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Explore the Jev ecosystem.

Learn, build, and play with Jev — from deep technical guides to creative experiments.

Make & explore

Experiments.

See what builders made with Jev. Games, tools, repositories, articles, and more — traced to their sources.

Featured experiment Jev experiments by Nader Dabit
Recorded frame from Jev experiments
Explore 2,140 entries
The directory03 / 03 · Experiments

Find something worth exploring.Experiments

Every category, from games and tools to repositories and writeups, traced to a primary source.

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Same ideas.
Different paths.

This is the full registry, not just the featured picks. Figures like speed, cost, and stars are maker-reported or captured snapshots, not JevMade measurements.

388 experiments · showing 1–60

DeerFlow includes opt-in Jev extensions for text classification, prompt-injection screening, and pruning old tool results.

Source screenshot of deer-flow
SOURCE SCREENSHOTEXPAND ↗
from the source async def _probability(self, client: httpx.AsyncClient, key: str, excerpt: str) -> f... body = { "model": self.options.model, "state": {"content": excerpt}, "questions": {"injection": _question()},
noulchoice ★ 83k Agent tooling

Oh My ClaudeCode can compare Jev with its existing coding-agent heuristics in shadow mode, then optionally let Jev make those decisions while retaining the original behavior as a fallback.

Source screenshot of oh-my-claudecode
SOURCE SCREENSHOTEXPAND ↗
from the source export async function queryJev( state: unknown, questions: JevQuestions, options: JevClientOptions,): Promise<JevResponse> {
choicescorenoul ★ 39.4k Agent tooling

Oh My Pi lets coding agents use Jev for structured decisions such as choosing an option or scoring a candidate.

Source screenshot of oh-my-pi
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from the source async #attempt<T>(path: string, body: string, key: string, signal: AbortSignal | undefi... const url = `${this.baseUrl}${path}`; const headers: Record<string, string> = { ...this.#headers, Authorization: `Bearer ${key}`,
choicescorenoul ★ 33.3k Agent tooling

jcode lets its fast browser agent ask Jev to choose the next move from the actions currently on screen.

Source screenshot of jcode
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from the source async fn decide(&self, request: &DecisionRequest) -> Result<Decision> { let body = request_body(request)?; let questions = body["questions"] .as_object() .context("Browser decision questions are missing")?
choice ★ 20.1k Agent tooling

OpenChamber asks Jev to choose a model for Auto sessions and, when enabled, flag risky tool permissions for human review.

Source screenshot of openchamber
SOURCE SCREENSHOTEXPAND ↗
from the source export const createJevClient = ({ fetchImpl = fetch, timeoutMs = JEV_TIMEOUT_MS } = {})... /** Resolves to the parsed answers; throws with `status` on an HTTP error and `code: '... ask: async (request, token) => { const abort = new AbortController(); const timer = setTimeout(() => abort.abort(), timeoutMs);
choicenoul ★ 10.6k Agent tooling

Sure can ask Jev to sort bank transactions into a family's categories and benchmark those choices against labeled examples.

Source screenshot of sure
SOURCE SCREENSHOTEXPAND ↗
from the source def decide!(state:, questions:, model: "") raise Error, "No questions provided" if questions.blank? questions = questions.transform_keys(&:to_s) questions.each { |key, question| validate_question!(key, question) }
choice ★ 10k Agent tooling

Qlty Slop One asks Jev to score code-quality traits in source excerpts, then rolls those scores into per-file reports.

Source screenshot of qlty
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from the source /// The validated answers for one excerpt, from the cache when the exact /// request was answered before. pub fn request(&self, state: &RequestState) -> Result<Answers> { let body = state.body()?; let key = canonical::digest(&body)?;
scorenoul ★ 3.2k Agent tooling

DSCode asks Jev whether a coding agent's risky action should proceed, stop or receive a fuller review.

Source screenshot of DSCode Jev auto-review
SOURCE SCREENSHOTEXPAND ↗
from the source verdict = await jev.approval({ action, context, sessionId: req.agent.session.id, signal:...if (verdict !== undefined) { if (verdict.decision !== 'defer') return applyVerdict({ decision: verdict.decision, re...}
choicenoulscore ★ 506 Agent tooling

See where a Claude Code session made progress and where it went in circles, with Jev reviewing a redacted trace.

Source screenshot of Claude Code Trace
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from the source key: "progressingEfficiently", label: "Progress", question: "Did the agent make steady, meaningful progress toward the user's task... higher_probability_is_better: true, answer: MetricAnswer::Noul,
noulscore ★ 370 Agent tooling

Autopilot's /jev command asks typed yes-or-no, choice or score questions about the current coding session.

Source screenshot of Autopilot /jev
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from the source const answer = await askJev(apiKey, question, { context });const presentation = presentJevAnswer(question, answer);setResult(eventKey, { kind: "jev", result: presentation.text,
noulchoicescore ★ 176 Agent tooling

CC Settings asks Jev whether a Claude Code prompt is broad enough to delegate.

Source screenshot of CC Settings delegation detector
SOURCE SCREENSHOTEXPAND ↗
from the source const jev = key ? await jevNoul({ key, prompt, instructions: JEV_INSTRUCTIONS, timeoutMs: JEV_TIMEOUT_... : null;if (jev !== null) { if (jev < JEV_THRESHOLD) return;
noul ★ 46 Agent tooling

Jeff reviews source files with a catalogue of Jev questions and reports pass, violation or inconclusive for each rule.

Source screenshot of Jeff
SOURCE SCREENSHOTEXPAND ↗
from the source response, err := (*client).Evaluate(ctx, catalog.Model, state, misses)for code, answer := range response.Answers { answers[code] = answer store.Put(catalog.Model, state, misses[code], *answer.Noul)}
noul ★ 38 Agent tooling

A semantic Hono router that directs HTTP requests according to their meaning.

Source screenshot of hono-jev-router
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from the source import type { JevResult } from 'hono-jev-router'const app = new Hono<{ Variables: { jev: JevResult } }>({ router: new JevRouter({ apiKey...app.on('jev', 'a request from an AI agent', (c) => { const { route, confidence, probabilities } = c.get('jev') // route: 'a request from an AI agent'
noul ★ 26 Agent tooling

Jev, briefly

A decision model, not a chatbot.

A chat model generates text token by token and hopes you parse it. Jev never generates a word. Your code sends the state of the world plus typed questions; Jev returns every answer in one parallel pass — typed values with calibrated probabilities and a confidence score your code can trust. About 70–500 ms end to end, $0.042 per million input tokens, output free. Trained with what TypeSafe calls Reinforcement Learning for Calibrated Decisions.

choice Pick one of these.

You define the options. Jev returns the chosen option, a probability for every option, and a confidence. The workhorse of routing, agents, and games — the answer space is always legal, so there is nothing to parse and nothing to hallucinate.

score Rate this on a rubric.

You describe ordered levels — say trivial / normal / critical. Jev returns the level, the probability of each, and a confidence. Scores turn fuzzy judgment (“how severe is this log line?”) into a number-free decision your code can branch on.

noul Is this statement true?

You assert a statement; Jev returns the probability that it is true — a noul. Moderation, verification, guardrails, “does this diff actually fix the bug?”: one question, one calibrated probability.

Official material lives at typesafe.ai and docs.typesafe.ai. JevMade is an independent community registry — not affiliated with or endorsed by TypeSafe AI.

Recurring lessons

Patterns that keep showing up.

One decision per tick

Games, drones, trading bots, and browser agents all converge on the same loop: serialize the state, ask one decisive question, act, repeat. Jev’s latency makes the loop feel instant — the model lives inside the control loop, not outside it.

Confidence as a gate

The answer says what; the confidence says whether to act. The most reliable entries threshold on confidence to route edge cases to a slower model or a human — automation with an honest escape hatch.

Shrink the choice space in code

Makers rarely ask Jev to pick from everything. Local tactics prune 225 gomoku moves to ~40; DOM filters turn a page into an element table; code narrows options, Jev judges within them.

Jev judges, LLMs talk

A recurring split: Jev makes every decision cheaply and instantly, and a small LLM is only invoked when a human-facing string must actually be written. Decision and generation are separate budgets.

Made something with Jev?

Send the link — repo, live demo, post, or video — plus a line on what it does and which primitives it uses. Every entry is verified against its primary source before it ships.

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