Amazon Web Services

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Turning the Flywheel: A Monograph to Accompany Good to Great by Jim Collins

Amazon Web Services, fulfillment center, index fund, Jeff Bezos, Socratic dialogue, Vanguard fund

Hansen, Great by Choice: Uncertainty, Chaos and Luck—Why Some Thrive Despite Them All (New York, NY: HarperBusiness, 2011), 91–95. 21Amazon, Fiscal 2015 Annual Letter to Shareholders (Seattle, WA: Amazon, 2015); Amazon, Fiscal 2016 Annual Report (Seattle, WA: Amazon, 2016); Amazon, Fiscal 2017 Annual Report (Seattle, WA: Amazon, 2017); Alex Hern, “Amazon Web Services: the secret to the online retailer’s future success,” The Guardian, February 2, 2017, https://www.theguardian.com/technology/2017/feb/02/amazon-web-services-the-secret-to-the-online-retailers-future-success; Robert Hof, “Ten Years Later, Amazon Web Services Defies Skeptics,” Forbes, March 22, 2016, https://www.forbes.com/sites/roberthof/2016/03/22/ten-years-later-amazon-web-services-defies-skeptics/#244356466c44. 22Amazon, Fiscal 2017 Annual Report (Seattle, WA: Amazon, 2017); “Global Retail Industry Worth USD 28 Trillion by 2019 - Analysis, Technologies & Forecasts Report 2016–2019 - Research and Markets,” Business Wire, June 30, 2016, https://www.businesswire.com/news/home/20160630005551/en/Global-Retail-Industry-Worth-USD-28-Trillion. 23Jim Collins, How the Mighty Fall: And Why Some Companies Never Give In (Boulder, CO: Jim Collins, 2009), 29–36. 24Alan Wurtzel, Good to Great to Gone: The 60 Year Rise and Fall of Circuit City (New York, NY: Diversion Books, Kindle Edition, 2012), Chapter 8; Circuit City Stores, Inc., Fiscal 2002 Annual Report (Richmond, VA: Circuit City Stores, Inc., 2002); Michael Janofsky, “Circuit City Takes a Spin at Used Car Marketing,” The New York Times, October 25, 1993, http://www.nytimes.com/1993/10/25/business/circuit-city-takes-a-spin-at-used-car-marketing.html; Mike McKesson, “Circuit City at Wheel of New Deal for Used-Car Shoppers: Megastores,” Los Angeles Times, January 28, 1996, http://articles.latimes.com/1996–01-28/news/mn-29582_1_circuit-city. 25Alan Wurtzel, Good to Great to Gone: The 60 Year Rise and Fall of Circuit City (New York, NY: Diversion Books, Kindle Edition, 2012), Chapter 8; Circuit City Stores, Inc., Fiscal 2002 Annual Report (Richmond, VA: Circuit City Stores, Inc., 2002). 26Alan Wurtzel, Good to Great to Gone: The 60 Year Rise and Fall of Circuit City (New York, NY: Diversion Books, Kindle Edition, 2012), Loc 3542 of 5094. 27Alan Wurtzel, Good to Great to Gone: The 60 Year Rise and Fall of Circuit City (New York, NY: Diversion Books, Kindle Edition, 2012), Chapter 8 and Chapter 10; Jesse Romero, “The Rise and Fall of Circuit City,” Federal Reserve Bank of Richmond, 2013; Jim Collins, How the Mighty Fall: And Why Some Companies Never Give In (Boulder, CO: Jim Collins, 2009), 29–36.

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Most seeming “second flywheels” come about organically, as bullet-to-cannonball extensions of a primary flywheel. Amazon showed this precise pattern with its Amazon Web Services, which enables organizations big and small to efficiently buy computing power, store data, host websites, and avail themselves of other technology services. Amazon Web Services began as an internal system to provide backend technology support for Amazon’s own e-commerce efforts. In 2006, the company fired a bullet on offering these very same services to outside companies. The bullet hit its target and Amazon had enough calibration to fire a cannonball. A decade later, Amazon Web Services (while still contributing less than 10 percent of Amazon’s overall net sales) generated a substantial portion of Amazon’s operating income.21 Even though Amazon Web Services first appears to be a very different activity than the consumer-retail business, it has substantial similarities.

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A decade later, Amazon Web Services (while still contributing less than 10 percent of Amazon’s overall net sales) generated a substantial portion of Amazon’s operating income.21 Even though Amazon Web Services first appears to be a very different activity than the consumer-retail business, it has substantial similarities. As Bezos wrote in his 2015 annual letter to shareholders, “Superficially, the two could hardly be more different. One serves consumers and the other serves enterprises . . . Under the surface, the two are not so different after all.” Amazon Web Services aims to lower prices and expand offerings to an ever-growing cadre of customers, leading to increasing revenues per fixed costs, which then drives the flywheel around again.

Learning Ansible 2 - Second Edition by Fabio Alessandro Locati

Amazon Web Services, anti-pattern, business logic, cloud computing, continuous integration, Debian, DevOps, don't repeat yourself, Infrastructure as a Service, inventory management, Kickstarter, revision control, source of truth, web application

In the next sections we will provision the environment that we have used in the previous chapters (two web servers and one database server) in the following environments: • Simple Amazon Web Service deployment: Where all machines will be placed in the same Availability Zone and same network • Complex Amazon Web Service deployment: Where the machines will be split in multiple Availability Zones as well as networks • DigitalOcean: DigitalOcean does not allow us to do many networking tweaks so it will be similar to the first one • Docker: We will create a simple deployment in this case Amazon Web Service Amazon Web Service is the most used public cloud by a fair amount and it's often chosen due to their huge amount of available services as well as the huge amount of documentation, answered questions, and articles that can be expected from such a popular product.

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The Ansible inventory scripts can be written in any language but, for consistency reasons, dynamic inventory scripts should be written in Python. Remember that these scripts need to be executable directly, so please remember to set them with the executable flag (chmod + x inventory.py). In this chapter, we will take a look at Amazon Web Services and DigitalOcean scripts that can be downloaded from the official Ansible repository. Amazon Web Services To allow Ansible to gather data from Amazon Web Services (AWS) about your EC2 instances, you need to download the following two files from Ansible's GitHub repository at https://github.com/ ansible/ansible: • The ec2.py inventory script • The ec2.ini file, which contains the configuration for your EC2 inventory script Ansible uses AWS Python library boto to communicate with AWS using APIs.

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To simplify this, Amazon provides some widely used DBaaS, more specifically: • • • • • • Aurora MariaDB MySQL Oracle PostgreSQL SQL Server For each one of those engines, Amazon offers different features and price models but the specifics of each is beyond the goal of this book. Setting up an account with AWS The first thing we will need before starting to work on our Amazon Web Service is an account. Creating an account on Amazon Web Services is pretty straightforward and very well-documented by Amazon official documentation as well as by multiple independent sites and therefore it will not be covered in these pages. After you have created your AWS account, you need to go into the AWS and do the following: • Upload your SSH key in EC2 | Keypairs • Create a new user in Identity & Access Management | Users | Create new user and create a file in ~/.aws/credentials with the following lines: [default] aws_access_key_id = YOUR_ACCESS_KEY aws_secret_access_key = YOUR_SECRET_KEY After you have created your AWS Keys and uploaded your SSH key, you need to set up Route53.

Working Backwards: Insights, Stories, and Secrets From Inside Amazon by Colin Bryar, Bill Carr

Amazon Web Services, barriers to entry, Big Tech, Black Lives Matter, business logic, business process, cloud computing, coronavirus, COVID-19, data science, delayed gratification, en.wikipedia.org, fulfillment center, iterative process, Jeff Bezos, late fees, loose coupling, microservices, Minecraft, performance metric, search inside the book, shareholder value, Silicon Valley, six sigma, Steve Jobs, subscription business, Toyota Production System, two-pizza team, web application, why are manhole covers round?

They were Amazon software engineers. They found Amazon Web Services easier to use than some of our existing internal software tools they had been working with to build amazon.com. At this point there was little doubt that web services were going to become a new way of building things. We just didn’t know how big it could be or how quickly developers would adopt it. Within a year, we had a pretty good idea—over 25,000 developers enrolled in the program6 and were constantly surprising us with what they built. Though this program was called Amazon Web Services, it bears little resemblance to today’s AWS.

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Over the ensuing years, the wording has been tweaked to reflect lessons learned and scars earned, but Amazon has never wavered in its commitment to these four core principles. And they are in large part the reason that in 2015 Amazon became the company that reached $100 billion in annual sales faster than any other in the world. Remarkably, that same year Amazon Web Services (AWS) was reaching $10 billion in annual sales—at an even faster pace than the one Amazon had set.3 Of course, these four cultural touchstones don’t quite get at the “how,” that is, how people can work, individually and collectively, to ensure that they are maintained. And so Jeff and his leadership team crafted a set of 14 Leadership Principles, as well as a broad set of explicit, practical methodologies, that constantly reinforce its cultural goals.

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Those two years began in the summer of 2003 when Jeff asked me to become his technical advisor, a role that is known colloquially as “Jeff’s shadow,” and is similar to a chief of staff role at other companies. The position had been formalized about 18 months earlier when Andy Jassy, now CEO of Amazon Web Services, became Jeff’s first full-time technical advisor. It had two main responsibilities. One was to help Jeff be as effective as possible. The other was, as Jeff put it to me, to “model and learn from each other” so that the person who held the position could eventually move on to a bigger role in the company.

Ask Your Developer: How to Harness the Power of Software Developers and Win in the 21st Century by Jeff Lawson

Airbnb, AltaVista, Amazon Web Services, barriers to entry, big data - Walmart - Pop Tarts, Big Tech, big-box store, bitcoin, business process, call centre, Chuck Templeton: OpenTable:, cloud computing, coronavirus, COVID-19, create, read, update, delete, cryptocurrency, data science, David Heinemeier Hansson, deep learning, DevOps, Elon Musk, financial independence, global pandemic, global supply chain, Hacker News, Internet of things, Jeff Bezos, Kanban, Lean Startup, loose coupling, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, microservices, minimum viable product, Mitch Kapor, move fast and break things, Paul Graham, peer-to-peer, ride hailing / ride sharing, risk tolerance, Ruby on Rails, Salesforce, side project, Silicon Valley, Silicon Valley startup, Skype, social distancing, software as a service, software is eating the world, sorting algorithm, Startup school, Steve Ballmer, Steve Jobs, Telecommunications Act of 1996, Toyota Production System, transaction costs, transfer pricing, two-pizza team, Uber and Lyft, uber lyft, ubercab, web application, Y Combinator

But they’re not alone—many multibillion-dollar SaaS companies arose over the years since the turn of the millennium, representing tens of billions of dollars in revenue, and hundreds of billions of dollars of market cap. But as good as SaaS companies are, the fastest-growing software company in the history of the world didn’t look anything like Salesforce or Workday. Amazon Web Services Changes the Game I joined Amazon in 2004 in the early days of Amazon Web Services (AWS). Once I joined, my boss explained the mission. Amazon was going to build enormous data centers and rent compute-power and storage capacity not as applications, but as building blocks that developers and other companies can use to build their apps.

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As Jeff Immelt, the former CEO of GE and a member of our Twilio board of directors, once told his executive team at GE: “If we don’t become the best technology company in the world, we’re doomed. We’re dead. There’s no Plan B.” “It’s a quest for survival,” says Werner Vogels, the legendary chief technology officer at Amazon and one of the chief architects of Amazon Web Services, the world’s biggest cloud computing platform, with dozens of data centers girdling the globe. Vogels is a huge guy, six foot six and built like an NFL linebacker. He has a doctorate in computer science and spent more than a decade in academia before joining Amazon. These days a big part of his job involves traveling the world and helping traditional companies adapt and survive.

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It took a year to write the first version of the software, with Ali writing 20 percent of the code himself. In 2016 Bunq opened for business, and by the end of 2019 it was operating in thirty European countries. The entire customer experience was a mobile app—they didn’t even have a web version until 2019. Bunq operates entirely in the cloud, using Twilio, Amazon Web Services, and others. They also run incredibly lean, with fewer than two hundred employees. Yes, you read that right. This is the thing that should terrify incumbent banks—the scale and efficiency of software is unprecedented. The culture is so engineering-driven that Ali doesn’t even describe Bunq as a bank but rather “a tech company that happens to have a bank attached to it.”

One Click: Jeff Bezos and the Rise of Amazon.com by Richard L. Brandt

Amazon Web Services, automated trading system, big-box store, call centre, cloud computing, deal flow, drop ship, Dynabook, Elon Musk, Free Software Foundation, inventory management, Jeff Bezos, Kevin Kelly, Kickstarter, Larry Ellison, Marc Andreessen, new economy, Pershing Square Capital Management, science of happiness, search inside the book, Silicon Valley, Silicon Valley startup, skunkworks, software patent, Steve Jobs, Stewart Brand, Tony Hsieh, two-pizza team, Whole Earth Catalog, Y2K

Netflix can’t afford (at least not yet) to buy all the computing power needed to load up films instantly and stream them to thousands of customers at any moment. So it rents computers from Amazon’s vast store at pennies per minute to handle the tasks, tapping into just as much computer power as it needs at any given moment. It’s all part of a surprising business from the online retailing company, called Amazon Web Services, which is part of a larger trend known as cloud computing. Services like this bring in half a billion dollars annually in revenues to Amazon. Buying companies is a relatively easy way for a stock-rich company to expand its business. But sometimes a great executive will stumble upon an unexpected new idea, or one of his employees may come up with something.

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Despite some concerns from higher-ups about security, Frederick recalled, “The funny thing is that it did not take a great deal of convincing.” The executives soon realized that they could have a gold mine in their cubicles. By making its data and tools available to outside programmers, Amazon could actually outsource the development of new products—for free. Amazon Web Services was launched in July 2002. “We’re putting out a welcome mat for developers,” announced Bezos. “This is an important beginning and new direction for us.” Developers began creating new sites with original features that could send new buyers to Amazon and help them find and buy products. One former Amazon developer, for example, created a site that he dubbed “Amazon Light.”

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No, but it was a great feature that benefitted customers. Besides, if the customers did decide to buy from the outside site, the transaction actually went through Amazon, which collects a fee for the transaction. Site owners who sent customers to Amazon to buy Amazon products got about a 15 percent cut of any sale they sent to Amazon. Web services—the process of sharing services between Web sites—had been discussed by Internet pundits for years. Amazon was the first to make the concept a reality in a big way. About two years after the launch of Web services, Amazon boasted sixty-five thousand developers using the program and sending some ten million queries a day to Amazon’s servers.That’s a lot of new customers.

The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power by Michael A. Cusumano, Annabelle Gawer, David B. Yoffie

activist fund / activist shareholder / activist investor, Airbnb, AltaVista, Amazon Web Services, AOL-Time Warner, asset light, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, business logic, Cambridge Analytica, Chuck Templeton: OpenTable:, cloud computing, collective bargaining, commoditize, CRISPR, crowdsourcing, cryptocurrency, deep learning, Didi Chuxing, distributed ledger, Donald Trump, driverless car, en.wikipedia.org, fake news, Firefox, general purpose technology, gig economy, Google Chrome, GPS: selective availability, Greyball, independent contractor, Internet of things, Jeff Bezos, Jeff Hawkins, John Zimmer (Lyft cofounder), Kevin Roose, Lean Startup, Lyft, machine translation, Mark Zuckerberg, market fundamentalism, Metcalfe’s law, move fast and break things, multi-sided market, Network effects, pattern recognition, platform as a service, Ponzi scheme, recommendation engine, Richard Feynman, ride hailing / ride sharing, Robert Metcalfe, Salesforce, self-driving car, sharing economy, Silicon Valley, Skype, Snapchat, SoftBank, software as a service, sovereign wealth fund, speech recognition, stealth mode startup, Steve Ballmer, Steve Jobs, Steven Levy, subscription business, Susan Wojcicki, TaskRabbit, too big to fail, transaction costs, transport as a service, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, Vision Fund, web application, zero-sum game

The network effects come from the increasing number or utility of the complements: The more there are or the higher quality they are, the more attractive the platform becomes to users and complementors, as well as other potential market actors such as advertisers (and investors). Microsoft Windows, Google Android, Apple iOS, and Amazon Web Services are commonly used operating systems and cloud computing services that serve as innovation platforms for computer and smartphone ecosystems. The second type we call transaction platforms.22 These platforms are largely intermediaries or online marketplaces that make it possible for people and organizations to share information or to buy, sell, or access a variety of goods and services.

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Amazon is even more complicated because of how it integrated and mixed platform and non-platform businesses. How much of its market value derives from scale and scope economies associated with the online store and its enormous physical warehousing system as compared to Amazon’s “pure” platform businesses, Amazon Marketplace and Amazon Web Services (AWS)? In 2017, the online retail store accounted for about two-thirds of Amazon’s revenues and Amazon Marketplace about 17 percent. AWS accounted for less than 10 percent of revenues but 60 percent of operating profits, and more in prior years.25 Amazon did not always break out these numbers, which made it difficult to do historical analysis.

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There are also unique barriers to entry in platform markets that we rarely see in the conventional world. First, network effects create barriers in the form of an existing stock of complements around a particular platform. When a platform has millions of applications that run on—and only on—Android, iOS, Windows, Amazon Web Services, Facebook, or WeChat, they increase the barriers to switching to a new or competing platform. Second, and closely related, is that new entrants are often challenged by the difficulty of replicating a platform ecosystem. Successful platform companies have established virtual armies of complementors, such as the thousands of software developers who have already joined the Android, iPhone, Facebook, or WeChat developer networks, or the millions of people with rooms to rent or cars to drive who have already registered with Airbnb, Uber, Lyft, and Didi Chuxing.

The Everything Store: Jeff Bezos and the Age of Amazon by Brad Stone

airport security, Amazon Mechanical Turk, Amazon Web Services, AOL-Time Warner, Apollo 11, bank run, Bear Stearns, Bernie Madoff, big-box store, Black Swan, book scanning, Brewster Kahle, buy and hold, call centre, centre right, Chuck Templeton: OpenTable:, Clayton Christensen, cloud computing, collapse of Lehman Brothers, crowdsourcing, cuban missile crisis, Danny Hillis, deal flow, Douglas Hofstadter, drop ship, Elon Musk, facts on the ground, fulfillment center, game design, housing crisis, invention of movable type, inventory management, James Dyson, Jeff Bezos, John Markoff, junk bonds, Kevin Kelly, Kiva Systems, Kodak vs Instagram, Larry Ellison, late fees, loose coupling, low skilled workers, Maui Hawaii, Menlo Park, Neal Stephenson, Network effects, new economy, off-the-grid, optical character recognition, PalmPilot, pets.com, Ponzi scheme, proprietary trading, quantitative hedge fund, reality distortion field, recommendation engine, Renaissance Technologies, RFID, Rodney Brooks, search inside the book, shareholder value, Silicon Valley, Silicon Valley startup, six sigma, skunkworks, Skype, SoftBank, statistical arbitrage, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, the long tail, Thomas L Friedman, Tony Hsieh, two-pizza team, Virgin Galactic, Whole Earth Catalog, why are manhole covers round?, zero-sum game

The company held its first developer conference that spring and invited all the outsiders who were trying to hack Amazon’s systems. Now developers became another constituency at Amazon, joining customers and third-party sellers. And the new group, run by Colin Bryar and Rob Frederick, was given a formal name: Amazon Web Services. It was the trailhead of an extremely serendipitous path. Amazon Web Services, or AWS, is today in the business of selling basic computer infrastructure like storage, databases, and raw computing power. The service is woven into the fabric of daily life in Silicon Valley and the broader technology community. Startups like Pinterest and Instagram rent space and cycles on Amazon’s computers and run their operations over the Internet as if the high-powered servers were sitting in the backs of their own offices.

