# Two tools, same job, different mental models Plain version first: these are **code-to-video** tools. You describe a timeline in HTML or React; the tool screenshots each frame and **FFmpeg** (a common stitcher for video files) glues the frames into an MP4. **Remotion** is the React-flavored one. You do not need Remotion, React, or FFmpeg installed to read this lesson. Programmatic video on the web has settled into two open-source options: **Remotion**, the established one, and **HyperFrames**, the 2026 challenger from HeyGen. They both render an MP4 from web code via a headless browser. They differ in *what you write*. | Tool | You write | The runtime is | The renderer is | |---|---|---|---| | **HyperFrames** | HTML + CSS + a paused GSAP timeline | Headless Chrome bundled by the CLI | `npx hyperframes render` (local FFmpeg pipeline) | | **Remotion** | React components with a `useCurrentFrame()` hook | Chromium under `@remotion/renderer` | `npx remotion render` locally, or `@remotion/lambda` on AWS | Both produce the same kind of output: a deterministic MP4 that's reproducible across runs. The pick is about how your team thinks, not what the output looks like. ## What "the same job" actually means A composition in either tool has: 1. A **timeline** with explicit duration in seconds (or frames). 2. **Layers** — visual elements positioned in space and time. 3. **Media** — images, video, audio, ideally synced to the timeline. 4. A **render** step that walks frame-by-frame, screenshots each frame, and stitches them into an MP4 via FFmpeg. The big idea is the same in both: **treat video composition as a deterministic function of inputs**. Same inputs → same MP4, byte for byte. That's what makes parametric rendering possible. ## What they're not - **Not real-time playback engines.** They don't compete with Unity or Unreal. They render offline, then play back. - **Not video editors.** They don't compete with Premiere or DaVinci. You write code, not click on a timeline. - **Not AI video models.** They don't generate frames from prompts. They composite frames you give them — and those frames can come from AI gen models, stock libraries, or hand-shot footage. ## Why this lesson exists The two tools are well-documented separately. What's missing is the *decision*: when do you reach for which? This lesson tours both, lays out the decision tree, and ends with you writing `pick_tool(project)` — a rule-based classifier that takes a project profile and returns `"hyperframes"`, `"remotion"`, `"both"`, or `"ai-gen-only"`. You'll also see where the community `design-html` skill fits (a third-party GStack skill for Claude Code, wrapping Cheng Lou's Pretext) — it produces Pretext-native HTML for design finalization, which is *not* the same as HyperFrames HTML but lives in the same neighborhood.2e:Td47,# HyperFrames — HTML is the source of truth for video HyperFrames is what happens when someone notices that web developers already know how to author rich visuals (HTML + CSS + GSAP) and stops trying to invent a new authoring environment for video. You write an HTML file. The CLI renders it to MP4. ## The file you write A HyperFrames project is an `index.html` file plus optional sub-compositions in `compositions/`. The HTML is plain — no framework, no build step from your side. Elements get `data-*` attributes for timing: ```html

Launch day

``` Notes that matter: - **Every clip needs `data-composition-id`** for the renderer to identify it. - **The timeline is `paused: true`** because the framework drives playback. - **You register the timeline on `window.__timelines`** so the renderer can find it. - **Duration comes from `data-duration`**, not from the GSAP timeline length. ## Rules HyperFrames enforces A few non-negotiable rules from the skill ground truth — these will save you debugging time: - **Deterministic only.** No `Math.random()`, no `Date.now()`, no `setTimeout` for timing. Use a seeded PRNG (like mulberry32) if you need pseudo-random values. The render must be reproducible. - **No `repeat: -1`.** Infinite repeats break the capture engine. Calculate a finite repeat count from duration. - **Synchronous timeline construction.** Build the timeline at page load. The capture engine reads `window.__timelines` synchronously. - **Animate visual properties only.** `opacity`, `x`, `y`, `scale`, `rotation`, `color`, `borderRadius`, transforms. Do NOT animate `visibility` or `display`. Do NOT call `video.play()` — the framework owns playback. ## The CLI surface Look at the table in the editor. The workflow is always: `init` once, then a loop of `lint` → `preview` → render small previews, with `render` at the end for the final MP4. Render flags worth knowing: - `--quality draft` while iterating, `--quality high` for delivery. - `--fps 30` is default; `--fps 60` doubles render time. - `--format webm` if you need a transparent video. - `--docker` produces byte-identical output across machines (important for CI). - `--strict` fails the render if lint produces any errors. ## Why this design wins for some teams HyperFrames is built on three bets: 1. **CSS animations are good enough for most motion design.** With GSAP on top, you have the same toolbox web designers use every day. 2. **HTML is the most reviewable text format for visual work.** Diff-friendly, version-control-friendly, easy to read in a PR. 3. **The browser's compositor is the best frame engine that already exists.** Why reinvent it? If your team thinks in CSS and GSAP — if "I'd just write a div with `transform: translateX(...)` for that" is your first instinct — HyperFrames matches your mental model. If your team thinks in React state machines, the next section is for you.2f:Te84,# Remotion — React components, frame by frame Remotion's pitch: "if you can write React, you can write video." A composition is a React component. The renderer evaluates that component once per frame, screenshots the output, and stitches the frames into an MP4. ## The component you write ```tsx import { useCurrentFrame, interpolate, AbsoluteFill } from "remotion"; export const Title: React.FC<{ headline: string }> = ({ headline }) => { const frame = useCurrentFrame(); const opacity = interpolate(frame, [0, 30], [0, 1], { extrapolateRight: "clamp", }); return (

{headline}

); }; ``` `useCurrentFrame()` returns the current frame number (0 at start, `fps * duration` at end). `interpolate(frame, [0, 30], [0, 1])` maps frames 0-30 to opacity 0-1. There's no timeline object you push tweens onto — every frame is a fresh render of the component with the current frame number wired through props or hooks. ## How a project is laid out A Remotion project is a normal React app under `src/`, plus a `remotion.config.ts` and a `Root.tsx` that registers compositions: ```tsx import { Composition } from "remotion"; import { Title } from "./Title"; export const Root = () => ( <> ); ``` That `` block is the same shape as a HyperFrames clip — name, duration, dimensions. Props are explicitly typed, which is the React-y advantage: TypeScript catches "I forgot to pass the headline" at compile time. ## The render path Two rendering options: - **Local:** `npx remotion render hero-title out.mp4 --props='{"headline":"Hello"}'`. Spins up Chromium, renders frame by frame, stitches via FFmpeg. Good for laptop iteration. - **AWS Lambda:** `@remotion/lambda` distributes the render across many Lambda functions running in parallel. Each function renders a chunk of frames; the result is concatenated in S3. This is how production teams render long videos in seconds instead of hours. Lambda renders cost a few cents per render on AWS, plus Remotion's licensing layer on top. ## Licensing — read the fine print Remotion is "open source" but commercial usage at scale requires a paid license. As of 2026: - **Free license** for individuals or companies with ≤ 3 employees. - **Company license** is per-developer-seat ($25/dev/month) plus per-render fees for self-hosted ($10 per 1000 renders/month), with a $100/month minimum. - **Enterprise license** starts at $500/month. HyperFrames is also open source but does not have a per-seat or per-render licensing wall. If cost across a large org matters, that's a real consideration. ## Why this design wins for some teams Remotion is built on three bets, parallel to HyperFrames' three: 1. **React is the most expressive language for declarative UI**, and video is UI through time. 2. **The component model handles parametric rendering naturally.