Walkthroughs, demos, and deep dives. Our notes first, the original next.
Real-time research powered by
Getting started
Choose an introduction, then explore a worked example.
99 videos · showing 25–48
This video breaks down TypeSafe AI's Jev model, explaining how it evaluates application state against schema-declared questions in a single pass. It covers Jev's primitive question categories, its typed probability outputs, and critical perspectives on vendor latency and cost claims.
Duration: 3 minutes 9 seconds.This video explains Jev AI in Hindi, contrasting it with conversational LLMs as a specialized decision model. It outlines how Jev evaluates support tickets across categories like billing and urgency, and illustrates agent safety checks that use confidence score thresholds to either execute actions automatically or escalate them to human reviewers.
Duration: 2 minutes 27 seconds.Benoît Ferrere explains TypeSafe AI's Jev model, contrasting its non-autoregressive decision architecture (RLCD) with standard LLMs. He reviews reported benchmarks, explains Jev's three output primitives (choice, score, Noul), and demonstrates how probabilistic outputs can drive enterprise ontology systems.
Duration: 18 minutes 25 seconds.This Portuguese-language tutorial explains Jev's probabilistic decision model architecture. The presenter showcases Playground modes including yes/no, score, and choice with custom criteria, then plans an architectural integration into an existing recommendation application using an LLM skill.
Duration: 34 minutes 21 seconds.This video introduces TypeSafe AI's Jev decision model using a duplicate charge support scenario. It details fixed-weight inference across three primitives—Choice, Noul, and Score—explains probability calibration via RLCD, and demonstrates why deterministic application logic must handle downstream execution.
Duration: 5 minutes 27 seconds.ZazenCodes demonstrates how to query TypeSafe's Jev model using the Python SDK. The tutorial covers Jev's core decision primitives—Noul, Choice, and Score—along with parallel multi-question evaluation and an automated support ticket triage loop.
Duration: 20 minutes 58 seconds.This video introduces TypeSafe AI's decision model Jev, explaining its probability-focused architecture, vendor-claimed low latency and pricing, and community experiments like fast moderation and trading.
Duration: 6 minutes 17 seconds.Julian Ivanov demonstrates Jev through interactive emoji and Wikipedia browser-automation apps, contrasting its zero-shot classification and probabilities against generative LLMs. He reviews agent routing, SEO auditing, and ad-filtering use cases, while highlighting hard limits around arithmetic, adversarial prompt susceptibility, context windows, and privacy trade-offs against open local alternatives like Laya.
Duration: 24 minutes 53 seconds.This video breaks down Jev, a non-chat classification model by TypeSafe. It explains why software decisions do not require text generation, how Jev returns probabilities and confidence scores over fixed options, the three supported question types, and how confidence thresholds route uncertain cases to humans.
Duration: 2 minutes 46 seconds.This video breaks down TypeSafe's Jev model, explaining how it replaces token-by-token text generation with direct, structured choices across three question formats. It also explores using Jev's confidence scores to route uncertain decisions to human reviewers or larger LLMs rather than replacing general-purpose generative models entirely.
Duration: 3 minutes 4 seconds.Midudev explores Jev by TypeSafe AI, demonstrating how it produces typed decisions instead of text. Through live examples like chat moderation and shell guardrails, he explains its parallel evaluation API, non-deterministic nature, and potential as a UI or model router.
Duration: 26 minutes 24 seconds.Rafael Voss explains TypeSafe AI's Jev model as a fast, low-cost System 1 decision engine. He contrasts it with generative System 2 LLMs and outlines three tiers of practical usage: LLM/skill routing, discrete data and email classification, and agentic computer use.
Duration: 14 minutes 47 seconds.Learn how to integrate TypeSafe AI's Jev model into Python scripts using typesafe-sdk and OpenRouter, classifying text inputs with the Noul primitive.
Duration: 17 minutes 2 seconds.Schovia explains Jev's classification design, playground state-versus-questions setup, SDK usage, parallel evaluation, confidence scoring, and browser agent benchmark results.
Duration: 12 minutes 56 seconds.This video walks through TypeSafe's Jev console, showing how to test Noul, score, and choice schemas in the playground before building an email triage demo.
Duration: 17 minutes 5 seconds.StatLearn Tech explains how to represent state and independent typed questions, then works through Choice, Score, and Noul criteria. The reviewed sections focus on selecting the right question shape and making binary criteria explicit.
Duration: 46 minutes 48 seconds.The Jev section of Vaibhav Sisinty's AI roundup describes choosing among fixed options: routing smaller jobs to models, selecting game moves, taking a browser's next step and setting several controls in a smart-home simulator.
Duration: 17 minutes 12 seconds.This video introduces TypeSafe AI's Jev model, explaining its fast System 1 decision architecture and three distinct query modes. It also details pricing, API access, and reported real-world demonstrations.
Duration: 10 minutes 7 seconds.Abhijeet Kalamkar introduces TypeSafe AI's Jev decision model in Hindi. He explains Jev's core assessment categories—Noul, choice, and score—explores official playground templates, and tests a custom decision dashboard with live customer support, sponsorship triage, and comment classification examples.
Duration: 18 minutes 42 seconds.This video breaks down TypeSafe AI's Jev model, contrasting System 1 fast structured decision-making against sequential generative LLMs. It explains parallel sampling, RLCD calibration, claimed sub-500ms latencies, and practical use cases.
Duration: 10 minutes 20 seconds.CoderOne introduces TypeSafe's Jev System 1 classification model alongside open-source alternatives like Laya. The video examines browser automation workflows, contrasts System 1 fast decisions with System 2 reasoning, and demonstrates Jev's API schema in the developer playground using state definitions and typed classification criteria.
Duration: 13 minutes 2 seconds.This video introduces TypeSafe AI's Jev, contrasting text-generating LLMs with parallel typed decision-making. It demonstrates ticket routing workflows using predefined questions and confidence scores.
Duration: 1 minutes 30 seconds.Farzam Arefi demonstrates using the TypeSafe AI playground to configure Jev primitives. The video breaks down state evaluation and details how to format Noul, choice, and score question types, interpret probability distributions and confidence scores, and structure multi-primitive parallel requests.
Duration: 15 minutes 9 seconds.Fireship introduces Jev's fixed answer types, contrasts them with generated text, and questions launch benchmarks and claims about how the model works.
Duration: 5 minutes 27 seconds.
