karpathy

karpathy / jobs

Public

Analyzing how susceptible every occupation in the US economy is to AI and automation, using data from the Bureau of Labor Statistics

154
23
100% credibility
Found Mar 15, 2026 at 146 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

This project scrapes data on 342 US occupations from the Bureau of Labor Statistics, uses an AI model to score their exposure to AI disruption, and generates an interactive treemap visualization.

How It Works

1
🌐 Discover the Job Map

You find an exciting interactive map showing how AI might change every job in the US economy.

2
🗺️ Explore AI Job Risks

Hover over colorful boxes sized by job numbers to see pay, growth, education needs, and AI exposure from safe green to risky red.

3
📥 Grab the Free Guide

Download the simple project files to create your own fresh version of the map.

4
📊 Collect Job Details

Automatically gather the latest facts on 342 occupations like salaries, job counts, and future outlooks from the official government handbook.

5
🤖 AI Scores the Risks

Smart AI reads each job's full description and gives a 0-10 score on how much AI will reshape it, with reasons why.

6
🎨 Build Your Map

Mix the job stats and AI scores into a beautiful interactive treemap you can view in your browser.

📈 See the Future of Work

Zoom into sectors, spot safe hands-on jobs or high-risk desk ones, and share insights on AI's big impact.

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AI-Generated Review

What is jobs?

This Python project scrapes the US Bureau of Labor Statistics Occupational Outlook Handbook for 342 occupations, extracts stats like pay and job growth into CSV, scores each for AI exposure (0-10) using an LLM like Gemini via OpenRouter, and generates an interactive HTML treemap visualization. Area scales by employment size, color by exposure risk—green for safe manual jobs, red for digital ones like developers. You get raw data files, a ready-to-serve static site, and a massive prompt file for feeding into any LLM to analyze AI's job market impact, like github jobs depends on or outputs.

Why is it gaining traction?

Karpathy's name draws eyes to this timely take on AI disruption, but the hook is the turnkey pipeline: run a few scripts with Playwright and uv, plug in an API key, and own structured BLS data plus LLM rationales—no reinventing scraping or scoring. The treemap instantly reveals patterns, like high-exposure clusters in knowledge work, beating scattered spreadsheets or manual LLM chats. Devs love the prompt.md for quick AI-for-analyzing-github-repo style queries on labor trends.

Who should use this?

AI researchers modeling job displacement, economists studying automation like analyzing how AI and emotional intelligence affect Indian IT professionals' decision making, or career coaches eyeing BLS outlooks. Devs building tools for github jobs germany or permissions analysis can fork the data pipeline; policymakers or journalists need the viz for reports on visual elements contributing to AI exposure narratives.

Verdict

Solid proof-of-concept for AI job analysis at 65 stars—docs are clear, pipeline reproducible, but 1.0% credibility score flags its early, niche maturity with no tests. Grab it if you're prototyping labor market dashboards; otherwise, watch for updates.

(198 words)

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