Supercharging Unit Testing in .NET with Moq, Bogus, and GitHub Copilot by Yuxin Wang

After attending Microsoft Build 2025, one message stood out: AI is no longer just a coding assistant—it’s becoming an essential part of the entire software development lifecycle, including testing. One area where this is especially impactful is unit testing, a task that’s often tedious, time-consuming, and frequently postponed until later in the development process.

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Csuite Insights: How to Use Generative AI and Agentic AI in the Enterprise By Mark Hewitt

Enterprise organizations are entering a new era defined by generative AI and agentic AI. These technologies are not simply augmentations; they represent a reconfiguration of how businesses operate, innovate, and compete. This eBook outlines the strategic opportunities, current landscape, key risks, mitigation strategies, and implementation pathways for leveraging generative and agentic AI to drive enterprise value.

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Mark Hewitt
Reflections on Microsoft Build 2025: Strategic Insights for Decision Makers by Russ Harding

At Microsoft Build 2025, GitHub Copilot and the Model Context Protocol (MCP) were not just announcements—they were signals. We are not being asked to adopt new tools; we are being asked to rethink how software is built, governed, and scaled. Copilot is evolving from autocomplete to autonomous collaborator. MCP is laying the groundwork for secure, interoperable AI agents. The opportunity is massive—but only for teams willing to pair velocity with vigilance. Plan for autonomy, build for control.

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AI Coding Agents Will Promote You To Tech Lead by Ed Lyons

Many experienced software developers have remained individual contributors and are not currently playing the role of tech lead, but coding agents will now be their direct reports. Like it or not, human engineers will have to talk to the agents daily, correct them, and make sure they do a good job. These human developers are now faced with a choice: either be in charge of the agents, or compete with them directly. 

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Ed Lyons
Before AI Gets Smart, Your Data Needs to Grow Up by Ranjan Bhattacharya

The modern data stack (MDS) is a critical enabler for enterprise AI success, providing the trusted infrastructure that ensures data is clean, consistent, and governed. While AI captures executive attention, its effectiveness hinges on the quality of the data it consumes. The MDS addresses long-standing challenges like data silos, inconsistent metrics, and governance gaps—ensuring AI outputs are accurate, compliant, and reliable.

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Ranjan Bhattacharya