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 1–60
QuantDinger can ask Jev to review a live trade entry before the order proceeds.
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from the source
response = requests.post( url, headers={"Authorization": f"Bearer {config['api_key']}"}, json={"model": model, "state": self._state_payload(request), "questions": JEV_QUESTI...)
GenOffice can ask Jev to reorder local search results by how well each document excerpt matches a query.
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from the source
export const fetchTransport: JevTransport = async (url, body, key, signal) => { const res = await fetch(url, { method: 'POST', headers: { Authorization: `Bearer ${key}`, 'Content-Type': 'application/json' }, body,
Reads supported Android chat screens, judges conversational intent, and offers replies that users may fill before sending manually.
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An AI stock-research team that can use Jev to rate an investment and flag risks before writing its report.
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from the source
"rating": { "type": "choice", "instructions": "综合团队讨论结论，该股票的最终投资评级是哪一档？", "criteria": dict(RATING_MAP), },
Kuest asks Jev to spot unclear prediction-market rules and rank useful context for a draft.
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from the source
export async function requestOpenRouterDecisions( request: OpenRouterDecisionRequest, options?: { apiKey?: string; timeoutMs?: number },): Promise<OpenRouterDecisionResponse> { const apiKey = options?.apiKey
A trading bot asks Jev what to do on every Monad block.
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Type one thought and watch the text box morph into an event card, checklist, conversion, calculation, or other fitting tool.
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Uses local OCR and Jev to judge Windows chat messages, rank three drafted replies, and fill one without sending it.
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Shows intent, risk, and ranked reply suggestions beside supported macOS chats while keeping message sending under user control.
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Identify IRS forms and schedules from their text with Jev.
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from the source
const q1: Record<string, ChoiceQuestion> = { kind: { type: 'choice', instructions: 'What kind of page is this?', criteria: KIND_C... form: { type: 'choice', instructions: FORM_INSTRUCTIONS, criteria: firstList }, } const r1 = await opts.backend.ask(state, q1)
Shortlist coding approaches, score them in a loop, and inspect the gates surrounding an LLM implementation run.
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Point DocJev at a PDF and a list of categories you wrote in plain English, and Jev names the category or marks where each new document starts.
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noul
Does page number 5 start a new source document, rather than continue page number 4?
Jobseek asks Jev to sort batches of listings by a job seeker's plain-language preferences, showing accepted and rejected matches in separate groups.
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from the source
decision: parseChoiceAnswer(answers[job.key]),
A browser video editor whose Jev-powered Director turns plain-language requests into structured timeline operations.
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from the source
// Shared shapes for the Director's timeline operations and vault-media placement.// Filled by Jev on the server (POST /director/route), executed in Home.tsx.export type TimelineOperation = | 'delete' // remove clip(s) | 'split' // cut clip(s) at a time
Notra turns material from your work into publishable content.
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Provides a visual editor for connecting Jev decision nodes into runnable workflows.
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A web playground compares parallel and one-shot LLM judgments with a compatible open decision-model backend.
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Float a translucent panel over WeChat or QQ to see intent, risk, urgency, and Jev-ranked reply candidates.
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Search multilingual text by meaning, with grep-like thresholds and AND, OR and NOT combinations over exact source lines.
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Prism shadows deterministic Solana liquidity rules with Jev judgments about strategy, toxic flow, recovery, and market stress.
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Lurk uses Jev to find Reddit posts that show useful buyer intent for a product.
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from the source
const answers = await askJev({ purpose: "score", projectId, ...judgeRequest(product, batch, readings),});
Crawls a site from its homepage and uses Jev to judge page-level SEO findings for downloadable reports.
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Read English aloud and see word-level mismatches plus passage-level meaning judgments as the transcript settles.
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Find sponsor reads in YouTube captions or live transcription, then skip only the time ranges controlled by the extension.
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Watch Gmail previews cross a Jev checkpoint and drop into reply, update, promotion, sales, or spam trays.
