Embargo Phrase Guard
An editor pastes a press release and a photo package’s extracted date and restriction phrases, and Jev selects the applicable embargo phrase and checks whether the draft publishes before it.
The kernel.
Instead of writing new text, the app finds real candidates in the material and Jev picks the right one, so photographers get exact values for Editorial compliance.
Why this primitiveSelecting the original restriction wording preserves its exact terms and helps prevent costly timing mistakes.
Required key.
Add this in your Lovable project under Settings → Secrets before pasting the prompt below.
The build prompt.
Paste into a fresh Lovable project. Make sure the key above is set first. read the build strategy →
Build "Embargo Phrase Guard" 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
An editor pastes a press release and a photo package’s extracted date and restriction phrases, and Jev selects the applicable embargo phrase and checks whether the draft publishes before it.
Discipline: Photography (Editorial compliance).
Jev pattern: Select, Don't Generate (pick the real value) — code extracts candidate spans/values from the source, then a Jev `choice` (with a 'none' option) selects the intended one so the app copies real text instead of generating it.
Why typed decisions: Selecting the original restriction wording preserves its exact terms and helps prevent costly timing mistakes.
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": <Press release and draft plus extracted embargo phrases and dates>, "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)
{
"embargo_phrase": {
"type": "choice",
"instructions": "Which candidate phrase states the applicable photo embargo?",
"criteria": {
"span_1": "Explicit embargo date and time",
"span_2": "Explicit embargo date without a time",
"span_3": "General release or availability timing",
"none": "No candidate states an embargo"
}
},
"early_publish": {
"type": "noul",
"instructions": "Does the draft’s scheduled publication time precede the stated embargo time?",
"criteria": {
"true": "Scheduled publication is before the embargo lifts",
"false": "It is not before the stated embargo"
}
}
}
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
the source text with candidate spans highlighted and the selected one locked in. 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.
Indicative figures for hackathon pitches — refine with your own research before raising.