This project built an end-to-end automated content publishing system, aimed at solving the “information gap” between the Traditional Chinese community and international tech communities.

📋 Abstract

Traditional technical knowledge dissemination relies on manual translation and transfer, often resulting in delays of days or even weeks. To solve this problem, I set up n8n automation workflow on a Linux server and integrated Ollama local large language model (gpt-oss) to achieve a fully unattended workflow from RSS monitoring, content extraction, AI summary translation, smart image selection, to final WordPress publishing. This system can instantly transform first-hand international technical information into high-quality Chinese content, significantly improving the efficiency and breadth of information acquisition.

🛠️ Technical Case Study

1. System Architecture & Infrastructure

To ensure system stability and privacy security, this project adopted a fully privatized deployment solution:

  • Linux Server: Serves as the foundational environment for computing and service hosting.
  • Workflow Orchestration (n8n): Uses n8n as the automation hub, responsible for connecting various API nodes and logical judgments, replacing traditional cumbersome Python Crontab scripts.
  • Local LLM Inference (Ollama): Deploys Ollama framework running gpt-oss model, enabling large-scale text processing without relying on expensive external APIs with privacy concerns.

2. Core Technology: AI-Driven Content Pipeline

The core of this system lies in transforming unstructured web content into structured publishing format through AI.

2.1 Ingestion & Cognitive Processing

The workflow begins with RSS monitoring of specific technical domains. Once new articles are discovered, the system triggers the following processing chain:

  1. Content Cleaning: Automatically crawls original web pages, removing ads and irrelevant HTML tags.
  2. Local LLM Inference: Feeds cleaned text to Ollama (gpt-oss) for multi-dimensional processing:
    • Summarization: Extracts core technical points from articles.
    • Translation: Accurately converts English technical terminology to Traditional Chinese.
    • Auto-Tagging: Based on semantic context, automatically determines the technical domain (e.g., DevOps, AI, Security) and generates corresponding tags.

2.2 Context-Aware Media Retrieval

To make articles visually engaging, I designed a “text-to-image” retrieval logic rather than simply using random images.

  1. Visual Intent Recognition: LLM analyzes article content to generate a set of precise English “Visual Keywords.”
  2. API Matching: System automatically calls image library API (Unsplash) using these keywords for search.
  3. Optimized Selection: Based on download count and relevance scores, automatically selects the most suitable image as the article’s Featured Image.

3. Automated Delivery

In the final stage, n8n creates drafts or directly publishes through the WordPress REST API using processed titles, content, tags, and image links. This not only standardizes article format but also achieves 24/7 uninterrupted information updates.

Conclusion

This project demonstrates how to use Low-Code tools (n8n) and Local LLM technology to rapidly build automated systems with commercial value. It not only solves the timeliness of information but also proves the feasibility of running complex AI workflows on low-cost hardware.