akashpal-21

;; org-roam-node-read+.el

(require 'org-roam-node)

;; Add a new constructor function to the org-roam-node struct
;; This constructor function avoids using &keys, and only takes a
;; curated lists of slots to construe the node struct.
(cl-defstruct (org-roam-node (:constructor org-roam-node-create)
			     (:constructor +org-roam-node-create
					   (id &optional file-title file-mtime level point file title olp))

ld100

Migrating from Ubuntu on WSL to ArchLinux on WSL2

Obsolete notice

This document was created back in 2020 and might not be actual nowadays. It is not supported anymore, so use thise information at your own risk.

Upgrading to WSL 2

• Download WSL2 Kernel
• run wsl --set-default-version 2 in windows command line, so that all future WSL machine will use WSL2.

dive

(defun dive/org-mark-as-reviewed ()
  "Mark current selected items as REVIEWED."
  (org-set-property "REVIEWED" (org-time-stamp '(16) nil)))

(add-to-list 'org-agenda-bulk-custom-functions
             '(?R dive/org-mark-as-reviewed))

wolterlw

diff --git a/tensorflow/lite/kernels/internal/BUILD b/tensorflow/lite/kernels/internal/BUILD
index 4be3226938..6bc39a7194 100644
--- a/tensorflow/lite/kernels/internal/BUILD
+++ b/tensorflow/lite/kernels/internal/BUILD
@@ -37,6 +37,18 @@ NEON_FLAGS_IF_APPLICABLE = select({
     ],
 })
 
+cc_library(
+    name = "common",

stvhwrd

Building from Source

sudo apt install autoconf automake libtool curl make g++ unzip -y
git clone https://github.com/google/protobuf.git
cd protobuf
git submodule update --init --recursive
./autogen.sh
./configure
make

mortenege

1. This assumes an SSH keypair exists, usually in ~/.ssh (windows C:\Users\<username>\.ssh)

2. This assumes a git repository exists, otherwise create one as usual `git init`

3. Create a Repository on GitHub

4. Add SSH key to GitHub

5. Add the remote origin from gthub

jwhendy

import cv2
import subprocess

### for reference, the output of v4l2-ctl -d /dev/video1 -l (helpful for min/max/defaults)
#                     brightness (int)    : min=0 max=255 step=1 default=128 value=128
#                       contrast (int)    : min=0 max=255 step=1 default=128 value=128
#                     saturation (int)    : min=0 max=255 step=1 default=128 value=128
# white_balance_temperature_auto (bool)   : default=1 value=1
#                           gain (int)    : min=0 max=255 step=1 default=0 value=0
#           power_line_frequency (menu)   : min=0 max=2 default=2 value=2

dusty-nv

#!/bin/bash
#
# EDIT:  this script is outdated, please see https://forums.developer.nvidia.com/t/pytorch-for-jetson-nano-version-1-6-0-now-available
#
sudo apt-get install python-pip

# upgrade pip
pip install -U pip
pip --version
# pip 9.0.1 from /home/ubuntu/.local/lib/python2.7/site-packages (python 2.7)

dkarchmer

__author__ = 'dkarchmer'
'''
This script emulates a stand-alone Python based client. It relies on Boto3 to access AWS, but
requires your Django server to have an API for your user to access Cognito based credentials

Because of Cognito, the client (this script) will only get temporary AWS credentials associated
to your user and only your user, and based on whatever you configure your AIM Policy to be.

Most Cognito examples demonstrate how to use Cognito for Mobile Apps, so this scripts demonstrate
how to create a stand-alone Python script but operating similarly to these apps.

ExpandOcean

# coding:utf-8
from kivy.app import App
from kivy.uix.image import Image
from kivy.clock import Clock
from kivy.graphics.texture import Texture
import cv2


class KivyCamera(Image):
    def __init__(self, capture, fps, **kwargs):