marked VS lighthouse

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/

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.

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.

Eleventy can take an .md file and output a .html but if you want to go as vanilla as possible you can just use a module like marked to do that.

Once you have the article data, you’ll likely have to process its body_markdown to a format required by your website, like HTML. There are many tools you can do this with — here’s an example using Marked.js:

While changeset still lacks some features like a unified changelog for all the package, this can be handled with some scripts. Using for example marked one could set up some parsing to create a unified changelog for the whole system.

did you look at the marked parser? (https://github.com/markedjs/marked) I'm using it for an upcoming plugin I'm working on

Google Core Vitals now represent the most important metrics to focus on when it comes to technical SEO. Google Core Vitals are a set of standardized metrics that Google uses to evaluate the user experience offered by a web page and assign it a technical SEO grade. Several tools exist to measure and report technical SEO performance, but the most reliable is Google Lighthouse.

- referred to https://github.com/GoogleChrome/lighthouse/issues/6512 but didnt see network.disable error - added staticdistdir as per https://github.com/GoogleChrome/lighthouse-ci/blob/main/docs/configuration.md,

There has been discussion about adding a visual indicator to make the mode more obvious, but it has not made it into Chrome devtools!

When we implemented the MVP of the fintech app, we had a quite complicated form. At that time, I was still young and inexperienced. And eventually, we realized that our project was slowing down. We had to spend additional hours figuring out the reason. We had many unnecessary re-renders because we ignored basic rules related to props in React. I wanted to do everything possible to avoid such situations in the future. So, I added to the project linters like this and an additional starting configuration to package.json to run why-did-you-render. In short, this plugin issues a warning if something is re-rendered unnecessarily and suggests how to avoid it. Also, we included running Lighthouse in headless mode. Some people say that premature optimizations are bad, but for me, it's a principle: do it right from the start.

There's a link to what they mean by mobile network there (https://github.com/GoogleChrome/lighthouse/blob/main/docs/throttling.md) and it says:

Here's the non-psuedo code equivalent that can leverage CompressionStream rather than using a browserified-gzip-library: https://github.com/GoogleChrome/lighthouse/blob/437eb4d757c5...

We use it in Lighthouse for the exact same purpose, a URL #hash full of state. We found that modern browsers support 10mb of data in the hash. https://github.com/GoogleChrome/lighthouse/pull/12509#discus...

You can find out if your SaaS app is slowing down with Google Chrome Lighthouse. Lighthouse should be a staple in your monitoring because it lets you see the performance, accessibility, and SEO metrics related to your app's performance.

[2] https://github.com/GoogleChrome/lighthouse/issues/10023#issu...

Lighthouse: A tool developed by Google to measure a website’s performance using an average of six metrics.