In the middle of the desert you can say anything you want

04 Jun 2022

My custom keyboard layout with dvorak and LEDs

3439 words, ~13 min read


My keyboard setup has always been weird, and usually glued together with multiple programs. Some time ago I decided to re-do it from scratch and this led to some BIG improvements and simplifications I’m really happy about, and want to describe here.

Context: I regularly type in English, German, Russian, Ukrainian, and write a lot of Python code. I use vim, qutebrowser, tiling WMs and my workflows are very keyboard-centric.

TL;DR: This describes my current custom keyboard layout that has:

  • only two sub-layouts (latin/cyrillic)
  • the Caps Lock LED indicating the active one
  • Caps Lock acting both as Ctrl and Escape
  • things like arrow keys, backspace accessible without moving the right hand
  • Python characters moved closer to the main row
  • UPDATE Feb. 2023: Added numpad keys on level5! But not level5 itself. For now, -option 'lv5:ralt_switch_lock' to setxkbmap works. For an updated picture, see the github repo.

It looks like this1: kl_cut.png

and is available on Github.

(Read More)

26 May 2022

Three stories

942 words, ~3 min read

Three war stories that happened to me and I want to write down. First two happened when I was a Werkstudent, third one when working full-time.

Bachelor’s thesis

Native language identification on english tweets.

In: english-language tweets. Things like tokens, length, punctuation, and other typical features.
Out: in which geographic area (US, India, Russia, …) does this person live, as a proxy of his first language.

Decided to try using Tensorflow and tf.estimator for that, why not learn something new.

My very first version performed incredibly well. There had to be an error somewhere. There was.

Due to my unfamiliarity with the libs I left the lat/lon in the input data. And the network learned to predict the country name based on lat/lon instead of native language identification.

Most interesting bug in my career

I was trying to train a language model and see what happens if random words in the training data are replaced by garbage tokens.

So with a certain probability a sentence was picked, in it with a certain probability a token was masked or replaced by random text. So picking some sequences and in them randomizing a token with a certain probability.

In the town where I [PREDICT_ME] born, lived a [GARBAGE], who sailed the sea.

Training LMs is hard, my dataset was HUGE, and training took days/weeks.

Randomly sometimes on day 2-3-4 training crashed, with a nondescript error, or sometimes it didn’t and training went on.

And I think it was TF1 and outputting the specific examples that made it fail was nontrivial, though I don’t remember the details.

Debugging it was nightmarish. And running a program that runs for a week and SOMETIMES crashes and you have to start again was very, very frustrating.

The cause:

  1. In one place in the code, I tokenized by splitting the sentence by whitespaces, in another one - by spaces.
  2. In the infinite-billion-sentences-dataset, TWO contained single double-spaces.
  3. The dataset is shuffled, and sooner or later we got to one of these sentences. When split, we got a token with length 0:
    >>> "We all live in a yellow  submarine".split(' ')
    ['We', 'all', 'live', 'in', 'a', 'yellow', '', 'submarine']
  4. If randomization decided to randomize one of these sentences and in them SPECIFICALLY the '' token, everything crashed.

For this to happen, the randomness gods had to give a very specific set of circumstances, low probability by themselves but likely to happen at least once during a week-long training.

Quickly creating a synthetic-but-human dataset

I’m really proud of this one.

The problem

Given: 10 example forms. Mostly consistent ones, containing a table with rows. Some rows contain text, some contain signatures, all by different people. The forms themselves were similar but not identical, and scan quality, pen colors, printing etc. varied.

Task: detect which rows and cells have signatures. Rows might have gaps, rows could have text but no signature. And do it really fast: both providing the proof of concept and the runtime of the network had to be quick.1

Problem: you need data to evaluate your result. … and data to train your NN, if you use a NN. You need this fast.

Attempt 1 - vanilla computer vision

I tried to do vanilla computer vision first. Derotate, get horizontal/vertical lines, from them - the cells, and see if they have more stuff (darker color) than some average value.

It failed because the signatures and text belonged people filling hundreds of such forms per day - the signatures and text could be bigger than the row they’re on. You never knew if it’s someone who wrote a shorter text, or text ’leaking’ from the rows above/below.

Attempt 2 - ML

I knew that Detectron2 could be trained to detect handwriting/signatures on relatively few examples. But again, we need evaluation data to see how well we do. 10 documents are too little.

.. okay, but what would evaluation look like?

I don’t really need the pixel-positions of the signatures. I need just rows/cells info. And filling forms is fast.

Crowdsourcing the eval dataset.

