karpathy/jobs — BLS Job Market Visualizer Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection. A research tool for visually exploring Bureau of Labor Statistics [Occupational Outlook Handbook](https://www.bls.gov/ooh/) data across 342 occupations. The interactive treemap colors rectangles by employment size (area) and any chosen metric (color): BLS growth outlook, median pay, education requirements, or LLM scored AI exposure. The pipeline is fully forkable — write a new prompt, re run scoring, get a new color layer. Live demo: [karpathy.ai/jobs](https://karpathy.ai/jobs/) Installation & Setup Create a .env file with your OpenRouter API key (required only for LLM scoring): Full Pipeline — Key Commands Run these in order for a complete fresh build: Key Files Reference File Description occupations.json Master list of 342 occupations (title, URL, category, slug) occupations.csv Summary stats: pay, education, job count, growth projections scores.json AI exposure scores (0–10) + rationales for all 342 occupations prompt.md All data in one ~45K token file for pasting into an LLM html/ Raw HTML pages from BLS (~40MB, source of truth) pages/ Clean Markdown versions of each occupation page site/index.html The treemap visualization (single HTML file) site/data.json Compact merged data consumed by the frontend score.py LLM scoring pipeline — fork this to write custom prompts Writing a Custom LLM Scoring Layer The most powerful feature: write any scoring prompt, run score.py , get a new treemap color layer. 1. Edit the prompt in score.py 2. Run the scoring pipeline 3. Rebuild site data Data Structures occupations.json entry occupations.csv columns Example row: site/data.json entry (merged frontend data) Frontend Treemap ( site/index.html ) The visualization is a single self contained HTML file using D3.js. Color layers (toggle in UI) Layer What it shows BLS Outlook BLS projected growth category (green = fast growth) Median Pay Annual median wage (color gradient) Education Minimum education required Digital AI Exposure LLM scored 0–10 AI impact estimate Adding a new color layer to the frontend Then update build site data.py to include your new score field in data.json . Generating the LLM Ready Prompt File Package all 342 occupations + aggregate stats into a single file for LLM chat: Scraping Notes The BLS blocks automated bots, so scrape.py uses non headless Playwright (real visible browser window): If scraping fails or is rate limited: The html/ directory already contains cached pages in the repo You can skip scraping entirely and run from process.py onward If re scraping, add delays between requests to avoid blocks Common Patterns Re score only missing occupations Parse a single occupation page manually Load and query occupations.csv Combine CSV with AI scores for analysis Troubleshooting playwright install fails BLS scraping blocked / returns empty pages Ensure headless=False in scrape.py (already the default) Add manual delays; do not run in CI The cached html/ directory in the repo can be used directly score.py OpenRouter errors Verify OPENROUTER API KEY is set in .env Check your OpenRouter account has credits Default model is Gemini Flash — change model in score.py for a different LLM site/data.json not updating after re scoring Treemap shows blank / no data Confirm site/data.json exists and is valid JSON Serve with python m http.server (not file:// — CORS blocks local JSON fetch) Check browser console for fetch errors Important Caveats (from the project) AI Exposure ≠ job disappearance. A score of 9/10 means AI is transforming the work, not eliminating demand. Software developers score 9/10 but demand is growing. Scores are rough LLM estimates (Gemini Flash via OpenRouter), not rigorous economic predictions. The tool does not account for demand elasticity, latent demand, regulatory barriers, or social preferences for human workers. This is a development/research tool , not an economic publication.

karpathy-jobs-bls-visualizer