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Garry Trinder

Cloud developer advocate at Microsoft focused on Microsoft 365 Copilot extensibility, contributing technical perspective and updates on Foundry, Agent 365, MCP servers, and developer tooling in the episode.

Top 3 podcasts with Garry Trinder

Ranked by the Snipd community
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Nov 29, 2025 • 1h

CCDev3 - Ignite roundup on Dev Story for Copilot and Agents

In this engaging discussion, Garry Trinder, a Cloud developer advocate at Microsoft, dives into the latest from the Ignite Conference. He shares insights on the renaming of Azure AI Foundry to Microsoft Foundry, highlighting its broad platform vision. Garry explores new features like the Foundry portal’s visual workflow designer and the streamlined publishing to Teams. They also touch on the integration of Claude models and emphasize the importance of experimenting with language models while maintaining responsible AI practices. A must-listen for tech enthusiasts!
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Jul 29, 2025 • 1h 9min

CCDev 2 - what you need to think about with MCP

In this discussion, Garry Trinder, a Developer Advocate at Microsoft, dives into the Model Context Protocol (MCP) and its game-changing potential for developers. He highlights the challenges of integrating MCP, from enhancing documentation to establishing governance. Trinder stresses the importance of crafting user-friendly materials and the adaptability of MCP servers for efficient software development. The conversation also delves into practical strategies for getting started with MCP while unlocking significant productivity gains in AI applications.
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Jun 20, 2025 • 46min

CCDev 1 - Navigating Copilot extensibility

Garry Trinder, a Developer Advocate at Microsoft, dives into Copilot extensibility and its potential. He discusses why extending Copilot is crucial for tailored solutions, outlining various methods for enhancement, whether for end users or specialists. The importance of personalizing AI tools and integrating external knowledge sources for improved productivity is emphasized. Trinder also highlights the delicate balance between deterministic and non-deterministic approaches in AI applications, making a case for responsible AI practices and continuous adaptation in an evolving landscape.

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