I wrote a pseudorandom numbers generator, that based on a number returns which cells and which columns have to be filled.

Bob, your number is 123. This means:

  • in document 1 you fill row 2,4,7 in column 1, row 3,8,9 in column 3, and sign row 2 and 7 in column 3. Save it as 123-1.pdf
  • in document 2, …

Zip the results and email them to me as

Then 100+ forms were printed out and distributed. Everyone got a number and N forms and which numbers to fill.

Then they send us zips, from which we could immediately parse the ground truth. And we knew which cells contained what in each image without needing to manually label each.

The dataset was not too synthetic: we got different people using different pens, different handwriting and different signatures to fill the forms.2

That dataset then gave us a good idea of how well our network would perform in real life.

  1. This meant that we had to use only one NN, because running separate ones for detecting different things would have been slow. So we had to train a checkpoint that predicts ALL THE CLASSES, ergo we couldn’t train something to detect handwriting using another dataset. (Using a NN to find signatures and using something else to find other classes would work, though.) Honestly I don’t remember that part well, but there were reasons why we couldn’t just train something to find handwriting on another dataset without generating our own. ↩︎

  2. Much better than we could have done by, say, generating ‘handwriting’ using fonts and distortions and random strings. ↩︎

29 Nov 2021

Attempting to parse Obsidian tags with Templater templates

1504 words, ~6 min read


After more than a month of use, I’m still totally completely absolutely in love with Obsidian.

The standard scenario for any new technology I get excited about is something like

  1. Discover something really cool
  2. Read the entire Internet about it
  3. Try to use it to solve all my problems, to see what sticks
  4. After a week or so, either I stop using it completely, or I keep using it for a narrow use-case. I consider the latter a success.

I think this is the first time the opposite happened: I started to play with Obsidian as a way to quickly sync notes between my computer and phone (better than one-person Telegram groups or Nextcloud or Joplin or…), then started using it to write the Diensttagebuch, then realized I can drag-n-drop pictures/PDFs into it and can use it to keep my vaccination certificate and other stuff I need often on my phone, then oh nice it’s also a markdown editor, and we’re not done yet.

I’d usually avoid closed-source solutions, but it has everything I could ask for except that:

  • a really active community, with blog posts being written, repositories, plugins etc being constantly developed by very different people
  • notes being stored markdown, which then can be version-controlled and edited using any of my existing tools
    • this is much better than being able to “export” them to markdown, one less step to do (or forget, or easily disable). Fits seamlessly into the backup strategies being used.
  • Downloadable program that works even without an Internet connection, even if Obsidian HQ gets hit by a meteorite
  • Obsidian themselves having paid options1, which means a chance, clear plan and incentive to survive, and clear ways to support them. Better than whatever I’d be able to do with an abandoned open source project I rely on. (That said, I’d love them to open source at least the client at some point.)

This mix between personal-and-not-personal stuff, and having both my phone and my laptop as first-class citizens, is something I’ve never had before, and something I like, then if I also try to make it into my published blog I’m bound to find edge cases, one of them being tags. That said until now, and including this bit, everything I tried to do worked seamlessly.

This post describes my attempt to set up tags in a way that both Obsidian’s native autocompletion/search AND Hugo’s tagging work.

Two types of tags

This blog is written in markdown and converted into a static website with Hugo. Both the Diensttagebuch and the journal take files from specific subfolders in a specific Obsidian vault, and get converted into Hugo-friendly markdown using Obyde.

There are two kinds of tags as a result of that:

  • Obsidian’s #tags in the body of the text. They are the main kind supported, they get autocompleted, various #tag/subtag options can happen, etc. They get ignored by obyde/Hugo during conversion.
  • Front matter tags. They look like this in yaml/obsidian:
    Obsidian metadata
    They are parsed by Obsidian, as in searching by them works, but adding them manually is painful and has no autocompletion. One needs to set the ‘metadata’ to be shown in settings for them to be displayed.
    They are the only ones understood by Obyde/Hugo, though.

I decided to write something that takes care of this for me easily.

Obsidian templates and Templater

Logical first step was Templater, which is a plugin for Obsidian I already use for my templating needs, such as generating a YAML frontmatter based only on a filename (see Day 1021).

I wanted:

  • obsidian’s #tags to become part of the yaml frontmatter
  • optionally - adding a line at the end of the file, like “Tags: #one #two #three” with the tags found in the frontmatter but not in the body.

I found a template2 doing pretty much this in the Template Showcase. It uses purely Obsidian’s/Templater’s JS/Templating language and is very close to what I want - but not exactly, and somehow I didn’t want to write more Javascript than needed.

