Read & learn
Written guides.
Understand Jev, one idea at a time. Walkthroughs, recipes, and writeups — our notes first, the original next.
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Explore the Jev ecosystem.
Learn, build, and play with Jev — from deep technical guides to creative experiments.
Watch & learn
Video guides.
See an idea take shape. Tutorials, demos, and deep dives, organized by topic and credited to their creators.
Make & explore
Experiments.
See what builders made with Jev. Games, tools, repositories, articles, and more — traced to their sources.
The directory03 / 03 · Experiments
Find something worth exploring.
Every category, from games and tools to repositories and writeups, traced to a primary source.
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.
1613 experiments · showing 1561–1613
Compare a fixed eight-second traffic-light cycle with a simulation where Jev decides whether to retain the current green signal.
ACTUAL RECORDING12 SEC ↗
Name a dish or cocktail and its ingredients rise from a pile of roughly 1,000 illustrated stickers.
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A “jevin keyboard” explores subtle ambient intelligence at the edge of generative interfaces.
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A Jev-powered copy-and-paste utility with bring-your-own-key support and a local mode announced as forthcoming.
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Audit Shopify catalogs for answer-engine problems such as products filed under unrelated categories.
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A planning model tracks an Overcooked level while Jev agents make the game's moment-to-moment decisions.
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Let Jev select sparse-attention rates across MiniMax H3 layers to shorten video generation on an RTX 4070.
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A Minority Report-inspired macOS interface uses Jev to drive its futuristic computer interactions.
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Read browser-agent activity logs every three seconds and average the resulting risk score across the session.
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Browses Jev projects by category, recency, or name, linking each card and detail page back to its maker's original post.
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Adds Jev's typed decisions as a LangChain Runnable, with experimental middleware for model routing and tool-risk checks.
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wmoto ran a local decision-model prototype and published a ten-second demonstration while noting that its speed still needed work.
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Erik Kokalj ran an 8-bit MLX conversion of Jev-Omni locally to choose jump, duck, or wait in Chrome Dino from cropped road images.
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Jacky Ko and collaborators introduced an Apache-2.0 contrastive language model that connects states to actions as an open System One model.
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Compare open-source Jev alternatives in Beam's playground, then take the request into your own application.
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Browse Jev projects by category, stars or age, with links to their repositories, demos and writeups.
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Find community Jev systems through their typed questions, with source snippets and maker posts alongside each listing.
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Speak a voice diary, let Jev sort your thoughts, then review the notes, tasks and reminders before sending them to Notion.
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Search an English–Icelandic dictionary by meaning, with Jev checking whether the shortlist contains a match and ranking the results.
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Bring Jev Search's ranked links into Claude Code, Codex or a terminal through an MCP server, CLI and optional WebSearch hook.
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Play Pong against Jev and watch its movement, shot selection, probabilities, and fallbacks in a live decision monitor.
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Four ways of asking Jev to play 2048, compared with random moves and fixed rules, with replays of the resulting games.
ACTUAL RECORDING12 SEC ↗
OpenRouter's Jev Router picks the model and reasoning effort for each request, and holds a model while its prompt cache stays warm.
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from the source
Jev Router picks the best model and reasoning effort for each request, balancing quality...It runs on Jev, TypeSafe's first System One model, and adapts as your conversation evolv...
OpenCodex asks Jev to choose a model and reasoning level for each Codex request, using the proxy's configured route whenever Jev is unavailable or returns an invalid answer.
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from the source
requestBody = JSON.stringify({ model: JEV_MODEL, state, questions: buildJevRouteQuestion(options.candidates),});
A Phoenix Evals example pits Jev against a small grounded-versus-hallucinated answer benchmark.
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from the source
const jev = typeSafeAi.evaluationModel("jev-latest");const jevEvaluator = createHallucinationEvaluator({ model: jev });const [jevResult, jevMs] = await timed(() => jevEvaluator.evaluate(example));
Docker Agent asks Jev whether a pending tool call is clearly read-only or needs the user's approval, treating uncertain calls cautiously.
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from the source
type: choicechoices: read_only: Clearly read-only, with no credential access or external data transfer. risky: Modifies or deletes data, executes opaque code, accesses credentials, or transf... unknown: Insufficient information to establish the operation's effects.
GPTCache asks Jev whether a cached answer still fits a new request, taking differences in the requests, answer, and dates into account.
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from the source
scores = { key: float(data["answers"][key]["noul"]) for key in self.DIMENSIONS}self.last_scores = scoresreturn min(scores.values())
Agent Native asks Jev which optional tools an agent should see at the start of a request, based on the user's recent context.
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from the source
candidateStateKey: "candidate_tools",answerKey: "best_tool",question:Which tools should be loaded into the agent context first for this task? Pick the most u...limit: prefetchLimit,
Memorax asks Jev whether searching saved coding memories would materially help with the current prompt and, when available, the previous turn.
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from the source
body: JSON.stringify({ model: JEV_MODEL, state, questions: { search_needed: SEARCH_QUEST...const answer = isRecord(body) && isRecord(body.answers) ? body.answers.search_needed : u...const probability = answer.noul;return { ok: true, decision: probability >= 0.5 ? "search" : "skip", probability, model:...
Interactive Shell uses Jev to decide when terminal work needs attention and which pre-authorized action may help.
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from the source
questions.attention = { type: "choice", instructions: `Choose the primary visible state using only explicit current evidence..... criteria: { working: "routine work is active, retrying, progressing, or healthily waiting...",
haiku.rag provides a reusable System One judge for benchmark verdicts such as whether two answers are equivalent.
