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Explore the Jev ecosystem.

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.

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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 1141–1200

Experimental Study: Non-Autoregressive Decision Architecture in Strategic Chess using TypeSafe AI Jev

by Deepender25 · TypeScript

Jev picks strategic preferences while a traditional engine searches for moves on a chess.com board.

Source screenshot of Experimental Study: Non-Autoregressive Decision Architecture in Strategic Chess using TypeSafe AI Jev
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choicescorenoul ★ 0 Benchmarks & research
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Agentic GraphRAG over S&P 10-Ks

by AnthonyAlcaraz · Python

A GraphRAG talk demo where Jev routes questions, checks evidence and decides whether deeper retrieval is needed.

Source screenshot of Agentic GraphRAG over S&P 10-Ks
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from the source JEV_MODEL = os.getenv("DEMO_JEV_MODEL", "jev-1.13.0")JEV_URL = "https://api.typesafe.ai/v1/systemone"LAYA_REPO = os.getenv("DEMO_LAYA_REPO", "convaiinnovations/laya")LAYA_MODEL_ID = "laya-421m-en"llm.PRICES_PER_MTOK[JEV_MODEL] = (0.042, 0.0)
choicenoul ★ 0 Apps & data pipelines
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PyroShield AI

by CodeWithEugene · TypeScript

A wildfire console that verifies citizen reports, rates evacuation routes and chooses a tactical response as the incident changes.

Source screenshot of PyroShield AI
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from the source import { TypeSafeClient, choice, noul, score } from "@typesafe-ai/sdk";/** * Server-side TypeSafe Jev client. Never import this from a client component: * the API key must stay on the server (Next.js Route Handlers only).
choicenoulscore ★ 0 Apps & data pipelines
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RazorRisk

by Dikshu015 · Python

Adds an optional second opinion to fraud investigations, comparing Jev's action and grounding check with the existing evidence pipeline.

Source screenshot of RazorRisk
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from the source questions={ "recommended_action": { "type": "choice", "instructions": "Given this fraud-risk evidence for one transaction, whi... "criteria": ACTION_CRITERIA,
choicenoul ★ 0 Apps & data pipelines
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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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