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99 videos · showing 49–72
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.Prakash introduces TypeSafe's Jev model, explaining how its three primitives—Noul, Score, and Choice—score predefined answers with probabilities. The tutorial covers pairing reflexive System 1 routing with larger generative models and demonstrates use cases like agent skill selection.
Duration: 4 minutes 58 seconds.Franklin reviews TypeSafe AI's Jev decision engine, demonstrating its structured choice-making across gameplay, guardrails, classification playground examples, and developer documentation.
Duration: 11 minutes 14 seconds.Héctor de León explains TypeSafe AI's Jev decision model and demonstrates querying its HTTP API in C#. He walks through formatting JSON payloads for binary Noul evaluations and multiclass choice categorizations.
Duration: 10 minutes 20 seconds.Kilo Loco follows TypeSafe's quick start through the playground, direct requests, a Python library and a Claude Code-built tool for rating his articles.
Duration: 50 minutes 34 seconds.Marius explains how TypeSafe AI's Jev differs from generative LLMs by outputting fast structured decisions rather than text. He demonstrates its three decision types—choice, score, and Noul—and shares informal custom comparison experiments evaluating speed, parallel evaluation, and classification consistency.
Duration: 10 minutes 24 seconds.Yash Thakker introduces TypeSafe AI's Jev model, explaining its positioning as a fast system-one decision engine. He walks through Jev's core primitives—Noul, choice, and score—in the web playground and demonstrates a demo repository testing decision tasks against GPT-4o mini.
Duration: 10 minutes 7 seconds.This video breaks down TypeSafe AI's Jev model, explaining its parallel decision architecture, pricing and speed claims, evaluation caveats, and practical software automation use cases.
Duration: 14 minutes 41 seconds.Carlos Alarcón explains TypeSafe AI's Jev model architecture, contrasting System 1 parallel sampling against autoregressive LLMs. He reviews its core decision primitives, tests moderation and triage scenarios, and implements a ticket classification pipeline using the Python SDK.
Duration: 40 minutes 40 seconds.David Ondrej explains Jev's parallel decision architecture and demonstrates building and deploying an intelligent lead qualification web app powered by Jev on a Hostinger VPS with Coolify.
Duration: 32 minutes 12 seconds.This tutorial introduces TypeSafe's Jev decision model in Hindi. The presenter contrasts parallel option scoring against autoregressive LLM generation, walks through the TypeSafe web playground, inspects JSON scoring and resume screening schemas, and demonstrates sending requests to the REST API via a Python script.
Duration: 16 minutes 16 seconds.This overview explains TypeSafe AI's Jev as a System 1 decision engine that scores and chooses among predefined options instead of generating tokens. It addresses viral demo misconceptions and explores practical applications like model routing, security guardrails, and parallel agent execution.
Duration: 5 minutes 2 seconds.This tutorial shows how to access TypeSafe AI's Jev model on Vercel's AI Gateway. It explains API key generation, card-on-file verification requirements, and provides demonstrations of Jev executing fast real-time decisions for voice browser control and image modification workflows.
Duration: 4 minutes 10 seconds.This deep dive details Jev, a non-generative decision model designed for backend automation using choice, score, and Noul primitives. It explores its synthetic RLCD training, parallel evaluation architecture, vendor-claimed performance metrics, and the developer burden of confidence-based thresholding.
Duration: 22 minutes 7 seconds.