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Various divisions of the U.S. government, such as NASA and the Central Intelligence Agency, are high-profile AWS customers as well. Though Amazon keeps AWS’s financial performance and profitability a secret, analysts at Morgan Stanley estimate that in 2012, it brought in $2.2 billion in revenue. The rise of Amazon Web Services brings up a few obvious questions. How did an online retailer spawn such a completely unrelated business? How did the creature that was originally called Amazon Web Services—the group working on the commerce APIs—evolve into something so radically different, a seller of high-tech infrastructure? Early observers suggested that Amazon’s retail business was so seasonal—booming during the holiday months—that Bezos had decided to rent his spare computer capacity during the quieter periods.

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Chapter 7: A Technology Company, Not a Retailer 1 Gary Rivlin, “A Retail Revolution Turns Ten,” New York Times, July 27, 2012. 2 Gary Wolf, “The Great Library of Amazonia,” Wired, October 23, 2003. 3 Ibid. 4 Luke Timmerman, “Amazon’s Top Techie, Werner Vogels, on How Web Services Follows the Retail Playbook,” Xconomy, September 29, 2010. 5 Shobha Warrier, “From Studying under the Streetlights to CEO of a U.S. Firm!,” Rediff, September 1, 2010. 6 Tim O’Reilly, “Amazon Web Services API,” July 18, 2002, http://www.oreillynet.com/pub/wlg/1707. 7 Damien Cave, “Losing the War on Patents,” Salon, February 15, 2002. 8 O’Reilly, “Amazon Web Services API.” 9 Steve Grand, Creation: Life and How to Make It (Darby, PA: Diane Publishing, 2000), 132. 10 Hybrid machine/human computing arrangement patent filed October 12, 2001; http://www.google.com/patents/US7197459. 11 “Artificial Artificial Intelligence,” Economist, June 10, 2006. 12 Katharine Mieszkowski, “I Make $1.45 a Week and I Love It,” Salon, July 24, 2006. 13 Jason Pontin, “Artificial Intelligence, with Help from the Humans,” New York Times, March 25, 2007. 14 Jeff Bezos, interview by Charlie Rose, Charlie Rose, PBS, February 26, 2009.

Terraform: Up and Running: Writing Infrastructure as Code by Yevgeniy Brikman

Amazon Web Services, cloud computing, DevOps, en.wikipedia.org, full stack developer, functional programming, general-purpose programming language, microservices, Ruby on Rails

The good news is that thanks to the DevOps Rebel Alliance, there is now a better way to do things: Terra‐ form. Terraform is an open source tool that allows you to define your infrastructure as code using a simple, declarative programming language, and to deploy and manage that infrastructure across a variety of cloud providers (including Amazon Web Services, Azure, Google Cloud, DigitalOcean, and many others) using a few commands. For example, instead of manually clicking around a webpage or running dozens of com‐ mands, here is all the code it takes to configure a server: resource "aws_instance" "example" { ami = "ami-40d28157" instance_type = "t2.micro" } And to deploy it, you just run one command: > terraform apply Thanks to its simplicity and power, Terraform is rapidly emerging as a dominant player in the DevOps world.

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To become fluent in French, you’ll have to spend time talking with native French speakers, watching French TV shows, and listening to French music. To become fluent in Terraform, you’ll have to write real Terraform code, use it to manage real software, and deploy that software on real servers (don’t worry, all the examples use Amazon Web Service’s free tier, so it shouldn’t cost you anything). Therefore, be ready to read, write, and execute a lot of code. What you will find in this book Here’s an outline of what the book covers: Chapter 1, Why Terraform How DevOps is transforming the way we run software; an overview of infra‐ structure as code tools, including configuration management, orchestration, and server templating; the benefits of infrastructure as code; a comparison of Terra‐ form, Chef, Puppet, Ansible, Salt Stack, and CloudFormation.

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My goal is to get you up and running quickly by discussing why you may want to use Terraform in the first place, how to fit it into your workflow, and what practices and patterns tend to work best. x | Preface To demonstrate these patterns, I’ve included a number of code examples. Just about all of them are built on top of Amazon Web Services (AWS), although the basic pat‐ terns should be largely the same no matter what cloud provider you’re using. My goal was to make it as easy as possible for you to try the examples at home. That’s why the code examples all focus on one cloud provider (you only have to register with one service, AWS, which offers a free tier for the first year).

The New Kingmakers by Stephen O'Grady

AltaVista, Amazon Web Services, barriers to entry, cloud computing, correlation does not imply causation, crowdsourcing, David Heinemeier Hansson, DevOps, Hacker News, Jeff Bezos, Khan Academy, Kickstarter, Marc Andreessen, Mark Zuckerberg, Netflix Prize, Paul Graham, Ruby on Rails, Silicon Valley, Skype, software as a service, software is eating the world, Steve Ballmer, Steve Jobs, The future is already here, Tim Cook: Apple, Y Combinator

As creative as they could now be with their software infrastructure, to build anything of size, they would eventually have to procure hardware. This meant either purchasing it outright or renting it, typically for the minimum of a month, with the attendant set up, management, and maintenance fees on top. Enter Amazon Web Services (AWS). The idea was simple. Driven relentlessly by Moore’s Law, hardware doubled in speed every two years. Like Google and other Internet giants, Amazon discovered early that the most-economical model for scaling its technology was on cheap, commodity servers deployed by the hundreds or thousands.

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Fielding, one the authors of HTTP, the protocol that still powers the Internet today—you’ve probably typed http:// many times yourself—advocated for a simple style that both reflected and leveraged the way the Internet itself had been constructed. Unsurprisingly, this simpler approach proved popular. At Amazon, for example, developers were able to choose between the two different mechanisms to access data: SOAP or REST. Even in 2004, 80% of those leveraging Amazon Web Services did so via REST. Two years after that, Google deprecated their SOAP API for search. And they were just the beginning. It’s hard to feel too sorry for SOAP’s creators, however—particularly if what one Microsoft developer told Tim O’Reilly is true: “It was actually a Microsoft objective to make [SOAP] sufficiently complex that only the tools would read and write this stuff,” he explained, “and not humans.”

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Developers are attracted to its platform because of the size of the market…those developers create thousands of new applications…the new applications give consumers thousands of additional reasons to buy Apple devices rather than the competition…and those new Apple customers give even more developers reason to favor Apple. Apple not only profits from this virtuous cycle, it benefits from ever-increasing economies of scale, realizing lower component costs than competitors. None of which would be possible without the developers Apple has recruited and, generally, retained. Amazon Web Services The company that started the cloud computing craze was founded in 1994 as a bookstore. The quintessential Internet company, Amazon.com competed with the traditional brick-and-mortar model via an ever-expanding array of technical innovations: some brilliant, others mundane. The most-important of Amazon’s retail innovations co-opted its customers into contributors.

Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson

"World Economic Forum" Davos, 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, algorithmic bias, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, Andy Rubin, AOL-Time Warner, artificial general intelligence, asset light, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, backtesting, barriers to entry, behavioural economics, bitcoin, blockchain, blood diamond, British Empire, business cycle, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, CRISPR, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, Dean Kamen, deep learning, DeepMind, Demis Hassabis, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, Evgeny Morozov, fake news, family office, fiat currency, financial innovation, general purpose technology, Geoffrey Hinton, George Akerlof, global supply chain, Great Leap Forward, Gregor Mendel, Hernando de Soto, hive mind, independent contractor, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, Jim Simons, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, Kiva Systems, law of one price, longitudinal study, low interest rates, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Marc Benioff, Mark Zuckerberg, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Mustafa Suleyman, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Project Xanadu, radical decentralization, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Robert Solow, Ronald Coase, Salesforce, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, synthetic biology, tacit knowledge, TaskRabbit, Ted Nelson, TED Talk, the Cathedral and the Bazaar, The Market for Lemons, The Nature of the Firm, the strength of weak ties, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, Two Sigma, two-sided market, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, ubercab, Vitalik Buterin, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

Kramer, “The Biggest Thing Amazon Got Right: The Platform,” Gigaom, October 12, 2011, https://gigaom.com/2011/10/12/419-the-biggest-thing-amazon-got-right-the-platform. 142 Dalzell was described as a bulldog: Matt Rosoff, “Jeff Bezos ‘Makes Ordinary Control Freaks Look like Stoned Hippies,’ Says Former Engineer,” Business Insider, October 12, 2011, http://www.businessinsider.com/jeff-bezos-makes-ordinary-control-freaks-look-like-stoned-hippies-says-former-engineer-2011-10. 143 launched Amazon Web Services in 2006: Amazon Web Services, “About AWS,” accessed February 4, 2017, https://aws.amazon.com/about-aws. 143 Amazon S3: Amazon Web Services, “Amazon Simple Storage Service (Amazon S3)—Continuing Successes,” July 11, 2006, https://aws.amazon.com/about-aws/whats-new/2006/07/11/amazon-simple-storage-service-amazon-s3---continuing-successes. 143 Amazon EC2: Amazon Web Services, “Announcing Amazon Elastic Compute Cloud (Amazon EC2)—Beta,” August 24, 2006, https://aws.amazon.com/about-aws/whats-new/2006/08/24/announcing-amazon-elastic-compute-cloud-amazon-ec2---beta. 143 more than 290,000 developers using the platform: Amazon, “Ooyala Wins Amazon Web Services Start-up Challenge, Receives $100,000 in Cash and Services Credits Plus Investment Offer from Amazon.com,” December 7, 2007, http://phx.corporate-ir.net/phoenix.zhtml?

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Might these resources be valuable to people who wanted to build a database, application, website, or other digital resource but didn’t want to go through the trouble of maintaining all required hardware and software themselves? Amazon decided to find out and launched Amazon Web Services in 2006. It originally offered storage (Amazon S3) and computing (Amazon EC2) services on the platform. Within eighteen months, Amazon claimed to have more than 290,000 developers using the platform. Amazon Web Services added more tools and resources over time, maintained hardened interfaces, and kept growing dramatically. By April 2016, it was contributing 9% of Amazon’s total revenue and, remarkably, over half of the company’s total operating income.

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Neural networks become much more powerful and capable as their size increases, and it’s only recently that sufficiently large ones have become cheap enough that they are available to many researchers. Cloud computing has helped open up AI research to these smaller budgets. Technology entrepreneur Elliot Turner estimates that the computing power required to execute a cutting-edge machine learning project could be rented from a cloud computing provider like Amazon Web Services for approximately $13,000 by the fall of 2016. Oddly enough, the popularity of modern video games has also been a great boost to machine learning. The specialized graphics processing units (GPUs) that drive popular gaming consoles turn out to be extremely well suited to the kinds of calculations required for neural networks, so they’ve been drafted in large numbers for this task.

Platform Capitalism by Nick Srnicek

"World Economic Forum" Davos, 3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Big Tech, Californian Ideology, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, collaborative economy, collective bargaining, data science, deindustrialization, deskilling, Didi Chuxing, digital capitalism, digital divide, disintermediation, driverless car, Ford Model T, future of work, gig economy, independent contractor, Infrastructure as a Service, Internet of things, Jean Tirole, Jeff Bezos, knowledge economy, knowledge worker, liquidity trap, low interest rates, low skilled workers, Lyft, Mark Zuckerberg, means of production, mittelstand, multi-sided market, natural language processing, Network effects, new economy, Oculus Rift, offshore financial centre, pattern recognition, platform as a service, quantitative easing, RFID, ride hailing / ride sharing, Robert Gordon, Salesforce, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, software as a service, surveillance capitalism, TaskRabbit, the built environment, total factor productivity, two-sided market, Uber and Lyft, Uber for X, uber lyft, unconventional monetary instruments, unorthodox policies, vertical integration, warehouse robotics, Zipcar

Amazon, for example, is often seen as an e-commerce company, yet it rapidly broadened out into a logistics company. Today it is spreading into the on-demand market with a Home Services program in partnership with TaskRabbit, while the infamous Mechanical Turk (AMT) was in many ways a pioneer for the gig economy and, perhaps most importantly, is developing Amazon Web Services as a cloud-based service. Amazon therefore spans nearly all of the above categories. Advertising Platforms The elders of this new enterprise form, advertising platforms are the initial attempts at building a model adequate to the digital age. As we will see, they have directly and indirectly fostered the emergence of the most recent technological trends – from the sharing economy to the industrial internet.

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By all accounts, the Amazon Prime delivery service loses money on every order, and the Kindle e-book reader is sold at cost.34 On traditional metrics for lean businesses, this is unintelligible: unprofitable ventures should be cut off. Yet rapid and cheap delivery is one of the main ways in which Amazon entices users onto its platform in order to make revenues elsewhere. In the process of building a massive logistical network, Amazon Web Services (AWS) was developed as an internal platform, to handle the increasingly complex logistics of the company. Indeed, a common theme in the genesis of platforms is that they often emerge out of internal company needs. Amazon required ways to get new services up and running quickly, and the answer was to build up the basic infrastructure in a way that enabled new services to use it easily.35 It was quickly recognised that this could also be rented to other firms.

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An Intuit survey, on the other hand, reportedly found that 6% of the population in Britain is working in the sharing economy, but the actual data do not appear to be available. See Stokes, Clarence, Anderson, and Rinne, 2014: 25; Hesse, 2015. 76. Henwood, 2015. 77. Berg, 2016. 78. Knight, 2016. 79. See many more examples at Amazon Web Services, 2016. 80. Huet, 2016. 81. Ibid. 82. While government surveillance is often the focus of public attention today, corporate surveillance is just as pernicious a phenomenon. Pasquale, 2015. 83. ‘Reinventing the Deal’, 2015. 84. CB Insights, 2015. 85. Ibid. 86. CB Insights, 2016a. 87. National Venture Capital Association, 2016: 9; Crain, 2014: 374. 88.

Ansible for DevOps: Server and Configuration Management for Humans by Jeff Geerling

Abraham Maslow, AGPL, Amazon Web Services, cloud computing, continuous integration, database schema, Debian, defense in depth, DevOps, fault tolerance, Firefox, full text search, Google Chrome, inventory management, loose coupling, microservices, Minecraft, MITM: man-in-the-middle, punch-card reader, Ruby on Rails, web application

If you’re interested in the current status of this effort, or would like to help in migrating to the v2 API, visit the v2 API support issue on GitHub. Dynamic inventory with AWS Many of this book’s readers are familiar with Amazon Web Services (especially EC2, S3, ElastiCache, and Route53), and likely have managed or currently manage an infrastructure within Amazon’s cloud. Ansible has very strong support for managing AWS-based infrastructure, and includes a dynamic inventory script to help you run playbooks on your hosts in a variety of ways. There are a few excellent guides to using Ansible with AWS, for example: Ansible - Amazon Web Services Guide Ansible for AWS I won’t be covering dynamic inventory in this chapter, but will mention that the ec2.py dynamic inventory script, along with Ansible’s extensive support for AWS infrastructure through ec2_* modules, makes Ansible the best and most simple tool for managing a broad AWS infrastructure.

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Now that we’ve tested our infrastructure on DigitalOcean, we can destroy the droplets just as easily (change the state parameter in provisioners/digitalocean.yml to default to 'absent' and run $ ansible-playbook provision.yml again). Next up, we’ll build the infrastructure a third time—on Amazon’s infrastructure. Provisioner Configuration: Amazon Web Services (EC2) For Amazon Web Services, provisioning works slightly different. Amazon has a broader ecosystem of services surrounding EC2 instances, and for our particular example, we will need to configure security groups prior to provisioning instances. To begin, create aws.yml inside the provisioners directory and begin the playbook the same ways as with DigitalOcean: 1 --- 2 - hosts: localhost 3 connection: local 4 gather_facts: false EC2 instances use security groups as an AWS-level firewall (which operates outside the individual instance’s OS).

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Ansible Examples Other resources Chapter 1 - Getting Started with Ansible Ansible and Infrastructure Management On snowflakes and shell scripts Configuration management Installing Ansible Creating a basic inventory file Running your first Ad-Hoc Ansible command Summary Chapter 2 - Local Infrastructure Development: Ansible and Vagrant Prototyping and testing with local virtual machines Your first local server: Setting up Vagrant Using Ansible with Vagrant Your first Ansible playbook Summary Chapter 3 - Ad-Hoc Commands Conducting an orchestra Build infrastructure with Vagrant for testing Inventory file for multiple servers Your first ad-hoc commands Discover Ansible’s parallel nature Learning about your environment Make changes using Ansible modules Configure groups of servers, or individual servers Configure the Application servers Configure the Database servers Make changes to just one server Manage users and groups Manage files and directories Get information about a file Copy a file to the servers Retrieve a file from the servers Create directories and files Delete directories and files Run operations in the background Update servers asynchronously, monitoring progress Fire-and-forget tasks Check log files Manage cron jobs Deploy a version-controlled application Ansible’s SSH connection history Paramiko OpenSSH (default) Accelerated Mode Faster OpenSSH in Ansible 1.5+ Summary Chapter 4 - Ansible Playbooks Power plays Running Playbooks with ansible-playbook Limiting playbooks to particular hosts and groups Setting user and sudo options with ansible-playbook Other options for ansible-playbook Real-world playbook: CentOS Node.js app server Add extra repositories Deploy a Node.js app Launch a Node.js app Node.js app server summary Real-world playbook: Ubuntu LAMP server with Drupal Include a variables file, and discover pre_tasks and handlers Basic LAMP server setup Configure Apache Configure PHP with lineinfile Configure MySQL Install Composer and Drush Install Drupal with Git and Drush Drupal LAMP server summary Real-world playbook: Ubuntu Apache Tomcat server with Solr Include a variables file, and discover pre_tasks and handlers Install Apache Tomcat 7 Install Apache Solr Apache Solr server summary Summary Chapter 5 - Ansible Playbooks - Beyond the Basics Handlers Environment variables Per-play environment variables Variables Playbook Variables Inventory variables Registered Variables Accessing Variables Host and Group variables group_vars and host_vars Magic variables with host and group variables and information Facts (Variables derived from system information) Local Facts (Facts.d) Variable Precedence If/then/when - Conditionals Jinja2 Expressions, Python built-ins, and Logic register when changed_when and failed_when ignore_errors Delegation, Local Actions, and Pauses Pausing playbook execution with wait_for Running an entire playbook locally Prompts Tags Summary Chapter 6 - Playbook Organization - Roles and Includes Includes Handler includes Playbook includes Complete includes example Roles Role scaffolding Building your first role More flexibility with role vars and defaults Other role parts: handlers, files, and templates Handlers Files and Templates Organizing more complex and cross-platform roles Ansible Galaxy Getting roles from Galaxy Using role requirements files to manage dependencies A LAMP server in six lines of YAML A Solr server in six lines of YAML Helpful Galaxy commands Contributing to Ansible Galaxy Summary Chapter 7 - Inventories A real-world web application server inventory Non-prod environments, separate inventory files Inventory variables host_vars group_vars Ephemeral infrastructure: Dynamic inventory Dynamic inventory with DigitalOcean DigitalOcean account prerequisites Connecting to your DigitalOcean account Creating a droplet with Ansible DigitalOcean dynamic inventory with digital_ocean.py Dynamic inventory with AWS Inventory on-the-fly: add_host and group_by Multiple inventory sources - mixing static and dynamic inventories Creating custom dynamic inventories Summary Chapter 8 - Ansible Cookbooks Highly-Available Infrastructure with Ansible Directory Structure Individual Server Playbooks Main Playbook for Configuring All Servers Getting the required roles Vagrantfile for Local Infrastructure via VirtualBox Provisioner Configuration: DigitalOcean Provisioner Configuration: Amazon Web Services (EC2) Summary ELK Logging with Ansible ELK Playbook Forwarding Logs from Other Servers Summary GlusterFS Distributed File System Configuration with Ansible Configuring Gluster - Basic Overview Configuring Gluster with Ansible Summary Mac Provisioning with Ansible and Homebrew Running Ansible playbooks locally Automating Homebrew package and app management Configuring Mac OS X through dotfiles Summary Docker-based Infrastructure with Ansible A brief introduction to Docker containers Using Ansible to build and manage containers Building a Flask app with Ansible and Docker Data storage container Flask container MySQL container Ship it!

