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86 videos · showing 1–24
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Codevolution takes a customer message from the TypeSafe playground into TypeScript, explaining Noul, Choice, and Score responses before combining them in one request. The closing examples make the boundary clear: use Jev for bounded judgments, a text model for writing, and ordinary code for exact calculations.
Duration: 22 minutes 12 seconds. View notes : What is Jev and How to Use it?More walkthroughs, examples, and different perspectives.
Durgesh explains TypeSafe AI's Jev model, showing how it specializes in rapid, low-cost structured decisions (classification, rating scales, boolean checks) rather than open-ended text generation. He details how Jev pairs with traditional LLMs in hybrid workflows, while explicitly cautioning that picking valid constrained choices does not guarantee decision correctness.
Duration: 24 minutes 11 seconds.Gaurav Khurana demonstrates how to obtain Jev API keys, configure HTTP payloads with state and question objects in Postman or Hopscotch, and run automated QA triage, agent guardrail checks, RAG grounding verification, and failure case tests.
Duration: 14 minutes 7 seconds.Explains TypeSafe's Jev decision model using customer support routing. Covers Choice, Score, and Noul primitives while emphasizing verification, schema versus accuracy separation, and calibration testing.
Duration: 9 minutes 46 seconds.The Cloud Girl explains TypeSafe AI's Jev model, examining how it avoids autoregressive text generation to make bounded, categorical and rubric decisions in a single forward pass. She reviews reported benchmark trade-offs and outlines an Observe-Judge-Reason-Act-Verify workflow pattern.
Duration: 11 minutes 52 seconds.This Hindi tutorial introduces TypeSafe's Jev as a System 1 fast decision model rather than a conversational LLM. It demonstrates running API calls, using the Python SDK for structured outputs (noul, choice, score), and running alternative local decision models like Laya and Laya MLX on Mac hardware with latency timing.
Duration: 25 minutes 59 seconds.This Hindi tutorial introduces TypeSafe AI's Jev model, explaining its primitive outputs (Noul/boolean, choice, and score). The presenter then demonstrates calling Jev 1.13 via OpenRouter in Python for customer support routing and human agent escalation, concluding with workflow integration patterns.
Duration: 13 minutes 22 seconds.Tomi Tokko demonstrates how TypeSafe AI's Jev model evaluates GitHub issue descriptions to classify responsible teams, score severity, and return decision probabilities via Vercel AI Gateway.
Duration: 8 minutes 1 seconds.Eli the Computer Guy reviews TypeSafe AI's Jev announcement, contrasting narrow, typed decision models against conversational LLMs. He examines claimed pricing, latency, confidence scores, and architectural tradeoffs.
Duration: 40 minutes 39 seconds.This Telugu presentation explains TypeSafe AI's Jev model, focusing on structured semantic decision-making rather than conversational text generation. It covers Jev's output primitives, architectural differentiation from autoregressive LLMs, confidence thresholding, and critically examines reported vendor cost and speed benchmarks.
Duration: 15 minutes 5 seconds.Mikey No Code demonstrates how to set up and use TypeSafe Jev's playground, detailing its three core primitives—Noul, score, and choice—and showing multi-question evaluation for resume screening, ticket routing, and content moderation.
Duration: 17 minutes 28 seconds.This Japanese tutorial explains TypeSafe's Jev decision model, distinguishing its selection and scoring paradigm from text-generating LLMs. It details stateless criteria evaluation, sensory parsing pipelines, confidence metrics, and practical interactive logic design.
Duration: 12 minutes 36 seconds.KodeKloud explains Jev through support-ticket routing, then builds a Papers, Please-inspired approval game with Claude Code and KodeKey. The most useful section shows how several independent ticket questions can guide escalation even when the department choice remains uncertain.
Duration: 21 minutes 51 seconds.Sumit Singh Rajput explains Jev's decision-oriented model architecture compared to generative LLMs, demonstrating how Jev handles routing, fast classification, and agent tool selection without text generation.
Duration: 10 minutes 28 seconds.NeuralNine tests Jev in the TypeSafe console and Python, using binary probabilities, categories, and ordered scores. The examples cover prompt routing, guardrail checks, sentiment, and game decisions, with speed kept separate from decision quality.
Duration: 22 minutes 21 seconds.This video breaks down TypeSafe AI's Jev model, explaining how it evaluates application state against schema-declared questions in a single pass. It covers Jev's primitive question categories, its typed probability outputs, and critical perspectives on vendor latency and cost claims.
Duration: 3 minutes 9 seconds.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.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.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.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.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.
