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
PrimitivesDot-and-index path
Backtick path reference
Naming a state field inside instructions with backticked dot-and-index paths, such as `ticket.messages[0].text`.
- Category
- Primitives
- Also known as
- Dot-and-index path
- Related terms
- 4
- Directory entries
- 2
- Docs
- docs.typesafe.ai
- Added
- 2026-09-24
Definition
When a question is about one part of a structured state, the docs recommend naming it in the instructions with a backticked path. The model then knows which part of the state to judge, and readers can see it too.
Explicit paths reduce ambiguity and indirection, which are two documented accuracy risks. The same convention is used to refer to data fields embedded in structured instructions.
Tagsprimitivesdesign
Related terms
Definitions that connect to this one.
StateThe content a request asks Jev to evaluate: a string, a named object, or an array of messages, shared by every question in the request.Core ConceptsInstructionsThe field that states what a question should judge; accepts a string, an object, or an array for extra structure.PrimitivesStructured instructionsPutting the question in one field and its supporting data, examples, or focus notes in others instead of one long string.PrimitivesIndirectionQuestions about a property of a property, or with double negatives, cost accuracy; write instructions as directly as possible.Evaluation & Training
From the directory
The three TypeSafe question types, the typed answers they return, how to choose between them, and how to ask several in a single call.
Practices & PatternsDocs#official#primitives#choice
Official
Instructions, option descriptions, score levels, and noul criteria all accept JSON, so you can label question parts, pass schemas and taxonomies, and keep candidate paths alive.
Practices & PatternsDocs#official#primitives#architecture
Official
From the community
Posts from builders shipping with Jev right now.
Full Jev video tutorial
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
Screening agent actions with Jev
Tested TypeSafe’s Jev (no-text, probability-only model) as an AI agent safety monitor. Checking each action first worked well caught most attacks with almost no false blocks, and much faster than Gemini.
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x