Built for Growth: How Builder Personality Shapes Your Business, Your Team, and Your Ability to Win by Chris Kuenne, John Danner

Airbnb, Amazon Web Services, asset light, Benchmark Capital, Berlin Wall, Bob Noyce, business climate, business logic, call centre, cloud computing, disruptive innovation, don't be evil, Fairchild Semiconductor, Fall of the Berlin Wall, Gordon Gekko, Jeff Bezos, Kickstarter, Larry Ellison, Lean Startup, Mark Zuckerberg, pattern recognition, risk tolerance, Sand Hill Road, self-driving car, Silicon Valley, solopreneur, Steve Jobs, Steve Wozniak, sugar pill, super pumped, supply-chain management, systems thinking, TED Talk, work culture , zero-sum game

All they cared about was maximizing their return.” Explorer in Action The Bezos commitment to innovation inspires a level of inventiveness that does not occur in other companies. —Chris Pinkham, Amazon Web Services Chris Pinkham had just convinced the higher-ups at Amazon to let him return to his native South Africa for the birth of his first child and to continue supporting his work on a project that would ultimately become AWS (Amazon Web Services). Before this move, when he was working for Amazon in Seattle in 2003, his curiosity led him to wonder whether there was a way to create “an infrastructure service for the world.”

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We share the stories of well-known entrepreneurs like Ben Cohen and Jerry Greenfield (of ice cream fame) and Jack Dorsey of Twitter and Square, in addition to lesser-known founders like Nate Morris, Grace Choi, and Steve Breitman, who have built noteworthy value in their own endeavors—in sanitation, cosmetics, and apartment laundries, respectively. We include corporate entrepreneurs such as Chris Pinkham, the inventor of the underlying technology that enabled Amazon Web Services; the late Charlie Cawley, who built MBNA, the credit card behemoth, out of a small department within Maryland National Bank; and legends like Norbert Berta, who created the caplet dosage form that helped the Tylenol brand recovery from the notorious 1982 poisonings. Using many of these examples, we illustrate how each Builder Type approaches the dynamic challenges at play in building a highly successful business—where does each create value, and where does each risk destroying it?

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Ability to attract talent: In many ways, an Explorer can become a bit of a folk hero among his or her community of people drawn to a specific kind of problem. Explorers use the question “Is there a better way?” as a magnet to attract enormously talented and like-minded systems thinkers. Earlier in this chapter, we saw Chris Pinkham and Mark Bonfigli each attract the talent who could build, respectively, the foundation for Amazon Web Services and one of the fastest-growing digital marketing firms for auto dealers. In some ways your articulation of the problem and perhaps the beginning of a vision on how to solve it create the same force of attraction that Crusaders enjoy (see chapter 4). This gift allows you to recruit the followers you need to take on successively larger and more complex problems.

Always Day One: How the Tech Titans Plan to Stay on Top Forever by Alex Kantrowitz

accounting loophole / creative accounting, Albert Einstein, AltaVista, Amazon Robotics, Amazon Web Services, Andy Rubin, anti-bias training, augmented reality, Automated Insights, autonomous vehicles, Bernie Sanders, Big Tech, Cambridge Analytica, Clayton Christensen, cloud computing, collective bargaining, computer vision, Donald Trump, drone strike, Elon Musk, fake news, Firefox, fulfillment center, gigafactory, Google Chrome, growth hacking, hive mind, income inequality, Infrastructure as a Service, inventory management, iterative process, Jeff Bezos, job automation, Jony Ive, Kiva Systems, knowledge economy, Lyft, Mark Zuckerberg, Menlo Park, new economy, Nick Bostrom, off-the-grid, Peter Thiel, QR code, ride hailing / ride sharing, robotic process automation, Salesforce, self-driving car, Sheryl Sandberg, Silicon Valley, Skype, Snapchat, SoftBank, Steve Ballmer, Steve Jobs, Steve Wozniak, super pumped, tech worker, Tim Cook: Apple, uber lyft, warehouse robotics, wealth creators, work culture , zero-sum game

Inside Amazon, Bezos has developed a culture that empowers employees to invent and lets them run the thing they’ve created (another leadership principle: Ownership). The deeper you dig in, the more apparent it becomes that this culture, bolstered by Wall Street investors who don’t demand Amazon turn a profit, is what’s behind the company’s array of beloved products and services: Echo, Kindle, Prime, Amazon Web Services, and Amazon.com. It is, in no uncertain terms, Amazon’s competitive advantage. Meet Amazon’s Science Fiction Writers On June 9, 2004, at 6:02 P.M., Jeff Bezos banned PowerPoint at Amazon. Not one for subtlety, he delivered the news right in an email subject line: “No powerpoint presentations from now on,” he wrote to his senior leadership team.

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In 2011, Amazon’s VP of pricing and promotions, Dilip Kumar, left his position in the retail organization to shadow Bezos for two years as his “technical adviser.” Amazonians covet the technical adviser position. Those who inhabit it attend every meeting Bezos takes, getting a chance to look at Amazon through his eyes, and often gain license to take a major swing when they’re done. Andy Jassy, Bezos’s first technical adviser, went on to found Amazon Web Services (AWS), Amazon’s cloud services division, which now brings in some $9 billion per quarter with Jassy as its CEO. Kumar—whose LinkedIn profile describes his time as Bezos’s technical adviser as “Quite possibly the best job that I’ve ever had!”—also set out to do something big upon completing his tour of duty.

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“You also had to be an expert in A/B testing platforms, and continuous improvement, and machine learning. Satya was an expert in all of those things.” When Nadella took over Server & Tools in 2011, he understood that simply providing servers and tools to companies building software for desktop machines wasn’t going to be viable. Watching Amazon Web Services’ early success, and reading his economics team’s analysis, Nadella decided that acting any slower would set Microsoft back. While working on Bing, Nadella learned how difficult it was to be a distant number-two player in a market, and he wasn’t eager to repeat the scenario. Despite the risks to the core Windows asset—not to mention a thriving Server & Tools business—Nadella made clear that Server & Tools under him would focus on enabling cloud computing.

Roads and Bridges by Nadia Eghbal

AGPL, Airbnb, Amazon Web Services, barriers to entry, Benevolent Dictator For Life (BDFL), corporate social responsibility, crowdsourcing, cryptocurrency, data science, David Heinemeier Hansson, Debian, DevOps, en.wikipedia.org, Firefox, Free Software Foundation, GnuPG, Guido van Rossum, Ken Thompson, Khan Academy, Kickstarter, leftpad, Marc Andreessen, market design, Network effects, platform as a service, pull request, Richard Stallman, Ruby on Rails, Salesforce, side project, Silicon Valley, Skype, software is eating the world, the Cathedral and the Bazaar, Tragedy of the Commons, Y Combinator

Like electricity or water utilities, access to a server or database can be metered, charged for, and shut off if fees go unpaid. Heroku (mentioned at the beginning of this paper) and Amazon Web Services are two prominent examples of platforms that sell digital infrastructure services to software developers for a fee. (Note that neither project is open source.) Similar open source projects at this level of infrastructure, such as OpenStack (a platform competitive to Amazon Web Services) or MySQL (a database), have found corporate homes. OpenStack is funded by a consortium of companies and MySQL was acquired by Oracle. Part of what makes these services financially attractive is the lack of noise.

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Suddenly infrastructure software was nearly free. We paid 10% of the normal costs for the software and that money was for software support. A 90% disruption in cost spawns innovation – believe me. [17] The availability of free software components today (as well as cheaper hosting and cloud services, like Amazon Web Services and Heroku) means that a technology startup no longer requires millions of dollars to get off the ground. Entrepreneurs can conceivably release a product and find a market without spending a single dollar, then raise money from venture capitalists only after they’ve shown strong signs of demand.

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Following Heartbleed, the Linux Foundation announced the formation of the Core Infrastructure Initiative (CII) in order to prevent a similar situation from happening again. Jim Zemlin, the executive director of the Linux Foundation, gathered nearly $4 million in pledges from thirteen corporate donors, including Amazon Web Services, IBM, and Microsoft, to support security-related infrastructure projects over the next three years. [178] They are also building government support, including support from the White House. [179] The CII is officially a project of the Linux Foundation. Since its formation in April 2014, the CII has sponsored development work on a number of projects, including OpenSSL, NTP, GnuPG (a communication encryption system), and OpenSSH (a set of security-related protocols).

Nothing But Net by Mark Mahaney

Airbnb, AltaVista, Amazon Web Services, AOL-Time Warner, augmented reality, autonomous vehicles, Big Tech, Black Swan, Burning Man, buy and hold, Cambridge Analytica, Chuck Templeton: OpenTable:, cloud computing, COVID-19, cryptocurrency, discounted cash flows, disintermediation, diversification, don't be evil, Donald Trump, Elon Musk, financial engineering, gamification, gig economy, global pandemic, Google Glasses, Jeff Bezos, John Zimmer (Lyft cofounder), knowledge economy, lockdown, low interest rates, Lyft, Marc Andreessen, Mark Zuckerberg, Mary Meeker, medical malpractice, meme stock, Network effects, PageRank, pets.com, ride hailing / ride sharing, Salesforce, Saturday Night Live, shareholder value, short squeeze, Silicon Valley, Skype, Snapchat, social graph, Steve Jobs, stocks for the long run, subscription business, super pumped, the rule of 72, TikTok, Travis Kalanick, Uber and Lyft, uber lyft

S&P, 279t, 282t as platform company, 241–242 playing quarters at, 57–58, 57t and pricing power flywheel, 192–194, 193f, 196–197 revenue, 82, 106–108, 153, 153t rise of, 5 sell-offs of, 48–52 share price, 51f underestimations of, 2 Amazon Kindle, 50, 115–116 Amazon Prime, 115, 174, 180–183, 192, 205 Amazon Subscribe & Save, 115 Amazon Web Services (AWS): launch of, 50 product innovation, 114–119, 179 revenue, 117f “Amazon.Bomb,” 10, 177–178 “Amazon’s Antitrust Paradox,” 308 American Technology Research, 156, 181 Android, ubiquity of, 63 Antitrust regulations, 308–310 AOL, 5, 7–8, 82 Apple: growth of, 305 management teams of, 203–204, 204t, 220t market cap of, 7 product innovation at, 114 revenue, 108, 153, 153t share price, 48–49 and Snap, 63 APRN (Blue Apron), 18–23, 19f, 33 Athar, Sohaib, 134 Auletta, Ken, 149 AWS (see Amazon Web Services [AWS]) Ayers, Charlie, 145 BABA (see Alibaba [BABA]) Bad stocks, 15–33, 293 Blue Apron, 18–23 and fundamentals of stock-picking, 15–18 Groupon, 27–32 Zulily, 23–27 Bain Capital, 24 Barron’s magazine, 9–10, 90, 91, 177–178 Barton, Rich, 188, 191, 209 Bezos, Jeff: acknowledgments of mistakes by, 219, 221 and Amazon Prime, 181, 193 and Amazon Web Services, 115, 116 and Burning Man, 220t as company founder, 204t competition for, 8 innovation by, 10, 205–206 shareholder letters by, 214–216, 216t–217t “Big baggers,” 1–2 “The Big Long,” 5–7 The Big Short (Lewis), 5 Black swan events, 18 Blockbuster, 10 Blue Apron (APRN), 18–23, 19f, 33 Blue Nile (NILE), 23–24 Booking.com (BKNG): acquisition of, 80 as big bagger, 1 during Covid-19 pandemic, 303 fundamentals at, 261t market cap of, 7 net income of, 5, 6t as tech stock, 3 Bow Street, 24 Boyd, Jeff, 91, 210 Brin, Sergey, 145, 146, 204t, 209, 219, 220t Broadcast.com, 87 Brown, Josh, 226 Buffett, Warren, 214 Burning Man, 219, 220t “Burning Up,” 177 Buyer, Lise, 145–146 Calico, 208 Cambridge Analytica, 37 Candor, of management teams, 221 Carpenter, Matt, 311 Case, Steve, 7 Cavens, Darrell, 23–24 Chernobyl (miniseries), 131 Chewy (CHWY): as competitor, 310 and Covid-19 pandemic, 3, 4, 17 during Covid-19 pandemic, 303 fundamentals at, 261t as long-term investment, 66–70 market cap of, 247t share price, 70f as tech stock, 3 CIA, AWS used by, 118 Cisco Systems, 6 Cloud computing, 117–118 Cloudflare, 17 CNBC, 68 Cohen, Ryan, 68, 302 Cohort performance, 250 Companies: judging high-quality, 292 live-from-home and work-from-home, 303 with minimal earnings, 235–242 with no earnings, 242–254, 243t with robust earnings, 232–235 user-generated content, 248–249 Competitive moats, 169–170 Comps, 105–107 Consumerism, as research, 140 ConsumerVoice.org, 21 Content moderation, on Facebook, 37 “A Conventional Look at an Unconventional IPO,” 156 Cooper, Bradley, 135 Covid-19 pandemic, 302–305 Amazon during, 3 Chewy during, 69 DoorDash during, 161–162 eBay during, 85 effect of, on markets, 17–18, 226, 246 and forecasting, 255 Google during, 46 and Internet sector, 3–4 Netflix during, 45 and revenue, 105–107 Stitch Fix during, 124–125, 127 stock dislocations due to, 276 Covid-19 pandemic tech stocks during, 131 Uber during, 159, 161 Cramer, Jim, 157 Criteo (CRTO), 288–289, 289f Cruz, Ted, 37 Customer focus: of Amazon, 180 of DoorDash, 185–186 long-term investments in companies with, 187 of management teams, 214–216, 216t–217t and pricing power flywheel, 194 of product innovation, 295–296 Customer value propositions, 173–199, 297 Amazon vs. eBay, 174–183 DoorDash vs.

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The intriguing question is: When did Amazon become a “reasonably sane” growth stock? When did it morph from being a speculative investment on the future of Internet retail to being a growth investment (still risky, but not speculative) on the future of one of the Internet’s largest platforms? My answer is 2006–2007, because that’s when Amazon launched both Amazon Web Services (AWS), its cloud offering, and the Amazon Kindle, its e-reader. Although I was skeptical at the time and viewed AWS as a limited, commodity-esque business, AWS eventually became arguably the most important growth engine at Amazon, and today it accounts for arguably half of the company’s market cap.

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This, combined with the stock being highly dislocated, made me believe that both fundamentals and stock price performance could materially inflect up. Yes, luck was involved, but SFIX shares did rally from $12 in April of 2020 to $70 by the end of the year, as customer metrics improved and the revenue growth outlook strengthened, thanks in part to those new features. For a big example, I’d point to the 2006 launch of Amazon Web Services (AWS), the company’s cloud computing offering, which goes down as one of the single most impressive, most impactful product innovations of this millennium (since 2000), along with those iPhone and Tesla launches. AWS is widely known and widely regarded by tech investors today. Though that was far from the case when it was launched.

Bezonomics: How Amazon Is Changing Our Lives and What the World's Best Companies Are Learning From It by Brian Dumaine

activist fund / activist shareholder / activist investor, AI winter, Airbnb, Amazon Robotics, Amazon Web Services, Atul Gawande, autonomous vehicles, basic income, Bernie Sanders, Big Tech, Black Swan, call centre, Cambridge Analytica, carbon tax, Carl Icahn, Chris Urmson, cloud computing, corporate raider, creative destruction, Danny Hillis, data science, deep learning, Donald Trump, Elon Musk, Erik Brynjolfsson, Fairchild Semiconductor, fake news, fulfillment center, future of work, gig economy, Glass-Steagall Act, Google Glasses, Google X / Alphabet X, income inequality, independent contractor, industrial robot, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kevin Roose, Lyft, Marc Andreessen, Mark Zuckerberg, military-industrial complex, money market fund, natural language processing, no-fly zone, Ocado, pets.com, plutocrats, race to the bottom, ride hailing / ride sharing, Salesforce, Sand Hill Road, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Steve Jobs, Stewart Brand, supply-chain management, TED Talk, Tim Cook: Apple, too big to fail, Travis Kalanick, two-pizza team, Uber and Lyft, uber lyft, universal basic income, warehouse automation, warehouse robotics, wealth creators, web application, Whole Earth Catalog, work culture

The company he founded controlled, as of 2019, nearly 40 percent of all online retailing in the U.S. and is one of the largest e-tailers in Europe. Amazon has expanded its Prime membership program to seventeen countries, and the number of people who have signed up for the service globally has hit more than 150 million. Bezos built Amazon Web Services (AWS) into the world’s largest cloud computing company, and Prime Video into a streaming media giant nipping at the heels of Netflix, and he’s the driving force behind the Echo, a smart speaker with Alexa inside that sold nearly 50 million units in its first few years of existence. Throughout the 2010s, this profitable company grew at an average rate of 25 percent a year—an astounding performance for such a large corporation (as of 2018, it had $233 billion in annual revenues).

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After breakfast, Ella takes the subway to her office. For her work, she searches for Bluetooth keyboards; no surprise, Amazon has the best selection. She clicks twice and knows they’ll be at her desk the next or maybe even the same day if she really needs them fast. She backs up important company files on the cloud built by Amazon Web Services, researches small-business loans offered by Amazon Lending, then gathers her team to discuss her start-up’s next major milestone: launching a new product on the Amazon site. That evening, on her way home, she stops at a cashier-less Amazon Go store to pick up a snack, and when she leaves, sensors and cameras automatically charge her Amazon account for what she carries out.

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Along those lines, Amazon already has a deal with Lennar, the nation’s largest homebuilder, to preinstall Alexa in all the company’s new homes. Another way that Amazon invades a new industry is to take something that it does well internally and then offer that service to others. The computer expertise that Amazon acquired while selling books online was impressive. Why not share that capability with other businesses? In 2006, Amazon Web Services was born. After a decade of building web applications, Amazon realized it had developed a core competency in operating computer infrastructure and data centers at a massive scale and that it could offer cloud services for customers at a great price. Today, AWS is the largest cloud computing company in the world by a wide margin.

Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies by Reid Hoffman, Chris Yeh

"Susan Fowler" uber, activist fund / activist shareholder / activist investor, adjacent possible, Airbnb, Amazon Web Services, Andy Rubin, autonomous vehicles, Benchmark Capital, bitcoin, Blitzscaling, blockchain, Bob Noyce, business intelligence, Cambridge Analytica, Chuck Templeton: OpenTable:, cloud computing, CRISPR, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, database schema, DeepMind, Didi Chuxing, discounted cash flows, Elon Musk, fake news, Firefox, Ford Model T, forensic accounting, fulfillment center, Future Shock, George Gilder, global pandemic, Google Hangouts, Google X / Alphabet X, Greyball, growth hacking, high-speed rail, hockey-stick growth, hydraulic fracturing, Hyperloop, initial coin offering, inventory management, Isaac Newton, Jeff Bezos, Joi Ito, Khan Academy, late fees, Lean Startup, Lyft, M-Pesa, Marc Andreessen, Marc Benioff, margin call, Mark Zuckerberg, Max Levchin, minimum viable product, move fast and break things, Network effects, Oculus Rift, oil shale / tar sands, PalmPilot, Paul Buchheit, Paul Graham, Peter Thiel, pre–internet, Quicken Loans, recommendation engine, ride hailing / ride sharing, Salesforce, Sam Altman, Sand Hill Road, Saturday Night Live, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Skype, smart grid, social graph, SoftBank, software as a service, software is eating the world, speech recognition, stem cell, Steve Jobs, subscription business, synthetic biology, Tesla Model S, thinkpad, three-martini lunch, transaction costs, transport as a service, Travis Kalanick, Uber for X, uber lyft, web application, winner-take-all economy, work culture , Y Combinator, yellow journalism

Growing wheat is a low-margin business, as is selling goods in a store or serving food in a restaurant. One of the most amazing things about Amazon’s success is that it has been able to build a massive business based on retailing, which is generally a low-margin industry. And even Amazon now relies heavily on its high-margin SaaS business, Amazon Web Services (AWS). In 2016, AWS accounted for 150 percent of Amazon’s operating income, which means that the retail business actually lost money. Most of the valuable companies we’re focusing on in this book have gross margins of over 60, 70, or even 80 percent. In 2016, Google had a gross income of $54.6 billion on sales of $89.7 billion, for a gross margin of 61 percent.

