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

   Sure! Here's your short article (approx. 1000 words max) based on the provided n8n workflow:
   
   ---
   
   **Title:**  
   Automating Hacker News "Who is Hiring?" Scrapes with n8n, OpenAI, and Airtable
   
   **Meta Description:**  
   Learn how to build a fully automated pipeline that scrapes the monthly "Ask HN: Who is hiring?" threads, extracts structured job data using GPT, and writes it to Airtable—all using n8n.
   
   **Keywords:**  
   Hacker News, n8n, job scraping workflow, GPT-4, Airtable automation, OpenAI, HN Who is Hiring, Algolia API, HN API, automated job board, no-code automation
   
   ---
   
   ### How to Automate the Hacker News "Who is Hiring?" Thread Using n8n, OpenAI, and Airtable
   
   Every month, Hacker News becomes a treasure trove for job opportunities through its “Ask HN: Who is hiring?” threads. While developers and companies share job openings manually, finding, organizing, and acting on these leads can be time-consuming.
   
   What if you could automate that?
   
   In this guide, we walk through an n8n-powered workflow that:
   1. Scrapes the latest “Who is hiring?” thread from Hacker News.
   2. Fetches all job replies from the thread.
   3. Uses GPT-4 through OpenAI to extract structured job data.
   4. Saves the results into Airtable for easy browsing and filtering.
   
   Let’s break down each part of this workflow.
   
   ---
   
   ### Step 1: Fetch the Latest “Who’s Hiring” Thread Using Algolia
   
   The first step leverages the Hacker News Algolia Search API. Using a POST request, the workflow searches for thread titles that match the exact string: `"Ask HN: Who is hiring?"`. Results are sorted by date to ensure we always get the most recent post.
   
   👉 Pro Tip: You can find the Algolia API call using the Chrome Developer Tools (Network tab) on https://hn.algolia.com and "Copy as cURL”.
   
   The n8n HTTP Request node authenticates the Algolia request using a header-based token and returns an array of posts. From there, we "split out" results and filter based on creation date (e.g., from the past 30 days) to ensure freshness.
   
   ---
   
   ### Step 2: Pull Full Data on the Main Post and Its Comments
   
   Once we locate the most recent “Who is hiring?” thread, we use the official Hacker News API to fetch the post itself, including an array of comment IDs (called `kids`). These comments typically contain individual job listings, posted directly by companies or engineers.
   
   Using another Split Out node, we iterate over each comment ID and call the HN Firebase API to fetch the full reply data. Now we have the raw job post text for each listing.
   
   Sample API call to fetch a comment:
   ```
   https://hacker-news.firebaseio.com/v0/item/COMMENT_ID.json?print=pretty
   ```
   
   ---
   
   ### Step 3: Clean and Prepare the Text
   
   The job listings posted in HN comments often include HTML escape characters (`&`, `/`, etc.), links, and inconsistent formatting.
   
   To handle this, a JavaScript Function node in n8n is used to process and clean each block of text. Key transformations include:
   - Removing HTML tags
   - Unescaping HTML entities
   - Normalizing whitespace
   - Pulling out URLs
   
   This gives us a clean, plaintext version of each job listing, ready for parsing.
   
   ---
   
   ### Step 4: Transform Unstructured Text into Structured Job Data
   
   Here’s where the magic happens.
   
   We send each cleaned job text to OpenAI’s GPT-4o (via LangChain’s integration in n8n). The prompt instructs the model to extract key job attributes such as:
   - Company name
   - Role/title
   - Location (e.g., remote, hybrid, on-site)
   - Employment type (e.g., full-time, contract)
   - Salary
   - Job description
   - Application URL
   - Company website
   
   OpenAI returns this info in a structured JSON format, which is validated against a schema using the Structured Output Parser node.
   
   Example output:
   ```json
   {
     "company": "Acme Corp",
     "title": "Senior Backend Engineer",
     "location": "Remote (US timezones)",
     "type": "FULL_TIME",
     "salary": "$120k–$150k",
     "description": "We're building scalable APIs for financial services...",
     "apply_url": "https://acme.jobs/apply",
     "company_url": "https://acme.com/careers"
   }
   ```
   
   ---
   
   ### Step 5: Store Results in Airtable
   
   With our job posts now structured, the final step is storing the data. We use the Airtable node to write each job entry directly into a base.
   
   The database schema includes the above job fields and can be extended with tags, review status, or dates for custom workflows.
   
   This makes the job postings easy to review, filter, sort, and even share with teammates or job seekers.
   
   ---
   
   ### Summary of Workflow Components
   
   - n8n triggers and orchestrates the entire flow.
   - The Algolia API helps locate the most recent HN hiring post.
   - The Hacker News Firebase API fetches full threads and job replies.
   - JavaScript cleans comment text.
   - OpenAI GPT-4 (via LangChain) converts messy paragraphs into clean job objects.
   - Airtable is the final home where everything gets stored.
   
   ---
   
   ### Benefits of This Automated Job Scraping System
   
   - 🧠 Uses AI to process natural language job descriptions with high accuracy.
   - 🧹 Cleans and deduplicates messy posts, making the data reliable.
   - 🕒 Saves hours of manual work—you only need to run or schedule the workflow.
   - 🗃️ Outputs structured jobs you can use for your hiring platform, email digests, or personal job hunt.
   
   ---
   
   ### Inspired? Do More
   
   What’s great is this system isn’t limited to “Who is hiring?”—you can adapt the same template to scrape:
   - “Ask HN: Freelancer? Seeking freelancer?”
   - Product launches
   - AMA threads
   
   And you can also easily change the output destination from Airtable to Google Sheets, a database, or a Slack notification.
   
   ---
   
   ### Third-Party APIs Used:
   
   1. **Algolia API (https://hn.algolia.com/api)** – Searches Hacker News content using fast, full-text indexing.
   2. **Hacker News Firebase API (https://github.com/HackerNews/API)** – Provides programmatic access to HN posts and comments.
   3. **OpenAI API via LangChain** – Used for extracting structured data with GPT-4 from job descriptions.
   4. **Airtable API** – Stores structured data in a spreadsheet-like table format.
   
   ---
   
   By combining data scraping, AI language understanding, and simple no-code automation with n8n, this workflow streamlines a deeply manual task into a reliable, repeatable pipeline.
   
   Want to try it? Get started with the official Airtable base here: [Airtable Base Template](https://airtable.com/appM2JWvA5AstsGdn/shrAuo78cJt5C2laR)
   
   Happy automating! 🚀
   
   ---

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.