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LinkedIn Content Automation Pipeline

  • Hours → seconds per content cycle
  • Multi-API orchestration (Discord, Drive, LinkedIn)
  • Stateful dedup via Supabase
project.zabesync.local
LinkedIn Content Automation Pipeline project screenshot

Problem: LinkedIn content creation is time-intensive, repetitive, and often lacks real-time relevance, reducing posting consistency.

Solution: Built an AI-driven n8n automation pipeline with live research, LLM-based generation, and Discord-triggered workflows with Supabase state management.

Impact: Automated end-to-end ideation to publishing, reducing effort from hours to seconds and enabling consistent, high-quality output.

Overview

End-to-end automation from research and ideation to drafted posts and one-click publish, with human-in-the-loop via Discord and durable state for deduplication.

Results

Hours to seconds per content cycle

Zero duplicate topics via Supabase state tracking

5-API orchestration (Discord, Drive, LinkedIn, Supabase, LLM)

Consistent posting cadence with minimal manual effort

Before / after

Publishing flow

Before: Manual context-switching across tools

After: Single pipeline with checkpointed state

Ideation

Before: Ad hoc notes and repetition

After: Deduped AI-assisted ideas delivered on schedule

Stack

AWS EC2 + Docker
n8n workflows
Supabase
LLM + web research
LinkedIn & Google APIs

Decisions

Key trade-offs and design calls that shaped the final delivery.

n8n over custom code

Context: Needed rapid workflow iteration without deployments for a content automation tool

Decision: Chose n8n visual workflow builder for AI-powered pipelines—allowed iterating on LLM prompts and API integrations without code changes

Discord as control plane

Context: Team already used Discord for daily communication

Decision: Made Discord the single interface for triggering, reviewing, and publishing—no separate dashboard needed, minimal context switching

Architecture

The primary system boundaries, runtime pieces, and how the project was structured in production.

n8n workflows with Gemini LLM

AI Content Engine

Three interconnected workflows: daily AI-powered idea pitcher with live web research and deduplication, content creator triggered by Discord that drafts posts and generates images, and one-click publisher that posts to LinkedIn.

Supabase

State Management

Stores idea deduplication state, user preferences, and tracks which ideas have been pitched—prevents topic repetition across campaigns.

Discord Bot

Control Interface

Sole control interface for the entire pipeline interactive buttons for approve/reject/publish, no manual LinkedIn interaction required.

Mermaid source. Paste into mermaid.live to visualize the diagram.

flowchart LR
  subgraph ingest [Trigger & control]
    D[Discord]
  end
  subgraph compute [Automation]
    N[n8n on EC2]
    L[LLM + research]
  end
  subgraph data [State & assets]
    S[(Supabase)]
    G[Google Drive]
  end
  subgraph publish [Outbound]
    LI[LinkedIn API]
  end
  D --> N
  N --> L
  N --> S
  N --> G
  N --> LI
  S --> N

Pipeline

How changes moved from development through validation and deployment.

1

Trigger

Discord Bot

Interactive buttons for /generate-ideas action

2

Research

Web scraping + LLM

Live web research and idea generation with deduplication

3

Drafting

n8n workflow

AI-powered post drafting with image generation

4

Review

Discord Bot

Interactive buttons for select ideas with dropdown action

5

Approval

Discord Bot

Interactive buttons for approve/reject actions

6

Processing

n8n on AWS EC2

3 interconnected workflows handle ideation → drafting → publishing with LLM reasoning

7

Storage

Supabase + Google Drive

State management and deduplication in Supabase, AI-generated images stored in Drive

8

Publish

LinkedIn API

One-click automated posting after Discord approval

Incidents

Operational failures, rehearsals, or recovery moments that changed how the system was run.

Free tier API limits causing workflow failures

P2

Resolution: Implemented multi-AI fallback mechanism and concise prompt engineering to reduce token usage

Lesson: Free tiers have hard limits—design fallback chains and optimize prompts for cost efficiency

Discord bot webhook connectivity issues

P3

Resolution: Configured n8n webhook node with retry logic and proper Discord bot token management

Lesson: Webhook integrations need retry logic and proper authentication handling

Web search API rate limiting

P3

Resolution: Cached research results and batched searches to minimize API calls

Lesson: Research APIs are expensive—cache aggressively and batch requests