Clawdemy Lessons

A roadmap to the pull request workflow: the mechanical PR loop, description structure, merge strategies, review etiquette, and eight anti-patterns.

A reading map of the multi-agent integration lesson: the three patterns, the lead's role, and the semantic-conflict failure mode git cannot detect.

Overview of the merge conflict lesson: five core ideas, prerequisites, a three-pass reading map, and what the lesson covers and skips.

A quick orientation to commit hygiene: what belongs in a message, why commits stay atomic, and when teams adopt Conventional Commits.

Brief on cherry-pick and stash: what the lesson covers, learning outcomes, prerequisites, the reading map, and the scope boundaries it deliberately skips.

A learning brief on git branches: what a branch is, why branches are cheap, how HEAD moves, and when to branch versus commit directly.

Overview of the lesson on AI-authored commits and PRs: scope, learning outcomes, reading map, and prerequisites for attributing and reviewing AI code.

Overview of the Clawless memory system: history versus memory, the four tiers, three ways memories get in, the panel controls, and the privacy rule.

Overview of the first hands-on Clawless lesson: the four screen zones, the model picker, mid-conversation switching, and what you need before you start.

What CostGuard and the privacy lesson cover: a spending safety net for BYOK usage against a monthly cap, and a data path with no Clawless server in it.

A roadmap to the API keys lesson: what a key is, the BYOK model with no Clawless markup, where keys live, the Codex OAuth path, and why keys turn red.

Overview of the generative-modeling synthesis: the four paradigms recapped, systems placed on the map, and the paradigm-fluency procedure for reading releases.

What to expect from the SDE diffusion lesson: prerequisites, outcomes, time, and how score matching, DDPM, and DDIM unify into one continuous-time framework.

Orientation for the production-grade diffusion lesson: DDIM accelerated sampling, classifier-free guidance, and the latency-quality Pareto frontier.

Editorial brief for the offline RL problem lesson: the fixed-dataset setting, the extrapolation-error failure mode, and the BCQ, CQL, IQL fixes to come.

Editorial overview of the offline RL algorithms lesson: how BCQ, CQL, and IQL fix the divergence problem, plus the decision rubric and BC sanity check.

Editorial brief on multi-task RL versus meta-RL, the three meta-RL families (MAML, RL2, PEARL), and the foundation-model parallel to academic meta-learning.

Editorial brief for the exploration lesson: three strategy families, the easy-vs-hard distinction as the main decision criterion, and the RND breakthrough.

Editorial brief outlining deep RL's four open frontiers, how each maps to the core algorithms, the tensions among them, and the syllabus recap.

What you will learn about Subagents (definition, four benefits, fields, creation paths) and Claude Managed Agents, plus the decision frame between them.

A brief on shipping a Claude app to production: the five disciplines, the Usage and Cost Admin API, and the rollout checklist as a deploy gate.

A quick orientation to the alignment problem: three failure modes (specification, proxy, deceptive) and why robustness and monitoring do not solve it.

Orientation to the safety-engineering lesson: the three transferable tools (nines of reliability, eight safe-design principles, tail events) and prerequisites.

Overview of the deployment-time safety lesson: what robustness and monitoring failures are, their six sub-mechanisms, and the classify-and-defend protocol.

Brief on Hendrycks' four AI risk buckets, the sub-mechanisms and historical analogies inside each, and the classify-and-defend three-step move.