This article provides a complete, practical walkthrough of the Telegram Splitout 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

   Sure! Below is a short-form article based on the provided n8n workflow:
   
   Title:
   🚀 “On This Day in Hacker News”: Automating Tech Trend Reflections with n8n + LLMs
   
   Meta Description:
   Learn how a no-code automation workflow using n8n, Google Gemini, and Hacker News creates daily AI-generated retrospectives on past tech headlines — published directly to Telegram.
   
   Keywords:
   n8n workflow, Hacker News automation, LLM chain, Google Gemini API, chatbots, Telegram bots, tech retrospectives, AI content generation, scraping news headlines, automation for developers
   
   Estimated Length: ~750 words
   
   Article:
   
   📰 “On This Day on Hacker News”: Using n8n & AI to Time Travel Through Tech Headlines
   
   In a digital era where trends fade as quickly as they rise, contextualizing today’s technology with its historical trajectory offers rich insight. Enter “On This Day HN”, an automated daily pipeline engineered in n8n that time-travels through Hacker News front pages going back to 2007 — analyzing what's changed, what endured, and what was once deemed disruptive.
   
   This post breaks down how this clever automation works using no-code workflows, HTML scraping, large language models (LLMs), and Telegram — all orchestrated seamlessly using the n8n automation platform.
   
   🎯 Objective: Daily AI-Driven Tech Retrospective
   
   The goal was to build a fully autonomous system that:
   
   - Retrieves Hacker News front-page headlines that appeared on the same calendar day across multiple years.
   - Groups themes and identifies evolution in tech topics.
   - Generates AI-formatted summaries in Markdown with links, neatly categorized under themes.
   - Publishes that to a public Telegram channel @OnThisDayHN.
   
   🧠 The Workflow Design in n8n
   
   Let’s dissect how the workflow operates.
   
   1. ⏰ Scheduled Trigger
   The Schedule Trigger node initiates the process daily at 21:00 (9 PM). This ensures timely daily retrospectives to publish during peak reading hours globally.
   
   2. 🧮 Custom Date Generation
   The node CreateYearsList generates a list of all calendar-matching dates from today across every year since 2007. For instance, if today is June 1, the workflow will query:
   - 2024-06-01
   - 2023-06-01
   - …
   - Back to 2007-06-01 (if valid, as 2007 starts from Feb 19).
   
   3. 🧼 Date Cleanup and Splitting
   Two nodes — CleanUpYearList and SplitOutYearList — format and separately iterate over each “same day” date for processing.
   
   4. 🕵️ Fetching Front Page Snapshots
   With the list of dates, the workflow uses the GetFrontPage HTTP node to request Hacker News archives. These pages are parsed by the ExtractDetails node, using CSS selectors to pull out:
   - Headlines
   - Corresponding URLs
   - Date metadata
   
   5. 🧩 Merging, Aggregating, and JSON Formatting
   Separate branch nodes GetHeadlines and GetDate merge the rows into one output using MergeHeadlinesDate. Followed by SingleJson to convert extracted data into a single clean JSON blob.
   
   From here, the magic begins.
   
   6. 🤖 AI-Powered Categorization with LangChain + Google Gemini Pro
   This data feeds into the Basic LLM Chain node, powered by LangChain, which is connected to Google’s Gemini Chat Model (Gemini 1.5 Pro) via n8n’s native GooglePalm API node.
   
   Prompted with specific instructions (e.g., "Act like a skilled news categorizer"), the LLM receives the historical JSON and responds with:
   
   - Markdown-formatted summary with years prefixing headlines
   - Categorized by themes (e.g., AI, Open Source, Privacy)
   - Link-wrapped headlines using proper Markdown
   
   It even analyzes trends with one-liner commentary such as: “AI discussions surged post-2017, replacing earlier focus on database frameworks.”
   
   7. 📲 Automatically Publishing to Telegram
   The final leg sends the formatted AI Markdown output via the Telegram node directly to the public channel @OnThisDayHN.
   
   With “Markdown” parse_mode enabled, Telegram renders it beautifully with link previews and bolded titles.
   
   🛠️ Third-Party APIs Used
   
   - Hacker News (Unofficial Scraping of Front Page using https://news.ycombinator.com/front)
   - Google Gemini API (via GooglePalm API) for AI summarization
   - Telegram Bot API (via n8n’s Telegram node) for broadcasting updates
   
   🔍 Why It’s Cool
   
   - Demonstrates how to combine human oversight with ML-generated insights.
   - Uses no external databases — just scraping, processing, and LLM usage.
   - Archives a daily footprint of tech evolution without writing backend code.
   - Can be scaled to retroactively populate GitHub repos, newsletters, etc.
   
   💡 Room for Expansion
   
   - Include voting score metadata for each article, to prioritize breakout ideas.
   - Auto-tweet most interesting headline of the day using n8n’s Twitter node.
   - Add database storage to allow trend graphs by year/theme using Grafana.
   
   Conclusion
   
   This n8n workflow is a powerful example of pairing simple automation with cutting-edge AI. It surfaces latent stories from our recent tech past — enabling better understanding of where we’ve been and where we might be going. As AI and automation tools continue to democratize access to knowledge, workflows like this remind us that history is just a trigger away.
   
   —
   
   Follow @OnThisDayHN on Telegram to get your daily dose of annotated tech nostalgia — brought to you by a robot.
   
   🧩 Built with ❤️ using n8n, LLMs, HTML parsing, and clever thinking.
   
   📌 Keywords Recap:
   n8n automations, LangChain, Google Gemini API, Telegram bot, Hacker News archive, AI Markdown summaries, tech retrospectives, no-code news analysis
   
   📘 Want the source? Request the n8n workflow here.

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.