Systems | Development | Analytics | API | Testing

Google just released A2A (Agent2Agent)—a new open protocol for agent to agent communication—and it left people wondering if they just started a protocol war against MCP (Model Context Protocol).

With the release of WSO2 Identity Server 7.1, WSO2 continues to lead with innovation in the open-source identity space by introducing AI-powered development capabilities. The new features introduced, enable developers to secure applications more efficiently using natural language instructions, reducing time, effort, and the required identity domain expertise.

In the prior blog, we previewed some of the reasons that we settled on using Anthropic’s Computer Using Agent (CUA) over other alternatives and promised to provide more information as to what and why, with facts and figures. If you haven’t read that blog, check it out here. In this blog, I hope we can bring to light some of the “why” behind our decisions, and what makes our agentic AI so powerful.

Migrating to BigQuery, the data to AI platform from Google Cloud, just got simpler, with AI-powered BigQuery Migration Services enhancements.

“AI is the new electricity,” as Andrew Ng, Co-founder of Google Brain and Coursera, famously put it, AI is now the driving force behind unprecedented innovation, reshaping everything from healthcare to finance and beyond. All of these sectors revolve around data.

Whether it’s a financial services firm or a major healthcare provider, no other technology is poised to be as transformative for businesses as artificial intelligence (AI). It is redefining how every organization approaches its data and business applications. And so, it’s within this reality that Cloudera has consistently recognized how important it is to set out a clear and actionable roadmap to tap into the potential of AI and GenAI.

Artificial Intelligence (AI) has revolutionized various industries, including software development. One particular area where AI has shown significant promise is generating unit tests effortlessly. With the help of AI-based tools, developers can automate the process of creating unit tests, saving time and effort. In this blog, we will delve into the pros and cons of AI-generated unit tests, highlighting their potential benefits and addressing any concerns.