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wtf is jobs?

karpathy/jobs — explained in plain English

Analysis updated 2026-07-17 · repo last pushed 2026-03-16

★ 1,834HTMLAudience · researcherComplexity · 2/5MaintainedSetup · easy

TL;DR

An interactive treemap that visualizes all 342 US job categories from government labor data, colored by growth, salary, education, or estimated AI exposure.

Mindmap

mindmap
  root((repo))
    What it does
      Visualize US job data
      Score AI exposure
      Color by metric
    Tech stack
      HTML
      Data pipeline
      LLM scoring
    Use cases
      Explore job growth
      Compare salary and education
      See AI exposure by job
    Audience
      Researchers
      Curious explorers
      Policy analysts

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

Why would anyone build with this?

REASON 1

Explore the interactive treemap to find fast-growing jobs that only require a high school diploma.

REASON 2

Compare occupations by median salary versus estimated AI exposure to spot high-pay, low-exposure roles.

REASON 3

Use the underlying JSON dataset of 342 BLS occupations for your own labor-market research or visualization.

REASON 4

Study the data pipeline as a template for scraping a government website and scoring text with an LLM.

What's in the stack?

HTMLJavaScriptPython

How it stacks up

karpathy/jobsop7418/guizang-social-card-skilln8n-io/n8n-docs
Stars1,8341,7631,635
LanguageHTMLHTMLHTML
Last pushed2026-03-16——
MaintenanceMaintained——
Setup difficultyeasyeasymoderate
Complexity2/52/52/5
Audienceresearchervibe coderpm founder

Figures from each repo's GitHub metadata at analysis time.

How do you spin it up?

Difficulty · easy Time to first run · 5min

The visualization is a static site, no setup needed to explore it, regenerating the JSON requires running the scrape and LLM-scoring pipeline.

No license information was mentioned in the explanation.

Yoink these prompts

Prompt 1
Using the data behind karpathy/jobs, help me find the occupations with the highest median salary but lowest AI exposure score.
Prompt 2
Explain how this repo's pipeline scrapes the BLS Occupational Outlook Handbook and turns it into the structured JSON used by the treemap.
Prompt 3
Show me how the LLM scoring step in this repo assigns an AI exposure score to an occupation description, and what the reasoning output looks like.
Prompt 4
Help me extend this repo's treemap to add a new color-by option based on job growth rate instead of salary.

Frequently asked questions

wtf is jobs?

An interactive treemap that visualizes all 342 US job categories from government labor data, colored by growth, salary, education, or estimated AI exposure.

What language is jobs written in?

Mainly HTML. The stack also includes HTML, JavaScript, Python.

Is jobs actively maintained?

Maintained — commit in last 6 months (last push 2026-03-16).

What license does jobs use?

No license information was mentioned in the explanation.

How hard is jobs to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is jobs for?

Mainly researcher.

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