vue

A progressive, incrementally-adoptable JavaScript framework for building UI on the web. http://vuejs.org

Intro

Vue.js is a library for building interactive web interfaces. It provides data-reactive components with a simple and flexible API. Core features include:

• Declarative rendering with a plain JavaScript object based reactivity system.
• Component-oriented development style with tooling support
• Lean and extensible core
• Flexible transition effect system
• Fast without the need for complex optimization

Note that Vue.js only supports ES5-compliant browsers (IE8 and below are not supported). To check out live examples and docs, visit vuejs.org.

https://github.com/vuejs/vue

cloudboost

One Complete Cloud Platform for next web. Add Storage, Real time, Search, Notifications, Auth and more with one simple API.https://www.cloudboost.io

CloudBoost is the Complete NoSQL Database Service for your app. Think of CloudBoost as Parse + Firebase + Algolia + Iron.io all combined into one :

• Data-Storage / JSON Storage / BLOB Storage
• 100% data ownership
• Realtime
• Search
• Cache
• Queues
• More - ACL's, User Authentication, Server-less apps and more.

https://github.com/CloudBoost/cloudboost

pytorch

 or 

PyTorch is a python package that provides two high-level features:

• Tensor computation (like numpy) with strong GPU acceleration
• Deep Neural Networks built on a tape-based autograd system

You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed.

We are in an early-release Beta. Expect some adventures and rough edges.

• More About PyTorch
• Installation
  • Binaries
  • From source
• Getting Started
• Communication
• Releases and Contributing
• The Team

Python	Linux CPU	Linux GPU
2.7.8
2.7
3.5
Nightly

More about PyTorch

At a granular level, PyTorch is a library that consists of the following components:

_	_
torch	a Tensor library like NumPy, with strong GPU support
torch.autograd	a tape based automatic differentiation library that supports all differentiable Tensor operations in torch
torch.nn	a neural networks library deeply integrated with autograd designed for maximum flexibility
torch.optim	an optimization package to be used with torch.nn with standard optimization methods such as SGD, RMSProp, LBFGS, Adam etc.
torch.multiprocessing	python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and hogwild training.
torch.utils	DataLoader, Trainer and other utility functions for convenience
torch.legacy(.nn/.optim)	legacy code that has been ported over from torch for backward compatibility reasons

Usually one uses PyTorch either as:

• A replacement for numpy to use the power of GPUs.
• a deep learning research platform that provides maximum flexibility and speed

Elaborating further:

A GPU-ready Tensor library

If you use numpy, then you have used Tensors (a.k.a ndarray).

PyTorch provides Tensors that can live either on the CPU or the GPU, and accelerate compute by a huge amount.

We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, math operations, linear algebra, reductions. And they are fast!

Dynamic Neural Networks: Tape based Autograd

PyTorch has a unique way of building neural networks: using and replaying a tape recorder.

Most frameworks such as TensorFlow, Theano, Caffe and CNTK have a static view of the world. One has to build a neural network, and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch.

With PyTorch, we use a technique called Reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes from several research papers on this topic, as well as current and past work such as autograd, autograd, Chainer, etc.

While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date. You get the best of speed and flexibility for your crazy research.

Python first

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc. You can write your new neural network layers in Python itself, using your favorite libraries and use packages such as Cython and Numba. Our goal is to not reinvent the wheel where appropriate.

Imperative experiences

PyTorch is designed to be intuitive, linear in thought and easy to use. When you execute a line of code, it gets executed. There isn't an asynchronous view of the world. When you drop into a debugger, or receive error messages and stack traces, understanding them is straight-forward. The stack-trace points to exactly where your code was defined. We hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.

Fast and Lean

PyTorch has minimal framework overhead. We integrate acceleration libraries such as Intel MKL and NVIDIA (CuDNN, NCCL) to maximize speed. At the core, it's CPU and GPU Tensor and Neural Network backends (TH, THC, THNN, THCUNN) are written as independent libraries with a C99 API.
They are mature and have been tested for years.

Hence, PyTorch is quite fast -- whether you run small or large neural networks.

The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives. We've written custom memory allocators for the GPU to make sure that your deep learning models are maximally memory efficient. This enables you to train bigger deep learning models than before.

