when a designer gets access to Jev
PatternsAutoresearch feature discovery
Autoresearch
A loop that proposes questions, turns free text into numeric features, and uses model errors to improve a classical regressor.
- Category
- Patterns
- Also known as
- Autoresearch feature discovery
- Related terms
- 3
- Directory entries
- 7
- Docs
- docs.typesafe.ai
- Added
- 2026-09-24
Definition
The cookbook turns 2,000 WineMag tasting notes into 67 numeric columns for a CatBoost model: 38 questions became 29 Score questions (mean plus spread) and 9 Nouls. Five rounds of reading its own errors cut held-out RMSE from 3.09 predicting the mean to 1.77 across 800 unseen reviews.
Request count grows with rows rather than questions — 100,000 rows means 100,000 requests per round — so the pattern fits offline feature generation more than real-time paths.
Tagspatternsresearchextraction
Related terms
Definitions that connect to this one.
Composite scoringBreak a judgment into atomic Score questions, normalize each, and combine them with weights you control in code.PatternsWeighted scoreThe Score answer computed as each level number times its probability, which is why a score can land between two levels.Answers & ConfidenceCost/quality frontierPlotting accuracy against cost and time on log scales, where up and to the left is better; cascades are designed to sit there.Evaluation & Training
From the directory
Turn wine tasting notes into 67 numeric columns for a CatBoost regressor: five rounds of reading its own errors cut held-out RMSE from 3.09 to 1.77 across 800 unseen reviews.
Cookbooks & DemosDocs#official#cookbook#research
Official
632GitHub stars
Nokia Applied Research's package that turns any open LLM into a Jev-style decision model: typed questions are read from a single prefill, and the calibration fixes need no fine-tuning.
Repos & SDKs#community#python#open-models
Community
nokia-applied-research/AnyJevPython
nokia-applied-research/AnyJevPython34GitHub stars
An independent local alternative to Jev: it takes English or Chinese context, questions, and candidate answers, and returns probabilities over the supplied candidates from a frozen Qwen3-4B backend.
Repos & SDKs#community#python#open-models
Community
zhihz/openjevPython
zhihz/openjevPython27GitHub stars
A bilingual field map of where Jev fits and where it fails, separating the author's raw API suites from cited third-party results and TypeSafe's own claims.
Practices & Patterns#community#python#evaluation
Community
Zaious/jev-capability-atlasPython
Zaious/jev-capability-atlasPythonA browsable atlas of 1,018 AI papers from August 2025 to August 2026, sorted into 24 topic collections with labs, citations, and code links, plus a reproducible cost benchmark for summarizing 1,000 papers.
Sites & GuidesArticle#community#directory#benchmarks
Community
1.3kGitHub stars
Train a small model that chooses among a changing list of text options, one probability per option in a single pass. Includes Doom, chess, and Wikispeedia demos.
Repos & SDKs#community#training#research
Community
vinnylarouge/jevlikePython
vinnylarouge/jevlikePython1 more matching entry in the full directory.
From the community
Posts from builders shipping with Jev right now.
When a designer gets Jev
Full Jev video tutorial
Full Jev Tutorial What it is, how you can build with it and what new applications it can unlock → 0:00 Intro → 0:34 Jev explained → 4:06 API setup → 5:59 Demo 1: Voice-controlled browser → 11:33 Demo 2: AI memory → 17:27 Demo 3: YouTube predictor
The case against Jev-scored compaction
This is a terrible compaction strategy that fundamentally doesn't understand how compaction and context management work. Seems like a lot of people are confused so let's break this down. 1. Compaction isn't a filter The role of compaction is to clean up history to keep the Show more
found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant
Classifying rows in DuckDB
I made a DuckDB extension where you can use @typesafeai 's Jev to do quick classification of rows in any csv/parquet file or duckdb table about 10sec for 1k rows ~ better than using an LLM, way more ergonomic than a classifier game-changing for data analysis!
A playable 16-judgment demo
typesafe's jev is fun! live demo you can play with: typesafe-demo.val.run
AI multiple choice, not essay writing
WTF is Jev by @typesafeai? Here’s the tl;dr ELI5: Think AI multiple choice, not AI essay writing. It doesn’t chat. It makes decisions your software can act on: “Spam or not?” “Which tool should this agent use?” “Does this need a human?” The exciting part: roughly 200x faster Show more