The Piracy Index Website
A comprehensive index of piracy-related resources, built
with modern web technologies.
HTML
CSS
JavaScript
Web Scraping
// Sample code from The Piracy Index Website const
fetchSites = async () => { try { const response = await
fetch('/api/sites'); const data = await response.json();
return data; } catch (error) { console.error('Error
fetching sites:', error); return []; } }; const
renderSites = (sites) => { const siteList =
document.getElementById('site-list'); siteList.innerHTML
= sites.map(site => `
${site.name}
${site.category}
`).join(''); }; // Initialize the site list (async () =>
{ const sites = await fetchSites(); renderSites(sites);
})();
Telegram - Libadwaita Theme
A custom GTK4 / Libadwaita theme for Telegram Desktop,
bringing a native look to the app on Linux.
CSS
GTK
Libadwaita
UI Design
/* Sample CSS from Telegram - Libadwaita Theme */
.window { background-color: @theme_bg_color; } .header {
background-color: @theme_header_bg_color; border-bottom:
1px solid @borders; } .message-in { background-color:
alpha(@theme_selected_bg_color, 0.1); border-radius:
18px 18px 18px 0; } .message-out { background-color:
@theme_selected_bg_color; border-radius: 18px 18px 0
18px; color: @theme_selected_fg_color; } button.text {
background: none; border: none; box-shadow: none;
min-height: 24px; padding: 2px 8px; } button.text:hover
{ background-color: alpha(@theme_fg_color, 0.1); }
Face Recognition using CV2
A Python-based face recognition system using OpenCV and
machine learning techniques.
Python
OpenCV
Machine Learning
Computer Vision
# Sample code from Face Recognition using CV2 import cv2
import numpy as np from sklearn.neighbors import
KNeighborsClassifier def train_face_recognizer(faces,
labels): knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(faces, labels) return knn def
recognize_face(frame, face_cascade, recognizer): gray =
cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces =
face_cascade.detectMultiScale(gray, 1.3, 5) for (x, y,
w, h) in faces: roi_gray = gray[y:y+h, x:x+w] roi_gray =
cv2.resize(roi_gray, (100, 100)) roi_gray =
roi_gray.flatten() prediction =
recognizer.predict([roi_gray])[0] cv2.rectangle(frame,
(x, y), (x+w, y+h), (255, 0, 0), 2) cv2.putText(frame,
f"Person: {prediction}", (x, y-10),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 2) return
frame # Main loop for real-time face recognition #
(implementation details omitted for brevity)
vfetch
A minimalist, optimized system information tool designed
to run in all POSIX shells.
Shell Scripting
POSIX
System Info
Performance Optimization
#!/bin/sh # vfetch - A minimal system info script #
Colors c0='[0m'; c1='[31m'; c2='[32m' c3='[33m';
c4='[34m'; c5='[35m' c6='[36m'; c7='[37m'; c8='[38m' #
Get system information user=$(whoami) host=$(hostname)
os=$(uname -s) kernel=$(uname -r) uptime=$(uptime -p |
sed 's/up //') packages=$(ls /var/lib/dpkg/info/*.list
2>/dev/null | wc -l) shell=$(basename "$SHELL") # Print
system information cat << EOF ${c1} _____ ${c2} /\\ _\\
${c0}${user}${c6}@${c0}${host} ${c3} / \\ \\ \\ ${c6}OS:
${c0}${os} ${c4} / \\ \\ \\ ${c6}KERNEL: ${c0}${kernel}
${c5} / /\\ \\ \\__\\ ${c6}UPTIME: ${c0}${uptime} ${c6}
/ /__\\ \\ \\${c6}PACKAGES: ${c0}${packages} ${c7} /
______\\ \\ ${c6}SHELL: ${c0}${shell} ${c8}/__/ \\__\\
EOF