Walkthroughs, demos, and deep dives. Our notes first, the original next.
Agent workflows
40 videos · showing 1–24
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Boundary hosts explain TypeSafe AI's Jev model for fast structured decisions, discussing state-machine agent design, constrained action spaces, and necessary harness guardrails.
Duration: 42 minutes 37 seconds. View notes : Jev Explained: The Fast AI Model We Tried to Break on PurposeMore walkthroughs, examples, and different perspectives.
Creator Magic walks through building, testing, and deploying three autonomous crypto trading bots using Claude Code and TypeSafe's Jev decision engine. He shows how periodic market snapshots are evaluated by Jev against bot personas, filtered through a local risk layer, tested in dry-run mode, and hosted 24/7 on a VPS.
Duration: 17 minutes 40 seconds.Kuuki Design evaluates TypeSafe's decision-focused model Jev within an agentic workflow, explaining its constrained output format, testing it on database triage and pull request pre-checks, and detailing cost trade-offs alongside security and complexity limitations.
Duration: 18 minutes 16 seconds.Chase AI demonstrates an agentic OS architecture utilizing Jev to route user requests across three execution tiers. The setup pairs Jev classification with Whisper, Kokoro, Obsidian vault memory, and Claude Code execution.
Duration: 22 minutes 16 seconds.Sascha Hoffmann demonstrates using Jev to offload high-volume discrete choices from costly LLMs. He reviews structured question definitions, context limitations, and two implementations: approving CRO agent copy edits and pre-filtering large Google Search Console queries.
Duration: 13 minutes 35 seconds.This Python tutorial uses Noul, Choice, and Score questions to route support tickets. It demonstrates request configuration, confidence gates, composite priority scores, and parallel questions that inform a handoff to application code, an LLM, or a person.
Duration: 40 minutes 17 seconds.This video breaks down Jev's architecture as a System 1 decision model rather than a generative chat LLM. It explains parallel sampling, probabilistic outputs (Noul, choice, score), and how Jev offloads fast routing tasks in agentic pipelines while evaluating competitive moats.
Duration: 17 minutes 32 seconds.Henri8 explores TypeSafe's Jev decision model via Vercel AI Gateway. He explains how typed questions replace conversational text for agent routing, triage, and guardrails, detailing two-stage architectures where LLMs interpret, Jev decides, and code executes based on confidence thresholds.
Duration: 20 minutes 29 seconds.Kaya Rezende examines practical use cases for TypeSafe AI's Jev model, spanning browser-based flight booking, automated ad classification via OCR, and community experiments like trading bots. The video highlights how low-latency structured decisions can serve as lightweight intermediaries for LLM routing, guardrails, lead qualification, and large-scale catalog analysis.
Duration: 5 minutes 39 seconds.This overview explains TypeSafe AI's Jev model, exploring its architecture as a fast System 1 model that outputs predefined structured decisions rather than text. It covers agent routing, latency tradeoffs, and TypeSafe's reported benchmarks.
Duration: 7 minutes 30 seconds.This video breaks down recent Jev updates, comparing TypeSafe's cloud decision model with alternatives like DiffusionGemma and local Laya, while detailing practical production patterns including RAG routing, semantic search, and agent gating.
Duration: 21 minutes 44 seconds.This overview explains TypeSafe AI's Jev as a 'system one' decision model for agent routing and classification. It covers Jev's output primitives, parallel evaluation architecture, REST API differences from standard chat endpoints, and crucial accuracy versus latency trade-offs compared to traditional LLMs.
Duration: 7 minutes 5 seconds.AI Jason explains Jev's architecture as a discrete decision-making model that outputs probability distributions over defined options. He illustrates how to construct business guardrails using confidence thresholds and demonstrates three real-world pipelines: fraud detection, buyer intent lead qualification, and social media post classification.
Duration: 14 minutes 29 seconds.This video explains how to build front-door model routing for AI agents using Jev. It covers creating model lanes with descriptive criteria, routing on confidence thresholds, handling multi-query task profiling, choosing between sticky and turn-by-turn routing, and establishing fallback mechanisms when network calls fail.
Duration: 12 minutes 37 seconds.This video breaks down TypeSafe AI's Jev model, explaining its discrete three decision modes (boolean, categorical, and graded rating scales) and showing how to integrate it into automated customer support routing workflows as an ultra-fast classification filter and post-generation safety check.
Duration: 13 minutes 40 seconds.This video examines TypeSafe AI's Jev model, explaining its non-autoregressive typed primitives (Noul, Choice, Score) for fast decision-making. It outlines confidence-gated routing cascades and practical operational limitations.
Duration: 10 minutes 12 seconds.This video introduces TypeSafe's Jev model as a fast, low-cost system-one classifier. It demonstrates integrating Jev with Claude Code across three practical levels: internal agent routing and skill selection, high-volume batch triage like lead qualification, and application-level features including semantic UI filtering and element removal.
Duration: 11 minutes 46 seconds.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.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.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.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.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.
