Project
Off-page distribution engine
A structured authority and distribution system across Reddit, TradingView, Medium, and Paragraph — built to survive Reddit's spam filters, adapt content per platform, and feed performance data back into what gets researched next.
The problem
Off-page growth for a crypto exchange blog is usually run as a checklist: post here, post there, hope something sticks. That breaks fast on Reddit specifically — crypto accounts get flagged and banned almost immediately if they post links or promotional language before they've built account history. It happened three times before the real constraint became clear: this isn't a content problem, it's a trust and sequencing problem.
The system
Market trend research feeds a platform adaptation layer, which rewrites the same underlying research per platform — unbranded and comment-first for Reddit, chart-analysis writeups for TradingView, long-form articles for Medium, and crypto-native essays for Paragraph — before distribution. Cision and Lark Base then track brand-mention lift and content performance, feeding the next research cycle.
The phased Reddit strategy
Reddit is the highest-risk, highest-value channel, so it runs its own internal phasing. This sequencing is what took the account from three bans to zero.
Account foundation
No crypto activity; general-interest commenting only
Karma in-niche
Unbranded comments in relevant subs, no links
Authority participation
Higher-effort, research-informed comments
Soft mentions
Brand mentions only when directly relevant, ~1:12 ratio
Distribution
Content shared only where rules explicitly allow it
Results — 10 months in
Illustrative figures pending verified export
Referring domains
month 1 → month 10
Domain rating
month 1 → month 10
Off-page organic traffic
month 1 → month 10
Earned brand mentions
month 1 → month 10
Reddit karma
month 1 → month 10
Competitor mention share
month 1 → month 10
What I'd automate next
The manual bottleneck is the adaptation layer — rewriting one research piece into four platform-specific formats by hand. The next step is routing that through an LLM-assisted drafting stage, with a live attribution layer telling the research stage which topics and formats are actually converting, not just getting views.