Brand System
About
Voice Profile System
Built LinkedIn voice DNA profiles for both Daeda founders by ranking the top 10 posts each (weighted reactions × engagement rate) and reverse-engineering the patterns into reusable style guides the AI curation pipeline can draft against.
Outputs
- tikita-voice.md - short, punchy fragments, problem-first hooks, British-casual builder-sharing-workshop tone, moderate emoji, Unicode bold for emphasis. Average post 150-300 words.
- jack-voice.md - long-form thesis-argument-conclusion (300-500+ words), provocative claim hooks, tab-indented numbered lists, gaming and fantasy metaphors, almost no emoji or hashtags.
- Side-by-side comparison table so the right voice can be picked consciously when drafting.
- Source data preserved as CSV per founder per year.
Why It Matters
Tone is the hardest thing to keep consistent across an AI writing pipeline. By turning each founder's top performers into a documented style, the curation workflow drafts in either voice on command instead of producing generic LinkedIn-ese.
Branded Asset Library
A reusable library of branded assets - UI animation clips, motion templates, type treatments, colour pairings, product UI captures - aggregated to slot into the AI loop for video production.
Why a library rather than per-asset generation: brand consistency at volume is hard. Pulling from a tagged, curated set means generated content can't drift off-brand.
Currently used as the constraint layer in the daeda_tech YouTube AI loop.
Tech Stack
Discussion & Feedback
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