Social — W35 Tue — Temerarii Media Back Office

postW35-Tue-social-3kindSocialweekW35dayTuedate2026-09-01campaignlongform-youtubecadence5/day floor × 9 channels

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Growth & Performance Marketing

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formatnative cut · 9:16 · 1:1 · 16:9 dims1080×1920 · 1080×1080 · 1920×1080 cadence5/day floor × 9 channels

Channels 9 destinations

TikTokInstagramLinkedInX/TwitterFacebookThreadsPinterestBlueskyYouTube

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social-W35-Tue-3

5-distinct-social/day · 9:16 master → 1:1 / 16:9 cuts per channel

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Channel	Native caption
Tiktok	Most 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.)
Instagram	Start 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
Linkedin	Takeaway 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.
X	AI 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
Facebook	Plenty 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.
Threads	Most 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.
Pinterest	AI 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.
Bluesky	AI 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
Youtube	Shipping 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

Composition layer × scene 9 scenes · this post's OWN storyboard (distinct per asset)

#	Layer (comp_id · still · tier)	Beat	Timecode	Motion	Logo	Audio	VO / on-screen / caption
1	still pending matches intent shared field signature-3d	open	0–6.6s	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	still pending 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	still pending 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	still pending 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	still pending 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	still pending 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	still pending 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	still pending 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	still pending 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

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