** Props in, frames out. A loop over a JSON file becomes a loop over `defaultProps`. 3. **AWS Lambda is the right scale model.** Hundreds of renders in parallel without provisioning servers. If your team already lives in React, ships Next.js apps, and would rather write `interpolate(...)` than tween config objects, Remotion's mental model is the right match. If your team is half designers, half engineers, and "let's write some CSS" is the natural first instinct, HyperFrames probably wins.30:Tcc5,# Pick by team fit, not feature list The two tools have ~95% feature overlap. They both render parametric, deterministic MP4s from web code, run in a headless browser, and support audio + video tracks. Trying to pick based on "which has more features" is the wrong frame. Pick based on **who's on the team** and **how the renders are deployed**. Four signals do most of the work. ## Signal 1 — team_familiarity_react The biggest predictor of which tool wins is whether your team already ships React. If yes, Remotion's mental model (`useCurrentFrame()`, `interpolate(...)`, props) is a natural extension of what they do all day. If no — if your team is designers, freelancers, or people who think in CSS first — the HyperFrames learning curve is much shorter. **Rule:** React-heavy team → Remotion bias. CSS/design-heavy team → HyperFrames bias. ## Signal 2 — needs_text_reflow If headlines have to auto-wrap based on actual rendered font metrics — "this could be 'Welcome' or 'Welcome to the demo experience', and the layout has to look good either way" — HyperFrames has an edge. It runs in real Chrome with real font metrics. There's a `window.__hyperframes.fitTextFontSize(...)` helper specifically for dynamic text overflow. Remotion can do this too, but you'll be writing it yourself in React. HyperFrames ships the helper. **Rule:** Text-heavy parametric work → HyperFrames slight edge. ## Signal 3 — render_target If you're rendering on one developer's laptop, both tools are roughly equivalent. If you're rendering 1000s of videos a day, you want Remotion + AWS Lambda. The Lambda distribution is the production scale move — render chunks in parallel, concatenate the result. HyperFrames doesn't have an equivalent of `@remotion/lambda` baked in; you'd build your own queue + worker pool. **Rule:** `render_target == "scale"` (cloud, 1000s/day) → Remotion bias. ## Signal 4 — complexity For very simple compositions (a single shot with a headline), both tools are overkill. AI gen might be the right answer. For high-complexity work (10+ scenes with custom shader transitions), HyperFrames has a built-in shader-transitions package and a richer scene-transition system in the skill files. Remotion can do it; you'll just write more glue. **Rule:** Very simple → AI-gen-only might do it. Very high complexity → HyperFrames bias on transitions, Remotion bias on Lambda parallelization. ## The combined rule (what you'll code) ```python def pick_tool(project): if project["complexity"] == "simple" and project["render_target"] == "local": return "ai-gen-only" # don't reach for a framework if project["team_familiarity_react"] and project["render_target"] == "scale": return "remotion" # React team + Lambda need if project["needs_text_reflow"] or not project["team_familiarity_react"]: return "hyperframes" # design-first team or heavy reflow return "both" # in-house engineers, pick the one with momentum ``` `"both"` isn't a cop-out — some shops run a Remotion pipeline for parametric ads and a HyperFrames pipeline for designer-driven brand work. Different tools for different jobs is fine. The point is to *choose intentionally*, not by accident.31:Tae5,# The Claude Code `design-html` skill and where it fits You'll see the term "Claude design" come up in two related but distinct senses. Worth pinning down so you don't mix them up. ## Sense 1 — the `design-html` skill (inside Claude Code) `design-html` is a gstack skill that runs inside Claude Code. It produces **Pretext-native HTML/CSS** — production-quality web pages where text actually reflows and heights are computed dynamically. The skill is for design finalization: someone has approved a mockup (from `/design-shotgun`, a CEO plan, or a back-of-napkin sketch) and now needs the actual HTML. It is *not* a video tool. It produces static web pages, not MP4s. The neighborhood overlap is that the HTML it produces is the same kind of asset HyperFrames composes into video — clean, deterministic, web-native. If your pipeline is "design the page, then turn it into a 20-second promo video," you'd run `design-html` to ship the page and then HyperFrames (via `website-to-hyperframes`) to capture the page into a video. ## Sense 2 — "Claude Design" the standalone product There's also Claude Design, Anthropic's design tool. The `claude-design-hyperframes` skill exists specifically for that environment — it produces valid HyperFrames first drafts inside Claude Design that the user can then download, open in Claude Code, and refine. Two things to know: 1. The Claude Design skill uses **pre-valid skeletons** so the output passes `npx hyperframes lint` immediately. 2. It explicitly hands off to a coding agent for polish: "Claude Design creates the first cut of the film, Claude Code does the edit-bay refinement." This handoff pattern matters for the pipeline lesson coming up. Different stages of the work want different tools, and the same HyperFrames project file is what passes between them. ## When `design-html` enters the video pipeline Three real cases: - **You need a landing page first, then a video about that page.** Run `design-html` to ship the page. Run `website-to-hyperframes` against the URL of that page to produce the promo. Same brand, same copy, same colors — because both came from the same DESIGN.md. - **You need to embed a static "frame" inside a video.** A pricing table, a feature comparison, a stat callout. `design-html` produces the HTML; HyperFrames embeds it as a clip. - **You're doing a `/design-shotgun` exploration that ends in motion.** Generate variants statically, pick one, finalize with `design-html`, then bring it into motion via HyperFrames. The mental model: `design-html` owns the *page*. HyperFrames owns the *page through time*. Remotion owns the *component through time*. Three tools, three slightly different jobs, one shared DNA — production-quality web output that's reproducible because it's code.32:T4f8,projects = [ ("one-off mood reel", {"team_familiarity_react": True, "needs_text_reflow": False, "render_target": "local", "complexity": "simple"}), ("5000 yearly recap", {"team_familiarity_react": True, "needs_text_reflow": False, "render_target": "scale", "complexity": "medium"}), ("designer brand reel", {"team_familiarity_react": False, "needs_text_reflow": False, "render_target": "local", "complexity": "medium"}), ("reflow-heavy ads", {"team_familiarity_react": True, "needs_text_reflow": True, "render_target": "local", "complexity": "medium"}), ("in-house mixed team", {"team_familiarity_react": True, "needs_text_reflow": False, "render_target": "local", "complexity": "high"}), ] def pick_tool(project): complexity = project.get("complexity", "medium") render_target = project.get("render_target", "local") react = project.get("team_familiarity_react", False) reflow = project.get("needs_text_reflow", False) if complexity == "simple" and render_target == "local": return "ai-gen-only" if react and render_target == "scale": return "remotion" if reflow or not react: return "hyperframes" return "both" for name, p in projects: print(f"{name+':':22s}{pick_tool(p)}") 33:T4f1,projects = [ {"name": "founder vision reel", "team_familiarity_react": True, "needs_text_reflow": False, "render_target": "local", "complexity": "simple"}, {"name": "spotify wrapped clone", "team_familiarity_react": True, "needs_text_reflow": False, "render_target": "scale", "complexity": "high"}, {"name": "brand kit promo", "team_familiarity_react": False, "needs_text_reflow": False, "render_target": "local", "complexity": "medium"}, {"name": "multilingual product", "team_familiarity_react": True, "needs_text_reflow": True, "render_target": "local", "complexity": "medium"}, {"name": "agency in-house team", "team_familiarity_react": True, "needs_text_reflow": False, "render_target": "local", "complexity": "high"}, ] def pick(p): if p["complexity"] == "simple" and p["render_target"] == "local": return "ai-gen-only" if p["team_familiarity_react"] and p["render_target"] == "scale": return "remotion" if p["needs_text_reflow"] or not p["team_familiarity_react"]: return "hyperframes" return "both" def route_projects(projects): return {p["name"]: pick(p) for p in projects} out = route_projects(projects) for p in projects: print(f"{p['name']+':':24s}{out[p['name']]}") 34:Td3a,# Seven steps, none of them optional A shippable video has seven canonical steps. Skip any one and you either don't ship or ship something broken. AI gen models give you huge leverage on steps 2 and 4 — generating reference frames and generating narration audio — but they don't *replace* the pipeline. They make each step cheaper. The pipeline: 1. **Concept** — what's this video doing? Who's it for? What action does it drive? This is the hardest step, and the one humans still own. AI can suggest concepts; it can't decide whether the brand should run them. 2. **Reference-gen** — generate visual references for the look. Hero shots, b-roll, color palettes, moodboards. Veo, Sora, Midjourney, Runway. The cost is now low enough to generate 10 references and pick the best. 3. **Composition** — assemble the references (plus copy, captions, brand assets) into a timeline. This is where HyperFrames or Remotion lives. The output of step 3 is a project file you can render. 4. **Audio** — narration (TTS via HyperFrames' built-in Kokoro voices, or ElevenLabs, or Cartesia), music (licensed or AI-generated via Suno, Udio), sound effects. Audio is the single biggest predictor of whether a video feels professional. 5. **Captions** — transcribe the audio to word-level timestamps and layer captions on screen. Much of feed video is watched muted, and captions lift retention even on sound-on platforms. They aren't optional. 6. **Render** — go from project file to MP4. Verify lint passes, verify WCAG contrast on text, verify the timeline duration matches the audio. 7. **Review** — show the rough cut to stakeholders, collect notes, re-render. The reproducibility property from lesson 01 is what makes this loop converge. ## What changes when you go AI-native Pre-AI, this pipeline took a team — copywriter, motion designer, voice actor, sound engineer, video editor. The fast version was two weeks. The slow version was two months. The AI-native version compresses each step by 10-100×: | Step | Pre-AI cost | AI-native cost | |---|---|---| | Concept | 2-3 days of meetings | 1-2 hours with an AI brainstorm partner | | Reference-gen | $500-2000 stock photo/footage license | 5-10 cents per Veo/Sora generation | | Composition | Motion designer at $1500/day | Engineer + HyperFrames for free | | Audio | $300-800 voice actor + studio | Kokoro TTS at ~free, or ElevenLabs at ~$0.30/min | | Captions | $1-3/minute via Rev | Whisper local for free | | Render | $50-200 render farm time | Lambda at a few cents, or local for free | | Review | 2-3 day async cycles | Same-day if the renders are fast enough | The cost compression is the headline. The bigger story is the *throughput* compression. A team that used to ship 1 video a week can now ship 1 video a day. Or — with parametric variation — 100 videos a day, each personalized. ## What this lesson teaches The rest of the lesson walks through where AI fits at each step, the `website-to-hyperframes` pattern as a worked example, and the cost model. Then you'll write `score_pipeline(steps)` — a function that audits a team's pipeline and tells you which steps they're missing. Most pipelines fail not because step 3 is wrong but because someone forgot step 5 (captions) or step 7 (review). Scoring the pipeline against the seven canonical steps surfaces the gap before it costs you a ship date.35:T11cf,# Different model for different step The "AI model" inside this pipeline isn't one model — it's at least four, each doing what it's best at. Routing the right work to the right model is what makes the pipeline cheap and good. ## Step 1 — Concept A reasoning-strong text model. Claude Opus, GPT-5, Gemini Ultra. The job is to take a fuzzy brief ("we want to launch our new SKU") and produce a structured concept doc: who's the audience, what action are we driving, what's the hook in the first 3 seconds, what's the CTA at the end. The token bill is small (a few thousand input, a few hundred output). You'll iterate maybe 3-5 times before locking the brief. Tools that sit in this slot: gstack's `/plan-ceo-review`, `/design-shotgun`, `/plan-design-review`. They're concept-stage thinking partners. The output is a written plan, not pixels. ## Step 2 — Reference-gen A video generation model. As of mid-2026, the strongest are Veo 3 (Google), Sora 2 (OpenAI), Runway Gen-4, Pika 2. Pick by what you need: - **Veo** for realistic motion and lighting. - **Sora** for stylized scenes and stronger physics. - **Runway** for image-to-video (start from a still, animate it). - **Pika** for fast iteration and lipsync. Costs in 2026: usually a few cents to a couple dollars per 5-10 second clip. Generate 10, throw away 9, use 1. The economics work because the rejection rate is much higher than for human-made footage but the per-unit cost is 100× lower. ## Step 3 — Composition Not an AI step. This is where a coding agent (Claude Code, Cursor, Codex, Aider) drives the HyperFrames or Remotion project. The agent reads the brief, picks scenes, writes the HTML or React components, runs `npx hyperframes lint`, and iterates. Where models *do* help: writing the composition. The HyperFrames skill files are designed specifically for an LLM agent to read — they're long, detailed, and ground-truth-authoritative. A coding agent can author a HyperFrames composition in one shot given the SKILL.md and a brief. ## Step 4 — Audio Three sub-steps, three different models: - **Narration (TTS):** HyperFrames ships with Kokoro-82M (the `npx hyperframes tts` command). Voices like `af_nova`, `bf_emma`, etc. Free, local, deterministic. ElevenLabs is the paid alternative when you need a specific celebrity-style voice — costs ~$0.30/minute. Cartesia is the low-latency alternative. - **Music:** Suno or Udio for AI-generated tracks. Generally licensed for commercial use under their subscription tiers. Both are at the "good enough for B-roll backing track, not good enough for the hero piece" stage. - **SFX:** ElevenLabs Sound Effects, or pre-licensed Sonniss/Freesound libraries. AI gen still doesn't beat a real foley library for short percussive hits. ## Step 5 — Captions Whisper (OpenAI) for transcription. HyperFrames bundles it via `npx hyperframes transcribe`. Runs locally, free, supports multiple model sizes (`tiny.en`, `base.en`, `medium.en`, `large-v3`). The output is word-level timestamps that the composition layer can sync against. For non-English content, deepgram or AssemblyAI tend to outperform Whisper on accuracy. ## Step 6 — Render Not an AI step. `npx hyperframes render` locally