Extensions without pain

Writing new neural network modules, or interfacing with PyTorch's Tensor API was designed to be straight-forward and with minimal abstractions.

You can write new neural network layers in Python using the torch API or your favorite numpy based libraries such as SciPy.

If you want to write your layers in C/C++, we provide an extension API based on cffi that is efficient and with minimal boilerplate.
There is no wrapper code that needs to be written. You can see an example here.

Installation

Binaries

• Anaconda

conda install pytorch torchvision -c soumith

From source

Instructions for an Anaconda environment.

If you want to compile with CUDA support, install

• NVIDIA CUDA 7.5 or above
• NVIDIA CuDNN v5.x

Install optional dependencies

export CMAKE_PREFIX_PATH=[anaconda root directory]
conda install numpy mkl setuptools cmake gcc cffi
conda install -c soumith magma-cuda75 # or magma-cuda80 if CUDA 8.0

Install PyTorch

export MACOSX_DEPLOYMENT_TARGET=10.9 # if OSX
pip install -r requirements.txt
python setup.py install

Getting Started

Three pointers to get you started:

• Tutorials: notebooks to get you started with understanding and using PyTorch
• Examples: easy to understand pytorch code across all domains
• The API Reference: http://pytorch.org/docs/

bluepill

 or 

Bluepill is a reliable iOS testing tool that runs UI tests using multiple simulators on a single machine

DEMO

parrot

 or 

Features

• Built-in UI (web app) ready to deploy.
• REST API to easily extend or integrate Parrot into your pipeline.
• Export to various formats: keyvaluejson, po, strings, properties, xmlproperties, android, php, xlsx, yaml and csv.
• Easily rename project strings, Parrot takes care of keeping locales in sync.
• Manage your project's team, assign collaborators and their roles.
• Control API Client access for your projects.
• Easy install/deploy using Docker.

Try it out

The easiest way to get started is using docker and docker-compose. Simply clone this repo, navigate to the root of it and start the services:

$ git clone https://github.com/anthonynsimon/parrot.git
$ cd parrot
$ sudo /bin/bash scripts/buildweb.sh
$ sudo /bin/bash scripts/start.sh

Please note that to build the web app, npm is required.

This will build the web app and launch 3 containers: a Postgres database, the Parrot API server and Nginx as the reverse proxy and static file server.

Navigate to https://localhost/api/v1/ping and you should be able to see if the API is up (your browser will complain about an unknown certificate, see the HTTPS notice below for more info).

And to view the web app simply navigate to https://localhost, it should open the login page of the web app.

Important note on HTTPS

For convinience, self-signed SSL certificates are provided for the reverse-proxy (nginx). Do NOT use them for anything other than development, use your own certificates instead. We recommended automating the generation and renewal of the certificates via Let's Encrypt. The /etc/nginx/certs and /etc/nginx/vhost.d volumes on the nginx container has been made available for this purpose.

webslides

 or 

WebSlides = Good Karma

Finally, everything you need to make HTML presentations in a beautiful way. Just the essentials. You can create your own presentation instantly. Simply choose a demo and customize it in minutes — https://webslides.tv/demos

Why WebSlides?

Good karma and productivity. Just a basic knowledge of HTML and CSS is required. Designers, marketers, and journalists can now focus on the content.

Features

Version 0.1 (Jan 8, 2017):

• Navigation (horizontal and vertical sliding): touchpad, keyboard shorcuts, and swipe.
• Slide counter.
• Permalinks: go to a specific slide.
• Simple CSS alignments. Put content wherever you want (vertical centering...)
• 40+ components: background images/videos, quotes, cards, covers...
• Flexible blocks with auto-fill and equal height.
• Fonts: Roboto, Maitree (Serif), and San Francisco.
• Vertical rhythm (use multiples of 8).

Markup

• Code is clean and scalable. It uses intuitive markup with popular naming conventions. There's no need to overuse classes or nesting.
• Each parent <section> in the #webslides element is an individual slide.

<article id="webslides">
    <section>
        <h1>Slide 1</h1>
    </section>
    <section class="bg-black aligncenter">
    <!-- .wrap = container 1200px -->
        <div class="wrap">
            <h1>Slide 2</h1>
        </div>
    </section>
</article>

Vertical Sliding

<article id="webslides" class="vertical">

What's in the download?