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Even with this strenuous effort, it took several years to “tame” the Fail Whale; it wasn’t until after Twitter made it through the 2012 US presidential election night without melting down that the company’s then–creative director Doug Bowman announced that the Great Blue Whale had been put to death. One of the main reasons for the very large increase in the growth of valuable Web companies that we’ve seen in recent years is Amazon’s cloud offering, Amazon Web Services (AWS), which has helped many such businesses navigate around infrastructure limitations. Dropbox, for example, was able to scale up its storage infrastructure much more quickly and easily because it used AWS storage, eliminating the need to build and maintain its own arrays of hard disks. AWS reflects one of the ways that Amazon has made operational scalability a competitive advantage.

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Since then, Amazon has steadily expanded from books into many other verticals, and today very nearly lives up to Bezos’s vision of an “everything store” (though you still can’t buy automobiles on Amazon…yet). Retail is a truly gargantuan market and Amazon has captured an almost unthinkable portion of it and even made its market much bigger by launching Amazon Web Services. Now, in addition to being “the everything store,” Amazon also provides much of the Internet’s computing power, bandwidth, and storage (including for other dominant companies like Netflix). Distribution Amazon was one of the first companies to fully grasp the possibilities of the Internet as a distribution platform in creating the first successful affiliate program, Amazon Associates, which incentivizes individuals and owners of other websites to refer customers to Amazon in exchange for a share of the revenues generated.

Data Wrangling With Python: Tips and Tools to Make Your Life Easier by Jacqueline Kazil

Amazon Web Services, bash_history, business logic, cloud computing, correlation coefficient, crowdsourcing, data acquisition, data science, database schema, Debian, en.wikipedia.org, Fairphone, Firefox, Global Witness, Google Chrome, Hacker News, job automation, machine readable, Nate Silver, natural language processing, pull request, Ronald Reagan, Ruby on Rails, selection bias, social web, statistical model, web application, WikiLeaks

Advanced Python Setup. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 E. Python Gotchas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453 F. IPython Hints. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 G. Using Amazon Web Services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 x | Table of Contents Preface Welcome to Data Wrangling with Python!

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Shared Jupyter notebooks Now that you are familiar with using Jupyter notebooks, you can set one up to share your code with others using a shared server. This will allow others to access your notebook on the normal Internet (not just localhost, like the notebook run from your terminal). There are some great tutorials available on how to set up a notebook server using DigitalOcean, Heroku, Amazon Web Services, Google DataLab, or whatever server you’d like. Remember to use secure passwords with your notebook server to ensure your notebooks are being used only by those with the pass‐ word. This will keep your server and data safe. We recommend setting up a version control system like Git (explored in more depth in Chapter 14) for your Jupyter notebooks as well, so you can have a history of your notebooks on a daily or weekly basis.

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Aside from these fairly simple and straightforward ways to parse your data and to give your script extra pieces of information, you can use more sophisticated and dis‐ tributed approaches like cloud data and databasing. We’ll look at these next. Using the Cloud for Data Processing The cloud is a term used to refer to a shared pool of resources, such as servers. There are many companies that offer cloud services—Amazon Web Services, more com‐ monly referred to as AWS, is one of the best known. The term cloud is often overused. If you are running your code on a cloud-based server, it is better to say “I am running it on a server” rather than “I am running it in the cloud.” When is a good time to use the cloud? The cloud is a good way to process data if the data is too large to process on your own computer or the procedure takes too long.

Amazon Unbound: Jeff Bezos and the Invention of a Global Empire by Brad Stone

activist fund / activist shareholder / activist investor, air freight, Airbnb, Amazon Picking Challenge, Amazon Robotics, Amazon Web Services, autonomous vehicles, Bernie Sanders, big data - Walmart - Pop Tarts, Big Tech, Black Lives Matter, business climate, call centre, carbon footprint, Clayton Christensen, cloud computing, Colonization of Mars, commoditize, company town, computer vision, contact tracing, coronavirus, corporate governance, COVID-19, crowdsourcing, data science, deep learning, disinformation, disintermediation, Donald Trump, Downton Abbey, Elon Musk, fake news, fulfillment center, future of work, gentrification, George Floyd, gigafactory, global pandemic, Greta Thunberg, income inequality, independent contractor, invisible hand, Jeff Bezos, John Markoff, Kiva Systems, Larry Ellison, lockdown, Mahatma Gandhi, Mark Zuckerberg, Masayoshi Son, mass immigration, minimum viable product, move fast and break things, Neal Stephenson, NSO Group, Paris climate accords, Peter Thiel, Ponzi scheme, Potemkin village, private spaceflight, quantitative hedge fund, remote working, rent stabilization, RFID, Robert Bork, Ronald Reagan, search inside the book, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, social distancing, SoftBank, SpaceX Starlink, speech recognition, Steve Ballmer, Steve Jobs, Steven Levy, tech billionaire, tech bro, techlash, TED Talk, Tim Cook: Apple, Tony Hsieh, too big to fail, Tragedy of the Commons, two-pizza team, Uber for X, union organizing, warehouse robotics, WeWork

With another like-minded deputy, Andy Jassy, Bezos also expanded in an even more surprising direction. Contemplating the way his own engineers worked, and the expertise the company had developed in building a stable computing infrastructure that could withstand enormous seasonal spikes in traffic, he conceived of a new business called Amazon Web Services. The idea was that Amazon would sell raw computing power to other organizations, who could access it online and use it to economically run their own operations. The business plan was barely understandable to many of Amazon’s own employees and board members. But the forty-year-old Bezos believed in it, micromanaging the project and sending extraordinarily detailed recommendations and goals to AWS team leaders, often late at night.

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His bet there was a renewal of Amazon’s manifest destiny—to sell not only everything, but everywhere. Bezos had missed an earlier opportunity to invest in India. In 2004, Amazon opened one of its first overseas software development centers in Bangalore, in a small office above an auto dealership. Employees working on its floundering search engine, A9, and its nascent cloud business, Amazon Web Services, repeatedly pitched plans to start a local online store. But as Amazon recovered from the dot-com bust and concentrated its energies on launching in China, India was practically an afterthought. As a result, some of Amazon’s early employees in India quit to start their own firms. In 2007, two engineers, Sachin Bansal and Binny Bansal—unrelated friends and former classmates at the Indian Institute of Technology (IIT) in New Delhi—left Amazon to start their own company, Flipkart, to try to replicate Bezos’s original magic of selling books online.

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As we have seen, Bezos avidly pursued that goal by plunging billions into projects like Alexa, the Fire Phone, and the Go store, as well as future dominance in India and Mexico and other secret initiatives never known to the public. None of those bets had yet borne fruit. But in 2015, an earlier wager finally started to pay off. In its April earnings report, Amazon revealed for the first time the financial health of its ten-year-old cloud business, Amazon Web Services, and shocked Wall Street with its underlying sales growth and profitability. Then in June, Amazon copied a competitor in China and introduced the first Prime Day, capitalizing on a decade of growth from its two-day shipping program. Both Wall Street and the media began to show a newfound interest in Amazon, and shortly after the twentieth anniversary of its launch, the company came under a new kind of scrutiny commensurate with its growing size.

Internet for the People: The Fight for Our Digital Future by Ben Tarnoff

4chan, A Declaration of the Independence of Cyberspace, accounting loophole / creative accounting, Alan Greenspan, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic management, AltaVista, Amazon Web Services, barriers to entry, Bernie Sanders, Big Tech, Black Lives Matter, blue-collar work, business logic, call centre, Charles Babbage, cloud computing, computer vision, coronavirus, COVID-19, decentralized internet, deep learning, defund the police, deindustrialization, desegregation, digital divide, disinformation, Edward Snowden, electricity market, fake news, Filter Bubble, financial intermediation, future of work, gamification, General Magic , gig economy, God and Mammon, green new deal, independent contractor, information asymmetry, Internet of things, Jeff Bezos, Jessica Bruder, John Markoff, John Perry Barlow, Kevin Roose, Kickstarter, Leo Hollis, lockdown, lone genius, low interest rates, Lyft, Mark Zuckerberg, means of production, Menlo Park, natural language processing, Network effects, Nicholas Carr, packet switching, PageRank, pattern recognition, pets.com, profit maximization, profit motive, QAnon, recommendation engine, rent-seeking, ride hailing / ride sharing, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, single-payer health, smart grid, social distancing, Steven Levy, stock buybacks, supply-chain management, surveillance capitalism, techlash, Telecommunications Act of 1996, TikTok, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, undersea cable, UUNET, vertical integration, Victor Gruen, web application, working poor, Yochai Benkler

For more on the origins of AWS, see former Amazonian Steve Yegge’s (since removed) rant on Google+, available at gist.github.com/chitchcock/1281611; Jeff Barr, “My First 12 Years at Amazon.com,” August 19, 2014; Andy Jassy’s review of Stone’s The Everything Store on Amazon.com; Benjamin Black, “EC2 Origins,” January 25, 2009; Jack Clark, “How Amazon Exposed Its Guts: The History of AWS’s EC2,” ZDNet, June 7, 2012; Bharath Kumar Gowru et al., “Cloud Computing Using Amazon Web Services,” International Journal for Modern Trends in Science and Technology 3, no 1 (February 2017): 55; Brandon Butler, “The Myth about How Amazon’s Web Service Started Just Won’t Die,” NetworkWorld, March 2, 2015. “Let’s make it …”: Jeff Bezos, interview by Om Malik at the D6 Conference in Carlsbad, California, in May 2008, quoted by Nicholas Carr, “Understanding Amazon Web Services,” Rough Type (blog), May 31, 2008. Not the only ones feeling the pain: Furrier, “Exclusive: The Story of AWS and Andy Jassy’s Trillion Dollar Baby.” 106, The team in Cape Town … Barefoot juggling: Marcin Kowalski’s remarks in “Amazon EC2—10 Year Anniversary Celebration,” YouTube, starts around 2:14.

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After a year and a half of development in Cape Town, EC2 launched in 2006. Following the same playbook as the e-commerce division, prices were kept low to drive growth: renting a fairly robust virtual server for an hour cost ten cents. Along with Simple Storage Service (S3), an online storage system released that same year, EC2 would be the flagship offering of Amazon Web Services (AWS). AWS created, and still dominates, the market for cloud infrastructure services. Its arrival was well timed. The post-dot-com period, as Google demonstrated, would be about amassing data and finding a way to monetize it. AWS offered a relatively affordable set of tools for manufacturing, storing, organizing, and processing this precious resource.

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Common Sense Social media is only one genre of online mall. To abolish the rest, more imagination is needed. Nick Srnicek imagines a publicly owned cloud service that “ensures privacy, security, energy efficiency and equal access for all.” Such a service might be carved out of a corporate provider like Amazon Web Services, which could be required to donate a portion of its capacity. Picture a “public lane” for the cloud, serving the digital infrastructure needs of a growing deprivatized sector. This sector would include a variety of cooperative ventures, not just in social media but across all realms of online life.

Fulfillment: Winning and Losing in One-Click America by Alec MacGillis

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", Airbnb, Amazon Web Services, Bernie Sanders, Big Tech, Black Lives Matter, call centre, carried interest, cloud computing, cognitive dissonance, company town, coronavirus, COVID-19, data science, death of newspapers, deindustrialization, Donald Trump, edge city, fulfillment center, future of work, gentrification, George Floyd, Glass-Steagall Act, global pandemic, Great Leap Forward, high net worth, housing crisis, Ida Tarbell, income inequality, information asymmetry, Jeff Bezos, Jeffrey Epstein, Jessica Bruder, jitney, Kiva Systems, lockdown, Lyft, mass incarceration, McMansion, megaproject, microapartment, military-industrial complex, new economy, Nomadland, offshore financial centre, Oklahoma City bombing, opioid epidemic / opioid crisis, plutocrats, Ralph Nader, rent control, Richard Florida, ride hailing / ride sharing, Robert Mercer, Ronald Reagan, San Francisco homelessness, shareholder value, Silicon Valley, social distancing, strikebreaker, tech worker, Travis Kalanick, uber lyft, uranium enrichment, War on Poverty, warehouse robotics, white flight, winner-take-all economy, women in the workforce, working-age population, Works Progress Administration

Why not build an infrastructure platform that would make Amazon’s own software development more efficient and also offer it to other companies? Those companies could design applications to run on the infrastructure—everything from computing to payments to messaging—and thus be spared the cost and hassle of building their own infrastructure and running their own servers and data centers. The company created Amazon Web Services, its cloud-computing branch, in 2003, and began offering its first data storage service in 2006. By 2017, AWS was providing cloud services to, among others, GE, Capital One, News Corp, Verizon, Airbnb, Slack, Coca-Cola, and even direct rivals like Apple and Netflix, while bringing in more than $17 billion in revenue for the year—a tenth of Amazon’s total.

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“It would be my pleasure,” said Alexa, her recently upgraded voice—redesigned to be more humanoid and less robotic, and unveiled in a Super Bowl commercial—booming through the hugely capacious hall of the Washington Convention Center. “Please welcome to the stage vice president of Worldwide Public Sector, Amazon Web Services: Teresa Carlson.” It was the ninth annual AWS Public Sector Summit, an event that had begun with a crowd of 50 in a Marriott across the river in Arlington, and now, in the spring of 2018, drew some 14,000 to the convention center in D.C. The summit’s steroidal expansion was visual affirmation of Amazon’s triumph in the cloud: by year’s end, its cloud infrastructure would bring in $15.5 billion, nearly as much as all its industry rivals combined.

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” * * * Three months later, the guests were starting to tuck into their salads in the Washington Hilton ballroom as the big Economic Club of Washington banquet for Jeff Bezos got underway. While they ate, the club president, David Rubenstein, ticked off the dinner’s sponsors, among them Boeing, JPMorgan Chase, and Amazon Web Services. “Everyone in this room by definition is a distinguished person,” said Rubenstein to appreciative laughter, but he would, he said, take the time to mention some of the most distinguished of all. He recognized all seventeen ambassadors in attendance: “Singapore … Indonesia … South Africa … United Kingdom … Australia … Ireland…” He recognized the postmaster general, and the head of the General Services Administration, and Maryland governor Larry Hogan, and Stephen Moret, who was overseeing Northern Virginia’s bid for Amazon’s second headquarters, and Washington mayor Muriel Bowser, and three former mayors also in attendance, and the chairman of the Washington Metro subway system.

Dual Transformation: How to Reposition Today's Business While Creating the Future by Scott D. Anthony, Mark W. Johnson

activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Web Services, Andy Rubin, Apollo 13, asset light, autonomous vehicles, barriers to entry, behavioural economics, Ben Horowitz, Big Tech, blockchain, business process, business process outsourcing, call centre, Carl Icahn, Clayton Christensen, cloud computing, commoditize, corporate governance, creative destruction, crowdsourcing, death of newspapers, disintermediation, disruptive innovation, distributed ledger, diversified portfolio, driverless car, Internet of things, invention of hypertext, inventory management, Jeff Bezos, job automation, job satisfaction, Joseph Schumpeter, Kickstarter, late fees, Lean Startup, long term incentive plan, Lyft, M-Pesa, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Minecraft, obamacare, Parag Khanna, Paul Graham, peer-to-peer lending, pez dispenser, recommendation engine, Salesforce, self-driving car, shareholder value, side project, Silicon Valley, SimCity, Skype, software as a service, software is eating the world, Steve Jobs, subscription business, the long tail, the market place, the scientific method, Thomas Kuhn: the structure of scientific revolutions, transfer pricing, uber lyft, Watson beat the top human players on Jeopardy!, Y Combinator, Zipcar

Many of these, such as Amazon Prime (free shipping for members) and customer reviews, fit within the rubric of transformation A, part of Amazon’s continual transformation from a retailer of books to achieve its vision of the world’s most customer-centered retailer. Other innovations push Amazon in new, disruptive directions. Perhaps the company’s most notable transformation B effort is the creation of Amazon Web Services (AWS), which has become the leading provider of cloud computing services. Imagine going back to 2005, when Amazon commissioned the work that ultimately spawned AWS, and predicting that a decade later the company would be the dominant market leader. People would have laughed. AWS began as an effort led by programmer John Dalzell to accelerate internal IT projects.

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Accelerate Capability Development via Acquisitions and External Hires Generally, a company pursuing transformation B needs to assess the new competitors it will encounter. If your competitive set isn’t changing, after all, it suggests you aren’t stretching far enough. For example, in its core retailing business, Amazon competes against other retailers, such as Walmart and Barnes & Noble. Its Amazon Web Services business has a completely different set of competitors, such as Infosys, Accenture, Microsoft, and IBM. It’s easy to discount the challenges of moving into new markets with a different set of competitors, particularly in a disruptive circumstance, when many of the competitors will be fast-moving entrants.

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Singapore Postal Services Statistics: Infocomm Development Authority of Singapore, http://www.ida.gov.sg/Policies-and-Regulations/Industry-and-Licensees/Standards-and-Quality-of-Service/Quality-of-Service/Postal-Services, accessed July 2, 2016. Simon Israel’s commitment to transformation and transparency: Speech at the 24th SingPost Annual General meeting, July 14, 2016, http://infopub.sgx.com/FileOpen/Chairman%20Speech.ashx?App=Announcement&FileID=412793. Creation of Amazon Web Services: Charles O’Reilly and Michael Tushman, Lead and Disrupt: How to Solve the Innovator’s Dilemma (Stanford, CA: Stanford University Press, 2016); Brad Stone, The Everything Store: Jeff Bezos and the Age of Amazon (New York: Little, Brown and Company, 2013). AWS market share: “AWS, Google, Microsoft and IBM pull away from pack in race for cloud market share,” Business Cloud News, April, 29, 2016, www.businesscloudnews.com/2016/04/29/aws-google-microsoft-and-ibm-pull-away-from-pack-in-race-for-cloud-market-share/.

Architecting For Scale by Lee Atchison

Amazon Web Services, business logic, business process, cloud computing, continuous integration, DevOps, Internet of things, microservices, platform as a service, risk tolerance, software as a service, web application

If you struggle with managing technical debt and associated application failures, this book will provide guidance in reducing that technical debt to make your application able to handle larger scale more easily. Why I Wrote This Book After spending many years at Amazon building highly scaled applications in both the retail and the Amazon Web Services (AWS) worlds, I moved to New Relic, which was in the midst of hyper growth. The company felt the pain of needing the systems and processes required to manage highly scaled applications, but hadn’t yet fully developed the processes and disciplines to scale its application. At New Relic, I saw firsthand the struggles of a company going through the process of trying to scale, and realized that there were many other companies experiencing the same struggles every day.