or `@remotion/lambda` for parallel cloud renders. Verify lint passes, verify contrast (HyperFrames runs a WCAG audit automatically), verify the timeline matches the audio length. ## Step 7 — Review A vision-strong model can help with QA — Claude with vision, GPT-5 vision. Feed it the rendered MP4 (or screenshots at key timestamps) and ask: "any visual issues?". HyperFrames also has an animation-map script that produces a structured Gantt chart of every tween. Reading the JSON is faster than re-watching the video 50 times. ## The routing table | Step | Model class | Example tools (2026) | |---|---|---| | Concept | Reasoning text | Claude Opus, GPT-5, Gemini Ultra | | Reference-gen | Video gen | Veo 3, Sora 2, Runway Gen-4, Pika 2 | | Composition | Coding agent | Claude Code, Cursor, Codex | | Audio (TTS) | Speech synthesis | Kokoro (local), ElevenLabs, Cartesia | | Audio (music) | Music gen | Suno, Udio | | Captions | Transcription | Whisper, Deepgram, AssemblyAI | | Render | None (mechanical) | HyperFrames CLI, Remotion CLI, @remotion/lambda | | Review | Vision model | Claude vision, GPT-5 vision | The routing matters. Using a reasoning model for transcription is wasteful. Using a transcription model for concept work is malpractice. The pipeline wins when each step calls the right model for the job.36:T11f0,# The `website-to-hyperframes` worked example The cleanest example of the seven-step pipeline in the wild is a real `website-to-hyperframes` skill the author runs in production. It takes one input — a URL — and produces a finished MP4 promo. Every canonical step from the previous lesson maps to one of its sub-steps. ## The user prompt > "Capture https://acme.com and make me a 25-second product launch video." That's the whole brief. The skill runs seven gated steps to turn it into a finished HyperFrames project. Each step produces an artifact that gates the next. ## Step-by-step ### 1. Capture & Understand Scrape the site (Puppeteer-style). Extract the brand: top colors, fonts, hero image, key headlines, vibe sentence. The output is a working summary the rest of the pipeline reads. **Gate:** print a site summary (name, colors, fonts, assets, one-sentence vibe). ### 2. Write `DESIGN.md` Six sections, ~90 lines. Brand cheat sheet: palette with hex values + roles, typography (1-2 families), motion guidelines, "what NOT to do" anti-patterns. This is the document every later step references. Notably, every HyperFrames composition is required by the SKILL.md "Visual Identity Gate" to trace its palette and typography back to a `DESIGN.md`. **Gate:** `DESIGN.md` exists in the project directory. ### 3. Write `SCRIPT.md` The narration script. Story backbone. Scene durations come from the narration — not from guessing. If the script says "Today, Acme is launching..." and that's 3.2 seconds in TTS, the first beat is 3.2 seconds. Audio drives timing. **Gate:** `SCRIPT.md` exists. ### 4. Write `STORYBOARD.md` Per-beat creative direction: mood, camera, animations, transitions, assets, depth layers, SFX. This is the creative north star — the document the engineer follows to build each composition. **Gate:** `STORYBOARD.md` exists with beat-by-beat direction and an asset audit table. ### 5. Generate VO + map timing Generate TTS audio (HyperFrames `npx hyperframes tts` with a Kokoro voice). Transcribe the TTS output (`npx hyperframes transcribe narration.wav`) to get word-level timestamps. Map timestamps to beats. Update `STORYBOARD.md` with real durations. **Gate:** `narration.wav` + `transcript.json` exist; beat timings in `STORYBOARD.md` updated. ### 6. Build compositions Build each composition in HyperFrames following the storyboard. After each one: self-review for layout, asset placement, animation quality. The skill enforces non-negotiable rules: every multi-scene composition uses transitions (no jump cuts), every scene has entrance animations on every element, and no exit animations except on the final scene (the transition IS the exit). **Gate:** every composition self-reviewed. No overlapping elements, no misplaced assets, no static images without motion. ### 7. Validate & Deliver Run `npx hyperframes lint` and `npx hyperframes validate`. Validate runs a WCAG contrast audit — every text element gets sampled against its background at 5 timestamps. Deliver the preview to the user first. Only render to MP4 on explicit request. **Gate:** `lint` and `validate` pass with zero errors. ## What this pattern teaches Three things to notice: 1. **The gates matter more than the steps.** Each step produces a file that the next step reads. If `DESIGN.md` doesn't exist, step 4 can't start. The pipeline forces sequencing. 2. **Audio drives timing, not the other way around.** Step 5 generates the TTS first, then step 6 builds visuals to match. Building the visuals first and then trying to record narration to fit always produces unnatural pacing. 3. **Lint + validate are non-skippable.** A WCAG-failing video still renders, but it ships with text you can't read on the actual background. The pipeline catches this before render. ## Why this maps to the canonical seven steps The seven canonical steps from the previous lesson map directly: | Canonical step | `website-to-hyperframes` step | |---|---| | 1. Concept | Sub-steps 1-3 (capture + DESIGN.md + SCRIPT.md) | | 2. Reference-gen | Sub-step 4 (STORYBOARD references assets from capture) | | 3. Composition | Sub-step 6 (build compositions) | | 4. Audio | Sub-step 5 (TTS narration) | | 5. Captions | Sub-step 5 (transcript for word timing) | | 6. Render | Sub-step 7 (validate + preview) | | 7. Review | Sub-step 7 (deliver preview, await notes) | If your team's pipeline doesn't have an artifact equivalent to each of these — no `DESIGN.md`, no `SCRIPT.md`, no `STORYBOARD.md` — you're skipping a step. The next two screens will give you a way to score that.37:Tc47,# The cost model — what one render actually costs The headline most people miss: the *render* itself is free. The HyperFrames render runs on your laptop's CPU; the @remotion/lambda render is a few cents. The real cost is the AI inputs. ## Per-render cost breakdown A representative 30-second product video in 2026: - **Concept:** ~$0.10 in Claude/GPT tokens for the brief. - **Reference-gen:** $0.10 - $5.00 depending on how many AI gen attempts you make per shot. Veo charges per second of output; Sora is similar. Generate 10 attempts at $0.30 each and keep the best, you're at $3. - **Composition:** free in terms of API cost (Claude Code writes the HyperFrames HTML in a session you'd be running anyway). Maybe $0.50 - $2 in Anthropic API cost if you're using Claude Code in production with the chapter-23 prompt-cache hits. - **Audio:** free if you use Kokoro TTS. ElevenLabs is ~$0.30/minute. Music via Suno/Udio is a flat subscription, ~$10/month for effectively unlimited renders. - **Captions:** free via local Whisper. - **Render:** free on local CPU. ~$0.05 on @remotion/lambda for a 30-second clip. - **Review:** human time costs human-time money. The QA pass with a vision model is ~$0.10. **Total per render: $0.30 - $7.00 depending on how many AI gen attempts you make.** Compare to the pre-AI cost — voice actor at $300/hour, motion designer at $1500/day, stock licenses at $200/clip. The same 30-second video used to cost $2000-$5000 and 1-2 weeks. Now it costs $5 and one afternoon. ## What this unlocks Three different business models live in the gap: ### Model A — Volume at zero marginal cost A DTC brand renders a fresh ad for every product, every week. 200 products × 52 weeks = 10,400 ads/year. At $2/ad that's $20,800 — cheaper than one human-made campaign would have been. The cost-per-render is so low that the question "is this product worth a video?" stops mattering. Every product gets a video. ### Model B — Quality at constant cost A high-end agency keeps the per-render budget around the pre-AI level ($500-1000) but uses the AI compression to do 50 iterations per render instead of 3. The output quality goes up because the rejection rate is now affordable. ### Model C — Personalization at scale The Spotify-Wrapped pattern. A subscription product generates a per-user video as a renewal hook or year-in-review. With 1M users at $2/render, the program costs $2M to run — but if it lifts retention by 1%, the LTV math wins easily. ## Where the cost still bites Three real cost areas left: - **Reference-gen rejection rate.