Walkthroughs, demos, and deep dives. Our notes first, the original next.
The full collection
197 videos · showing 97–120
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.Abhishek explains the distinction between generative LLMs and TypeSafe's Jev model, highlighting Jev's focus on rapid decision-making. He walks through a Python demonstration using the Jev API to route user support tickets into discrete categories with confidence scores.
Duration: 10 minutes 33 seconds.Julian Goldie explains TypeSafe AI's Jev model, detailing how its no-text, constrained-choice architecture handles classification and routing tasks. He demonstrates parallel batching and confidence-threshold gating while noting key vendor evaluation caveats.
Duration: 17 minutes 49 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.Mayank Aggarwal evaluates TypeSafe's Jev model by contrasting its single-pass decision architecture with traditional auto-regressive LLMs. He walks through playground scoring and demonstrates custom local web applications including prompt-based LLM routing, support ticket triage, batch inbox categorization, feed filtering, and browser agent navigation.
Duration: 17 minutes 42 seconds.Julian Goldie explains TypeSafe AI's Jev decision model, demonstrating how offloading classification and routing reduces token overhead across browser automation, task boards, and app pipelines.
Duration: 64 minutes 38 seconds.Eric Siu proposes Jev classification gates for marketing and business-agent workflows. The examples include checking for duplicate topics, screening leads, and routing selected items to another agent; some dashboards are illustrative mockups.
Duration: 11 minutes 0 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.Meydeey explains Jev’s bounded decisions and reports a 30,000-test comparison with a generative model. He reviews timing, costs, confidence, and failure cases, including repeatability and nested structures.
Duration: 18 minutes 37 seconds.MG explains TypeSafe's Jev model as a dedicated decision-making architecture rather than an LLM. The video demonstrates using Jev alongside Claude Code to route requests, filter unnecessary MCP tool contexts, and enforce execution guardrails, while critically addressing marketing hype and the need for rigorous accuracy evaluation.
Duration: 22 minutes 11 seconds.This video demonstrates integrating Jev as a steerable reranker in RAG pipelines. It explains how Jev primitives evaluate passages against policy criteria that static cross-encoders cannot follow, comparing throughput, chunk scaling, and reported top-1 retrieval accuracy.
Duration: 11 minutes 27 seconds.