My solution is more of a hack, less portable, and uses Python, because that’s what I know how to write best.

My solution

Python script to parse/add the tags to frontmatter

I wrote a (quick-n-dirty) Python script that:

  • gets the tags from the frontmatter of the input .md file
  • gets the tags found by Obsidian from an environment variable
  • finds tags found by obsidian but not in frontmatter
  • rewrites the .md file with all the tags added to the frontmatter.
from frontmatter import Frontmatter
from pathlib import Path
import os
import argparse
import yaml

ENV_VAR_TAGS = 'tags'
TAGS_LINE = "\nTags: "
IGNORE_TAGS = {"zc"}

def get_args() -> argparse.Namespace:
  parser = argparse.ArgumentParser(description='Remove tags')
  parser.add_argument('--input_file', type=Path, help='Input file')
  parser.add_argument('--print', default=False, action="store_true", help='if set, will print to screen instead of rewriting the input file')
  parser.add_argument('--add_new_tagline', default=False, action="store_true", help='if set, will create/edit a "Tags: " line at the end of the file with tags found in front matter but not text body')
  return parser.parse_args()

def str_tags_to_set(tags: str) -> set:
  Converts tags like "#one,#two,#three" into a set
  of string tags without the "#"s:
    {'one', 'two', 'three'}
  def parse_tag(tag: str):
    ret_tag = tag.lower() if TAGS_TO_LOWER else tag
    return ret_tag[1:]

  return_set = set([parse_tag(x) for x in tags.split(",")])
  return return_set

def set_tags_to_str(tags: set, ignore_tags: set = IGNORE_TAGS) -> str:
  The opposite of str_tags_to_set, returns space-separated "#tag1 #tag, ..".
  Ignores tags that contain even part of any ignore_tags.

  final_tags = ''
  for tag in tags:
    for i in ignore_tags:
      if i in tag:
      tag: str = tag.lower()
    final_tags+=f"#{tag} "
  return final_tags

def line_is_tag_line(line: str) -> bool:
  return line[0:len(TAGS_LINE)] == TAGS_LINE

def get_tags_in_tagline(tagline: str) -> set:
  words = tagline.split(" ")
  return {w for w in words if w[0]=="#"}

def main() -> None:
  args = get_args()
  input_file = args.input_file

  # input tags
  tags_frontmatter = set()
  tags_all = set()
  tags_obs = set()

  # output tags
  missing_obs_tags = set()
  missing_yaml_tags = set()

  parsed_yaml_fm = Frontmatter.read_file(input_file)
  frontmatter_dict = parsed_yaml_fm['attributes']
  post_body = parsed_yaml_fm['body']

  has_tags_in_fm = 'tags' in frontmatter_dict

  # all tags (yaml + #obsidian)
  env_tags_by_obsidian = os.getenv(ENV_VAR_TAGS)
  tags_all = str_tags_to_set(env_tags_by_obsidian)

  # tags in yaml frontmatter 
  tags_frontmatter = set()
  if has_tags_in_fm:
    tags_frontmatter = set(frontmatter_dict['tags'])

  # "obsidian" tags (basically #tags in the text)
  tags_obs = tags_all.difference(tags_frontmatter)

  # print(f"{input_file}: \n\
  #     all_tags: {tags_all}\n \
  #     obs_tags: {tags_obs}\n \
  #     fm_tags: {tags_frontmatter}\n"
  #     )

  # tags found in frontmatter but not in #obsidian
  missing_obs_tags = tags_frontmatter.difference(tags_obs)
  # #obsidian tags not found in frontmatter
  missing_fm_tags = tags_obs.difference(tags_frontmatter)

  if missing_fm_tags:
    if not has_tags_in_fm:
      frontmatter_dict['tags'] = list()

  if missing_obs_tags and args.add_new_tagline:
    final_tags = set_tags_to_str(missing_obs_tags)

    # If last line is "Tags: "
    last_line = post_body.splitlines()[-1]
    if line_is_tag_line(last_line):
      # tags_in_tl = get_tags_in_tagline(last_line)
      # Remove last "\n" in post body
      post_body = post_body[:-1]
      # Add the missing tags to the last line
      post_body += " " + final_tags + "\n"
      # If we have no "Tags: " line, we add one
      final_string = TAGS_LINE + final_tags

  new_fm_as_str = "---\n" + yaml.dump(frontmatter_dict)  + "\n---"
  final_file_content = new_fm_as_str + "\n" + post_body

  # print(final_file_content)
  if args.print:

if __name__ == "__main__":

Obsidian templates

To use the python file, I added to Templater a “System command user function” 3 add_tags with this code:

python3 _templates/tags/ --input_file "<% tp.file.path() %>" 

It calls the python file and passes to it the location of the currently edited file.