** AI video models still produce a lot of unusable output. Budgeting for 5-10× over-generation is normal. - **Music licensing.** AI-generated music is fine for B-roll but still doesn't beat a licensed track for hero pieces. Real licenses still cost real money. - **Review cycles.** If a stakeholder requests 30 revisions, "rendering is free" doesn't help — your engineer's time is the bottleneck. The pipeline doesn't eliminate cost. It moves cost from "rendering and production" to "deciding what to render." Which is the right place for the cost to be.38:T9eb,# Score the pipeline against the canonical seven You've seen the canonical seven steps and the worked example (`website-to-hyperframes`). Now the practical question: when you look at a real team's video workflow, how do you know if it's complete? ## The scoring rule Score 15 points for each canonical step the team covers. Seven steps × 15 points = 105 points max, but we cap the score at 100. So if a team covers all seven, they hit 100. Missing any one drops them to 85. Missing two drops them to 70. The function returns an integer. The seven canonical step names are: ```python CANONICAL = [ "concept", "reference-gen", "composition", "audio", "captions", "render", "review", ] ``` ## What "covered" means A step is covered when the team has a *named tool or artifact* responsible for it. Not "we talk about concept in a meeting." A real artifact: a `concept.md`, a SCRIPT, a reference shot library, a TTS tool, a captioning tool, a render command, a review checklist. The input to your function will be a list of step dicts shaped like: ```python [ {"step": "concept", "tool": "Claude Opus"}, {"step": "reference-gen", "tool": "Veo 3"}, {"step": "composition", "tool": "HyperFrames"}, # ... ] ``` A step counts as covered if its `step` name is in `CANONICAL` AND it has a non-empty `tool`. Anything else doesn't count. ## Why 15 points instead of dividing 100 by 7 Two reasons: 1. **Round numbers are easier to debug.** 15 + 15 + 15 = 45, easy to see at a glance. 14.28... × 3 = 42.85 is not. 2. **The cap rewards completeness.** If you cover all 7 you get 105, capped to 100. That asymmetry signals "all 7 is the goal" without making the math weird. ## What scores actually mean | Score | Verdict | |---|---| | 100 | Full pipeline (all 7 covered → 105 capped to 100). Ready to scale. | | 90 | One missing. Find it, fix it. | | 75 | Two missing. Probably captions + review. | | 60 or below | Multiple gaps. The team isn't shipping; they're rendering proofs. | The most common gaps in 2026: - **Captions.** Teams render to MP4 and don't add captions, then wonder why feed performance is bad. (A large share of feed video is watched muted, and captions lift retention even on sound-on platforms like TikTok.) - **Review.** Teams render once and ship. No structured review = no convergence on quality. - **Reference-gen.** Teams skip AI gen, hire stock, and pay 100× more per shot than they need to. Coverage scoring catches all three. The next screen has you write it.39:T551,team_alpha = [ {"step": "concept", "tool": "Claude Opus"}, {"step": "reference-gen", "tool": "Veo 3"}, {"step": "composition", "tool": "HyperFrames"}, {"step": "audio", "tool": "Kokoro TTS"}, {"step": "captions", "tool": "Whisper"}, {"step": "render", "tool": "npx hyperframes render"}, {"step": "review", "tool": "Claude vision QA"}, ] team_beta = [ {"step": "concept", "tool": "Claude Opus"}, {"step": "reference-gen", "tool": "Veo 3"}, {"step": "composition", "tool": "Remotion"}, {"step": "audio", "tool": "ElevenLabs"}, {"step": "render", "tool": "remotion lambda"}, {"step": "review", "tool": "stakeholder meeting"}, # missing: captions ] team_gamma = [ {"step": "concept", "tool": "Claude Opus"}, {"step": "composition", "tool": "HyperFrames"}, {"step": "render", "tool": "local CPU"}, {"step": "totally-fake-step", "tool": "some random tool"}, # not canonical {"step": "audio", "tool": ""}, # empty tool, doesn't count # missing: reference-gen, captions, review (and audio doesn't count) ] # define score_pipeline(steps) below print(f"team alpha: {score_pipeline(team_alpha)}") print(f"team beta: {score_pipeline(team_beta)}") print(f"team gamma: {score_pipeline(team_gamma)}") 3a:T61a,team_alpha = [ {"step": "concept", "tool": "Claude Opus"}, {"step": "reference-gen", "tool": "Veo 3"}, {"step": "composition", "tool": "HyperFrames"}, {"step": "audio", "tool": "Kokoro TTS"}, {"step": "captions", "tool": "Whisper"}, {"step": "render", "tool": "npx hyperframes render"}, {"step": "review", "tool": "Claude vision QA"}, ] team_beta = [ {"step": "concept", "tool": "Claude Opus"}, {"step": "reference-gen", "tool": "Veo 3"}, {"step": "composition", "tool": "Remotion"}, {"step": "audio", "tool": "ElevenLabs"}, {"step": "render", "tool": "remotion lambda"}, {"step": "review", "tool": "stakeholder meeting"}, ] team_gamma = [ {"step": "concept", "tool": "Claude Opus"}, {"step": "composition", "tool": "HyperFrames"}, {"step": "render", "tool": "local CPU"}, {"step": "totally-fake-step", "tool": "some random tool"}, {"step": "audio", "tool": ""}, ] CANONICAL = ["concept", "reference-gen", "composition", "audio", "captions", "render", "review"] def score_pipeline(steps): covered = set() for entry in steps: name = entry.get("step") tool = entry.get("tool", "") if name in CANONICAL and isinstance(tool, str) and tool.strip(): covered.add(name) return min(100, len(covered) * 15) print(f"team alpha: {score_pipeline(team_alpha)}") print(f"team beta: {score_pipeline(team_beta)}") print(f"team gamma: {score_pipeline(team_gamma)}") 3b:T445,Final design drill. You're consulting for five teams. Audit each one's pipeline against the canonical seven steps. Write `audit_pipelines(teams)` that takes a list of team dicts and returns a dict mapping each team's `name` to a verdict dict shaped like: ```python {"score": int, "missing": list[str]} ``` Each team dict has a `name` and a `steps` list (same shape as the previous drill — each step has `step` and `tool` fields). - Use the same scoring rule: 15 points per UNIQUE canonical step covered (`step` name in canonical list AND non-empty `tool`), capped at 100. - `missing` is a list of canonical step names that were NOT covered. Preserve canonical order: `["concept", "reference-gen", "composition", "audio", "captions", "render", "review"]`. - Five teams run. Expected output: ``` acme: score=100 missing=[] bravo: score=90 missing=['captions'] charlie: score=75 missing=['audio', 'captions'] delta: score=60 missing=['reference-gen', 'audio', 'captions'] echo: score=45 missing=['reference-gen', 'audio', 'captions', 'review'] ``` 3c:T724,teams = [ {"name": "acme", "steps": [ {"step": "concept", "tool": "Claude"}, {"step": "reference-gen", "tool": "Veo"}, {"step": "composition", "tool": "HyperFrames"}, {"step": "audio", "tool": "Kokoro"}, {"step": "captions", "tool": "Whisper"}, {"step": "render", "tool": "hyperframes render"}, {"step": "review", "tool": "vision QA"}, ]}, {"name": "bravo", "steps": [ {"step": "concept", "tool": "GPT-5"}, {"step": "reference-gen", "tool": "Sora"}, {"step": "composition", "tool": "Remotion"}, {"step": "audio", "tool": "ElevenLabs"}, {"step": "render", "tool": "lambda"}, {"step": "review", "tool": "stakeholder review"}, ]}, {"name": "charlie", "steps": [ {"step": "concept", "tool": "Claude"}, {"step": "reference-gen", "tool": "Veo"}, {"step": "composition", "tool": "HyperFrames"}, {"step": "render", "tool": "hyperframes