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However, one time in late summer it dropped below our 80% SLA for a short period of time (we say we failed our SLA). Note In certain industries, businesses have contractual agreements with customers that require them to meet established SLAs, perhaps with financial or other consequences for failing to meet them. Amazon Web Services, for example, has SLAs with its customers, and in some cases provides financial compensation if they fail to meet those SLAs. For example, with Amazon EC2 instances, if AWS’s monthly uptime percentage falls below 99.95%, it gives a service credit of 10% to affected customers. If it falls below 99.0%, AWS gives a service credit of 30%.

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Amazon Elastic Load Balancer (ELB), Changing Allocations Amazon Kinesis, Internet of Things Data Intake Amazon S3, Managed Resource (Non-server-based)Lambda and, Event Processing limits of, The “Magic” of Usage-Based Resource Allocation usage-based allocation, The “Magic” of Usage-Based Resource Allocation Amazon Web Services (see AWS) API contracts, The Ownership Benefit API Gateway, Mobile Backend application availability (see availability) applicationsbuilding with failure in mind, Focus #1: Build with Failure in Mind building with scaling in mind, Focus #2: Always Think About Scaling distributing across the cloud, Distributing the Cloud-Maintaining Location Diversity for Availability Reasons effects of growth, Preface guidelines for separating into services, What Should Be a Service?

Python for Unix and Linux System Administration by Noah Gift, Jeremy M. Jones

Amazon Web Services, bash_history, Bram Moolenaar, business logic, cloud computing, create, read, update, delete, database schema, Debian, distributed revision control, Firefox, functional programming, Guido van Rossum, industrial robot, inventory management, job automation, Mark Shuttleworth, MVC pattern, skunkworks, web application

Bayer, Michael, SQLAlchemy ORM Bicking, Ian, virtualenv blocks of code, editing, Magic Edit bookmark command, bookmark bookmarks, cd navigating to bookmarked directories, cd bootstrapped virtual environment, custom, Creating a Custom Bootstrapped Virtual Environment Boto (Amazon Web services), Amazon Web Services with Boto Buildout tool, Buildout, Developing with Buildout, Developing with Buildout developing with, Developing with Buildout bzip2 compression, Using tarfile Module to Create TAR Archives C callbacks, Callbacks, Callbacks capitalization, Built-in methods for str data extraction (see case) case (capitalization), Built-in methods for str data extraction converting for entire string, Built-in methods for str data extraction cd command, cd, cd, cd, dhist -<TAB> option, dhist -b option, cd charts, creating, Graphical Images checksum comparisons, MD5 Checksum Comparisons, MD5 Checksum Comparisons choices usage pattern (optparse), Choices Usage Pattern close( ) function (socket module), socket close() method, Creating files close() method (shelve), shelve cloud computing, Cloud Computing, Building a sample Google App Engine application, Amazon Web Services with Boto, Google App Engine, Building a sample Google App Engine application Amazon Web services with Boto, Amazon Web Services with Boto cmp() function (filecmp module), Using the filecmp Module combining strings, Built-in methods for str data extraction command history, History command line, Introduction, Summary, Basic Standard Input Usage, Basic Standard Input Usage, Introduction to Optparse, Option with Multiple Arguments Usage Pattern, Unix Mashups: Integrating Shell Commands into Python Command-Line Tools, Hybrid Kudzu Design Pattern: Wrapping a Unix Tool in Python to Spawn Processes, Integrating Configuration Files, Integrating Configuration Files basic standard input usage, Basic Standard Input Usage, Basic Standard Input Usage integrating configuration files, Integrating Configuration Files, Integrating Configuration Files integrating shell commands, Unix Mashups: Integrating Shell Commands into Python Command-Line Tools, Hybrid Kudzu Design Pattern: Wrapping a Unix Tool in Python to Spawn Processes optparse, Introduction to Optparse, Option with Multiple Arguments Usage Pattern community of Python users, Why Python?

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to obtain object information, pinfo ' (quotation mark, single), Creating strings creating strings with, Creating strings " (quotation marks, double), Creating strings creating strings with, Creating strings _ (underscore), rehash, psearch, psearch, History results, History results for results history, History results, History results __ (in variable names), rehash __ object, psearch ___ object, psearch “magic” functions, alias (see also specific function) A Active Directory, using with Python, Using LDAP with OpenLDAP, Active Directory, and More with Python, Importing an LDIF File active version of package, changing, Change Active Version of Package alias function, alias, alias, alias alias table, rehash, rehashx Amazon Web services (Boto), Amazon Web Services with Boto Apache config file, hacking (example), Apache Config File Hacking, Apache Config File Hacking Apache log reporting, Apache Log Reporting, Apache Log Reporting Apache Log Viewer, building (example), Building an Apache Log Viewer Using PyGTK, Building an Apache Log Viewer Using PyGTK, Building an Apache Log Viewer Using Curses, Building an Apache Log Viewer Using Curses, Apache Log Viewer Application, Apache Log Viewer Application with curses library, Building an Apache Log Viewer Using Curses, Building an Apache Log Viewer Using Curses with Django, Apache Log Viewer Application, Apache Log Viewer Application with PyGTK, Building an Apache Log Viewer Using PyGTK, Building an Apache Log Viewer Using PyGTK Apache logfile, parsing (example), Log Parsing, Log Parsing appscript project, OS X Scripting APIs archiving data, Archiving, Compressing, Imaging, and Restoring, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files examining TAR file contents, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files ARP protocol, Creating Hybrid SNMP Tools asr utility, Automatically Re-Imaging Machines attachments (email), sending, Sending attachments with Python attrib attribute (Element object), ElementTree authentication, Authenticating to a Password Protected Site when installing eggs, Authenticating to a Password Protected Site authentication (SMTP), Using SMTP authentication automated information gathering, Automated Information Gathering, Receiving Email, Receiving Email, Receiving Email receiving email, Receiving Email, Receiving Email automatically re-imaging routines, Automatically Re-Imaging Machines automation, with IPython shell, Automation and Shortcuts, rep B background threading, IPython and Net-SNMP backslash (\), Creating strings escape sequences, list of, Creating strings backups, Introduction, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files examining TAR file contents, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files bar charts, creating, Graphical Images Bash, Python versus, Why Python?

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Bayer, Michael, SQLAlchemy ORM Bicking, Ian, virtualenv blocks of code, editing, Magic Edit bookmark command, bookmark bookmarks, cd navigating to bookmarked directories, cd bootstrapped virtual environment, custom, Creating a Custom Bootstrapped Virtual Environment Boto (Amazon Web services), Amazon Web Services with Boto Buildout tool, Buildout, Developing with Buildout, Developing with Buildout developing with, Developing with Buildout bzip2 compression, Using tarfile Module to Create TAR Archives C callbacks, Callbacks, Callbacks capitalization, Built-in methods for str data extraction (see case) case (capitalization), Built-in methods for str data extraction converting for entire string, Built-in methods for str data extraction cd command, cd, cd, cd, dhist -<TAB> option, dhist -b option, cd charts, creating, Graphical Images checksum comparisons, MD5 Checksum Comparisons, MD5 Checksum Comparisons choices usage pattern (optparse), Choices Usage Pattern close( ) function (socket module), socket close() method, Creating files close() method (shelve), shelve cloud computing, Cloud Computing, Building a sample Google App Engine application, Amazon Web Services with Boto, Google App Engine, Building a sample Google App Engine application Amazon Web services with Boto, Amazon Web Services with Boto cmp() function (filecmp module), Using the filecmp Module combining strings, Built-in methods for str data extraction command history, History command line, Introduction, Summary, Basic Standard Input Usage, Basic Standard Input Usage, Introduction to Optparse, Option with Multiple Arguments Usage Pattern, Unix Mashups: Integrating Shell Commands into Python Command-Line Tools, Hybrid Kudzu Design Pattern: Wrapping a Unix Tool in Python to Spawn Processes, Integrating Configuration Files, Integrating Configuration Files basic standard input usage, Basic Standard Input Usage, Basic Standard Input Usage integrating configuration files, Integrating Configuration Files, Integrating Configuration Files integrating shell commands, Unix Mashups: Integrating Shell Commands into Python Command-Line Tools, Hybrid Kudzu Design Pattern: Wrapping a Unix Tool in Python to Spawn Processes optparse, Introduction to Optparse, Option with Multiple Arguments Usage Pattern community of Python users, Why Python?

Designing Web APIs: Building APIs That Developers Love by Brenda Jin, Saurabh Sahni, Amir Shevat

active measures, Amazon Web Services, augmented reality, Big Tech, blockchain, business logic, business process, cognitive load, continuous integration, create, read, update, delete, exponential backoff, Google Hangouts, if you build it, they will come, Lyft, machine readable, MITM: man-in-the-middle, premature optimization, pull request, Salesforce, Silicon Valley, Snapchat, software as a service, the market place, uber lyft, web application, WebSocket

Network I/O Network bottlenecks in modern applications are frequently caused by dependencies on external services requiring API calls across data centers. CPU Inefficient code performing expensive computations is one of the common causes of CPU bottlenecks. Memory Memory bottlenecks typically occur when systems do not have sufficient RAM. Most of the cloud hosting providers offer solutions to measure these bottlenecks. If you’re on Amazon Web Services (AWS), you can use Amazon CloudWatch. Heroku has New Relic. And on Google, you can use Stackdriver to access metrics and get insights into health, performance, and availability. To pinpoint specific bottlenecks, you can monitor the categories we just listed, homing in on your most frequently called API methods.

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Building a Developer Strategy | 149 Attribute Platform of choice Preferred development language and frameworks Common use cases Preferred means of communication Market size and geographical distribution Description Windows and Linux scripting, web developers, SharePoint or Confluence. Java, .NET. Many work with Amazon Web Services (AWS; e.g., AWS Step Functions), some integrate with Slack for reporting. Some developers are exploring Node.js. Internal use cases—approval processes (time off, expenses, general), reports, and looking up clients in the customer relationship management (CRM) and ticketing systems seem like the most requested set of use cases.

