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64 videos · showing 1–24
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Cole Medin shows how Jev screens incoming email, decides which messages deserve a draft, filters news and checks duplicates. He keeps a richer model for judgments needing more personal context.
Duration: 13 minutes 16 seconds. View notes : Jev Can SERIOUSLY Supercharge Your AI Second Brain (Here's How)More walkthroughs, examples, and different perspectives.
Derek Cheung demonstrates how to build an automated speed-to-lead system in n8n using Jev and OpenRouter. The workflow qualifies leads by analyzing text derived from user-submitted photos and voice notes, routing qualified prospects to receive generated media while pausing uncertain cases for human review in Slack.
Duration: 12 minutes 56 seconds.John Lindquist demonstrates using Jev to turn messy inputs into typed decisions for agent workflows, highlighting its speed, cost-efficiency, and limitations like menu size constraints.
Duration: 94 minutes 21 seconds.Parker Prompts builds four small apps with Claude Code and Jev: a support inbox sorter, a prompt checker, a podcast clip finder and a Y Combinator company matcher. Each gives Jev a defined decision rather than asking it to write a long answer.
Duration: 12 minutes 3 seconds.Sydney Runkle explains how LangChain chooses a model tier for each Open SWE task, using past task records, routing criteria and measured outcomes.
Duration: 13 minutes 39 seconds.Diogo Almeida explains why TypeSafe built Jev for machine-to-machine decisions rather than prose, and describes its probability outputs and training approach.
Duration: 89 minutes 40 seconds.IndyDevDan discusses model usage on OpenRouter and describes adding Jev to his Pi coding agent. His rough 20% token-spend saving is a personal estimate, not a controlled comparison of cost, speed or work quality.
Duration: 26 minutes 8 seconds.Cmpnd explores DSPy’s Jev integration and the ReAnchor optimizer. Typed signatures describe small decisions, and agent examples apply them to shell-command checks, tool selection and filtering history.
Duration: 62 minutes 13 seconds.Hosts Hannes Rudolph and Patrick Erichsen discuss Jev with Allie Laabs and Josh Lehman, then OpenClaw Enterprise with Kevin Lin. They explore small decisions in agent software, experimental harness and plugin integrations, and the separate security boundaries needed to manage agents at work.
Duration: 59 minutes 23 seconds.Pico AI Lab demonstrates choosing a skill in two steps and ranking retrieved passages, with fallbacks and limits for each decision.
Duration: 10 minutes 20 seconds.This Hindi/Hinglish walkthrough builds a Python movie assistant: Jev chooses a route, LangGraph connects the steps, and a Groq-hosted language model supplies prose and extracts movie details.
Duration: 102 minutes 46 seconds.Aishwarya Srinivasan explains Jev’s three question types through support-ticket routing and agent checks, separating a valid answer format from a correct decision.
Duration: 22 minutes 2 seconds.Cole Medin demonstrates Jev checks before tool calls, game and browser testing, and classification of GitHub work into workflow steps and model tiers.
Duration: 17 minutes 39 seconds.Claire Vo and John Lindquist discuss Jev demonstrations spanning voice commands, record matching, layered routing, chess, agent coordination and presentation coaching.
Duration: 46 minutes 7 seconds.KodeKloud follows a support ticket through small decisions that sit beside an LLM: choosing a team and a reply model, asking for human approval before a refund, and checking a drafted response against policy. It also explains why confidence needs testing on your own tickets before you let the program act.
Duration: 21 minutes 1 seconds.Prompt Engineering reports testing a Pi coding agent on three database tasks with five harness configurations. Selecting relevant instructions helped one task; tool gates exposed bypasses and added work.
Duration: 15 minutes 32 seconds.This walkthrough demonstrates how to integrate Jev decision models into an agent harness to handle routing, risk gating, and tool selection without relying on full LLM calls.
Duration: 20 minutes 24 seconds.Work through ten levels of TypeScript examples, from yes-or-no checks and support-ticket scores to Pi tool hooks, context compaction and an agent that writes its own Jev questions.
Duration: 35 minutes 17 seconds.Use a GitHub Action to ask Jev which kind of issue arrived and which coding agent should handle it. The walkthrough creates labels, adds decision logs and tests an issue before showing how a separate software factory picks up labelled work.
Duration: 16 minutes 25 seconds.The AI Automators combines separate ticket judgments into a support workflow, showing how application code groups questions, checks uncertainty, sets priority and passes reply writing to an LLM.
Duration: 14 minutes 20 seconds.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.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.
