samwit/llm-tutorials resource
A set of LLM Tutorials from my youtube channel observed · 2026-08-28
Health v2 · maintenance only
29/100
- Activity 0
- Release rhythm 35
- Longevity 84
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1177
- days_rel: n/a
- days_push: 1177
- n_releases_24m: 0
Adoption not part of the score
1163 stars · 301 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A collection of Jupyter Notebook tutorials accompanying Sam Witteveen's YouTube channel on large language models. It covers hands-on examples of working with LLMs, including prompting, agents, and related tooling.
Use cases
- learn how to build LLM applications
- follow along with LLM youtube tutorials in notebooks
- examples of langchain and LLM agents
- getting started with prompt engineering
- study notebooks for RAG with LLMs
When to choose
- you prefer learning by reading and running notebook code
- you want free tutorial material matching a YouTube course
- you are exploring LLM tooling like agents and prompting
When to avoid
- you need production-ready, maintained library code
- you require a licensed or supported codebase
- you need up-to-date content for rapidly changed LLM APIs
Facets
learning-resource · maturity maintenance
llm-inference prompt-engineering rag agent-framework large-language-models tutorials artificial-intelligence developer-tools python cross-platform jupyter-notebooks youtube-tutorials langchain openai educational
1 source
- readme: https://github.com/samwit/llm-tutorials · fetched 2026-08-28 · 57abd955144d
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| samwit/llm-tutorials | main | 29 |
For agents
markdown · JSON · MCP: product_card(name="samwit/llm-tutorials")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem