This is the reference point. All the other options are based off this.
|-- app
| |-- controllers
| | |-- admin
The standard way of understanding the HTTP protocol is via the request reply pattern. Each HTTP transaction consists of a finitely bounded HTTP request and a finitely bounded HTTP response.
However it's also possible for both parts of an HTTP 1.1 transaction to stream their possibly infinitely bounded data. The advantages is that the sender can send data that is beyond the sender's memory limit, and the receiver can act on
# 0 is too far from ` ;) | |
set -g base-index 1 | |
# Automatically set window title | |
set-window-option -g automatic-rename on | |
set-option -g set-titles on | |
#set -g default-terminal screen-256color | |
set -g status-keys vi | |
set -g history-limit 10000 |
Ansible playbook to setup HTTPS using Let's encrypt on nginx. | |
The Ansible playbook installs everything needed to serve static files from a nginx server over HTTPS. | |
The server pass A rating on [SSL Labs](https://www.ssllabs.com/). | |
To use: | |
1. Install [Ansible](https://www.ansible.com/) | |
2. Setup an Ubuntu 16.04 server accessible over ssh | |
3. Create `/etc/ansible/hosts` according to template below and change example.com to your domain | |
4. Copy the rest of the files to an empty directory (`playbook.yml` in the root of that folder and the rest in the `templates` subfolder) |
const tf = require('@tensorflow/tfjs-node'); | |
const Jimp = require('jimp'); | |
// Directory path for model files (model.json, metadata.json, weights.bin) | |
// NOTE: It can be obtained from [Export Model] -> [Tensorflow.js] -> [Download my model] | |
// on https://teachablemachine.withgoogle.com/train/image | |
const MODEL_DIR_PATH = `${__dirname}`; | |
// Path for image file to predict class | |
const IMAGE_FILE_PATH = `${__dirname}/example.jpg`; |