marked VS requests

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.

For many years now, Requests has been frozen. Being left in a vegetative state and not evolving, this blocked millions of developers from using more advanced features.

- Ubiquitous “requests” library used in most docs examples, no async support https://github.com/psf/requests

Explore here.

It's Lukasa (his name is Cory, there's Łukasz in PSF though, but that's a different person). Looking at him, he made significant contributions to the requests repo: https://github.com/psf/requests/graphs/contributors

> I'd love to see the look on some of these people's faces when they find out that tool/software/whatever they use is actually using libcurl under the hood.

Python dependencies (does not include curl)

https://devguide.python.org/getting-started/setup-building/i...

The "requests" module in Python (does not use curl)

https://github.com/psf/requests

This part can be found in the README of the GitHub repository.

Looking around for similar errors I found this issue where they recommended trying to use a newer version of the urllib3 library.

but I appreciate the fact that there is an issue about it, it's acknowledged and .. unfixable, it would now break too many things https://github.com/psf/requests/issues/2002

The requests package has been the golden standard for package structure for as long as I can remember.