Read & learn
Written guides.
Understand Jev, one idea at a time. Walkthroughs, recipes, and writeups — our notes first, the original next.
A public registry · independent & community-run
Explore the Jev ecosystem.
Learn, build, and play with Jev — from deep technical guides to creative experiments.
Watch & learn
Video guides.
See an idea take shape. Tutorials, demos, and deep dives, organized by topic and credited to their creators.
Make & explore
Experiments.
See what builders made with Jev. Games, tools, repositories, articles, and more — traced to their sources.
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 1141–1200
A parody MCP oracle that hashes your words into arbitrary confident answers, but sends serious health, money and safety questions to humans.
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from the source
// Jef. The entire model. A System Zero Model by TypoSafe AI (typesafe.lol).// Jef never writes a sentence. Jef only picks from what you gave it, scores it,function hash(s){ var h=2166136261>>>0; for(var i=0;i<s.length;i++){ h^=s.charCodeAt(i); h=Math.imul(h,16777619)>>>0; } return h; }
Builds an intentionally bad language model by asking Jev which word comes next, one Choice request at a time.
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choice
The text below is an excerpt from a longer document, cut off mid-sentence. Which word or punctuation mark comes next, immediately after the last word...
A pixel-art 8-Ball that scores nine yes-or-no propositions in parallel instead of asking one multiple-choice question.
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noul
Is the user’s text in `question` asking a question that can naturally be answered yes or no? · yes or no
A Victorian 8-Ball whose answer, paradox check and risk score animate its needle, steam and glowing fluid.
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choice
Determine if the statement or question is true/affirmative, false/negative, or ambiguous, and select the corresponding canonical Magic 8-Ball answer....
Scores LinkedIn posts for corporate performance, then invites Grok to roast only the ones that cross the line.
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score
How much of this post is performance rather than substance? · plain direct writing / mostly substantive / noticeably inflated / mostly performance / p...
A voice-controlled claw machine with Jev cast as the fruit-fly brain behind it.
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choice
Which claw command does `call` give? The call can be phrased freely, colloquially, rudely, in English or German, and may address the fly by name. · le...
Ask Jeeves, with Jev behind the search box and a choice of yes/no, multiple-choice or scored answers.
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noul
`instructions` is the visitor’s own question, verbatim, with an empty `state` unless they add context · Yes / No · Choice · Score
Query messy text with SQL-style filters and plain-language questions that Jev can answer.
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Check posts against moderation rules written in plain English, before your app decides what to publish.
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Helps an agent find the right tool or skill without loading the whole catalog into its main conversation.
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Give a Playwright page a task in words and let Jev choose the clicks and form actions.
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Run open-Jev decisions in your browser without an API key or inference server.
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Check generated answers against several rubrics, with Jev judging them in batches.
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A Pi tool for asking typed questions, with interchangeable Jev and chat-model backends.
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Ask Jev to label nytka datasets and keep a record of the questions and model used.
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An unofficial Jev community hub with a playground, small tools and projects to try in your browser.
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Test what an answer means, not just which words it contains, with Jev assertions for Vitest and Jest.
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Pick a question or type your own, and Jev answers it twice: once alone, once with six live Exa results in its state.
ACTUAL RECORDING12 SEC ↗
choice
Six Exa results, each `Title — domain (date): highlights`, sit in `state.search_results` beside the typed question · yes / no
Fifty HR tool logos sit in a pile at the bottom of the page. Type an HR problem and the ones that fit rise into rows; Jev scores all fifty at once.
ACTUAL RECORDING12 SEC ↗
from the source
POST /api/jev/search {"q":"payroll for a small startup with eight employees"}{"picks":[{"id":"gusto","noul":0.91},{"id":"payfit","noul":0.71}],"ms":191}{"scale":{"choice":"smb","confidence":1}}
Four models play parallel Pong lanes; Jev chooses a paddle move whenever the ball needs another decision.
ACTUAL RECORDING12 SEC ↗
Watch Jev and a fly-connectome player race across separate Minesweeper boards, one move at a time.
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Steer a Chicago traffic simulation with Jev choosing citywide priorities and bounded corridor policies.
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Post in a simulated town and see how thousands of Jev-driven residents react to your message.
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Type a color request and compare Jev’s ranked palette commands with an ordinary fuzzy match.
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Make a tier list from your own items and criterion, with Jev scoring each item before code places it into S–F tiers.
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Ask typed questions of a value with BAML’s .feels(), .how(), .matches() and .fill() methods backed by Jev.
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Play a mahjong hand while Jev recommends a discard and rates its efficiency, safety and wait.
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Compare Jev’s typed classifications with classical ML pipelines across the maker’s eight datasets.
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Hide or highlight LinkedIn posts after Jev scores their writing against several slop signals and counter-signals.
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Solve a bilingual lateral-thinking mystery by asking questions while Jev plays the keeper of the hidden answer.
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Get a shell completion from your command history that changes as you type, rather than a static prefix match.