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Hear It Fresh asks Jev to find themes in song lyrics so listeners can include or exclude them from a playlist.
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from the source
const answers = await classifySongThemes(row, signal, plan.stale);const themes = deriveThemes(raw);await setSongThemes(row.id, themes, raw, QUESTION_VERSIONS, DERIVE_VERSION);
Screens datasets with Jev, separates keep, review, and reject records, then trains and compares a configured language model.
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Long-press a WeChat message to see its likely intent, emotion, urgency, and a suggested reply posture in a local popup.
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A video-editing tool that scores each spoken sentence with Jev and renders the results as a live 16:9 meter.
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Describe a startup by purpose, logo appearance, or visible text and watch matching YC logos rise from a physics pile.
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Control Ableton Live in English or Japanese from a short typed or dictated command, with Jev choosing among only currently valid targets and actions.
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Build a systematic-review table from verbatim evidence in papers rather than model-written answers.
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A SillyTavern extension measures chat with custom Jev sensors, then applies matching rules to narration, lists, rerolls, or scripts.
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Filter an X timeline with plain-language rules, per-author overrides, and correction feedback using Jev or a chat model.
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Say “Hey Jev” to a Mac and watch a compact assistant route commands, questions, timers, and reminders.
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A Discord bot that uses Jev to filter spam and scam links, escalate repeat offenses, and help moderators review member history.
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Describe a visual world in one sentence and let Jev turn it into a coordinated dashboard design system.
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Reveal emotional probabilities, implied meaning, and communication advice beside WeChat messages in an Android overlay.
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A Rust tool that uses Jev to classify Git commits by change type, bug-fix status, and security relevance.
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Scores social posts for hook strength and lets creators compare rewrites inside a Chrome extension.
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Builds knowledge-graph relations from extracted evidence and typed Jev decisions, retaining provenance for each edge.
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A social-deduction game where Jev chooses which bot speaks next and separately screens text written by human players.
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from the source
export const JEV_REPLY_SCORE_LEVELS = [ 'not part of the current thread of the discussion', 'mentioned in passing or only loosely connected to the current thread',
Talk to a macOS desktop app that combines realtime voice conversation with optional Jev-guided browser actions.
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Suggests internal links by judging candidate target pages and choosing honest anchor phrases already present in the copy.
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from the source
def noul(instructions: str) -> dict: return {"type": "noul", "instructions": instructions}
A Jev-powered tool for searching codebases by meaning.
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from the source
bun installexport TYPESAFE_API_KEY="your-key"
Write down a dilemma, let an LLM arrange the possibilities, then open a book-like answer chosen by Jev.
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Scroll X while behavioral labels and warning chips appear beneath posts before they enter view.
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Navigate a YouTube transcript by topic, chapter, and semantic heat map without asking Jev to rewrite it.
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Chooses and gates small reusable programs from natural-language requests, asking before irreversible actions.
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Read a visible WeChat conversation and see Jev’s judgments about intent, emotion, urgency, and how to respond.
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Create inbox rules that use Jev probabilities to tag, move, flag, or notify on matching messages.
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Drives a mobile app through a QA task and finishes with a clear pass, fail or incomplete result.
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Adds semantic predicates to PostgreSQL queries, evaluating them outside the database and folding typed judgments back into results.
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Deletes Telegram group spam only when Jev’s probability clears a configured threshold, leaving messages untouched on failures.
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A spatial reference browser for creators, with source-linked collections, metadata highlights, and local Jev query choices.
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Jev chooses instruments, harmony, and bar patterns for a composition you can refine in a piano roll and export as MIDI.
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Compare six small demos of Jev handling routing, triage, inbox classification, filtering, and scoring.
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Stamp low-quality X posts with a translucent STOP and mark promotions already identified by the platform as AD.
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Screens each morning’s arXiv feed against your interests, then publishes the strongest matches with per-interest probabilities.
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A Python trading bot that uses Jev for market decisions and includes backtesting support.
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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?
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 experimentSign in first so we can reply to you. Submissions are reviewed, not published automatically.