OpenStax-LLM: tools for OpenStax LLM access
openstax-llm π§ π Pedagogical semantic chunking, RAG dataset preparation, and LLM fine-tuning pipelines from OpenStax textbooks. Built on top of openstax-md, openstax-llm transforms OpenStax college textbooks into structured, citation-aware, formula-safe datasets for vector search (RAG) and model fine-tuning. This repository ships three things that share one core: Artifact What it is Entry point openstax-llm Python library and CLI openstax-llm openstax-llm-mcp Model Context Protocol server openstax-llm-mcp skills/openstax-llm Agent skill for coding assistants /skill:openstax-llm β‘ Why openstax-llm?
- βͺopenstax-llm π§ π Pedagogical semantic chunking, RAG dataset preparation, and LLM fine-tuning pipelines from OpenStax textbooks.
- βͺBuilt on top of openstax-md, openstax-llm transforms OpenStax college textbooks into structured, citation-aware, formula-safe datasets for vector search (RAG) and model fine-tuning.
- βͺThis repository ships three things that share one core: Artifact What it is Entry point openstax-llm Python library and CLI openstax-llm openstax-llm-mcp Model Context Protocol server openstax-llm-mcp skills/openstax-llm Agent skill for cod
Hacker News (AI / LLM) files mainly under ai. We currently carry 7,368 of its stories.
Story provenance
Source Β· retrieval Β· rights Β· ranking β open for full record
inspect β
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/michaelnavazhylau/openstax-llm |
| Publication time | Fri, 02 Oct 2026 20:03:08 +0000 |
| Retrieval time | 2026-10-02T20:06:16.290Z |
| Last seen | 2026-10-02T20:06:16.290Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch Β· cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 40-XwiheQlu5 Β· 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes β open original |
| Substitutes article? | No β link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
openstax-llm π§ π Pedagogical semantic chunking, RAG dataset preparation, and LLM fine-tuning pipelines from OpenStax textbooks. Built on top of openstax-md, openstax-llm transforms OpenStax college textbooks into structured, citation-aware, formula-safe datasets for vector search (RAG) and model fine-tuning. This repository ships three things that share one core: Artifact What it is Entry point openstax-llm Python library and CLI openstax-llm openstax-llm-mcp Model Context Protocol server openstax-llm-mcp skills/openstax-llm Agent skill for coding assistants /skill:openstax-llm β‘ Why openstax-llm? Generic chunkers (simple character or recursive token splitters) break down on technical academic textbooks: They cut mathematical formulas in half ($x^2 + \dots$ split from \dots + y^2$).
β¦
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.