This article provides a complete, practical walkthrough of the Splitout Http Send 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 Personalized Learning Recommendations with Hacker News and AI using n8n
   
   Meta Description:
   Discover how an n8n workflow scrapes Hacker News, uses Google Gemini AI for analysis, and sends personalized learning recommendations based on real community feedback — fully automated from form to inbox.
   
   Keywords:
   n8n workflow, Google Gemini AI, Hacker News API, AI recommendations, learning resources, AI automation, personalized learning, LangChain, LLM Chain, sentiment analysis, email automation, form-based recommendations
   
   Article:
   
   In the age of overwhelming information, finding the best resources to learn any topic can be a time-consuming challenge. What if you could rely on a curated list of recommendations—backed by real user experiences—to learn something new? An innovative n8n workflow does exactly that by fetching the most useful learning resources from Hacker News posts and delivering them straight to your inbox using the power of automation and large language models (LLMs).
   
   Let’s explore how this workflow orchestrates multiple services and tools to create a personalized and intelligent learning assistant.
   
   📬 Step 1: Collecting User Intent with a Simple Form
   
   It all begins with a user submitting a form. Using n8n’s built-in Form Trigger node, users answer a straightforward question: “What do you want to learn?”. They also provide their email address, so the final curated recommendations can be directly emailed to them. For example, a user might enter “DevOps” or “Python” or even more advanced topics like “prompt engineering.”
   
   This minimal, frictionless interface ensures that the system captures only what it needs: the learning topic and a way to contact the user.
   
   🔍 Step 2: Mining Discussions with Hacker News API
   
   Once the topic is captured, the workflow uses the Hacker News API to search for relevant discussions, particularly tagged as “Ask HN.” These Ask HN posts often result in rich conversations where community members share insightful resources, advice, and learning paths.
   
   The SearchAskHN node fetches up to 150 such posts, narrowing it down to the most relevant threads for learning the desired topic.
   
   🗨️ Step 3: Digging Into the Comments
   
   With the post IDs collected, the workflow drills into the commentary layer using the FindHNComments node, which performs an HTTP request to retrieve each post’s children (i.e., comment threads). These individual responses are then split out for separate processing.
   
   All the comments are then aggregated together using the CombineIntoSingleText node. This transformation combines raw narrative knowledge—practical tips, resource links, and shared experiences—from the community.
   
   🧠 Step 4: Leveraging AI via LangChain and Google Gemini
   
   This is where magic meets machine. The compiled text is processed by a Basic LLM Chain node, which is supercharged by Google’s Gemini 1.5 Flash model—a powerful AI conversational model invoked through LangChain’s integration.
   
   The prompt fed into the AI is a deceptively complex set of instructions: Find learning resources, ignore noise, analyze sentiment, categorize by resource type (e.g., books, courses, YouTube videos), and classify by difficulty (beginner to expert). Finally, it formats everything neatly into Markdown.
   
   This AI-driven classification ensures that the user gets not only a list of links but actual insights derived from authentic discussions.
   
   📄 Step 5: Converting to Email-Friendly HTML
   
   Once the AI-generated Markdown is ready, the Convert2HTML node transforms it into email-compatible HTML. The final email body includes a header with how many Hacker News comments were analyzed, followed by the categorized learning recommendations.
   
   📧 Step 6: Hitting Send with Email Automation
   
   In the final step, the SendEmailWithTopResources node dispatches an email to the user with curated content. The email is sent via an SMTP account connected inside the workflow. It’s personalized, valuable, and delivered automatically—bringing true meaning to "AI assistant."
   
   At the end of the chain lies a simple Finished node—marking the success of a complete digital pipeline tailored to democratize learning.
   
   Why This Workflow Stands Out
   
   - 🧠 Uses LLM intelligence (Google Gemini 1.5 Flash model) to analyze noisy, text-heavy Hacker News threads.
   - 🕸️ Taps into trusted community wisdom—using real opinions, not just SEO-optimized clickbait.
   - 🔄 Fully automated: From form submission to email delivery without any manual intervention.
   - ✨ Highly customizable prompt design in the LLM Chain makes it adaptable across skill levels and content types.
   
   All Tools and Services Used
   
   Here are the third-party APIs and services integrated in this workflow:
   
   1. Hacker News API — used to fetch Ask HN posts and related comments.
   2. Google Gemini (PaLM) API — used via LangChain to provide LLM-based natural language understanding and generation.
   3. SMTP Email Service — used to send custom HTML emails to users (configured Gmail SMTP in the example).
   4. LangChain — integrates with the Gemini model to process comments with a defined prompt structure.
   
   Final Thoughts
   
   This workflow is a perfect showcase of low-code automation and AI working harmoniously. Whether you’re a lifelong learner or someone building ed-tech tools, this pipeline brings together crowdsourced wisdom and AI intelligence to help people make faster, better decisions about what and how to learn.
   
   Even better, it’s all implemented in n8n—a free, open-source workflow automation platform—showing that cutting-edge personalization can be both powerful and accessible.
   
   So, what do you want to learn next?
   
   🧠✨
   
   — Written with help from Google Gemini & built with n8n
   
   💡 Want to try this workflow? Clone it into your own n8n instance and hook it up with your API credentials to start curating learning paths instantly.

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.