Back Office · office.temerarii.xyz
One published post, granular — the Social for W42 Mon, its copy, its output-policy format, and the video master it derives from. Part of the day's full output set.
postW42-Mon-social-5kindSocialweekW42dayMondate2026-10-19campaignlongform-youtubecadence5/day floor × 9 channels

Copy the published post text

Minus the Hype
copy ready · render pending

Output-policy spec format · dimensions (asset_specs.output_policy)

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

Channels 9 destinations

TikTokInstagramLinkedInX/TwitterFacebookThreadsPinterestBlueskyYouTube

This post a distinct social asset — its own angle, storyboard, and cuts

thread-minus-the-hype-W42-Mon
5-distinct-social/day · 9:16 master → 1:1 / 16:9 cuts per channel

Channel-cuts this asset → 9 native captions (one master · per-platform aspect+copy)

ChannelNative caption
TiktokYour output is weak so you reach for the most expensive model. Wrong fix. We run small fast models for the easy jobs and save the big one for the hard thinking. Same result, a tenth of the bill. (AI-assisted)
InstagramThe reflex: bigger model fixes everything. The move: route by task. Small fast models do extraction and sorting. The big model only handles the hard reasoning. Same output. A tenth of the cost. #AItools #aiworkflow #builtnotbought #automation #costcontrol
LinkedinMost teams default to the most expensive model when their output looks weak. The bill goes up tenfold and the output does not change. What we actually do: route by task. Small fast models handle extraction and classification. The big model only gets the hard reasoning that needs it. The takeaway: pick the model per job, not per habit. You get the same answer for a fraction of the spend. We run our own pipeline this way.
X"Bigger model fixes everything" is a reflex, not a strategy. Route by task: small fast models for easy jobs, the big one for hard reasoning. Same output, a tenth of the bill. Method here:
FacebookWhen your AI output looks weak, the reflex is to reach for the most expensive model. The bill jumps tenfold and the answer stays the same. The real move is to route by task. Use small fast models for the easy jobs and save the big model for the hard thinking. Try sorting your tasks into easy and hard before you pick a model.
ThreadsEveryone has the same reflex: my output is weak, so I need the biggest model. That is a tenfold bill for output you would have gotten anyway. Route by task instead. Small fast models for easy jobs. Big model only for the hard reasoning. Same answer, way less spend.
PinterestHow to cut AI costs without losing quality: route by task. Use small fast models for extraction and classification, and reserve the expensive large model only for hard reasoning. Model routing strategy for cheaper AI workflows and lower API bills.
BlueskyThe reflex: my output is weak, so I need the biggest model. The result: a tenfold bill for the same answer. Route by task instead. Small models for easy jobs, big model for hard reasoning.
YoutubeTitle: Stop Paying ten times for AI. Route by Task Instead. The reflex is to reach for the most expensive model when output looks weak. That is a tenfold bill for the same answer. We route by task: small fast models for extraction and classification, the big model only for hard reasoning. This short shows the method so you can sort your own tasks and pick the right model per job. (AI-assisted)

Composition layer × scene 5 scenes · this post's OWN storyboard (distinct per asset)

#Layer (comp_id · still · tier)BeatTimecodeMotionLogoAudioVO / on-screen / caption
1s1
matches intent
shared field
signature-3d
hook0–5.6skinetic-buildicon·liquid-chrome♪ node_lock
The reflex everyone has: my output is weak, so I need the most expensive model.
on-screen: "Bigger model fixes everything"
expected on screen: red ground · knot hero in the shared Signal Field · Magister leads · mark · kinetic-build · icon·liquid-chrome logo · caption bottom-left
spec (the prompt): comp_id shared Signal Fieldvisual markshape knotground redtreatment liquid-chromemotion kinetic-buildpower laser-lockinstrument laser-trace→"Bigger model fixes everything"
2s2
matches intent
JsonDiff
template
diff5.6–13.399999999999999scrossfade-8ficon·white-knockout♪ node_lock
What we really do: small fast models do the easy jobs. The big model is saved for the hard thinking.
on-screen: Reality: route by task
expected on screen: red ground · a JsonDiff panel over a dimmed Signal Field · Magister leads · code · crossfade-8f · icon·white-knockout logo · caption bottom-left
spec (the prompt): comp_id JsonDiffvisual codeshape knotground redtreatment white-knockoutmotion crossfade-8fpower morphinstrument morph→Reality: route by taskcurate codeLines, fileName, lines
3s3
matches intent
ChecklistCard
template
teach13.4–19.2skinetic-buildicon·liquid-chrome♪ node_lock
The real cost of defaulting to the biggest model is a tenfold bill for output you'd have gotten anyway.
on-screen: The cost: tenfold bill, same output
expected on screen: red ground · a ChecklistCard panel over a dimmed Signal Field · Magister leads · node-graph · kinetic-build · icon·liquid-chrome logo · caption bottom-left
spec (the prompt): comp_id ChecklistCardvisual node-graphshape knotground redtreatment liquid-chromemotion kinetic-buildpower morphinstrument morph+laser→The cost: tenfold bill, same outputcurate items, nodes
4s4
first render · fix pending
StatScoreboard
templatedead_airgeneric_scene
proof19.2–24.2sreceipts-counticon·white-knockout♪ node_lock
Minus the Hype
expected on screen: red ground · a StatScoreboard panel over a dimmed Signal Field · Magister leads · receipts · receipts-count · icon·white-knockout logo · caption bottom-left
spec (the prompt): comp_id StatScoreboardvisual receiptsshape knotground redtreatment white-knockoutmotion receipts-countpower receiptsinstrument spotlight→Minus the Hypecurate pillar, stats, statsLabels
5s5
first render · fix pending
shared field
signature-3ddead_airgeneric_scene
resolve24.2–29.2scoalescenceicon·white-knockout♪ bed_out
Minus the Hype
expected on screen: red ground · knot hero in the shared Signal Field · Magister leads · coalescence · coalescence · icon·white-knockout logo · caption bottom-left
spec (the prompt): comp_id shared Signal Fieldvisual coalescenceshape knotground redtreatment white-knockoutmotion coalescencepower coalescenceinstrument coalescence→Minus the Hype

Cross-links this post in the day's output set