Instance | Branch |
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When hosting our web applications, we often have one public IP
address (i.e., an IP address visible to the outside world)
using which we want to host multiple web apps. For example, one
may wants to host three different web apps respectively for
example1.com
, example2.com
, and example1.com/images
on
the same machine using a single IP address.
How can we do that? Well, the good news is Internet browsers
/* | |
##Device = Desktops | |
##Screen = 1281px to higher resolution desktops | |
*/ | |
@media (min-width: 1281px) { | |
/* CSS */ | |
No need for homebrew or anything like that. Works with https://www.git-tower.com and the command line.
- Install https://gpgtools.org -- I'd suggest to do a customized install and deselect GPGMail.
- Create or import a key -- see below for https://keybase.io
- Run
gpg --list-secret-keys
and look forsec
, use the key ID for the next step - Configure
git
to use GPG -- replace the key with the one fromgpg --list-secret-keys
"use strict"; | |
/** | |
* Hypertext Transfer Protocol (HTTP) response status codes. | |
* @see {@link https://en.wikipedia.org/wiki/List_of_HTTP_status_codes} | |
*/ | |
enum HttpStatusCode { | |
/** | |
* The server has received the request headers and the client should proceed to send the request body |
This document details some tips and tricks for creating redux containers. Specifically, this document is looking at the mapDispatchToProps
argument of the connect
function from [react-redux][react-redux]. There are many ways to write the same thing in redux. This gist covers the various forms that mapDispatchToProps
can take.
Combining TensorFlow for Poets and TensorFlow.js.
Retrain a MobileNet V1 or V2 model on your own dataset using the CPU only.
I'm using a MacBook Pro without Nvidia GPU.
MobileNets can be used for image classification. This guide shows the steps I took to retrain a MobileNet on a custom dataset, and how to convert and use the retrained model in the browser using TensorFlow.js. The total time to set up, retrain the model and use it in the browser can take less than 30 minutes (depending on the size of your dataset).
Example app - HTML/JS and a retrained MobileNet V1/V2 model.