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

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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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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.

409 experiments · showing 301–360

Benchmarks & research Clear all

JEV versus LLMs: Accuracy, Cost and Calibration on Seven Political Science Replications

by Matthew DiGiuseppe / Steven Denney · Research / hosted model APIs

Compare Jev with human reference labels and language models across seven political-science replications, including annotation, scaling and probability calibration.

Source screenshot of JEV versus LLMs: Accuracy, Cost and Calibration on Seven Political Science Replications
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choicescorenoul Benchmarks & research
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A World Model for LLM Agents: Comparing a Decision Model with General-Purpose LLMs

by Yong Hwi Song and Keon Myung Lee, Chungbuk National University · Python TextWorld evaluation harness, Qwen3-4B policy and world-model baselines; manuscript and run logs

Song and Lee use Jev to predict a text game’s next state, then feed those probabilities to a fixed planner and compare against writing-model predictions.

Source screenshot of A World Model for LLM Agents: Comparing a Decision Model with General-Purpose LLMs
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choice Benchmarks & research
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Evaluating and Benchmarking the System One Model Jev

by Tobias Deußer, Lorenz Sparrenberg, Rafet Sifa

In 2026, Tobias Deußer and colleagues evaluated the Jev 1.13.0 model without task-specific training across 37 datasets, comparing it to direct answer probabilities from two open models.

Source screenshot of Evaluating and Benchmarking the System One Model Jev
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choicenoulscore Benchmarks & research
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TypedBench: A Benchmark for Calibration, Framing Sensitivity, and Cost in System One Decision Models

by Rahul Sharma; Andrew B. Duncan; Gaétan Marceau Caron; Sebastian J. Vollmer

TypedBench is a benchmark for System One decision models that evaluates policy adherence, wording robustness, probability quality, and induced decision outcomes.

Source screenshot of TypedBench: A Benchmark for Calibration, Framing Sensitivity, and Cost in System One Decision Models
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choicescorenoul Benchmarks & research
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Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks

by Xing Li; Qingcheng Chang; Jinzhong Ning; Changfeng Xu; Shenlong Zhang; Yijia Zhang; Ling Luo; Hongfei Lin

A preprint reports Jev matching frontier models on knowledge and commonsense tests while lagging on mathematical word problems.

Source screenshot of Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks
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choicescorenoul Benchmarks & research
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SecJev: Bringing Security Expertise to System One Decision Models

by Zheng Chen; Fei Yu; Haohao Huang; Yang Li; Anlong Chen; Lei Chen

SecJev is a family of security-specialized Jev-like decision models, ranging from 0.8B to 9B parameters, built on Kev's single-pass candidate scorer.

Source screenshot of SecJev: Bringing Security Expertise to System One Decision Models
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choicescore Benchmarks & research
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SearchJev + SearchDecision-Bench

by Congfeng Cao; Lipeng Zuo; Konstantinos Papakostas; Qiwei Xu; Songwei Xu; Lun Zhou; Zhaochun Ren; Yougang Lyu; Xiaohui Yan

SearchJev takes a search agent's quick calls, such as whether a page is relevant or enough evidence is in, and leaves planning and writing to a larger model.

Source screenshot of SearchJev + SearchDecision-Bench
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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?

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