Clawdemy Lessons

What policy iteration covers: the evaluate-then-improve loop, the improvement theorem, a worked MDP, prerequisites, and where it sits in RL planning.

Overview of the policy gradient capstone: what REINFORCE, actor-critic, PPO, and RLHF cover, prerequisites, the math involved, and time and difficulty.

Model-free policy evaluation explained: first-visit vs every-visit Monte Carlo, a 3-state worked example, and the unbiased but high-variance trade with TD.

A study brief for the Markov Decision Process lesson: what you will learn, where it fits in the track, prerequisites, and a note on the notation ahead.

An orientation to function approximation and deep RL: why tables fail, the linear-Q gradient step, the deadly triad, and how DQN's fixes stabilize learning.

Preview of the multimodal AI opener: learning outcomes, prerequisites, where the lesson fits, and the read and practice time to expect.

Diffusion image generation, in brief: learning outcomes, prerequisites, where DiT and MM-DiT fit, and read and practice time.

Brief on transformer video generation: what spacetime patches change, the two compression strategies, the captioned-data constraint, and failure modes.

Brief for the multimodal reasoning lesson: learning outcomes, prerequisites, scope, and the four-layer stack of perception, reasoning, tools, and alignment.

Preview of the native multimodal lesson: learning outcomes, prerequisites, and time needed to grasp how one transformer trains on all modalities at once.

What this lesson covers: applying the multimodal world model framing to drug discovery, plus how to tell ML benchmark claims from clinical claims.

Orientation for the JEPA lesson: what you will learn, prerequisites, how JEPA's embedding-space objection to generative pretraining sets up world modeling.

Overview of the encode-then-fuse vision-language recipe: the three pieces, CogVLM's visual expert, two-stage training, prerequisites, and time to read.

A survey brief mapping six directions the LLM field is moving, with learning outcomes, prerequisites, and how three of them set up the deeper lessons ahead.

The five core LUI UX patterns (streaming, citations, regeneration, hedging, recoverable failure), where the lesson fits, prerequisites, and supporting details.

Overview of when and how to fine-tune your own LLM: the three-part decision test, the staged pipeline, the tools, the economics, and learning outcomes.

Orientation for the prompt-engineering lesson: the toolkit, prompt-fix vs code-fix triage, the versioning-and-testing discipline, and where prompts run out.

Preview of reading askFSDL, a real RAG Q&A app, end to end: the production decisions at each pipeline stage and a checklist you can reuse on any app.

A guided tour of LLMOps: the five pillars, a per-request log schema, evaluation in production, prompt versioning, and regression testing for safe changes.

A working picture of a hosted LLM: what it is at the API level, the generation loop, and the three limits (context, cost, latency) you design against.

What this opening lesson covers: the five parts of a minimal LLM app, prerequisites, where it fits in the track, and the skills you will walk away with.

Brief overview of the capstone: the track arc, three rules for reading an industry fireside, five durable bets, and three reader moves to act on next.

A roadmap to the augmented-LLM lesson covering RAG and tool use: what you will learn, where it fits, the prerequisites, and the read and practice time.

A primer on LLM agents: the loop, the three foundational patterns, the three tests for when to use one, the five failure modes, and how to operate one.

Orientation for the generative models track: what generative means, the four paradigms, prerequisites, and the math and time each lesson will ask of you.