| // ==UserScript== | |
| // @name Kindle Download | |
| // @namespace http://tampermonkey.net/ | |
| // @version 2025-02-20 | |
| // @description Download all your kindle books | |
| // @author You | |
| // @match https://www.amazon.com/hz/mycd/digital-console/contentlist/booksPurchases/* | |
| // @icon https://www.google.com/s2/favicons?sz=64&domain=amazon.com | |
| // @grant none | |
| // ==/UserScript== |
I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.
So below I made a list of leetcode problems that are as close to grokking problems as possible.
| { | |
| "cmd": ["g++", "-o", "${file_path}/${file_base_name}.exe", "-static-libgcc", "-static-libstdc++", "*.cpp"], | |
| "file_regex": "^(..[^:]*):([0-9]+):?([0-9]+)?:? (.*)$", | |
| "working_dir": "${file_path}", | |
| "selector": "source.c, source.cpp, source.c++", | |
| "path": "c:/Program Files/mingw-w64/mingw64/bin", | |
| "shell": true, | |
| "variants": [ | |
| { | |
| "name": "Run", |
A while ago I did some rage tweeting about job ads. Here is a list of helpful things people sent me to read then and since:
I recently built a small agent-based model using Python and wanted to visualize the model in action. But as much as Python is an ideal tool for scientific computation (numpy, scipy, matplotlib), it's not as good for dynamic visualization (pygame?).
You know what's a very mature and flexible tool for drawing graphics? The DOM! For simple graphics you can use HTML and CSS; for more complicated stuff you can use Canvas, SVG, or WebGL. There are countless frameworks, libraries, and tutorials to help you draw exactly what you need. In my case, this was the animation I wanted:
(Each row represents a "worker" in my model, and each rectangle represents a "task.")
L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns
Compress 1K bytes with Zippy ............. 3,000 ns = 3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns = 20 µs
SSD random read ........................ 150,000 ns = 150 µs
Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs
This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.
###Array ####Definition: