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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.

2140 experiments · showing 361–420

Jev Driving Lab

by kavehmz · JavaScript

A collection of Jev experiments spans support routing and a 3D driving simulation with visible sensor input.

Source screenshot of Jev Driving Lab
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from the source # Decision Labdocker compose -f demo01/compose.yaml up -d --build# Jev Driving Labdocker compose -f demo02/compose.yaml up -d --build# Fable implementation — work in progress
— ★ 8 Playable demos & bots
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问问Jev (Ask Jev)

by kuhung · TypeScript

Ask Jev is a retro Chinese advice desk for everyday dilemmas, with yes-or-no, choice and regret-risk modes.

Source screenshot of 问问Jev (Ask Jev)
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from the source const typesafeReq = buildTypeSafeRequest(question.trim(), mode, context?.trim());const remoteRes = await fetch(endpoint, { method: 'POST', body: JSON.stringify(typesafeReq), cache: 'no-store',
choicescorenoul ★ 8 Playable demos & bots
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zod-jev

by jomatsu · TypeScript

Zod checks whether your data has the right shape. Jev checks whether it makes sense.

Source screenshot of zod-jev
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from the source import { createJevZod, getSemanticIssues } from "zod-jev";const z = createJevZod(); // reads TYPESAFE_API_KEYconst Ticket = z.semantic(z.object({ subject: z.string(), body: z.string() }), [ { id: "refund_requested",
choicescorenoul ★ 7 SDKs & clients
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jevlogs

by reachjalil · TypeScript

An OpenTelemetry log-triage tool that uses Jev to decide which signals warrant deeper language-model analysis.

Source screenshot of jevlogs
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from the source # Evaluate the included sample logs with Jevnpx jevlogs --live --sample# Evaluate a local log filenpx jevlogs --live --file ./app.log --limit 20# Pipe JSONL in; get machine-readable decisions out
choicescore ★ 7 Apps & data pipelines
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system-one

by sgoedecke · Python

system-one performs batched, single-token choice inference with open language models through a TypeSafe-compatible interface.

Recorded frame of system-one
ACTUAL RECORDING12 SEC ↗
from the source from system_one import SystemOnefrom typesafe_sdk import Choiceengine = SystemOne.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")result = engine.system_one( state="My shoes arrived in the wrong size. Can I exchange them?",
choice ★ 7 Benchmarks & research
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jgrep

by keltokhy · Python

A command-line filter that uses Jev to match lines by a natural-language description rather than a text pattern.

Source screenshot of jgrep
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from the source export JEV_GATEWAY_URL=https://gateway.example.com/v1/systemoneexport JEV_GATEWAY_API_KEY=... # or ~/.config/jev/gateway.key and gateway.urljgrep "a stack trace" build.log # picked automatically when it is the only key set
choicescore ★ 6 Agent tooling
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jev-clerk

by stas4000 · Python

A Mac assistant that enters supplier invoices into Frappe Books, with Jev choosing the on-screen actions.

Source screenshot of jev-clerk
SOURCE SCREENSHOTEXPAND ↗
from the source URL = os.environ.get("TYPESAFE_URL", "https://api.typesafe.ai/v1/systemone")def choice(instructions: str, criteria: dict[str, str]) -> dict: return {"type": "choice", "instructions": instructions, "criteria": criteria}def ask(state: dict, questions: dict) -> dict: body = {"model": MODEL, "state": state, "questions": questions}
choicenoul ★ 6 Browser & computer use
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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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