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16 Commits
i-love-pro
...
00b810599a
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| a6f3cd3f9a | |||
| ccc8d6f0eb |
12
agda.rb
12
agda.rb
@@ -46,7 +46,17 @@ class AgdaContext
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# assumes that links can't span multiple pages, and that links
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# aren't nested, so ensure that the parent of the textual node
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# is the preformatted block itself.
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raise "unsupported Agda HTML output" if at.parent.name != "pre"
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if at.parent.name != "pre"
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# Costmetic highlight warnings are sometimes applied to newlines.
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# If they don't have content, treat them as normal newlines at the
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# top level.
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#
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# This is an <a class="CosmeticProblem">\n</a> node.
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unless at.parent.name == "a" and at.parent['class'] == "CosmeticProblem" and at.content.strip.empty?
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raise "unsupported Agda HTML output in file #{file} at line #{line} (content #{at.content.inspect})"
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end
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end
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# Increase the line and track the final offset. Written as a loop
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# in case we eventually want to add some handling for the pieces
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19
assets/scss/onspiders.scss
Normal file
19
assets/scss/onspiders.scss
Normal file
@@ -0,0 +1,19 @@
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@import "variables.scss";
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@import "mixins.scss";
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.spiderweb {
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position: fixed;
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top: 0;
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right: 0;
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opacity: 0.25;
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max-width: 900px;
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@include below-container-width {
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width: $container-width-threshold;
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}
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}
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code {
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background: none;
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border: none;
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}
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@@ -1,51 +1,3 @@
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@import "variables.scss";
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body {
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background-color: #1c1e26;
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--text-color: white;
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font-family: $font-code;
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}
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h1, h2, h3, h4, h5, h6 {
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text-align: left;
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font-family: $font-code;
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}
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h1::after {
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content: "(writing)";
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font-size: 1rem;
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margin-left: 0.5em;
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position: relative;
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bottom: -0.5em;
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color: $primary-color;
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}
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nav .container {
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justify-content: flex-start;
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a {
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padding-left: 0;
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margin-right: 1em;
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}
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}
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.header-divider {
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visibility: hidden;
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}
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hr {
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height: auto;
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border: none;
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&::after {
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content: "* * *";
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color: $primary-color;
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font-size: 2rem;
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display: block;
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text-align: center;
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}
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}
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/* Code for the CSS glitch effect. Originally from: https://codepen.io/mattgrosswork/pen/VwprebG */
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.glitch {
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47
assets/scss/writing.scss
Normal file
47
assets/scss/writing.scss
Normal file
@@ -0,0 +1,47 @@
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@import "variables.scss";
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body {
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background-color: #1c1e26;
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--text-color: white;
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font-family: $font-code;
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}
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h1, h2, h3, h4, h5, h6 {
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text-align: left;
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font-family: $font-code;
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}
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h1::after {
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content: "(writing)";
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font-size: 1rem;
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margin-left: 0.5em;
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position: relative;
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bottom: -0.5em;
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color: $primary-color;
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}
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nav .container {
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justify-content: flex-start;
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a {
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padding-left: 0;
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margin-right: 1em;
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}
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}
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.header-divider {
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visibility: hidden;
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}
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hr {
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height: auto;
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border: none;
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&::after {
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content: "* * *";
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color: $primary-color;
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font-size: 2rem;
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display: block;
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text-align: center;
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}
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}
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718
assets/svg/spiderweb.svg
Normal file
718
assets/svg/spiderweb.svg
Normal file
@@ -0,0 +1,718 @@
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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
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<svg
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width="197.21727mm"
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viewBox="0 0 197.21727 106.16592"
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version="1.1"
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id="svg1"
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xml:space="preserve"
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inkscape:version="1.4.2 (ebf0e940, 2025-05-08)"
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sodipodi:docname="spiderweb.svg"
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xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
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xmlns="http://www.w3.org/2000/svg"
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xmlns:svg="http://www.w3.org/2000/svg"><sodipodi:namedview
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pagecolor="#000000"
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bordercolor="#000000"
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inkscape:showpageshadow="2"
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inkscape:pageopacity="0.0"
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inkscape:pagecheckerboard="0"
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inkscape:deskcolor="#d1d1d1"
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inkscape:document-units="mm"
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inkscape:zoom="1.2119635"
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inkscape:cy="235.56816"
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inkscape:window-width="1728"
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inkscape:window-height="996"
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inkscape:window-x="0"
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inkscape:window-y="38"
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inkscape:window-maximized="1"
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inkscape:current-layer="mainlayer" /><defs
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inkscape:groupmode="layer"
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inkscape:label="Main Layer"
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d="m 205.1224,79.314854 4.64282,-2.64383"
|
||||
id="path225" /><path
|
||||
style="fill:none;stroke:#ffffff;stroke-width:0.365;stroke-dasharray:none;stroke-opacity:1"
|
||||
d="m 208.34658,83.635256 1.41864,-0.838287"
|
||||
id="path226" /><path
|
||||
style="fill:none;stroke:#ffffff;stroke-width:0.365;stroke-dasharray:none;stroke-opacity:1"
|
||||
d="M 29.690113,9.1690057 175.30265,0"
|
||||
id="path227" /></g></svg>
|
||||
|
After Width: | Height: | Size: 43 KiB |
@@ -412,4 +412,4 @@ ideas to absurd and humorous extremes. So, my final reason:
|
||||
kind, welcoming and enthusiastic people, who dedicate much of their
|
||||
time to spreading the joy of the field.
