Checklist the per-video bar — engine/sim
81.9/100
quantitative quality · weights learn from your reviews (
engine.sim.memory review thread-minus-the-hype-W46-Thu good|bad)⚠ 6 flag(s) — not yet ship-ready: copy_genericlow_vo_coveragedead_airgeneric_scene · see docs/strategy/VIDEO-CHECKLIST.md
Composition comp · template family · expected output
9:16 Reelrendered1:1 Squarepending16:9 Widepending9:16 4Kpending1:1 4Kpending16:9 4KpendingGIF (SMS)pending
expected output: 1/7 rendered — same matrix the /media preview surfaces for this asset.
Composition layer × scene 5 scenes · 27.3s · comp_id + rendered still + tier + the script
| # | Layer (comp_id · still · tier) | Beat | Timecode | Motion | Logo | Audio | VO / on-screen / caption |
|---|---|---|---|---|---|---|---|
| 1 |  matches intent shared field signature-3d | hook | 0–5.1s | kinetic-build | icon·wireframe | ♪ node_lock | The forecasting pitch: feed it your numbers, get next quarter's revenue to the dollar. on-screen: "AI predicts your revenue" |
expected on screen: white ground · cone hero in the shared Signal Field · Augur leads · mark · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual markshape coneground whitetreatment wireframemotion kinetic-buildpower laser-lockinstrument laser-trace→"AI predicts your revenue" | |||||||
| 2 |  matches intent CodeWindow template | diff | 5.1–12.1s | crossfade-8f | icon·wireframe | ♪ node_lock | Here is what we do. We use the model to build scenario ranges from your pipeline. Then we show which guesses move the result most. on-screen: Ranges, not one number |
expected on screen: white ground · a CodeWindow panel over a dimmed Signal Field · Augur leads · code · crossfade-8f · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id CodeWindowvisual codeshape coneground whitetreatment wireframemotion crossfade-8fpower morphinstrument morph→Ranges, not one numbercurate codeLines, windowTitle | |||||||
| 3 |  matches intent NumberedList template | teach | 12.1–17.3s | kinetic-build | icon·wireframe | ♪ node_lock | A point forecast looks exact, but it is fiction. You end up planning a whole budget on a number that was never real. on-screen: The cost: planning on fiction |
expected on screen: white ground · a NumberedList panel over a dimmed Signal Field · Augur leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id NumberedListvisual node-graphshape coneground whitetreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→The cost: planning on fictioncurate items, nodes | |||||||
| 4 |  first render · fix pending ComparisonTable templatedead_airgeneric_scene | proof | 17.3–22.3s | receipts-count | icon·wireframe | ♪ node_lock | Minus the Hype |
expected on screen: white ground · a ComparisonTable panel over a dimmed Signal Field · Augur leads · receipts · receipts-count · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id ComparisonTablevisual receiptsshape coneground whitetreatment wireframemotion receipts-countpower receiptsinstrument spotlight→Minus the Hypecurate colA, colB, rows, statsLabels | |||||||
| 5 |  first render · fix pending shared field signature-3ddead_airgeneric_scene | resolve | 22.3–27.3s | coalescence | icon·wireframe | ♪ bed_out | Minus the Hype |
expected on screen: white ground · cone hero in the shared Signal Field · Augur leads · coalescence · coalescence · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual coalescenceshape coneground whitetreatment wireframemotion coalescencepower coalescenceinstrument coalescence→Minus the Hype | |||||||
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 9 destinations
LinkedInX/TwitterYouTubeInstagramFacebookThreadsTikTokPinterestBluesky
Social captions supplemental published copy · per channel (comp_id level)
tiktokThe pitch: feed AI your numbers, get next quarter's revenue to the dollar. What we really do: use the model to build scenario ranges from your pipeline and see which guesses move the result. A single number is a budget built on fiction. (AI-assisted)
instagram"AI predicts your revenue."
The pitch: feed it numbers, get next quarter to the dollar.
Reality: we build scenario ranges, not one number.
Then we see which assumptions move the result.
The cost of a point forecast: planning on fiction.
#Forecasting #AItools #MinusTheHype #SmallBusiness #TheBigTM
linkedin"AI predicts your revenue" is the forecasting pitch: feed it your numbers, get next quarter to the dollar.
Here is what we actually do. We use the model to build scenario ranges from your pipeline data, then surface which assumptions drive the result. The output is a range and a list of bets, not a single confident number.
The real cost of a point forecast is a whole budget built on a number that was never real. Plan on ranges.
x"AI predicts revenue" is the pitch: feed it numbers, get next quarter to the dollar.
Reality: we build scenario ranges from pipeline data and show which assumptions move the result.
A point forecast is a budget built on fiction.
facebookThe pitch sounds tempting: feed AI your numbers and get next quarter's revenue to the dollar. Here is what we actually do. We use the model to build scenario ranges from your pipeline data, then show which assumptions move the result the most. A single point forecast leaves you planning a whole budget on a number that was never real. Plan on ranges instead.
threads"AI predicts your revenue."
The pitch: feed it your numbers, get next quarter to the dollar.
What we really do: build scenario ranges from pipeline data, then show which assumptions move the result.
The cost of one number? A budget built on fiction.
pinterestCan AI predict your revenue? The honest answer for small businesses: use the model to build scenario ranges from your pipeline data, not one exact number. Then surface which assumptions drive the result. A point forecast leads to a budget built on a number that was never real.
bluesky"AI predicts revenue" is the pitch. Reality: we build scenario ranges from pipeline data and show which assumptions move the result. A point forecast is a budget built on fiction.
youtubeTitle: Can AI Predict Your Revenue? Minus the Hype
The forecasting pitch says feed the model your numbers and get next quarter's revenue to the dollar. Here is what The Big T-M actually does: use the model to build scenario ranges from your pipeline data and surface which assumptions drive the result. The real cost of a single point forecast is a whole budget built on a number that was never real. Plan on ranges, not fiction.