🎬 Filmmaking & Animation · Motion design

Motion Spec Sentinel

Motion designers paste animation specs and timing notes, and Jev decides whether each delivered motion claim is supported, escalating uncertain deviations for review.

Verify & Escalate· check then hand off
Section · Jev

The kernel.

full primer →

Jev checks each claim about Motion design against its evidence; anything unclear goes to a person, so filmmakers never act on a guess.

Why this primitiveTyped compliance decisions are easy to audit against specifications and do not fabricate revised motion concepts.

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
Appendix · Secrets

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.

Paste into a fresh Lovable project. Make sure the key above is set first. read the build strategy →

Build "Motion Spec Sentinel" 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
Motion designers paste animation specs and timing notes, and Jev decides whether each delivered motion claim is supported, escalating uncertain deviations for review.
Discipline: Filmmaking & Animation (Motion design).
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: Typed compliance decisions are easy to audit against specifications and do not fabricate revised motion concepts.

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": <Motion specs, timing notes, and delivery observations>, "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)
{
  "timing_claim_supported": {
    "type": "noul",
    "instructions": "Does the evidence support the claimed timing compliance?",
    "criteria": {
      "true": "Observed timing falls within the cited specification.",
      "false": "Timing conflicts with or is not verified against the specification."
    }
  },
  "motion_deviation": {
    "type": "choice",
    "instructions": "Classify the potential motion deviation.",
    "criteria": {
      "duration": "The animation duration differs from the spec.",
      "easing": "The easing or motion curve differs from the spec.",
      "sequence": "The order or presence of motion events differs.",
      "none": "No deviation is evidenced."
    }
  }
}

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
Appendix · Market

Market sizing.

TAM
$8B+ motion design software
SAM
$1B motion QA and review
SOM
$4M design studios

Indicative figures for hackathon pitches — refine with your own research before raising.

See also

Adjacent entries.