Guides / video walkthroughs

Jev Illustrated Video Guides

Subtitle-driven, frame-verified walkthroughs with unique alt text for image search. Six curated videos rebuilt as scannable step guides—with proof frames and timestamp jumps back to source.

Six curated

Each guide ships 4-6 key frames, numbered steps, FAQ, and YouTube timestamp deep links.

Typesafe AI founder Dio Almada presents Jev as a System One classification model that accepts state text and typed Choice, Score, or Noul questions without autoregressive token generation.

Runtime: 16:19•Sam Witteveen

Jev Classification Quickstart: OpenRouter API, Primitives & Real Probabilities

OpenRouter integration, three typed primitives, and live probability distributions for routing, sentiment, and yes/no gates.

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Diagram explaining how ChatGPT-era LLMs optimized for human chat create downward pressure on workflow automation layers that demand sub-second categorical decisions.

Runtime: 7:12•Caleb Writes Code

Jev Architecture Explained: Why 70ms Decision Models Beat LLMs for Workflow Automation

Why chat-optimized LLMs fail automation SLAs, and how Jev parallel sampling becomes the logic gate layer at 70–500ms.

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Google Flights browser agent benchmark overlay showing 178 millisecond Jev decision loop time after migrating from sequential VLM screenshot polling to parallel structured sampling.

Runtime: 8:48•Alex Hitt

Ultra-Fast Browser Agents with Jev: 178ms DOM Loops & Dual Model Orchestration

DOM snapshot filtering, parallel sampler actions, RLCD calibration, and dual-model loops for sub-second browser automation.

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JevBench Score leaderboard ranking Jev 1.13.0 first among open models at 75.3, just above Semif at 74.6, djev at 74.3, and Laya at 70.1 on a 25 percent blend of intelligence, calibration, speed, and cost.

Runtime: 21:32•Sam Witteveen

Open Jev Models Are Here: Semif, Nimble, Decider, DiffusionGemma & Laya Hands-On

A field guide to the open Jev ecosystem: how six open implementations work, where JevBench ranks them, and which one to run locally.

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Whiteboard diagram of an LLM reasoning loop labeled Thinking, Reasoning, Tools and ReAct loop, where the LLM acts as the brain and token optimization matters because text generation consumes tokens at every turn.

Runtime: 23:27•Krish Naik

Jev vs LLM: Will Jev Replace LLMs? Krish Naik's Whiteboard Explainer

A whiteboard walkthrough of Jev vs LLM: what the ReAct loop costs in tokens, how Jev turns unstructured queries into probabilities, and the hybrid architecture that uses both.

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Hand-drawn terminal window running the bun install command with a magenta cursor block, the only supported installer for the Jev Trader repository cloned from GitHub.

Runtime: 6:32•Alex Hitt

Jev Trader Tutorial: Build a Subsecond AI Trading Bot on Monad

A six-minute build log for Jev Trader: clone the repo, install with bun, split your RPC paths, pass the 18ms benchmark, and watch a dry-run market maker think inside the 300ms block window.

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