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Jev AI decision models

Jev models provide typed probabilistic decisions for software, taking unstructured state and returning structured answers.

69 signals · 68 builders · First seen Sep 19 · 30.8× momentum

Jev AI Model Testing

Jev is an AI model that provides typed decisions for code to act upon, achieving 20/20 accuracy in testing.

@pybankers51m

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Jev: Typed Probabilistic Decisions

Jev is an AI system designed for machines that returns typed probabilistic decisions with a fast response time.

@nrkhaled111h

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TypeSafe AI Releases Jev TypeSafe AI released Jev on September 15 2026 as a machine native AI mo

Jev uses Choice Score and Noul question primitives to return structured responses with probabilities.

@ApollonVisual1h

74

Jev: Typed Probabilistic Decisions

Jev is a model that provides typed probabilistic decisions instead of generating text, designed for routing, scoring, and real-time agents.

@_Show_TimeZ_1h

Jev AI Model

Jev is an AI model that indicates its confidence level in its responses, unlike traditional AI that may present uncertain answers with false certainty.

@MichaelKaiserAI1h

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Typesafe AI for Decision Making

Typesafe AI is a system for structured decision-making problems that selects from predefined answers and provides probabilities, rather than generating text like a traditional LLM.

@NavDoesTech1h

TypeSafe AI System One Model

TypeSafe AI's System One Model, named Jev, processes messy input and predefined questions to produce structured, type-safe judgments with calibrated confidence, avoiding prose generation.

@jimmy_longbow_1h

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Jev Typed Question Answering Model

Jev is a novel model that answers typed questions with associated probabilities, operating in approximately half a second.

@mihai_respira1h

TypeSafe AI Jev Model Integration

This project integrates the TypeSafe AI Jev model into a system layer to process unstructured state into typed decisions, aiming to eliminate issues with parsing unstructured data.

@TaseedMustafa1h

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Jev as an LLM Guardrail and Verification Layer

Jev acts as a verification layer for generative LLM responses, checking for safety, factual alignment, and policy compliance before the response is delivered to the user.

@kayodigitalza1h

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Jev AI Model

Jev is an AI model that takes 'state' and typed questions to return typed decisions with calibrated confidence, excelling at classification tasks.

@ivantinkers1h

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Meet Jev: The AI That Sorts the Orders Instead of Writing the Recipe Imagine you love cooking an

A Choice question asks Jev to select from those options.

@FoundOnArdy1h

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Play video

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Typesafe AI's Jev Model

Jev is an AI model that provides typed decisions, such as yes/no probabilities, list selections, or scores, instead of conversational text, operating significantly faster and cheaper than full LLM calls.

@oliviscusAI1h

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Jev: AI for Decisions

Jev is an AI model designed to make specific, bounded decisions within software applications, outputting typed decisions and probabilities rather than conversational text.

@GonnabeNikhil1h

Jev System One Model

Jev is a "System One" model designed for fast, structured decisions, accepting a state and typed questions to return structured answers with probabilities.

@Srimugunthan_D1h

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Jev: A System One Model for Efficient AI Evaluation

Jev is a System One model that takes state as input and outputs typed choices, scores, or booleans with calibrated probabilities, avoiding string generation and performing parallel evaluations.

@ayyazdev1h

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Jev: A Decision-Making AI Model

Jev is an AI model that provides typed decisions, probabilities, and confidence scores for software to act upon, differing from traditional LLMs by not generating answers token by token.

@riabcevv1h

Jev System One Model

Jev is a new System One model from TypeSafe AI that answers typed questions by returning a value and a confidence number for code branching.

@alltechkevin1h

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Typesafe AI: Machine-Native Intelligence

Typesafe AI is developing machine-native intelligence that makes decisions within software, contrasting with previous chatbot approaches that operated alongside workflows. The project is currently testing its first system, JEV, focusing on decision latency and performance within local agent runtimes.

@Motion_Viz1h

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Jev Performance Comparison

This project compares the performance and cost of Jev against existing LLM-based structured decisions, showing Jev to be significantly faster and cheaper.

@aiadjuncts2h

TypesafeAI Jev Classifier Integration

This project integrates TypesafeAI Jev into a classifier to improve model, agent, and use case selection, resulting in significant cost, time, and evaluation improvements.

@root4ccess2h

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Jev Transaction Categorization

Jev is a tool that performs transaction categorization, achieving comparable accuracy to GPT-5.6 while being significantly faster and cheaper, and it can identify when it is not 100% certain about a categorization above a 0.85 confidence threshold.

@alilibx2h

TypeSafe AI Jev Early Access

This project is testing Jev, a system for structured, typed decisions and confidence scores across classification, routing, scoring, and verification.

@topshoh2h

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Jev: AI for Software Decisions

Jev is an AI model designed to make decisions within software applications by processing state and narrow questions to return typed answers with probabilities, enabling semantic if-statements where code handles the final branching and actions.