|
||||
|
||||
I ❤️ you.
|
||||
I ❤️ the programming languages community.
|
||||
|
||||
190
content/blog/llm_personal_software/index.md
Normal file
190
content/blog/llm_personal_software/index.md
Normal file
@@ -0,0 +1,190 @@
|
||||
---
|
||||
title: "Personal Software with the Help of LLMs"
|
||||
date: 2026-04-05T16:03:00-07:00
|
||||
tags: ["LLMs"]
|
||||
series: ["LLM-Assisted Flashcard Generator"]
|
||||
description: "In this post, I describe an inherently individual and outcome-focused class of LLM-generated software"
|
||||
---
|
||||
|
||||
In [the previous post in this series]({{< relref "pdf_flashcards_llm" >}}),
|
||||
I wrote about a little utility I created for detecting underlined words
|
||||
in a book and creating vocabulary study material for them.
|
||||
Like I mentioned earlier, this was one of my earliest experiences with
|
||||
LLM-driven development, and I think it shaped my outlook on the technology
|
||||
quite a bit. For me, the bottom line is this: _with LLMs, I was able to
|
||||
rapidly solve a problem that was holding me back in another area of my life_.
|
||||
My goal was never to "produce software", but to "acquire vocabulary",
|
||||
and, viewed from this perspective, I think the experience has been a
|
||||
colossal success.
|
||||
|
||||
As someone who works on software, I am always reminded that end-users rarely
|
||||
care about the technology as much as we technologists; they care about
|
||||
having their problems solved. I find taking that perspective to be challenging
|
||||
(though valuable) because software is my craft, and because in thinking
|
||||
about the solution, I have to think about the elements that bring it to life.
|
||||
|
||||
With LLMs, I was able --- allowed? --- to view things more from the
|
||||
end-user perspective. I didn't know, and didn't need to know, the API
|
||||
for `PyMuPDF`, `argostranslate`, or `spaCy`. I didn't need to understand
|
||||
the PDF format. I could move one step away from the nitty-gritty and focus
|
||||
on the 'why' and the 'what', on the challenge of what I wanted to accomplish.
|
||||
I wrestled with the inherent complexity and
|
||||
avoided altogether the unrelated difficulties that merely happened to be
|
||||
there (downloading language modules; learning translation APIs; etc.)
|
||||
|
||||
By enabling me to do this, the LLM let me make rapid progress, and to produce
|
||||
solutions to problems I would've previously deemed "too hard" or "too tedious".
|
||||
This did, however, markedly reduce the care with which I was examining
|
||||
the output. I don't think I've _ever_ read the code that produces the
|
||||
pretty colored boxes in my program's debug output. This shift, I think,
|
||||
has been a divisive element of AI discourse in technical communities.
|
||||
I think that this has to do, at least in part, with different views
|
||||
on code as a medium.
|
||||
|
||||
#### The Builders and the Craftsmen
|
||||
There are two perspectives through which one may view software:
|
||||
as a craft in and of itself, and as a means to some end.
|
||||
My flashcard extractor can be viewed in vastly different ways when faced
|
||||
from these two perspectives. In terms of craft, I think that it is at best
|
||||
mediocre; most of the code is generated, slightly verbose and somewhat
|
||||
tedious. The codebase is far from inspiring, and if I had written it by hand,
|
||||
I would not be particularly proud of it. In terms of product, though,
|
||||
I think it tells an exciting story: here I am, reading Camus again, because
|
||||
I was able to improve the workflow around said reading. In a day, I was able
|
||||
to achieve what I couldn't muster in a year or two on my own.
