Master n8n Fast With These 17 Essential Nodes (real examples)

Introduction to AI Automations in Naden

Overview of AI Automations

• The speaker has created over 200 AI automations using Naden, serving various purposes including client work, YouTube content, and personal business needs.

• The focus will be on 17 core nodes that are frequently used across these automations, with real examples provided for clarity.

• A comprehensive 39-page document detailing these nodes and their applications is available for free by joining the speaker's community.

Core Nodes in Naden

Scheduled Trigger

• The first node discussed is the scheduled trigger, which allows workflows to activate based on a set schedule (e.g., daily at specific times).

• Users can configure triggers with various intervals such as days, hours, or even custom cron settings for precise control.

• An example given is a personal AI news summary that scrapes information daily at 5 a.m. and sends tailored newsletters via email.

Event Triggers

• Event triggers respond to actions from different apps (e.g., new emails in Gmail or messages in Slack).

• These triggers can also include events like new CRM records or form submissions on websites.

• A practical application involves an email agent that classifies incoming emails and manages them automatically based on Gmail triggers.

Subworkflows

• Subworkflows allow one workflow to trigger another, enhancing modularity and scalability within automation processes.

• This feature enables users to create reusable tools (like sending emails), which can be utilized across multiple workflows without redundancy.

Split Out Node

• The split out node takes an array of items and separates them into individual elements for processing.

• For instance, if given a list of sports, it outputs each sport as separate items allowing distinct operations per item.

• An example includes generating images for multiple animals by splitting requests so each animal gets its own image generation request.

Understanding Data Manipulation: Splitting and Aggregating

The Process of Splitting and Aggregating Data

• The concept of splitting data involves breaking down a dataset into smaller components, which can then be processed individually. In this case, three items are split to allow for detailed processing.

• After processing individual items, the next step is aggregating them back into a single list. This is essential for tasks like writing data back into a Google Sheet or rendering final outputs.

Utilizing the Edit Fields Node

• The edit fields node (often referred to as the set node) allows users to define and manipulate various data types such as strings, numbers, booleans, and arrays. For example, setting fields like name ("Nate"), age (23), gender (true), and interests (["AI", "traveling", "dogs"]).

• This node serves as a source of truth by storing core data points that can be referenced later in workflows. It ensures consistency across processes by maintaining key information.

Dynamic Data Management with Set Node

• In dynamic workflows, the set node helps maintain updated versions of critical information. For instance, it can store the most recent version of an email during revisions made by sales agents.

• Users can also leverage this node to customize their input dynamically within workflows—allowing for personalized settings based on user-defined parameters.

Conditional Logic with If Node

• The if node checks conditions (e.g., whether a person's age is greater than 21). Depending on the outcome (true or false), it directs data along different paths in the workflow.

• An example includes polling where requests are made to check if a task is completed. If not done yet, it loops back until success is achieved.

Advanced Routing with Switch Node

• The switch node expands upon conditional logic by allowing multiple routing options beyond just true/false branches. It enables complex decision-making pathways based on various criteria.

• A practical application involves checking file types in Google Drive; depending on whether it's a PDF, text file, or Excel document, different actions can be taken accordingly.

Understanding Workflow Nodes in Automation

Types of Windows Docs and Code Nodes

• The discussion begins with an overview of three different types of Windows documents, emphasizing the importance of setting conditions to guide workflows effectively.

• A code node is introduced as a powerful tool for manipulating predictable data structures. It is often preferred over AI solutions due to cost-effectiveness and speed.

• An example involving YouTube transcript metadata illustrates how a code node can be utilized to process messy data into a structured format.

• The speaker explains how they collaborated with Claude (an AI assistant) to write code that combines text fields from transcripts and extracts timestamps for better organization.

• Emphasis is placed on iterative testing when using code nodes, highlighting the need for adjustments based on initial outputs.

HTTP Requests: Essential for Integration

• HTTP requests are identified as crucial components in automation workflows, allowing integration beyond the internal environment of NAND.

