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The most methodologically rigorous, fully transparent AI job-exposure index.

History of apps/worker Clear filter
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  1. Wed, Aug 5, 2026 6

    1. simon-pierre boucher · 1 file +25 −12
    2. chore: add file headers (author, contact, copyright, description) to all source files
      …
      Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
      simon-pierre boucher · 8 files +72
    3. fix(worker): tolerate truncated/invalid batch results; raise rater max_tokens
      …
      Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
      simon-pierre boucher · 1 file +15 −2
    4. feat(worker): parallel per-model rating batches with resume via state file
      …
      Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
      simon-pierre boucher · 1 file +32 −8
    5. feat: full O*NET universe — real ETL, deep detail pages, search, browse, rating scripts
      …
      - etl: OEWS wage download, O*NET 30.3 transform (occupations/tasks/importance),
        wages transform, psycopg loader; 1,016 occupations + 18,796 tasks loaded
      - web: live search, /occupations browse grouped by SOC major group, home with
        hero stats + top/least exposed + group averages; detail page adds wages,
        employment, rank/percentile, weighted dimension breakdown, full per-task
        rater audit trail (model/rating/rationale), related occupations
      - worker: one-shot rate script (batch submit/poll/ingest, --limit pilot mode),
        purge-demo script, thinking disabled for the rater
      - layout: preview banner now driven by whether the latest run is demo-based
      
      Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
      simon-pierre boucher · 4 files +126 −1
    6. feat: scaffold AI Risk Index platform (research corpus, methodology v1 draft, monorepo)
      …
      - docs/research: 4-document research corpus grounding v1 (indices, data
        sources, rater API, landscape/evidence), compiled 2026-08-05
      - docs/methodology: METHODOLOGY.md v1 draft, changelog, worked example
      - packages/scoring: pure deterministic engine (5 weighted dimensions,
        barriers inverted, exposure/substitution/augmentation sub-scores with
        CI bounds), snapshot + fast-check property tests
      - packages/db: Prisma schema with rating audit trail and immutable runs
      - apps/web: Next.js 14 site + public API v1 (health, methodology,
        occupations)
      - apps/worker: BullMQ multi-model rater pipeline (Message Batches,
        versioned rubric prompt v1) + score:recompute
      - apps/etl: Python skeleton with O*NET 30.3 downloader
      - infra: compose files, deploy.sh, ngrok.yml, env template
      
      Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
      simon-pierre boucher · 9 files +515
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