── any source, one personalized program

Any source. A personalized program in minutes.

A textbook, a role you're chasing, or a program someone already built. Turn it into a course tuned to what you know — and take it into any AI agent.

from a book

Turn any material into a course

Drop a textbook, paper, or your own notes. We map it to a knowledge graph and skip what you know.

from a job

Learn exactly what a role needs

Paste a posting — we reverse-engineer the skills and build the path.

from a program

Start one that already works

Pick a program someone built, learn it as-is, or fork it and make it yours.

AI Engineering Programme

Nebular

learn

Database Design

Nebular

learn

A Byte of Python

Nebular

learn

Browse programs

Works with any AI agent — Claude, GPT, Gemini, Cursor, OpenCode.

── How it works

From your materials, to any agent.

Feed in what you've got. Nebular distills it into one living learner model — which any agent can read from and write back to.

What you feed in

materials

Textbook

Intro to ML · 412 pp

Lecture notes

CS229 · 14 weeks

Syllabus

Stanford · Spring

Your goal

“ship an agent”

The craft

learner knowledge graph

Learner knowledge graph

alex@stanford · 247 nodes

MasteredIn progressGapUnexplored

ObjectivesPrerequisitesAssessmentsPedagogy

Any agent

learn anywhere

◆Claude

◇GPT-4

✦Gemini

⌘Cursor

Alex · one learner

Same memory across every agent

── Three ways in

Whether you learn, teach, or hack your own path.

Nebular meets you where you are. Pick a starting point — the learner model is the same underneath.

◐For learners

Learn anything, with any agent.

Pick a program, connect your agent of choice, and study at your level. Your progress is remembered across sessions and tools.

▸Browse 240+ community programs
▸Connect Claude, GPT, Gemini, or Cursor
▸Adaptive practice tuned to your gaps

Browse programs →Or upload your own materials

◆For creators & educators

Turn your knowledge into a learning program.

Drop in a textbook, lecture notes, or a syllabus. Creator Studio extracts objectives, prerequisites, and assessments — ready to share with the world.

▸Auto-generate skill graphs from sources
▸Edit objectives, prereqs, pedagogy
▸Publish or remix community programs

Open Creator Studio →See an example program

⌘For power users & explorers

Build your own learning journey.

Mix three textbooks, a paper, and a podcast into a single path toward whatever you want to master. No curriculum required — just a goal and the agents you trust.

▸Define your own goal and trajectory
▸Remix sources across domains
▸Track mastery in one persistent graph

Start a custom path →See power-user examples

── What you know

See your knowledge take shape.

Every concept you learn becomes part of your personal knowledge map. Strong areas glow. Gaps fade. Misconceptions are flagged, so you can see not just what you missed — but why.

247

Concepts mapped

Mastered

Gaps to fix

Open your knowledge map →

Alex's knowledge map

Machine Learning · Updated after your last session

live

Misconception detected

You understand what gradient descent does, but not yet why the step size matters.

Next best step

Review Gradient descent before Backprop.

● Mastered● Learning now● Needs review● Misconception● Not started

── What you can do with it

One learner model. Every agent stays in sync.

Connect any AI agent — Claude, ChatGPT, Gemini, Claude Code, Cursor — to your Nebular learner model over MCP. Each agent reads the same mastery state, picks up where the last one left off, and records evidence back as you work.

Conversational agentClaude / ChatGPT / Gemini

→ Connected via MCP

Quick check before we go further. You're standing on a steep slope of the loss surface. You double the step size. What happens to the loss after one step?

It should drop faster — bigger step, faster descent.

That's the misconception I want to fix. The step doesn't scale with how fast loss drops — it scales with curvature. Let me build you a one-screen interactive so you can see it overshoot.

Building skills/gradient-descent/intuition/step-size.html

>_Claude Code (local workspace)MCP + .nebular/

→ Pedagogy rules shipped as files in your repo

$ curl -fsSL nebular.live/setup/dynamic-programming | bash
✓ wrote .nebular/AGENT.md (pedagogy contract)
✓ wrote .nebular/PROGRAMME.md (curriculum + skills)
✓ wrote .nebular/LEARNER.md (empty — fill on interview)
✓ registered MCP server nebular-mind
 
$ claude
Read .nebular/AGENT.md, .nebular/PROGRAMME.md, .nebular/LEARNER.md. Running interview first, then session_bootstrap.
Lessons are interactive HTML artifacts under skills/<slug>/, not chat.
 
$ 

Nebular learner model→Agent context→Lesson + evidence←Evidence back

MCP calls · session #1184 context evidence

✓session_bootstrap:recommended: state designconversational

✓lesson_start:essential: what is the state?conversational

✓skill_neighborhood:prereq: recursion (mastered)claude code

✓evidence_record:state_design · partialconversational

✓evidence_record:memoisation · weakclaude code

✓session_end:summary writtenclaude code

The interface can change. The learner model stays persistent.

Nebular

Built by the Nebular team

"Blend of mathematics, engineering and AI"

Newest programme

AI Engineering Programme

Blend of mathematics, engineering and AI

NPublished 6/12/2026

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Database Design

Based on: Database Design - 2nd Edition covers database systems and database design concepts. New to this edition are SQL info, additional examples, key terms and review exercises at the end of each chapter.

software engineeringdatabases

Nebular

Built by the Nebular team

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