Stop learning n8n? Build NEW AI Systems in 2026

The AI Automation Leap [00:00]

• The biggest leap in AI has occurred, enabling fully automated AI systems to be built in minutes, not hours, thanks to Gemini 3.0 and Claude Code.
• This shift allows for automation essentially through words, moving beyond traditional workflow building.
• An example demonstrated is scraping the number one AI newsletter, "The AI Rundown," to extract article information without touching any code.
• Previously, building such workflows on platforms like Make.com or Natan required significant skill and time; now, users can simply type to build complex AI systems.
• The video promises to show how to build amazing AI systems, like the newsletter scraper, in minutes.

"And thanks to Gemini 3.0 and claude code, it is now possible to build fully automated AI systems in minutes instead of hours."

Integrating Traditional and Generative AI [01:50]

• Platforms like Make.com and Natan are integrators that connect different tools and services.
• Gemini and Claude are executors at the code level, capable of performing tasks.
• These traditional platforms are not obsolete; they become more powerful when integrated with new AI capabilities.
• The key is knowing how to build AI systems and when to use these platforms effectively to save time and build profit.
• The video will demonstrate deploying AI systems across three levels of difficulty, catering to all skill levels.

"But the thing is these platforms here are not dead. They actually become more powerful. You just need to know how to build AI systems and when to actually use them."

Level One: Lindy AI - Quick Deployment [03:36]

• Lindy AI is an example of how to quickly stand up many pre-developed AI systems.
• Users can browse and deploy systems in seamless ways through platforms like Natan and Make.
• An example is an automated meeting scheduler that can be deployed by connecting it to your email and calendar.
• The system can handle scheduling conversations, eliminating the need for a personal assistant.
• Users can build apps by typing simple prompts, such as requesting a scraper for 20 dentists in Leeds, asking for their email and phone numbers, and generating a personalized one-liner for their agency.
• The AI then builds this scraper in the background, performing internet searches to gather the requested information.
• Lindy AI completed the dentist scraper task, providing contact details, website catalog, and a unique agency one-liner, saving significant manual effort.

"So, what if I said something like, 'Hey, I want you to build for me a scraper. I want you to scrape for me 20 dentists in leads. I want to know their email address. I want to know their phone number. And I would like you to write for me a personalized one-sentence outliner for my agency that is unique.'"

Level Two: Natan - AI-Assisted Workflow Building [07:32]

• Natan has introduced a "Build with AI" feature that significantly enhances workflow creation.
• This feature allows users to generate workflows by describing their desired outcome, such as creating a Reddit scraper to analyze top posts and identify relevant content.
• While the AI editor is powerful for certain flows, it has limitations for very large or complex scenarios, where coding knowledge becomes beneficial.
• Learning Natan and Make is still important for understanding workflow building foundations and how systems connect.
• The video demonstrates debugging a workflow by changing the AI model and API key, showcasing the ability to refine AI-generated processes.
• The AI successfully scraped the Natan subreddit, analyzed posts, and provided five relevant articles with hooks, all generated through a text prompt.

"Well, the truth is no. You should be learning NAM. And if I show you actually just I've set it out this way because this I believe is the best way to do it."

Level Three: Gemini and Claude Code - Advanced App Creation [10:57]

• This level involves using Gemini and Claude Code to build apps that can perform virtually any desired function.
• Instead of relying solely on existing scenarios, users can describe their needs to the model, which will then build the application.
• This process may involve some back-and-forth as the AI models are not always "one-shot" solutions.
• A common starting point is using AI Studio to generate a prompt, then creating a Standard Operating Procedure (SOP) with models like Claude or ChatGPT, and finally feeding it into Gemini.
• The output can be highly sophisticated, such as building dashboards inspired by designs from platforms like Dribbble.com.
• Users can leverage tools like Claude Code, VS Code, or Cursor to build and develop these applications directly on their laptops by providing prompts.
• The video showcases an example of building a content feed application that can be connected to services like Superbase or Appify through conversations.
• A strategy was developed to scrape articles from a newsletter website, creating a unique card interface with images, titles, and AI summaries.
• The AI scraper successfully identified HTML elements like H1s and images, parsing the data efficiently.

"So instead of using any and crank scenarios, which is still amazing and that's still really important, what we can do now is just say to the model, what do I want? And it will physically build it for us."

Future of AI Development and Skill Sets [15:26]

• The video highlights the rapid pace of technological advancement, where the delta between an idea and its realization is shrinking.
• For beginners, learning platforms like Make and Natan is still crucial for understanding workflow building.
• There will remain scenarios where traditional platforms are preferred over generative AI for finer control and customization.
• Gemini and Claude have significantly advanced AI capabilities, making them go-to tools for many tasks.
• For clients and businesses, Natan is still used in many automation scenarios.
• The most valuable skill set involves understanding prompt engineering, AI architecture, and having the context to leverage new AI advancements.
• The future of AI development is about building applications faster and knowing what to build, making strategic understanding paramount.

"The only real key differentiator becomes a couple of things. Number one is how quickly you can build it and secondly what you build."