🎥 Videography & Film · Production planning

Shotlist Feasibility Ranker

Paste a shoot brief and proposed shots, and Jev scores each shot for story value, location feasibility, and setup efficiency so a weighted ranking guides the schedule.

Score Ranker· graded ranking
Section · Jev

The kernel.

full primer →

Jev rates every option for Production planning on a few named qualities; videographers move sliders to decide what matters and the ranking updates instantly.

Why this primitiveComparable scores help crews prioritize shots under real constraints without replacing the director’s judgment.

Kernel
comparable Jev `score` questions per candidate on 3-4 named dimensions, normalised with score/(levels-1) and combined with user-controlled weights in code
Drives the UI as
a ranked list with weight sliders that re-rank instantly without calling Jev again
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 "Shotlist Feasibility Ranker" 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
Paste a shoot brief and proposed shots, and Jev scores each shot for story value, location feasibility, and setup efficiency so a weighted ranking guides the schedule.
Discipline: Videography & Film (Production planning).
Jev pattern: Score Ranker (graded ranking) — comparable Jev `score` questions per candidate on 3-4 named dimensions, normalised with score/(levels-1) and combined with user-controlled weights in code.
Why typed decisions: Comparable scores help crews prioritize shots under real constraints without replacing the director’s judgment.

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": <Shoot brief, shot descriptions, and constraints>, "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)
{
  "story_value": {
    "type": "score",
    "instructions": "How important is this shot to communicating the story?",
    "criteria": [
      "0 — No meaningful story value",
      "1 — Nice-to-have detail",
      "2 — Supports an important beat",
      "3 — Essential story beat"
    ]
  },
  "location_feasibility": {
    "type": "score",
    "instructions": "How feasible is this shot under the stated location constraints?",
    "criteria": [
      "0 — Not feasible",
      "1 — Major obstacles",
      "2 — Feasible with adjustments",
      "3 — Straightforward to shoot"
    ]
  },
  "setup_efficiency": {
    "type": "score",
    "instructions": "How efficiently can this shot be captured relative to its value?",
    "criteria": [
      "0 — Very costly for little value",
      "1 — Inefficient setup",
      "2 — Reasonable effort",
      "3 — High value for minimal setup"
    ]
  }
}

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
a ranked list with weight sliders that re-rank instantly without calling Jev again. 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
Production planning software: $500M–$2B
SAM
Small and mid-size production teams: $100M–$500M
SOM
Independent production crews: $1M–$5M

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

See also

Adjacent entries.