Read & learn
Written guides.
Understand Jev, one idea at a time. Walkthroughs, recipes, and writeups — our notes first, the original next.
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.Experiments
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.
394 experiments · showing 181–240
Solve Japanese lateral-thinking mysteries by asking questions that Jev judges against a hidden truth.
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A point-cloud rigid-body simulator asks Jev to classify possible collisions after conventional broad-phase filtering.
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Paste a story or find one on Wikipedia, then chart three characters’ action intensity and feelings across the plot.
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Experiment with pixel art by asking Jev to map simple silhouettes or judge generations of code-made sprites.
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Turn a document into a sentence-by-sentence heat map of requirements, tension, rhetorical role, or other chosen dimensions.
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Direct a music-video pipeline in which Jev chooses camera, composition, lighting, and tone before other tools generate each cut.
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Decode an ad’s persuasion pattern, then rank template-built rewrites against a synthetic focus-group rubric.
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Load books, PDFs, Word files or images on Windows or Android and ask bounded questions with answers expressed as probabilities.
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A read-only crypto alert bot that studies settled signals and adjusts its filters without ever placing a trade.
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A GraphRAG talk demo where Jev routes questions, checks evidence and decides whether deeper retrieval is needed.
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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)
Tell Jev what you want to watch and let it pick from a catalog of 4,800 films.
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from the source
class ChoiceQuestion(BaseModel): type: Literal["choice"] = "choice" instructions: JSONContent = None criteria: dict[str, JSONContent]
A wildfire console that verifies citizen reports, rates evacuation routes and chooses a tactical response as the incident changes.
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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).
Adds an optional second opinion to fraud investigations, comparing Jev's action and grounding check with the existing evidence pipeline.
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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,
Review your Valorant rounds with ratings from local rules or an optional Jev judge.
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from the source
const payload = { state: input.state, model: this.model, questions: toPayloadQuestions(questions), }
A local second brain where Jev reranks search evidence and suggests useful links between related notes.
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from the source
export function passageScoreQuestion(query: string, pointer: string): JudgmentQuestion { return { type: 'score', instructions: `Score how useful the passage at \`${pointer}\` is as evidence for the... criteria: [
A Spanish-language cancer podcast library where Jev sorts new videos by format, topic and relevance before code decides publication.
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from the source
export const editorialQuestions={ category:{type:'choice',instructions:'Identify the PRIMARY topic of this video from its...
Turns a text and a pack of criteria into a shareable wall of verdict cards, appearing as each batch returns.
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from the source
const TYPES = new Set(['noul', 'score', 'choice']); if (item.type === 'score' && (!Array.isArray(item.criteria) || item.criteria.length...function asQuestion(item) { return { type: item.type, instructions: item.instructions, ....
A crossword agent where Python manages the grid and crossings while Jev ranks short wordlist candidates for each clue.
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from the source
answers = self._jev( state, { "fits": { "type": "noul",
A Telegram fantasy league and token-research copilot that asks Jev whether deeper Nansen data is worth the credits.
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from the source
"worth_deep_dive": { "type": "noul", "instructions": "Is this token move worth spending 5 credits of deep Nan... },
A small playground that turns one line into several visible Jev judgments about romance, social posting or city routing.
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from the source
import { choice, noul, score, type Questions } from '@typesafe-ai/sdk';romantic_frame: choice(love_signal_strength: score(is_passionate: noul('Does `text` read as intense, burning romantic feeling?', {
Play music and get one of twenty house cocktails chosen to match its sound.
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from the source
const upstream=await fetch('https://api.typesafe.ai/v1/systemone',{method:'POST',headers...const data=await upstream.json(), answer=data.answers?.cocktail;const recipe=candidates(input).find(r=>r.id===answer?.choice);
A local research desk where Jev chooses methods, searches and leads, then checks evidence and claims under fixed budgets.
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from the source
from typesafe_sdk import AsyncTypeSafeClient,Choice,Score,Noul,RetryPolicy if a.get('type')!=q.type: raise DecisionError('Answer type mismatch') if q.type=='choice': if a.get('choice') not in q.criteria or set(a.get('probabilities',{}))!=set(...
Add classification, probability and scoring columns to CSV rows from questions written in YAML.
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from the source
questions: - name: department type: choice question: Which team should handle this message? options:
Jev dispatches taxis through a simulation of Baku's streets.
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from the source
const INSTRUCTIONS = { surucu: "Bu sifarişi hansı sürücü götürməlidir? Məsafə, gözlənilən çatma vaxtı və reyt... tecililik: "Sərnişinin mesajına əsasən sifariş nə qədər təcilidir?", saxta_sifaris: "Bu sifariş saxta, zarafat və ya sui-istifadə cəhdidir?", xidmet_tipi: "Sərnişinə hansı xidmət tipi uyğundur?",
A customer-support demo that uses GPT-4o to write replies and Jev to check them.
