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This coaching package is designed to give you a clear, practical introduction to prototyping and vibe coding. By the end, you’ll understand the fundamentals, have hands-on experience building a lightweight prototype, and know exactly how to take your next steps. Package topics can include, but are not limited to: • Idea exploration: identify a concept worth prototyping and define the minimum viable features • Tool selection and setup: get oriented with the most accessible low-code, no-code, and AI-assisted prototyping tools • Rapid build fundamentals: learn the basics of vibe coding—fast iteration, leveraging templates, and AI copilots • Guided build: create a simple, testable prototype or clickable demo together • Next steps: roadmap and resources to refine, expand, and showcase your prototype Outcome: By the end of this package, you’ll have both a practical introduction to prototyping and vibe coding and your very first working demo. You’ll walk away with the clarity and confidence to keep iterating and experimenting on your own.
A functional prototype or clickable demo of your idea
Hands-on experience with low-code and AI-assisted prototyping tools
A documented roadmap for refining and scaling your prototype
Practical workflows for rapid iteration and vibe coding
Coaching delivered via live sessions.
Services included:
AI Fundamentals
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Joined November 2025
5.0
Production AI Agents at Live Nation | Databricks, MCP, & Vertex Expert
I build AI agents for real analytics environments - not demos, not proofs of concept. As Director of Business Intelligence and Analytics at Live Nation Entertainment, I've designed and shipped production agent systems that combine LLM reasoning, MCP servers, structured data querying, and document search into workflows that replace manual analytical processes. My hands-on stack includes Databricks, Gemini, Vertex AI Search, Streamlit, and custom MCP server development. I've also built open-source tooling in this space, including a video understanding MCP server using local/free models. I come from a decade-plus background in BI and data engineering, which means I understand the messy reality agents actually have to operate in - legacy data models, inconsistent schemas, stakeholder trust issues, and the gap between what LLMs promise and what they deliver in production. If you're trying to build your first agent, figure out the right architecture for your use case, or get an agent system actually adopted inside your organization - I can help you avoid the mistakes I've already made.
5h of coaching