|
||||
|
||||
The truth is, the "builder vs. craftsman" distinction is a simplifying one,
|
||||
another in the long line of "us vs. them" classifications. Any one person is
|
||||
capable of being any combination of these two camps at any given time. Indeed,
|
||||
different sorts of software demand to be viewed through different lenses.
|
||||
I will _still_ treat work on my long-term projects as a craft, because
|
||||
I will come back to it again and again, and because our craft has evolved
|
||||
to engender stability and maintainability.
|
||||
|
||||
However, I am more than happy to settle for 'underwhelming' when it means an
|
||||
individual need of mine can be addressed in record time. I think this
|
||||
gives rise to a new sort of software: highly individual, explicitly
|
||||
non-robust, and treated differently from software crafted with
|
||||
deliberate thought and foresight.
|
||||
|
||||
#### Personal Software
|
||||
|
||||
I think as time goes on, I am becoming more and more convinced by the idea
|
||||
of "personal software". One might argue that much of the complexity in many
|
||||
pieces of software is driven by the need of that software to accommodate
|
||||
the diverse needs of many users. Still, software remains somewhat inflexible and
|
||||
unable to accommodate individual needs. Features or uses that demand
|
||||
changes at the software level move at a slower pace: finite developer time
|
||||
needs to be spent analyzing what users need, determining the costs of this new
|
||||
functionality, choosing which of the many possible requests to fulfill.
|
||||
On the other hand, software that enables the users to build their customizations
|
||||
for themselves, by exposing numerous configuration options and abstractions,
|
||||
becomes, over time, very complicated to grasp.
|
||||
|
||||
Now, suppose that the complexity of such software scales superlinearly with
|
||||
the number of features it provides. Suppose also that individual users
|
||||
leverage only a small subset of the software's functionality. From these
|
||||
assumptions it would follow that individual programs, made to serve a single
|
||||
user's need, would be significantly less complicated than the "whole".
|
||||
By definition, these programs would also be better tailored to the users'
|
||||
needs. With LLMs, we're getting to a future where this might be possible.
|
||||
|
||||
I think that my flashcard generator is an early instance of such software.
|
||||
It doesn't worry about various book formats, or various languages, or
|
||||
various page layouts. The heuristic was tweaked to fit my use case, and
|
||||
now works 100% of the time. I understand the software in its entirety.
|
||||
I thought about sharing it --- and, in a way, I did, since it's
|
||||
[open source](https://dev.danilafe.com/DanilaFe/vocab-builder) --- but realized
|
||||
that outside of the constraints of my own problem, it likely will not be
|
||||
of that much use. I _could_ experiment with more varied constraints, but
|
||||
that would turn it back into the sort of software I discussed above:
|
||||
general, robust, and complex.
|
||||
|
||||
Today, I think that there is a whole class of software that is amenable to
|
||||
being "personal". My flashcard generator is one such piece of software;
|
||||
I imagine file-organization (as served by many "bulk rename and move" pieces
|
||||
of software out there), video wrangling (possible today with `ffmpeg`'s
|
||||
myriad of flags and switches), and data visualization to be other
|
||||
instances of problems in that class. I am merely intuiting here, but
|
||||
if I had to give a rough heuristic, it would be problems that:
|
||||
|
||||
* __fulfill a short-frequency need__, because availability, deployment,
|
||||
etc. significantly raises the bar for quality.
|
||||
* e.g., I collect flashcards once every two weeks;
|
||||
I organize my filesystem once a month; I don't spend nearly enough money
|
||||
to want to regenerate cash flow charts very often
|
||||
* __have an "answer" that's relatively easy to assess__, because
|
||||
LLMs are not perfect and iteration must be possible and easy.
|
||||
* e.g., I can see that all the underlined words are listed in my web app;
|
||||
I know that my files are in the right folders, named appropriately,
|
||||
by inspection; my charts seem to track with reality
|
||||
* __have a relatively complex technical implementation__, because
|
||||
why would you bother invoking an LLM if you can "just" click a button somewhere?
|
||||
* e.g., extracting data from PDFs requires some wrangling;
|
||||
bulk-renaming files requires some tedious and possibly case-specific
|
||||
pattern matching; cash flow between N accounts requires some graph
|
||||
analysis
|
||||
* __have relatively low stakes__, again, because LLMs are not perfect,
|
||||
and nor is (necessarily) one's understanding of the problem.
|
||||
* e.g., it's OK if I miss some words I underlined; my cash flow
|
||||
charts only give me an impression of my spending;
|
||||
* I recognize that moving files is a potentially destructive operation.
|
||||
|
||||
I dream of a world in which, to make use of my hardware, I just _ask_,
|
||||
and don't worry much about languages, frameworks, or sharing my solution
|
||||
with others --- that last one because they can just ask as well.
