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Automating My Video Workflow with N8N and AI: A Real-World Test
By Dimitri Bellini03 Aprile, 2025Blog, Uncategorized

Automating My Video Workflow with N8N and AI: A Real-World Test

Good morning everyone, Dimitri Bellini here! Welcome back to Quadrata, my channel dedicated to the open-source world and the IT topics I find fascinating – and hopefully, you do too.

This week, I want to dive back into artificial intelligence, specifically focusing on a tool we’ve touched upon before: N8N. But instead of just playing around, I wanted to tackle a real problem I face every week: automating the content creation that follows my video production.

The Challenge: Bridging the Gap Between Video and Text

Making videos weekly for Quadrata is something I enjoy, but the work doesn’t stop when the recording ends. There’s the process of creating YouTube chapters, writing blog posts, crafting LinkedIn announcements, and more. These tasks, while important, can be time-consuming. My goal was to see if AI, combined with a powerful workflow tool, could genuinely simplify these daily (or weekly!) activities.

Could I automatically generate useful text content directly from my video’s subtitles? Let’s find out.

The Toolkit: My Automation Stack

To tackle this, I assembled a few key components:

  • N8N: An open-source workflow automation tool that uses a visual, node-based interface. It’s incredibly versatile and integrates with countless services. We’ll run this using Docker/Docker Compose.

  • AI Models: I experimented with two approaches:

    • Local AI with Ollama: Using Ollama to run models locally, specifically testing Gemma 3 (27B parameters). The latest Ollama release (0.1.60 at the time of recording, though versions update) offers better support for models like Gemma.

    • Cloud AI with Google AI Studio: Leveraging the power of Google’s models via their free API tier, primarily focusing on Gemini 2.5 Pro due to its large context window and reasoning capabilities.

  • Video Transcripts: The raw material – the subtitles generated for my YouTube videos.

Putting it to the Test: Automating Video Tasks with N8N

I set up an N8N workflow designed to take my video transcript and process it through AI to generate different outputs. Here’s how it went:

1. Getting the Transcript

The first step was easy thanks to the N8N community. I used a community node called “YouTube Transcript” which, given a video URL, automatically fetches the subtitles. You can find and install community nodes easily via the N8N settings.

2. Generating YouTube Chapters

This was my first major test. I needed the AI to analyze the transcript and identify logical sections, outputting them in the standard YouTube chapter format (00:00:00 - Chapter Title).

  • Local Attempt (Ollama + Gemma 3): I configured an N8N “Basic LLM Chain” node to use my local Ollama instance running Gemma 3. I set the context length to 8000 tokens and the temperature very low (0.1) to prevent creativity and stick to the facts. The prompt was carefully crafted to explain the desired format, including examples.

    Result: Disappointing. While it generated *some* chapters, it stopped very early in the video (around 6 minutes for a 25+ minute video), missing the vast majority of the content. Despite the model’s theoretical capabilities, it failed this task with the given transcript length and my hardware (RTX 8000 GPUs – good, but maybe not enough or Ollama/model limitations).

  • Cloud Attempt (Google AI Studio + Gemini 2.5 Pro): I switched the LLM node to use the Google Gemini connection, specifically targeting Gemini 2.5 Pro with a temperature of 0.2.

    Result: Much better! Gemini 2.5 Pro processed the entire transcript and generated accurate, well-spaced chapters covering the full length of the video. Its larger context window and potentially more advanced reasoning capabilities handled the task effectively.

For chapter generation, the cloud-based Gemini 2.5 Pro was the clear winner in my tests.

3. Crafting the Perfect LinkedIn Post

Next, I wanted to automate the announcement post for LinkedIn. Here, the prompt engineering became even more crucial. I didn’t just want a generic summary; I wanted it to sound like *me*.

  • Technique: I fed the AI (Gemini 2.5 Pro again, given the success with chapters) a detailed prompt that included:

    • The task description (create a LinkedIn post).

    • The video transcript as context.

    • Crucially: Examples of my previous LinkedIn posts. This helps the AI learn and mimic my writing style and tone.

    • Instructions on formatting and including relevant hashtags.

    • Using N8N variables to insert the specific video link dynamically.

  • Result: Excellent! The generated post was remarkably similar to my usual style, captured the video’s essence, included relevant tags, and was ready to be published (with minor review).

4. Automating Blog Post Creation

The final piece was generating a draft blog post directly from the transcript.

  • Technique: Similar to the LinkedIn post, but with different requirements. The prompt instructed Gemini 2.5 Pro to:

    • Generate content in HTML format for easy pasting into my blog.

