Are the benchmark numbers real or just marketing?

They are labeled-data runs in notebook 02, not promises. On the harder 8-way task Jev matched frontier accuracy at 92% while much faster and cheaper; on the easy task all three were near 98-100%, with personal the weakest class.

What does the full course cost and what do I need to start?

You need one OpenRouter key for Jev and the LLM. Clone, run the setup script, put the key in .env, run the doctor check, then open the notebooks. The author reports the whole course costs less than $0.20.

What if I do not have Jev access yet?

Set JEV_BACKEND=adapter in .env and your LLM answers the same questions. Every notebook except the benchmark still runs, which is useful for working through the ideas first.

Will this teach agent design or only isolated Jev calls?

It teaches the combination: Jev makes small typed decisions, the LLM writes and plans, and plain code enforces hard rules. The five agent insertion points are router, tool, guard, done gate, and judge.

What are the five places Jev goes into an agent?

Router, tool, guard, done gate, and judge. The router picks the cheapest model that can do the job, the tool turns Jev into a callable the agent uses, the guard screens actions, the done gate answers whether the task is finished, and the judge grades output. The notebooks build each one in turn and the capstone combines all of them.

What do the individual tool results actually show?

The README's own runs, not projections: 40 emails triaged in 2.4 s at 92% accurate, 20 of 20 scam texts caught with 0 real texts blocked, 9 of 9 planted vulnerabilities found with no false alarms, 14 of 14 correct guard decisions, a router that was 47% cheaper, a tool picker right 10 of 10 times with 90% fewer tokens, and a capstone that routed 24 of 25 mixed items correctly.

Do I have to run the notebooks to learn from them?

No. All 12 are committed with real outputs, so you can read them on GitHub or on the docs site before running anything. Each runs in about a minute, checks itself against labeled data, and prints what it cost, and the README suggests paths by time: notebooks 01 to 02 for 15 minutes, 01 to 03 to 04 to 07 for an hour, or all 12 over a weekend.

CommunityJev Practices & Patterns

Learn Jev End To End

Free hands-on course of 12 notebooks that pairs Jev decisions with LLM writing across 13 small tools, needing one OpenRouter key and printing cost and self-checks per lesson.

See highlights

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Stars: 16
Language: Jupyter Notebook
License: MIT
Published by: Community
Author: harshithsunku
Last verified: Oct 2026

harshithsunku/learn-jev-end-to-end

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Learn Jev end to end: a free hands-on course. Build 13 AI agent use cases with a fast brain (Jev) and a slow brain (LLM). One OpenRouter key.

Forks: 4
Last pushed: Updated Sep 2026

Repo stats from the GitHub API, cached Oct 2026.Homepage

About this project

📖 Read the docs · 🚀 Quick start · 🎓 The course · 🛠️ Use cases · 📊 Benchmarks. Learn Jev end to end is a free, hands-on course. In 12 short notebooks you go from "what is Jev?" to building 13 real AI tools with it: an email triage job, a scam-text detector, a code vulnerability hunter, an agent safety guard and more. You need one API key, and running the whole course costs less than $0.20. The Jev Playground , recorded live (real time): Jev vs an LLM race · scam-text shield · agent guard.

Quoted from the project's README. Read the full README.

Tagscommunity awesomejev catalog-import jupyternotebook

Works forJev for Classification Jev for Routing Jev for Tool calling

Used inJev for Software & DevTools

Highlights

Teaches a fast-brain and slow-brain split: Jev decides yes or no, picks one, or scores while the LLM writes and plans.
Builds 13 tools including email triage, scam-text checks, vulnerability hunting, guards, routers, and an ops copilot.
Each notebook runs in about a minute, checks itself against labeled data, and ships with real outputs to read first.
Runs with one OpenRouter key for Jev and the LLM, with an adapter fallback that lets most notebooks run without Jev.

Watch out

Reported speeds, costs, and accuracy figures are the author's own runs on labeled sample data, not guarantees for your workload. Copy reviewed against the repository README on 2026-10-04, not from running it.

Questions about this entry

Short answers drawn from the linked project or page.

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