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PharrellWANG / setup.md
Created October 5, 2025 15:55 — forked from jdesive/setup.md
Mount NFS share from synology NAS to Ubuntu

Mount Synology NFS share to Ubuntu 16.04

I have all hardware virtualized in ESXi 6.5...
Synology DSM 5.2-5644
Ubuntu 16.04

Start

On your fresh install on Ubuntu 16.04:

  1. Click the Connections icon on the top bar
  2. Goto Edit Connections
@PharrellWANG
PharrellWANG / how-to-install-latest-gcc-on-ubuntu-lts.txt
Created July 13, 2021 12:41 — forked from application2000/how-to-install-latest-gcc-on-ubuntu-lts.txt
How to install latest gcc on Ubuntu LTS (12.04, 14.04, 16.04)
These commands are based on a askubuntu answer http://askubuntu.com/a/581497
To install gcc-6 (gcc-6.1.1), I had to do more stuff as shown below.
USE THOSE COMMANDS AT YOUR OWN RISK. I SHALL NOT BE RESPONSIBLE FOR ANYTHING.
ABSOLUTELY NO WARRANTY.
If you are still reading let's carry on with the code.
sudo apt-get update && \
sudo apt-get install build-essential software-properties-common -y && \
sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y && \
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PharrellWANG / How to Access Data in the Backbuffer in Direct3D 9.cpp
Created February 27, 2021 04:07 — forked from karlgluck/How to Access Data in the Backbuffer in Direct3D 9.cpp
This is the code for accessing pixel data from the backbuffer in a D3D9 application. Keywords: LPDIRECT3DSURFACE9 read backbuffer copy back buffer directly access back buffer Direct3D device DirectX 9
void demoExtractBackBufferPixels(LPDIRECT3DDEVICE9 d3d_device) {
// TODO: In your app, add FAILED() macros to check the HRESULTs passed back
// by each of the API calls. I leave these out for clarity.
// Grab the backbuffer from the Direct3D device
LPDIRECT3DSURFACE9 back_buffer = NULL;
d3d_device->GetBackBuffer(0, 0, D3DBACKBUFFER_TYPE_MONO, &back_buffer);
// Get the buffer's description and make an offscreen surface in system memory.
@PharrellWANG
PharrellWANG / Demo.c
Created January 6, 2021 08:15 — forked from mikeando/Demo.c
Example of using C++ from C.
#include "HMyClass.h"
#include <stdio.h>
void my_eh( const char * error_message, void * unused)
{
printf("my_eh: %s\n", error_message);
}
int main()
{
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PharrellWANG / Install Zsh and Oh-my-zsh on CentOS 7.md
Created October 24, 2020 02:46 — forked from OndrejValenta/Install Zsh and Oh-my-zsh on CentOS 7.md

Install Zsh and Oh-my-zsh on CentOS 7

Based on this article

ALL INSTALLATIONS ASSUME YES WHEN PROMPTED, that's what -y does

This script can be copy paste to ssh as is. No hands installation. :-)

yum install zsh -y
@PharrellWANG
PharrellWANG / BytesStreamFormat-AnnexB-H264.svg
Created September 3, 2020 07:09
cdn-svg-264-bitstream-structure
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@PharrellWANG
PharrellWANG / keybase.md
Created December 19, 2019 02:36

Keybase proof

I hereby claim:

  • I am pharrellwang on github.
  • I am aliwang (https://keybase.io/aliwang) on keybase.
  • I have a public key ASB5ajalS_G-aneb_wQ9P-JKYyc7wIgtkMOfyRa8I_zntgo

To claim this, I am signing this object:

@PharrellWANG
PharrellWANG / youtube_format_code_itag_list.md
Created January 6, 2019 08:07 — forked from sidneys/youtube_format_code_itag_list.md
YouTube video stream format codes itags

YouTube video stream format codes

Comprehensive list of YouTube format code itags

itag Code Container Content Resolution Bitrate Range VR / 3D
5 flv audio/video 240p - - -
6 flv audio/video 270p - - -
17 3gp audio/video 144p - - -
18 mp4 audio/video 360p - - -
22 mp4 audio/video 720p - - -
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PharrellWANG / moving_mnist.py
Created December 16, 2018 14:52 — forked from tencia/moving_mnist.py
from PIL import Image
import sys
import os
import math
import numpy as np
###########################################################################################
# script to generate moving mnist video dataset (frame by frame) as described in
# [1] arXiv:1502.04681 - Unsupervised Learning of Video Representations Using LSTMs
# Srivastava et al
@PharrellWANG
PharrellWANG / tf.py
Created November 23, 2018 02:54 — forked from koaning/tf.py
tensorflow layer example
import tensorflow as tf
import numpy as np
import uuid
x = tf.placeholder(shape=[None, 3], dtype=tf.float32)
nn = tf.layers.dense(x, 3, activation=tf.nn.sigmoid)
nn = tf.layers.dense(nn, 5, activation=tf.nn.sigmoid)
encoded = tf.layers.dense(nn, 2, activation=tf.nn.sigmoid)
nn = tf.layers.dense(encoded, 5, activation=tf.nn.sigmoid)
nn = tf.layers.dense(nn, 3, activation=tf.nn.sigmoid)