Introducing Decide, a System One model in the browser

Decide reads a piece of text and picks one of your labels, inside the user's browser, in about 8 ms in Chrome. It needs no server and no API key, and its confidence means what it says: answers at 0.9 are right about 90% of the time. When it isn't sure, it asks an LLM and learns from the reply. Watching real Llama 3.1 8B answers, the share of messages it sent fell from 30% to 19%. Try it in your browser.

A small, local Jev

TypeSafe's Jev made the term "System One model" stick: a model that doesn't write, it reads a text once and returns a typed decision with a calibrated confidence. Jev runs behind an API. Decide is the same kind of model, small enough to run on the user's device, so each decision is free, private and a few milliseconds. It's less accurate than a large hosted model, which is why the confidence matters: your code can act alone when Decide is sure and ask for help when it isn't.

Coding against Jev already? decidekit/typesafe has the same systemOne({ state, questions }) shape for Choice questions, and the guide lists what differs.

Try it

Decide ships in the decidekit library:

npm install decidekit

import { createDecider } from "decidekit";

const d = await createDecider();
const { choice, confidence } = await d.decide("my card still hasn't arrived", ["card_arrival", "lost_card", "refund"]);
// choice: "card_arrival", typed as your labels

BANKING77, 77 labels, all training data	90.7% (93.1% since 0.2)
CLINC150, 150 labels, all training data	94.4% (96.3% since 0.2)
BANKING77 with 5 examples per label	82.3%
Calibration error, trained heads with 10+ examples per label	0.008 to 0.029
A decision in Chrome (worker, median)	7.7 ms

Measured on a Ryzen 7 260 laptop with the default model (bge-small-en-v1.5, 34 MB, downloaded once). The README has the full table and the scripts that produced it.

Your labels are the type

choice has the literal type of the labels you pass, so the compiler checks every branch:

const labels = ["refund", "bug_report", "praise"] as const;
const { choice } = await d.decide(text, labels);
switch (choice) {
  case "refund": return openRefund();
  case "bug_report": return fileBug();
  case "praise": return thank();
}

Rename a label and TypeScript shows you every place that handled the old one. Trained heads carry their labels too: decidekit train examples.csv --types writes a .ts file with the labels as a union, so a head trained by someone else still types your code.

Three ways to start

1. From label names. Pass ["refund", "bug_report", "praise"] and it decides zero-shot. Add a one-line description per label and it gets 7 to 10 points better; describeLabels() asks an LLM to write them once.
2. From examples, in the page. train() fits a small head on the user's own data in a Web Worker. BANKING77's 10,003 examples trained without a page stall longer than 18 ms.
3. From an LLM, as it goes. withFallback() answers what it's sure of and sends the rest to your ask function. Each answer becomes a training example, so the share it sends keeps falling.

Learning from an LLM that's sometimes wrong

LLM answers are not ground truth. Llama 3.1 8B agreed with BANKING77's labels on 89% of our demo messages. Wrong answers barely moved the head's accuracy, but they ruined its confidence, so it kept asking. Decide now fits its confidence on the answers zero-shot was already sure of, and learns the LLM's "none of these" answers so it can turn away off-topic text itself. The method and the numbers are in the research post, When the LLM is wrong.

The two lanes demo replays those real Llama answers, so you can watch the share fall.

What it doesn't do

• Tone. It reads what a text is about better than how it feels: 61% on tweet sentiment, 49% on emotion with 32 examples per label.
• Long documents. It reads about a paragraph. Split longer text.
• Languages other than English with the default model. multilingual-e5-small reaches 76% on Japanese and Chinese intents against 55% and 40%, with calibrated confidence, at about 118 MB.

It's 0.1 and MIT licensed. Tell us what it gets right and wrong on your data in Discussions or the debuff Discord.

npm install decidekit