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arosstale / jobs

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

28
99
100% credibility
Found Mar 15, 2026 at 28 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

Scripts that scrape US Bureau of Labor Statistics occupation data, parse it into summaries, score AI exposure using a language model, and generate an interactive treemap visualization.

How It Works

1
🔍 Discover the job explorer

You stumble upon a cool online tool showing how AI might affect every job in America, grouped in a colorful map.

2
💡 Get curious to try it yourself

Inspired, you decide to build your own version to dive deeper into the job data and scores.

3
📥 Gather job stories

You collect detailed stories about 342 real jobs from the government's job handbook, saving them safely on your computer.

4
✂️ Tidy up the details

You clean and organize the job descriptions into easy-to-read summaries with key facts like pay and growth.

5
🧠 Let AI score the risks

You connect a smart AI helper to read each job story and rate its vulnerability to AI changes on a 0-10 scale.

6
🖼️ Build your visual map

You combine the job facts and AI scores to create a zoomable treemap where job size shows employment and color shows risk.

🎉 Explore your insights

Open your personal job map to hover over careers, see safe green zones for hands-on work and red alerts for desk jobs, and understand the future of work.

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AI-Generated Review

What is jobs?

This Python tool scrapes the US Bureau of Labor Statistics Occupational Outlook Handbook, covering all 342 major occupations, to extract job stats like pay, employment projections, and duties. It then uses an LLM via OpenRouter to score each role's exposure to AI and automation on a 0-10 scale, factoring in digital vs. physical work. Output includes clean CSV/JSON datafiles, Markdown summaries, and a static interactive treemap visualization where bubble size shows job volume and color grades AI risk.

Why is it gaining traction?

Unlike generic scrapers, it delivers ready-to-use AI exposure scores with rationales, plus a polished treemap demo live at karpathy.ai/jobs—perfect for quick job market analysis. Developers dig the end-to-end pipeline from scrape to viz, bypassing manual data wrangling, and the calibrated scoring anchors like "software devs at 9/10" make AI impact tangible without building from scratch.

Who should use this?

Labor economists modeling AI disruption, career coaches assessing safe sectors like plumbing (low exposure), or data journalists building dashboards on US job trends. It's ideal for devs prototyping AI-for-analyzing-github-repo tools or studying how AI affects roles in github jobs germany.

Verdict

Solid for one-off analysis with excellent README and CLI workflow, but at 28 stars and 1.0% credibility score, it's early-stage—expect tweaks for production scale. Fork and extend if you're analyzing how AI reshapes occupations.

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