Yeah Jev by @typesafeai is very cool. It classified 500 emails in seconds. And it costed 3.5 cents.
Answers & ConfidenceP(yes)
Noul probability
The single 0-1 value a Noul returns: the probability that the answer is yes, with no separate confidence field.
- Category
- Answers & Confidence
- Also known as
- P(yes)
- Related terms
- 4
- Directory entries
- 2
- Docs
- docs.typesafe.ai
- Added
- 2026-09-24
Definition
A Noul answer is the probability of yes. Near 1 is a strong yes, near 0 a strong no, and 0.5 means yes and no are equally likely — it is not a medium skill level or a magnitude.
Because there is no separate confidence, the value itself carries both the decision and the uncertainty, which is why thresholds matter: pick 0.5 when either error is equally easy to act on and go higher when a false yes is expensive.
Tagsconfidenceprimitives
Related terms
Definitions that connect to this one.
NoulThe yes/no primitive: one probability from 0 to 1, where the value is the answer and no separate confidence is returned.PrimitivesThresholdThe value your code compares against a score, a Noul probability, or a confidence to turn an answer into an action.Answers & ConfidenceUncertaintyThe honest "I'm not sure" signal that lets a system defer instead of guessing, exposed through probabilities and confidence.Answers & ConfidenceConfidenceA 0-1 number on every Choice and Score answer that collapses the probability distribution into one certainty score.Answers & Confidence
From the directory
The yes/no primitive: one probability from 0 to 1, where the value is the answer and no separate confidence is needed. True and false criteria pin down subtle boundaries.
Practices & PatternsDocs#official#primitives#noul
Official
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
From the community
Posts from builders shipping with Jev right now.
500 emails for 3.5 cents
Headless Chromium agent
Acabo de terminar la implementación de @typesafeai + Chromium Headless para que mis agentes puedan navegar por internet a una buena velocidad! En este ejemplo le pido al agente que entre a la página del término "Café" en Wikipedia y navegue por los hipervínculos hasta terminar Show more
Tremendo esto!! Voy a trabajar en una implementacion propia.
Custom Jev-style models for agent workflows
Prediction: millionaires will be made using custom Jev style models (parallel constrained decoding) to make the agent systems companies already run more token efficient. Let me explain with a scenario: Imagine a company already has an agent workflow running where an llm reviews Show more
They were building in stealth for 2 years, I was building in stealth for 2 hours… Happy to open source Qwen-2.5-1B-RLCD, 5x faster on-device inference for JSON workloads that need to be type-safe. ⚡️Demo below on a M4 MacBook⚡️ every LLM has the ability to efficiently batch
Live viral post analyzer
Just created this with Jev by @typesafeai. A live viral post analyzer. As soon as you stop typing for .5 seconds it analyzes the viral potential. Going to try and actually make this good, will need to scrape a lot of twitter data... Notice how it also categorizes the tweet Show more
Jev plays Tetris: 134 lines in two minutes
Puse a Jev, la nueva IA de @typesafeai, a jugar al Tetris en modo súper difícil. Decidió cada jugada en unos 0,3 segundos. Acomodó 357 piezas e hizo 134 líneas en solo 2 minutos. Increíble.
Support answers in a Mac app
First @typesafeai use case, live in our Mac app: setup and troubleshooting help when no model is loaded. Model downloading, load failed, API returning 503, phone won't pair: the user asks, Jev reads the question with the whole built-in manual as state and decides, with Show more
