Luca Simone lukefx

Create new Automator application with Run Shell script action (exact steps below).
1. Open Automator.app (e.g. from Spotlight)
2. Select File → New in menu
3. Select "Application", click "Choose"
4. With "Actions" toggle selected, type "Run Shell Script" in filter box, double click on filtered item
Put contents from lock_spotify_version.sh into script's body (exact steps below).
In opened right-hand item select /bin/sh as "Shell" type and copy contents of Gist's "lock_spotify_version.sh" below/in text area

Docker compose as a systemd unit

Create file /etc/systemd/system/docker-compose@.service. SystemD calling binaries using an absolute path. In my case is prefixed by /usr/local/bin, you should use paths specific for your environment.

[Unit]
Description=%i service with docker compose
PartOf=docker.service
After=docker.service

	package io.license.core.validation

	import javax.validation.Constraint
	import javax.validation.ConstraintValidator
	import javax.validation.ConstraintValidatorContext
	import javax.validation.Payload
	import kotlin.reflect.KClass


	@Target(AnnotationTarget.FIELD)

	package com.aj.samplemessaging.receiver;

	import org.springframework.amqp.AmqpRejectAndDontRequeueException;
	import org.springframework.amqp.rabbit.annotation.RabbitListener;
	import org.springframework.messaging.handler.annotation.Header;
	import org.springframework.messaging.handler.annotation.Payload;
	import org.springframework.stereotype.Component;

	import java.util.List;
	import java.util.Map;

	#!/bin/sh

	# check for where the latest version of IDEA is installed
	IDEA=`ls -1d /Applications/IntelliJ\ * \| tail -n1`
	wd=`pwd`

	# were we given a directory?
	if [ -d "$1" ]; then
	# echo "checking for things in the working dir given"
	wd=`ls -1d "$1" \| head -n1`

	var express = require('express');
	var bodyParser = require('body-parser')
	var crypto = require('crypto');

	// Calculate the X-Hub-Signature header value.
	function getSignature(buf) {
	var hmac = crypto.createHmac("sha1", process.env.FB_APP_SECRET);
	hmac.update(buf, "utf-8");
	return "sha1=" + hmac.digest("hex");
	}

	"""Sequence-to-sequence model with an attention mechanism."""
	# see https://www.tensorflow.org/versions/r0.10/tutorials/seq2seq/index.html
	# compare https://github.com/tflearn/tflearn/blob/master/examples/nlp/seq2seq_example.py
	from __future__ import print_function
	import numpy as np
	import tensorflow as tf

	vocab_size=256 # We are lazy, so we avoid fency mapping and just use one class per character/byte
	target_vocab_size=vocab_size
	learning_rate=0.1

	package com.example;

	import org.springframework.beans.factory.annotation.Autowired;
	import org.springframework.beans.factory.annotation.Qualifier;
	import org.springframework.boot.SpringApplication;
	import org.springframework.boot.autoconfigure.SpringBootApplication;
	import org.springframework.web.bind.annotation.RequestMapping;
	import org.springframework.web.bind.annotation.RestController;

	import java.util.List;

	alias c='docker-compose'
	alias cb='docker-compose build'
	alias cup='docker-compose up'
	alias cr='docker-compose run --service-ports --rm'
	alias crl='docker-compose run --service-ports --rm local'
	alias crd='docker-compose run --service-ports --rm develop'
	alias crt='docker-compose run --rm test'
	alias crp='docker-compose run --rm provision'
	alias crci='docker-compose run --rm ci'
	alias crwt='docker-compose run --rm watchtest'