A written library built from platform documentation and open-sourced ranking code — not recycled advice. Every claim is cited, every number is checkable, and the workflows are the ones that survive contact with the systems they target.
Karma stopped being the gate. Contributor Quality Score runs on five tiers — Lowest, Low, Moderate, High, Highest — and large subreddits filter submissions against it directly. We cover what actually feeds CQS (account age, email verification, IP history, posting pattern, prior enforcement), the warming sequence that gets a cold account to Moderate before it posts anything, and the 10:1 contribution ratio that keeps it there.
Also: the four distinct things people call a shadowban — sitewide admin suppression, AutoMod filtering triggered by low CQS, domain-level URL suppression, and per-subreddit Crowd Control. They have different symptoms and different fixes, and a profile that still loads publicly proves none of them are absent.
X published its feed algorithm to GitHub in January 2026, so the engagement weights are no longer guesswork. Replies carry roughly 2–3× a like. A retweet is worth about 20×, a bookmark about 10×, and a reply that the author replies to is worth on the order of 150× a like.
What follows from those numbers is a completely different posting strategy than chasing likes: the first 30–60 minutes of engagement is the single largest distribution lever, external links in the main post are actively suppressed, and Premium amplifies signals you already generate rather than substituting for them.
The production pipeline in full: research vault → one pillar split into 30 angles → script templates → voiceover and visuals → batch production → QC → publish. Planning thirty unrelated videos is the mistake; thirty angles on one pillar is what lets a channel accumulate topical authority.
On Shorts, YouTube's own viewed-versus-swiped-away report is close to a direct readout of hook strength, and it is a different diagnosis from retention drop-off. We cover why AI voiceover fails when scripts are written in essay prose instead of conversational rhythm, and which parts of the pipeline still have to be human — niche selection, the editorial pass, thumbnail testing.
TikTok states plainly that "neither follower count nor whether the account has had previous high-performing videos are direct factors in the recommendation system." That single sentence invalidates most of the advice built on growing followers first.
They also name completion as the heavyweight signal: "whether a user finishes watching a longer video from beginning to end, would receive greater weight than a weak indicator." We work through what that means for length, for looping, and for the early recommendation round that your first likes, comments and replays trigger.
Nearly every plateau is one of two problems, and the fix for one makes the other worse. High retention with low views means the content worked but the premise was too narrow for distribution to widen — broaden the entry point, not the production quality. High views with low retention means the opening wrote a cheque the video did not cash. Diagnosing these backwards is the most expensive mistake in short-form, and it is why the tracking sheet in Module 03 puts retention before view count.
Plus: the weekly batching model that survives a bad week, why a three-post buffer is the highest-return habit available, why deleting underperformers destroys the only data you have, and why you review every thirty posts rather than every day — before thirty rows you are pattern-matching on noise.
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