jevish

September 19, 2026 · View on GitHub

Semantic pattern matching and zero-shot judgment in JavaScript. Jev-ish: behaves like TypeSafe Jev in local CPU cache (<0.05ms, 0 deps), and speculatively escalates to cloud Jev when needed.

Live Interactive Playground →

npm install jevish

Quick start

import jevish from 'jevish';

await jevish('Checkout button returns 500 internal server error', {
  'bug @ >0.8': (t, meta) => fileJira(t, meta.score),
  'bug':        (t) => queueTriage(t),
  'billing':    (t) => openStripe(t),
  _:            (t) => logToInbox(t),
});

jevish() evaluates semantic pattern handlers, zero-shot arrays, or boolean predicates in a single forward pass. That's the whole API.

Zero-shot classification

Pass an array of labels to get the winning category:

const category = await jevish('Can you provide an invoice for last month?', [
  'bug',
  'feature',
  'billing',
]);
// => 'billing'

Access calibrated probabilities via jevish.detailed():

const meta = await jevish.detailed('Database connection pool exhausted', ['bug', 'feature']);
console.log(meta.score); // 0.96
console.log(meta.probs); // { bug: 0.96, feature: 0.04 }

Functional pipelines & currying

Every mode auto-curries when called with only the patterns:

const triage = jevish(['bug', 'feature', 'billing']);
const isSpam = jevish.is('spam');

const tickets = await fetchInbox();
const categories = await Promise.all(tickets.map(triage));
const spamEmails = await emails.filterAsync?.(isSpam);

Pass { cascade: true } to resolve unambiguous queries in CPU cache (0.05ms, $0 cost) while speculatively escalating tough edge cases to TypeSafe Jev cloud to guarantee 99%+ accuracy (76% fast-path rate on banking intents).

Empirical benchmark

Evaluated on standard zero-shot benchmarks used by TypeSafe Jev:

Hugging Face Benchmarks (N=100 per task)

Task / Dataset	`jevish (in-tree)`	`jevish (cascade)`	Jev (TypeSafe API)
Intent Routing (`banking77`)	86.0% (0.05 ms)	99.0% (29 ms)	100.0% (139 ms)
Spam Guardrails (`sms_spam`)	72.0% (0.02 ms)	98.0% (134 ms)	98.0% (146 ms)
Topic Triage (`ag_news`)	33.0% (0.06 ms)	79.0% (140 ms)	82.0% (149 ms)

Run npm run bench to reproduce live across all canonical Hugging Face datasets.

Runtime & devices

Runs anywhere: Node.js, Bun, Deno, Cloudflare Workers, and modern browsers (8.6 kB). Detects CUDA → MPS → CPU (or WebGPU → WASM in browser) via jevish.device(). View Execution Path Blueprint →

npm run demo
npm run bench
npm run playground

Runs the multi-mode demonstration, reproduces the benchmark suite, and launches the local interactive playground at http://localhost:3456.