Checklist the per-video bar — engine/sim
85.1/100
quantitative quality · weights learn from your reviews (
engine.sim.memory review social-W35-Tue-3 good|bad)⚠ 4 flag(s) — not yet ship-ready: copy_generictoo_complexdup_sequence_across_assetsdup_set_across_assets · 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 · 57.6s · 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 | 0–6.6s | spatial-parallax | icon·liquid-chrome | ♪ bed_in | Plenty of teams bolt AI onto a product as a buzzword, then wonder why nobody uses it. on-screen: AI bolted on as a buzzword |
expected on screen: red ground · knot hero in the shared Signal Field · Magister leads · node-graph · spatial-parallax · icon·liquid-chrome logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual node-graphshape knotground redtreatment liquid-chromemotion spatial-parallaxpower summoninstrument summon→AI bolted on as a buzzword | |||||||
| 2 |  matches intent shared field signature-3d | hook | 6.6–12.899999999999999s | kinetic-build | icon·white-knockout | ♪ node_lock | Start from the most painful repeated task, not from the model you happen to find shiny. on-screen: Start from the painful task |
expected on screen: red ground · knot hero in the shared Signal Field · Magister leads · mark · kinetic-build · icon·white-knockout logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual markshape knotground redtreatment white-knockoutmotion kinetic-buildpower laser-lockinstrument laser-trace→Start from the painful task | |||||||
| 3 |  matches intent NumberedList template | teach | 12.9–19.2s | kinetic-build | icon·liquid-chrome | ♪ node_lock | Write the test set first, real inputs with known good answers, before you build the feature. on-screen: Write the eval before the feature |
expected on screen: red ground · a NumberedList panel over a dimmed Signal Field · Magister leads · node-graph · kinetic-build · icon·liquid-chrome logo · caption bottom-left spec (the prompt): comp_id NumberedListvisual node-graphshape knotground redtreatment liquid-chromemotion kinetic-buildpower morphinstrument morph+laser→Write the eval before the featurecurate items, nodes | |||||||
| 4 |  matches intent RankList template | proof | 19.2–25.5s | receipts-count | icon·white-knockout | ♪ node_lock | Score each candidate model against that same set, so the choice is evidence, not a vibe. on-screen: Score every model the same way |
expected on screen: red ground · a RankList panel over a dimmed Signal Field · Magister leads · receipts · receipts-count · icon·white-knockout logo · caption bottom-left spec (the prompt): comp_id RankListvisual receiptsshape knotground redtreatment white-knockoutmotion receipts-countpower receiptsinstrument spotlight→Score every model the same waycurate rows, statsLabels | |||||||
| 5 |  matches intent TerminalRun template | diff | 25.5–31.8s | crossfade-8f | icon·liquid-chrome | ♪ node_lock | The winner is rarely the biggest model, it is the cheapest one that passes your test. on-screen: Not the biggest, the right-sized |
expected on screen: red ground · a TerminalRun panel over a dimmed Signal Field · Magister leads · code · crossfade-8f · icon·liquid-chrome logo · caption bottom-left spec (the prompt): comp_id TerminalRunvisual codeshape knotground redtreatment liquid-chromemotion crossfade-8fpower morphinstrument morph→Not the biggest, the right-sizedcurate codeLines | |||||||
| 6 |  matches intent ChecklistCard template | teach | 31.8–38.4s | kinetic-build | icon·white-knockout | ♪ node_lock | Keep a human in the loop on the cases your eval flags as low confidence, every time. on-screen: Keep the human on the edge cases |
expected on screen: red ground · a ChecklistCard panel over a dimmed Signal Field · Magister leads · node-graph · kinetic-build · icon·white-knockout logo · caption bottom-left spec (the prompt): comp_id ChecklistCardvisual node-graphshape knotground redtreatment white-knockoutmotion kinetic-buildpower morphinstrument morph+laser→Keep the human on the edge casescurate items, nodes | |||||||
| 7 |  matches intent StatScoreboard template | proof | 38.4–44.699999999999996s | receipts-count | icon·liquid-chrome | ♪ node_lock | When a new model version drifts, that same test set catches it before your users do. on-screen: The eval catches the drift |
expected on screen: red ground · a StatScoreboard panel over a dimmed Signal Field · Magister leads · receipts · receipts-count · icon·liquid-chrome logo · caption bottom-left spec (the prompt): comp_id StatScoreboardvisual receiptsshape knotground redtreatment liquid-chromemotion receipts-countpower receiptsinstrument spotlight→The eval catches the driftcurate pillar, stats, statsLabels | |||||||
