Checklist the per-video bar — engine/sim
98.5/100
quantitative quality · weights learn from your reviews (
engine.sim.memory review social-W25-Fri-2 good|bad)⚠ 1 flag(s) — not yet ship-ready: copy_generic · see docs/strategy/VIDEO-CHECKLIST.md
Composition comp · template family · expected output
9:16 Reelpending1:1 Squarepending16:9 Widepending9:16 4Kpending1:1 4Kpending16:9 4KpendingGIF (SMS)pending
render pending — silent master not yet on disk
expected output: 0/7 rendered — same matrix the /media preview surfaces for this asset.
Composition layer × scene 9 scenes · 47.0s · 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 | open | 0–5.2s | spatial-parallax | icon·white-knockout | ♪ bed_in | The old way of using machine learning needed a whole team and months. on-screen: Models used to need a team |
expected on screen: black ground · cone hero in the shared Signal Field · Augur leads · node-graph · spatial-parallax · icon·white-knockout logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual node-graphshape coneground blacktreatment white-knockoutmotion spatial-parallaxpower summoninstrument summon→Models used to need a team | |||||||
| 2 |  matches intent shared field signature-3d | hook | 5.2–10.4s | kinetic-build | icon·wireframe | ♪ node_lock | Here is the move now: start with one clear yes or no question. on-screen: Now you start with a clear question |
expected on screen: black 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 blacktreatment wireframemotion kinetic-buildpower laser-lockinstrument laser-trace→Now you start with a clear question | |||||||
| 3 |  matches intent NumberedList template | teach | 10.4–15.9s | kinetic-build | icon·wireframe | ♪ node_lock | Frame your problem as a guess the model can score, like will this churn. on-screen: Frame it as a guess to score |
expected on screen: black 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 blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→Frame it as a guess to scorecurate items, nodes | |||||||
| 4 |  matches intent ComparisonTable template | proof | 15.9–21.1s | receipts-count | icon·wireframe | ♪ node_lock | Ask will this customer leave, then let the model score each one daily. on-screen: Will this customer leave? Score it |
expected on screen: black 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 blacktreatment wireframemotion receipts-countpower receiptsinstrument spotlight→Will this customer leave? Score itcurate colA, colB, rows, statsLabels | |||||||
| 5 |  matches intent TerminalRun template | diff | 21.1–26.1s | crossfade-8f | icon·wireframe | ♪ node_lock | A sharp question beats fancy math. Bad framing breaks the smartest model. on-screen: A sharp question beats fancy math |
expected on screen: black ground · a TerminalRun panel over a dimmed Signal Field · Augur leads · code · crossfade-8f · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id TerminalRunvisual codeshape coneground blacktreatment wireframemotion crossfade-8fpower morphinstrument morph→A sharp question beats fancy mathcurate codeLines | |||||||
| 6 |  matches intent ChecklistCard template | teach | 26.1–31.3s | kinetic-build | icon·wireframe | ♪ node_lock | Let the tool try a simple model first before anyone reaches for complex. on-screen: Let AI try the simple model first |
expected on screen: black ground · a ChecklistCard panel over a dimmed Signal Field · Augur leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id ChecklistCardvisual node-graphshape coneground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→Let AI try the simple model firstcurate items, nodes | |||||||
| 7 |  matches intent KpiGrid template | proof | 31.3–36.5s | receipts-count | icon·wireframe | ♪ node_lock | A simple model often wins, so you check it before spending on more. on-screen: Simple often wins; check it first |
expected on screen: black ground · a KpiGrid panel over a dimmed Signal Field · Augur leads · receipts · receipts-count · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id KpiGridvisual receiptsshape coneground blacktreatment wireframemotion receipts-countpower receiptsinstrument spotlight→Simple often wins; check it firstcurate kpis, statsLabels | |||||||
| 8 |  matches intent WireframeMock template | step | 36.5–42.0s | kinetic-build | icon·wireframe | ♪ node_lock | Frame one clear question, try a simple model, then check if it is enough. on-screen: Frame the question, try simple, then check |
expected on screen: black ground · a WireframeMock panel over a dimmed Signal Field · Augur leads · pipeline · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id WireframeMockvisual pipelineshape coneground blacktreatment wireframemotion kinetic-buildpower throwinstrument laser-trace→Frame the question, try simple, then checkcurate stages, steps | |||||||
| 9 |  matches intent shared field signature-3d | resolve | 42.0–47.0s | coalescence | icon·white-knockout | ♪ bed_out | The Big T-M starts machine learning with the question, not the algorithm. on-screen: The Big T-M starts with the question |
expected on screen: black ground · cone hero in the shared Signal Field · Augur leads · coalescence · coalescence · icon·white-knockout logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual coalescenceshape coneground blacktreatment white-knockoutmotion coalescencepower coalescenceinstrument coalescence→The Big T-M starts with the question | |||||||
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)
tiktokMachine learning without a data team (AI-assisted): start with ONE clear yes/no question, like 'will this customer leave?' Frame it as a guess the model scores. Try a simple model first. Bad framing breaks the smartest math. #machinelearning #ai #datascience
instagramML starts with a question, not an algorithm.
"Will this customer leave?"
Try simple first.
#machinelearning #ai #datascience #tech #emergingtech
linkedinMachine learning does not start with an algorithm, it starts with a question. The method: frame your problem as one clear yes or no guess the model can score, like will this customer leave. The takeaway: a sharp question beats fancy math, and a simple model often wins. Try the simple version first and check if it is enough.
xMachine learning starts with a question, not an algorithm. Frame it as a yes/no guess to score. Try simple first. temerarii.xyz
facebookYou don't need a whole data team to use machine learning. Start with one clear yes/no question, like 'will this customer leave?', frame it as a guess the model scores, and try a simple model first. The method is on the site.
threadsML truth: it starts with a question, not an algorithm. Frame your problem as a yes/no guess the model can score (like 'will this customer churn?'), and try a simple model first. Bad framing breaks the best math.
pinterestMachine learning method: start with one clear yes/no question, frame it as a guess the model scores, and try a simple model first. A sharp question beats fancy math. Machine learning basics, data science tips, AI for business.
blueskyML starts with a question, not an algorithm. Frame it as a yes/no guess to score, try simple first. temerarii.xyz
youtubeMachine Learning Starts With a Question, Not an Algorithm
You do not need a whole data team to start. We show the method: frame your problem as one clear yes or no guess the model can score, like will this customer leave. A sharp question beats fancy math, and a simple model often wins. Try the simple version first and check if it is enough.