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API Implementation Checklist: ❏ Define specific developer problem to solve ❏ Write internal API specification ❏ Get internal feedback on API specification ❏ Build API ❏ Authentication ❏ Authorization ❏ Error handling ❏ Rate-limiting ❏ Pagination ❏ Monitoring and logging ❏ Write documentation ❏ Run beta test with partners on new API ❏ Gather feedback from beta partners and make changes ❏ Create communication plan to notify developers of changes ❏ Release API changes API Design Worksheets | 205 Index A additions to APIs, 131 additive-change strategy, 133 Amazon Web Services (AWS), 83 ambassador programs, 195 analytics dashboards, 56 Apache Thrift, 14 API testers, 179 APIs about, 1 attributes and traits of good APIs, 199 business case for, 3-7 APIs as a product, 6 APIs for external developers first, internal developers second, 5 APIs for internal developers first, external developers second, 4 characteristics of great APIs, 7 description language, 122-126 design paradigms, 9 designing (see designing APIs, best practices; designing APIs, practical exercise in) event-driven, 19-25 comparison of different types, 24 HTTP Streaming, 23-24 WebHooks, 19-22 WebSockets, 22-23 request–response comparison of types, 18 GraphQL, 14-18 REST, 10-13 RPC, 13-14 security (see security) uses of, 2 application names, misleading, pro‐ hibiting in OAuth, 40 application-level caching for APIs, 87 archiving a GitHub repository, HTTP request for, 12 asynchronous operations, 89 authentication, 27 choosing mechanism for MyFiles API (example), 66 authorization, 28 OAuth 2.0 as standard for, 29 Authorization header (HTTP) for Basic Authentication, 28 with OAuth tokens and scope, 34 automated testing (see testing) awareness tactics examples, 156 AWS (see Amazon Web Services) B backward compatibility, maintaining, 58, 127-128 Basic Authentication, 28 beta programs, 188-190 beta testers, 79 Botkit framework, 177 bottlenecks, finding, 82 207 breadth and depth analysis for devel‐ oper programs, 185 breadth developer programs, 192-197 bulk operation endpoints, support‐ ing, 95 business case for APIs, 3-7 business objectives, defining, 62-64 business-focused tech savvy audience, 145 C cache invalidation, 87 caching in developer SDKs, 115 using to scale thoughput, 87 change, managing, 117-142 aiming for consistency, 117-126 using automated testing, 120-126 backward compatibility, 127-128 planning for and communicating change, 128-141 additions to the API, 131 communication plan, 129 removing fields or endpoints, 132 versioning, 133-141 changelogs, 170 CI pipeline, 121 clickjacking, 40 client secret, ability to reset in OAuth, 39 cloud hosting providers, solutions for measuring bottlenecks, 83 code samples, 173 code snippets, 174 communication means, preferred, of developers, 149 communication plan for changes, 129 community contributions, 182-183 community, building, 192 computing resources, adding to scale applications, 85 consistency in an API, 50, 117-126 hallmarks of consistency, 118 using automated testing, 120-126 continuous integration (CI), 121 Conversations APIs (Slack), 13, 93 208 | Index CPU bottlenecks, 83 Create, Read, Update, and Delete operations (see CRUD operations) credit programs, 197 cross-site request forgery (CSRF), 27, 38 cross-site scripting (XSS), 27 CRUD operations, 10 HTTP verbs and REST conven‐ tions, 11 in MyFiles API (example), 67 in request–response API para‐ digms, 18 pros and cons of API paradigms for MyFiles API (example), 65 current actual numbers for developer funnel indicators, 155 cursor-based pagination, 99-101 advantages and disadvantages, 100 ID as cursor, 101 opaque strings as cursor, 101 timestamp as cursor, 101 custom HTTP headers rate-limit response headers, 110 with OAuth token and scope, 34 D dark launching rate-limiting, 109 data access patterns (new), introduc‐ ing in your API, 90 database indexes, 86 database profiling, 84 database replication, 85 database sharding, 85 date filters, 96 debugging tools, 179 DELETE method (HTTP), 10 (see also CRUD operations) depth developer programs, 187-192 design sprints, 160, 191 designing APIs, best practices, 47-59 design for great developer experi‐ ence, 48-59 making it fast and easy to get started, 48 making troubleshooting easy, 52-56 making your API extensible, 56 working toward consistency, 50 design for real-life use cases, 47-48 focusing on users, 2 designing APIs, practical exercise in, 61-79 scenario 1, 62-72 defining business objectives, 62-64 outlining key user stories, 64 selecting technology architec‐ ture, 65 writing an API specification, 68-72 scenario 2, 72-79 defining problem and impact statement, 73 getting feedback on the API spec, 77-79 selecting technology architec‐ ture, 74 writing an API specification, 74-77 developer ecosystem strategy, build‐ ing, 143-161 building a developer strategy, 147 defining your developer funnel, 152-154 funnel indicators, 154 deriving measurements, 160 developer segmentation, 147-150 common use cases and tasks, 148 examples of segmentation analysis, 149 identity, 147 market size and geographical distribution, 149 platform of choice, 148 preferred development lan‐ guage, framework, and tools, 148 preferred means of communi‐ cation, 149 proficiency, 148 developers, 144-147 business-focused tech savvy audience, 145 hackers, 145 hobbyist, 144 professional developers, 146 variations on categories men‐ tioned, 146 distilling the value proposition, 151 mapping current and future state, 155-156 outlining your tactics, 156-160 awareness tactics examples, 156 developer relations high-level quarterly plan, 159 proficiency tactics examples, 157 success tactics examples, 158 usage tactics examples, 157 developer programs, 185-198 breadth and depth analysis for, 185 breadth programs, 192-197 credit programs, 197 hackathons, 194 meetups and community, 192 online videos and streaming, 196 speaking at events and event sponsorships, 194 support, forums, and StackO‐ verflow, 196 train-the-trainer and ambassa‐ dor programs, 195 depth programs, 187-192 design sprints, 191 early access/beta program, 188 top partner program, 187 measuring, 197 developer relations, 143 Developer Relations core activities, 185 developer resources, 163-184 API documentation, 163-172 code samples and snippets, 172-175 Index | 209 community contribution, 182-183 development tools, 179 frameworks, 177 office hours, 181 rich media, 180 videos, 180 software development kits (SDKs), 175-177 webinars and online training, 182 developer SDKs (see software devel‐ opment kits) developers APIs for external developers first, internal developers second, 5 APIs for internal developers first, external developers second, 4 communicating with about API changes, 129 removal of fields or endpoints, 132 rate limits and, 110-112 trying APIs without signing up, 49 disk I/O, 83 documentation for APIs, 49, 163-172 changelog, 170 frequently asked questions, 168 Getting Started guides, 163 landing page, 169 reference documentation, 165 terms of service (ToS), 171 tutorials, 167 E early access/beta program, 188 edge caching, 87 error handling and exponential back‐ off in SDKs, 115 errors HTTP status codes in MyFiles API specification (example), 71 meaningful, 52-55 actionable errors and recom‐ mended error codes, 52 grouping errors into high-level categories, 53 210 | Index organizing into status codes, headers, and machinereadable and humanreadable codes, 54 event objects for MyFiles API techni‐ cal spec (example), 75 event-driven APIs, 19-25 comparison of different types, 24 HTTP Streaming, 23-24 pros and cons for MyFiles API (example), 74 WebHooks, 19-22 WebSockets, 22-23 events (developer), 194 Events API (Slack), 91 evolving API design, 90-97 adding new API methods, 92 best practices, 97 introducing new data access pat‐ terns, 90 new options to filter results, 95 supporting bulk endpoints, 95 explicit-version strategy, 134-138 exponential back-off, 115 extensibility of APIs, 56 F Facebook, ToS violations, 41 failures and retries (WebHooks), 20 feedback, getting on API specifica‐ tion, 77-79 fields in API responses, filtering, 96 filtering results, providing options for, 95 firewalls, WebHooks and, 21 fixed-window counter (rate-limiting), 107 Flannel (Slack), 88 forums, 196 frameworks, 177 frequently asked questions (FAQ), 168 function names, versioned, 136 G geographical distribution (develop‐ ers), 149 GET method (HTTP), 10 (see also CRUD operations) Getting Started guides, 163 GitHub, 5 addressing scalability challenges, 91 archiving a repository, HTTP request for, 12 OAuth scope headers in API response, 35 rate-limit response header, 111 rate-limiting at, 112 Gmail phishing attack on, 40 thin WebHook message payload, 45 Google Cloud Platform (GCP), 83 Google Developer Groups (GDG), 192 Google Hangouts, versioning case study, 140 GraphQL, 6, 14-18, 91 advantages over REST and RPC, 16 comparison to REST and RPC APIs, 18 Object Field deprecation, 133 pros and cons for MyFiles API (example), 66 gRPC, 14 gzip compression, using in SDKs, 114 custom rate-limit response head‐ ers X-RateLimit-Limit, 111 X-RateLimit-Remaining, 111 X-RateLimit-Reset, 111 organizing errors into, 54 specifying API versions in, 135 X-Frame-Options, 40 HTTP methods CRUD operations and REST con‐ ventions, 11 in REST APIs, 10 in REST, RPC, and GraphQL APIs, 18 in RPC-style APIs, 13 HTTP status codes description for errors in MyFiles API specification (example), 71 in REST APIs, 10 indicating redirection for moved/ moving resources, 135 organizing errors into, 54 returning for rate limits, 110 HTTP Streaming, 23-24 comparison with WebHooks and WebSockets, 24 pros and cons for MyFiles eventdriven API (example), 74 HTTPs endpoints (OAuth), 39 human-readable errors, 52 H I hackathons, 194 hackers, 145 hash-based message authentication code (HMAC), 43 Hello World exercise, 163 hobbyist developers, 144 horizontal scaling, 85 HTTP, 9 (see also request–response APIs) in RPC-style APIs, 13 HTTP headers custom OAuth headers X-Accepted-OAuth-Scopes, 35 X-OAuth-Scopes, 35 ID as cursor, 101 identity (developers), 147 iframes, rendering of authorization screen, disallowing, 40 impact statement for MyFiles API (example), 63 scenario 2, 73 indexes (database), 86 integrated development environ‐ ments (IDEs), 148 interface definition language, 122-126 interface description language (IDL), 122 interfaces, 1 Index | 211 J JavaScript object notation (JSON), 122 JSON responses in REST APIs, 11 JSON web APIs, 122-126 describing and validating requests, 125 describing and validating respon‐ ses, 123 K key indicators status report (devel‐ oper funnel), 155 key performance indicators (KPIs), connecting to developer activities, 160 L landing page for API documentation, 169 load testing, 84 logging changelog, 170 use in troubleshooting developer issues, 55 M machine-readable error codes, 52 Macys.com responsive checkout, 78 MAJOR, MINOR, and PATCH ver‐ sions, 137 managing change (see change, man‐ aging) market potential (developer funnel indicators), 155 market size, 149 measurements of developer activities, 160 measuring developer programs, 197 meetups and community, 192 memory bottlenecks, 83 methods, adding to APIs, 92 MINOR versions, 137 mocking data for interactive user testing, 78 Mutual TLS (Transport Layer Secu‐ rity), 44 212 | Index N network I/O, 83 noise (in WebHooks), 21 non-CRUD operations in REST APIs, 12 O OAuth, 28-42, 50 benefits of, 29 best practices, 38 listing and revoking authoriza‐ tions, 37 scopes, 32 Slack's move to granular OAuth scopes, 34 selection for use in MyFiles API (example), 66 token and scope validation, 34 token expiry and refresh tokens, 35 token generation, 30 objective key results (OKRs), 159 office hours, 181 offset-based pagination, 97 advantages and disadvantages, 98 opaque strings as cursor, 101 OpenAPI, 125 order filters, 96 P paginating APIs, 97-102 best practices, 102 cursor-based pagination, 99-101 advantages and disadvantages, 100 choosing cursor contents, 101 offset-based pagination, 97 advantages and disadvantages, 98 pagination support in developer SDKs, 114 partner engineers, 187 PATCH method (HTTP), 10 (see also CRUD operations) PATCH versions, 137 personally identifiable information (PII), 55 phishing attacks using misleading application names, 40 platform of choice (developers), 148 polling, 19 solving as API scaling problem in REST APIs, 90 WebHooks vs., 19 POST method (HTTP), 10 (see also CRUD operations) problem and impact statement for MyFiles API (example), 63 scenario 2, 73 professional developers, 146 proficiency (developer), 148 proficiency tactics examples, 157 profiling code, 83 programming languages, 148 implementing code snippets in, 174 PUT method (HTTP), 10 (see also CRUD operations) Q quarterly plan for developer relations, 159 R rate-limit response headers, 110 rate-limiting APIs, 102-114 best practices, 112 implementation strategies, 105-110 fixed-window counter, 107 sliding-window counter, 108 token bucket algorithm, 105 rate limits and developers, 110-112 documenting rate limits, 111 rate-limit status API, 111 rate-limiting policy, 103 Slack's rate-limiting, lessons learned from, 113 Stripe's rate-limiting strategies, 104 Read method, 10 (see also CRUD operations) read/write scopes, 32 Real-Time Messaging API, 91 reference apps, 173 reference documentation, 165 refresh tokens, 36 one-time-use, 39 remote procedure calls (RPCs), 13 (see also RPC APIs) removing endpoints or fields from APIs, 132 replay attacks, 43 request logs, providing for develop‐ ers, 55 requests adding request parameters to con‐ trol output, 131 describing and validating, 125 request parameters in version schemes, 136 request–response APIs, 9-19 comparison of different types, 18 GraphQL, 14-17 REST, 10-13 RPC, 13-14 resources (in REST APIs), 10 showing relationships between, 11 responses adding response fields, 131 describing and validating, 123-125 REST APIs, 10-13 comparison to RPC and GraphQL, 18 CRUD operations, HTTP verbs, and REST conventions, 11 general rules for, 10 non-CRUD operations, 12 payload creep, 17 polling as scaling problem, solv‐ ing, 90 pros and cons for MyFiles API (example), 66 showing relationships among resources, 11 retries (WebHooks), 20 rich media, 180-181 rich site summary (RSS) feed, adding to changelog, 170 RPC APIs, 13-14 Index | 213 comparison to REST and GraphQL, 18 general rules for, 13 HTTP request to Slack API, 13 pros and cons for MyFiles API (example), 66 Slack, Conversations APIs, 13 using protocols other than HTTP, 14 RSpec test using JSON Schema speci‐ fication, 125 S sandboxes and API testers, 179 scaling APIs, 81-116 evolving your API design, 90-97 adding new API methods, 92 best practices, 97 introducing new data access patterns, 90 providing new options to filter results, 95 supporting bulk endpoints, 95 providing developer SDKs, 114-116 caching frequently used data, 115 error handling and exponen‐ tial back-off, 115 pagination support, 114 rate-limiting support, 114 SDK best practices, 115 using gzip compression in SDKs, 114 scaling throughput, 82-90 adding computing resources, 85 best practices, 89 caching, 87 database indexes, 86 doing expensive operations asynchronously, 89 finding bottlenecks, 82 using pagination, 97-102 best practices, 102 cursor-based pagination, 99-101 offset-based pagination, 97 214 | Index using rate-limiting, 102-114 best practices, 112 implementation strategies, 105-110 rate limits and developers, 110-112 scopes (OAuth), 32 for sensitive information, 39 for use in MyFiles API (example), 66 in MyFiles API (example) scopes, operations, and resour‐ ces, 67 Slack's move to granular OAuth scopes, lessons learned from, 34 SDKs (see software development kits) search filters, 96 search operations in REST APIs, 12 security, 27-46 authentication and authorization, 27 for WebHooks, 20 OAuth, 28-42 best practices, 38 listing and revoking authoriza‐ tions, 37 scopes, 32 token and scope validation, 34 token expiry and refresh tokens, 35 token generation, 30 WebHooks, 42-45 semantic versioning specification (SemVer), 137 server-sent events (SSE), streaming data via, 24 short-lived authorization codes (OAuth), 39 short-term targets and market poten‐ tial (developer funnel indicators), 155 signatures (WebHook), 43 Slack APIs, 4 adding new API methods, 92 addressing scalability challenges with Events API, 91 API Metadata, 126 app credentials of Slack app with verification token, 42 changelog, 170 Conversations API, 132 supporting bulk operations, 95 developer segmentation for Slack, 149 early access/beta program, 189 Flannel, application-level edge cache, 87 inconsistency in, 118 long-lived tokens, 37 missing field on message pay‐ loads, 127 move to granular OAuth scopes, lessons learned from, 34 rate-limiting, lessons learned from, 113 RPC-style web API, 13 translation layer to maintain backward compatibility, 57 WebSocket-based real-time mes‐ saging API, 22 sliding-window counter (ratelimiting), 108 snippets (code), 174 software as a service (SaaS) compa‐ nies, 4 software development kits (SDKs), 50, 148, 175-177 developer SDKs, 114-116 best practices, 115 caching frequently used data, 115 error handling and exponen‐ tial back-off, 115 pagination support, 114 rate-limiting support, 114 using gzip compression, 114 maintaining, 178 SoundCloud API, value proposition, 151 speaking at developer events, 194 specification (spec), writing for an API, 68-72 MyFiles API WebHooks (exam‐ ple), 74-77 SQL databases, queries based on cur‐ sor values, 99 Stack Overflow, 196 Stackdrivers, 83 stakeholders, reviewing API specifi‐ cation with, 77 state parameter support (OAuth), 38 streaming, 196 Stripe online testing of API by develop‐ ers without signing up, 49 rate-limiting, 104 value proposition, 151 versioning case study, 139 subresources in APIs, 11 success tactics examples, 158 support for developers, 196 T task queues, 89 TCP (Transport Control Protocol), 22 technology architecture, selecting scenario 1 for MyFiles API (exam‐ ple), 65 scenario 2 for MyFiles API (exam‐ ple), 74 terms of service (ToS) violations of Facebook ToS, 41 writing, 171 testing automated, 120-126 describing and validating requests, 125 describing and validating responses, 123-125 sandboxes and API testers, 179 Thrift, 14 throughput, scaling, 82-90 adding computing resources, 85 best practices, 89 caching, 87 database indexes, 86 doing expensive operations asyn‐ chronously, 89 finding bottlenecks, 82 the Time to Hello World (TTHW), 164 Index | 215 timestamps, using as cursors, 101 TLS (Transport Layer Security), 44 token bucket algorithm (ratelimiting), 105 tokens (OAuth) and scope, validation of, 34 expiry and refresh tokens, 35 generation of, 30 Slack's long-lived tokens, 37 top partner program, 187 train-the-trainer and ambassador programs, 195 transformations between versions, 137 Transport Control Protocol (see TCP) transport patterns for MyFiles API (example), 65 troubleshooting, making easy for developers, 52-56 building tooling, 55 meaningful errors, 52-55 providing troubleshooting tools, 179 tutorials for APIs, 49, 167 Twitch, deprecation of an API, 59 Twitter, 90 cursor-based pagination, 99 U Uber developers and rate-limiting, 109 Unix timestamp as cursor, 99 Update method, 10 (see also CRUD operations) URI components, specifying versions in, 135 URIs, specification for MyFiles API (example), 70 usage tactics examples, 157 user interfaces (UIs), 88 user stories (key), outlining scenario 1 for MyFiles API (exam‐ ple), 64 scenario 2 for MyFiles API (exam‐ ple), 73 users, focusing on in API design, 2 216 | Index users.conversations API method (Slack), 94 V value proposition, distilling for your API, 151 verification tokens, 42 versioning APIs, 57, 133-141 additive-change strategy, 133 case study, Google Hangouts, 140 case study, Stripe, 139 explicit-version strategy, 134-138 policies for MAJOR and MINOR changes, 138 process management, 141 vertical scaling, 85 videos creating, 180 online videos and streaming, 196 W WebHooks, 19-22, 90 comparison with WebSockets and HTTP Streaming, 24 considerations for use in MyFiles API (example), 66 MyFiles API Webhooks Spec (example), 74-77 polling vs., 19 pros and cons for MyFiles eventdriven API (example), 74 security, 42-45 best practices, 45 Mutual Transport Layer Secu‐ rity, 44 request signing and WebHook signatures, 43 thin payloads and API retrieval, 44 verification tokens, 42 supporting, additional complexi‐ ties added by, 20 webinars and online training, 182 WebSockets, 22-23, 90 comparison with WebHooks and HTTP Streaming, 24 pros and cons for MyFiles eventdriven API (example), 74 Y YouTube API, value proposition, 151 X XML responses, REST APIs, 11 Index | 217 About the Authors Brenda Jin is an entrepreneur and software engineer.

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It) by Salim Ismail, Yuri van Geest

23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, anti-fragile, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, behavioural economics, Ben Horowitz, bike sharing, bioinformatics, bitcoin, Black Swan, blockchain, Blue Ocean Strategy, book value, Burning Man, business intelligence, business process, call centre, chief data officer, Chris Wanstrath, circular economy, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, commoditize, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, data science, Dean Kamen, deep learning, DeepMind, dematerialisation, discounted cash flows, disruptive innovation, distributed ledger, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fail fast, game design, gamification, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, holacracy, Hyperloop, industrial robot, Innovator's Dilemma, intangible asset, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Joi Ito, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, lifelogging, loose coupling, loss aversion, low earth orbit, Lyft, Marc Andreessen, Mark Zuckerberg, market design, Max Levchin, means of production, Michael Milken, minimum viable product, natural language processing, Netflix Prize, NetJets, Network effects, new economy, Oculus Rift, offshore financial centre, PageRank, pattern recognition, Paul Graham, paypal mafia, peer-to-peer, peer-to-peer model, Peter H. Diamandis: Planetary Resources, Peter Thiel, Planet Labs, prediction markets, profit motive, publish or perish, radical decentralization, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Rutger Bregman, Salesforce, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, SpaceShipOne, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Jurvetson, subscription business, supply-chain management, synthetic biology, TaskRabbit, TED Talk, telepresence, telepresence robot, the long tail, Tony Hsieh, transaction costs, Travis Kalanick, Tyler Cowen, Tyler Cowen: Great Stagnation, uber lyft, urban planning, Virgin Galactic, WikiLeaks, winner-take-all economy, X Prize, Y Combinator, zero-sum game

And they have profoundly changed the way we communicate and interact with one another. One reason for that change is that the cost of distributing a product or service, particularly if can be converted almost entirely to information, has dropped almost to zero. It used to require millions of dollars in servers and software to launch a software company. Thanks to Amazon Web Services (AWS), it now costs just a tiny fraction of that amount. Similar stories can be found in every department in every industry of the modern economy. History and common sense make clear that you cannot radically transform every part of an organization—and accelerate the underlying clock of that enterprise to hyper-speed—without fundamentally changing the nature of that organization.

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It’s hard to pin down exactly when this new organizational form emerged. Various aspects of ExOs have been around for decades, but it is only over the last few years that they have really started to matter. If we had to pick an official ExO origin date, it would be March 2006, when Amazon launched Amazon Web Services and created the low-cost “Cloud” for medium and small businesses. From that date on, the cost of running a data center moved from a fixed CAPEX (Capital Expenditure) cost to a variable cost. Today, it is almost impossible to find a single startup that doesn’t use AWS. We have even found a simple metric that helps to identify and distinguish emerging Exponential Organizations: a minimum 10x improvement in output over four to five years.

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The information age now enables Apple and other companies to access physical assets anytime and anywhere, rather than requiring that they actually possess them. Technology enables organizations to easily share and scale assets not only locally, but also globally, and without boundaries. As we noted earlier, the launch of Amazon Web Services in March 2006 was a key inflection point in the rise of ExOs. The ability to lease on-demand computing that would scale on a variable cost basis utterly changed the IT industry. A new Silicon Valley phenomenon called TechShop is another example of this trend. In the same way that gyms use a membership model to aggregate expensive exercise machinery that few could afford to have at home, TechShop collects expensive manufacturing machinery and offers subscribers a small monthly fee ($125 to $175, depending on the location) for unlimited access to its assets.

Rule of the Robots: How Artificial Intelligence Will Transform Everything by Martin Ford

AI winter, Airbnb, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, basic income, Big Tech, big-box store, call centre, carbon footprint, Chris Urmson, Claude Shannon: information theory, clean water, cloud computing, commoditize, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Elon Musk, factory automation, fake news, fulfillment center, full employment, future of work, general purpose technology, Geoffrey Hinton, George Floyd, gig economy, Gini coefficient, global pandemic, Googley, GPT-3, high-speed rail, hype cycle, ImageNet competition, income inequality, independent contractor, industrial robot, informal economy, information retrieval, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, job automation, John Markoff, Kiva Systems, knowledge worker, labor-force participation, Law of Accelerating Returns, license plate recognition, low interest rates, low-wage service sector, Lyft, machine readable, machine translation, Mark Zuckerberg, Mitch Kapor, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Ocado, OpenAI, opioid epidemic / opioid crisis, passive income, pattern recognition, Peter Thiel, Phillips curve, post scarcity, public intellectual, Ray Kurzweil, recommendation engine, remote working, RFID, ride hailing / ride sharing, Robert Gordon, Rodney Brooks, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Silicon Valley startup, social distancing, SoftBank, South of Market, San Francisco, special economic zone, speech recognition, stealth mode startup, Stephen Hawking, superintelligent machines, TED Talk, The Future of Employment, The Rise and Fall of American Growth, the scientific method, Turing machine, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, universal basic income, very high income, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y Combinator

One analysis from the research firm Gartner, for example, projects that neuromorphic designs will largely displace GPUs as the primary hardware platform for AI by 2025.2 CLOUD COMPUTING AS THE PRIMARY INFRASTRUCTURE FOR ARTIFICIAL INTELLIGENCE Today’s cloud computing industry got its start in 2006 with the launch of Amazon Web Services, or AWS. Amazon’s strategy was to leverage its expertise in building and managing the massive data centers that powered its online shopping service by selling flexible access to computing resources hosted in similar facilities to a wide range of clients. As of 2018, Amazon Web Services operated more than one hundred data centers located in nine different countries throughout the world.3 The growth of the cloud services provided by Amazon and its competitors has been staggering.

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In other words, women and people of color are more likely to generate an incorrect match. In 2018, the American Civil Liberties Union compared images of all 538 members of the U.S. Congress to a large dataset of booking photographs taken of people who had been arrested. The ACLU used the Rekognition system available through Amazon Web Services, which is becoming increasingly popular with police departments because it is available at a very low cost. The ACLU was able to conduct the experiment for only about $12. The system flagged twenty-eight members of Congress as arrestees whose photos were contained in the mug shot dataset. Assuming that none of the individuals who were arrested have, in fact, been elected to either the House or the Senate, these were all false positives.

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Annie Palmer, “Judge temporarily blocks Microsoft Pentagon cloud contract after Amazon suit,” CNBC, February 13, 2020, www.cnbc.com/2020/02/13/amazon-gets-restraining-order-to-block-microsoft-work-on-pentagon-jedi.html. 11. Lauren Feiner, “DoD asks judge to let it reconsider decision to give Microsoft $10 billion contract over Amazon,” CNBC, March 13, 2020, www.cnbc.com/2020/03/13/pentagon-asks-judge-to-let-it-reconsider-its-jedi-cloud-contract-award.html. 12. “TensorFlow on AWS,” Amazon Web Services, accessed May 4, 2020, aws.amazon.com/tensorflow/. 13. Kyle Wiggers, “Intel debuts Pohoiki Springs, a powerful neuromorphic research system for AI workloads,” VentureBeat, March 18, 2020, venturebeat.com/2020/03/18/intel-debuts-pohoiki-springs-a-powerful-neuromorphic-research-system-for-ai-workloads/. 14.

Platform Revolution: How Networked Markets Are Transforming the Economy--And How to Make Them Work for You by Sangeet Paul Choudary, Marshall W. van Alstyne, Geoffrey G. Parker

3D printing, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, Andrei Shleifer, Apple's 1984 Super Bowl advert, autonomous vehicles, barriers to entry, Benchmark Capital, big data - Walmart - Pop Tarts, bitcoin, blockchain, business cycle, business logic, business process, buy low sell high, chief data officer, Chuck Templeton: OpenTable:, clean water, cloud computing, connected car, corporate governance, crowdsourcing, data acquisition, data is the new oil, data science, digital map, discounted cash flows, disintermediation, driverless car, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, Free Software Foundation, gigafactory, growth hacking, Haber-Bosch Process, High speed trading, independent contractor, information asymmetry, Internet of things, inventory management, invisible hand, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, Kevin Roose, Khan Academy, Kickstarter, Lean Startup, Lyft, Marc Andreessen, market design, Max Levchin, Metcalfe’s law, multi-sided market, Network effects, new economy, PalmPilot, payday loans, peer-to-peer lending, Peter Thiel, pets.com, pre–internet, price mechanism, recommendation engine, RFID, Richard Stallman, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Salesforce, Satoshi Nakamoto, search costs, self-driving car, shareholder value, sharing economy, side project, Silicon Valley, Skype, smart contracts, smart grid, Snapchat, social bookmarking, social contagion, software is eating the world, Steve Jobs, TaskRabbit, The Chicago School, the long tail, the payments system, Tim Cook: Apple, transaction costs, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, winner-take-all economy, zero-sum game, Zipcar

Cecil, “Uber, Lyft, and Other Rideshare Drivers Now Have Insurance Options,” Policy Genius, https://www.policygenius.com/blog/uber-lyft-and-other-rideshare-drivers-now-have-insurance-options/, accessed June 14, 2015. 48. Huckman, Pisano, and Kind, “Amazon Web Services.” 49. Jillian D’Onfro, “Here’s a Reminder Just How Massive Amazon’s Web Services Business Is,” Business Insider, June 16, 2014, http://www .businessinsider.com/amazon-web-services-market-share-2014-6. 50. Annabelle Gawer and Michael A. Cusumano, Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry Innovation (Boston: Harvard Business School Press, 2002). 51. Adapted from Gawer and Cusumano, Platform Leadership. 52.