| 8 |  matches intent WireframeMock template | step | 44.7–51.300000000000004s | kinetic-build | icon·white-knockout | ♪ node_lock | Step one is the eval set, fifty real examples, before you write a line of feature code. on-screen: Build the eval set first |
expected on screen: red ground · a WireframeMock panel over a dimmed Signal Field · Magister leads · pipeline · kinetic-build · icon·white-knockout logo · caption bottom-left spec (the prompt): comp_id WireframeMockvisual pipelineshape knotground redtreatment white-knockoutmotion kinetic-buildpower throwinstrument laser-trace→Build the eval set firstcurate stages, steps | |||||||
| 9 |  matches intent shared field signature-3d | resolve | 51.3–57.599999999999994s | coalescence | icon·liquid-chrome | ♪ bed_out | Useful beats impressive, and the eval is how you tell them apart at The Big T-M. on-screen: Useful beats impressive |
expected on screen: red ground · knot hero in the shared Signal Field · Magister leads · coalescence · coalescence · icon·liquid-chrome logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual coalescenceshape knotground redtreatment liquid-chromemotion coalescencepower coalescenceinstrument coalescence→Useful beats impressive | |||||||
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)
tiktokMost AI features fail because teams pick the model before the problem. Reverse it: write an eval set first, real inputs with known-good answers, then score every model against it. The winner is the cheapest one that passes, not the biggest. (AI-assisted edit.)
instagramStart from the painful task, not the shiny model.
Write the eval set first.
Score every model the same way.
Right-sized beats biggest.
The eval catches drift.
#artificialintelligence #ai #machinelearning #tech #temerarii
linkedinTakeaway for teams shipping AI features: write the eval before the feature.
Start from the most painful repeated task, not the model you find shiny. Build a test set of real inputs with known-good answers, then score every candidate model against that same set so the choice is evidence rather than vibe. The winner is rarely the biggest model; it's the cheapest one that passes. Keep a human in the loop on low-confidence cases, and when a new model version drifts, that same eval catches it before your users do. Useful beats impressive.
xAI truth: most features fail because teams pick the model before the problem. Write an eval set first, real inputs with known answers, then score every model against it. The winner is the cheapest that passes, not the biggest. temerarii.com
facebookPlenty of teams bolt AI onto a product as a buzzword, then wonder why nobody uses it. The method: start from the most painful repeated task, write an eval set first (real inputs with known-good answers), and score every candidate model against it. The winner is the cheapest one that passes, not the biggest. Keep a human on the low-confidence cases. Useful beats impressive. More at temerarii.com.
threadsMost AI features fail because teams pick the model before the problem. Write an eval set first, real inputs with known-good answers, then score every model against it. The winner is the cheapest one that passes, not the biggest. The eval also catches version drift.
pinterestAI feature development guide: start from the painful task, write an evaluation set of real inputs with known answers, score every candidate model the same way, choose the cheapest that passes, and use the eval to catch model drift. Evergreen AI product and machine learning strategy.
blueskyAI truth: most features fail because teams pick the model before the problem. Write an eval set first, real inputs with known answers, then score every model. The winner is the cheapest that passes, not the biggest. temerarii.com
youtubeShipping AI Features: Write the Eval Before the Feature
Plenty of teams bolt AI on as a buzzword, then wonder why nobody uses it. The method: start from the most painful repeated task, not the shiniest model. Write a test set first, real inputs with known-good answers, then score every candidate model against that same set so the choice is evidence, not vibe. The winner is rarely the biggest; it's the cheapest one that passes. Keep a human in the loop on low-confidence cases, and let the eval catch version drift before your users do.
More at temerarii.com.
#artificialintelligence #ai #machinelearning