Rights Boundary Check
Artists paste usage requests alongside license terms, and Jev verifies each requested use against the terms and sends uncertain matches to a human queue.
Verify & Escalate· check then hand off
Section · Jev
full primer →The kernel.
Jev checks each claim about Image licensing against its evidence; anything unclear goes to a person, so painters never act on a guess.
Why this primitiveA yes/no fit decision per usage field is safer than generated legal-sounding prose.
Kernel
a Jev `noul` checks each claim/field against its evidence; low or mid probabilities are escalated to a human review queue instead of auto-acting
Drives the UI as
claim cards marked supported / unsupported / needs review, with a review queue column
Required key.
TYPESAFE_API_KEY
Your TypeSafe key. The mega-prompt asks for it with a secure form and keeps it on the server. Jev returns typed decisions: choice, noul (yes/no probability) and score.
open ↗Add this in your Lovable project under Settings → Secrets before pasting the prompt below.
Appendix · Mega-prompt
The build prompt.
budget · 1 message
Paste into a fresh Lovable project. Make sure the key above is set first. read the build strategy →
Build "Rights Boundary Check" as a ONE-SHOT Lovable build (~5 credits, no follow-ups).
Single-page TanStack Start app, one route, cut scope ruthlessly.
THIS PROMPT IS SELF-CONTAINED
Do not assume the participant knows Jev or has read any other page. TypeSafe Jev is a decision model,
not a text generator. It receives `state` plus a map of typed `questions` and returns JSON that code can
use directly. Use Choice to select exactly one defined option, Noul for a yes/no probability from 0 to 1,
and Score for a probability-weighted position across ordered levels. Keep rules, thresholds, arithmetic,
and actions in code. Use a generative model only if the product separately needs prose.
CONCEPT
Artists paste usage requests alongside license terms, and Jev verifies each requested use against the terms and sends uncertain matches to a human queue.
Discipline: Visual Art (Image licensing).
Jev pattern: Verify & Escalate (check then hand off) — a Jev `noul` checks each claim/field against its evidence; low or mid probabilities are escalated to a human review queue instead of auto-acting.
Why typed decisions: A yes/no fit decision per usage field is safer than generated legal-sounding prose.
ENGINE: TypeSafe Jev (typed decisions, not text)
- Before writing code, ask the participant for `TYPESAFE_API_KEY` with Lovable's secure secret form. Never
ask them to paste it into chat, never prefix it with `VITE_`, and never expose it to browser code.
- Put the callable server function in `src/lib/jev.functions.ts`; do not import a server-only module from UI code.
- Validate the user's input before the handler and read `process.env['TYPESAFE_API_KEY']` inside the handler.
- From the server, call:
POST https://api.typesafe.ai/v1/systemone
Authorization: Bearer <TYPESAFE_API_KEY>, Content-Type: application/json
body: { "model": "jev-latest", "state": <License terms, requested uses, and artwork metadata>, "questions": QUESTIONS }
- Ask all questions in ONE request (they run in parallel and cannot see each other).
- Read Choice from `answers[id].choice`, `.probabilities`, and `.confidence`; Score from `.score`,
`.probabilities`, `.legend`, and `.confidence`; Noul from `.noul` only (it has no separate confidence).
