31
Solving the “Miracle Sudoku” in Prolog
Writing a solver for “Miracle Sudoku” puzzles in Prolog.
Top posts
Every blog post that took off on Hacker News, ranked by the votes of its best submission. 173 posts.
32
Swift Algorithms
I’m excited to announce Swift Algorithms, a new open-source package of sequence and collection algorithms, along with their related types.
33
34
35
The stable marriage problem and modern dating
Attempting to model modern dating using the lessons learned from the stable marriage problem
36
Lagrangian Neural Networks
A blog by Sam Greydanus
37
38
The Sun seen through the Earth in “neutrino light” (2007)
Physics blog quantum computation
39
Test for Divisibility by 13
How to manually test whether a large number is divisible by 7, 11, and 13 all at the same time.
40
Life Is Made of Unfair Coin Flips
It’s time we did a non-pandemic related newsletter issue. So this week we’ll do something more fun. Today we’re going to talk about an interesting journal article that came out two weeks ago,…
41
The Recursive Universe
A few years ago I came across this video, showing a complex machine built entirely in Conway’s Game of Life:
42
Calculate the day of the week for any date in your head
The Doomsday Algorithm gives the day of the week for any date (and you can do it in your head)
43
Crooked to the Millimetre
The technology pioneered by the ETH spin-off incon.ai allows blocks to be positioned with pinpoint accuracy, creating structures with aesthetic designs and augmented acoustics.
44
When Bloom filters don't bloom
Last month finally I had an opportunity to use Bloom filters. I became fascinated with the promise of this data structure, but I quickly realized it had some drawbacks. This blog post is the tale of my brief love affair with Bloom filters.
45
Building a computer in Conway's game of life
A new (and better) version of the GOL computer is available here : https://github.com/nicolasloizeau/scalable-gol-computer
46
47
Is Probability Real?
Today, I want to address an issue with statements involving chance. To demonstrate, let’s first consider a statement that doesn’t involve chance:
48
The Most Common Errors in Undergraduate Mathematics (2009)
This page describes the errors that I have seen most frequently in undergraduate mathematics, the likely causes of those errors, and their remedies.
49
Anxiety as an algorithm
Founder, Investor, Author
50
51
Cache Oblivious Algorithms
Cache-Oblivious Algorithms Cache-oblivious algorithms seemed incredible to me when I first heard about it in 6.854 (Advanced Algorithms). It’s relatively straightforward to imagine algorithms that utilizes information about page size $B$ and cache size $M$ to create efficient data structures. However, cache-oblivious…
52
Entropy explained, with sheep (2016)
An explorable blog post about entropy, with sheep.
53
Ramanujan's Easiest Formula
A while ago I decided to figure out how to prove one of Ramanujan’s formulas. I feel this is the sort of thing every mathematician should try at least once. I picked the easiest one I could f…
54
55
Math is an insurance policy
We live in interesting times. For instance, we are witnessing several extinction events all at once. One of them is the massive extinction of species. The other is the extinction of jobs. Both are …
56
Gamedev Tutorial: Dot Product, Rulers, and Bouncing Balls
[latexpage] 本文之中文翻譯在此 Prerequisites Trignometry Basics – Sine & CosineTrigonometry Basics – Tangent, Triangles, And CannonballsInverse Trigonometric Functions, Slope Angles, And Facing Objects Overview The dot product is a simple yet extremely useful mathematical tool. It encodes the relationship between two vectors'…
57
How much math you need for programming (2014)
Whenever I wanted to learn Algorithms, Mathematics used there somehow seemed to be an obstacle. I admit my Math is not that good but it ain’t that bad either but this “ain’t bad&#…
58
The largest city in each 10x10 degree latitude/longitude box
From the highly eclectic blog of Mark Dominus
59
60
The Case for Bayesian Deep Learning
The Case for Bayesian Deep Learning Andrew Gordon Wilson Abstract Bayesian inference is especially compelling for deep neural networks. The key distinguishing property of a Bayesian approach is margin