• The speaker notes that many integrations available in applications are essentially HTTP requests wrapped in user-friendly formats, making them less intimidating.

• Guidance is provided on finding API documentation for tools lacking native integrations, stressing the importance of understanding endpoints and parameters.

Utilizing Loops in Workflows

• The loop node is discussed as vital for processing large datasets without overwhelming system resources or hitting rate limits.

• Originally named "split in batches," this node allows customization of batch sizes and branching options within workflows.

• Practical examples illustrate how loops can manage multiple items efficiently by processing them one at a time, ensuring control over workflow execution.

• The speaker shares experiences using loops in various projects, including managing file downloads and controlling workflow overload through strategic pacing.

Conclusion

The session emphasizes the significance of understanding different nodes—code nodes, HTTP requests, and loops—in building effective automation workflows. Each component plays a critical role in enhancing efficiency while managing complex data interactions.

Self-Hosting NDN with Hostinger

Introduction to Self-Hosting

• The speaker discusses self-hosting their NDN (No-Code Development Network) using Hostinger VPS, highlighting the ease of use for individuals without a DevOps background.

• Emphasizes that Hostinger provides an AI assistant to help users manage their instances effectively.

Setting Up the VPS

• A brief overview of selecting a plan, specifically recommending the KVM2 plan due to its popularity and flexibility for future changes.

• Mentions pricing details: $6 per month for 24 months, with an option for daily auto backups at an additional cost.

• Users can apply a coupon code "Nate Herk" for a 10% discount on annual plans, with additional discounts available for community members.

Dashboard and Application Management

• After setup, users are directed to their dashboard where they can manage settings and track metrics once the instance is operational.

• The speaker demonstrates setting up a Nitin account, noting it functions similarly to typical NIDN workflows but without execution limits.

Webhook Functionality

• Introduces webhook nodes as similar to event triggers; these allow integration with services lacking native support.

• Explains how webhooks can send data back to original sources after processing through NDN workflows.

AI Agent Capabilities

• Discusses the AI agent node which utilizes LLM (Large Language Model), allowing users to connect various chat models without needing external tools or memory.

• Highlights customization options within the AI agent node, including system prompts and structured output parsers.

Tool Integration in Workflows

• Describes how agents can perform actions via HTTP requests or by connecting with other services like databases or email platforms.

• Notes that agents automatically fill in parameters based on instructions given, enhancing workflow efficiency.

How to Automate Email Creation and Data Management

Automating Email Creation

• The agent utilizes its brain to generate an email, automatically filling in the recipient, subject, and message body. For example, it addressed Nate with a lunch invitation.

• If the tool is removed, the agent can still create the email output but requires a structured output parser to separate the components (recipient, subject, message) for sending via Gmail.

• A structured output parser is essential; it specifies how information should be formatted into distinct fields like "to," "subject," and "email."

• Once configured correctly with the structured output parser, the agent can effectively send emails by populating individual fields in Gmail.

• The speaker emphasizes that they would use AI tools like Claude or ChatGPT to assist in writing structured output parsers rather than doing it manually.

Utilizing Google Sheets for Data Management

• Google Sheets is highlighted as a versatile tool for storing data externally during workflows that involve processing large amounts of information.

• In deep research workflows, data from various chapters is stored in Google Sheets to manage extensive datasets without cluttering the workflow.

• For UGC ads workflows, Google Sheets helps track product details and statuses (e.g., ready or finished), allowing users to manage creatives efficiently.

• The workflow checks conditions based on user input from Google Sheets (like model selection), demonstrating how front-end management integrates with backend processes.

Conclusion and Resources

• The speaker shares insights from their experience building over 200 workflows using various nodes within Nen for real clients and personal automation needs.

• A comprehensive 39-page document detailing node functionalities and best practices is available for free through their community platform.

• Additional resources include courses on AI automation fundamentals and advanced techniques available through their paid community membership.