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from the source
response = await self.client.system_one( self._state(ctx), {name: self._question()}, model=self.model)p = response.nouls[name].noulverdict = EvaluationReason(value=p >= threshold, reason=f"system_one p={p:.3f}")
Kimaki's OpenCode auto-mode asks Jev whether a pending tool action may run without another prompt, using the action, its arguments, and the latest user message.
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from the source
state: serializedState,questions: { allow: { type: 'boolean', instructions: `Should this pending OpenCode tool action run automatically? ${CLASSIF...
ctx sift uses Jev to keep useful or contradictory passages while shrinking an agent's tool output.
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from the source
let judgment = client.select(selection.task(), selection.candidates(text));if let Some(scores) = judgment.scores.as_deref() && let Some(proposal) = selection.propose(scores){ selected_count = proposal.kept_count();
TypeSafe Jev Gate adds a fail-closed review step to Hermes Agent's consequential tool calls.
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from the source
answers = evaluate_tool(tool_name, args, **kwargs)secret_safe = _noul(answers, "no_secret_egress")score = risk(answers)eligibility = answers.get("eligibility", {}).get("choice")
Lintus turns plain-language YAML rules into Jev checks and reports threshold-crossing answers as lint offenses.
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from the source
let mut query = jev::Query::new(format!("File: {path}\n\n{content}"));for rule in &task.rules { query.ask(&rule.id, rule.to_noul())?;}let response = self.client.perform(&query)?;
Search a retained window of Kubernetes or file logs with Jev, then inspect matching, possible, and unrelated evidence.
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from the source
questions.insert(format!("e{i}_relevance"), json!({ "type": "choice", "criteria": criteria, "instructions": "Does events[i] address the log-search query..."}));
mem-jev asks Jev to judge ambiguous agent memories, then folds five semantic signals back into retrieval ranking.
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from the source
evaluation, err := service.evaluator.Evaluate(ctx, request.State)record, err := NewJudgmentRecord(...)return ServiceResult{ Disposition: DispositionCommitted, Features: record.Features,
Mistype a subcommand and jevyoumean asks Jev which documented command you probably intended.
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from the source
"intended": { Type: "choice", Instructions: instructions, Criteria: criteria(commands),}
A JevNPC trial asks whether a threatened Minecraft villager should ignore danger or call the guards.
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from the source
criteria.put("IGNORE", "Start no new action...");criteria.put("CALL_GUARD", "Request help from the listed eligible guards...");String instructions = "Which available response should this NPC take now to stay safe?";
Keel can ask Jev which installed coding provider and model should receive a new task.
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from the source
let input = SelectionInput { state: DecisionState { task, context, state_version }, candidates: prepared_routes,};let outcome = selector.select(input, &DecisionBudget::new(1)).await;
Triage messages, classify CSV rows, interpret voice commands, or let Jev steer Snake from a Java web playground.
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from the source
var state = game.state(semantics);var decision = model.decide(state, Game.QUESTIONS);var choice = Answer.get(answers, Game.MOVE, Answer.Choice.class);var move = Direction.valueOf(choice.choice().toUpperCase(Locale.ROOT));return new Turn(game.step(move, random), ...);
Send text through a named semantic rule or your own question and receive a valid, invalid, or uncertain result.
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from the source
"result": map[string]any{ "type": "choice", "instructions": question, "criteria": map[string]string{"true": "El texto cumple...", "false": "El texto no cump...}
Record live Jev decisions as redacted cassettes, then replay or perturb them without another provider call.
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from the source
JevResponse response = delegate.send(request);repository.save(new Cassette(...));
Watch Jev choose a legal Tetris placement from a heuristic shortlist, then play the selected move.
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from the source
client.choose(QUESTION_ID, JevPromptSupport.state(snapshot), INSTRUCTIONS, criteria);return selected.move();
AutoNomousBot shows Jev a Minecraft world snapshot, lets it choose from available actions, then performs the selection.
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from the source
jevClient.askChoice(state, question, fallbackId)actionExecutor.execute(client, chosen);
Give Jev Pilot a mobile goal and it observes the screen, chooses a bounded UI action, and performs it.
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from the source
let answers = self.judge.evaluate(...)?;let mut decided = catalog.resolve(&answers, &floors);
Add Jev-backed tool approval, chat triage, transcript curation, and model routing to OpenClaw.
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from the source
const scored = await this.client.score({if (scored.level >= this.approvalLevel) {
TweetGuard uses Jev to sort individual X posts into spam categories while leaving uncertain posts visible.
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from the source
const uncertain = answer.confidence < JEV_MIN_CONFIDENCE;const isSpam = answer.choice !== 'normal' && !uncertain;
DLQ Inspector asks Jev whether an ambiguous dead-letter message should be replayed, fixed, avoided, or investigated.
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from the source
resp, err = a.Client.Evaluate(ctx, state, jev.ClassificationQuestions())out.Classification = jevChoiceToClassification(verdict.Classification)
Any Auto asks Jev whether coding-agent tool calls are risky, authorized, and policy-compliant before approving them.
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from the source
json!({"model":self.config.approver.model,"state":input.state,"questions":questions})let mut result = into_review(&response)?;
Supercov asks named quality and security questions about source files, then turns Jev's answers into inspectable findings.
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from the source
if let Some(health) = catalog::health(values) {weighted.push((answer.bytes, health));
Sentio classifies email with Jev and adjusts spam scores from unsolicited-mail and phishing probabilities.
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from the source
let state = self.build_state(message_text, envelope_from, envelope_to);let score_delta = self.score_delta(unsolicited, phishing, confidence);
Run Laya’s typed decisions natively on Apple Silicon, then watch its terminal Snake demo choose moves with safety corrections.
ACTUAL RECORDING12 SEC ↗
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.
Submit your experiment → hello@JevMade.com