My Name is Jev: Meet the New Type of AI Model

This week I asked an AI model 1,140 questions. It answered all of them in 2.3 seconds. The bill was two and a half cents.

It did not write a single word back.

That’s Jev, and I think it’s one of the more interesting models to show up this year.

A "My name is Jeff" meme that reads "My name is Jev"

Yes, I made the meme. Jev couldn’t say that line if it tried. You’d have to ask it a yes/no question.

What Jev is

Jev comes from TypeSafe, a San Francisco lab that came out of stealth on September 15. OpenRouter added it three days later as typesafe/jev-1.13, so if you have an OpenRouter key, you already have Jev.

Every frontier lab has spent the last two years teaching models to think longer. TypeSafe went the other way. Jev reads whatever you hand it and answers one of three kinds of question, using only the options you give it.

Noul: yes or no, as a probability.
Choice: pick one of up to 255 options you describe.
Score: place it on a scale you define, “Can wait” to “Blocking revenue right now.”

Every answer comes back with probabilities. No prose to parse, no JSON that’s almost JSON, no invented option. It’s an if-statement that understands English.

It’s also cheap: $0.042 per million input tokens, and output is free. My first call, three questions about one support ticket, came back in 0.6 seconds and cost $0.000017.

Where it fits

Most decisions inside software aren’t essays. They’re “which team gets this ticket,” “is this safe to run,” “is this spam,” “which of these 40 things matters.” We’ve been paying a chatbot to write a paragraph so we could regex one word out of it.

Routing and triage. Tickets, emails, alerts, pull requests. Pick the queue, score the urgency.
Gating agents. Before an agent issues a refund or runs a command, ask three yes/no questions. At 0.9 or higher, go. At 0.1 or lower, stop. In between, a human looks. OpenRouter published exactly this recipe.
Checking an LLM’s homework. Did the answer stick to the source, or make something up? Ship the supported ones, escalate the rest.
Sorting big piles. Thousands of documents, one question each, ranked by probability.
Picking instead of writing. Pull the candidate dates or amounts out with a regex, then let Jev choose the right one.

The probabilities are the part I didn’t expect to love. When the model is unsure, it says so, and you fix things by moving a threshold, not rewriting a prompt.

What I built with it

My research repo has 1,140 items in it: voice briefs, news digests, insights, bets, trend signals, blog drafts. I built a thing called Sieve that asks Jev the same question about every one of them and ranks the answers. “Talk-ready.” “Evidence against my community thesis.” “Feeds chapter two of the book.” “A hands-on build that could be demoed live at a developer meetup.” (My CABLE 82 talk came back first, at 95%. Correct.)

Then I checked it against my own filing. I’d already sorted every insight and trend signal into swimlanes by hand, so I stripped the labels and asked Jev to do it. Overall it agreed with me about 8 times in 10. When it said it was confident, 9 times in 10. When it was unsure, it was close to a coin flip, and it told me so.

Sieve is open source

I split Sieve into the engine and my stuff, and put the engine on GitHub: github.com/nothans/sieve. MIT licensed, just Node, no dependencies. Point it at any folder of Markdown, describe what counts as an item and which questions you care about in one config file, and it sifts the whole pile from the command line or a little web UI. My research notes and my config stay on my machine.

It ships with two demos: a made-up research notebook, and all 313 of Aesop’s fables. Ask the fables for “a trick that backfires on the trickster” and it reads every one of them in 1.4 seconds for about a third of a cent.

The Moral preset files every fable under its moral. The Dog and the Shadow came back 100% greed, which seems fair to the dog.

The repo also carries the Jev command-line tool on its own, so any script or agent can grab a typed answer when it needs one.

What tripped me up

Jev reads literally. Really literally. “Projects I could demo live on stage” ranked a Google product launch first, because Jev has no idea who “I” is. “Is this evidence against my thesis” came back as a shrug on everything. Asking for the concrete thing instead (“does this report a community that did fine without human trust?”) fixed it. TypeSafe documents all of this: no math, no counting, no date comparisons, and adversarial text can push it around. It also can’t tell you why, so you get a number and no reason.

The idea is already loose, too. Six open-source clones showed up within about 48 hours, at least one of them running on a single gaming GPU. What got copied is the shape: state in, typed questions in, probabilities out. I think that shape sticks around, whoever ends up serving it.

Creative uses I want to try

Board game bots. A playtest bot that picks its move from the legal moves, with a persona written in the question.
A notification bouncer. One yes/no before anything pings my phone: “Is this urgent enough to interrupt someone at dinner?” (Not “would I want this.” Jev doesn’t know who I am.)
CABLE 82 tonight. Score every headline on “cozy Friday night viewing” and let the 1982 cable channel build its own lineup.
Moderation with the thresholds in public. Every post gets the same small set of typed judgments, and members can see, and vote on, the numbers that decide what happens.
A code review tripwire. “Does this diff touch authentication or payments?” before an agent is allowed to merge.
Alt text that checks itself. Does this description actually say what the chart shows?
Spreadsheet columns that think. Add a column called “urgency,” type what it means, fill 300 rows in seconds.
Training its own replacement. Label a big pile of examples with Jev, then train a tiny local model on the labels.