This script enables you to launch your Rails application in production environment (port:80) with Nginx and Unicorn.
Please make sure that your Gemfile in your rails application includes unicorn.
| #!/usr/bin/ruby | |
| # | |
| # This script fixes /usr/local only. | |
| # | |
| # 6th January 2010: | |
| # Modified the script to just fix, rather than install. - rpavlik | |
| # | |
| # 30th March 2010: | |
| # Added a check to make sure user is in the staff group. This was a problem | |
| # for me, and I think it was due to me migrating my account over several |
| #!/bin/bash | |
| # https://gist.github.com/949831 | |
| # http://blog.carbonfive.com/2011/05/04/automated-ad-hoc-builds-using-xcode-4/ | |
| # command line OTA distribution references and examples | |
| # http://nachbaur.com/blog/how-to-automate-your-iphone-app-builds-with-hudson | |
| # http://nachbaur.com/blog/building-ios-apps-for-over-the-air-adhoc-distribution | |
| # http://blog.octo.com/en/automating-over-the-air-deployment-for-iphone/ | |
| # http://www.neat.io/posts/2010/10/27/automated-ota-ios-app-distribution.html |
| //This is the Backbone controller that manages the content of the app | |
| var Content = Backbone.View.extend({ | |
| initialize:function(options){ | |
| Backbone.history.on('route',function(source, path){ | |
| this.render(path); | |
| }, this); | |
| }, | |
| //This object defines the content for each of the routes in the application | |
| content:{ | |
| "":_.template(document.getElementById("root").innerHTML), |
Sometimes you want to have a subdirectory on the master branch be the root directory of a repository’s gh-pages branch. This is useful for things like sites developed with Yeoman, or if you have a Jekyll site contained in the master branch alongside the rest of your code.
For the sake of this example, let’s pretend the subfolder containing your site is named dist.
Remove the dist directory from the project’s .gitignore file (it’s ignored by default by Yeoman).
BEFORE YOU CONTINUE:
mrt is no longer used with Meteor 1.0These days some people were discussing at meteor-talk group about running Meteor at Windows and I’ve recommended them using Vagrant. It’s a very developer-friendly piece of software that creates a virtual machine (VM) which let you run any operating system wanted and connect to it without big efforts of configuration (just make the initial installation and you have it working).
Many packages (I've tested) for running Meteor+Vagrant fails because Meteor writes its mongodb file and also other files inside local build folder into a shared folder between the Windows host and the Linux guest, and it simply does not work. So I've put my brain to work and found a solution: do symlinks inside the VM (but do not use ln. Use mount so git can follow it). It’s covered on
| { | |
| name: "rechazo", | |
| label: "Rechazos", | |
| editable: false, | |
| cell: Backgrid.Cell.extend({ | |
| render: function () { | |
| if(this.model.attributes['rechazos'] > 0) { | |
| this.$el.html('<a class="botonrechazo-centro-periodo" data-id="' + this.model.attributes['id'] + '" role=button href="#modalAccionesCPRechazos" data-toggle="modal"><button class="btn">Historial de Rechazos ('+ this.model.attributes['rechazos'] +')</button></a>'); | |
| } else { | |
| this.$el.html('<button class="btn" disabled>Historial de Rechazos ('+this.model.attributes['rechazos']+')</button>'); |
| 'use strict'; | |
| module.exports = function(grunt) { | |
| // load all grunt tasks | |
| require('matchdep').filterDev('grunt-*').forEach(grunt.loadNpmTasks); | |
| // configurable paths | |
| var paths = { |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.