DEV Community: James T

Introduction to n8n: Beginner Course Summary

James T — Sun, 31 May 2026 00:15:44 +0000

In this blog, I’ll give a clear brief summary of the n8n beginner course. You can watch the full video course on the official n8n website (link in the references below).

What is n8n?

n8n is a powerful workflow automation platform that combines AI capabilities with business process automation. It offers a node-based visual interface while giving you full control to write custom JavaScript or Python code directly in the canvas.

APIs and Webhooks

Understanding APIs

An API (Application Programming Interface) allows different applications to communicate with each other.

Almost every modern app has an API you can connect to.
Example: Google Sheets API lets you read or update data in spreadsheets.

When working with APIs, we make requests and receive responses.

Components of an HTTP Request

There are four main components:

URL – The unique address of the resource (page, image, data, etc.).
- Includes: Scheme, Host, Port (optional), Path, Query Parameters (optional).
Method – Defines the action you want to perform:
- GET – Retrieve data
- POST – Send data
- PUT / PATCH / DELETE – Update data (less common)
Headers – Provide additional context (language, device type, location, etc.).
Body – Contains data being sent (used mainly with POST requests).

Authentication (Credentials)

To prove you’re allowed to make a request:

API Key (via query parameter or header)
OAuth (most secure common method)

HTTP Response Components

Status Code – Tells if the request was successful:
- 200 = Success
- 401 = Unauthorized
- 404 = Not Found
- 500 = Server Error
Headers – Metadata about the response (content type, length, expiration, etc.).
Body – The actual data returned (usually JSON, HTML, or binary).

What are Webhooks?

Webhooks are used when an external service needs to notify your workflow automatically (e.g., every time a payment is made in Stripe).
You provide a URL that receives a POST request when the event occurs.

Nodes in n8n

Nodes are the building blocks of every workflow. There are three main categories:

Entry Points (Triggers) – Start the workflow
Functions – Transform, filter, or format data
Exit Points – Send data to apps or final destinations

Tip: When adding the first node, type the name of the app or trigger instead of scrolling.

Data Handling in n8n

Credentials

Used to authenticate with external services
Saved at the instance level
Can be selectively shared with specific workflows or users

Data Views

You can view data in three formats:

Table View – Columns and rows (easiest to read)
JSON View – Key-value pairs
Schema View – Shows available keys with example values

Expressions

Use {{ }} to reference and transform data dynamically.

Examples:

{{ $json.first_name }}
{{ $json.first_name }} {{ $json.last_name }}
{{ $json.last_name.toUpperCase() }}

Core Workflow Concepts

Canvas – Main workspace where you build workflows
Activation – Workflows must be activated to run automatically
Triggers – Orange lightning icon; starting point of every workflow
Branching:
- Use IF or Switch nodes for conditional logic
- Drag multiple output connections to send data to multiple paths

Useful Nodes

Node	Purpose
Schedule Trigger	Run workflow at specific times
Webhook Trigger	Receive data from external services
Filter	Remove items that don’t match conditions
IF / Switch	Create conditional branches
Edit Fields (Set)	Clean, restructure, and combine data
Aggregate	Combine multiple items into one
Remove Duplicates	Eliminate repeated items
Limit	Restrict number of items
Item Lists	Break arrays into individual items

Error Handling

Create a dedicated Error Workflow (using Error Trigger node)
Recommended: Send notifications via Slack/Email with execution details
Use Stop and Error node for manual error handling
Validate data early using IF nodes

Debugging Tips

Check Execution Log for all runs
Use Copy to Editor (Debugflow) to test with real data
Retry failed executions
Keep workflow version history
Always validate missing or invalid data

Collaboration & Security

Active community forum
Many ready-made workflow templates
Different user roles: Owner, Admin, Member
Secure credential sharing (without exposing keys)
Workflow sharing with Creator or Editor permissions

References

n8n Official Video Course: https://docs.n8n.io/video-courses/

How AI and Cursor Are Transforming Product Management: From PM to Builder

James T — Wed, 13 May 2026 20:21:45 +0000

By James Tang

Introduction
The role of a Product Manager (PM) has evolved. You are now expected to be a builder — someone who can quickly prototype ideas using AI, validate them with users, and then hand them off to engineers for production.