Then I create the template that calls it:

<% tp.user.add_tags({"tags": tp.file.tags}) %>

tp.file.tags returns all (all) tags found by Obsidian, so #body tags and frontmatter ones.

They get passed to the python script as an environment variable, canonical way to do this as per Templater docu4.

Initially I tried to pass them as parameter, but tp.file.tags passed in the system command user function always returned an empty list.


I write the text in Obsidian as usual, adding any #body tags I like, then at the end run that template through the hotkey I bound it to (<C-S-a>), done.

Better ways to do this

Problems with this solution:

  • won’t work on Android
  • too many moving parts

Ways to improve it:

  • It’s possible to do this without Python. The template I found2 uses JS to find the editor window, then the text in it, then the YAML node. Though intuitively I like parsing the YAML as YAML more.
  • I wrote it with further automatization in mind, for example running it in so that it parses and edits all the markdown files in one go. This would mean splitting the thing by words, taking care of correctly grouping #tags-with/subtags and not looking inside code blocks etc. Not sure here. I’d still have Obsidian as ultimate source of truth about what it considers a tag and what not.

What now?

This is the first long-ish blog post I’ve written in a while. Feels awesome. Let’s see if I can get this blog started again.

26 Mar 2019

Pchr8board - a mirrored left-hand keyboard layout for Dvorak

1055 words, ~4 min read


Pchr8board is a keyboard layout for Linux based on Randall Munroe’s Mirrorboard idea and implements it in Linux for the Dvorak keyboard layout.

It looks like this:

(Read More)

09 Dec 2016

Semantic Mediawiki for personal knowledge management, using templates and a custom userscript

1368 words, ~5 min read

Here I’ll try to document my current setup for links management, which is slowly starting to take form.

Как мы пришли к такой жизни

Since the social bookmarking site Delicious (old links page) is seriously falling apart (which is very sad, I liked it almost as much as I liked Google Reader) I started looking for other alternatives. For some time, I used WordPress LinkLibrary plugin until I felt the hard category system lacked flexibility (you can see on the “Links” page of this blog how cluttered and repetitive it is), I needed _tags _and more ways to organize the links and, possibly, the relationships between them.

Then for a very short time I set up a WordPress installation specifically for links. I was not the first one who attempted this ( as an example), but it did not work out well for me.

As for the existing social bookmarking services, for example or, I did not want to pay and wanted control of my data (thank God the export feature in Delicious worked more often than not, but I don’t want to risk it anymore).

As for the need to “share” it, I want to have access to it from various places and, since there’s nothing private, putting it in the cloud and putting a password on it sounds like an unneeded layer of complication. Lastly — who knows — maybe someone will actually get some use out of it.

Semantic Mediawiki

Mediawiki is the software Wikipedia runs on. Semantic Mediawiki is an open-source extension for it that adds the ability to store and query data on a whole another level.

Semantics means, basically, meaning. The difference between “60”, “60kg”, “My weight is 60kg.”

Traditionally, Mediawiki allows the pages to link to each other, but the exact nature of the connection is not clear, and you can’t use the connections much. Semantic Mediawiki allows to define additional data for every page, and allows to define relationships between pages. The data “Benjamin Franklin was born in the USA in 1706” suddenly becomes searchable, for example as “Give me the people born in America before 1800” and “Give me the list of countries where people named Ben were born”. A link “Benjamin Franklin -> Philadelphia” becomes “Benjamin Franklin was (BORN IN) Philadelphia”.

This is awesome.

After looking at it, I understood that I have immense power in my hands, and that I have no idea how to use it. As in, how to create an architecture that was both meaningful and easy to adhere to.

Seeing all this, I thought it would make sense to upgrade my “Link database” to something much more interconnected and useful, a personal knowledge management system.

And here it is.


The system

Take this page.

Every page has 5 values:

  • l: The actual URI
  • t: the title
  • c: the complexity (how easy/hard is it to read; sometimes I just don’t want to think too much), 1 to 10
  • r: the rating, also 1 to 10
  • o: If it’s a page with only one link, around which the content of the page has been built. (As opposed to “Here are 5 links about X”)

Plus, of course, any additional text.

Properties can be set:

1) In the text itself, for example like this:

    - [[t::Uncoiling the spiral: Maths and hallucinations.]]