@abhijeetdevv2h

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Play video

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Jev Model for Help Center Classification

This project implements the Jev model for real-time help center classification, analyzing user questions to identify the most relevant support article.

@v_sattinger2h

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Jev AI: Agent Decision Layer

Jev AI provides a cost-effective mechanism for agents to determine when not to act, returning structured decisions and confidence levels for routing, risk scoring, approval, or human escalation.

@ShinkaIoT2h

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Jev Routing Function Testing

Jev was tested on core routing functions, demonstrating accuracy comparable to or exceeding current LLMs, with significantly improved speed and reduced cost.

@gusfraser2h

Jev Model Performance Evaluation

This project evaluates Jev, a probability-based model, on two real-world tasks: analyzing SEC filings and checking extracted facts, highlighting its strengths in narrow, yes-or-no questions and its limitations with complex document interpretation.

@RegenbaumShaun2h

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Jev Model for Legal Use Cases

This project tests the Jev model from typesafeai for legal applications within stll_app, noting that Jev is optimized for decisions rather than text generation and provides typed answers with probabilities.

@jan__kubica2h

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Jev Independent Index

Jev is an AI model effective for classifying inputs into predefined categories when the criteria are explicitly defined within the input data, offering significant cost and latency advantages over existing models.

@drewdil2h

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Typesafe AI Jev

Jev is an AI technology that provides faster initial question classification compared to traditional LLM calls, as demonstrated by Barnacle Labs.

@duncsand2h

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Jev Independent Index

Jev is an AI model that returns a choice or score with confidence based on predefined possible answers, rather than acting as a chat model.

@MichalKiliany2h

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Jev: Typed AI Agent Decisions

Jev is an AI model that takes shared state and typed questions, providing direct code-usable answers with choices, scores, and probabilities, enabling efficient decision-making for AI agents.

@adidshaft2h

Play video

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Jev: Fast Decision-Making for Agentic Systems

Jev is a new System One model from TypeSafe AI designed to accelerate decision-making in agentic systems by processing state and typed questions to produce calibrated decisions in under a second, eliminating the need for text parsing.

@eandualem2h

Jev Production Deployment

Jev has been deployed in production for Proofpress via Vercel AI Gateway, enabling default evidence review due to its speed and low cost.

@richardt8302h

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Play video

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Jev Integration for AI Agents

This project integrates Jev by @typesafeai into a SaaS AI Agent to provide faster and more cost-effective responses compared to traditional LLMs, with a mechanism to identify when a full LLM is still required.

@LennartPrange2h

Play video

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Jev is what happens when you stop forcing AI to talk.

Jev is what happens when you stop forcing AI to talk. ​ It’s the first public “System One” model from TypeSafe AI, built for fast decisions inside software—not conversations. ​ You give it: ​ • Unstructured state: text,

@jurlycat2h

Jev: A TypeScript-Ready Decision Model

Jev is a model that takes a state and typed questions to provide choices, scores, and yes/no probabilities for code to branch on, enabling automated decision-making in applications.

@markargyelan_2h

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Jev: An Independent Index for Decision Making

Jev is a model that takes program state and typed questions as input, returning calibrated yes/no answers, choices, or scores with low latency, designed to handle decision-making tasks separate from text generation.

@AlexFreitasAI2h

Play video

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TypeSafe AI System One (Jev) vs. RLHF LLMs

This project compares TypeSafe AI's System One model, Jev, against current RLHF-based LLMs for decision-making tasks, specifically on a smart model routing use case.

@kishan07252h

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TypeSafe Jev Model

The Jev model is an AI component that makes structured decisions for immediate program use, rather than engaging in dialogue or lengthy explanations.

@21_borv2h

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Jev: An Independent Index for AI Engineering

Jev is a framework designed to optimize AI agent workflows by identifying and automating decisions that do not require lengthy LLM responses, thereby building reliable loops around them.

@Av1dlive2h

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Jev Decision Model Performance

Jev is a decision model that demonstrated superior speed and efficiency compared to other LLMs in evaluations.

@OpenRouter2h

Jev Primer

This project provides an objective explanation and a clear demonstration of Jev, a new model from @typesafeai that answers questions with a fixed shape based on a given situation, enabling its use in code.

@marc_bara2h

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Jev by @typesafeai is incredible I added 18 Jev use cases to my personal bot harness and have be

Jev by @typesafeai is incredible I added 18 Jev use cases to my personal bot harness and have been collecting some insane metrics. Jev can provide tons of info live, it can also provide info BEFORE the model asks keepi

@Divine_machine2h

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My X feed is full of posts on Jev by @typesafeai and as a Growth Marketer i've been really confu

Choice - picks from a set of options.

@AryacChandra2h

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Play video

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I’ve been testing Jev, the new model from @typesafeai , co-founded by ChatGPT contributor @Compl

It chooses from defined options and returns probabilities.

