jmingtan

{:aliases
 {:dev {:extra-deps {com.bhauman/rebel-readline {:mvn/version "0.1.2"}
                     org.clojure/tools.nrepl {:mvn/version "0.2.12"}
                     cider/cider-nrepl {:mvn/version "0.17.0-SNAPSHOT"}}
        :main-opts ["-m" "repl"]}}}

roman01la

(ns figma.specs
  (:require [clojure.spec.alpha :as s]))

(def color-chan
  (s/and number? #(<= 0 % 1)))

(def alpha-chan
  (s/and number? #(<= 0 % 1)))

;;=============================================

Jannis

;; Run using a local build of ClojureScript master, e.g.:
;; clj -Sdeps '{:deps {org.clojure/clojurescript {:mvn/version "1.10.155"}}}' -i build.clj

(require '[cljs.build.api :as b])

(b/watch "src"
  {:output-dir "out"
   :output-to "out/main.js"
   :optimizations :none
   :verbose true

jsiebern

module Store = {
  type action =
    | Increase
    | Decrease
    | SetUsername(string);
  type state = {
    count: int,
    username: string,
  };
  type storeBag = {

Geoyi

Overview

When you're working on multiple coding projects, you might want a couple different version of Python and/or modules installed. That way you can keep each project in its own sandbox instead of trying to juggle multiple projects (each with different dependencies) on your system's version of Python. This intermediate guide covers one way to handle multiple Python versions and Python environments on your own (i.e., without a package manager like conda). See the Using the workflow section to view the end result.

Use cases

1. Working on 2+ projects that each have their own dependencies; e.g., a Python 2.7 project and a Python 3.6 project, or developing a module that needs to work across multiple versions of Python. It's not reasonable to uninstall/reinstall modules every time you want to switch environments.
2. If you want to execute code on the cloud, you can set up a Python environment that mirrors the relevant

simonw

Running in Datasette at https://vice-police-shootings.now.sh/vice-bc7c892/ViceNews_FullOISData

Here's what I did:

First, I exported as CSV the data from https://docs.google.com/spreadsheets/d/1CaOQ7FUYsGFCHEqGzA2hlfj69sx3GE9GoJ40OcqI9KY/edit#gid=1271324584

Then I converted it to a SQLite database using https://github.com/simonw/csvs-to-sqlite like so:

csvs-to-sqlite ~/Downloads/ViceNews_FullOISData.csv /tmp/vice.db \

-c Fatal -c SubjectArmed -c SubjectRace -c SubjectGender -c OfficerRace \

levand

Since it has come up a few times, I thought I’d write up some of the basic ideas around domain modeling in Clojure, and how they relate to keyword names and Specs. Firmly grasping these concepts will help us all write code that is simpler, cleaner, and easier to understand.

Clojure is a data-oriented language: we’re all familiar with maps, vectors, sets, keywords, etc. However, while data is good, not all data is equally good. It’s still possible to write “bad” data in Clojure.

“Good” data is well defined and easy to read; there is never any ambiguity about what a given data structure represents. Messy data has inconsistent structure, and overloaded keys that can mean different things in different contexts. Good data represents domain entities and a logical model; bad data represents whatever was convenient for the programmer at a given moment. Good data stands on its own, and can be reasoned about without any other knowledge of the codebase; bad data is deeply and tightly coupled to specific generating and

willwm

### Base Windows Configuration ###

# Enable Windows Features...
Enable-WindowsOptionalFeature -FeatureName Microsoft-Hyper-V-All -Online -NoRestart
Enable-WindowsOptionalFeature -FeatureName Containers -Online -NoRestart
Enable-WindowsOptionalFeature -FeatureName Microsoft-Windows-Subsystem-Linux -Online -NoRestart

### Chocolatey Installs ###

# Install Chocolatey: https://chocolatey.org/install

pesterhazy

Why

Ripgrep is a fast search tool like grep. It's mostly a drop-in replacement for ag, also know as the Silver Searcher.

helm-ag is a fantastic package for Emacs that allows you to display search results in a buffer. You can also jump to locations of matches. Despite the name, helm-ag works with ripgrep (rg) as well as with ag.

How

enjalot

Quick, Draw! ndjson data

The Quick, Draw! dataset uses ndjson as one of the formats to store its millions of drawings.

We can use the ndjons-cli utility to quickly create interesting subsets of this dataset.

The drawings (stroke data and associated metadata) are stored as one JSON object per line. e.g.:

{