How I Built “Viral Ink” - An AI System That Turns Ideas Into Viral LinkedIn Content

Source: Dev.to

Goals

• Generate high‑quality content daily
• Feel personal (not generic AI)
• Improve over time using feedback

Workflow: ONBOARD → GENERATE → SELECT → PUBLISH → LEARN

The system analyzes my past posts, extracts tone, structure, and vocabulary, and injects those rules into every generation. The result is content that sounds like me, not AI. It also tracks topic momentum (velocity + growth) and identifies emerging trends early.

Multi‑Agent System

1. Researcher – finds ideas
2. Writer – drafts posts
3. Critic – scores quality
4. Revision – improves output

Virality Scoring

Each post receives:

• 3 hook variants
• A score (0–100 %) based on:
  • Hook strength
  • Format
  • Engagement potential
  • Trend timing

Daily Output

Every morning the system delivers:

• 7–10 ready‑to‑post ideas
• Hook variations
• Virality scores

Pick one and post in about 2 minutes. After publishing, the system:

• Tracks likes, comments, shares
• Compares predicted vs. actual performance
• Updates scoring weights, persona preferences, and topic memory

The system improves every week.

Technical Stack

# Example configuration (YAML)
language_models:
  - OpenAI
  - Anthropic
  - Ollama
tools:
  - SMTP / Resend
testing:
  - pytest

Why It’s Different

• Uses your voice, not a generic AI style
• Learns from real results
• Avoids repeating content
• Employs multi‑agent reasoning

Try It

https://github.com/Sohamp2809/viral-ink

AI won’t replace creators, but creators who use AI systems like this will win.