, 72.8% of testers are prioritizing AI-powered QA for 2026. This includes tools that handle test planning, test management, test writing, and even analyzing test results. Contents Toggle.

We’re excited to announce that Speedscale’s proxymock is now available as an OpenClaw skill on ClawHub, bringing powerful API traffic replay capabilities directly into your Claude workflows.

The original promise of AI was that it would write most of the code for us. In reality, we’re not there yet. So where can AI meaningfully improve developer productivity today? In this post, we look at how AI powers development productivity across the SDLC, practical tools to use and frameworks for overcoming AI operationalization bottlenecks.

Over the past few weeks, an unusual experiment has been unfolding quietly and then very publicly.

A global leader in entertainment, gaming, and hospitality partnered with WSO2 to take the organization’s first step to becoming an agentic enterprise by building an intelligent solution that would transform how support teams operate. The solution, a virtual support engineer, automated a manual issue resolution process and reduced resolution times from 2 hours to 1 minute, helping to provide a better support experience for both customers and internal operations teams alike.

One year ago, we introduced Analyst Studio, ThoughtSpot’s unified workspace for preparing and managing AI-ready data, with a vision: to transform analysts from report generators into business catalysts. SQL, Python, and visual analysis finally worked together in one workspace, letting data teams move seamlessly between ad-hoc queries and advanced modeling, all while preparing data for the AI revolution we knew was coming.

In the age of AI, where delivery continues to accelerate, release confidence shouldn’t lag behind. Today’s software changes continuously, often generated or modified by AI. That raises complexity while shrinking the time quality teams have to plan, test, and make decisions. Manual workflows and static automation weren’t built for this pace.

Gartner has named Qlik a Leader in the 2026 Gartner Magic Quadrant for Augmented Data Quality Solutions, our seventh time being recognized as a Leader in this Magic Quadrant. As AI becomes operational, data quality matters more than ever. We’re past the phase where AI just produces outputs. AI is starting to initiate, route, and act across real workflows.

Creating AI writing tools is messier than you’d think. You start with this grand vision of an assistant that actually helps people write better, not just spits out generic text. Then reality hits. Models hallucinate. Users have wildly different needs. And suddenly you’re facing questions about responsibility, accuracy, and whether you’re building something genuinely useful or just another gimmick.

Today, we’re thrilled to announce that Koyeb has entered into a definitive agreement to join Mistral AI to advance cutting-edge AI infrastructure. Koyeb will bring its platform, technology, and team to accelerate Mistral Compute offering. Compute is designed to provide leading teams across the globe the same state-of-the-art infrastructure Mistral AI uses to build, run, and scale frontier models and AI software.