render"}, {"step": "review", "tool": "team review"}, ]}, {"name": "delta", "steps": [ {"step": "concept", "tool": "Claude"}, {"step": "composition", "tool": "HyperFrames"}, {"step": "render", "tool": "hyperframes render"}, {"step": "review", "tool": "team review"}, ]}, {"name": "echo", "steps": [ {"step": "concept", "tool": "Claude"}, {"step": "composition", "tool": "HyperFrames"}, {"step": "render", "tool": "hyperframes render"}, ]}, ] # define audit_pipelines(teams) below results = audit_pipelines(teams) for t in teams: r = results[t["name"]] print(f"{t['name']+':':12s} score={r['score']:<3} missing={r['missing']}") 3d:T952,teams = [ {"name": "acme", "steps": [ {"step": "concept", "tool": "Claude"}, {"step": "reference-gen", "tool": "Veo"}, {"step": "composition", "tool": "HyperFrames"}, {"step": "audio", "tool": "Kokoro"}, {"step": "captions", "tool": "Whisper"}, {"step": "render", "tool": "hyperframes render"}, {"step": "review", "tool": "vision QA"}, ]}, {"name": "bravo", "steps": [ {"step": "concept", "tool": "GPT-5"}, {"step": "reference-gen", "tool": "Sora"}, {"step": "composition", "tool": "Remotion"}, {"step": "audio", "tool": "ElevenLabs"}, {"step": "render", "tool": "lambda"}, {"step": "review", "tool": "stakeholder review"}, ]}, {"name": "charlie", "steps": [ {"step": "concept", "tool": "Claude"}, {"step": "reference-gen", "tool": "Veo"}, {"step": "composition", "tool": "HyperFrames"}, {"step": "render", "tool": "hyperframes render"}, {"step": "review", "tool": "team review"}, ]}, {"name": "delta", "steps": [ {"step": "concept", "tool": "Claude"}, {"step": "composition", "tool": "HyperFrames"}, {"step": "render", "tool": "hyperframes render"}, {"step": "review", "tool": "team review"}, ]}, {"name": "echo", "steps": [ {"step": "concept", "tool": "Claude"}, {"step": "composition", "tool": "HyperFrames"}, {"step": "render", "tool": "hyperframes render"}, ]}, ] CANONICAL = ["concept", "reference-gen", "composition", "audio", "captions", "render", "review"] def audit_pipelines(teams): out = {} for team in teams: covered = set() for entry in team["steps"]: name = entry.get("step") tool = entry.get("tool", "") if name in CANONICAL and isinstance(tool, str) and tool.strip(): covered.add(name) score = min(100, len(covered) * 15) missing = [s for s in CANONICAL if s not in covered] out[team["name"]] = {"score": score, "missing": missing} return out results = audit_pipelines(teams) for t in teams: r = results[t["name"]] print(f"{t['name']+':':12s} score={r['score']:<3} missing={r['missing']}") 1c:[["$","$L24",null,{"payload":{"catalog":"The AI-native design pipeline — concept to shipped MP4 in one workflow (step 3/8) · programmatic design · promptdojo","byPath":{"developer":"The AI-native design pipeline — concept to shipped MP4 in one workflow (step 3/8) · programmatic design · promptdojo","marketer":"The AI-native 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oint"}]},{"slug":"the-paper-trail","steps":[{"id":"people-studio/the-paper-trail/s01-01-policy-is-legal-surface"},{"id":"people-studio/the-paper-trail/s02-02-the-boundary-as-a-state-machine"},{"id":"people-studio/the-paper-trail/s03-03-who-signs-off"},{"id":"people-studio/the-paper-trail/s04-04-predict-the-version-log"},{"id":"people-studio/the-paper-trail/s05-05-fix-the-shortcut"},{"id":"people-studio/the-paper-trail/s06-06-the-evidence-trail"},{"id":"people-studio/the-paper-trail/s07-07-write-decision-record"},{"id":"people-studio/the-paper-trail/s08-08-checkpoint"}]}]},{"slug":"ops-studio","lessons":[{"slug":"the-sop-is-data","steps":[{"id":"ops-studio/the-sop-is-data/s01-01-the-two-weeks-notice-problem"},{"id":"ops-studio/the-sop-is-data/s02-02-the-sop-as-data"},{"id":"ops-studio/the-sop-is-data/s03-03-spot-the-invented-step"},{"id":"ops-studio/the-sop-is-data/s04-04-fill-the-sop-lint"},{"id":"ops-studio/the-sop-is-data/s05-05-predict-the-diff"},{"id":"ops-studio/the-sop-is-data/s06-06-the-diff-is-the-change-control"},{"id":"ops-studio/the-sop-is-data/s07-07-write-lint-sop"},{"id":"ops-studio/the-sop-is-data/s08-08-checkpoint"}]},{"slug":"auto-run-or-named-approval","steps":[{"id":"ops-studio/auto-run-or-named-approval/s01-01-the-checklist-that-lied"},{"id":"ops-studio/auto-run-or-named-approval/s02-02-the-schedule-is-data"},{"id":"ops-studio/auto-run-or-named-approval/s03-03-which-item-may-auto-run"},{"id":"ops-studio/auto-run-or-named-approval/s04-04-fix-the-silent-auto-complete"},{"id":"ops-studio/auto-run-or-named-approval/s05-05-predict-the-exception-queue"},{"id":"ops-studio/auto-run-or-named-approval/s06-06-exceptions-are-a-queue-not-a-shrug"},{"id":"ops-studio/auto-run-or-named-approval/s07-07-write-run-checklist"},{"id":"ops-studio/auto-run-or-named-approval/s08-08-checkpoint"}]},{"slug":"numbers-with-receipts","steps":[{"id":"ops-studio/numbers-with-receipts/s01-01-the-number-that-moves-money"},{"id":"ops-studio/numbers-with-receipts/s02-02-recompute-dont-trust"},{"id":"ops-studio/numbers-with-receipts/s03-03-which-claim-has-no-receipt"},{"id":"ops-studio/numbers-with-receipts/s04-04-predict-the-verification"},{"id":"ops-studio/numbers-with-receipts/s05-05-fill-the-receipt-check"},{"id":"ops-studio/numbers-with-receipts/s06-06-the-anomaly-is-a-question"},{"id":"ops-studio/numbers-with-receipts/s07-07-write-verify-report"},{"id":"ops-studio/numbers-with-receipts/s08-08-checkpoint"}]},{"slug":"the-pii-proxy","steps":[{"id":"ops-studio/the-pii-proxy/s01-01-the-ticket-that-left-the-building"},{"id":"ops-studio/the-pii-proxy/s02-02-mask-then-unmask"},{"id":"ops-studio/the-pii-proxy/s03-03-which-payload-may-leave"},{"id":"ops-studio/the-pii-proxy/s04-04-fill-the-leftover-check"},{"id":"ops-studio/the-pii-proxy/s05-05-predict-the-two-sides"},{"id":"ops-studio/the-pii-proxy/s06-06-unmask-is-a-named-human-act"},{"id":"ops-studio/the-pii-proxy/s07-07-write-the-proxy"},{"id":"ops-studio/the-pii-proxy/s08-08-checkpoint"}]}]},{"slug":"legal-studio","lessons":[{"slug":"the-cite-check-gate","steps":[{"id":"legal-studio/the-cite-check-gate/s01-01-the-brief-that-reads-perfectly"},{"id":"legal-studio/the-cite-check-gate/s02-02-three-verdicts-as-data"},{"id":"legal-studio/the-cite-check-gate/s03-03-which-cite-needs-no-check"},{"id":"legal-studio/the-cite-check-gate/s04-04-predict-the-verification-run"},{"id":"legal-studio/the-cite-check-gate/s05-05-fix-the-verifier-that-asks-again"},{"id":"legal-studio/the-cite-check-gate/s06-06-write-verify-authorities"},{"id":"legal-studio/the-cite-check-gate/s07-07-checkpoint"}]},{"slug":"clause-extraction-with-pinpoints","steps":[{"id":"legal-studio/clause-extraction-with-pinpoints/s01-01-a-claim-without-a-pinpoint-is-a-rumor"},{"id":"legal-studio/clause-extraction-with-pinpoints/s02-02-the-playbook-as-data"},{"id":"legal-studio/clause-extraction-with-pinpoints/s03-03-which-row-is-unusable"},{"id":"legal-studio/clause-extraction-with-pinpoints/s04-04-predict-the-triage"},{"id":"legal-studio/clause-extraction-with-pinpoints/s05-05-fill-the-walk-away-route"},{"id":"legal-studio/clause-extraction-with-pinpoints/s06-06-write-triage-extraction"},{"id":"legal-studio/clause-extraction-with-pinpoints/s07-07-checkpoint"}]},{"slug":"privilege-rules-and-the-review-log","steps":[{"id":"legal-studio/privilege-rules-and-the-review-log/s01-01-privilege-is-a-routing-question"},{"id":"legal-studio/privilege-rules-and-the-review-log/s02-02-the-routing-table"},{"id":"legal-studio/privilege-rules-and-the-review-log/s03-03-which-request-is-the-waiver-risk"},{"id":"legal-studio/privilege-rules-and-the-review-log/s04-04-predict-the-router"},{"id":"legal-studio/privilege-rules-and-the-review-log/s05-05-fix-the-privilege-hole"},{"id":"legal-studio/privilege-rules-and-the-review-log/s06-06-the-log-that-survives-a-motion"},{"id":"legal-studio/privilege-rules-and-the-review-log/s07-07-write-log-ready"},{"id":"legal-studio/privilege-rules-and-the-review-log/s08-08-checkpoint"}]}]},{"slug":"agent-studio","lessons":[{"slug":"