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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
Recorded frame from Jev experiments
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.

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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 601–660

A Codex plugin that uses Jev to restore verbatim context after session compaction.

Source screenshot of fast-dev-compaction
SOURCE SCREENSHOTEXPAND ↗
from the source npm installnpm run typecheck # librarynpm run check:hooks # node --check on every hook scriptnpm test # unit tests, fake Jev, no networknpm run build # emit dist/ (checked in: hooks import it)
— ★ 2 Agent tooling

Put a Jev check between an agent and its MCP tools to screen for unsafe calls and results.

Source screenshot of jev-shield
SOURCE SCREENSHOTEXPAND ↗
from the source npx -y github:caiovicentino/jev-shield install-hooks # opt in: hook + plugin (merges...npx -y github:caiovicentino/jev-shield uninstall-hooks # opt out: removes hook + plugi...JEV_HOOK_OFF=1 # kill switch (env), JEV_FAIL_M...
— ★ 2 Agent tooling

Skill Router uses Jev to match a Claude Code session's goal with relevant installed skills.

Source screenshot of skill-router
SOURCE SCREENSHOTEXPAND ↗
from the source $ skill-router intent set "write the cold outreach sequence for our fintech prospects"[skill-router] Session goal: write the cold outreach sequence for our fintech prospectsJev: task kind `marketing_growth`, needs-a-skill 0.84.Relevant installed skills for this session: /cold-email (probability 1.00) — Write B2B cold emails and follow-up sequences that ge...
— ★ 2 Agent tooling

A model router that uses Jev and a YAML policy to choose the least expensive LLM suited to each request.

Source screenshot of tiershift
SOURCE SCREENSHOTEXPAND ↗
from the source request ─► Jev: 11 questions, one call, ~180 ms ─► your YAML policy ─► model, fallback,... difficulty · stakes · needs_reasoning thresholds on probabilities; safety · domain · output_length · ... context, capability, and budget...
— ★ 2 Agent tooling

Browser automation with Jev choosing what to click and Playwright doing the clicking.

Source screenshot of jev-browser (tontoko)
SOURCE SCREENSHOTEXPAND ↗
from the source npx jev-browser open https://example.com --session worknpx jev-browser snapshot --session work# Use the ref returned by snapshot, or a caller-authored Playwright selector:npx jev-browser click 'a' --session worknpx jev-browser take_screenshot --args '{"filename":"page.png","fullPage":true}' --sessi...
choice ★ 2 Browser & computer use

A TypeScript library that recursively navigates a taxonomy so Jev can choose among more than 255 options.

Source screenshot of jev-tree
SOURCE SCREENSHOTEXPAND ↗
from the source import { createJevTree } from 'jev-tree';const tree = createJevTree({ maxFanout: 32 });const result = await tree.select({ state: 'Replica lag 47m on primary still accepting writes', question: 'Which incident type is this?',
choice ★ 2 Apps & data pipelines

A terminal interface pairs OpenAI answers with Jev routing, clarification and uncertainty decisions.

Source screenshot of jev-system-one
SOURCE SCREENSHOTEXPAND ↗
from the source flowchart TD U[Human asks a question once] --> J1[TypeSafe Jev: routing decision] J1 --> D1[Choice: response mode] J1 --> D2[Noul: clarification required] J1 --> D3[Noul: uncertainty notice required]
choicescorenoul ★ 2 Playable demos & bots

Test Jev on chess puzzles and on figuring out which game character you're talking to.

Recorded frame of jev-benchmark
ACTUAL RECORDING12 SEC ↗
from the source jevcommon/client.py rate-limited async TypeSafe client; persists every raw request/...jevchess/ chess benchmark state.py board → state at three levels (fen / ascii / rich); move descri... experiments.py A–G: Choice, Score fan-out, hierarchical, perception, mate-in-1... engine.py positions.py Stockfish ground truth; position generation
choicescorenoul ★ 2 Benchmarks & research

trade-jev backtests Jev as a buy, sell, or hold trader on Nasdaq futures order-book data.

Source screenshot of trade-jev
SOURCE SCREENSHOTEXPAND ↗
from the source uv run python -m trade_jev.run --days 2026-06-23 # one...uv run python -m trade_jev.run --days 2026-06-08,2026-06-09,2026-06-10 # sev...uv run python -m trade_jev.run --all # all...uv run python -m trade_jev.run --days 2026-06-23 --policies hold,random,imbalance # bas...
— ★ 2 Benchmarks & research

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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