This article provides a complete, practical walkthrough of the Splitout Http 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: Harnessing Hacker News and AI to Discover the Best Learning Resources with n8n Automation
   
   Meta Description:
   Learn how an n8n workflow leverages Hacker News, Google Gemini AI, and intelligent automation to analyze user queries and deliver curated learning resources directly to your inbox.
   
   Keywords:
   n8n, Google Gemini, Hacker News, AI workflow, learning resources, automation, educational recommendations, LLM, LangChain, sentiment analysis, AI content generation
   
   Third-Party APIs Used:
   
   1. Hacker News API – To fetch “Ask HN” posts and related comments.
   2. Google Gemini (PaLM) API – To perform large language model-based summarization and sentiment analysis.
   3. SMTP Email Service – Gmail SMTP credentials are used to send personalized response emails to users.
   
   —
   
   Article:
   
   Discover the Best Learning Resources from Hacker News Using AI and n8n
   
   In today’s information-rich world, discovering high-quality learning resources tailored to your needs can be overwhelming. Whether you're exploring Python, DevOps, AI, or a new domain entirely, the volume of recommendations can feel like noise. But what if you could automate the discovery of trusted, community-recommended resources curated from thousands of insightful discussions on Hacker News?
   
   That's precisely what this n8n workflow achieves. By combining the power of Hacker News, Google Gemini's large language model (LLM), and structured workflow automation, the system intelligently fetches, filters, and synthesizes the most helpful educational resources—delivered straight to your inbox.
   
   Let’s break down how this powerful AI-powered curation engine works.
   
   Step 1: A Simple Form to Start the Journey
   
   It begins with the user-friendly form presented via the n8n “Form Trigger” node:
   
   - The user specifies what they want to learn, like “Machine Learning” or “Web3”.
   - They provide an email address where the results will be sent.
   
   Once submitted, the workflow is triggered to kick off the data-gathering and analysis pipeline.
   
   Step 2: Mining Hacker News for Raw Educational Insights
   
   The next step is powered by the “Hacker News” node. It specifically targets the “Ask HN” community posts and searches for posts that match the entered topic using keyword filtering. "Ask HN" is known for its dense concentration of experienced professionals and curious learners interacting over knowledge-sharing threads.
   
   For each relevant post, the workflow:
   
   - Extracts comment IDs (through a “Split Out Children” node).
   - Fetches the actual comment content for each discussion thread via HTTP requests to the Hacker News API.
   
   Step 3: Aggregating the Collective Wisdom
   
   Once all comments are retrieved, the system aggregates them into a single body of text using the “Aggregate” node. This consolidated data becomes the knowledge corpus for the AI to analyze.
   
   Step 4: AI-Powered Content Analysis and Classification
   
   Now the real magic begins.
   
   The aggregated Hacker News discussion text is passed to the “Basic LLM Chain” node, powered by Google Gemini's 1.5 Flash model. This AI model has the critical task of:
   
   - Scanning all the comments for resource mentions (like books, tutorials, MOOCs, articles).
   - Filtering out unrelated or low-value content.
   - Categorizing the resources by type (books, online courses, lectures, etc.).
   - Assigning a difficulty level based on content context (beginner, intermediate, advanced).
   - Performing sentiment analysis to gauge how well-received or recommended each resource is by the community.
   
   All results are then formatted using a precise Markdown template for readability and structure.
   
   Example Output Format:
   
   ```
   ## Top HN Recommended Resources To Learn Machine Learning
   
   ### Online Courses
   - [Coursera: Machine Learning by Andrew Ng](https://coursera.org/learn/machine-learning) – Widely recommended for foundational ML concepts.
   - [fast.ai](https://www.fast.ai/) – Project-focused and fast-paced ML course.
   
   ### Books
   - "Hands-On Machine Learning with Scikit-Learn and TensorFlow" – Comprehensive for intermediate learners.
   - "The Elements of Statistical Learning" – Considered top-tier for advanced learners.
   
   ### Tools & Libraries
   - [scikit-learn](https://scikit-learn.org/) – Easy-to-use machine learning tools for Python programmers.
   ```
   
   Step 5: Formatting and Delivery
   
   Once the AI produces the Markdown-formatted content, another node (“Convert2HTML”) translates it into HTML to ensure the output email is clean and visually structured.
   
   The final email is sent via the “Send Email” node using SMTP (Gmail) credentials. The message includes:
   
   - A brief summary, including the number of Hacker News comments analyzed
   - A structured list of community-recommended learning resources
   - Hyperlinks to books, online courses, articles, or videos where available
   
   Users receive this output in just a few minutes after submitting their learning interest.
   
   Why Is This Innovation Important?
   
   This workflow transforms open data from Hacker News—a goldmine of peer-reviewed insights—into actionable knowledge using AI and automation.
   
   What makes this stand out?
   
   - Real feedback: Hacker News users often provide resources they’ve used and trust.
   - AI-powered insights: Google's LLM processes hundreds of comments to distill reliable patterns.
   - Workflow sophistication: Modular automation allows easy adjustment and scalability for any learning topic.
   - Privacy-first and fast: No logins, no accounts. Just a form, a topic, and an email.
   
   Potential Use Cases
   
   - Personal learning journeys: Help self-learners cut through content clutter.
   - HR/Training teams: Auto-discover resources for team upskilling.
   - EdTech platforms: Recommend top-rated content curated from real discussions.
   - Newsletters: Feed curated educational content into mailers for followers.
   
   Final Thoughts
   
   This n8n-powered automation embodies the true synergy of community-driven content and large language models. By seamlessly fetching, analyzing, categorizing, and delivering the most endorsed learning material from Hacker News, it redefines how learners discover quality content online.
   
   Education isn’t about information overload—it's access to the right knowledge at the right time. With workflows like this, AI helps bridge that gap effortlessly.
   
   Try integrating this in your own learning platform or just build it to discover your next favorite resource.
   
   👨‍💻 | Automated. Personalized. AI-Powered Learning Curation.

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.