The best YouTube downloaders:

• The best YouTube downloader for Windows is Stacher. It’s free, open-source, and simple. It’s an easy-to-use graphical application that does the setup for you.
• The best YouTube downloader for the command line is yt-dlp.
• The best YouTube downloader for Android is NewPipe. This third-party YouTube app has a built-in download tool.
• The best YouTube downloader for web based is Cobalt , can be self deployed

Source: https://windowsread.me/p/best-youtube-downloaders

Abstract

Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we introduce the Well: a large-scale collection of datasets containing numerical simulations of a wide variety of spatiotemporal physical systems. The Well draws from domain experts and numerical software developers to provide 15TB of data across 16 datasets covering diverse domains such as biological systems, fluid dynamics, acoustic scattering, as well as magneto-hydrodynamic simulations of extra-galactic fluids or supernova explosions. These datasets can be used individually or as part of a broader benchmark suite. To facilitate usage of the Well, we provide a unified PyTorch interface for training and evaluating models. We demonstrate the function of this library by introducing example baselines that highlight the new challenges posed by the complex dynamics of the Well. The code and data is available at this URL.

Source: https://gfw.report/blog/geedge_and_mesa_leak/en/

1. Introduction

The Great Firewall of China (GFW) experienced the largest leak of internal documents in its history on Thursday September 11, 2025. Over 500 GB of source code, work logs, and internal communication records were leaked, revealing details of the GFW’s research, development, and operations.

The leak originated from a core technical force behind the GFW: Geedge Networks (whose chief scientist is Fang Binxing) and the MESA Lab at the Institute of Information Engineering, Chinese Academy of Sciences. The documents show that the company not only provides services to governments in places like Xinjiang, Jiangsu, and Fujian, but also exports censorship and surveillance technology to countries such as Myanmar, Pakistan, Ethiopia, Kazakhstan, and other unidentified country under the “Belt and Road” framework.

Book: Designing with Algorithms a Mathematical Guide

Source:

• https://en.wikipedia.org/wiki/Artificial_Intelligence:_A_Guide_for_Thinking_Humans

Book: Artificial Intelligence A Guide for Thinking Humans

Source:

• https://en.wikipedia.org/wiki/Artificial_Intelligence:_A_Guide_for_Thinking_Humans

Kadang-kadang kita perlu mengetahui suatu tanggal itu hari ke berapa dalam suatu tahun. Berikut ini cara menghitungnya dengan bahasa Python.

from datetime import datetime, timedelta
day_of_year = 265
start_of_year = datetime(2025, 1, 1)
target_date = start_of_year + timedelta(days=day_of_year - 1)
weekday_num = target_date.weekday()+1
print("weekday num", weekday_num)

source:

• The Art of Quantitative Finance vol 1 https://link.springer.com/book/10.1007/978-3-031-23873-4
• The Art of Quantitative Finance vol 2 https://link.springer.com/book/10.1007/978-3-031-23870-3
• The Art of Quantitative Finance vol 3 https://link.springer.com/book/10.1007/978-3-031-23867-3

Source: https://zachwills.net/how-to-use-claude-code-subagents-to-parallelize-development/

This book offers a comprehensive and accessible introduction to game theory, emphasizing both noncooperative and cooperative aspects of strategic decision-making. In the chapters on noncooperative game theory, you will explore advanced topics such as perfect equilibrium, evolutionary stable strategies, and correlated equilibrium, along with a range of subjects often underrepresented in other textbooks.The cooperative game theory sections cover essential topics like coalitional games, cake-cutting and fairness, cooperative bargaining, and matching theory. Additionally, the book includes an insightful chapter on mechanism design.Designed for use in one-semester advanced undergraduate or graduate-level courses, this textbook stands apart from others at the same level. Each chapter begins with clear theoretical definitions, followed by carefully detailed examples. Select chapters include propositions that either demonstrate the existence of equilibrium in abstract games or interrelate various game-theoretic concepts.While rigorous in its scope, the book assumes no advanced background in calculus or algebra. The mathematical exposition is kept as straightforward and self-contained as possible, ensuring that readers can easily apply theoretical ideas to practical examples and follow proofs with ease.

Problem:

create pair of of people with similar properties. Minimize difference between members of a pair

Solution

Algorithm for optimal node pairing that minimizes the total distance between paired nodes. This is essentially a minimum-weight perfect matching problem.I’ve created a comprehensive implementation of optimal node pairing algorithms. Here’s what the code includes:

Key Components:

1. Node Class: Represents a point in 2D space with distance calculation
2. Multiple Algorithm Approaches:
   • Greedy: Fast O(n³) algorithm that repeatedly finds closest pairs
   • Nearest Neighbor: O(n²) algorithm that pairs each node with its nearest unpaired neighbor
   • Approximation with Local Search: Improves greedy solution using 2-opt style swaps
   • Brute Force Optimal: Guaranteed optimal solution via backtracking (exponential time)

Algorithm Trade-offs:

• Greedy: Fast but may get stuck in local optima
• Nearest Neighbor: Faster, good for large datasets, decent quality
• Approximation: Best balance of speed and quality for medium datasets
• Brute Force: Perfect solution but only practical for small instances (≤8-10 nodes)

Usage Recommendations:

• Small datasets (≤8 nodes): Use brute force for guaranteed optimal
• Medium datasets (10-100 nodes): Use approximation algorithm
• Large datasets (>100 nodes): Use nearest neighbor or greedy

The code includes a test function that demonstrates all algorithms on a sample dataset, showing the total distance and individual pairs for each approach. You can easily adapt it for your specific coordinate system or distance metric.