Render a 'model comparison grid' video from a config — a fal-style "same prompt, N contenders" showcase — a dark real-DOM stage where per beat a monospace prompt fades in centered, docks to a small top strip, then a labeled 2-4 panel grid (static images OR muted video clips, mixable per cell) staggers in and holds for comparison, plus a minimal end card — frame-stepped via Playwright (video cells are frame-seeked deterministically) and encoded with FFmpeg. Deterministic assembly, FREE (cell media comes from create-image-fal / create-video-fal, music from create-music-elevenlabs), text stays pixel-crisp. Use for the model-comparison-grid format.
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Render the 'model comparison grid' format from a config. The signature of this format is a
"Same prompt. N models." gauntlet: a dark stage where, per beat, a PROMPT eyebrow +
the (condensed) prompt fades in centered in monospace and holds readable ~0.8s, then
docks to a small top strip while a grid of 2-4 labeled panels staggers in (0.15s apart)
and holds for side-by-side comparison. A persistent model/variant label sits under each
panel; column order is identical on every beat. Ends on a minimal end card (headline +
column names only — no meta-stats line).
The grid is media-agnostic per cell: any cell is a static image or a muted video clip (i2v outputs, screen recordings), mixable within one beat. Video cells loop during the hold and are frame-seeked deterministically (the renderer awaits each seek), so the render never depends on wall-clock playback timing.
The renderer itself is FREE/deterministic (Playwright frame-step + FFmpeg). The paid inputs
are separate capabilities: the cell images come from create-image-fal, the cell
clips from create-video-fal, and the music bed from create-music-elevenlabs.
Prompt text and labels are real DOM — never AI-rendered.
Default shape: 5 beats × 4.5s + 2.5s end card = 25.0s @ 1280×720/30fps, all configurable
from one config.json.
build_composition.py --config config.json --output hyperframe.html ; render_seekable_hyperframe.py hyperframe.html master-silent.mp4 <duration> --fps 30 --width 1280 --height 720 — dark stage, staggered grid, deterministic, $0. The config schema is documented at the top of scripts/build_composition.py; scripts/config.example.json IS the shipped worked example (re-point the cell paths at your own media; its contenders, beat axes, prompts, end-card copy and music prompt are the demo's — the recipe's user choices supply yours, never copy them as defaults).
build_composition.py validates every cell path and the column count (2-4), infers each
cell's media type from its extension (.png/.jpg/.jpeg/.webp → image; .mp4/.mov/.webm/.m4v
→ muted video), and emits a self-contained HTML that exposes window.mediaReady() +
window.renderAt(t). render_seekable_hyperframe.py awaits both, so <video> cells seek
to the right frame before each screenshot — never a frozen first frame.
renderAt(t) — never CSS animation-delay/transitions
(Playwright scrubbing traps delayed animations in pre-state)..mov
ProRes to H.264 first). An images-only grid has no decode dependency.还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
中文简介正在准备中。当前显示原文说明。
Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
中文简介正在准备中。当前显示原文说明。
For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
中文简介正在准备中。当前显示原文说明。
The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
中文简介正在准备中。当前显示原文说明。
Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
中文简介正在准备中。当前显示原文说明。
Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
中文简介正在准备中。当前显示原文说明。