wknapik

# This function greps everything between the last two prompts in the current tmux pane.
# Arguments are passed to `grep -i', so any valid `grep' options can be supplied.
# Requirements: coreutils, grep, sed, tmux, zsh.

just() {
    local -r max=10000 psone="$(print -P "$PS1"|sed "s,\x1B\[[0-9;]*[a-zA-Z],,g")"
    local inside=0;
    tmux capture-pane -pS-"$max" -E"$max"|tac|\
        while IFS= read -r line; do
            case "$inside,$line" in

pandasauce

# 75.0.3770.142
0x04b58615 7626 => 9090 0x04b58615
0x04b5861d 31c0488945 => 9090909090 0x04b5861d

Due to a beautiful bug in Chromium, it turns out you don't need this binary patch and can **enable** subpixel positioning by asking Chrome **to disable it**: https://bugs.chromium.org/p/chromium/issues/detail?id=824153#c39

Needless to say, I won't be maintaining this gist any longer.

Rich-Harris

I've been deceiving you all. I had you believe that Svelte was a UI framework — unlike React and Vue etc, because it shifts work out of the client and into the compiler, but a framework nonetheless.

But that's not exactly accurate. In my defense, I didn't realise it myself until very recently. But with Svelte 3 around the corner, it's time to come clean about what Svelte really is.

Svelte is a language.

Specifically, Svelte is an attempt to answer a question that many people have asked, and a few have answered: what would it look like if we had a language for describing reactive user interfaces?

A few projects that have answered this question:

djspiewak

Quick Tips for Fast Code on the JVM

I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.

This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea

ah45

(ns railway-oriented-programming
  "An adaptation of [Railway Oriented Programming](rop) using the
  [Cats](cats) library in Clojure.

  [rop]: http://fsharpforfunandprofit.com/posts/recipe-part2/
  [cats]: https://github.com/funcool/cats"
  (:require [cats.builtin]
            [cats.core :as cats]
            [cats.monad.either :as either]))

ghoseb

(ns clj-spec-playground
  (:require [clojure.string :as str]
            [clojure.spec :as s]
            [clojure.test.check.generators :as gen]))

;;; examples of clojure.spec being used like a gradual/dependently typed system.

(defn make-user
  "Create a map of inputs after splitting name."
  ([name email]

Deraen

• (:identity req) is auth backend independent way to access user data
• login and logout implementation depends on auth backend
• :current-user doesn't imply that authentication is required, route should also have :auth-rules if authentication is required

prakhar1989

Rich Hickey on becoming a better developer

Rich Hickey • 3 years ago

Sorry, I have to disagree with the entire premise here.

A wide variety of experiences might lead to well-roundedness, but not to greatness, nor even goodness. By constantly switching from one thing to another you are always reaching above your comfort zone, yes, but doing so by resetting your skill and knowledge level to zero.

Mastery comes from a combination of at least several of the following:

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

jwo

class Api::RegistrationsController < Api::BaseController
  
  respond_to :json
  def create

    user = User.new(params[:user])
    if user.save
      render :json=> user.as_json(:auth_token=>user.authentication_token, :email=>user.email), :status=>201
      return
    else