barefootstache @barefootstache@qoto.org

Spent the last 10 hours trying to get any #linux operating system to work on a #dell #inspiron15 with #windows10.

First tried to get #fedora to boot via #yumi, but it black screened.

Next tried to use the same USB stick with #Ubuntu only to realize that it was too small.

Got myself a second stick, but Windows wasn't reading it via "This PC", thus had to format it via Disk Management before using YUMI to install Ubuntu onto it.

And it didn't help that Windows was very sluggish with the existing hardware. Making the whole experience a mental challenge onto one's patience!

The most fun I had was dealing with a a pre-existing reference as in

```
const page = { foo: 0, bar: 0};
const pageList = [];
if (someNumber > 4) {
for (let ii = 0; ii < someNumber; ii++ ) {
page.foo = ii * ii;
page.bar = ii + ii;
pageList.push(page);
}
return;
}
pageList.push(page);
```

When console logging within the for-loop everything worked as expected, but if one saved the `JSON.stringify(pageList)` then each item that was created in the for-loop equated to the last item created.

The solution is to create a `structureClone(page)` within the for-loop and reference it over `page`.

#javascript #programming

As neat as #jquery or #cheerio is, I miss the abilities of #VanillaJavaScript in the browser.

I don't remember how many times I tried to grab certain properties, which would have been available in the browser, but don't exist in cheerio.

And it is a bit annoying to constantly put various html elements into the cheerio wrapper class to get access to the various functionalities it offers. Thus instead grabbed the minimal viable data and just worked further with arrays.

#javascript

After spending 7 hours to publish the code while going through a 71 page PDF.

The 71 pages were reduced to 43 pages in the `clean-html.js` step. And in the next step of `create-question.js` it was expanded to 63 pages.

Most of the time I was in the cleaning phase, since this is where one can remove pages and add questions quickly without the need of either copy-pasting the wording directly to #anki or manually typing it out.

One thing that has been holding me back is not having a #LuaSnip in #NeoVim to generate snippets quickly.

Just published the preliminary tool #pdf4anki on #codeberg

https://codeberg.org/barefootstache/pdf4anki

It mainly describes how to do it and is a semi-automation tool to get PDFs into #anki.

In the current version one will still need to modify the pattern constant in the clean-html.js file to align with the PDF in use.

#javascript #opensource #jq #pdftohtml #nodejs

The last five days been working on getting lecture slides semi automatically into #anki.

For the first three days battling with #python in extracting the data from a 77 page PDF.

On the fourth day finally got the first complete PDF worth of lecture slides into Anki after 8 hours. Most of the time manual pattern matching and setting up a good enough data structure.

On the fifth day, got the second set of lecture slides which are only 44 pages and were decreased down to 12 pages and took 5 hours. Most of the time was converting the manual pattern matching from the fourth day into an automatic sequence and writing up documentation on how to reproduce.

After spending so much time in trying to semi automate the process, I have been questioning if it would have been faster to do it all manually. Hopefully the upcoming sets of slides will go much faster and plan to release the code in the next couple of days.

#studying

After struggling to get #python #PyMuPDF to work and being close the deadline, I shifted to using a combination of other commands.

First using the #linux #pdftohtml command, which is so much faster than PyMuPDF and packages the result similar to saving a website.

Next with #NeoVim and #RegEx format the #HTML file to be able to be quickly processed with #NodeJs #cheerio and eventually through #json to be saved in #sqlite.

Is it elegant and automatic? No, though it works!

#JavaScript

#TIL that #SnakeCase is a less frequently used tagging scheme on the #fediverse.

This could be due to that some services break their internal tagging schema, e.g. #Minds doesn't work well with #KebabCase.

Or it could be due to the laziness of the users and subjectively arguing that #snake_case doesn't add to the readability of #PascalCase or #camelCase tags.

In return the argument is that snake_case can add value if the tag has a not obvious word break, especially if the tag is completely written in lowercase or UPPERCASE.

Or if the the underscore replaces a different character other than space like slash, pipe, hyphen, etc.

And in AReallyLongTag / a_really_long_tag it could aid readability.

Thus if one wants grouping and discoverability of posts while creating a brand identity consider using snake_case tags.

#mastodon #ActivityPub #friendica #pixelfed