u/Mohd_Hamid

Hey everyone,

I wanted to share a look at an autonomous content engine I’ve been fine-tuning recently. The goal was to build a system that handles everything from ideation and video rendering to final asset management and social media publishing without any manual intervention.

I’ve attached the full canvas architecture in image_48bbaa.png. Here is how the technical pipeline handles the heavy lifting:

⚙️ How It Works:
Structured Ideation: A Schedule Trigger fires up a Google Gemini Chat Model node. I’m utilizing a Structured Output Parser here to ensure the AI output strictly adheres to a predictable JSON schema (captions, hashtags, visual prompt data) so it never breaks the down-funnel nodes.

Async Video Generation (Google Veo 3): The visual prompts are sent via HTTP requests directly to the Google Veo API. Because video generation takes time, the workflow passes through a conditional check (If node) and a Wait loop to poll the endpoint until the asset rendering is complete.

Data Sanitization & Storage: A custom JavaScript node cleans up the API response. The video is downloaded, pushed to Google Drive, and permission-shared automatically to create a clean, accessible URL for the social platforms.

Meta API Publishing: The final asset URL and Gemini-generated caption are sent to the Instagram Graph API (INSTA node). It pauses momentarily via a Wait step to let the platform finish processing the media container before triggering the final container publish step. Everything is logged in a Google Sheet at both the start and finish for auditing.

🛠️ Key Takeaways from Building This:
Handling Async APIs: When dealing with heavy GenAI video models like Veo, robust webhook polling or carefully configured wait-loops are essential to prevent workflow timeouts.

Strict Schemas are Life: If you don't parse your LLM outputs structurally, minor formatting variations in captions or hashtags will crash your downstream HTTP requests.

Happy to answer any questions about the node configurations, the Meta API payload structure, or working with Veo endpoints! Let me know what you think.

Hey everyone,

I wanted to share a look at an autonomous content engine I’ve been fine-tuning recently. The goal was to build a system that handles everything from ideation and video rendering to final asset management and social media publishing without any manual intervention.

I’ve attached the full canvas architecture in image_48bbaa.png. Here is how the technical pipeline handles the heavy lifting:

⚙️ How It Works:
Structured Ideation: A Schedule Trigger fires up a Google Gemini Chat Model node. I’m utilizing a Structured Output Parser here to ensure the AI output strictly adheres to a predictable JSON schema (captions, hashtags, visual prompt data) so it never breaks the down-funnel nodes.

Async Video Generation (Google Veo 3): The visual prompts are sent via HTTP requests directly to the Google Veo API. Because video generation takes time, the workflow passes through a conditional check (If node) and a Wait loop to poll the endpoint until the asset rendering is complete.

Data Sanitization & Storage: A custom JavaScript node cleans up the API response. The video is downloaded, pushed to Google Drive, and permission-shared automatically to create a clean, accessible URL for the social platforms.

Meta API Publishing: The final asset URL and Gemini-generated caption are sent to the Instagram Graph API (INSTA node). It pauses momentarily via a Wait step to let the platform finish processing the media container before triggering the final container publish step. Everything is logged in a Google Sheet at both the start and finish for auditing.

🛠️ Key Takeaways from Building This:
Handling Async APIs: When dealing with heavy GenAI video models like Veo, robust webhook polling or carefully configured wait-loops are essential to prevent workflow timeouts.

Strict Schemas are Life: If you don't parse your LLM outputs structurally, minor formatting variations in captions or hashtags will crash your downstream HTTP requests.

Happy to answer any questions about the node configurations, the Meta API payload structure, or working with Veo endpoints! Let me know what you think.

I've been building an n8n workflow that automatically monitors and replies Facebook comments and uses AI to decide what action to take.

Current workflow:
• Facebook Webhook receives new comments
• AI analyzes sentiment, intent, and lead quality
• Positive leads are routed for follow-up
• Negative comments are immediately emailed to the manager
• AI can generate a suggested reply
• Different paths handle complaints and sales inquiries automatically

The goal is to reduce response time and ensure no important comment gets missed.

I'm planning to add:

* CRM integration
* WhatsApp notifications
* Automatic lead scoring
* Dashboard with analytics

I'd love any feedback or suggestions on improving this workflow!

Built my first AI Customer Support Agent 🚀

Here's what I built.

An AI-powered customer support workflow that:

Monitors incoming customer emails
Retrieves relevant information from a knowledge base using RAG
Analyzes customer sentiment (Positive, Neutral, or Negative)
Automatically responds to customers when appropriate
Escalates important conversations to a manager
Creates drafts for cases that need human review

Tech Stack: OpenAI, Pinecone, Embeddings, Gmail, AI Workflow Automation

This is my first AI agent, and building it gave me hands-on experience with AI workflows, retrieval systems, and real-world automation.

Here's how it works. What would you improve? 👇

#AI #AIAgents #OpenAI #RAG #Pinecone #Automation #CustomerSupport #BuildInPublic #GenerativeAI