Jev, explained simply

The version with no jargon

Think of the models you already know as writers. You ask a question, they write you an answer, and you read it. They are good at that and they charge for every word they produce.

Jev is not a writer. It is closer to a clerk with a clipboard and a set of boxes to tick. You say: here is a customer email, and here are three departments. Which one? It ticks a box. You say: here is a comment, is it abusive, yes or no? It ticks a box. It cannot write you a paragraph about the email even if you ask, because that is not what it does.

Because ticking a box is so much less work than writing, it is fast and cheap in a way that changes what is worth doing. One person here sorted 100,000 social posts in about twenty seconds for 67 cents. Another classified 500 emails for three and a half cents. Doing either of those with a writing model would take hours and cost real money, so in practice nobody would have done it at all.

What you give it, and what comes back

Two things go in. First, the text to judge — an email, a comment, a page, a row from a spreadsheet. Second, your question, with the answers you are willing to accept written out.

Three things come back. The answer it picked. How likely it thought each of your options was, as percentages. And a confidence score, which is the useful one: it says how sure Jev is overall. Most people use that number to build a rule like “if it is more than 80% sure, just do it; if not, show it to a human”.

You can also ask many questions at once about the same text, and they all come back together for almost no extra cost. Someone here asks 61 questions about a single draft post and gets every answer in about a second.

What it cannot do

• It cannot write. No drafts, no summaries, no code. A normal model still does all of that.
• It cannot invent an answer outside your list, which is a feature — but it also means you have to know the list in advance.
• It cannot look at pictures, audio or video. Text only.
• It can still be wrong. “Cannot make things up” means the answer will always be one of yours, not that it is always the right one.

Why it is called Jev

After William Stanley Jevons, who noticed in 1865 that as steam engines got better at burning coal, Britain burned more coal rather than less, because suddenly it was worth using engines everywhere. The bet behind the name is the same: make a decision cheap enough and software will start making millions of them.

It was released on 15 September 2026 by TypeSafe AI, and people started building with it the same day. This site has been collecting what they made ever since — 457 projects so far.

Where to go next

If you want the technical version — what a call looks like, what it costs, how it scores against other models — read what is Jev. If you want to see what people do with it, the builds are grouped by job on Jev use cases. And if you want to feel it rather than read about it, the AI slop detector runs on Jev: paste a URL and it answers 35 questions about the page in about a quarter of a second.

Made with Jev is independent. The model itself is announced in TypeSafe’s launch thread.

Common questions

What does Jev mean?

It is a model name, not a word. TypeSafe AI named it after the Jevons paradox: when something gets cheap enough, people use far more of it, not less.

Is Jev a chatbot?

No. It never writes a sentence. You give it some text and a multiple-choice question about that text, and it gives you back one of your own answers.

What is Jev good for?

Decisions a program makes over and over: is this email urgent, which folder does this go in, is this comment abusive, which link should the agent click next. Thousands of them, for pennies.

Do I need to be a programmer to use it?

To use it directly, yes — it is an API that other software calls. You can use things built on it without writing any code, and some of them are free and open in a browser tab.