|
||||
|
||||
#### The Unfair Advantage of Being Technical
|
||||
I recognize that my success described here did not come for free. There
|
||||
were numerous parts of the process where my software background helped
|
||||
me get the most out of Codex.
|
||||
|
||||
For one thing, writing software trains us to think precisely about problems.
|
||||
We learn to state exactly what we want, to decompose tasks into steps,
|
||||
and to intuit the exact size of these steps; to know what's hard and what's
|
||||
easy for the machine. When working with an LLM, these skills make it possible
|
||||
to hit the ground running, to know what to ask and to help pluck out a particular
|
||||
solution from the space of various approaches. I think that this greatly
|
||||
accelerates the effectiveness of using LLMs compared to non-technical experts.
|
||||
|
||||
For another, the boundary between 'manual' and 'automatic' is not always consistent.
|
||||
Though I didn't touch any of the `PyMuPDF` code, I did need to look fairly
|
||||
closely at the logic that classified my squiggles as "underlines" and found
|
||||
associated words. It was not enough to treat LLM-generated code as a black box.
|
||||
|
||||
Another advantage software folks have when leveraging LLMs is the established
|
||||
rigor of software development. LLMs can and do make mistakes, but so do people.
|
||||
Our field has been built around reducing these mistakes' impact and frequency.
|
||||
Knowing to use version control helps turn the pathological downward spiral
|
||||
of accumulating incorrect tweaks into monotonic, step-wise improvements.
|
||||
Knowing how to construct a test suite and thinking about edge cases can
|
||||
provide an agent LLM the grounding it needs to iterate rapidly and safely.
|
||||
|
||||
In this way, I think the dream of personal software is far from being realized
|
||||
for the general public. Without the foundation of experience and rigor,
|
||||
LLM-driven development can easily devolve into a frustrating and endless
|
||||
back-and-forth, or worse, successfully build software that is subtly and
|
||||
convincingly wrong.
|
||||
|
||||
#### The Shoulders of Giants
|
||||
|
||||
The only reason all of this was possible is that the authors of `PyMuPDF`,
|
||||
`genanki`, `spaCy`, and `argos-translate` made them available for me to use from
|
||||
my code. These libraries provided the bulk of the functionality that Codex and I
|
||||
were able to glue into a final product. It would be a mistake to forget this,
|
||||
and to confuse the sustained, thoughtful efforts of the people behind these
|
||||
projects for the one-off, hyper-specific software I've been talking about.
|
||||
|
||||
We need these packages, and others like them, to provide a foundation for the
|
||||
things we build. They bring stability, reuse, and the sort of cohesion that
|
||||
is not possible through an amalgamation of home-grown personal scripts.
|
||||
In my view, something like `spaCy` is to my flashcard script as a brick is to
|
||||
grout. There is a fundamental difference.
|
||||
|
||||
I don't know how LLMs will integrate into the future of large-scale software
|
||||
development. The discipline becomes something else entirely when the
|
||||
constraints of "personal software" I floated above cease to apply. Though
|
||||
LLMs can still enable doing what was previously too difficult, tedious,
|
||||
or time consuming (like my little 'underline visualizer'), it remains
|
||||
to be seen how to integrate this new ease into the software lifecycle
|
||||
without threatening its future.
|
||||
239
content/blog/pdf_flashcards_llm/index.md
Normal file
239
content/blog/pdf_flashcards_llm/index.md
Normal file
@@ -0,0 +1,239 @@
|
||||
---
|
||||
title: "Generating Flashcards from PDF Underlines"
|
||||
date: 2026-04-05T16:02:00-07:00
|
||||
tags: ["LLMs", "Python"]
|
||||
series: ["LLM-Assisted Flashcard Generator"]
|
||||
description: "In this post, I describe my personal PDF-to-vocab-flashcards pipeline"
|
||||
---
|
||||
|
||||
__TL;DR__: I, with the help of ChatGPT, wrote [a program](https://dev.danilafe.com/DanilaFe/vocab-builder)
|
||||
that helps me extract vocabulary words from PDFs. Scroll just a bit further down
|
||||
to see what it looks like.
|
||||
|
||||
Sometime in 2020 or 2021, during the COVID-19 pandemic, I overheard from some
|
||||
source that Albert Camus, in his book _La Peste_ (The Plague), had quite
|
||||
accurately described the experience that many of us were going through
|
||||
at the time. Having studied French for several years, I decided that the
|
||||
best way to see for myself what _La Peste_ is all about was to read it
|
||||
in its original, untranslated form.
