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263 videos · showing 121–144
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.This video breaks down recent Jev updates, comparing TypeSafe's cloud decision model with alternatives like DiffusionGemma and local Laya, while detailing practical production patterns including RAG routing, semantic search, and agent gating.
Duration: 21 minutes 44 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 examines where TypeSafe Jev fits in software architectures, comparing it to BERT, analyzing multi-step browser execution, demonstrating 3-question customer support triaging, and using confidence thresholds for human escalation.
Duration: 12 minutes 24 seconds.The Stack explains a division of work: a language model writes, while Jev routes requests and checks proposed tool actions. It recommends testing decisions alongside the existing system before allowing them to control it.
Duration: 15 minutes 57 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 overview explains TypeSafe AI's Jev as a 'system one' decision model for agent routing and classification. It covers Jev's output primitives, parallel evaluation architecture, REST API differences from standard chat endpoints, and crucial accuracy versus latency trade-offs compared to traditional LLMs.
Duration: 7 minutes 5 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.AI Jason explains Jev's architecture as a discrete decision-making model that outputs probability distributions over defined options. He illustrates how to construct business guardrails using confidence thresholds and demonstrates three real-world pipelines: fraud detection, buyer intent lead qualification, and social media post classification.
Duration: 14 minutes 29 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.Nate B. Jones explores TypeSafe's Jev classifier, outlining how a dedicated multi-choice model fits into software architectures, agent orchestration, and automated pipelines alongside LLMs.
Duration: 33 minutes 1 seconds.This video explains how to build front-door model routing for AI agents using Jev. It covers creating model lanes with descriptive criteria, routing on confidence thresholds, handling multi-query task profiling, choosing between sticky and turn-by-turn routing, and establishing fallback mechanisms when network calls fail.
Duration: 12 minutes 37 seconds.Japanese tech commentators explain TypeSafe AI's Jev model, contrasting its fast System 1 parallel decision design with slow System 2 generative LLMs. They examine Diogo Almeida's 'Bitterest Lesson', RLCD decision training, and the practical software automation possibilities created by lower latency and cost.
Duration: 25 minutes 20 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.Dave Ebbelaar builds a Python support-ticket flow, inspects category probabilities, and combines category, frustration, and refund questions in one call. He also explains why sending client data to a new provider needs a separate privacy decision, even when the API is easy to use.
Duration: 16 minutes 50 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.This video breaks down TypeSafe AI's Jev model, explaining its discrete three decision modes (boolean, categorical, and graded rating scales) and showing how to integrate it into automated customer support routing workflows as an ultra-fast classification filter and post-generation safety check.
Duration: 13 minutes 40 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.This video examines TypeSafe AI's Jev model, explaining its non-autoregressive typed primitives (Noul, Choice, Score) for fast decision-making. It outlines confidence-gated routing cascades and practical operational limitations.
Duration: 10 minutes 12 seconds.This video introduces TypeSafe's Jev model as a fast, low-cost system-one classifier. It demonstrates integrating Jev with Claude Code across three practical levels: internal agent routing and skill selection, high-volume batch triage like lead qualification, and application-level features including semantic UI filtering and element removal.
Duration: 11 minutes 46 seconds.
