Systems | Development | Analytics | API | Testing

Why MCP is a major leap in the evolution of AI-driven software testing

Let’s say you’re renovating a house. It’s a big project, and you’ll need a lot of planning, expertise, and equipment. Now imagine you live in a world where the hammers you bought at one store don’t match up with the nails you bought at another, and certain wrenches wouldn’t work on anything but the parts they came with. What a pain, right?

How Qlik Is Powering Bystronic's GenAI Transformation

Some data problems are universal — like dealing with unstructured data. At Bystronic, a global leader in sheet metal processing, we have mountains of it. From technical documentation to sales decks, HR policies, and IT knowledge bases, data is scattered across folders, servers, and systems. Industry research shows that 80% of enterprise data is unstructured, meaning it’s often invisible to the teams that need it most. As a result, up to 68% of that data goes unused. The impact is real.

Confluent Cloud is now available in the new AWS Marketplace AI Agents and Tools category

Confluent announces the availability of Confluent Cloud in the new AI Agents and Tools category of AWS Marketplace. This enables AWS customers to easily discover, buy, and deploy AI agent solutions, including Confluent's fully managed data streaming platform Confluent Cloud, using their AWS accounts, for accelerating AI agent and agentic workflow development.

From Checklists to Discovery: Leveraging AI and Embracing Curiosity in Testing | Kunal Ashar

This session explores the shift from structured checklists to a more dynamic, curiosity-driven approach in software testing. Attendees will learn how AI tools can enhance exploratory testing by supporting deeper requirement analysis, generating actionable insights, and streamlining the capture of questions, risks, and test ideas.

AI at Scale Needs Control: Inside ClearML's Resource Allocation Policy Manager

By Erez Schnaider, Technical Product Marketing Manager, ClearML AI engineering today goes far beyond simply training a model. Teams are fine-tuning large language models on high-end GPUs, running massive, distributed experiments, and orchestrating hybrid workflows spanning on-premises clusters, private and public clouds. With great power comes great responsibility, and with powerful hardware comes complexity. Without robust controls, things can quickly descend into costly chaos: Who’s using what?

ZeroTrust for LLMs: Applying Security Principles Through DreamFactory's Gateway

The key to securing large language models (LLMs) lies in adopting a Zero‑Trust framework. This approach ensures that every interaction - whether from users, devices, or applications - is verified, authenticated, and authorized. With the rise of LLMs in enterprise environments, traditional security models no longer suffice. Here's how DreamFactory's Gateway helps implement Zero‑Trust principles effectively.

Cline Vs Cursor: Which Ai Coding Tool To Choose In 2025?

Choosing the appropriate development tool can either improve or inhibit your coding efficiency. Two tools that can confuse developers are Cline and Cursor because they are intended for different use cases in the software development lifecycle. Developer’s must understand the difference between Cline vs Cursor so that they can improve their workflow and select the appropriate tool for their project requirements.

From Static to Adaptive: Why Agentic AI is the Future of Enterprise Software

Over the first half of this year, I’ve had the unique privilege of traveling across EMEA, APAC, and the US, leading our global Agentic AI workshop series. From London to Singapore to Mumbai, I’ve had a front-row seat to how enterprises—across industries and continents—are rethinking what software can be in an age of intelligent systems. And I can confidently say: the era of Agentic AI has arrived.

QA Wolf vs Rainforest QA vs Alphabin: The 2025 AI-Driven QA Comparison

Today, Quality Assurance accounts for approximately 40% of overall development costs. Forty percent. Nearly half of your development budget is going to QA, and your best engineers are still doing repetitive work that should've been automated years ago. Something's broken in QA, and I think I know what it is. Alphabin’s approach to QA positions it as a leading option, on par with QA Wolf and Rainforest QA.