Interviews & Discussion — topic-mm-interviews

post topic-mm-interviewskind topicweek —date —campaign multimedia-pillarpillar multimediabeat —asset videoduration 54.3sground blackscenes 9

Checklist the per-video bar — engine/sim

94.9/100

plain languagevo coverageno dead airuniquenesscaption fitcompletenesscleanliness

quantitative quality · weights learn from your reviews (engine.sim.memory review topic-mm-interviews good|bad)

⚠ 3 flag(s) — not yet ship-ready: copy_generictoo_complexcurate_missing · see docs/strategy/VIDEO-CHECKLIST.md

Composition comp · template family · expected output

composition SceneReelfamily / template SceneReel

9:16 Reelrendered1:1 Squarepending16:9 Widepending9:16 4Kpending1:1 4Kpending16:9 4KpendingGIF (SMS)pending

▶ open rendered mp4

expected output: 1/7 rendered — same matrix the /media preview surfaces for this asset.

Composition layer × scene 9 scenes · 54.3s · comp_id + rendered still + tier + the script

#	Layer (comp_id · still · tier)	Beat	Timecode	Motion	Logo	Audio	VO / on-screen / caption
1	still pending matches intent shared field signature-3d	open	0–6.1s	spatial-parallax	icon·wireframe	♪ —	Inside two hours of raw tape there's a great ten-minute interview, and finding it used to be the whole job. on-screen: A great interview hides in raw tape
	expected on screen: black ground · octa hero in the shared Signal Field · Lumen leads · node-graph · spatial-parallax · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual node-graphshape octaground blacktreatment wireframemotion spatial-parallaxpower summoninstrument summon→three-mark
2	still pending first render · fix pending LegacyCard templatecurate_missing	legacy	6.1–11.8s	crossfade-8f	icon·wireframe	♪ —	The old way meant an editor scrubbing every minute by hand, hunting for the moments that mattered. on-screen: Old way
	expected on screen: black ground · a LegacyCard panel over a dimmed Signal Field · Lumen leads · crossfade-8f · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id LegacyCardshape octaground blacktreatment wireframemotion crossfade-8fpower morphinstrument decay→three-symbolic
3	still pending matches intent NumberedList template	teach	11.8–18.0s	kinetic-build	icon·wireframe	♪ —	Step one, we transcribe the whole thing with word-level timestamps, so the tape becomes searchable text. on-screen: Step 1: transcribe with timestamps
	expected on screen: black ground · a NumberedList panel over a dimmed Signal Field · Lumen leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id NumberedListvisual node-graphshape octaground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→html-in-canvas-panelcurate items, nodes
4	still pending matches intent ChecklistCard template	teach	18.0–23.9s	kinetic-build	icon·wireframe	♪ —	Step two, we ask the model to surface the strongest quotes and tag the moments that earn a clip. on-screen: Step 2
	expected on screen: black ground · a ChecklistCard panel over a dimmed Signal Field · Lumen leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id ChecklistCardvisual node-graphshape octaground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→three-diagramcurate items, nodes
5	still pending matches intent CheatSheet template	teach	23.9–30.4s	kinetic-build	icon·wireframe	♪ —	Step three, those timestamps drive the rough cut, so the edit assembles itself around the best lines. on-screen: Step 3: cut to the timestamps
	expected on screen: black ground · a CheatSheet panel over a dimmed Signal Field · Lumen leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id CheatSheetvisual node-graphshape octaground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→html-in-canvas-codecurate nodes, points, uses
6	still pending matches intent StepFlow template	teach	30.4–36.5s	kinetic-build	icon·wireframe	♪ —	Step four, captions burn in and the system slices vertical clips for social from the same master. on-screen: Step 4: caption and clip for social
	expected on screen: black ground · a StepFlow panel over a dimmed Signal Field · Lumen leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id StepFlowvisual node-graphshape octaground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→three-flowcurate nodes, steps
7	still pending matches intent StatScoreboard template	proof	36.5–42.6s	receipts-count	icon·wireframe	♪ —	We show the turnaround openly, two hours of raw tape to a captioned cut in a fraction of the old time. on-screen: Receipts
	expected on screen: black ground · a StatScoreboard panel over a dimmed Signal Field · Lumen leads · receipts · receipts-count · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id StatScoreboardvisual receiptsshape octaground blacktreatment wireframemotion receipts-countpower receiptsinstrument spotlight→html-in-canvas-receiptscurate pillar, stats, statsLabels
8	still pending matches intent shared field signature-3d	futurist	42.6–47.800000000000004s	spatial-parallax	icon·wireframe	♪ —	Soon you tell the edit what story you want and it re-cuts the interview around that thread. on-screen: Next: the edit you can talk to
	expected on screen: black ground · octa hero in the shared Signal Field · Lumen leads · spatial-parallax · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldshape octaground blacktreatment wireframemotion spatial-parallaxpower spatial-parallaxinstrument laser-fire→three-forward
9	still pending matches intent shared field signature-3d	resolve	47.8–54.3s	coalescence	icon·wireframe	♪ swell	Take your last interview, transcribe it, and pull one clip from the strongest quote. That's the first step. on-screen: Transcribe one interview, find the clip
	expected on screen: black ground · octa hero in the shared Signal Field · Lumen leads · coalescence · coalescence · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual coalescenceshape octaground blacktreatment wireframemotion coalescencepower coalescenceinstrument coalescence→three-coalescence