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from the source
JEV_URL = "https://api.typesafe.ai/v1/systemone"JEV_MODEL = "jev-1.13.0" # pinned on purpose; "jev-latest" can change under youHAZARD_BLOCK_AT = 0.5INTENT_MIN_CONFIDENCE = 0.5EVAL_PASS_AT = 0.75
A small web crawler that asks Jev which page to visit next.
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from the source
API = os.environ.get("JEV_API_URL", "https://api.typesafe.ai/v1/systemone")DEFAULT_MODEL = os.environ.get("JEV_MODEL", "jev-latest")def yes(answer: dict, key: str) -> bool: """Read a ``noul`` answer as a boolean.""" return bool((answer.get("answers", {}).get(key) or {}).get("noul"))
A tokenized-stock trading demo that uses Jev to interpret what you want to do.
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from the source
execution_intent: { type: "choice", instructions: "What does the user want to do about `request.text` given `mar... criteria: { market_now: "User explicitly accepts current on-chain price and wants imme...
Label rows in a CSV or JSONL file with Jev, and set uncertain answers aside for a person to review.
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from the source
questions: category: type: choice instructions: "Is `message` unsolicited commercial spam, or a message the recipient... options:
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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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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Hide or highlight LinkedIn posts after Jev scores their writing against several slop signals and counter-signals.
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Watch Jev and a chat model judge a Fed chair’s remarks as hawkish or dovish, line by line.
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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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Inspect listening ports on a Mac and ask Jev which processes to keep, clean up, or leave for your decision.
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Crawl a site to uncover orphan pages and get Jev-ranked internal links with suggested sentences and anchor text.
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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(
Every move in 2048 is one Jev Choice over the four directions, with no heuristic fallback and the probabilities, latency and cost shown live.
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from the source
res = await fetch(url, { method: "POST", headers, body: JSON.stringify(body) })
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 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, {
A garage of small inspectable demos where Jev supplies the judgment — fraud screening, cyber triage, medical routing, emergency dispatch — and plain Python policy decides what to do.
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from the source
return client.system_one(state=STATE, questions=QUESTIONS)
Maps threat-report prose onto MITRE ATT&CK techniques and emits a Navigator layer you can load straight into the ATT&CK interface.
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from the source
self._reflex = reflex or Reflex(model=model, lint=\"off\", guard=\"flag\", **reflex_kwargs
A local RAG workbench over Chinese League of Legends patch notes: every number comes from parsing the announcement, and Jev only reranks candidates and checks its own answer.
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from the source
ENDPOINT = os.environ.get(\"TYPESAFE_ENDPOINT\", \
A creator-inbox desk that asks Jev five questions per comment and lets code choose the action; a confidence slider re-routes the whole queue without another call.
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from the source
const result = await client.systemOne({state: { ... },questions: QUESTIONS,
A Next.js workbench for Jev's three primitives through OpenRouter: a 95-category Choice, Noul evaluation with transfer curves, and Score rubric rating.
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from the source
const endpoint =
A Chrome side panel that auto-trades an NSE stock through Zerodha Kite: Jev answers a direction question each tick and a risk gate sizes and places the order.
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from the source
const res = await fetch(`${JEV_BASE_URL}/v1/systemone`, {method: \"POST\",signal: ctrl.signal,headers: { \"Content-Type\": \"application/json\", Authorization: `Bearer ${apiKey}` },body: JSON.stringify({ state, model: JEV_MODEL, questions }),
Has Jev play chess against itself one decision per move, with a browser board showing the position and Jev's last answer.
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from the source
endpoint = f\
A real-time card-transaction fraud classifier where a System One model makes every live decision and an LLM agent runs only for the ambiguous slice.
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from the source
thin adapter around langchain_typesafe TypeSafeClassifier; wire asserted in backend/test
Play Jev at Gomoku, Go or chess: code builds a pool of legal candidate moves and Jev answers one Choice over them, with every call shown under the board.
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from the source
const NATIVE_URL = "https://api.typesafe.ai/v1/systemone";
A 2D fighting demo where Jev calls the intent every few hundred milliseconds and frame-level code turns that intent into footwork, blocks and punches.
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from the source
const JEV_URL = 'https://api.typesafe.ai/v1/systemone';
An agentic GraphRAG engine where every graph operation is a typed decision from a swappable System One model, and the LLM runs only once to write the answer.
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from the source
_JEV_API_URL =
A 24/7 market-making system where Jev answers six atomic judgments per tick and a code-side policy composes the action and holds the risk vetoes.
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from the source
response = await self._client.system_one(
Jev plays chess against Stockfish, against any OpenRouter model, or against you, with the moves it weighed drawn as arrows and their probabilities beside them.
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from the source
const data = await postJson(url, r.keyed ? key : "", { model: r.model, state, questions
An unofficial operator-level showcase: a local lab with a smart-home demo, worked examples for each primitive and pattern, and a TypeScript CLI harness for agent loops.
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from the source
f"{url}/v1/systemone", (the httpx client.post on the preceding line sends payload with s
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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