This article provides a complete, practical walkthrough of the Splitout Code Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automating Hacker News "Who is Hiring" Post Scraping and Structuring with n8n and OpenAI
   
   Meta Description:
   Discover how to automate the extraction and structuring of job listings from Hacker News "Who is Hiring" posts using n8n, OpenAI's GPT-4o-mini, and Airtable integration in this intelligent no-code workflow.
   
   Keywords:
   n8n workflow, Hacker News API, Algolia API, OpenAI GPT-4o-mini, Airtable automation, job scraping, Who is Hiring, job post parsing, AI data structuring, Hacker News career feed
   
   Third-Party APIs Used:
   
   1. Algolia Search API (https://hn.algolia.com/api)
   2. Hacker News API (https://github.com/HackerNews/API)
   3. OpenAI API (GPT-4o-mini)
   4. Airtable API
   
   Article:
   
   —
   Automating Hacker News "Who is Hiring" Scraping and Structuring with n8n and AI
   
   Each month, the Hacker News (HN) community posts its highly anticipated “Ask HN: Who is hiring?” thread, where tech startups and large companies alike share job listings. While great for discovering new opportunities, browsing and organizing jobs from the massive list of replies is time-consuming and often overwhelming.
   
   What if we could tap into this goldmine of job leads, extract only the relevant posts, convert free-form text into neatly structured data, and export them into a tool like Airtable—all automatically?
   
   This is exactly what an innovative no-code workflow built in n8n does by combining several powerful APIs and automation tools. Let’s break down how it works and how you can use it or modify it for your own hiring or data-gathering needs.
   
   🧠 Workflow Overview
   
   The n8n workflow titled “HN Who is Hiring Scrape” automates the entire process of:
   
   1. Scraping "Who is hiring?" posts using the Algolia-powered HN search.
   2. Filtering recent posts (within the last 30 days).
   3. Extracting all job-related replies via the Hacker News official API.
   4. Parsing and cleaning the raw text from those replies.
   5. Using OpenAI's GPT-4o-mini model to structure the data into a consistent format.
   6. Saving that structured content directly into an Airtable base, ready for browsing or analysis.
   
   ⚙️ Step-by-Step Breakdown
   
   1. Manual Trigger: The workflow is executed manually by the user through the n8n editor using a Manual Trigger node. This is useful for ad-hoc runs or testing during development.
   
   2. Searching Algolia (HN Mirror API): A custom HTTP node is configured to send a POST request to Algolia’s API (behind https://hn.algolia.com) using a properly constructed query. It filters for posts with the exact phrase "Ask HN: Who is hiring?", sorted by date to ensure freshness. This node requires authentication headers provided by Algolia.
   
   3. Extracting Post Details: The results (aka “hits”) are split out and processed individually. The workflow extracts information like title, creation date, and story ID, using the Set node to format the data for filtering.
   
   4. Filtering for Freshness: Posts older than 30 days are filtered out to avoid stale job listings, using a Filter node based on the 'createdAt' field.
   
   5. Getting Main Post Data from Hacker News API: Using the filtered story ID, the workflow sends an HTTP GET request to the official Hacker News API to retrieve the full data of the main post.
   
   6. Extracting Job Replies: Each job post is typically a "child" comment to the main “Who’s hiring” thread. The workflow extracts the comment IDs (“kids” array) and loops through them to fetch each comment individually.
   
   7. Cleaning Text: Job post replies come in HTML-escaped, messy formats. A Code node (JavaScript) is used to clean and format the text properly—removing HTML tags, decoding common entities, and inserting newlines for URLs and paragraphs for better readability.
   
   8. Structuring Content with OpenAI: The cleaned text is passed through the LangChain-integrated gpt-4o-mini model from OpenAI using the OpenAI Chat Model node. The prompt instructs the AI to extract structured data such as:
      - company name
      - job title
      - location
      - employment type
      - compensation
      - job description
      - application and company URLs
   
   9. Output Parsing: The response is validated and mapped using a structured output parser, enforcing a manual JSON schema to maintain consistency for Airtable import.
   
   10. Writing to Airtable: The final JSON is then written to a preconfigured Airtable base and table, mapping fields such as “title”, “company”, “salary”, “description”, and more.
   
   ✨ Example of Final Structured Output:
   ```json
   {
     "company": "Acme Inc.",
     "title": "Senior Frontend Engineer",
     "location": "Remote, US-based",
     "type": "FULL_TIME",
     "salary": "$120,000 - $140,000",
     "description": "We build productivity tools for designers. Looking for a frontend developer with 5+ years experience in React.",
     "apply_url": "https://acmejobs.com/apply",
     "company_url": "https://acme.com/careers"
   }
   ```
   
   🔗 Tools and APIs Involved
   
   - Algolia API: To search and retrieve posts from Hacker News with fine-grained filters and sorting functionality.
   - Hacker News API: To fetch the full contents of stories and replies based on comment IDs.
   - OpenAI API: Powering the GPT-4o-mini model to transform unstructured job posts into clean JSON data.
   - Airtable API: Used to store structured job data into a relational database for visualization, filtering, or exporting.
   
   💡 Why This Matters
   
   This workflow turns a chaotic community job listing into a programmatically usable dataset. For recruiters, startup founders, or job hunters, this means:
   
   - No need to scroll through hundreds of nested replies.
   - Richly structured data to filter by role, location, or type.
   - Real-time automation using scalable no-code tools.
   
   🧪 Customization Tips
   
   - Swap Airtable for Google Sheets, Notion, or your own database depending on where you want to store the results.
   - Modify the prompt for GPT-4 to extract other information like required skills or company size.
   - Expand to other HN post types like "Freelancer? Seeking freelancer?" or "Show HN".
   
   ✅ Final Thoughts
   
   The power of n8n lies in its extensibility and seamless integration with both APIs and AI tools. With this Hacker News automation, you've turned a community forum into a structured hiring feed—saving hours of manual parsing while gaining powerful insights.
   
   Whether you're building a job board, tracking industry demand, or just hunting for your next gig, this workflow arms you with AI-powered tools to elevate your process.
   
   —
   
   👨‍💻 Try it yourself and copy the Airtable base: https://airtable.com/appM2JWvA5AstsGdn/shrAuo78cJt5C2laR
   
   Let your data work for you—automated, enriched, and intelligently organized.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.