Format stack 3 aspects · same scenes[], re-cropped

9:16

1080×1920

Stories · TikTok · YouTube Shorts · Reels

1:1

1080×1080

LinkedIn · Facebook · Instagram

16:9

1920×1080

X/Twitter · YouTube · LinkedIn video

Channels 12 destinations

LinkedInX/TwitterYouTubeInstagramFacebookThreadsTikTokPinterestBlueskyEmailSMSBlog

Social captions supplemental published copy · per channel (comp_id level)

tiktokThere is a great ten-minute interview hiding in two hours of raw tape. AI-assisted way to find it: transcribe the whole thing with word-level timestamps so the tape becomes searchable text. Ask the model to surface the strongest quotes and tag the moments that earn a clip. Those timestamps drive the rough cut, so the edit assembles itself around the best lines. Captions burn in, and the same master gets sliced into vertical clips for social. temerarii.xyz

instagramA great interview is hiding in your raw tape. Transcribe with word-level timestamps. Ask the model for the strongest quotes. Let those timestamps drive the rough cut. Burn captions, slice vertical clips from the same master. The edit assembles itself around the best lines. temerarii.xyz #interviewediting #transcription #videoediting #contentclips #temerarii

linkedinInside two hours of raw tape there is a great ten-minute interview, and finding it used to be the whole job. Here is how we cut the scrubbing. Transcribe the whole thing with word-level timestamps, so the tape becomes searchable text instead of a timeline you scrub by hand. Then ask the model to surface the strongest quotes and tag the moments that earn a clip. Those timestamps drive the rough cut, so the edit assembles itself around the best lines. Captions burn in and the system slices vertical clips for social from the same master. We show the turnaround openly: raw tape to a captioned cut in a fraction of the old time. Take your last interview, transcribe it, and pull one clip from the strongest quote. That is the first step. temerarii.xyz

xA great ten-minute interview hides in two hours of raw tape. Transcribe with word-level timestamps, let the model surface the best quotes, drive the rough cut off those timestamps. Try it on your last interview. temerarii.xyz

facebookInside two hours of raw tape there is a great ten-minute interview. Finding it used to mean an editor scrubbing every minute by hand. Here is the faster way: transcribe the whole thing with word-level timestamps so the tape becomes searchable text, ask the model to surface the strongest quotes, then let those timestamps drive the rough cut. Captions burn in and the same master gets sliced into vertical clips. Take your last interview and pull one clip from the strongest quote at temerarii.xyz

threadsA great ten-minute interview hides in two hours of raw tape. Transcribe with word-level timestamps so the tape is searchable, ask the model for the strongest quotes, let those timestamps drive the rough cut. Captions burn in, vertical clips slice off the same master. Try it on your last interview. temerarii.xyz

pinterestInterview editing workflow: transcribe raw tape with word-level timestamps, surface the strongest quotes with AI, let the timestamps drive the rough cut, burn in captions, and slice vertical social clips from one master. Find the great ten-minute interview hiding in two hours of footage. Save this video editing method.

blueskyA great ten-minute interview hides in two hours of raw tape. Transcribe with word-level timestamps, let the model surface the best quotes, drive the rough cut off those timestamps, slice vertical clips from the master. temerarii.xyz

youtubeFind the Interview in the Raw Tape: Timestamp Transcription to Rough Cut Inside two hours of raw tape there is a great ten-minute interview, and finding it used to be the whole job, an editor scrubbing every minute by hand. Here is the method we use instead, given away. Step one: transcribe the whole thing with word-level timestamps, so the tape becomes searchable text. Step two: ask the model to surface the strongest quotes and tag the moments that earn a clip. Step three: those timestamps drive the rough cut, so the edit assembles itself around the best lines. Step four: captions burn in and the system slices vertical clips for social from the same master. We show the turnaround openly: two hours of raw tape to a captioned cut in a fraction of the old time. What is next: you tell the edit what story you want and it re-cuts the interview around that thread. Take your last interview, transcribe it, and pull one clip from the strongest quote. That is the first step. temerarii.xyz

Cross-links every lens is a view on this one record

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