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

   Title:
   "Building an Automated Hacker News Throwback Bot with n8n and Gemini AI"
   
   Meta Description:
   Discover how to build a fully automated Hacker News throwback bot using n8n, Google Gemini AI, and web scraping. Explore how headlines evolve across years for the same calendar day and learn how this system delivers curated summaries straight to Telegram.
   
   Keywords:
   
   - Hacker News
   - n8n workflow
   - Google Gemini AI
   - LLM summarization
   - automation
   - web scraping
   - Telegram bot
   - hacker news headlines
   - tech trends analysis
   - AI-powered curation
   
   Third-Party APIs Used:
   
   1. Google Gemini (via PaLM API)
   2. Telegram Bot API
   3. Hacker News (Unofficial - via web scraping from https://news.ycombinator.com)
   
   Article:
   
   📰 Building an Automated Hacker News Throwback Bot with n8n and Gemini AI
   
   Keeping up with how the technology narrative has unfolded over time can yield deep insights into industry trends and developer interests. But what if you could automate this evolution analysis every single day—surfacing the top Hacker News headlines for a specific date across previous years? That’s exactly what a clever n8n workflow does using AI and automation.
   
   In this article, we’ll take a deep dive into how this workflow functions, and how technologies like Google Gemini (via PaLM API), n8n, and Telegram come together to generate a daily retrospective digest of Hacker News trends.
   
   ⏰ The Premise: "On This Day in Hacker News"
   
   At its core, this automation collects Hacker News front page headlines for today’s date from the current year back to 2007. It identifies standout titles, clusters them into meaningful themes, and highlights how the technology discourse has shifted over time—delivered in Markdown via a Telegram bot.
   
   With zero manual intervention, this system leans on AI to make sense of large data sets and aligns perfectly with tech-savvy automation aficionados.
   
   🔧 Breaking Down the Workflow
   
   Here’s how this impactful automation pipeline works step-by-step within n8n:
   
   1. Schedule Trigger (Daily Run at 9 PM)
      The workflow kicks off with a Schedule Trigger to run daily at 21:00 hours. This ensures that content is refreshed consistently with the most up-to-date headlines.
   
   2. CreateYearsList: Generating the Date Range
      Using a JavaScript block, the system first takes today’s date and backtracks annually from the current year to 2007. It intelligently skips unsupported dates (e.g., before February 19, 2007, when Hacker News was launched).
   
   3. CleanUpYearList and SplitOutYearList
      Once generated, the list of dates is split into individual records to process each year separately.
   
   4. GetFrontPage & ExtractDetails
      Each target date is used to scrape Hacker News front-page headlines using a simple HTTP GET request to https://news.ycombinator.com/front. The HTML content is parsed with CSS selectors to extract headline titles and URLs.
   
   5. MergeHeadlinesDate
      For each scraped entry, the workflow merges the date with its respective headline list—producing a structured array of date-to-headlines mappings.
   
   6. Aggregate as JSON
      A single JSON object is compiled from all records—a unified dictionary of dates and their historical headlines.
   
   7. Basic LLM Chain (Powered by Google Gemini)
      This unified JSON is passed to a LangChain Node that uses Google Gemini’s AI to perform a creative summarization. The LLM is prompted to:
   
      - Extract 10–15 key headlines across the years.
      - Categorize headlines thematically (e.g., AI, security, programming languages).
      - Preface each item with the year.
      - Format everything via Markdown with clickable headlines.
      - Offer optional trend summarization (e.g., "Rust continues to gain popularity").
   
      This is the heart of the operation—the intersection of NLP and contextual comprehension, unlocking added value from dense data.
   
   8. Deliver the Output to Telegram
      Finally, the formatted summary is pushed to a Telegram channel (@OnThisDayHN) using the Telegram Bot API. Users subscribed to the channel get a daily, AI-curated retrospective of what was hot on Hacker News for this day in years past.
   
   🤖 Example Output:
   Here's what a user might receive on Telegram:
   ```
   # HN Lookback May-01 | 2007 to 2024
   
   ## AI and Machine Learning
   - 2022 [Anthropic launches Claude AI](https://example.com)
   - 2018 [DeepMind's AlphaZero beats Stockfish](https://example.com)
   
   ## Programming Languages
   - 2020 [Rust replaces C++ in core Firefox components](https://example.com)
   - 2015 [Go 1.5 released](https://example.com)
   
   ## HN Meta / Social Commentary
   - 2010 [Ask HN: Is Hacker News becoming too corporate?](https://example.com)
   
   ## Interesting Trend
   AI headlines sharply increase from 2017 onward, shifting from algorithmic research to product-focused launches.
   ```
   
   💡 Takeaways
   
   This workflow demonstrates how AI-enhanced automation can reshape content delivery. Instead of simply digesting today’s news, it delivers a unique time-series narrative: how our industry has arrived at the present, through the lens of one day repeated annually.
   
   With just a short burst of configuration through n8n and cloud-based AI, users gain a fully autonomous knowledge-delivery pipeline.
   
   Whether you're a developer fascinated by trends, a historian of technology, or someone who appreciates the art of automation—this Hacker News throwback bot offers a uniquely insightful experience.
   
   📦 Tech Stack Recap:
   - n8n: Visual workflow automation tool.
   - Google Gemini (PaLM): For summarization and theme classification.
   - Telegram Bot API: To deliver daily digests to users.
   - Hacker News: Source of raw tech innovation signals (scraped HTML).
   
   🧠 Final Thoughts
   
   This isn't just an automation—it's storytelling at scale. With LLMs like Gemini weaving summaries from raw headlines, and n8n orchestrating the heavy lifting, this solution is a prime example of how no-code tools and AI can bring content to life.
   
   Interested in building your own variant? Clone the workflow in n8n, plug in credentials, and start telling your own story—one headline at a time.

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.