the-tool-contract","steps":[{"id":"agent-studio/the-tool-contract/s01-01-the-feature-you-ship-today"},{"id":"agent-studio/the-tool-contract/s02-02-the-schema-is-the-blast-radius"},{"id":"agent-studio/the-tool-contract/s03-03-spot-the-loose-tool"},{"id":"agent-studio/the-tool-contract/s04-04-predict-the-validation"},{"id":"agent-studio/the-tool-contract/s05-05-fix-the-yolo-rerun"},{"id":"agent-studio/the-tool-contract/s06-06-write-validate-call"},{"id":"agent-studio/the-tool-contract/s07-07-checkpoint"}]},{"slug":"the-loop-with-a-meter","steps":[{"id":"agent-studio/the-loop-with-a-meter/s01-01-a-loop-that-cannot-stop-is-an-invoice"},{"id":"agent-studio/the-loop-with-a-meter/s02-02-caps-are-part-of-the-loop"},{"id":"agent-studio/the-loop-with-a-meter/s03-03-why-did-it-stop"},{"id":"agent-studio/the-loop-with-a-meter/s04-04-predict-the-stop-reason"},{"id":"agent-studio/the-loop-with-a-meter/s05-05-fill-the-budget-check"},{"id":"agent-studio/the-loop-with-a-meter/s06-06-the-trace-is-the-receipt"},{"id":"agent-studio/the-loop-with-a-meter/s07-07-write-run-loop"},{"id":"agent-studio/the-loop-with-a-meter/s08-08-checkpoint"}]},{"slug":"evals-before-it-works","steps":[{"id":"agent-studio/evals-before-it-works/s01-01-green-before-it-runs"},{"id":"agent-studio/evals-before-it-works/s02-02-a-golden-case-is-a-past-incident"},{"id":"agent-studio/evals-before-it-works/s03-03-which-case-catches-it"},{"id":"agent-studio/evals-before-it-works/s04-04-predict-the-eval-run"},{"id":"agent-studio/evals-before-it-works/s05-05-fix-the-router-to-green"},{"id":"agent-studio/evals-before-it-works/s06-06-the-ratchet-entry"},{"id":"agent-studio/evals-before-it-works/s07-07-write-lint-the-ratchet"},{"id":"agent-studio/evals-before-it-works/s08-08-checkpoint"}]}]}]},"detail":{"number":29,"slug":"programmatic-design","title":"programmatic design — code-driven video and the new design pipeline","blurb":"ai generates raw assets; code stitches them into something shippable. hyperframes, remotion, claude design — when each tool wins, how they combine, and the data-driven workflows that turn one template into a hundred videos.","overview":"$27","status":"live","lessons":[{"slug":"why-programmatic","title":"Why programmatic video — when AI gen alone isn't enough","estMinutes":12,"prerequisites":[],"status":"live","steps":[{"type":"read","id":"programmatic-design/why-programmatic/s01-01-intro","xp":1,"hint":[],"personalize":false,"phase":"warmup","estSeconds":150,"concept":"when-ai-gen-isnt-enough","body":"$28","cta":"Got it","runnable":true},{"type":"read","id":"programmatic-design/why-programmatic/s02-02-the-three-jobs","xp":1,"hint":[],"personalize":false,"phase":"build","estSeconds":180,"concept":"three-jobs-only-code-does","body":"$29","cta":"Got it","code":"# the three jobs that programmatic video does — and AI gen alone does not.\n\nJOBS = [\n (\"parametric variation\", \"1 template + N data rows → N videos\"),\n (\"data-driven rendering\", \"video composition reads from a database\"),\n (\"reproducibility\", \"same inputs → byte-identical output\"),\n]\nfor name, why in JOBS:\n print(f\"{name:24s} — {why}\")\n","runnable":true},{"type":"mc","id":"programmatic-design/why-programmatic/s03-03-which-task-is-code","xp":2,"hint":[{"level":1,"body":"Code wins when the brief implies a TEMPLATE (same every time, copy stays put) or a DATA SOURCE (scores, prices, names that change per render). Model wins when the brief is \"imagine a beautiful scene.\"","cost":0}],"personalize":false,"phase":"build","estSeconds":55,"concept":"code-job-vs-model-job","prompt":"Which TWO briefs are clearly code jobs (programmatic video), not\nAI-gen-only? Code wins when the deliverable is parametric, data-driven,\nor requires reproducibility. Model wins when the deliverable is one\nnovel scene with no structured inputs.\n","options":[{"id":"b_and_c","label":"B (channel intro) and C (NFL reel)"},{"id":"a_and_d","label":"A (fog shot) and D (abstract loop)","explain":"Both are exactly what AI gen models are best at — single novel scenes with no copy, no data, no template. These are model jobs."},{"id":"a_and_c","label":"A (fog shot) and C (NFL reel)","explain":"Half right. NFL reel is data-driven, definitely code. But the fog shot has no template, no parametric inputs, no data — pure model job."},{"id":"c_and_d","label":"C (NFL reel) and D (abstract loop)","explain":"Half right. NFL reel is code. But the abstract loop is a model job — no copy, no data, just a vibe."}],"answerIds":["b_and_c"],"shuffle":false,"code":"briefs = {\n \"A\": \"A 5-second cinematic shot of fog rolling over mountains at dawn.\",\n \"B\": \"A 30-second YouTube channel intro, same every video, with the host's name and channel logo overlaid in their brand red.\",\n \"C\": \"A 'top 10 plays of the week' NFL reel that pulls scores from the league API and ships every Monday.\",\n \"D\": \"A dreamlike abstract loop for a chill-hop album cover, no copy, no captions.\",\n}\nfor k, v in briefs.items():\n print(f\"{k}: {v}\")\n","runnable":true},{"type":"mc","id":"programmatic-design/why-programmatic/s04-04-parametric-vs-one-shot","xp":2,"hint":[{"level":1,"body":"Parametric variation = same template, N different rows of structured data, N different outputs. The \"rows\" field is the giveaway — 80 rows means 80 renders from one template.","cost":0}],"personalize":false,"phase":"build","estSeconds":60,"concept":"parametric-variation-defined","prompt":"Two workflows. Which one is \"parametric variation\" (and therefore\nbelongs in a programmatic video pipeline)?\n","options":[{"id":"workflow_x","label":"Workflow X — 80 product ads from one template"},{"id":"workflow_y","label":"Workflow Y — one dreamy album-art loop","explain":"One-off, no template, no structured inputs, no repetition. That's the canonical AI-gen-only brief — let the model paint one scene."},{"id":"both","label":"Both are parametric","explain":"Y has no parameters that change between renders — it's a single render. Parametric variation means N different outputs from the same template, driven by N rows of structured inputs."},{"id":"neither","label":"Neither — both are single videos","explain":"X is 80 videos, not one. The structured inputs (product_name, price, image_url, cta_text) and 80 rows of data are exactly what parametric variation means."}],"answerIds":["workflow_x"],"shuffle":false,"code":"# one shop runs both of these every week. one is a code job, one is a model job.\n\nworkflow_x = {\n \"frequency\": \"weekly\",\n \"inputs\": [\"product_name\", \"price\", \"image_url\", \"cta_text\"],\n \"rows\": 80, # 80 products in the catalog\n \"output\": \"80 vertical ads, all 15s, brand template\",\n}\n\nworkflow_y = {\n \"frequency\": \"one-off\",\n \"inputs\": [\"mood: dreamy, vaporwave, sunset, no copy\"],\n \"rows\": 1,\n \"output\": \"one 8-second loop for the album art reveal\",\n}\nprint(\"X:\", workflow_x)\nprint(\"Y:\", workflow_y)\n","runnable":true},{"type":"read","id":"programmatic-design/why-programmatic/s05-05-the-classifier","xp":2,"hint":[],"personalize":false,"phase":"build","estSeconds":130,"concept":"classify-by-three-signals","body":"$2a","cta":"Got it","code":"# three signals decide where a video brief lands.\n\nSIGNALS = {\n \"rows\": \"more than 1 means parametric — code wins\",\n \"needs_copy\": \"headlines, captions, CTAs — code wins (model can't reliably render text)\",\n \"data_source\": \"live DB, API, spreadsheet — code wins\",\n \"is_one_shot\": \"single novel scene, no copy, no template — model wins\",\n}\nfor k, v in SIGNALS.items():\n print(f\"{k}: {v}\")\n","runnable":true},{"type":"write","id":"programmatic-design/why-programmatic/s06-06-write-classify-video-task","xp":5,"hint":[{"level":1,"body":"Three branches in order. The first branch requires ALL four signals to point at the model. The second branch requires ANY one parametric/data signal. The third branch is the default fall-through (\"hybrid\").","cost":0},{"level":2,"body":"Use `task.get(\"rows\", 1)` so missing fields don't crash. The order of branches matters — check `ai-gen-only` first (most restrictive), then `code-driven`, then default to `hybrid`.","cost":0}],"personalize":false,"phase":"build","estSeconds":200,"concept":"classify-video-task","prompt":"Write `classify_video_task(task)` that takes a task dict and\nreturns one of `\"ai-gen-only\"`, `\"code-driven\"`, or `\"hybrid\"`.