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263 videos · showing 193–216
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 video examines TypeSafe's Jev model, explaining its three mathematical primitives (Choice, Score, Noul) and parallel classification architecture. It critically dissects TypeSafe's benchmark methodology, benchmark judge consensus limitations, why 0% hallucination guarantees schema format rather than factual truth, and the rapid emergence of open-source community replicas.
Duration: 9 minutes 26 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.Jack Roberts demonstrates TypeSafe's Jev model across practical micro-decision tasks, comparing observed latency and cost against frontier models. He explains Jev's output modes—binary decisions, preloaded option selection, and numerical scoring—and illustrates how to leverage Jev alongside generative LLMs for email triage, slop detection, design matching, and model routing.
Duration: 11 minutes 53 seconds.Jeremy Chone live-codes an ergonomic Rust library for TypeSafe AI's Jev model, inspecting its API request structure, typed criteria, probability outputs, and architectural trade-offs.
Duration: 164 minutes 40 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.Didier Sampaolo demonstrates Jev on support tickets, batch classification, and blog tags. He contrasts its zero-shot setup with a trained classifier and proposes testing alternative category trees before reorganizing a product catalog.
Duration: 17 minutes 51 seconds.Nate Herk tests Jev across various practical classification workflows, including inbox sorting, an X feed Chrome extension, meeting analytics, and real-time paper trading. He explains Jev's output modalities (boolean, categorical choices, numeric scores) and highlights that production use requires rigorous golden-set evals and handoffs to generative models for execution.
Duration: 16 minutes 8 seconds.This video examines TypeSafe's Jev model, explaining its non-autoregressive parallel sampler architecture, three question primitives, and RLCD training. It critically analyzes vendor speed and cost claims versus third-party tests by Every, and details where fast, typed probabilistic judgments fit within production AI stacks.
Duration: 8 minutes 8 seconds.This video examines TypeSafe AI's Jev model, explaining its parallel evaluation of structured questions over a single state. It covers supported question formats, access options, and key operational limitations.
Duration: 8 minutes 20 seconds.AgentenFabrik tests TypeSafe AI's Jev model for classification, browser automation, and gaming. Comparing it directly against GPT in small email triage tests, the creator evaluates execution speed against decision quality, including an XRP paper trading test and the practical engineering hurdles of connecting Jev to live game state feeds.
Duration: 7 minutes 12 seconds.This video breaks down TypeSafe AI's Jev model, explaining its typed decision structure, practical optimization tips for states and criteria, hands-on empirical comparisons against LLMs, and how to effectively divide labor between code, Jev, and generative LLMs.
Duration: 28 minutes 37 seconds.This Telugu walkthrough introduces TypeSafe's Jev model for classification tasks. It compares latency and costs against standard LLMs on a 100-tweet sentiment test, walks through client code setting custom choices like bot detection, and explains Jev's parallel decision-making design.
Duration: 6 minutes 47 seconds.Alex Hitt demonstrates deploying the open-source Jev Trader bot on Monad testnet. The walkthrough covers provider credentials, separate RPC paths for reads and writes, network timing, decision telemetry, and dry-run quote placement before live orders.
Duration: 6 minutes 32 seconds.Brandon Roberts explains TypeSafe AI's Jev model as a fast system-one decision engine. He reviews core differences from traditional chat LLMs, examines platform primitives like choices, scores, and booleans, and demonstrates an automated GitHub issue triage CLI script using the TypeSafe SDK.
Duration: 13 minutes 36 seconds.Dan Vega demonstrates how to interact with TypeSafe's Jev decision model using standard HTTP requests. He covers Jev's core primitives (Noul/boolean, choice, score) and shows how to call the REST endpoint using plain Java 25 HttpClient and Spring Boot RestClient with Java records.
Duration: 25 minutes 51 seconds.This video breaks down TypeSafe's non-generative Jev decision model, analyzing how parallel evaluation replaces autoregressive generation for software loops. It critically examines vendor-reported speed and cost claims against practical cheap-model routing, highlighting operational trade-offs and vendor eval caveats.
Duration: 7 minutes 31 seconds.EDteam explains Jev's architecture as a probability-based decision engine rather than a generative text LLM. They cover API payload design (state, questions, criteria) across choice, Noul, and score question types, run playground experiments highlighting prompt sensitivity, and showcase an MVP course recommender sorting real catalog entries using confidence scores.
Duration: 25 minutes 11 seconds.Fazt explores TypeSafe AI's Jev decision model, explaining its typed primitives (Noul, choice, score) and batch queries over state context. He builds a practical ticket triage application demonstrating automated classification and priority scoring.
Duration: 30 minutes 50 seconds.Ryan Vogel explains Jev, a specialized classification model by TypeSafe AI that outputs probabilities across schema options. He showcases fast email triage, lead routing, video clip detection, and browser automation, while detailing limitations on tasks requiring broader intelligence like trading.
Duration: 28 minutes 24 seconds.Hunter Bohm evaluates TypeSafe's Jev model, explaining its parallel structured schema scoring and non-generative nature. He explores community use cases like dispatch routing and computer use, then presents hybrid tests pairing Jev with Astra to offload code review, email classification, and browser steps to cut agent runtime and API costs.
Duration: 12 minutes 39 seconds.
