Daniel Chen sfyumi

Why COW was deemed ungood for std::string.

COW, short for copy on write, is a way to implement mutable strings so that creating strings and logically copying strings, is reduced to almost nothing; conceptually they become free operations like no-ops.

Basic idea: to share a data buffer among string instances, and only make a copy for a specific instance (the copy on write) when that instance's data is modified. The general cost of this is only an extra indirection for accessing the value of a string, so a COW implementation is highly desirable. And so the original C++ standard, C++98, and its correction C++03, had special support for COW implementations, and e.g. the g++ compiler's std::string implementations used COW.

So why was that support dropped in C++11?

In particular, would the same reason or reasons apply to a reference counted immutable string value class?

Pimping Up Your PowerShell & Cmder with Posh-Git, Oh-My-Posh, & Powerline Fonts

Backstory (TLDR)

I work as a full-stack developer at work. We are a Windows & Azure shop, so we are using Windows as our development platform, hence this customization.

For my console needs, I am using Cmder which is based on ConEmu with PowerShell as my shell of choice.

Yes, yes, I know nowadays you can use the Linux subsystem on Windows 10 which allow you to run Ubuntu on Windows. If you are looking for customization of the Ubuntu bash shell, check out this article by Scott Hanselman.

Switching between AHCI and RAID on the Dell XPS 15 (9560)

This guide likely applies to other models and, potentially, even laptops from other OEMs that have NVME drives. However, I've only tested this on my Dell XPS 15 (9560) with the OEM Windows installation from the Signature Edition model.

Switching from RAID to AHCI

Switching from RAID to AHCI is significantly simpler than switching from AHCI to RAID. All that's needed is a successful boot to Safe Mode.

To set the default boot mode to Safe Mode, use msconfig.exe or open an admin cmd/PowerShell window and run:

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

	INC = -I${TF_INSTALL_PATH}/include
	LIB = -L${TF_INSTALL_PATH}/xla_aot_runtime_src
	LIBS = -lpthread -ltf_xla_runtime
	CXXFLAGS = -D_GLIBCXX_USE_CXX11_ABI=0

	.phony: all clean

	all: test_model

	test_model: test_model.cc my_model.o

	require 'webrick'
	require 'fileutils'

	if ARGV.length != 0
	root = ARGV.first.gsub('\\', '/')
	else
	root = '.'
	end
	BACKUP_DIR = 'bak'

	const MY_DOMAIN = "agodrich.com"
	const START_PAGE = "https://www.notion.so/gatsby-starter-notion-2c5e3d685aa341088d4cd8daca52fcc2"
	const DISQUS_SHORTNAME = "agodrich"

	addEventListener('fetch', event => {
	event.respondWith(fetchAndApply(event.request))
	})

	const corsHeaders = {
	"Access-Control-Allow-Origin": "*",

	swagger: '2.0'
	info:
	title: Jupyter Notebook API
	description: Notebook API
	version: "4"
	contact:
	name: Jupyter Project
	url: jupyter.org
	# will be prefixed to all paths
	basePath: /api