\n\nInput fields (all optional, use `.get()` with sensible defaults):\n\n- `rows: int` (default 1)\n- `needs_copy: bool` (default False)\n- `data_source: str | None` (default None)\n- `is_one_shot: bool` (default False)\n\nRules, in order:\n\n- If `is_one_shot` AND `rows == 1` AND NOT `needs_copy` AND\n `data_source is None` → return `\"ai-gen-only\"`.\n- Else if `rows > 1` OR `data_source is not None` → return\n `\"code-driven\"`.\n- Else → return `\"hybrid\"`.\n\nFive briefs run. Expected output:\n```\nfog shot: ai-gen-only\nchannel intro: hybrid\nshopify ads: code-driven\nnfl reel: code-driven\nalbum loop: ai-gen-only\n```\n","starter":"briefs = [\n (\"fog shot\", {\"rows\": 1, \"needs_copy\": False, \"data_source\": None, \"is_one_shot\": True}),\n (\"channel intro\", {\"rows\": 1, \"needs_copy\": True, \"data_source\": None, \"is_one_shot\": False}),\n (\"shopify ads\", {\"rows\": 100,\"needs_copy\": True, \"data_source\": \"catalog.csv\",\"is_one_shot\": False}),\n (\"nfl reel\", {\"rows\": 1, \"needs_copy\": True, \"data_source\": \"nfl-api\", \"is_one_shot\": False}),\n (\"album loop\", {\"rows\": 1, \"needs_copy\": False, \"data_source\": None, \"is_one_shot\": True}),\n]\n\n# define classify_video_task(task) below\n\nfor name, task in briefs:\n print(f\"{name+':':16s} {classify_video_task(task)}\")\n","grader":{"kind":"stdout-equality","expected":"fog shot: ai-gen-only\nchannel intro: hybrid\nshopify ads: code-driven\nnfl reel: code-driven\nalbum loop: ai-gen-only\n","normalize":"collapse-trailing-newline"},"solution":"$2b","hiddenTests":[]},{"type":"checkpoint","id":"programmatic-design/why-programmatic/s07-07-checkpoint","xp":8,"hint":[],"personalize":false,"phase":"check","estSeconds":220,"concept":"route-five-real-briefs","prompt":"Final design drill. You're the in-house creative engineer at a DTC brand.\nFive briefs land in your inbox in one morning. Route each one.\n\nWrite `route_briefs(briefs)` that takes a list of brief dicts and\nreturns a dict mapping each brief's `name` to its classification\n(`\"ai-gen-only\"`, `\"code-driven\"`, or `\"hybrid\"`).\n\nUse the same rules as the previous step:\n\n- All four signals point at the model → `\"ai-gen-only\"`.\n- Any parametric signal (`rows > 1`) or data signal\n (`data_source is not None`) → `\"code-driven\"`.\n- Otherwise → `\"hybrid\"`.\n\nExpected output:\n```\nfounder dream sequence: ai-gen-only\nweekly sale ads: code-driven\nfeature launch: hybrid\ncustomer testimonial: hybrid\ninventory dashboard: code-driven\n```\n","starter":"briefs = [\n {\"name\": \"founder dream sequence\", \"rows\": 1, \"needs_copy\": False, \"data_source\": None, \"is_one_shot\": True},\n {\"name\": \"weekly sale ads\", \"rows\": 24, \"needs_copy\": True, \"data_source\": \"sale-feed\", \"is_one_shot\": False},\n {\"name\": \"feature launch\", \"rows\": 1, \"needs_copy\": True, \"data_source\": None, \"is_one_shot\": False},\n {\"name\": \"customer testimonial\", \"rows\": 1, \"needs_copy\": True, \"data_source\": None, \"is_one_shot\": False},\n {\"name\": \"inventory dashboard\", \"rows\": 1, \"needs_copy\": True, \"data_source\": \"warehouse\", \"is_one_shot\": False},\n]\n\n# define route_briefs(briefs) below\n\nresults = route_briefs(briefs)\nfor b in briefs:\n print(f\"{b['name']+':':24s}{results[b['name']]}\")\n","grader":{"kind":"stdout-equality","expected":"founder dream sequence: ai-gen-only\nweekly sale ads: code-driven\nfeature launch: hybrid\ncustomer testimonial: hybrid\ninventory dashboard: code-driven\n","normalize":"collapse-trailing-newline"},"solution":"$2c","passThreshold":0.66}],"xpTotal":21},{"slug":"hyperframes-and-remotion","title":"HyperFrames and Remotion — the two tools that own this space","estMinutes":16,"prerequisites":["why-programmatic"],"status":"live","steps":[{"type":"read","id":"programmatic-design/hyperframes-and-remotion/s01-01-the-two-tools","xp":1,"hint":[],"personalize":false,"phase":"warmup","estSeconds":180,"concept":"hyperframes-vs-remotion-at-a-glance","body":"$2d","cta":"Got it","runnable":true},{"type":"read","id":"programmatic-design/hyperframes-and-remotion/s02-02-hyperframes-tour","xp":2,"hint":[],"personalize":false,"phase":"warmup","estSeconds":220,"concept":"hyperframes-fundamentals","body":"$2e","cta":"Got it","code":"# the HyperFrames CLI surface, in order of how often you'll use each command.\n\nCOMMANDS = [\n (\"npx hyperframes init my-video\", \"scaffold a new project with templates\"),\n (\"npx hyperframes lint\", \"static checks on index.html + compositions/\"),\n (\"npx hyperframes preview\", \"dev server with hot reload (default port 3002)\"),\n (\"npx hyperframes render\", \"render to MP4 in renders/\"),\n (\"npx hyperframes transcribe audio.mp3\", \"Whisper transcription for caption sync\"),\n (\"npx hyperframes tts 'hello' --voice af_nova\", \"Kokoro TTS for narration\"),\n (\"npx hyperframes doctor\", \"diagnose Chrome, FFmpeg, Node, memory\"),\n]\nfor cmd, what in COMMANDS:\n print(f\"{cmd:48s} — {what}\")\n","runnable":true},{"type":"mc","id":"programmatic-design/hyperframes-and-remotion/s03-03-hyperframes-cli","xp":2,"hint":[{"level":1,"body":"The real HyperFrames CLI verbs are: init, lint, preview, render, transcribe, tts, doctor, browser, info, upgrade, compositions, docs, benchmark. `compile-to-bytecode` and `flatten-keyframes` were never part of the tool.","cost":0}],"personalize":false,"phase":"build","estSeconds":55,"concept":"hyperframes-cli-commands","prompt":"Three of those commands are real HyperFrames CLI commands. Two\nare made up. Which THREE are real?\n","options":[{"id":"a_b_d","label":"A (init), B (lint), and D (render --quality draft)"},{"id":"a_c_e","label":"A (init), C (compile-to-bytecode), and E (flatten-keyframes)","explain":"C and E are inventions. HyperFrames has no compile-to-bytecode or flatten-keyframes commands. The real CLI is: init, lint, preview, render, transcribe, tts, doctor, browser, info, upgrade, compositions, docs, benchmark."},{"id":"b_d_e","label":"B (lint), D (render --quality draft), and E (flatten-keyframes)","explain":"B and D are real. E is invented — HyperFrames does not have a flatten-keyframes command."},{"id":"a_b_e","label":"A (init), B (lint), and E (flatten-keyframes)","explain":"A and B are real. E is invented."}],"answerIds":["a_b_d"],"shuffle":false,"code":"# five commands, three of them are real HyperFrames CLI commands. two are made up.\n\ncommands = {\n \"A\": \"npx hyperframes init my-video\",\n \"B\": \"npx hyperframes lint\",\n \"C\": \"npx hyperframes compile-to-bytecode\",\n \"D\": \"npx hyperframes render --quality draft\",\n \"E\": \"npx hyperframes flatten-keyframes\",\n}\nfor k, v in commands.items():\n print(f\"{k}: {v}\")\n","runnable":true},{"type":"read","id":"programmatic-design/hyperframes-and-remotion/s04-04-remotion-tour","xp":2,"hint":[],"personalize":false,"phase":"build","estSeconds":200,"concept":"remotion-fundamentals","body":"$2f","cta":"Got it","code":"# Remotion's surface, paraphrased — the React side of programmatic video.\n\nREMOTION_BUILDING_BLOCKS = [\n (\"\", \"register a renderable component with a name, duration, fps, dimensions\"),\n (\"useCurrentFrame()\", \"hook that returns the current frame number — drives all motion\"),\n (\"interpolate(...)\", \"map a frame range to a value range, optional easing\"),\n (\"\", \"compose multiple components on a shared timeline\"),\n (\"

The AI-native design pipeline — concept to shipped MP4 in one workflow — step 3 of 8