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A public registry · independent & community-run

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
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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 1441–1500

LogJev

by DumoeDss

LogJev turns text, images, or audio into choices and scores using supported models’ log probabilities.

Source screenshot of LogJev
SOURCE SCREENSHOTEXPAND ↗
choicescore SDKs & clients
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Lurk

by Kevin Wang

Lurk monitors Reddit threads for citation opportunities and sends matches through email, Discord, or Slack.

Recorded frame of Lurk
ACTUAL RECORDING12 SEC ↗
— Apps & data pipelines
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Nitpicky

by Richard Meng

Nitpicky zooms into faces, fingers, text, numbers and poses, then asks Jev to judge whether a photo was AI-generated.

Recorded frame of Nitpicky
ACTUAL RECORDING12 SEC ↗
— Apps & data pipelines
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Not Slop

by Eric Yang

Eric Yang’s browser extension uses Jev to flag suspected AI slop on LinkedIn and X as the reader scrolls.

Recorded frame of Not Slop
ACTUAL RECORDING12 SEC ↗
— Apps & data pipelines
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jev() for PostgreSQL

by Zachi

Zachi’s PostgreSQL extension uses a jev() function to filter database rows with natural-language conditions, without indexes or embeddings.

Recorded frame of jev() for PostgreSQL
ACTUAL RECORDING12 SEC ↗
— Integrations
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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.

Submit your experiment → hello@JevMade.com