Video production: plan and research

The planned phase that turns the script and the voice-over into a video: visuals, rhythm, sound, other risks, the pipeline and the tools to build.

TL;DR

The phase after the script and the voice-over, turning them into a video, has been planned and researched, not built. The plan lives in research/video-production/; start at its README.md. The video is arranged to the voice: about 220 scenes for 1,217 seconds of Hinglish audio (the script has 14 graphic notes), each one a data scene from the fact ledger, a piece of typography, a flat diagram of the script's own metaphors, or a cropped passage of a real source, with a thin layer of licensed photographs. Claude writes a structured storyboard; tools check it, fetch and build the assets, compile a frame-exact timeline, render, mix and verify.

  • Proven here: a Node renderer ran a heavy 1080p scene at 22 frames per second in 186 MB with no frame files (this machine has 6 GB RAM and 7 GB free disk); ffmpeg has every filter needed; Wikimedia Commons, the Internet Archive and headless Chrome work without keys.
  • Not proven: every pacing, load, density and loudness default, whether viewers feel any of it, how YouTube treats a synthetic narrator, and anything about how the audio sounds (Claude has not heard it).
  • Folklore found: "1.6 second shots", "15 to 20 seconds static kills retention" and "65% at 3 seconds" have no primary source; Google AI Mode said so itself. They are not used as targets.
  • Waiting on the user: 14 decisions, each with a recommendation, listed below; the first slice proposed is beats B0 and B4 (183 seconds).

1. The four questions, answered

QuestionAnswerFile
Which visuals, and how to get themFive honesty classes (evidence, data, metaphor, typography, atmosphere); a closed catalogue of 26 scene types; a status grammar that draws verified, alleged and "not found" differently; rights and routes for each source; a 32-idea bank01-visual-assets.md
How to arrange them with the voiceA paragraph contract (anchor, evidence, figure, landing); scene per sentence; cuts on sentence ends and pauses; visual load opposite to verbal load; transition meanings and defaults; text and number rules02-visual-grammar.md
SoundEvery sound signals a visual event, mirrors what the audience remembers, or is silence; functional sounds synthesised in code; CC0 foley for the recognition moments; four music beds the user auditions; two-pass loudness03-sound.md
What else mattersYouTube's policy on templated and synthetic content; Indian fair dealing after ANI v RSY News (29 September 2026); honesty on screen; privacy and allegations; this machine's limits; how to review without hearing; measurement; delivery04-other-areas.md

2. The pipeline, V0 to V9

V0

Lock

Hash the script, ledger, 14 WAVs and word times. Every beat must be aligned (only B1 and B2 are).

V1

Meaning map

Per sentence: numbers with ledger status, dates, quotes, names, questions, pauses, hard or easy window.

V2

Storyboard (Claude)

One scene object per sentence or clause, in beat-sheet.json. The creative step.

V3

Beat-sheet lint

37 rules: honesty, traceability, sync, pacing, load, text, sound. Hard gates and warnings.

V4

Assets

Commons, Internet Archive, headless capture, pdf.js, Freesound. A licence and source row for each.

V5

Timeline

Integer frames at 30 fps, SFX events, holds, landings, a complexity score per second.

V6

Render

Per beat, cached by content hash, Node canvas piped into ffmpeg.

V7

Sound

Synthesised effects, foley, bed, ducking from word times, loudness normalisation.

V8

Check

ffmpeg detectors, text and contrast, flashes, a contact sheet for Claude to read, a verdict.

V9

Publish and learn

Captions, credits, an OpenTimelineIO export for DaVinci Resolve, retention joined to scenes.

Contracts, rules and the twelve tools to build, in order, are in research/video-production/BUILD-SPEC.md. Nothing in it exists yet.

3. Facts measured on the way

4. Decisions waiting for the user

#DecisionRecommendation
1Adopt the honesty classes and the never-list (no news video or article photographs, no logo, no photoreal AI imagery, no staged evidence, no faces)Yes
2Renderer: Node plus @napi-rs/canvas in a separate packageYes
3Renders and caches outside OneDriveYes
4CaptionsA closed-caption file from the script and word times; key-term typography on screen
5MusicLibrary bed per act that the user auditions, plus synthesised drones; AI music off
6Stock footageOff
7Pace (173 wpm)Keep for slice 1
8Gap between beats0.8 s
9Mahesh Sharma on screenText only; blocked until the 2019 article is opened
10A private benchmark of 6 to 10 reference videosUser decides (YouTube's terms restrict downloading)
11Unlisted test upload with the synthetic voiceUser, 10 minutes
12A free Freesound token in .envYes (never in chat)
13First sliceB0 plus B4, then B7 plus B11
14Trial the deadline clock, pivot ladder, status grammar and pivot motifYes; drop what is disliked
What Google AI Mode was worth

Fifteen threads were answered. It gave useful structure and vocabulary (sound as a signalling cue, a hierarchy of cut, reframe and in-scene change, a measurable proxy for each attention principle, the QA filter list), but contradicted itself on the pacing numbers between two questions and, asked to plan the opening two minutes, invented footage that matches no claim. Digests and verdicts are in research/video-production/sources/RESEARCH-LOG.md; they are digests, not verbatim threads.

5. Files

Written 2026-10-31 from the files in research/video-production/ and the probes run that day. If a page and a file disagree, the file is right: fix the page.