This article provides a complete, practical walkthrough of the Linkedin Telegram Automation Scheduled 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:
   From Hacker News to Social Feeds: How This AI-Powered Workflow Automates GitHub Curation
   
   Meta Description:
   Learn how an n8n workflow crawls Hacker News for trending GitHub links, uses GPT-4 to create narrative-driven social media posts, and schedules them for X (Twitter) and LinkedIn — all while updating Airtable and pinging the user via Telegram.
   
   Keywords:
   n8n automation, Hacker News GitHub scraper, social media bot, open-source content curation, AI social assistant, GPT-4 LinkedIn post generator, Twitter automation, Airtable social media tracker, Telegram notifications, GitHub to Twitter workflow
   
   Third-Party APIs Used:
   
   - OpenAI API (GPT-4): for generating AI-crafted social media content.
   - Airtable API: for storing, updating, and preventing duplication of social media posts.
   - Twitter API (OAuth 2): for posting updates to X (formerly Twitter).
   - LinkedIn API: for auto-posting content to LinkedIn profiles.
   - Telegram API: for notifying the user about newly prepared posts.
   - GitHub (via HTTP Request): for fetching project information from repo URLs.
   
   Article:
   
   How This AI Workflow Turns Hacker News Discoveries into Social Content Gold
   
   In an age where content is king and distribution is queen, staying ahead in the fast-paced tech world means sharing relevant, timely insights across platforms. If you're a developer, content strategist, or just someone who loves the intersection of automation and storytelling, this n8n-powered workflow might just be your new secret weapon.
   
   It crawls Hacker News. It sniffs out trending GitHub repositories. It writes compelling posts for X (formerly Twitter) and LinkedIn using GPT-4. Then, after notifying you via Telegram, it posts them—automatically.
   
   Let’s break it down.
   
   The Mission: From News Feed to Social Feeds
   
   Imagine automating your social presence around real, trending content without lifting a finger. That’s exactly what this workflow does.
   
   Every six hours, an n8n Schedule Trigger kicks things off. Its job? Crawl the Hacker News homepage using a simple HTTP request and pull in the HTML content.
   
   Extracting GitHub Gems
   
   Not all posts on Hacker News are worth sharing. This workflow uses a Python-based custom code node to extract only those posts that contain GitHub links. Using BeautifulSoup, a popular HTML parser, the system scans each post's metadata—title, author, score, comments, and more—and grabs the actual repository links.
   
   But we’re not posting every GitHub project we find. Why flood your feed with duplicates?
   
   Leveraging Airtable, the workflow checks each post ID to ensure it hasn't already been shared. A clever filtering script cross-references current items with existing Airtable records, marking only "unposted" entries for further processing.
   
   Digging Deeper: Visiting and Understanding Repos
   
   Once a new GitHub repo is identified, a separate HTTP Request node fetches the actual repo HTML. Then, using n8n’s built-in HTML-to-Markdown converter, the raw HTML is cleaned up for use in an AI prompt.
   
   This is where the magic happens.
   
   Enter GPT-4: Your AI Content Assistant
   
   Armed with the repository’s Markdown-formatted content, title, and URL, n8n calls the OpenAI GPT-4 API with specific, humanlike instructions:
   
   - Create two separate posts
   - Keep Twitter content under 280 characters
   - Focus on narrative, functionality, and professional tone
   - Avoid marketing buzzwords and personal pronouns
   
   The result? A JSON response with tailor-crafted posts ready for each platform.
   
   This GPT-generated content is validated through formatting checks, ensuring both Twitter and LinkedIn versions are present before proceeding.
   
   Notifying and Pausing: The Human-in-the-Loop Moment
   
   Once the content is approved, a Telegram bot sends the preformatted posts directly to you. This step introduces a buffer—a 5-minute wait node—giving you time to review or abort the posts if necessary.
   
   Social Publishing: Pressing the Button
   
   After the wait, n8n does what it does best—executes.
   
   It posts the tweet via Twitter’s API and the longer version to LinkedIn. Each post gets status-marked in Airtable:
   
   - “TDone” for Twitter
   - “LDone” for LinkedIn
   
   Thus, no content is posted twice, ensuring complete control and traceability.
   
   Why This Matters
   
   The real beauty of this workflow lies in its elegance. It marries real-time content discovery with modern storytelling thanks to AI—all while offering just the right amount of human accountability via Telegram notifications.
   
   Whether you're trying to build your personal brand, run a dev-focused newsletter, or manage social presence for a tech organization, this setup reduces manual overhead and increases content velocity.
   
   In Summary
   
   This n8n workflow is a compelling example of low-code automation done right. Here’s what it does in one continuous flow:
   
   - Crawls Hacker News homepage for trending GitHub links
   - Filters out non-GitHub and already-posted items
   - Fetches the GitHub page and cleans its content
   - Sends it to GPT-4 to generate social media-ready posts
   - Notifies the user via Telegram with drafts
   - Waits 5 minutes before publishing to Twitter and LinkedIn
   - Updates Airtable to prevent reposts
   
   It’s a powerful social media agent—built entirely with open tools, clean logic, and AI-driven creativity.
   
   Whether you're scaling up your personal brand or automating a company content stream, this is the future of social media content creation. And it’s already here.
   
   Ready to build yours?
   
   — 
   
   Let me know if you'd like a version tailored for a developer blog, startup product demo, or marketing use case!

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.