    • Avoid certain elements (like quotation marks unless necessary).

    • Recognize and correctly format specific terms (like “Quadrata”, my name “Dimitri Bellini”, or the “ZabbixItalia Telegram Channel” – https://t.me/zabbixitalia).

    • Structure the text logically with headings and paragraphs.

    • Include basic SEO considerations.

  • Result: Success again! While it took a little longer to generate (likely due to the complexity and length), the AI produced a well-structured HTML blog post draft based on the video content. It correctly identified and linked the channels mentioned and formatted the text as requested. This provides a fantastic starting point, saving significant time.

Key Takeaways and Challenges

This experiment highlighted several important points:

  • Prompt Engineering is King: The quality of the AI’s output is directly proportional to the quality and detail of your prompt. Providing examples, clear formatting instructions, and context is essential. Using AI itself (via web interfaces) to help refine prompts is a valid strategy!

  • Cloud vs. Local AI Trade-offs:

    • Cloud (Gemini 2.5 Pro): Generally more powerful, handled long contexts better in my tests, easier setup (API key). However, subject to API limits (even free tiers have them, especially for frequent/heavy use) and potential costs.

    • Local (Ollama/Gemma 3): Full control, no API limits/costs (beyond hardware/electricity). However, requires capable hardware (especially GPU RAM for large contexts/models), and smaller models might struggle with complex reasoning or very long inputs. Performance was insufficient for my chapter generation task in this test.

  • Model Capabilities Matter: Gemini 2.5 Pro’s large context window and reasoning seemed better suited for processing my lengthy video transcripts compared to the 27B parameter Gemma 3 model run locally (though further testing with different local models or configurations might yield different results).

  • Temperature Setting: Keeping the temperature low (e.g., 0.1-0.2) is vital for tasks requiring factual accuracy and adherence to instructions, minimizing AI “creativity” or hallucination.

  • N8N is Powerful: It provides the perfect framework to chain these steps together, handle variables, connect to different services (local or cloud), and parse outputs (like the Structured Output Parser node for forcing JSON).

Conclusion and Next Steps

Overall, I’m thrilled with the results! Using N8N combined with a capable AI like Google’s Gemini 2.5 Pro allowed me to successfully automate the generation of YouTube chapters, LinkedIn posts, and blog post drafts directly from my video transcripts. While the local AI approach didn’t quite meet my needs for this specific task *yet*, the cloud solution provided a significant time-saving and genuinely useful outcome.

The next logical step is to integrate the final publishing actions directly into N8N using its dedicated nodes for YouTube (updating descriptions with chapters) and LinkedIn (posting the generated content). This would make the process almost entirely hands-off after the initial video upload.

This is a real-world example of how AI can move beyond novelty and become a practical tool for automating tedious tasks. It’s not perfect, and requires setup and refinement, but the potential to streamline workflows is undeniable.

What do you think? Have you tried using N8N or similar tools for AI-powered automation? What are your favourite use cases? Let me know in the comments below! And if you found this interesting, give the video a thumbs up and consider subscribing to Quadrata for more content on open source and IT.

Thanks for reading, and see you next week!

Bye everyone,
Dimitri

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Automate Smarter, Not Harder: Exploring N8n for AI-Powered Workflows
By Dimitri Bellini31 Marzo, 2025Blog, Uncategorized

Automate Smarter, Not Harder: Exploring N8n for AI-Powered Workflows

Good morning everyone! Dimitri Bellini here, back on Quadrata, my channel where we dive into the fascinating world of open source and IT. As I always say, I hope you find these topics as exciting as I do!

This week, we’re venturing back into the realm of artificial intelligence, but with a twist. We’ll be looking at an incredibly interesting, user-friendly, and – you guessed it – open-source tool called N8n (pronounced “N-eight-N”). While we’ve explored similar solutions before, N8n stands out with its vibrant community and powerful capabilities, especially its recent AI enhancements.

What is N8n and Why Should You Care?

At its core, N8n is a Workflow Automation Tool. It wasn’t born solely for AI; its primary goal is to help you automate sequences of tasks, connecting different applications and services together. Think of it as a visual way to build bridges between the tools you use every day.

Why opt for a tool like N8n instead of just writing scripts in Python or another language? The key advantage lies in maintainability and clarity. While scripts work, revisiting them months later often requires deciphering complex code. N8n uses a graphical user interface (GUI) with logical blocks. This visual approach makes workflows much easier to understand, debug, and modify, even long after you’ve created them. For me, especially for complex or evolving processes, this visual clarity is a huge plus.

The best part? You can install it right on your own hardware or servers, keeping your data and processes in-house.

