Project
Reverse Dependencies for shoulda-context
The projects listed here declare shoulda-context as a runtime or development dependency
0.0
An ActiveSupport::Cache store using redis_failover (a zookeeper based implementation of HA redis) for Rails apps.
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It provides functions as find, find_by_alias, get, exists?, validate, save etc.
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Arel-style queries for Riak Yokozuna
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Native RRD file reader
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A Ruby wrapper for the Montage REST API
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A singleton test runner for Minitest
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scorm2004-manifest is a Ruby gem that provides a manifest file parser for SCORM 2004 4th edition. It parses and validates the manifest file according to SCORM 2004 4th Edition Content Aggregation Model (CAM) Version 1.1. After parsing and validating, it builds an object tree that captures XML's hierarchical structure.
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["Gem for creating web tests based on Selenium"]
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Finer control over which attributes of a model to serialize
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Sinatra authentification with saving user data to redis
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Run a block of code only if a system level file lock can be obtained for a given filepath.
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SkiPlan API client gem
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Sklik advertising PPC api for creating campaigns and updating them when they runs
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SoftLayer API Client using SOAP protocoal, supporting all services and models
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This numeric solver is an example of the use of Flt.
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SpreadsheetAgent is a framework for creating distributed pipelines across many
different servers. It is extensible, and flexible. It does not specify what goals
any pipeline should be working towards, or which goals are prerequisites for
other goals, but it does provide logic for easily defining these relationships
based on your own needs. It does this by providing a subsumption architecture,
whereby many small, highly focused agents are written to perform specific goals,
and also know what prerequisites and resources they require to perform them.
In addition, it is designed from the beginning to support the creation of simple
human-computational workflows.
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Rails 開發環境常用工具 Forms/Process/Spreadsheet
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swfu gem for guess
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A client for dictionaryapi.com
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The English language is extremely complicated. We have words that can have multiple
parts of speech. Natural language processing is difficult because it is hard to
tell if a word is a noun when it could be a verb or an adjective, etc.
The purpose of this project is to develop an algorithm that, given a sentence string,
has a ranking system that detects the part of speech of each word.
Why is the useful? Because understanding the correct parts of speech in a sentence
is the first step to teaching a robot how to read.
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