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Learn summit 2023 talks

Skill hiteshbandhu/skills-i-use/skills/ai-engineer-talks/learn-summit-2023-talks

Applies AI Engineer Summit 2023 main-stage playbooks for RAG, agents, interfaces, local LLMs, fine-tuning, product velocity, TypeChat/Pydantic, LangChain observability, and the discipline of AI engineering (Swyx, Willison). Use when grounding team practice in summit talks, shipping RAG/agents, or onboarding to AI engineering; or when the user says "summit 2023 talks", "1000x AI engineer", "production RAG Jerry Liu", "open questions Willison".From its SKILL.md

Install

npx -y skills add hiteshbandhu/skills-i-use --skill learn-summit-2023-talks

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
runs commandsInstructs the agent to run 2 commands, including `cp -r skills/learn-summit-2023-talks ~/.claude/skills/` and 1 more.

SKILL.md

3.1 KB, 723 tokens by cl100k_base, as published. Nobody here has run it

Learn Summit 2023 talks

Action playbook from twenty-two AI Engineer Summit 2023 talks. Do not summarize talks — pick a workflow and execute it.

Supporting files:

workflows.md — workflows A–N
source-index.md — src-NNN → learnings

Optional deliverables: {SKILL_OUTPUT_DIR}/learn-summit-2023-talks/

Step 0 — Pick workflow

What is the user trying to do?
├─ Define AI engineering career / 1000x leverage           → A  [src-018, src-002, src-022]
├─ Production RAG + vectors + wild deployments             → B  [src-006, src-015, src-017]
├─ Agents + context-aware reasoning                      → C  [src-019, src-005]
├─ Domain fine-tuning / adaptation                       → D  [src-009]
├─ Local / private LLM deployment                        → E  [src-010]
├─ Viral AI product / growth lessons                     → F  [src-001, src-011, src-003]
├─ LLM system building blocks (product)                  → G  [src-004]
├─ Interfaces & abstraction ladder                     → H  [src-008, src-021]
├─ Type-safe LLM I/O (Pydantic / TypeChat)               → I  [src-014, src-013]
├─ Data platform AI pivot                                → J  [src-020]
├─ Multimodal APIs (see/hear/speak/draw)                 → K  [src-016]
├─ Reactive / streaming AI apps                          → L  [src-007]
├─ Open questions & yearly retrospectives                → M  [src-012]
└─ Move fast without breaking prod                       → N  [src-011]

Open workflows.md for steps and deliverables.

Install

cp -r skills/learn-summit-2023-talks ~/.claude/skills/
cp -r skills/learn-summit-2023-talks ~/.cursor/skills/

Source: playlists/ai-engineer-summit-2023-talks/.

Cross-cutting rules

Rule	Source
AI engineering ≠ prompting; systems wrap non-AGI models	[src-018 @ 5:53]
Naive RAG fails in production — plan retrieval + eval	[src-006 @ 2:58]
Ask: impossible new builds vs faster builds (Willison)	[src-012 @ 1:01]
Structured outputs reduce integration risk	[src-014], [src-013]
Agents need product-grade interfaces, not chat-only	[src-019], [src-021]

Output to user

Name workflow (A–N) and artifact
Save under ./skill-outputs/learn-summit-2023-talks/ when requested

Invocation examples

@learn-summit-2023-talks production RAG checklist from Jerry Liu
framework for open questions in our AI roadmap
pydantic vs typechat for our API layer

What ships with it: 3 files

9.6 KB alongside SKILL.md

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.