- Paste or type your HTML code in the left editor panel.
- The Markdown output will appear instantly in the right panel.
- Customize the output using the options: heading style, code block style, and list markers.
- Enable GFM (GitHub Flavored Markdown) for table and strikethrough support.
- Click 'Copy' to copy the Markdown or 'Download' to save as a .md file.
What is the difference between ATX and Setext heading styles?
ATX style uses hash symbols (# Heading), while Setext style uses underlines (=== for h1, --- for h2). ATX is more common and supports all heading levels (h1-h6).
What is GitHub Flavored Markdown (GFM)?
GFM is an extension of standard Markdown that adds support for tables, strikethrough text, task lists, and other features commonly used on GitHub.
Can this tool convert complex HTML layouts?
This tool works best with semantic HTML content. Complex layouts with CSS positioning may not convert perfectly, but standard content elements like headings, paragraphs, lists, tables, and code blocks convert well.
Is my HTML data secure?
Yes! All conversion happens entirely in your browser using JavaScript. Your HTML code is never sent to any server, ensuring complete privacy.
Markdown
Markdown is a lightweight markup language created by John Gruber in 2004 for formatting plain text documents. It uses simple, intuitive syntax that can be easily converted to HTML and other formats while remaining readable in its raw form.
HTML
HTML (HyperText Markup Language) is the standard markup language for creating web pages and web applications. It defines the structure and content of web documents using a system of tags and attributes.
HTML Entity
HTML Entity (HTML Character Entity) is a string that begins with an ampersand (&) and ends with a semicolon (;), used to represent special characters in HTML that would otherwise be interpreted as HTML code or are not easily typed on a keyboard.
Text-to-Image
Text-to-Image is an artificial intelligence technology that generates visual images from natural language text descriptions, using deep learning models to interpret textual prompts and synthesize corresponding photorealistic or artistic images.