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263 videos · showing 145–168
Cole Medin explains Jev, TypeSafe's fast decision-making AI model trained with RLCD. He demonstrates practical implementations including PR triage, model routing, and game loop decision-making, comparing Jev's speed, cost, and reliability against traditional LLM structured outputs.
Duration: 17 minutes 13 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.This video shows how Jev acts as a fast tool-routing agent inside the Hyper Edit open-source video editor. It demonstrates invoking media sourcing, dead-air removal via FFmpeg, captioning, and fallback routing to Claude without standard LLM thinking overhead.
Duration: 5 minutes 17 seconds.Krish Naik explains TypeSafe AI's Jev decision model, contrasting its fast probabilistic classification with generative LLM reasoning loops. He walks through conceptual architectural diagrams showing how Jev can handle tool routing, multi-agent dispatch, and post-generation guardrail verification without replacing generative LLMs.
Duration: 23 minutes 27 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.LangChain product manager Sydney introduces Jev, a System 1 decision model from TypeSafe AI. The video covers how Jev handles parallel typed queries (choice, score, boolean) directly on structured state, contrasting it with LLMs and demonstrating harness use cases like model routing, risky tool call gating, and judge-based evals.
Duration: 9 minutes 14 seconds.TypeSafe CEO Diogo Almeida discusses Jev’s machine-oriented API, the distinction between assistance and automation, and why he favors small, testable decisions over long conversational prompts. The interview also explores external memory and coordination in multi-agent coding systems.
Duration: 142 minutes 22 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.This Portuguese-language tutorial demonstrates integrating Jev alongside DeepSeek to audit legal contracts. It covers sequential question-by-question API calls, confidence cutoff thresholds for manual review, and user-configurable risk checklists.
Duration: 21 minutes 28 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.Moon Dev evaluates Jev for crypto trading pipelines, contrasting single forward-pass parallel classification with generative LLMs. He reviews latency, context, and calibration claims, highlights arithmetic limitations, and shows how to run Jev in shadow mode.
Duration: 13 minutes 11 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.Omar Kamal demonstrates how to use the Jev decision API in Python. He details the choice, score, and Noul decision types, explains question batching constraints, builds a coding convention classifier across 14 modules, and packages the model into a Claude Code skill to steer automated code reviews.
Duration: 12 minutes 19 seconds.This Portuguese tutorial explains TypeSafe Jev's System 1 paradigm, demonstrating how to install the official skill in Codex and build real-time decision apps for ticket routing and automated Playwright browser flight searching.
Duration: 21 minutes 42 seconds.Luís demonstrates evaluating marketing leads in n8n using Jev compared to traditional LLMs. He explains Jev's structured choice, score, and probability outputs, illustrating how CRM event histories can be classified with low latency and favorable per-request economics.
Duration: 41 minutes 29 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.Sean Chen explains TypeSafe AI's Jev decision model, contrasting its System 1 fast JSON architecture with conversational LLMs. He walks through Noul, choice, and score questions in the playground, then evaluates accuracy, latency, and cost across custom agent benchmarks.
Duration: 22 minutes 29 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.Shubham Sharma evaluates Jev across real automation scenarios including batch email sorting, video frame text moderation, inbound scoring, and edge-case classification failures.
Duration: 15 minutes 13 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.Well Pires explains the architecture behind TypeSafe AI's Jev model, focusing on structured probabilistic decisions over conversational text generation. He demonstrates creating a Chrome extension that classifies browser feed posts, categorizes user direct messages, and predicts member churn scores in real time via OpenRouter API requests.
Duration: 16 minutes 0 seconds.Zeldo explains Jev AI by TypeSafe, testing it as a decision layer for pre-filtering and model routing. He explores how evaluating set options directly cuts token overhead in agent workflows compared to standard chat LLMs, while comparing Jev to open-source alternatives like NanoJev and Bespoke Nimble.
Duration: 8 minutes 37 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.
