There are so many great GIFs out there and I want to have copies of them. Twitter makes that harder than it should be by converting them to MP4 and not providing access to the source material. To make it easier, I made a bash pipeline that takes a tweet URL and a filename, extracts the MP4 from that tweet and uses ffmpeg to convert back to GIF.
brew install ffmpegapt install ffmpeg
| # Algorithm W (Damas-Hindley-Milner) in LiveScript. | |
| # By Paul Miller (paulmillr.com), Public domain. | |
| # | |
| # Based on Robert Smallshire's [Python code](http://bit.ly/bbVmmX). | |
| # Which is based on Andrew's [Scala code](http://bit.ly/aztXwD). | |
| # Which is based on Nikita Borisov's [Perl code](http://bit.ly/myq3uA). | |
| # Which is based on Luca Cardelli's [Modula-2 code](http://bit.ly/Hjpvb). | |
| # Something like that. | |
| prelude = require './prelude' |
| module Sorting where | |
| -- See https://www.twanvl.nl/blog/agda/sorting | |
| open import Level using () renaming (zero to ℓ₀;_⊔_ to lmax) | |
| open import Data.List hiding (merge) | |
| open import Data.List.Properties | |
| open import Data.Nat hiding (_≟_;_≤?_) | |
| open import Data.Nat.Properties hiding (_≟_;_≤?_;≤-refl;≤-trans) | |
| open import Data.Nat.Logarithm | |
| open import Data.Nat.Induction |
| // data comes from here http://stat-computing.org/dataexpo/2009/the-data.html | |
| // download 1994.csv.bz2 and unpack by running: cat 1994.csv.bz2 | bzip2 -d > 1994.csv | |
| // 1994.csv should be ~5.2 million lines and 500MB | |
| // importing all rows into leveldb took ~50 seconds on my machine | |
| // there are two main techniques at work here: | |
| // 1: never create JS objects, leave the data as binary the entire time (binary-split does this) | |
| // 2: group lines into 16 MB batches, to take advantage of leveldbs batch API (byte-stream does this) | |
| var level = require('level') |
The Racket platform provides a robust set of tools for developing languages, mostly centered around the macro system and the #lang protocol, which allows providing arbitrary readers to convert text input to syntax objects representing Racket modules. The REPL is a bit more complicated, however, because it has different rules—each expression needs to be dynamically read, assigned lexical context, expanded, compiled, evaluated, and printed, all in real time. (In that sense, perhaps the phrase “REPL” oversimplifies what Racket is doing, but that’s a separate conversation.)
So how does Racket accommodate this in the face of completely arbitrary user-defined languages, some of which may not even support interactive evaluation in a traditional sense? Racket mostly solves this problem by having a related but distinct set of protocols for managing runtime interactions that operates alongside #lang.
Racket’s evaluation model divides pretty much ev
A delayed computation is a function that will do some computation when called. Because of the delayed nature the computation may be asynchronous so the function takes a callback.
Note: the term "delayed computation" is just a temporary name so we can talk about this idea, I do not recommend using this name as it's a) confusing and b) this concept is hard to name.
| function attributes(xs, name) { | |
| return is_element(xs)? xs.getAttribute(name) | |
| : is_array(xs)? xs.map(attributes) | |
| : is_sequence(xs)? map(xs, attributes) | |
| : /* otherwise */ raise(TypeError('Not supported.')) | |
| } | |
| // vs | |
| function attributes(xs, name) { |
| # will suspend on every function call (very proof of concept, | |
| # it will be really easy to get access to local variables, a | |
| # full stack, and even live evaluate within any closure) | |
| % node test.out.js | |
| [suspended] foo() | |
| > c | |
| [suspended] bar(x) | |
| > c | |
| [suspended] bar(i - 1) |
| [ | |
| a: 1 | |
| , b: 2 | |
| , c: 3 | |
| ] | |
| [ a: 1 | |
| , b: 2 | |
| , c: 3 | |
| ] |
