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86 videos · showing 25–48
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.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.Inference Desk explains Jev’s binary, categorical, and scoring questions, then connects the request schema to application control flow. The examples cover independent questions over one state and confidence gates that send uncertain cases to another handler.
Duration: 5 minutes 5 seconds.Makoto Taniguchi demonstrates TypeSafe AI's Jev, explaining its structured probability output, testing criteria and states in the playground, and implementing an inquiry categorization web application.
Duration: 16 minutes 14 seconds.Kristiyan Stoyanov demonstrates TypeSafe's Jev model through direct API queries, illustrating its noul, choice, and score question types. He then showcases two end-to-end implementations: dynamic routing between local Qwen and hosted Claude Sonnet, and multi-agent pull request triage evaluating comment severity and relevance.
Duration: 24 minutes 32 seconds.This German-language tutorial introduces Jev by TypeSafe AI, framing it as a fast System 1 model that outputs classifications, scores, and probabilities instead of generative prose. It covers query structures, pricing, and how confidence scores enable automated routing workflows alongside LLMs.
Duration: 5 minutes 31 seconds.Mohamed Naji Aboo introduces TypeSafe AI's Jev model as a fast System 1 decision engine. He walks through the Python SDK using Noul, Choice, and Score in a single API call to classify customer support message topics, tone, and urgency.
Duration: 12 minutes 10 seconds.Niklas Hansen explains Jev's structured decision outputs (Choice, Score, Noul) and demonstrates six practical use cases: agent model routing, batch email classification, browser automation, lead scoring, custom RAG reranking, and coding assistant skills. He evaluates Jev against GPT-4o-mini and the local Laya alternative, highlighting realistic performance limits and failure modes.
Duration: 17 minutes 40 seconds.Gisela Torres explains Jev AI by TypeSafe, demonstrating its native decision primitives—boolean, choice, and score—alongside composite structured payloads and a step-by-step game loop demo using ViZDoom.
Duration: 20 minutes 33 seconds.Santiago Munoz explains Jev as a high-speed, discrete decision judge rather than a text-generating LLM. He demonstrates connecting Jev API keys with Claude Code, automating YouTube comment retrieval via Composio, and running high-volume classification across custom criteria for cost-effective sentiment and intent analysis.
Duration: 23 minutes 35 seconds.This overview introduces TypeSafe's Jev model, explaining its design as a non-conversational decision-making system. It details vendor pricing claims, latency differences compared to reasoning models, and suitable high-volume automation use cases.
Duration: 10 minutes 12 seconds.This Hindi overview explores TypeSafe AI's Jev, demonstrating the web playground's structured decision types, routing email queries, accessing the model via AI gateways, and testing experimental automated trading and safety gates.
Duration: 15 minutes 34 seconds.Zubair Trabzada explains that Jev is a fast, structured decision layer rather than a generative text-writing LLM. Using a simulated traffic grid demonstration, he explains discrete choice routing, contrasts Jev with traditional LLM generation cycles, and shows how to generate API keys on TypeSafe AI to integrate Jev into coding assistants.
Duration: 9 minutes 29 seconds.
