Rich-Harris

Stuff I wish I'd known sooner about service workers

I recently had several days of extremely frustrating experiences with service workers. Here are a few things I've since learned which would have made my life much easier but which isn't particularly obvious from most of the blog posts and videos I've seen.

I'll add to this list over time – suggested additions welcome in the comments or via twitter.com/rich_harris.

Use Canary for development instead of Chrome stable

Chrome 51 has some pretty wild behaviour related to console.log in service workers. Canary doesn't, and it has a load of really good service worker related stuff in devtools.

maxweber

(require '[datomic.api :as d]
         '[clojure.string :as str])

(defn normalize-query
  "Turns a vector formatted Datomic datalog query into a map formatted
   one."
  [query]
  (let [pairs (partition-by keyword? query)]
    (assert (even? (count pairs)))
    (into

brianreavis

#!/bin/sh
# Ensures libvips is installed and attempts to install it if not
# Currently supports:
# * Debian Linux
#   * Debian 7, 8
#   * Ubuntu 12.04, 14.04, 14.10, 15.04
#   * Mint 13, 17
# * Red Hat Linux
#   * RHEL/Centos/Scientific 6, 7
#   * Fedora 21, 22

henryw-erudine

(ns datascript-to-datomic-util
  (:require [datascript :as d]))

;;;; a utility to help with a datomic-datascript roundtrip process involving:
;;; 1. export some data from a datomic database and transact into a datascript instance.
;;; 2. perform one or more transactions against datascript.
;;; 3. transact the sum of all changes made against datascript back into datomic in a single tx

;;; this namespace contains two public functions:
;;; listen-for-changes: listen to datascript transactions and build up a record of changes

favila

-- Optimized MYSQL schema for datomic
-- Unfortunately the bin/sql/mysql-*.sql bootstrapping files for datomic are not
-- very good, and can actually cause failures if not adjusted.
-- One symptom of this is the following error:

-- SQL Error (1071): Specified key was too long; max key length is 767 bytes.

-- Reported here: https://support.cognitect.com/entries/28462168-MySQL-Caveats

-- This is caused by the default collation for the `id` column possibly being

aphyr

commit 8e51c34ca4d97e48750850d5ba09956f89783b4e
Author: Kyle Kingsbury <[email protected]>
Date:   Wed May 7 19:06:15 2014 -0700

    Improve Keyword.intern performance
    
    Keyword interning is an expensive factor in many Clojure
    serialization/deserialization paths, especially where the same set of
    keywords are created and freed repeatedly; e.g. iterating over records
    with similar structure. There are two principle costs to keyword

yangshun

function parseVideo (url) {
    // - Supported YouTube URL formats:
    //   - http://www.youtube.com/watch?v=My2FRPA3Gf8
    //   - http://youtu.be/My2FRPA3Gf8
    //   - https://youtube.googleapis.com/v/My2FRPA3Gf8
    // - Supported Vimeo URL formats:
    //   - http://vimeo.com/25451551
    //   - http://player.vimeo.com/video/25451551
    // - Also supports relative URLs:
    //   - //player.vimeo.com/video/25451551

azakordonets

#!/usr/bin/env python
 
import SimpleHTTPServer, BaseHTTPServer, SocketServer, socket, time, sys
 
class ThreadedHTTPServer(SocketServer.ThreadingMixIn,
                         BaseHTTPServer.HTTPServer) :
    """
    New features w/r to BaseHTTPServer.HTTPServer:
    - serves multiple requests simultaneously
    - catches socket.timeout and socket.error exceptions (raised from

Chouser

(ns n01se.externs-for-cljs
  (:require [clojure.java.io :as io]
            [cljs.compiler :as comp]
            [cljs.analyzer :as ana]))

(defn read-file [file]
  (let [eof (Object.)]
    (with-open [stream (clojure.lang.LineNumberingPushbackReader. (io/reader file))]
      (vec (take-while #(not= % eof)
                       (repeatedly #(read stream false eof)))))))

larsmans

#!/usr/bin/python
#
# K-means clustering using Lloyd's algorithm in pure Python.
# Written by Lars Buitinck. This code is in the public domain.
#
# The main program runs the clustering algorithm on a bunch of text documents
# specified as command-line arguments. These documents are first converted to
# sparse vectors, represented as lists of (index, value) pairs.

from collections import defaultdict