This article provides a complete, practical walkthrough of the Splitout Code Create 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 Insight Extraction with n8n, OpenAI, and Qdrant
   
   Meta Description:
   Discover how to build a powerful no-code automation using n8n to fetch Hacker News comments, extract insights via OpenAI's GPT and embeddings, store vectors in Qdrant, cluster feedback using K-means, and export actionable insights to Google Sheets.
   
   Keywords:
   n8n workflow, Hacker News API, Qdrant vector store, OpenAI Embeddings, GPT-4o, K-means clustering, comment analysis, LangChain, Google Sheets automation, no-code machine learning, sentiment analysis, community insights, LLM insights
   
   Third-party APIs and Services Used:
   
   1. Hacker News API — For retrieving story comments and metadata from Hacker News.
   2. Qdrant API — Used for storing and managing vectorized comment data with advanced filtering and clustering capabilities.
   3. OpenAI API — For generating embeddings (text-embedding-3-small) and extracting insights/sentiment via GPT-4o-mini.
   4. Google Sheets API — To export final insights and raw comment clusters into structured, shareable spreadsheets.
   
   Article:
   
   Unlocking Valuable Insights from Hacker News Using n8n Automation
   
   Hacker News is a treasure trove of thoughtful discussions and unfiltered feedback from the tech community. But sifting through hundreds of nested comments to extract meaningful patterns and community sentiment is a daunting task. This is where no-code platforms like n8n come to the rescue — enabling powerful automations by chaining together apps, APIs, and even machine learning models.
   
   In this article, we walk through a fully automated n8n workflow that ingests Hacker News story comments, clusters them by similarity, and extracts actionable insights using OpenAI’s GPT models. The results are neatly stored in a Google Sheet — ready for sharing or further analysis.
   
   Let’s break down the process.
   
   📌 Step 1: Select a Hacker News Story & Clear Existing Data
   
   At the outset, the workflow prompts the user to input a Hacker News story ID. To maintain a clean slate for each analysis, any previously stored data for that story in the Qdrant vector database is deleted using an HTTP request. This ensures that repeated runs don’t accumulate redundant vectors or outdated insights.
   
   📰 Step 2: Fetch Comments from Hacker News
   
   Using the built-in Hacker News API node in n8n, the workflow retrieves all comments tied to the provided story ID. A custom transformation step then flattens this deeply nested tree into an array of comment objects — including both top-level responses and multi-level replies — annotated with relevant metadata such as story ID, title, author, and text content.
   
   🧠 Step 3: Embed Files with OpenAI & Store in Qdrant
   
   Using OpenAI’s text-embedding-3-small model, each comment is transformed into a vector representation suitable for similarity searches. These embeddings are paired with their original text and metadata, then stored in Qdrant, a high-performance vector database designed for AI-powered applications.
   
   Qdrant allows advanced filtering, so we can easily fetch or delete subsets (e.g., all vectors tied to a specific story).
   
   📊 Step 4: Trigger the Insight Subworkflow
   
   Post-vectorization, the workflow programmatically triggers a dedicated subworkflow whose sole job is to process the insights — allowing clean separation of stages and easier debugging or reuse.
   
   🔍 Step 5: Retrieve Vectors & Cluster Comments
   
   The insight subworkflow begins by loading all vectors associated with the chosen Hacker News story. A K-means clustering algorithm is applied using a Python node to discover up to five groups of similar responses based on semantic similarity. Each cluster represents a recurring theme or shared sentiment among commenters.
   
   To ensure meaningful results, we only retain clusters with at least three comment vectors.
   
   💬 Step 6: Fetch Raw Comments by Cluster
   
   With each cluster’s point IDs in hand, the vector database is queried again — this time to retrieve the full text of each associated comment. These payloads will be used as prompts for GPT to generate insights and sentiment analysis.
   
   💡 Step 7: Generate Insights via OpenAI LLM
   
   Now the magic happens! Using OpenAI’s GPT-4o-mini model through LangChain's Information Extractor node, the workflow summarizes each cluster of comments, providing:
   - A concise thematic insight.
   - An overarching community sentiment (e.g., positive, neutral, negative).
   - Suggested improvements if applicable.
   
   This LLM-driven summarization helps turn unstructured text into easily digestible feedback suitable for product and UX teams.
   
   📊 Step 8: Export Results to Google Sheets
   
   Finally, all extracted data — from clustering insights to individual responses — is compiled into a structured row and appended into a pre-connected Google Sheet. Each row includes:
   - Story ID and Title
   - Number of Responses
   - Raw Truncated Comments
   - GPT-generated Insights and Sentiment
   
   Users can now sort, filter, or share the insights with stakeholders needing rapid feedback on trending topics, product launches, or tech discussions.
   
   👩‍💻 Use Cases and Customization
   
   This workflow is ideal for:
   - Product managers analyzing user feedback.
   - Startups gauging reception on their HN launch.
   - Market researchers tracking sentiments on hot tech debates.
   - Developers building commenting analysis dashboards.
   
   You can easily extend this workflow further — for instance, sending alerts if sentiment is negative, or pushing the summary into Slack or Notion.
   
   Conclusion
   
   Combining the power of n8n's visual workflow builder with AI and vector databases unlocks exciting possibilities. By automating the end-to-end process — from data retrieval to natural language summarization — we’ve turned the chaotic sea of Hacker News comments into actionable intelligence with minimal coding.
   
   Give it a try, tweak the clustering thresholds, swap in different LLMs, or connect to new endpoints. Community insight automation has never been this easy — or this powerful.
   
   Happy hacking!
   
   ✅ Reference Sheet:
   A sample output sheet can be found here:  
   https://docs.google.com/spreadsheets/d/e/2PACX-1vQXaQU9XxsxnUIIeqmmf1PuYRuYtwviVXTv6Mz9Vo6_a4ty-XaJHSeZsptjWXS3wGGDG8Z4u16rvE7l/pubhtml
   
   🙋Need Help?
   Join the n8n community on Discord or the official Forum for support and inspiration.
   
   —
   
   Let me know if you’d like this article turned into a blog-ready markdown format or published HTML snippet!

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.