|
||||
|
||||
I made good progress, but I certainly did not know every word. At the surface,
|
||||
I was faced with two choices: guess the words from context and read without
|
||||
stopping, or interrupt my reading to look up unfamiliar terms. The former
|
||||
seemed unfortunate since it stunted my ability to acquire new vocabulary;
|
||||
the latter was unpleasant, making me constantly break from the prose
|
||||
(and the e-ink screen of my tablet) to consult a dictionary.
|
||||
|
||||
In the end, I decided to underline the words, and come back to them later.
|
||||
However, even then, the task is fairly arduous. For one, words I don't recognize
|
||||
aren't always in their canonical form (they can be conjugated, plural, compound,
|
||||
and more): I have to spend some time deciphering what I should add to a
|
||||
flashcard. For another, I had to bounce between a PDF of my book
|
||||
(from where, fortunately, I can copy-paste) and my computer. Often, a word
|
||||
confused the translation software out of context, so I had to copy more of the
|
||||
surrounding text. Finally, I learned that given these limitations, the pace of
|
||||
my reading far exceeds the rate of my translation. This led me to underline
|
||||
fewer words.
|
||||
|
||||
I thought,
|
||||
|
||||
> Perhaps I can just have some software automatically extract the underlined
|
||||
> portions of the words, find the canonical forms, and generate flashcards?
|
||||
|
||||
Even thinking this thought was a mistake. From then on, as I read and went
|
||||
about underlining my words, I thought about how much manual effort I will
|
||||
be taking on that could be automated. However, I didn't know how to start
|
||||
the automation. In the end, I switched to reading books in English.
|
||||
|
||||
Then, LLMs got good at writing code. With the help of
|
||||
Codex, I finally got the tools that I was dreaming about. Here's what it looks
|
||||
like.
|
||||
|
||||
{{< figure src="./underlines.png" caption="Detected underlined words on a page" label="Detected underlined words on a page" >}}
|
||||
|
||||
{{< figure src="./result.png" caption="Auto-flashcard application" label="Auto-flashcard application" class="fullwide" >}}
|
||||
|
||||
This was my first foray into LLM-driven development. My commentary about that
|
||||
experience (as if there isn't enough of such content out there!) will be
|
||||
interleaved with the technical details.
|
||||
|
||||
### The Core Solution
|
||||
The core idea has always been:
|
||||
|
||||
1. Find things that look like underlines
|
||||
2. See which words they correspond to
|
||||
3. Perform {{< sidenote "right" "lemmatization-node" "lemmatization" >}}
|
||||
Lemmatization (<a href="https://en.wikipedia.org/wiki/Lemmatization">Wikipedia</a>) is the
|
||||
process of turning non-canonical forms of words (like <code>am</code> (eng) /
|
||||
<code>suis</code> (fr)) into their canonical form which might be found in the
|
||||
dictionary (<code>to be</code> / <code>être</code>).
|
||||
{{< /sidenote >}} and translate.
|
||||
|
||||
My initial direction was shaped by the impressive demonstrations of OCR
|
||||
models, which could follow instructions at the same time as reading a document.
|
||||
For these models, a prompt like "extract all the text in the red box"
|
||||
constituted the entire targeted OCR pipeline. My hope was that a similar
|
||||
prompt, "extract all underlined words", would be sufficient to accomplish
|
||||
steps 1 and 2. However, I was never to find out: as it turns out,
|
||||
OCR models are large and very expensive to run. In addition, the model
|
||||
that I was looking at was specifically tailored for NVIDIA hardware which
|
||||
I, with my MacBook, simply didn't have access to.
|
||||
|
||||
In the end, I came to the conclusion that a VLM is overkill for the problem
|
||||
I'm tackling. This took me down the route of analyzing the PDFs. The
|
||||
problem, of course, is that I know nothing of the Python landscape
|
||||
of PDF analysis tools, and that I also know nothing about the PDF format
|
||||
itself. This is where a Codex v1 came in. Codex opted (from its training
|
||||
data, I presume) to use the [`PyMuPDF`](https://pymupdf.readthedocs.io) package.
|
||||
It also guessed (correctly) that the PDFs exported by my tablet used
|
||||
the 'drawings' part of the PDF spec to encode what I penned. I was instantly
|
||||
able to see (on the console) the individual drawings.
|
||||
|
||||
The LLM also chose to approach the problem by treating each drawing as just
|
||||
a "cloud of points", discarding the individual line segment data. This
|
||||
seemed like a nice enough simplification, and it worked well in the long run.
