Last updated: 2026-05-12
A measured, non-commercial course on personal productivity for anyone working alongside an AI assistant. Twenty-two lessons across six modules: see your work before you augment it, learn the toolkit and its economics, build the knowledge layer, codify expertise as skills, orchestrate agents while knowing the limits of delegation, and close with the discipline of doing fewer things better. No pitches, no upsells, no proprietary frameworks invented for this course.
Self-paced. Honour-based. Not accredited.
Six short modules that move from the elimination-first hierarchy through the impact equation, model routing, the three-layer knowledge base, codified skills, sub-agents and worktrees, the peer-reviewed out-of-the-loop performance problem, and the slow-productivity counterweight at the close. Practical frame, no jargon, no salesmanship.
Each module sets up the next, and every lesson pairs a productivity-tradition idea with the AI pattern that translates it into practice. The arc takes a beginner through to advanced material without skipping the foundations.
The elimination-first hierarchy, the three-term impact equation, the context audit, and the deep-shallow distinction. The honest map of where time actually goes before any tool is chosen.
Renting versus owning. Models, context windows, and routing economics. Local versus cloud, tokens, and cost discipline. Visual, desktop, and terminal surfaces. Prompts, permissions, and safe defaults.
Capture without friction, the four-folder PARA structure, the three-layer raw, wiki, and schema knowledge base, and context engineering as a systems discipline at agentic scope.
The assembly-line decomposition, the codify-before-build rule, the five-step skill construction loop, persistent memory across sessions, and outcome-named shortcuts that stay legible months later.
Sub-agents, worktrees, the routing decision, chains of skills, hooks as the deterministic layer, MCP as the integration substrate, and the peer-reviewed out-of-the-loop performance literature.
Auto-research loops, the Ralph pattern, headless workflows on current agent CLIs, dashboards as the emerging interface, and the slow-productivity counterweight that closes the course.
Start with the order of operations the rest of the course rests on: eliminate first, then automate, then delegate. The opening lesson is short and changes how the next twenty-one are read.
The first two modules assume no prior knowledge and move slowly enough that a reader who has never used an assistant agent before will keep up. The later modules introduce sub-agents, hooks, MCP, and headless workflows, but each is grounded in the foundations laid earlier. If you have been using an assistant ad hoc and want a working frame for thinking about it, this is for you.
The course deliberately draws on a wide set of voices across two literatures. From the productivity tradition: Drucker, Newport (Deep Work, A World Without Email, Slow Productivity), Allen, Forte, Ahrens, Ferriss, Covey, Kahneman, Csikszentmihalyi, Clear, Ohno on the Toyota production system, Boyd on the OODA loop, and the peer-reviewed human-factors literature on automation (Endsley and Kiris, Parasuraman and Riley). From the contemporary AI discourse: Engelbart and Licklider on augmentation, the published Anthropic engineering posts on how internal teams use Claude, the Karpathy LLM knowledge-base pattern, the published Model Context Protocol specification, and the recent writing on context engineering. The point of the course is to let you read all of them — and then form your own view.
Course progress, exam results, and certificate details are stored in this browser on this device. They may be removed if you clear browser data. Nothing is sent to a server.