Explains the Model Context Protocol and how tool access complements structured outputs for LLM-powered apps. Walks through building a simple FastMCP email server that can list messages and return content from a sample mailbox. Shows how to expose the server through a Cloudflare tunnel and connect it to OpenAI's remote MCP support so models can call the tools. Discusses security considerations and the flexibility MCP provides for richer, multi-step workflows.

Introduces the structured output feature for LLMs and how JSON schemas make it easier to pass model results into downstream systems. Builds a book recommendation demo with Pydantic models and the OpenAI Python SDK to enforce predictable responses. Expands to an Azure DevOps scenario that turns a user story into epics, features, and tasks, then creates the work items via the SDK. Shares practical lessons about validation, schema design, and chaining LLM outputs into existing workflows.

Walks through exporting Apple Health data from the Watch app and loading the XML into R with tidy tools. Demonstrates filtering and counting record types, isolating heart rate measurements, and converting units for analysis. Shows how to visualize trends, build a simple Shiny interface, and experiment with moving averages to explore your own heart rate patterns.

Explains the slowdown caused by RStudio's default Knit behavior spawning a fresh R session for every render. Describes building a console-based RMarkdown rendering addin that mirrors a TextMate workflow and keeps loaded packages in memory. Highlights the speed benefits along with the risk of hidden dependencies on the console environment and encourages periodic clean renders with Knit.