@kevinlysocial2h

Jev: Fast, Calibrated Decisions

Jev is an AI system designed for fast, System 1-style decisions by selecting from a fixed list of answers with associated probabilities, unlike slower, System 2 LLMs that generate text.

@balavenkatesh222h

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Jev: AI Decision Engine

Jev is an AI system that makes typed decisions and probabilities based on state, contrasting with traditional AI models that generate text.

@aakash_env2h

Building a Harness with Jev

Jev is a 'System One model' from TypeSafe AI that handles decisions by returning typed answers with probabilities based on state and questions, rather than generating text.

@cv_usk2h

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Jev: AI Agent Decision Orchestration

Jev acts as a decision-making layer for AI agents, choosing the next move by splitting intelligence from execution and enabling parallel processing of decisions.

@MishaAi3332h

Jev Decision Model for Y Combinator Companies

This project utilizes Jev, a decision model from @typesafe, to evaluate all Y Combinator companies, returning probabilities rather than text.

@RohanBhanotAI2h

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Jev: Non-Autoregressive Decision Model

Jev is a non-autoregressive decision model that processes application state and typed questions in a single forward pass, returning calibrated probabilities for efficient filtering, gating, and selection tasks.

@heman10x2h

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Jev Decision Engine

Jev is a system designed for making rapid, structured decisions by processing state inputs and answering typed questions with numerical outputs, enabling automated software actions without human-readable text generation.

@AmrTawfik1602h

Jev: Type-Safe Probabilistic Reasoning

Jev is a non-chat model that takes typed state and questions, returning calibrated probabilities in a single pass without generating text.

@hazemomier2h

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Jev: Fast, Accurate Decision Model

Jev is a new model that generates zero text, taking messy input and providing a clean answer with a confidence score, significantly faster and cheaper than traditional LLMs for tasks like simple decision-making.

@LingLiAI2h

Play video

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Jev: Decision Intelligence Model

Jev is a decision-making AI model that provides typed answers with probabilities and confidence scores, trained using RLCD for reliable probability interpretation, and is significantly faster and cheaper than traditional LLMs for specific tasks.

@AlcidesTicllaCh2h

Jev: A System One Model for Classification and Scoring

Jev is a "System One" model that returns classifications, scores, and probabilities, unlike text-generating LLMs, and can evaluate multiple dimensions of AI agent sessions in parallel at a lower cost.

@aktwits2h

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Jev (TypeSafe) Model

Jev (TypeSafe) is a System One model designed for classification and routing tasks, providing calibrated decisions instead of text to improve speed and reduce costs.

@mehkra13h

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Jev: AI Judgement Model

Jev is a specialized AI model designed for performing judgements and verification tasks, acting as a quality control inspector for AI agents and workflows, rather than a general-purpose code or chat model.

@EthanChen2133h

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Jev AI for Customer Feedback Analysis

Jev is an AI tool designed to classify customer feedback into categories like bugs or feature requests and identify duplicate issues, returning typed answers with calibrated probabilities instead of generating text.

@ChrisDiNicolas9h

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Jev AI for Customer Feedback Analysis

Jev is an AI model designed to classify customer feedback into categories like bugs or feature requests and identify duplicate issues, offering a cheaper, faster, and more accurate alternative to traditional LLMs for these specific tasks.

@ChrisDiNicolas9h

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TypeSafe AI Jev System One Model

TypeSafe AI's Jev is a System One Model that takes context/state as input and returns fast, typed probabilistic decisions for software to act on, rather than generating strings like traditional LLMs.

@cliffbaldridge9h

TypesafeAI Jev for Assistant Routing

This project utilizes TypesafeAI's Jev mechanism to improve the routing accuracy and speed of a family assistant's tests across multiple languages, significantly outperforming keyword matching.

@ahmednadar9h

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Play video

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AI-Powered Puzzle Game Bot

A bot plays a puzzle game by querying an AI model for tap probabilities before each move, utilizing live API calls for decision-making.

@mikemenard_com9h

Play video

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Live Sales Call Analyzer

This project uses Jev, an AI model that makes decisions and classifies things, to analyze live sales calls by dissecting conversations, identifying key deal signals, and triggering actions to assist sellers.

@mfishbein11h

Jev as a Typed Judgment Layer

This project provides a practical guide on implementing Jev as a typed judgment layer in production, detailing how to design questions, manage uncertainty, and test workflows.

@heyjunpenn11h

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Jev: A Decision-Making AI Layer

Jev is an AI model that provides typed answers with probabilities to questions about a given state, functioning as a 'smart if statement' for automated decision-making.

@invinci11h

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TypeSafe AI Jev Decision Engine

Jev is an AI decision engine that evaluates typed schemas directly in a single forward pass, bypassing the text generation of LLMs for faster and more reliable routing choices.

@noxflux11h