ML feature extraction
Turn free text into numeric features for a classical downstream model.
8 rows are filed under this pattern, 8 of them with code. Rows are listed as the catalogue orders them: official first, then rows with code, then by stars.
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Rows marked “not Jev itself” or “shadow mode” stay listed, but they are not examples of deciding with Jev.
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Cookbook: Autoresearch feature discovery
An autoresearch loop that proposes questions, turns free text into numeric features, and uses model error to improve a supervised gradient-boosting regressor.
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nimble
Data, recipe and serving code for Bespoke-Nimble-9B, an open decision model inspired by Jev: a Qwen3.5-9B LoRA that answers choice and true-or-false questions about a text with probabilities. On 324 held-out examples it matched 90.1% of reference labels; scored alike, Jev 1.13.0 matched 93.2%.written by a model
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jev-align
Builds calibrated decision functions from human feedback.
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Prism
Does not place orders. It judges market conditions such as toxic flow and mean reversion, and hands the assessment to the existing strategy.
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JEV Sees
Gives Jev vision: local Florence-2 and CLIP models find the objects in an image, video or RGB-D frame and keep their IDs across frames, then Jev answers a choice, noul or score about each relevant object (bus colour, pedestrian risk, animal motion).written by a model
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tiershift
Model router that starts each request on the cheapest tier and lifts it only when Jev's signals (multi-step reasoning, difficulty, stakes, safety) cross a YAML policy. On its author's 120 prompts it matched the flagship's quality at 40% less cost; the fast model alone did as well at 3% of the cost.written by a model
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jev-board-lab
Password-protected browser workbench that turns CSV rows into Jev judgments: you write at most 20 noul, choice or score questions and run them on at most 25 selected rows, each row sent as one state through a Cloudflare Worker that holds the key. The CSV stays in the browser.written by a model
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jev-calibrated-narrative-coding
Code, question schema and aggregated results of a paper that sent 5,018,080 Texas police crash narratives (2017–2025) to Jev as noul, choice and score questions, checked their calibration against coded fields and a human gold set, and turned it into how many flagged records people must review.written by a model