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jev-workshop

fnusatvik07/jev-workshop

Hands-on workshop teaching Jev, the decision model, via notebooks and deck.

Automated README summary

Automatically summarized from the README on 18 Oct 2026; not a run by jevbooks. Run instructions and author-reported outcomes still need verification.

What it is

A workshop repo for developers and instructors: five live-executed Jupyter notebooks, a 48-slide deck with speaker notes, a cited reference, walkthrough apps, and live-build projects teaching Jev, TypeSafe AI's System One model that returns decisions instead of text.

How it uses Jev

Jev takes a state (text or JSON) plus typed questions and returns typed answers with calibrated probabilities. Notebooks and apps use Choice, Score, Noul and Box answers to branch in plain Python: routing, gating, triage, screening, and reranking decisions, with thresholds and actions owned by code.

Primitives:choicescorenoul

Technique worth stealing

Ask typed questions over a state and branch on calibrated probabilities in code, keeping thresholds and actions in plain Python rather than in prompts.

Try it

uv sync, export TYPESAFE_API_KEY, then uv run jupyter lab notebooks/ (outputs are pre-saved).

View on GitHub

Start using Jev: API key and your first request →

Model judgments from README excerpts

Project signals use repository metadata, an existing summary and the first 700 README characters, not the full project. “Not found” means not detected in that material; it does not establish absence. These are the original judgments with recorded models and dates, not source audits or run results.

Models & evaluation dates

Inclusion gate
jev-1.13.0
Project signals
jev-1.13.0
Category
jev-1.13.0
Pattern tags
jev-1.13.0

Evidence

These signals use repository metadata, an existing summary and at most 700 cleaned README characters. ≥ 0.60 means reported in that excerpt; ≤ 0.40 means not found there. The middle band is inconclusive. None is a source-code audit or a test result. The runnable-tool question excludes research experiments; the recommendation question asks for a learning shortlist, not a quality verdict.

  • Jev is core to the projectFound in material0.96
  • Shows a System One patternnot found in the excerpt0.36
  • Describes handling uncertaintynot found in the excerpt0.06
  • Reports measured numbersnot found in the excerpt0.14
  • Is a runnable toolnot found in the excerpt0.12
  • Top-10 learning shortlist signalFound in material0.72
  • Is a replica of the modelnot found in the excerpt0.03
  • Solves a common problem (0–2)score on a 0–2 scale1.11 / 2
  • About JevFound in material0.99
Signals
Jev is core to the project0.96Shows a System One pattern0.36Describes handling uncertainty0.06Reports measured numbers0.14Is a runnable tool0.12Top-10 learning shortlist signal0.72Is a replica of the model0.0300.51Solves a common problem (0–2)1.11 / 2012

no-judgment band (0.40–0.60)

Design patterns
Funnel0.97Traffic light0.97Questionnaire0.95Scorecard0.95Menu0.77Grey zone0.76Inspection0.65Ghostwriter0.56Check-up0.34Blind review0.30Question bank0.27Thermostat0.2500.51

no-judgment band (0.40–0.60)

The Jev model and date for each judgment stage are listed above. Card written by DeepSeek V4.1 Flash from the README on 18 Oct 2026.