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HackerNews Intelligent Learning Resource Recommendation Workflow

This workflow automatically filters relevant "Ask HN" posts and comments from HackerNews based on the learning topics submitted by users. It utilizes advanced language models for analysis, extracting high-quality learning resource recommendations, and generates a list in structured Markdown format, which is ultimately sent to the user via email. This process effectively addresses the issue of information overload, helping users quickly find practical learning materials and enhancing their learning efficiency and experience.

Workflow Diagram
HackerNews Intelligent Learning Resource Recommendation Workflow Workflow diagram

Workflow Name

HackerNews Intelligent Learning Resource Recommendation Workflow

Key Features and Highlights

This workflow automatically filters relevant "Ask HN" posts and their comments from HackerNews based on user-submitted learning topics. It leverages the Google Gemini language model to analyze and perform sentiment assessment on the comment content, intelligently extracting the highest-quality learning resource recommendations. These recommendations are then organized into a structured Markdown-formatted categorized list and sent to users via email. The entire process forms an automated closed loop, encompassing data retrieval, intelligent filtering, semantic analysis, and personalized delivery.

Core Problems Addressed

Users often struggle to quickly find high-quality, targeted learning materials amid the vast amount of online discussions and resource information. This workflow tackles information overload by automatically filtering out irrelevant content and distilling community-validated, practical learning resources, thereby saving users’ time in screening information and enhancing learning efficiency.

Application Scenarios

  • Independent learners seeking authoritative learning material recommendations
  • Educational institutions or training platforms providing personalized learning resources to students
  • Content aggregation and recommendation services requiring automated processing of community discussion data
  • Technical blogs or media outlets aiming to rapidly curate high-quality content on trending topics

Main Process Steps

  1. User submits learning topic and email address (Form Trigger node)
  2. Search for relevant "Ask HN" posts on the specified topic via HackerNews API (HackerNews node)
  3. Split comment IDs from posts and fetch full comment content individually (HTTP Request node)
  4. Aggregate all comment texts into a single text block (Aggregate node)
  5. Invoke Google Gemini large language model combined with sentiment analysis to intelligently extract and categorize recommended resources (LangChain node)
  6. Convert the Markdown-formatted recommendations into HTML (Markdown node)
  7. Send the recommendation email to the user (Email node)
  8. End the workflow (NoOp node)

Involved Systems or Services

  • HackerNews API (data retrieval)
  • Google Gemini (PaLM) language model (intelligent content understanding and generation)
  • SMTP email service (email delivery)
  • n8n workflow automation platform (process orchestration)

Target Users and Value Proposition

  • Independent learners: Quickly access community-validated high-quality learning resources to avoid information overwhelm
  • Educational content planners and training institutions: Automate personalized learning recommendations to improve service quality
  • Developers and tech enthusiasts: Enhance content mining and organization efficiency through automation workflows
  • Media and content platforms: Rapidly consolidate in-depth resources on user-interest topics to enrich content value

This workflow realizes intelligent mining and personalized delivery of community data, effectively helping users discover truly valuable learning materials within fragmented information, significantly enhancing the learning experience and the intelligence level of content services.