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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
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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.

Real-time research powered by Firecrawl

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 1801–1860

TypeSafe launched Jev because sequential LLMs are “totally useless for computers”

by Adrian Bridgwater / The New Stack

The New Stack follows Jev’s launch, Vercel’s early adoption figures, and a developer who asks the text-only model to recognize drawings.

Source screenshot of TypeSafe launched Jev because sequential LLMs are “totally useless for computers”
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— Articles & threads
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Matching records with Jev: how many candidates to put in one question, and what goes wrong

by Shaun Mataire · Python

Shaun Mataire compares Jev’s multiple-choice and yes-or-no questions for matching product listings and research papers.

Source screenshot of Matching records with Jev: how many candidates to put in one question, and what goes wrong
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choicenoul Benchmarks & research
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Beyond Calibration: Do a Typed-Decision Model's Probabilities Obey the Probability Axioms?

by Keyi Li, Yihao He and Quanyi Li · Python

A study asks Jev logically related questions to see whether its probabilities agree with one another, not just with correct answers.

Source screenshot of Beyond Calibration: Do a Typed-Decision Model's Probabilities Obey the Probability Axioms?
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noulchoice ★ 0 Benchmarks & research
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Decision-Oriented Recommendation Reranking: An Empirical Study of Jev

by Hanjia Lyu and Yinglong Xia

A study compares Jev with Qwen and recommendation models when reordering candidate books, games and films for a reader's next choice.

Source screenshot of Decision-Oriented Recommendation Reranking: An Empirical Study of Jev
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choice Benchmarks & research
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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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