Courses

An exhaustive, hands-on course on Mermaid - the JavaScript library that turns markdown-like text into diagrams. From the theory of why 'diagrams as code' matters, through the pipeline, flowcharts, sequence diagrams, Gantt charts, ER and class diagrams, state diagrams and more types, to theming, integration, and best practices. Includes knowledge checks in every module and a final exam.

2026-09-09

A practical, opinionated course on Knowledge Centered Service (KCS) — the industry practice where the people who answer customer questions capture, reuse, and improve knowledge as they do their jobs. Covers the Solve Loop and Evolve Loop, quality without review queues, KM technology, measuring activities vs outcomes, proving business value, and standing up a program with executive sponsorship and peer coaching. Expanded with researched statistics on the knowledge economy, the cost of knowledge loss, the economics of deflection, and the AI revolution in KM — with interactive charts. Grounded in the LinkedIn Learning course by David Kay (Consortium for Service Innovation).

2026-09-05

An exhaustive, research-grounded course on marimo — the reactive Python notebook. From the theory of why notebooks fail and how reactive execution fixes them, through installation, the dependency-graph execution model, building interactive apps, running as scripts and web apps, to AI agents and the ecosystem. Includes honest pros and cons throughout.

2026-09-04

A comprehensive course on Hermes Agent — an AI employee / agentic harness that grows with you. Learn how to set up Hermes Agent from scratch, connect it to Telegram/Slack/iMessage, give it memory with Honcho, manage a multi-agent workforce with Multica, build autonomous workflows (morning briefings, animated websites, property marketing kits, content repurposing, e-com ads, trading bots, health coaches, Jarvis command centers), and learn how to package and sell these builds for recurring revenue. Based on the 3-hour YouTube course by Samin Yasar.

2026-08-23

Learn how to build Retrieval-Augmented Generation (RAG) systems powered by knowledge graphs in Python. Covers GraphRAG theory, when to use knowledge graphs vs classic RAG, and a hands-on implementation using Neo4j, LangChain, and OpenAI to create an AI agent that answers multi-hop questions by querying a graph database.

2026-08-23