|
||||
|
||||
#### Iterating on the Heuristic
|
||||
The trouble with the LLM agent was that it had no good way of verifying
|
||||
whether the lines it detected (and indeed, the words it considered underlined)
|
||||
were actually lines (and underlined words). Its initial algorithm missed
|
||||
many words, and misidentified others. I had to resort to visual inspection
|
||||
to see what was being missed, and for the likely cause.
|
||||
|
||||
The exact process of the iteration is not particularly interesting. I'd
|
||||
tweak a threshold, re-run the code, and see the new list of words.
|
||||
I'd then cross-reference the list with the page in question, to see
|
||||
if things were being over- or under-included. Rinse, repeat.
|
||||
|
||||
This got tedious fast. In some cases, letters or words I penned would get picked
|
||||
up as underlines, and slightly diagonal strokes would get missed. I ended up
|
||||
requesting Codex to generate a debugging utility that highlighted (in a box)
|
||||
all the segments that it flagged, and the corresponding words. This
|
||||
is the first picture I showed in the post. Here it is again:
|
||||
|
||||
{{< figure src="./underlines.png" caption="Detected underlined words on a page" label="Detected underlined words on a page" >}}
|
||||
|
||||
In the end, the rough algorithm was as follows:
|
||||
|
||||
1. __Identify all "cloud points" that are not too tall__. Lines that
|
||||
vertically span too many lines of text are likely not underlines.
|
||||
* The 'height threshold' ended up being larger than I anticipated:
|
||||
turns out I don't draw very straight horizontal lines.
|
||||
|
||||
{{< figure src="tallmarks.png" caption="My angled underlines" label="My angled underlines" >}}
|
||||
2. __Create a bounding box for the line,__ with some padding.
|
||||
I don't draw the lines _directly_ underneath the text, but a bit below.
|
||||
* Sometimes, I draw the line quite a bit below; the upward padding
|
||||
had to be sizeable.
|
||||
|
||||
{{< figure src="lowmarks.png" caption="My too-low underlines" label="My too-low underlines" >}}
|
||||
3. __Intersect `PyMuPDF` bounding boxes with the line__. Fortunately,
|
||||
`PyMuPDF` provides word rectangles out of the box.
|
||||
* I required the intersection to overlap with at least 60% of the word's
|
||||
horizontal width, so accidental overlaps don't count.
|
||||
|
||||
{{< figure src="widemarks.png" caption="My too-wide underline hitting `Cela`" label="My too-wide underline hitting `Cela`" >}}
|
||||
* The smallest underlines are roughly the same size as the biggest strokes
|
||||
in my handwriting. The 60% requirement filtered out the latter, while
|
||||
keeping the former.
|
||||
|
||||
{{< figure src="flaggedmarks.png" caption="Letters of a hand-writing word detected as lines" label="Letters of a hand-writing word detected as lines" >}}
|
||||
4. __Reject underlines that overlap from the top__. Since, as I mentioned,
|
||||
my underlines are often so low that they touch the next line.
|
||||
|
||||
#### Lemmatization and Translation
|
||||
|
||||
I don't recall now how I arrived at [`spaCy`](https://github.com/explosion/spaCy),
|
||||
but that's what I ended up using for my lemmatization. There was only
|
||||
one main catch: sometimes, instead of underlining words I didn't know,
|
||||
I underlined whole phrases. Lemmatization did not work well in those
|
||||
contexts; I had to specifically restrict my lemmatization to single-word
|
||||
underlines, and to strip punctuation which occasionally got tacked on.
|
||||
With lemmatization in hand, I moved on to the next step: translation.
|
||||
|
||||
I wanted my entire tool to work completely offline. As a result, I had to
|
||||
search for "python offline translation", to learn about
|
||||
[`argos-translate`](https://github.com/argosopentech/argos-translate).
|
||||
Frankly, the translation piece is almost entirely uninteresting:
|
||||
it boils down to invoking a single function. I might add that
|
||||
`argos-translate` requires one to download language packages --- they
|
||||
do not ship with the Python package. Codex knew to write a script to do
|
||||
so, which saved a little bit of documentation-reading and typing.
|
||||
|
||||
The net result is a program that could produce:
|
||||
|
||||
```
|
||||
Page 95: fougueuse -> fougueux -> fiery
|
||||
```
|
||||
|
||||
Pretty good!
|
||||
|
||||
### Manual Intervention
|
||||
That "pretty good" breaks down very fast. There are several points of failure:
|
||||
the lemmatization can often get confused, and the offline translation
|
||||
fails for some of the more flowery Camus language.
|
||||
|
||||
In the end, for somewhere on the order of 70% of the words I underlined,
|
||||
the automatic translation was insufficient, and required small tweaks
|
||||
(changing the tense of the lemma, adding "to" to infinitive English verbs, etc.)
