This is most likely an only-for-fun use case, but as a team of Data Scientists that constantly uses Markdown (be it in our GitHub Issue Trackers or on organizational tools like Notion or Jira), we thought of automated styling techniques.
However, not everyone knows of such functionality, and often times it isn’t used correctly. So why not augment this with a Named Entity Recognition functionality that automatically detects code within some unstructured text and highlights it accordingly?
With kern, doing so becomes easy. You can craft regular expressions that detect beginnings and endings of code snippets, train ML extractors, and validate the quality of these information sources. This makes it so much easier to debug your code, as you can combine labeling with data programming. Once you have scaled your training data and achieve a sufficient prediction quality, you can either just export the ruleset you came up with, or train a new ML model to detect code at runtime.
If you’re interested in trying out such use cases, feel free to register for our free version. Anytime you have questions or run into trouble with your case, we are more than glad to help you!