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263 videos · showing 97–120
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.Steve from Builder.io tests Jev on automated browser and OS tasks, revealing significant failure rates when operating on raw text representations without visual input. He demonstrates where Jev fails standalone, how a hybrid fallback with multimodal LLMs works, and practical integration patterns like tool selection and priority classification.
Duration: 5 minutes 30 seconds.Cloudflare Developers shows a Worker calling Jev through AI Gateway. Its AI binding turns an ingredient description into a core label, while Unified Billing removes the need for a separate provider key.
Duration: 0 minutes 54 seconds.Eli Rigobeli explains TypeSafe AI's Jev decision engine, contrasts schema enforcement with answer accuracy, and integrates it into a GTD inbox classifier. He then runs a 100-request benchmark against 14 LLMs, showing Jev used as a specialized, low-cost classifier alongside generative LLMs rather than replacing them.
Duration: 42 minutes 17 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.The Fintech Builder presents 50 recorded Jev demos across financial workflows. Selected examples explain how to inspect saved answers, compare them with simple rules, and judge the limits of a test before trusting its headline result.
Duration: 121 minutes 28 seconds.Henri8 explores TypeSafe's Jev decision model via Vercel AI Gateway. He explains how typed questions replace conversational text for agent routing, triage, and guardrails, detailing two-stage architectures where LLMs interpret, Jev decides, and code executes based on confidence thresholds.
Duration: 20 minutes 29 seconds.LangChain and TypeSafe discuss integrating Jev, a System 1 decision model, into agent workflows for fast safety classification and model routing.
Duration: 48 minutes 4 seconds.Hugo Brua uses a personal support experience to explain why a chatbot needs a route to a person. He describes a Jev router that labels example messages for human or LLM handling instead of writing the reply itself.
Duration: 9 minutes 1 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.Jack Roberts builds three no-code web applications demonstrating Jev's capabilities in text classification, real-time rubric scoring, and semantic UI component ranking without traditional vector databases.
Duration: 14 minutes 3 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.Kaya Rezende examines practical use cases for TypeSafe AI's Jev model, spanning browser-based flight booking, automated ad classification via OCR, and community experiments like trading bots. The video highlights how low-latency structured decisions can serve as lightweight intermediaries for LLM routing, guardrails, lead qualification, and large-scale catalog analysis.
Duration: 5 minutes 39 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.This overview explains TypeSafe AI's Jev model, exploring its architecture as a fast System 1 model that outputs predefined structured decisions rather than text. It covers agent routing, latency tradeoffs, and TypeSafe's reported benchmarks.
Duration: 7 minutes 30 seconds.Explains TypeSafe AI's Jev model, showing how it processes input text in a single pass to return typed probabilities across three question primitives. It critiques vendor speed and hallucination marketing claims, detailing confidence calibration and a practical 100-row evaluation methodology.
Duration: 16 minutes 5 seconds.Philipp Acsany walks through an OpenRouter request to Jev, then asks Claude Code to adapt the official TypeSafe skill to use that connection only when requested.
Duration: 8 minutes 13 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.
