percollate VS parser

You can still choose automation. The easier route for me is to use wallabag to save the article. Then on my remarkable tablet I can grab a very readable document with https://github.com/koreader/koreader.

The other option is to use https://github.com/danburzo/percollate to convert a webpage to a nice document directly. I use both tools depending on my needs.

This function is just a simple combination with yt-dlp and percollate.

If someone hasn't already done this and I can't figure out how they are converting HTML, I have also considered using Percollate to convert, then sending to ReMarkable via rmapi.

The Cli Tool Percollate offers a different approach, but is also very good: https://github.com/danburzo/percollate

Concerning pdf there is the well known wkhtmltopdf , but let me say that I love the not so well known percollate

Since I'm working on a similar project, this is how I am planning to pull content from the web, utilizing percollate[1] to get the HTML content, I haven't written any implementation for this in Python yet.

If you don't mind me asking, how were you going to implement spaced repetition? Since the Incremental Reading algorithm has never been published as far as I know.

[1]: https://github.com/danburzo/percollate

Thoroughly scraping is challenging, especially in an environment where you don’t have (or want) a JavaScript runtime.

For content extraction, I found the approach the Postlight library takes quite neat. It scores individual html nodes based on some heuristics (text length, link density, css classes). It the selects the nodes with the highest score. [1] I ported it to Swift for a personal read later app.

[1] https://github.com/postlight/parser

I've been working on an enhanced reader mode extension for the last few months. I found that Mercury Reader's parser tool is useful for extracting content. If that's not exactly what you're looking for, readibility is another good option. It's a library used inside Firefox's reader moder that you can use in any project.

I currently have it turned off while I search for better sources, but I have a VM that runs a custom cron script that combines a custom RSS reader, podfox, mercury-parser, and coqui-ai to generate audio podcasts from RSS news feeds. I should probably clean it up and release the script/setup process. With a few tweaks and some AI text-to-speech and a little machine learning audio processing you can get a really good podcast experience from text posts.

It looks like Relay could be updated to convert it locally though, since the parser that it uses appears to be open source.

https://github.com/postlight/mercury-parser The only one I need, shit's too good

An alternative would be to extract the plain text from the article's page with either some "readability" API or a library like Mercury Parser: https://github.com/postlight/mercury-parser

I haven’t directly compared them, but I have also found mercury parser (https://github.com/postlight/mercury-parser) to be very reliable.

Since it turns a website into very plain (X)HTML it‘s fairly easy to use it to make a browsing proxy or automatically produce epub files for e-readers, which is what I do.

Make sure that at the end of the process you have the node and npm executables installed - the http.webpage integration uses the Mercury Parser API to convert web pages to Markdown.

Good luck! Those HTML issues you're coming across are tough and so varied across the web!

I was working with Mercury Parser (pluggable parsing for different sites) in the past.

https://github.com/postlight/mercury-parser