Bring any course into the AI tools you already use. Highlight, summarize, and customize lessons — rooted in reputable sources, connected to Claude, Codex, Cursor, and Gemini through MCP.
Free for learners. Free for teachers. No credit card.
Trapped in one publisher's app, one LMS, one URL.
Textbook + courseware bundles north of $300 per class.
Print cycles can't keep up with the field.
Bolted-on chatbots that don't know what you're reading.
Teachers can't customize, learners can't reshape.
LearnOS ingests course material from wherever you have it, then exposes it through MCP so the AI tools you already use — Claude, Codex, Cursor, Gemini — can read, summarize, quiz, and rewrite it on your terms.
Point LearnOS at any markdown-rendered textbook URL, or import a local NN-module/NN-lesson tree. We crawl, chunk by heading, and embed every section for retrieval — same the moment ingest returns.
github.com/stewart/calc-textbookEvery lesson renders as long-form prose — KaTeX-formatted math, Mermaid diagrams, syntax-highlighted code. Highlight, take notes, and chat with the lesson without leaving the page.
Select any passage. Highlights persist per-user and feed quick actions (summarize · flashcard · ask) right on the spot.
Every assistant message resolves cite:LESSON#ANCHOR markers into clickable chips that open the exact heading section.
One-click comprehensive audio overview of a lesson or module — a single AI narrator covers the whole arc. Streams while you read, or queues up for the gym.
Generate SM-2-scheduled decks from a lesson, a highlight, or a custom prompt — reviewed in /today.
Hover the badges on the screenshot to see each piece in detail.
For a function f, the derivative at a point a is defined as the limit of (f(a+h) − f(a)) / h as h approaches zero.
Geometrically, this is the slope of the tangent line at a — the best linear approximation to the curve at that point.
When the limit exists, we say f is differentiable at a.
Paste a URL, drop a markdown directory, point at a YouTube playlist. LearnOS chunks the content by heading, embeds with pgvector, and keyword + semantic search are live the moment ingest returns.
A single AI narrator walks through the entire lesson — citation-aware, comprehensive enough to stand on its own. Generated server-side, streamed in the background while you read, queued up for the gym.
Scope a question to one lesson, a module, or your whole library. Hybrid retrieval (pgvector + tsvector RRF) ranks the relevant sections; every answer cites the exact heading anchor it pulled from.
Select any passage, pick the Flashcard action, and a card lands in your deck — scheduled with SM-2, queued in /today. Generate a whole deck from a lesson in one tap.
A function is continuous at a point when the limit equals the value.
Everything you need to read, retain, and apply — built for serious self-directed learners. No paywalls. No vendor lock-in.
Generate a comprehensive audio overview of any lesson or module — one AI narrator, citation-aware, covers the full arc. Streams in the background while you read or commute.
Ask a question scoped to a lesson, a module, a course, or your whole library. Every answer cites the exact heading section it pulled from.
One-tap card generation from a lesson or highlight, scheduled with SM-2-lite. Review the queue in /today with cards bundled by course.
Markdown in. Citations checked. Quality scored. Push to git and your students see the update on next page load.
Every lesson is a README.md you can edit with any tool. Frontmatter sets the slot — `module`, `lesson`, `prereqs`, `objectives`. Push to git and the course rebuilds on import.
---
title: The derivative as a limit
module: 02-calculus-foundations
lesson: 04-derivative-limit
prereqs: [limits, function-notation]
objectives:
- Define the derivative as a limit
- Connect to tangent line geometry
---Claude Code, Codex, Gemini CLI, Cursor — point any of them at your course repo and let them draft modules, run a citation-verifier pass, regenerate flashcards.
Generate a structured quality report on any course: source citation density, prerequisite chain integrity, exercise coverage. Publish gates use the rubric so the catalog stays high-signal.
LearnOS exposes search, lessons, transcripts, flashcards, and notes over MCP. Drop a token into any compatible agent and your courses are right there.
The textbook hasn’t changed in fifty years. The LMS hasn’t changed in twenty. The AI tools that are changing — Claude, Codex, Cursor — can’t see what you’re trying to learn.
LearnOS is the missing layer: durable courses, ingested from wherever you already have them, with AI retrieval and MCP integration as table stakes — not a $20/month upsell.
We don't host content. We point at the best material that already exists — textbooks, lectures, open-source curricula — and add the layer that makes it studyable, citable, and yours.
Chat, summaries, flashcards, citation-grounded retrieval — all on by default, everywhere, for every learner. Not a Pro tier. Not a chatbot in a corner.
MCP-first. Your courses belong inside Claude, Codex, Cursor, Gemini — wherever you already work. We don't compete with the AI tools you love; we make them better.
When you're studying, the citation is the point. We dig into how LearnOS combines pgvector + tsvector RRF to make sure every assistant message resolves to a verifiable source.
The Model Context Protocol turns any LLM into a course-aware assistant — without the publisher lock-in that killed every previous interoperability effort.
We dispatched Claude Code subagents (lesson-writer + citation-verifier + source-scout) to draft a Calculus I course. Here’s what worked, what didn’t, and the SKILL definition we’d ship next.
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