2) Invisibly:


3) using the following nice template I’ve written: 

    [[l::{{{1}}}]] - [[t::{{{2}}}]]. 
    Complexity: [[c::{{{3|5}}}]]; Rating: [[r::{{{4|5}}}]]; Is only link: [[o::{{{5|true}}}]]
     |t={{{2|1}}} <!-- If no title given, use URI as name -->
     |c={{{3|5}}} <!-- 5 as default value -->
     |r={{{4|5}}} <!-- 5 as default rating unless something else given
     |o={{{5|true}}} <!-- only link by default -->

which can be used like this:
|Why our brains love high ceilings

My main goal for this was that it should be fast, and fast for me. I can type the above much faster than I can multiple input boxes in a hypothetical GUI.

Then I decided to write some bad javascript to simplify it even more.

The bookmarklet/userscript

An actual bookmarklet would be definitely the next thing I'm doing, until then I'll be adding the pages manually.

But I wrote a small script (two years since I've used any Javascript, haha), to minimize the text above to just this:
Why our brains love high ceilings

The (badbadbad) Javascript code is the following:

var lines = $('#wpTextbox1').val().split('\n');

for (i=0; i<5; i++) {
if (typeof lines[i] == 'undefined') {lines[i]='';}

if (!ValidURL(lines[0])) {alert(lines[0]+" doesn't look like a valid URL.")};
if (lines[1]=='') {lines[1]=lines[0]};
if (lines[2]=='') {lines[2]='5'};
if (lines[3]=='') {lines[3]='5'};

if (parseInt(lines[2]) > 10 || parseInt(lines[2])<0 || isNaN(lines[2])) {
alert(lines[2]+'is not a valid value, setting to default 5');

if (parseInt(lines[3]) > 10 || parseInt(lines[3])<0 || isNaN(lines[3])) {
alert(lines[3]+'is not a valid value, setting to default 5');

var text="{{B|\n"+lines[0]+"\n|"+lines[1]+"\n|"+lines[2]+"\n|"+lines[3];
if (lines[4]!='') text+="\n|"+lines[4];

var field = document.getElementById('wpTextbox1');
var textArray = field.value.split("\n");
textArray.splice(0, 4);
textArray[0] = text;
field.value = textArray.join("\n");

function ValidURL(str) {
var pattern = new RegExp('^(https?:\\/\\/)?'+
return pattern.test(str);

The minimized variant of the above now sits nice in my bookmarks bar, and is bound to a keypress in cvim. So I can fill just the URI, and it sets everything else to some default values and adds the Mediawiki template formatting.


  1. Getting the page title automagically (see, I'll need a PHP backend. It would be also interesting to check from the PHP if the IP making the request is currently logged in in my wiki, and get the title only then, to prevent abuse
  2. Making a bookmarklet which populates automatically most of the fields, like my old Delicious bookmarklet (sigh.)

Searching the wiki

The search in Semantic Mediawiki is explained pretty well here. Now I can do neat things like "Give me the pages in the Category 'To read' with complexity < 4". And lastly, categories can be inside other categories. If X is in category  A, which is a subcategory of B, it still shows up in searches for category B. (example) Pretty nice!

Knowledge Management

Things I want to learn or will probably need pretty often will have their own pages, like the Formulating Knowledge page. Simply because interacting with the material always helps much more than just reading it. Also I like that it will be represented in a way relevant for me, without unnecessary data and with additional material I think should be there.

For the link pages, there will be the link + very short summary (it has been working pretty well) + a couple of thoughts about it, + maybe relevant data or links to other pages.

TODO: Quotes + Move there my "To Read" / "To Listen to" lists. Also think of a better name for it.


Warum einfach, wenn es auch kompliziert geht? (A nice German phrase about avoiding the unbearable simplicity of being: "Why simple, when it can be complicated as well?")

On a serious note, I don't have any doubts that in the long run I'll be thankful for this system.

Firstly, I control all of this data. Feels good. Take that, capitalist ad-ridden surveillance corporations!

Secondly, working with a lot of information has always been something I do often and enjoy immensely, and it would make sense to start accumulating everything in one place. Every day I stumble upon a lot of material on the Internet, of very different nature, and with not-obvious connections between them. I have more interests than I can count.

Organizing everything like this so far looks to me the best alternative, and I'm reasonably certain it will work out. There's a lot that can be improved, and I think in a couple of moths it will morph into something awesome.

Finding ways to use all the accumulated data is a topic for another day.


A couple of nice relevant inspiring places:  - in Russian, a person using Mediawiki as central hub for everything.  - personal wiki, mostly design. a much more advanced version of what I'm trying to do, also using Semantic Mediawiki. I should drop him a line :)