Clawdemy Lessons

A brief on why correct components yield incorrect AI systems: four complex-systems properties, Perrow normal accidents, and when layered defenses break down.

Overview of how game theory frames multi-agent AI risks: Nash versus Pareto outcomes, three failure modes, and four cooperation mechanisms with their limits.

A guided overview of moral uncertainty in beneficial AI: the three strategies, social welfare functions, fairness criteria, and the link to outer alignment.

What the opening AI safety lesson covers: the four risk categories, the discipline-vs-stance frame, prerequisites, audience, and difficulty.

A preview of the four-layer AI governance lesson: what each layer covers, why compute governance leads, prerequisites, and the placement skill you will build.

What this lesson covers: the six agent patterns, their verbatim definitions, learning outcomes, prerequisites, and the decision tree for picking the right one.

A guided overview of prompt caching, context windows, compaction, and context editing: what you will learn, prerequisites, and how the levers fit.

A brief on Model Context Protocol (MCP): the connector request and response shapes, per-tool configuration, the L4/L5/L6 decision frame, and connector limits.

Roadmap for the agent-loop lesson: the workflow-vs-agent distinction, the 30-line loop, the stop_reason vocabulary, tool_choice modes, and loop disciplines.

A quick brief on Agent Skills (durable on-disk instructions Claude reads on demand) and Claude Code, the agent harness that makes reusable prompts shareable.

Brief overview of your first Claude API call: the request, structured response, stateless multi-turn conversations, and the system parameter.

Overview of the Claude tool-use lesson: what you will learn, prerequisites, where it fits, and the single capability it builds (the four-step loop).

The production Messages API at a glance: when to stream, batch, or do neither; stop_reason dispatch; the HTTP error map; SDK retries; and request_id logging.

A roadmap to the lesson on Anthropic-provided tools: the three categories, the server-tool response shape, the pricing rule, prerequisites, and read time.

Model selection roadmap: the capability the lesson builds, the three Claude families and effort dial covered, prerequisites, and the cost math.

The multimodal AI closer: six unifying threads across the track, what it deferred or never covered, and three trajectories for where the field heads next.

Orientation for the lesson on shipping multimodal AI: what you'll learn, the reasoning-stack prerequisite, learning outcomes, and time and difficulty.

Overview of agent security: the threat model, three attack categories and defenses, the defense-in-depth toolkit, and the Agents Rule of Two.

A roadmap to the Taylor series: why factorials matter, the e^x, sine, and cosine series, and how gradient descent and Newton's method are Taylor at work.

Overview of higher derivatives: notation, the second derivative as acceleration and curvature, the second-derivative test, and ML curvature uses.

A map of reinforcement learning fundamentals: how the agent-environment-reward loop works, what makes RL hard, and the exploration-exploitation tension.

An overview of value iteration: what you will learn, the prerequisites, the math involved, and how the update reappears later in Q-learning and DQN.

Overview of value functions and the Bellman equations: what V and Q are, how the one-step recursion is derived, and why optimal policies are greedy.

A brief on temporal-difference learning: the TD(0) update, a worked four-episode chain, the MC vs TD bias-variance tradeoff, and where TD powers modern RL.

Overview of the Q-learning lesson: what you will learn, prerequisites, the off-policy vs SARSA distinction, the DQN preview, and time and difficulty.