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Baldwin and K. B. Clark of Harvard Business School describe a well-designed platform as consisting of a stable core layer that restricts variety, sitting underneath an evolving layer that enables variety.12 Today’s best-designed platforms incorporate this structural principle. For example, Amazon Web Services (AWS), the most successful platform for providing cloud-based information storage and management, focuses on optimizing a handful of basic operations, including data storage, computation, and messaging.13 Other services, which are used by just a fraction of AWS customers, are restricted to the periphery of the platform and provided through purpose-built apps.

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All service interfaces, without exception, must be designed from the ground up to be externalizable. That is to say, the team must plan and design to be able to expose the interface to developers in the outside world. No exceptions. 6. Anyone who doesn’t do this will be fired. 7. Thank you; have a nice day! Astute application of this principle of transparency underlies the success of Amazon Web Services (AWS), the platform’s giant cloud services company. Andrew Jassy, Amazon’s vice president of technology, had observed how different divisions of Amazon kept having to develop web service operations to store, search, and communicate data.48 Jassy urged that these varied projects should be combined into a single operation with one clear, flexible, and universally comprehensible set of protocols.

Arriving Today: From Factory to Front Door -- Why Everything Has Changed About How and What We Buy by Christopher Mims

air freight, Airbnb, Amazon Robotics, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, big-box store, blue-collar work, Boeing 747, book scanning, business logic, business process, call centre, cloud computing, company town, coronavirus, cotton gin, COVID-19, creative destruction, data science, Dava Sobel, deep learning, dematerialisation, deskilling, digital twin, Donald Trump, easy for humans, difficult for computers, electronic logging device, Elon Musk, Frederick Winslow Taylor, fulfillment center, gentrification, gig economy, global pandemic, global supply chain, guest worker program, Hans Moravec, heat death of the universe, hive mind, Hyperloop, immigration reform, income inequality, independent contractor, industrial robot, interchangeable parts, intermodal, inventory management, Jacquard loom, Jeff Bezos, Jessica Bruder, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kaizen: continuous improvement, Kanban, Kiva Systems, level 1 cache, Lewis Mumford, lockdown, lone genius, Lyft, machine readable, Malacca Straits, Mark Zuckerberg, market bubble, minimum wage unemployment, Nomadland, Ocado, operation paperclip, Panamax, Pearl River Delta, planetary scale, pneumatic tube, polynesian navigation, post-Panamax, random stow, ride hailing / ride sharing, robot derives from the Czech word robota Czech, meaning slave, Rodney Brooks, rubber-tired gantry crane, scientific management, self-driving car, sensor fusion, Shenzhen special economic zone , Shoshana Zuboff, Silicon Valley, six sigma, skunkworks, social distancing, South China Sea, special economic zone, spinning jenny, standardized shipping container, Steve Jobs, supply-chain management, surveillance capitalism, TED Talk, the scientific method, Tim Cook: Apple, Toyota Production System, traveling salesman, Turing test, two-sided market, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, vertical integration, warehouse automation, warehouse robotics, workplace surveillance

Fast, cloud-based databases might seem mundane, but it’s worth noting that nothing that will happen next in this object’s journey through the warehouse would be possible were it not for the utter ubiquity of modern information technology. And the IT backbone of Amazon’s retail operation is, of course, its own industry-dominating cloud services, known as Amazon Web Services (AWS). AWS encompasses nearly 200 (and counting) cloud-based IT services, from databases and cloud computing to AI and general-purpose storage. Our cardboard box of several dozen individually packaged USB chargers is then processed by a “receiver,” a person who inspects them, scans the individual bar code on each item, confirms the items are intact and that they match their descriptions, and then drops them into yellow totes.

…

In the entire history of Amazon, there have only been four people in this role, while also being the public face of Amazon’s fulfillment and delivery operations. First there was Jimmy Wright, former vice president in charge of distribution at Walmart from 1990 to 1998, who came out of retirement for a fourteen-month stint at Amazon from 1998 to 1999. Then there was Jeff Wilke, who by the end of his time at Amazon in early 2021 was tied with Amazon Web Services chief Andy Jassy as Bezos’s most senior and trusted deputy. As of 2021, there’s Dave Clark, who is also the public face of Amazon’s fulfillment and delivery operations. In between Wilke and Clark, from 2006 until his retirement in 2013, there was Marc. While Marc was at Amazon, the company grew from $7 billion a year in revenue to $70 billion.

…

fish items out of the trash: Khadeeja Safdar, Shane Shifflett, and Denise Blostein, “You Might Be Buying Trash on Amazon—Literally,” Wall Street Journal, December 18, 2019, https://www.wsj.com/articles/you-might-be-buying-trash-on-amazonliterally-11576599910. AWS encompasses: “Cloud Products,” Amazon Web Services, https://aws.amazon.com/products. You’ve got to be crazy: Jennifer Alsever, “Robot Workers Take Over Warehouses,” CNN, November 9, 2011, https://money.cnn.com/2011/11/09/smallbusiness/kiva_robots/index.htm. pioneered by Walmart: Charles Fishman, The Wal-Mart Effect (London: Penguin Books, 2006). 16 percent of all retail sales: “Commerce Retail Sales as a Percent of Total Sales,” Federal Reserve Bank of St.

Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone by Satya Nadella, Greg Shaw, Jill Tracie Nichols

3D printing, AlphaGo, Amazon Web Services, anti-globalists, artificial general intelligence, augmented reality, autonomous vehicles, basic income, Bretton Woods, business process, cashless society, charter city, cloud computing, complexity theory, computer age, computer vision, corporate social responsibility, crowdsourcing, data science, DeepMind, Deng Xiaoping, Donald Trump, Douglas Engelbart, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, equal pay for equal work, everywhere but in the productivity statistics, fault tolerance, fulfillment center, Gini coefficient, global supply chain, Google Glasses, Grace Hopper, growth hacking, hype cycle, industrial robot, Internet of things, Jeff Bezos, job automation, John Markoff, John von Neumann, knowledge worker, late capitalism, Mars Rover, Minecraft, Mother of all demos, Neal Stephenson, NP-complete, Oculus Rift, pattern recognition, place-making, Richard Feynman, Robert Gordon, Robert Solow, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, Snow Crash, special economic zone, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, subscription business, TED Talk, telepresence, telerobotics, The Rise and Fall of American Growth, The Soul of a New Machine, Tim Cook: Apple, trade liberalization, two-sided market, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, young professional, zero-sum game

By 2008, storm clouds were gathering over Microsoft. PC shipments, the financial lifeblood of Microsoft, had leveled off. Meanwhile sales of Apple and Google smartphones and tablets were on the rise, producing growing revenues from search and online advertising that Microsoft hadn’t matched. Meanwhile, Amazon had quietly launched Amazon Web Services (AWS), establishing itself for years to come as a leader in the lucrative, rapidly growing cloud services business. The logic behind the advent of the cloud was simple and compelling. The PC Revolution of the 1980s, led by Microsoft, Intel, Apple, and others, had made computing accessible to homes and offices around the world.

…

He wrote about Bayesian estimators, machine learning, pattern recognition, and probabilistic decision making. “The advances in data management developed by Amazon engineers have been the starting point for the architectures underneath the cloud storage and data management services offered by Amazon Web Services (AWS),” he wrote. Amazon was leading a revolution and we had not even mustered our troops. Years earlier I had left Sun Microsystems to help Microsoft capture the lead in the enterprise market, and here we were once again far behind. As a company, we’d been very publicly missing the mobile revolution, but we were not about to miss the cloud.

…

People, the human element of any enterprise, are ultimately the greatest asset, and so I set about assembling the right team, starting with Scott Guthrie, a very accomplished Microsoft engineer. He had spearheaded a number of successful company technologies focused on developers. I tapped him to lead engineering for Azure on its way to becoming Microsoft’s cloud platform—our answer to Amazon Web Services. Over time, many others from both inside and outside the company joined our effort. Jason Zander, another key leader who built .Net and Visual Studio, joined to lead the core Azure infrastructure. We recruited the highly regarded Big Data researcher Raghu Ramakrishnan from Yahoo and James Phillips who had cofounded the database company Couchbase.

The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology by William Mougayar

Airbnb, airport security, Albert Einstein, altcoin, Amazon Web Services, bitcoin, Black Swan, blockchain, business logic, business process, centralized clearinghouse, Clayton Christensen, cloud computing, cryptocurrency, decentralized internet, disintermediation, distributed ledger, Edward Snowden, en.wikipedia.org, Ethereum, ethereum blockchain, fault tolerance, fiat currency, fixed income, Ford Model T, global value chain, Innovator's Dilemma, Internet of things, Kevin Kelly, Kickstarter, market clearing, Network effects, new economy, peer-to-peer, peer-to-peer lending, prediction markets, pull request, QR code, ride hailing / ride sharing, Satoshi Nakamoto, sharing economy, smart contracts, social web, software as a service, too big to fail, Turing complete, Vitalik Buterin, web application, Yochai Benkler

Blockchain virtual machines may be too expensive if we are to literally compare their functionality to a typical cloud service such as Amazon Web Services or DigitalOcean, but they will be be certainly useful for smart contracts that execute their logic on the blockchain’s virtual machinery, or decentralized applications, also called Dapps. As a sidenote, we could also see a future where client nodes can talk to each other directly in scenarios where blockchains are too expensive or slow. When you run an application in the cloud (for example, on Amazon Web Services or Microsoft Azure), you are billed according to a combination of time, storage, data transfer, and computing speed requirements.

…

These new areas will include banking without banks, gambling without the house’s edge, title transfers without central authorities stamping them, e-commerce without eBay, registrations without government officials overseeing them, computer storage without Dropbox, transportation services without Uber, computing without Amazon Web Services, online identities without Google, and that list will continue to grow. Take any services and add “without previous center-based authority,” and replace with “peer-to-peer, trust-based network,” and you will start to imagine the possibilities. The general characteristics of decentralization-based services include: Speed in settlements No intermediary delays Upfront identification and reputation Flat structure with no overhead Permission-less user access Trust built inside the network Resiliency against attacks No censorship No central point of failure Governance decisions by consensus Peer-to-peer communications THE CRYPTO ECONOMY What started as Bitcoin, the poster child cryptocurrency that captured our imagination, is leading to a multiplicity of blockchain-enabled businesses and implementations.

Deploying OpenStack by Ken Pepple

Amazon Web Services, cloud computing, database schema, Infrastructure as a Service, Kickstarter, Ruby on Rails, web application, x509 certificate

It is intended to provide the reader with a solid understanding of the OpenStack project goals, details of specific OpenStack software components, general design decisions, and detailed steps to deploy OpenStack in a few controlled scenarios. Along the way, readers would also learn common pitfalls in architecting, deploying, and implementing their cloud. Intended Audience This book assumes that the reader is familiar with public Infrastructure as a Service (IaaS) cloud offerings such as Rackspace Cloud or Amazon Web Services. In addition, it demands an understanding of Linux systems administration, such as installing servers, networking with iptables, and basic virtualization technologies. Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions.

…

# nova-manage db sync # To view the database scheme version, use the db version arguments: # nova-manage db version 14 Note The database version for Cactus is 14 Instance Types and Flavors Instance types (or “flavors,” as the OpenStack API calls them) are resources granted to instances in Nova. In more specific terms, this is the size of the instance (vCPUs, RAM, Storage, etc.) that will be launched. You may recognize these by the names “m1.large” or “m1.tiny” in Amazon Web Services EC2 parlance. The OpenStack API calls these “flavors” and they tend to have names like “256 MB Server.” Instance types or flavors are managed through nova-manage with the instance_types command and an appropriate subcommand. At the current time, instance type manipulation isn’t exposed through the APIs nor the adminclient.

The Authoritarian Moment: How the Left Weaponized America's Institutions Against Dissent by Ben Shapiro

2021 United States Capitol attack, active measures, affirmative action, Affordable Care Act / Obamacare, Alan Greenspan, Amazon Web Services, Bernie Sanders, Big Tech, Black Lives Matter, coronavirus, COVID-19, critical race theory, crowdsourcing, defund the police, delayed gratification, deplatforming, disinformation, don't be evil, Donald Trump, fake news, Ferguson, Missouri, future of work, gender pay gap, George Floyd, global pandemic, green new deal, Greta Thunberg, Herbert Marcuse, hiring and firing, illegal immigration, income inequality, Intergovernmental Panel on Climate Change (IPCC), It's morning again in America, Jon Ronson, Kevin Roose, lockdown, Mark Zuckerberg, mass incarceration, microaggression, mutually assured destruction, New Journalism, obamacare, Overton Window, Parler "social media", Robert Bork, Ronald Reagan, Saturday Night Live, Silicon Valley, Snapchat, social distancing, Social Justice Warrior, Steven Pinker, Susan Wojcicki, tech bro, the scientific method, TikTok, Tim Cook: Apple, War on Poverty, yellow journalism

Then, in the aftermath of the riot, Apple’s app store removed Parler, as did the Google Play store. The excuse: supposedly, Parler users had coordinated with regard to the January 6 protests, and Parler had allowed inflammatory and threatening material to remain up. The final blow came when Amazon Web Services—a company that merely provides cloud-based web infrastructure for companies—canceled Parler altogether, taking it offline. AWS, Parler CEO John Matze wrote, “will be banning Parler until we give up free speech, institute broad and invasive policies like Twitter and Facebook and we become a surveillance platform by pursuing guilt of those who use Parler before innocence.”27 As it turned out, Facebook and Twitter had been used by Capitol protesters to coordinate as well.

…

Leftist interest groups immediately began pressuring other major banks to do the same: American Federation of Teachers president Randi Weingarten said the union would not recommend Wells Fargo’s mortgage lending program to its members because of ties to the gun industry.40 In May 2019, Chase Bank began closing bank accounts for customers deemed radical, including Enrique Tarrio of the Proud Boys and radical activist Laura Loomer. Jamie Dimon, CEO of Chase Bank, said, “Very directly, we have not and do not debank people because of their political views.”41 For now, presumably. This threat extends beyond the financial services industry. When Amazon Web Services, whose sole job is to provide cloud services, decides to deplatform Parler, that’s polarizing. When Mailchimp, an email delivery service, refuses to do business with the Northern Virginia Tea Party, that’s polarizing.42 When PayPal announces that it uses slurs from the Southern Poverty Law Center to determine which groups to ban, that’s polarizing.43 When Stripe announces it will not process funds for the Trump campaign website after January 6, that’s polarizing.44 The question here isn’t whether you like any of these groups.

…

After the January 6 riots, based on vaguely sourced reports that Parler had been an organizing place for the rioters, Apple, Amazon, and Google all barred Parler. Apple’s App Store barred Parler on the basis that Parler’s processes were “insufficient” to “prevent the spread of dangerous and illegal content.” Amazon Web Services used its power to kick Parler off the internet entirely, denying it access to its cloud hosting service. Amazon’s excuse: Parler had allowed “posts that clearly encourage and incite violence,” and that it had no “effective process to comply with the AWS terms of service.”53 None of the big tech companies could explain what, precisely, a minimum standard would have looked like.

Attack of the 50 Foot Blockchain: Bitcoin, Blockchain, Ethereum & Smart Contracts by David Gerard

altcoin, Amazon Web Services, augmented reality, Bernie Madoff, bitcoin, Bitcoin Ponzi scheme, blockchain, Blythe Masters, Bretton Woods, Californian Ideology, clean water, cloud computing, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, distributed ledger, Dogecoin, Dr. Strangelove, drug harm reduction, Dunning–Kruger effect, Ethereum, ethereum blockchain, Extropian, fiat currency, financial innovation, Firefox, Flash crash, Fractional reserve banking, functional programming, index fund, information security, initial coin offering, Internet Archive, Internet of things, Kickstarter, litecoin, M-Pesa, margin call, Neal Stephenson, Network effects, operational security, peer-to-peer, Peter Thiel, pets.com, Ponzi scheme, Potemkin village, prediction markets, quantitative easing, RAND corporation, ransomware, Ray Kurzweil, Ross Ulbricht, Ruby on Rails, Satoshi Nakamoto, short selling, Silicon Valley, Silicon Valley ideology, Singularitarianism, slashdot, smart contracts, South Sea Bubble, tulip mania, Turing complete, Turing machine, Vitalik Buterin, WikiLeaks

As of March 2015, a full third of all Bitcoin exchanges up to then had been hacked, and nearly half had closed.80 Since the exchanges are largely uninsured, unregulated and not required to keep reserves, depositors’ money goes up in smoke. It’s not just scamminess on the part of the proprietors, but sheer jawdropping incompetence: Bitomat, then the third-largest exchange, were keeping the whole site’s wallet file on an Amazon Web Services EC2 server in the cloud that didn’t have separate backups and was set to “ephemeral,” i.e., it would disappear if you restarted it. Guess what happened in July 2011? Whoops.81 Bitcoinica was its sixteen-year-old creator’s first serious PHP project. He read up on PHP, Ruby on Rails, personal finance and startups, and wrote an exchange.82 It collapsed in May 2012: “No database backups … Everyone had root.”83 The exchange’s remaining funds were lost in further hacks, after the administrators turned out to be using their (leaked) Mt.

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This is clearly superior, for a certain type of seller, to the IPO bubble of the dot-com era, in that these aren’t actually shares, and the purchasers have no influence over the funded enterprise even in theory. The ideas themselves are as bad as the worst dot-com IPOs. Digix, the first token crowdsale on the Ethereum blockchain itself, is a cryptocurrency backed by gold;309 Golem offers a “decentralized” (buzzword alert!) market in computing, like Amazon Web Services except you can only pay using their token;310 Gnosis offers semiautomatic prediction markets using their token;311 SingularDTV is a bizarre plan to fund a TV show about the Singularity in which a Caribbean island adopts Ethereum as its currency and Austrian economics works (this one gets its own section later in the book); Iconomi is an index fund of other ICOs.312 The token smart contracts are often incompetent in both intended functionality and programming ability.313 This turns out not to matter as long as they do the basic job: attract buyers and sell tokens.

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Until he reinstalled the laptop they were on, he was the proud owner of six Dogecoins. Index 8chan 53 A company for carrying on an undertaking of great advantage, but nobody to know what it is 100 Accenture 122 address 12 AIDS Trojan 72 AllCrypt 43 Alloway, Tracy 111 AlphaBay 72 AlphaPoint 84 altcoins 91 Altoid 51 Amazon Web Services 43, 98 anarcho-capitalism 18, 49 Andresen, Gavin 65 anonymity 26, 52 AntMiner 57 Apple 127, 137 arbitrage 83, 88 artificial intelligence 103, 117, 137 ASCAP 126 Ash, Jordan 96 ASIC 56 Astrobotic 94 Atlantis 54 Augur 104 Australian Securities Exchange 121 Australian Tax Office 63 Austrian economics 23, 49, 98, 137 Automattic 75 Ayre, Calvin 63 Azure Blockchain 122 b-money 19 Back, Adam 19 Bancor 95, 97 Bandcamp 130 banking the unbanked 29 baphomet (8chan) 53 BBC 32, 66 Beanie Baby 35 Berklee Rethink 128 BFX 85 Bitcoin creation of new bitcoins 14 economic aims 22 economic equality 30 exchanges 42 invention 20 irreversibility 26 limited supply 31 Satoshi Nakamoto (creator) 59 security 25 transactions per second 28 Bitcoin Bowl 77 Bitcoin Jesus 37 Bitcoin Mining Accidents 55 Bitcoin Relay Network 58 Bitcoin Savings & Trust 40 Bitcoinica 43, 83 bitcoinmarket.com 35 Bitcointalk 40, 41 Bitfinex 40, 83 BitGo 84 Bitgold 19 Bitmain 57 Bitomat 43 BitPay 43, 74 BitShares 99 BitTorrent 130, 134 Blem Information Management 116 blockchain 13 blockchain (business) 111 Blockchain for Creative Industries 132 Blockchain or the Chaingang?

