Liquid VS marked

Liquid is a template language created by shopify. In my use case I use it for generate html that is almost similar looking but differs in data. So when iterating over my HTML, I need to preview the changes I made combined with my data.

Inside the blog directory, create an index.liquid file. This will be our blog’s homepage. Eleventy provides a number of options when selecting a template engine. For this project, we’ll use Liquid.

Templating engine: SSGs rely on templating engines to define the structure of web pages. These engines enable developers to create reusable templates and incorporate dynamic content. Popular templating engines include Liquid, Handlebars, Mustache, EJS, ERB, HAML, and Slim.

Assuming I understand the ask, I think my approach would be to have a trigger fire when a ticket's custom field is set to "add user to the application." That trigger would notify a webhook. That webhook would be set to the Organization API endpoint with a payload that uses liquid markup to add 1 to the existing Organization's value.

For example, their theme templates use Liquid, which is a html templating system for Ruby. Activemerchant also was released by Shopify, and it provides a interface to major payment providers like PayPal.

In the meantime, Liquid v4.0.4 has been released, and allows building a Jekyll site with the latest Ruby.

Liquid docs - https://shopify.github.io/liquid/

Then, let’s create the simplest template for our static Eleventy page. We’ll write it using Liquid, but since it’s so simple, it won’t take advantage of any useful templating tags for now. Let’s call it index.liquid:

Previously I have used Jekyll for blogging and it has served me well for simple blogs and static websites. Jekyll is a static site generator that relies on Markdown, Liquid, HTML, and CSS. Which means no JavaScript -- a Jamstack without the J. With GitHub Pages you can even host Jekyll sites directly from your repository.2

Next, install gray-matter to extract metadata from the front matter of markdown files, and marked to convert the markdown files to HTML:

📑 Marked Markdown parser. Use it to create your own markdown editor.

Vrite SDK provides a few built-in input and output transformers. These are functions, with standardized signatures to process the content from and into Vrite. In this case, gfmInputTransformer is essentially a GitHub Flavored Markdown parser, using Marked.js under the hood.

Another contestant in this realm is Bright[1]. It runs entirely on the server and doesn't increase bundle size as seen here[2]. Regarding parsing speed tree-sitter is without a doubt performant since it is written in Rust, but I don't have any problems "parsing on every keystroke" with a setup containing Marked[3], highlight.js[4] and a sanitizer. I did however experience performance issues with other Markdown parser libraries than Marked.

[1]: https://bright.codehike.org/

[2]: https://aihelperbot.com/test-suite

[3]: https://github.com/markedjs/marked

[4]: https://highlightjs.org/

The project uses markedJS to convert markdown into HTML, this is their GitHub page.

To handle pasting block Markdown content like this, I had to tap into ProseMirror and implement a custom mechanism (though somewhat based on TipTap’s paste rules), detecting starting and ending points of the blocks and parsing them with Marked.js.

I am using marked for markdown parsing together with marked-highlighting to handle syntax highlighting and everything is working as it should.

I'm trying to render user input using SvelteMarkdown (that uses marked).

GPT API returns data in markdown format. You can parse it using a Markdown library and string manipulation. On Electron app I developed https://jhappsproducts.gumroad.com/l/gpteverywhere, I used https://github.com/markedjs/marked and a code syntax highlighting package to display code blocks. And used JavaScript string manipulation to detect when code blocks start and end so I could add COPY/SAVE buttons to the blocks. I hope this helps, and happy coding! :)

Again, with streaming enabled, you’ll now receive new tokens as soon as they’re available. Given that OpenAI’s API uses Markdown in its response format, a full message will need to be put together from the incoming tokens and parsed to HTML, as accepted by the replaceContent function. For this purpose, I’ve used the Marked.js parser.