ACTUAL RECORDING12 SEC ↗
Watch Jev and a chat model judge a Fed chair’s remarks as hawkish or dovish, line by line.
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Take penalty kicks against a Jev goalkeeper in a 3D football shootout.
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Replay six Jev chess experiments, from unaided legal moves to tactical filters and Stockfish-assisted variants.
ACTUAL RECORDING12 SEC ↗
Turn a Telegram channel’s posts into a year-in-review card showing their mix of news, jokes, ads and more.
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Write a browser test in Gherkin and let Jev map its steps to the controls on the page.
ACTUAL RECORDING12 SEC ↗
Tour a graphical Jev dojo with Tetris stacking, task routing, swarm reactions and other decision scenes.
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Pitch a startup idea to a retro infomercial and compare how stupid Jev thinks it is with how fundable it looks.
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Inspect listening ports on a Mac and ask Jev which processes to keep, clean up, or leave for your decision.
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Let Jev steer a Solari browser or desktop: it picks actions from visible controls, and the agent checks each move before continuing.
ACTUAL RECORDING12 SEC ↗
Crawl a site to uncover orphan pages and get Jev-ranked internal links with suggested sentences and anchor text.
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Race Jev through ten snap-judgment rounds, deciding whether reviews, messages, and other prompts are genuine, sarcastic, urgent, or invented.
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Can a cheap classifier label every agent failure in a training run? Applied Compute clusters a sample of traces, then has Jev do the labeling.
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from the source
Each model outputs a floating-point score from 0 to 1 for each of 14 failure modes.A score at or above the decision threshold marks that failure mode as present.
Give a chat app a few named subagents and let Jev decide who takes each turn and whether they work at once or in turn.
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choice
Who must handle this turn? · main or an agent name · then parallel or sequence
Three Jev tools for the DeepSeek Harness on WSL: ask typed questions, check whether evidence supports a claim, or rank candidates.
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from the source
return await client.systemOne({ state: args.state, questions })
3,608 one-subject-swapped statements put to Jev as forced true/false picks, in three wordings and eleven languages, with every raw answer published.
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from the source
self.url, self.model = JEV_URL, "jev-latest"
A zsh plugin whose ghost-text command suggestions are ranked by Jev on demand, with shell history redacted before anything leaves the machine.
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from the source
response = self.client.system_one(
A zero-dependency Node playground whose four tabs send typed questions to Jev through OpenRouter and draw the probabilities.
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from the source
upstream = await fetch(ENDPOINT, {
Runs the CLASH cross-modal contradiction test against Jev with each image replaced by its COCO caption, then reports accuracy and modality bias.
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from the source
r = self.session.post(f"{BASE_URL}/v1/systemone", json=body, timeout=self.timeout)
Every move in 2048 is one Jev Choice over the four directions, with no heuristic fallback and the probabilities, latency and cost shown live.
ACTUAL RECORDING12 SEC ↗
from the source
res = await fetch(url, { method: "POST", headers, body: JSON.stringify(body) })
Auto-marks 2,054 MR-GSM8K maths scripts against a teacher rubric with one Jev call each, then checks the marks against human annotators.
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from the source
const result = await client.systemOne(
A CPU-first GUI agent where RapidOCR reads the screen, Jev picks the next action, pyautogui performs it and dHash checks the result.
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from the source
r = _retrying_post(self.base_url, json=payload, api_key=self.api_key)
A local visual playground for System One APIs: build Yes/No, Choice and Score decisions, run them against several endpoints, and diff the results.
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from the source
url=provider["endpoint"]
A companion that answers only with a face: Jev picks one of sixteen moods and blobatar morphs its expression, with no text reply.
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from the source
const res = await fetch("https://api.typesafe.ai/v1/systemone", {
A Home Assistant integration that turns AI Task fields and YAML sensors into System One questions and decodes the answers back into Home Assistant values.
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from the source
async with self._session.post(
Browser and computer use for coding agents where each step is a batch of small typed questions to Jev and code composes the next action.
ACTUAL RECORDING12 SEC ↗
from the source
response = await this.fetch(`${this.baseUrl}/v1/systemone`, {
A Chrome extension that covers YouTube comments until Jev decides whether, read against the video, they disclose a plot event.
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from the source
upstream = await fetch(env.TYPESAFE_URL, {
Greek backgammon against Jev, which reads the position with four questions and picks the move as one Choice while code enumerates and prices every legal play.
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from the source
upstream = await doFetch(UPSTREAM, init)
An autonomous Tibia bot: an OTClient Lua module streams health, mana, position and visible monsters every 250 ms and Jev decides survival, danger and targeting.
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from the source
const response = await this.client.systemOne({
A Chrome extension that filters ads and uninteresting cards out of Xiaohongshu and Bilibili feeds, with Jev classifying each card and judging your interest in it.
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from the source
const resp = await fetch(CONFIG.apiBase, {
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