Prerequisites
- A laptop you can install software on, with 8GB of memory or more
- Comfort finding your way around files and folders. No coding experience.
- Free Claude.ai (opens in a new tab) and Kilo (opens in a new tab) accounts, created before the day. Neither asks for a card.
- MarkText (opens in a new tab) installed before the day. Free, on Windows, Mac and Linux.
- Optional: a paid Claude plan (Pro), to do the Claude Code (opens in a new tab) and Claude Cowork exercises on your own laptop. Without one, you watch those parts and practise later from the workbook.
Syllabus outline
Foundations
Large language models as a function from text to text: parameters, weights, and what training actually searches for. Tokenisation and one-token-at-a-time generation. Sampling and temperature, and why one prompt yields two answers. Hallucination as a consequence of the objective rather than a defect. Knowledge cutoff, the context window as working memory, the model as distinct from the app around it, and the two meanings of "agent".
Prompting
The prompt as a brief rather than an incantation. Its five elements: the task, supplied context, the reader, output format, and a worked example. Zero-shot and few-shot. Iterative repair against restarting. The limits of prompting and what no wording can fix. Paired weak and strong prompts for each element, with both outputs.
Your Workbench
Three interfaces to the same tool: graphical, command line, and API. Terminal essentials: working directory, navigation, and the few commands in daily use. Markdown as a portable working format. APIs distinguished from applications. Claude Code installed and run against a working folder, with the common first-run failures.
The Claude Ecosystem
One subscription across several surfaces. Chat, Projects as persistent working context, and Artifacts as editable documents rather than downloads. Cowork for delegated jobs, Claude Code for local folders, Claude Design for visual output. Choosing a surface by task. Flat-rate plans against metered API pricing, and the equivalent products at OpenAI and elsewhere.
The No-Cost Path
Local inference against hosted inference. LM Studio for running a model on a laptop without a terminal, and the hardware each model size demands. Kilo Gateway as a single key across providers, and configuring a command-line tool against it. Goose as a no-subscription alternative to Cowork. Cost per route compared.
Skills, Agents and MCP
MCP connectors as access to other applications, and the permissions that access carries. Skills as encoded working method, contrasted with project instructions; ready-made skills, and writing one for a house format. Agents as a loop toward an outcome rather than a sequence of steps, and three characteristic failure modes.
Dates coming soon
We are arranging the dates and venue for the next batch. Interested? Get notified the moment it is announced.