One of the most valuable skills you can learn today is using Large Language Models (LLMs) together with Cursor, an AI-powered code editor, to generate and iterate on code rapidly.

I recently completed the full AI Foundations and Coding Agents tutorials from Cursor (created by Lee Robinson). In this article, I’ll share a concise summary of the key concepts and a practical guide to using Cursor effectively as a PM.

Privacy First: Protecting Your Code
Before diving in, a quick but important note on privacy:

When working with AI coding tools, protecting your code and company IP is important. Most modern tools, including Cursor, give you the option to disable training on your data. I recommend always turning off machine learning training in Cursor (and any other AI tool) when handling sensitive or proprietary work.

Be cautious with tools like Gemini’s Design Stitch or similar features — many don’t offer an opt-out for training, which creates a risk of your work leaking to competitors.

Course Overview
The Cursor course is divided into two main parts:

1. AI Foundations
Covers core concepts:

How AI models work
Hallucinations and limitations
Tokens and pricing
Context management
Tool calling and agents

2. Coding Agents
Focuses on practical application:

Working with agents
Understanding codebases
Developing features
Finding and fixing bugs
Reviewing and testing code
Customizing agents
Putting everything together

Key AI Concepts You Should Understand

Probabilistic, Not Deterministic

AI models don’t always give the same output even with identical prompts. This is why it’s smart to test multiple models (GPT, Claude, etc.) in Cursor using the Cmd + K (or Ctrl + K) menu.

Hallucinations

Models can confidently invent things that don’t exist (e.g., suggesting a non-existent Tailwind vX package). This often happens because their knowledge has a cutoff date.

Tokens & Pricing

Everything is broken into tokens.
You pay per token, not per word.
Output tokens usually cost more than input tokens.

Context Management

Cursor maintains conversation history as context. The more you chat, the more context it has — but this also consumes tokens. Tool calling helps the model dynamically retrieve information instead of stuffing everything into context.

Tools & Agents

Cursor equips agents with tools such as: reading/writing files, searching code, running terminal commands, web search, and more.

Agents shine when given a clear objective. They can decide which tools to use, call them, analyse results, and iterate.

MCP (Model Context Protocol)

A new standard that allows AI models to use tools in a consistent, universal way.

End-to-End Example: Adding a New Dashboard Report

Here’s how this looks in real life. Imagine you just joined a SaaS company and need to add a new report to the dashboard.

Step 1: Understand the Codebase

Cursor automatically indexes your project. You can:

Use semantic search (understands intent and meaning)

Use instant grep (exact text/pattern matching)

Ask Cursor to generate Mermaid diagrams to visualise architecture, data flows, or component relationships.

Step 2: Create a Plan

Switch to Plan mode, describe the feature, and let the agent generate a detailed plan. Review and refine it before proceeding.

Step 3: Build

Click Build to let the agent implement the first version.

Step 4: Debug

For simple errors: paste the error message into Cursor.

For complex issues: use Debug mode. The agent forms hypotheses, adds instrumentation, asks you to reproduce the issue, analyses logs, and suggests targeted fixes.

Step 5: Review & Test

Ask the agent to:

Review the code
Write unit tests
Create Playwright integration tests
Break changes into commits for a clean PR

Pro Tip – Rules

You can create custom rules for the agent (e.g., coding conventions, preferred patterns, build/test commands). Use them sparingly — too many rules increase context and can confuse the model.

Cursor Marketplace

Don’t forget to explore the Cursor Marketplace — a hub for plugins and extensions that further enhance the AI’s capabilities.

Final Thoughts

As a PM, mastering tools like Cursor allows you to move from idea to validated prototype at lightning speed. You don’t need to become a full-stack engineer, but you can become dangerously effective at turning ideas into working software.

Start small, protect your IP, experiment with different models, and iterate quickly.

Reference:

Cursor Learn