swarn

Semantic Highlighting in Neovim

• What is Semantic Highlighting?
• Default Highlighting
  • Tokens to Highlights
  • Changing Highlights
  • Disabling Highlights
• Using LspTokenUpdate for Complex Highlighting
  • Highlighting Based on More Than One Modifier
• Dealing with Ambiguity

kripken

Emscripten as a linker for Zig and C

This shows how to build a nontrivial program using Zig+Emscripten or C+Emscripten. In both cases Emscripten is only used as a linker, that is the frontend is either zig or clang.

"Nontrivial" here means the program uses interesting Emscripten features:

• Asyncify
• Full GLES3 support
• GLFW3 support

citruz

Running Linux and Windows on M1 with QEMU

30.11.2020: Updated with the new patchseries and instructions for Windows

02.12.2020: Added tweaks

08.12.2020: Updated with patchseries v4

31.01.2020: Updated with patchseries v6

3v1n0

#!/bin/bash

CONFIG_PATH=~/.config/Code

for i in $CONFIG_PATH/User/workspaceStorage/*; do
	if [ -f $i/workspace.json ]; then
		folder="$(python3 -c "import sys, json; print(json.load(open(sys.argv[1], 'r'))['folder'])" $i/workspace.json 2>/dev/null | sed 's#^file://##;s/+/ /g;s/%\(..\)/\\x\1/g;')"
	
		if [ -n "$folder" ] && [ ! -d "$folder" ]; then
			echo "Removing workspace $(basename $i) for deleted folder $folder of size $(du -sh $i|cut -f1)"

mkocabas

import numpy as np
import torch

def compute_similarity_transform(S1, S2):
    '''
    Computes a similarity transform (sR, t) that takes
    a set of 3D points S1 (3 x N) closest to a set of 3D points S2,
    where R is an 3x3 rotation matrix, t 3x1 translation, s scale.
    i.e. solves the orthogonal Procrutes problem.
    '''

rxwei

Make Swift AD support all numpy functions via Autograd/JAX

In the Python module:

let autograd = Python.import("jax.grad")

extension PythonObject {
  @differentiable(wrt: args, vjp: _vjpDynamicallyCall)
  @discardableResult

chaiyujin

Updated 4/11/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

xeekworx

#include <SDL.h>
#include <glad\glad.h>
#include <nanovg.h>
#define NANOVG_GL3_IMPLEMENTATION
#include <nanovg_gl.h>
#include <nanovg_gl_utils.h> // For framebuffer utilities not shown in this code
#include <string>
#include <iostream>
#include <iomanip> // for setw

pachadotdev

# Option 1: Use apt-get

# keys taken from https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo
cd ~/GitHub/r-with-intel-mkl/
wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB

sudo sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list'
sudo apt-get update && sudo apt-get install intel-mkl-64bit

zhanwenchen

Updated 4/11/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc