NADN Master Class Summary

Introduction

• Goal: Move from beginner knowledge of NADN to becoming an AI agent builder or implementing AI automations into daily life/work.
• Approach: Start from the basics and build up knowledge.
• NADN: A low-code/no-code automation tool that simplifies building workflows without requiring extensive coding knowledge.
• Features: Drag-and-drop interface, highly accessible for non-developers, but flexible for advanced users.
• Comparison: NADN allows building of tools directly within as opposed to other tools like Make or Zapier.

Importance of Automating Workflows

• Efficiency and Productivity: Eliminates repetitive tasks, reduces error, and allows focus on high-value work.
• Cost Savings: Reduces operational risks, saves time, and adapts to changing needs.
• Data Handling: Integrates data from various sources and provides real-time insights.
• Customer Experience: Enhances interactions and response times, leading to better satisfaction and loyalty.

Why Learn NADN?

• Empowers Non-developers: Anyone can build automations with minimal technical skills.
• Access to 300+ Integrations: Connects with popular tools like Gmail, Slack, Twitter, etc.
• Versatile Connectivity: Ability to connect to virtually any tool using APIs or webhooks.

Getting Started with NADN

• Setup Options: Self-hosted for control and flexibility vs. cloud-hosted for simplicity and managed services.
• Key Components: Workflows (recipes), nodes (steps/actions), executions (actual runs).
• Interface Overview: Drag-and-drop canvas for workflow creation.
• Community Resources: Access to templates and documentation for learning and support.

Core Concepts

• Node Types: Trigger, Action, Data Transformation, Logic.
• Building an Example Workflow: Demonstration of building a workflow to process customer orders and send summaries via email.

Part 3: RAG and Vector Databases

• RAG (Retrieval Augmented Generation): Combines retrieval of data from external sources with AI-generated responses.
• Vector Databases: Store data as vectors for efficient retrieval based on meaning rather than exact words.
• Embedding Data: Process of converting documents into vector stores for AI to use.

Building RAG AI Agents

• Example Workflow: Using Nike earnings PDF to build an AI agent capable of querying the document for information.

Expanding Agents with Custom Tools

• Custom Workflows as Tools: Build reusable tools in NADN for complex operations.
• Scalability: Use agents to manage specific tasks, allowing for efficient scaling.

APIs and HTTP Requests

• Understanding APIs: Bridges between different software systems.
• HTTP Requests: Methods for communicating with APIs (GET for fetching data, POST for sending data).
• Practical Examples: Demonstrated basic API calls using NADN.

Error Workflows

• Error Handling: Set up workflows to manage and notify errors automatically.

Best Practices

• Organization: Keep workflows organized to save time and effort in future modifications.
• Reusability: Use sub-workflows to avoid redundancy.
• Error Handling: Implement error workflows for robustness.
• Scalability: Optimize for handling larger tasks efficiently.

Next Steps

• Hands-On Learning: Encourage active building and experimentation.
• Community Engagement: Join the Free School Community for collaboration and support.
• Continuous Exploration: Try new integrations and share achievements with others.

Conclusion

• Achievement: From a beginner to being capable of creating impactful automations.
• Encouragement: Continue the journey of exploration and skill enhancement in NADN.