The download includes demos and images (devices and logos). All content is for demo purposes only. Images are property of their respective owners.

webslides/
├── index.html
├── css/
│   ├── base.css
│   └── colors.css
│   └── svg-icons.css (optional)
├── js/
│   ├── webslides.js
│   └── svg-icons.js (optional)
└── demos/
└── images/

CSS Syntax (classes)

• Typography: .text-landing, .text-data, .text-intro...
• Background Colors: .bg-primary, .bg-apple, .bg-blue...
• Background Images: .background,.background-center-bottom...
• Cards: .card-50, .card-40...
• Flexible Blocks: .flexblock.clients, .flexblock.metrics...

Extensions

You can add:

• Unsplash photos
• animate.css
• particles.js
• pt

Awesome-APIs

 or 

A curated list of APIs from round the web.

This is an attempt to categorise different APIs scoured from the web which make their resources available for consumption.

Big List of Naughty Strings

The Big List of Naughty Strings is an evolving list of strings which have a high probability of causing issues when used as user-input data. This is intended for use in helping both automated and manual QA testing; useful for whenever your QA engineer walks into a bar.

Why Test Naughty Strings?

Even multi-billion dollar companies with huge amounts of automated testing can't find every bad input. For example, look at what happens when you try to Tweet a zero-width space (U+200B) on Twitter:

FORK IT

Robotgo

or

Golang Desktop Automation. Control the mouse, keyboard, bitmap,read the screen,Window Handle and global event listener.

RobotGo supports Mac, Windows, and Linux(X11).

Installation:

go get github.com/go-vgo/robotgo

draco

 or 

Draco is a library for compressing and decompressing 3D geometric meshes and point clouds. It is intended to improve the storage and transmission of 3D graphics.

Draco was designed and built for compression efficiency and speed. The code supports compressing points, connectivity information, texture coordinates, color information, normals, and any other generic attributes associated with geometry. With Draco, applications using 3D graphics can be significantly smaller without compromising visual fidelity. For users, this means apps can now be downloaded faster, 3D graphics in the browser can load quicker, and VR and AR scenes can now be transmitted with a fraction of the bandwidth and rendered quickly.

Draco is released as C++ source code that can be used to compress 3D graphics as well as C++ and Javascript decoders for the encoded data.

 or 

botpress

 or

Botpress is an open-source bot creation tool written in Javascript. It is powered by a rich set of open-source modules built by the community. We like to say that Botpress is like the Wordpress of Chatbots; anyone can create and reuse other people's modules.

Installation

Botpress requires node (version >= 4.2) and uses npm as package manager.

npm install -g botpress

Creating a bot

Creating a bot is simple, you need to run botpress init in a terminal inside an empty directory:

mkdir my-bot && cd my-bot
botpress init

Once your bot is created, you need to run botpress start to start your bot:

botpress start

This will provide you locally a web interface available at http://localhost:3000

Adding stuff to your bot

At this point, your bot does nothing, you need to add features. There are two ways to add features:

• Installing and configuring modules
• Coding

Installing and configuring modules

For example, there's a botpress-messenger module that will make your bot connect to Facebook Messenger and easily send/receive messages.

You can install modules directly in the web interface, or by using the botpress install command:

botpress install messenger

Once installed, modules expose two things:

• A graphical interface (available in the left panel). This makes configuration easy and convenient. You don't need to know about coding to use the graphical interface.
• Features via APIs. Each module has a detailed documentation on how to use their API.

There are not a lot of modules yet, we count on the community to develop many useful ones! Please get in touch with us if you would like to develop modules but you are not sure on how to get started.

Coding to add features

As the number of modules increase, we expect that the amount of code you'll need to write will lower everyday. Developers can add code directly in the bot (i.e. index.js) and access the core and modules features. For example, if you wish to respond to a GETTING_STARTED event on Facebook Messenger, you might code something along these lines:

bp.hear({ type: 'postback', text: 'GETTING_STARTED' }, (event, next) => {
  bp.messenger.sendText(event.user.id, 'Hello, human!')
})

 or