Day 1: Foundations, Agents, Build on Agentic Security: Build, Audit, Break

Module 1: LLM Fundamentals

Mon, 01 Jan 0001 00:00:00 +0000

Module 1: LLM Fundamentals#

What an LLM actually is, from tokens to weights to the API you call: next-token prediction, sampling, context windows, the OpenAI API as the de facto standard, the model landscape and how to read benchmarks critically, and the practical craft of working with the context window. Compressed pace; the lab is where it sticks.

Questions this module answers#

What actually happens when I send a prompt to an LLM — what are tokens, weights, and sampling?
Why is “glorified autocomplete” both accurate and misleading?
What is a context window, and why do long sessions degrade?
If the API is stateless, why is resending a long conversation not ruinously expensive?
What’s the difference between a system prompt and a user message, and how do I use each well?
What actually makes the system prompt special? (less than you think)
How do I pick a model — and when does small-and-fast beat the frontier flagship?
How do I read benchmarks (and ArtificialAnalysis) without being fooled?
Why do alignment and refusals matter for security work?

Slides#

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Module 2: Agents

Mon, 01 Jan 0001 00:00:00 +0000

Module 2: Agents: Using and Extending Them#

From chatbot to agent: give the model tools and a loop, and it can act. Tool-calling mechanics at the API level, the pi harness (permission modes, supervision, where agents shine and where they waste your time), then making it yours: skills, custom tools, MCP servers, subagents, and extensions that hook the harness itself. The longest module of the class: it owns the middle of Day 1, and its lab runs in five parts woven through the lecture.

Module 3: Agentic Coding

Mon, 01 Jan 0001 00:00:00 +0000

Module 3: Agentic Coding: Build#

Using agents to write real code, and what it takes to make that code good: workflows that work (plan first, small steps, verification loops), where AI-generated code fails on security, model tiering, and accountability: you own what the agent ships. The lab closes Day 1 and kicks off the class arc: everyone builds the same networked app to spec, it freezes overnight, and Day 2 attacks it.