Key Functionalities of N8n

N8n packs a punch when it comes to features:

  • Visual Workflow Builder: Create complex automation sequences graphically using a web-based GUI. Drag, drop, and connect nodes to define your logic.

  • Extensive Integrations: It boasts a vast library of pre-built integrations for countless applications and services (think Google Suite, Microsoft tools, databases, communication platforms, and much more).

  • Customizable Nodes: If a pre-built integration doesn’t exist, you can create custom nodes, for example, to execute your own Python code within a workflow.

  • AI Agent Integration: This is where it gets really exciting for us! N8n now includes dedicated modules (built using Langchain) to seamlessly integrate AI models, including self-hosted ones like those managed by Ollama.

  • Data Manipulation: N8n isn’t just about triggering actions. It allows you to transform, filter, merge, split, and enrich data as it flows through your workflow, enabling sophisticated data processing.

  • Strong Community & Templates: Starting from scratch can be daunting. N8n has a fantastic community that shares workflow templates. These are invaluable for learning and getting started quickly.

Getting Started: Installation with Docker

My preferred method for running N8n, especially for testing and home use, is using Docker and Docker Compose. It’s clean, contained, and easy to manage. While you *can* install it using npm, Docker keeps things tidy.

  1. Use Docker Compose: I started with the official Docker Compose setup provided on the N8n GitHub repository. This typically includes N8n itself and a Postgres database for backend storage (though SQLite is built-in for simpler setups).

  2. Configure Environment: Modify the .env file to set up database credentials and any other necessary parameters.

  3. Launch: Run docker-compose up -d to start the containers.

  4. Access: You should then be able to access the N8n web interface, usually at http://localhost:5678. You’ll need to create an initial user account.

  5. Connect AI (Optional but Recommended): Have your Ollama instance running if you plan to use local Large Language Models (LLMs).

N8n in Action: Some Examples

Let’s look at a few examples I demonstrated in the video to give you a feel for how N8n works:

Example 1: The AI Calculator

This was a simple workflow designed to show the basic AI Agent block.

  • It takes a mathematical question (e.g., “4 plus 5”).

  • Uses the AI Agent node configured with an Ollama model (like Mistral) and Postgres for memory (to remember conversation context).

  • The “tool” in this case is a simple calculator function.

  • The AI understands the request, uses the tool to get the result (9), and then formulates a natural language response (“The answer is 9”).

  • The execution log is fantastic here, showing step-by-step how the input flows through chat memory, the LLM, the tool, and back to the LLM for the final output.

Example 2: AI Web Agent with SERP API

This workflow demonstrated fetching external data and using AI to process it:

  • It used the SERP API tool (requiring an API key) to perform a web search (e.g., “latest news about Zabbix”).

  • The search results were passed to the first AI Agent (using Ollama) for initial processing/summarization.

  • Crucially, I showed how to pass the output of one node as input to the next using N8n’s expression syntax ({{ $json.output }} or similar).

  • A second AI Agent node was added with a specific prompt: “You are a very good AI agent specialized in blog writing.” This agent took the summarized web content and structured it into a blog post format.

Example 3: Simple Web Scraper

This showed basic web scraping without external APIs:

  • Used the built-in HTTP Request node to fetch content from specific web pages.

  • Applied filtering and data manipulation nodes to limit the number of pages and extract relevant text content (cleaning HTML).

  • Passed the cleaned text to Ollama for summarization.

  • The visual execution flow clearly showed each step turning green as it completed successfully.

I also briefly mentioned a much more complex potential workflow involving document processing (PDFs, text files), using Quadrant as a vector database, and Mistral for creating embeddings to build a Retrieval-Augmented Generation (RAG) system – showcasing the scalability of N8n.

Conclusion: Your Automation Powerhouse

N8n is a remarkably powerful and flexible tool for anyone looking to automate tasks, whether simple or complex. Its visual approach makes automation accessible, while its deep integration capabilities, including first-class support for AI models via tools like Ollama, open up a world of possibilities.

Being open-source and self-hostable gives you complete control over your workflows and data. Whether you’re automating IT processes, integrating marketing tools, processing data, or experimenting with AI, N8n provides a robust platform to build upon.

What do you think? Have you tried N8n or other workflow automation tools? What kind of tasks would you love to automate using AI?

Let me know your thoughts, suggestions, and experiences in the comments below! Your feedback is incredibly valuable.

If you found this useful, please consider sharing it and subscribing to my YouTube channel, Quadrata, for more content on open source and IT.

Thanks for reading, and see you in the next one!

– Dimitri Bellini

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