|
||||
|
||||
I thought --- why not just make this interactive? Fortunately, there are
|
||||
plenty of Flask applications in Codex's training dataset. In one shot,
|
||||
it generated a little web application that enabled me to tweak the source word
|
||||
and final translation. It also enabled me to throw away certain underlines.
|
||||
This was useful when, across different sessions, I forgot and underlined
|
||||
the same word, or when I underlined a word but later decided it was not worth
|
||||
including in my studying. This application produced an Anki deck, using
|
||||
the Python library [`genanki`](https://github.com/kerrickstaley/genanki).
|
||||
Anki has a nice mechanism to de-duplicate decks, which meant that every
|
||||
time I exported a new batch of words, I could add them to my running
|
||||
collection.
|
||||
|
||||
Even then, however, cleaning up the auto-translation was not always easy.
|
||||
The OCR copy of the book had strange idiosyncrasies: the letters 'fi' together
|
||||
would OCR to '=' or '/'. Sometimes, I would underline a compound phrase
|
||||
that spanned two lines; though I knew the individual words (and would be surprised
|
||||
to find them in my list), I did not know their interaction.
|
||||
|
||||
In the end, I added (had Codex add) both a text-based context and a visual
|
||||
capture of the word in question to the web application. This led to the final
|
||||
version, whose screenshot I included above. Here it is again:
|
||||
|
||||
{{< figure src="./result.png" caption="Auto-flashcard application" label="Auto-flashcard application" class="fullwide" >}}
|
||||
|
||||
The net result was that, for many words, I could naively accept the
|
||||
automatically-generated suggestion. For those where this was not possible,
|
||||
in most cases I only had to tweak a few letters, which still saved me time.
|
||||
Finally, I was able to automatically include the context of the word in
|
||||
my flashcards, which often helps reinforce the translation and remember
|
||||
the exact sense in which the word was used.
|
||||
|
||||
To this day, I haven't found a single word that was underlined and missed,
|
||||
nor one that was mis-identified as underlined.
|
||||
|
||||
### Future Direction
|
||||
|
||||
In many ways, this software is more than good enough for my needs.
|
||||
I add a new batch of vocabulary roughly every two weeks, during which time
|
||||
I manually export a PDF of _La Peste_ from my tablet and plug it into
|
||||
my software.
|
||||
|
||||
In my ideal world, I wouldn't have to do that. I would just underline some
|
||||
words, and come back to my laptop a few days later to find a set of draft
|
||||
flashcards for me to review and edit. In an even more ideal world, words
|
||||
I underline get "magically" translated, and the translations appear somewhere
|
||||
in the margins of my text (while also being placed in my list of flashcards).
|
||||
|
||||
I suspect LLMs --- local ones --- might be a decent alternative technology
|
||||
to "conventional" translation. By automatically feeding them the context
|
||||
and underlined portion, it might be possible to automatically get a more
|
||||
robust translation and flashcard. I experimented with this briefly
|
||||
early on, but did not have much success. Perhaps better prompting or newer
|
||||
models would improve the outcomes.
|
||||
|
||||
That said, I think that those features are way beyond the 80:20 transition:
|
||||
it would be much harder for me to get to that point, and the benefit would
|
||||
be relatively small. Today, I'm happy to stick with what I already have.
|
||||
|
||||
In the [next part of this series]({{< relref "llm_personal_software" >}}),
|
||||
I will talk more about how this project influenced my views on LLMs.
|
||||
11
content/series/llm-assisted-flashcard-generator/_index.md
Normal file
11
content/series/llm-assisted-flashcard-generator/_index.md
Normal file
@@ -0,0 +1,11 @@
|
||||
+++
|
||||
title = "LLM-Assisted Flashcard Generator"
|
||||
summary = """
|
||||
In this series, I write up a little program I wrote for myself,
|
||||
which detects vocabulary words I underline in a book and turns them
|
||||
into flashcards. I view this through the lens of a first foray into
|
||||
development that heavily relies on LLMs.
|
||||
"""
|
||||
status = "complete"
|
||||
donotunique = true
|
||||
+++
|
||||
44
content/writing/onspiders.md
Normal file
44
content/writing/onspiders.md
Normal file
@@ -0,0 +1,44 @@
|
||||
---
|
||||
title: "On Spiders"
|
||||
date: 2026-03-22T01:03:00-05:00
|
||||
type: onspiders
|
||||
---
|
||||
|
||||
```
|
||||
Whenever I stay still,
|
||||
I feel the spiders weave their webs around me.
|
||||
Their tiny legs entangle me in silk,
|
||||
traverse my body as they seem to proudly
|
||||
inspect their work and into darkness sink.