SERVER IMPLEMENTATION (adapt the input name and result mapping to this concept)
```ts
import { createServerFn } from "@tanstack/react-start";
import { z } from "zod";
const Input = z.object({ text: z.string().trim().min(1).max(20000) });
export const decide = createServerFn({ method: "POST" })
.inputValidator((input: unknown) => Input.parse(input))
.handler(async ({ data }) => {
const apiKey = process.env['TYPESAFE_API_KEY'];
if (!apiKey) return { ok: false as const, status: 500, message: "TYPESAFE_API_KEY is not configured." };
const requestBody = {
model: "jev-latest",
state: { text: data.text },
questions: QUESTIONS,
};
let response: Response | undefined;
for (let attempt = 0; attempt < 3; attempt += 1) {
response = await fetch("https://api.typesafe.ai/v1/systemone", {
method: "POST",
headers: { Authorization: `Bearer ${apiKey}`, "Content-Type": "application/json" },
body: JSON.stringify(requestBody),
});
if (response.ok || (response.status !== 429 && response.status < 500)) break;
if (attempt < 2) await new Promise((resolve) => setTimeout(resolve, 750 * 2 ** attempt));
}
if (!response) return { ok: false as const, status: 503, message: "Jev did not respond." };
if (!response.ok) {
const message = (await response.text()).slice(0, 500) || `Jev request failed (${response.status}).`;
return { ok: false as const, status: response.status, message };
}
const result = await response.json();
return { ok: true as const, answers: result.answers };
});
```
In the page component, call `decide` with `useServerFn`, preserve the participant's input while loading or
on error, disable duplicate submissions, and render the returned typed values rather than parsing prose.
QUESTIONS (start here, refine wording if needed)
{
"use_permitted": {
"type": "noul",
"instructions": "Do the license terms permit this specific requested use?",
"criteria": {
"true": "The requested use falls within explicit terms.",
"false": "Terms exclude it, are silent, or conflict."
}
},
"use_category": {
"type": "choice",
"instructions": "Which usage category best fits the request?",
"criteria": {
"editorial": "Editorial or educational use.",
"commercial": "Advertising, product, or paid promotion.",
"display": "Exhibition, portfolio, or public display.",
"derivative": "Adaptation, modification, or merchandise."
}
},
"term_clarity": {
"type": "score",
"instructions": "How clear are the applicable license terms?",
"criteria": [
"Clear: explicit scope and conditions.",
"Partial: scope is clear but a condition is ambiguous.",
"Unclear: key terms are missing or conflicting."
]
}
}
DECISION RULES (in code, not in the model)
- choice/score: if confidence < 0.6 show a "needs review" badge instead of acting.
- noul: >= 0.75 = yes, <= 0.25 = no, otherwise "review".
- score: normalise with score / (levels - 1) before combining with weights.
- Missing or malformed answers go to review. Show safe HTTP status and message in the UI. Retry only 429/5xx,
at most twice with increasing delay; never retry 400/401/402/403/404 and never invent results.
- If the product needs prose (a summary, a message), use Lovable AI for that text only; Jev makes the decisions.
UI
claim cards marked supported / unsupported / needs review, with a review queue column. Include 2 sample inputs as one-click chips so the demo works instantly.
Tailwind + shadcn, editorial look, footer: "Built during the Creative AI & Quantum Hackathon organised by StreetKode Fam during Indian Krump Festival 14".
NO database, NO auth, NO extra pages, NO tests.
BEFORE YOU FINISH
- Run the app and test both sample inputs through the real server function.
- Confirm the browser receives no API key and no fabricated fallback result.
- Confirm Choice/Score confidence and Noul probability drive the stated review rules.
- Confirm loading, success, review, empty-input, and service-error states are readable on mobile.
Reference only if needed: https://docs.typesafe.ai/api and https://docs.typesafe.ai/primitives
Market sizing.
TAM
$2B–$6B visual licensing services
SAM
$200M–$600M independent art licensing
SOM
$2M–$8M artist licensing tools
Indicative figures for hackathon pitches — refine with your own research before raising.
Adjacent entries.
Art provenance
Provenance Proof Desk
Gallerists paste artwork records and supporting documents, and Jev checks each provenance claim, classifies its evidence, and routes uncertain claims to human review.
Artwork conservationCondition Report Triage
Conservators paste condition notes and image observations, and Jev checks whether each recorded damage claim is supported before uncertain cases enter review.
Artist representationPortfolio Fit Sorter
Artists paste portfolio entries and gallery submission criteria, and Jev verifies each fit claim and escalates borderline submissions for human assessment.
Printmaking editionsEdition Ledger Sentinel
Printmakers paste edition records and sale entries, and Jev checks each edition, proof, and availability claim against the ledger, routing inconsistencies to review.