|
||||
|
||||
|
||||
There're times I see the spiders' threads on others,
|
||||
They shimmer gently in the purple dusk,
|
||||
Or sway with zephyrs in the hair of lovers,
|
||||
stuck.
|
||||
|
||||
To listen to the webs is to hear echoes
|
||||
of social butterflies ensnared within
|
||||
arachnid galleries of human ethos,
|
||||
whose pieces come and go upon a whim.
|
||||
|
||||
Their curators are blind and want for stories,
|
||||
delivered as vibrations through the links,
|
||||
for righteous anger, anguish, joy and worries,
|
||||
superimposing frequencies that mix,
|
||||
reveberating through the sprawling complex
|
||||
of radial lines and newly captured prey,
|
||||
resisting first but ultimately hopeless,
|
||||
these signals grow in urgency and they
|
||||
form standing waves that make the victims sing
|
||||
and even from myself bring out a resonant ring.
|
||||
```
|
||||
|
||||
|
||||
<!--
|
||||
the signals cause a standing wave
|
||||
intensify with every passing moment
|
||||
arachnid galleries of human drama,
|
||||
temples to the human mind
|
||||
(traverse my body all while I sleep soundly,)
|
||||
They shimmer gently in the setting sun, -->
|
||||
11
convert.rb
11
convert.rb
@@ -68,13 +68,12 @@ files.each do |file|
|
||||
t.replace(rendered)
|
||||
end
|
||||
|
||||
found_any ||= document.at('meta[name="needs-latex"]')
|
||||
|
||||
# If we didn't find any mathematical equations, no need to include KaTeX CSS.
|
||||
# Disabled here because Bergamot technically doesn't require math blocks
|
||||
# on the page but does need the CSS.
|
||||
#
|
||||
# unless found_any
|
||||
# document.css('link[href$="katex.css"], link[href$="katex.min.css"]').each(&:remove)
|
||||
# end
|
||||
unless found_any
|
||||
document.css('link[href$="katex.css"], link[href$="katex.min.css"]').each(&:remove)
|
||||
end
|
||||
|
||||
File.write(file, document.to_html(encoding: 'UTF-8'))
|
||||
end
|
||||
|
||||
25
layouts/onspiders/baseof.html
Normal file
25
layouts/onspiders/baseof.html
Normal file
@@ -0,0 +1,25 @@
|
||||
{{- /* Note: changing the baseof template because the title, tags, etc. of a regular post are still valid. */ -}}
|
||||
<!DOCTYPE html>
|
||||
<html lang="{{ .Site.Language.Lang }}">
|
||||
{{- partial "head.html" . -}}
|
||||
<body>
|
||||
{{ $writingcss := resources.Get "scss/writing.scss" | css.Sass | resources.Minify }}
|
||||
<link rel="stylesheet" href="{{ $writingcss.Permalink }}">
|
||||
{{ $spidercss := resources.Get "scss/onspiders.scss" | css.Sass | resources.Minify }}
|
||||
<link rel="stylesheet" href="{{ $spidercss.Permalink }}">
|
||||
<svg class="spiderweb" viewBox="0 0 197.21727 106.16592">
|
||||
<use href="{{ (resources.Get "svg/spiderweb.svg").Permalink }}#mainlayer"></use>
|
||||
</svg>
|
||||
{{- partial "header.html" . -}}
|
||||
<div class="container"><hr class="header-divider"></div>
|
||||
<main class="container">
|
||||
|
||||
{{- if .Draft -}}
|
||||
{{- partial "warning.html" (i18n "postDraft") -}}
|
||||
{{- end -}}
|
||||
|
||||
{{- block "main" . }}{{- end }}
|
||||
</main>
|
||||
{{- block "after" . }}{{- end }}
|
||||
</body>
|
||||
</html>
|
||||
@@ -3,6 +3,8 @@
|
||||
<html lang="{{ .Site.Language.Lang }}">
|
||||
{{- partial "head.html" . -}}
|
||||
<body>
|
||||
{{ $writingcss := resources.Get "scss/writing.scss" | css.Sass | resources.Minify }}
|
||||
<link rel="stylesheet" href="{{ $writingcss.Permalink }}">
|
||||
{{ $voidcss := resources.Get "scss/thevoid.scss" | css.Sass | resources.Minify }}
|
||||
<link rel="stylesheet" href="{{ $voidcss.Permalink }}">
|
||||
{{- partial "header.html" . -}}
|
||||
|
||||
Submodule themes/vanilla updated: 952502e690...3937ed4172
Reference in New Issue
Block a user