Agile Project Management with Kanban (Developer Best Practices) by Eric Brechner

Amazon Web Services, cloud computing, continuous integration, crowdsourcing, data science, DevOps, don't repeat yourself, en.wikipedia.org, index card, Kaizen: continuous improvement, Kanban, loose coupling, minimum viable product, pull request, software as a service

Read further about your favorite advanced project-management topics in the books I listed in this chapter. Index A acceptance test-driven development (ATDD), 121–122 Agile practice Kaizen and, 110 mixing with Kanban, 120–123 Agile Project Management with Scrum (Schwaber), 57 Åhlström, Pär, 135 Amazon Web Services (AWS), 80 Anderson, David, 57, 118 Astels, Dave, 122 ATDD (acceptance test-driven development), 121–122 AWS (Amazon Web Services), 80 B backlog adapting from Waterfall, 41, 45, 50, 52, 54 checklist of actions to take, 24 evolving from Scrum, 58–61, 65–68 large organizations, 85–87 ordering work, 28–29 populating, 25–27 SE workflow, 109 troubleshooting problems, 17–23 Barry, Tanianne DeMaria, 120 BDD (behavior-driven development), 122 Beck, Kent, 121 behavior-driven development (BDD), 122 Benson, Jim, 120 Brooks, Frederick P., Jr., 34 buffer (drum-buffer-rope), 126–128 bugs, software adapting from Waterfall, 45–46, 48–51 defined, 102 evolving from Scrum, 62–65 ordering work, 28–29 sustained engineering, 109–111 troubleshooting problems, 20 business applications, scaling Kanban, 118–119 C chaos, setting limits on, 10–12 checklist of actions to take adapting from Waterfall, 56 deadline management, 37 deploying finished work, 83–84 evolving from Scrum, 70 Kanban, 24, 136 large organizations, 100 for management consent, 5 sustained engineering, 115–116 Chelimsky, David, 122 completed tasks measurement adapting from Waterfall, 48–51 evolving from Scrum, 62–65 conference presentations, 21 constraints defined, 11 elevating, 125 theory of constraints, 124–128 continuous delivery, 80 Continuous Delivery (Humble and Farley), 123 continuous deployment, 79–83 continuous integration, 72–75 continuous publishing, 77–79 continuous push, 75–77 core engineering team about, 102–103 ownership, 104–105 roles and responsibilities, 104 troubleshooting problems, 112–113 Cox, Jeff, 126 Crenshaw, Dave, 124 critical chain, 129–130 CTE (current task estimate), 32–35 Cumulative Flow Diagram, 32–33 current task estimate (CTE), 32–35 customers and customer interaction adapting from Waterfall, 46–48 communicating status to, 92–93 demos for customers, 21 evolving from Scrum, 67 sustained engineering, 105–106, 111, 113 D daily standup meetings about, 14–17 adapting from Waterfall, 42–43, 52 evolving from Scrum, 61, 68 Personal Kanban and, 120 sustained engineering, 115 troubleshooting problems, 23, 115 De Bonte, Austina, 119 deadline management about, 25 checklist of actions to take, 37 establishing MVP, 27–28 estimating features and tasks, 29–31 ordering technical debt, 28–29 ordering work, 28–29 populating backlog, 25–27 right-sizing teams, 33–37 tracking expected completion date, 31–33 Xbox example, 31 defects (Lean waste category), 131 deliver work step guidelines, 8–9 troubleshooting problems, 21–23 demos for customers, 21 Dennis, Zach, 122 dependencies dealing with late or unstable, 94–98 ordering work based on, 87–90 deploying finished work about, 71 checklist of actions to take, 83–84 continuous deployment, 79–83 continuous integration, 72–75 continuous publishing, 77–79 continuous push, 75–77 Xbox examples, 74–75, 77–79, 81–82 design work adapting from Waterfall, 44–45 ordering, 28–29 troubleshooting problems, 21, 23 DevOps practice, 122–123 distributed version control, 73, 75–77 distribution lists, communicating via, 93 done rule (pull criteria) about, 12–13, 15 adapting from Waterfall, 54 best practices, 13–14 evolving from Scrum, 69 implementing, 13 specifying, 13 validating, 13 drum-buffer-rope (TOC application), 126–128 E escalations defined, 102 in SE workflow, 109 troubleshooting problems, 113 estimating features and tasks, 29–31 executive reviews, 21 expected completion date, tracking, 31–33 F Farley, David, 123 features adapting from Waterfall, 42–44 estimating, 29–31 Fletcher, Drew, 119 Fowler, Martin, 121 Freedom from Command and Control (Seddon), 135 G global optimization, 132–135 Goal, The (Goldratt, et al.), 126 Goldratt, Eliyahu M., 126 H Hellesoy, Aslak, 122 Helmkamp, Bryan, 122 high-level routine capturing, 7–8 posting steps for, 8–10 hotfix, defined, 102 Humble, Jez, 123 I implement work step in daily standup meetings, 15–16 done rule, 13 guidelines, 8–9 troubleshooting problems, 17–20 improvement, product.

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There are frameworks, such as Microsoft Azure’s Application Insights, that can help you achieve the monitoring and instrumentation you need. Naturally, continuous deployment depends on an automated system to deploy builds. In case an issue comes up, the deployment system must also be capable of an automated rollback (switching back to a previous good build). Fortunately, modern cloud computing providers such as Amazon Web Services (AWS) and Microsoft Azure provide these automated systems. With audience control, monitoring, instrumentation, and automated deployment and rollbacks in place, you no longer need preproduction environments. Those environments are costly to build and maintain, and they rarely function quite like production environments because of differences in scale, load, networking, hardware, customer data, business data, and configuration settings.

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy by George Gilder

23andMe, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AlphaGo, AltaVista, Amazon Web Services, AOL-Time Warner, Asilomar, augmented reality, Ben Horowitz, bitcoin, Bitcoin Ponzi scheme, Bletchley Park, blockchain, Bob Noyce, British Empire, Brownian motion, Burning Man, business process, butterfly effect, carbon footprint, cellular automata, Claude Shannon: information theory, Clayton Christensen, cloud computing, computer age, computer vision, crony capitalism, cross-subsidies, cryptocurrency, Danny Hillis, decentralized internet, deep learning, DeepMind, Demis Hassabis, disintermediation, distributed ledger, don't be evil, Donald Knuth, Donald Trump, double entry bookkeeping, driverless car, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, fake news, fault tolerance, fiat currency, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, floating exchange rates, Fractional reserve banking, game design, Geoffrey Hinton, George Gilder, Google Earth, Google Glasses, Google Hangouts, index fund, inflation targeting, informal economy, initial coin offering, Internet of things, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, Jim Simons, Joan Didion, John Markoff, John von Neumann, Julian Assange, Kevin Kelly, Law of Accelerating Returns, machine translation, Marc Andreessen, Mark Zuckerberg, Mary Meeker, means of production, Menlo Park, Metcalfe’s law, Money creation, money: store of value / unit of account / medium of exchange, move fast and break things, Neal Stephenson, Network effects, new economy, Nick Bostrom, Norbert Wiener, Oculus Rift, OSI model, PageRank, pattern recognition, Paul Graham, peer-to-peer, Peter Thiel, Ponzi scheme, prediction markets, quantitative easing, random walk, ransomware, Ray Kurzweil, reality distortion field, Recombinant DNA, Renaissance Technologies, Robert Mercer, Robert Metcalfe, Ronald Coase, Ross Ulbricht, Ruby on Rails, Sand Hill Road, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Singularitarianism, Skype, smart contracts, Snapchat, Snow Crash, software is eating the world, sorting algorithm, South Sea Bubble, speech recognition, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, stochastic process, Susan Wojcicki, TED Talk, telepresence, Tesla Model S, The Soul of a New Machine, theory of mind, Tim Cook: Apple, transaction costs, tulip mania, Turing complete, Turing machine, Vernor Vinge, Vitalik Buterin, Von Neumann architecture, Watson beat the top human players on Jeopardy!, WikiLeaks, Y Combinator, zero-sum game

Scaled up by a distributed queue-processing system, “it allowed us to add new nodes to the pool on demand.” The flaw was that all the GPUs were controlled by Amazon Web Services, which had to be paid. Stephen Balaban was following the Google model of giving your product away and charging for “premium” subscriptions. The problem was that most of his “customers” found the free access to addictive psychedelic photo-paintings good enough. There were a hundred thousand takers for the $9.95 “premium edition,” but a million dollars wasn’t enough. Within a few months, Dreamscope pretty much died of its own success. Amazon Web Service bills mounted to forty thousand dollars a month. The company still had $150,000 in the bank, but it was running out of runway.

…

It fought back, as it normally does, with a stream of YouTube speeches and technical presentations demonstrating the superiority of Google’s cloud offerings, its global SQL reach, its facile user interfaces, its instant responses, its MapReduce, Hadoop, and “Spanner” big-database schemes, its massive fiber deployments and world-spanning data centers, its idealism, its tech conference éclat. But somehow when people had to choose a cloud service, they were turning not to Google but to Amazon Web Services. Who would have thunk it? Google, meanwhile, under its new CEO, Sundar Pichai, pivoted away from its highly publicized “mobile first” mantra, which had led to its acquisitions of Android and Ad Mob, and toward “AI first.” Google was the recognized intellectual leader of the industry, and its AI ostentation was widely acclaimed.

…

Balaban remembered appreciating that about Strachman and Gibson when he first encountered them: “Mike and Danielle recognize the emotional component of starting a company that a lot of people neglect—the emotional rollercoaster that is very draining. I observed that they were really good at making sure that everyone had support networks for when you are, in Elon Musk’s apt description, staring into the abyss and chewing on glass.” Facing the ballooning Amazon Web Services bills, Balaban went back to 1517; Danielle and Mike supplied another $150,000. That extended the runway another four or five months. Not enough. Austin Russell invested twenty thousand dollars in it (he later added another hundred thousand), and so did Gary Bradski at Magic Leap, among others.

Secrets of Sand Hill Road: Venture Capital and How to Get It by Scott Kupor

activist fund / activist shareholder / activist investor, Airbnb, Amazon Web Services, asset allocation, barriers to entry, Ben Horowitz, Benchmark Capital, Big Tech, Blue Bottle Coffee, carried interest, cloud computing, compensation consultant, corporate governance, cryptocurrency, discounted cash flows, diversification, diversified portfolio, estate planning, family office, fixed income, Glass-Steagall Act, high net worth, index fund, information asymmetry, initial coin offering, Lean Startup, low cost airline, Lyft, Marc Andreessen, Myron Scholes, Network effects, Paul Graham, pets.com, power law, price stability, prudent man rule, ride hailing / ride sharing, rolodex, Salesforce, Sand Hill Road, seminal paper, shareholder value, Silicon Valley, software as a service, sovereign wealth fund, Startup school, the long tail, Travis Kalanick, uber lyft, VA Linux, Y Combinator, zero-sum game

A few years into my job, on the eve of finishing an IPO for E.piphany, one of the marketing executives I had worked with to help them prepare for the IPO told me he was leaving to join a new startup called LoudCloud. Cofounded by Marc Andreessen, the already revered cofounder of Netscape, LoudCloud was trying to create a compute utility (much like Amazon Web Services has now created). Among the other cofounders was Ben Horowitz. This was the fall of 1999, and the dot-com excitement was in full swing. I had finally opened my eyes to what was happening around me, and I wanted to be a part of it. When my friend at E.piphany offered me the chance to meet Marc Andreessen and Ben Horowitz and see what they were doing, it was too much to pass up.

…

As an engineer, you should be able to develop your custom application and then just “plug it in” to the compute utility that could run the application seamlessly for you. You shouldn’t have to worry about what kind of database, networking equipment, application servers, etc., underlie the utility; it should simply work. It was a great idea—one that Amazon Web Services has built into a multibillion-dollar business today. LoudCloud was probably about ten years ahead of its time, an oft-repeated lesson, by the way, in the startup world. Though timing isn’t everything, timing is definitely something—it’s a big reason why we now see many ideas that failed in the dot-com bubble being reincarnated as successful businesses two decades later.

…

As cloud computing began to take off, the unit costs of all these hardware and software products began to fall. A variant of Moore’s law was sweeping through every segment of the technology stack. At the same time, software development systems also progressed, and engineering efficiency increased correspondingly. Today, developers can go to Amazon Web Services or competing providers and rent compute utility on demand, providing incremental pricing coupled with dramatically lowered input costs. Thus the costs required to start a company have fallen significantly, and therefore the amount of money that startups need to raise at an early stage has declined accordingly.

Amazon: How the World’s Most Relentless Retailer Will Continue to Revolutionize Commerce by Natalie Berg, Miya Knights

3D printing, Adam Neumann (WeWork), Airbnb, Amazon Robotics, Amazon Web Services, asset light, augmented reality, Bernie Sanders, big-box store, business intelligence, cloud computing, Colonization of Mars, commoditize, computer vision, connected car, deep learning, DeepMind, digital divide, Donald Trump, Doomsday Clock, driverless car, electronic shelf labels (ESLs), Elon Musk, fulfillment center, gig economy, independent contractor, Internet of things, inventory management, invisible hand, Jeff Bezos, Kiva Systems, market fragmentation, new economy, Ocado, pattern recognition, Ponzi scheme, pre–internet, QR code, race to the bottom, random stow, recommendation engine, remote working, Salesforce, sensor fusion, sharing economy, Skype, SoftBank, Steve Bannon, sunk-cost fallacy, supply-chain management, TaskRabbit, TechCrunch disrupt, TED Talk, trade route, underbanked, urban planning, vertical integration, warehouse automation, warehouse robotics, WeWork, white picket fence, work culture

It is Amazon’s acceptance of failure as a learning experience that sets them apart from other businesses. ‘[E]very single important thing that we have done has taken a lot of risk taking, perseverance, guts, and some of them have worked out, most of them have not’, says Bezos. Let’s be clear, the ones that have worked out – for example, Prime, Amazon Web Services (AWS) and Amazon Echo – have been colossal successes for the company. The 20-year bet and importance of consistency ‘We’re going to be unprofitable for a long time. And that’s our strategy.’ Jeff Bezos, 199716 Wall Street is inherently short-termist, leaving most public companies focused on maximizing profitability and stock performance from quarter to quarter.

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Amazon has cleverly taken Prime from a scheme that initially centred on delivery perks to an all-encompassing content-streaming, book-lending, photo-storing beast of a membership programme. The result? Higher spend, shopper frequency and retention. We’ll discuss this in greater detail in the next chapter. Amazon Web Services Amazon’s cloud storage service may not directly benefit shoppers, but it has certainly proven to be Amazon’s white knight. Operating margins are consistently in the high single digits and in 2017 the division was responsible for more than 100 per cent of Amazon’s operating profit. Remember Brad Stone’s point about feeding any part of the flywheel to accelerate it?

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It cannot be underestimated, however, how much it helps that Amazon’s core business is founded on technology innovation. Before looking at this innovation, let’s take a step back here, as it wasn’t always this way. Going back to 2002, with necessity truly being the mother of all invention, Amazon Web Services (AWS) was first born of the need for sufficient number-crunching capacity and standardized, automated computing infrastructures on which to run its retail marketplace. Capitalizing on advances in networking, storage, compute power and virtualization, Amazon began reselling its cloud computing capabilities as services in 2006.

The AI-First Company by Ash Fontana

23andMe, Amazon Mechanical Turk, Amazon Web Services, autonomous vehicles, barriers to entry, blockchain, business intelligence, business process, business process outsourcing, call centre, Charles Babbage, chief data officer, Clayton Christensen, cloud computing, combinatorial explosion, computer vision, crowdsourcing, data acquisition, data science, deep learning, DevOps, en.wikipedia.org, Geoffrey Hinton, independent contractor, industrial robot, inventory management, John Conway, knowledge economy, Kubernetes, Lean Startup, machine readable, minimum viable product, natural language processing, Network effects, optical character recognition, Pareto efficiency, performance metric, price discrimination, recommendation engine, Ronald Coase, Salesforce, single source of truth, software as a service, source of truth, speech recognition, the scientific method, transaction costs, vertical integration, yield management

Going from bottom to top, these are the steps taken to bring it to life: financed it with internal capital; developed it using the Amazon Web Services computing and data infrastructure; collected data from early users of the product and ancillary, spoken word datasets; prepared that data internally without letting it leave the company, partly for privacy reasons; hired its own people to label snippets of voice files; built its own voice recognition models; used its own monitoring systems, again, through Amazon Web Services; monitored usage internally; built the speaker that sits in your home; distributed the speaker directly to customers with supporting setup tools (through mobile applications) to implement as they wish—such as connected to other speakers or as a stand-alone device; and got usage data to train the models and update the product over the Internet.

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Below is a table of tools that individual contributors may use to illustrate variety, cost, and functionality: category cost examples Integrated development environment Dollars per month JupyterLab, Jupyter Notebooks, Google Colaboratory, RStudio, PyCharm, Microsoft Visual Studio, MathWorks MATLAB, and Vim/Emacs/VS Code Collaboration Hundreds of dollars per month Dataiku and Amazon SageMaker Frameworks Free Scikit-learn, Keras, XGBoost, Google TensorFlow, PyTorch, and Apache Spark MLib Cloud platform Based on usage Amazon Web Services, Google Cloud Platform, Microsoft Azure, IBM Cloud, and SAP Cloud Platform Pretrained models Free Various open-source software, Google Cloud Platform, Amazon Rekognition, and Microsoft Azure AutoML Free Google Cloud Platform AutoML, AWS Comprehend & Rekognition, Microsoft AutoML, H2O, and DataRobot Data labeling Based on usage Appen, Labelbox, and SuperAnnotate Visualization Hundreds of dollars per month Tableau, Looker, Domo, and Microsoft Excel Data preparation Based on usage Informatica, Trifacta, Alteryx, Tamr, and Paxata AI-FIRST MANAGEMENT AI-First companies might need new types of managers.

The Alliance: Managing Talent in the Networked Age by Reid Hoffman, Ben Casnocha, Chris Yeh

Airbnb, Amazon Web Services, centralized clearinghouse, cloud computing, disruptive innovation, Jeff Bezos, Jony Ive, Larry Ellison, Marc Andreessen, new economy, pre–internet, Salesforce, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, software as a service, Steve Jobs

And that’s how Lasseter ended up back at Disney as its chief creative officer of Disney Animation Studios.9 Disney’s management hired an entrepreneurial talent like Lasseter, but they treated him as a commodity rather than an ally, and in the process, they lost their chance to develop a multibillion-dollar business. Lasseter would have been happy to develop that business within Disney, but his managers wouldn’t let him. Benjamin Black and Amazon Web Services Amazon didn’t make the same mistake as Disney. Recently, it used the principles of the alliance to generate a new multibillion-dollar business. Amazon has become a leader in the field of cloud computing, thanks to Amazon Web Services (AWS), which allows companies to rent online storage and computing power, rather than buying and operating their own servers. Companies ranging from Fortune 500 giants to one-person start-ups run their businesses on AWS.

Python Data Analytics: With Pandas, NumPy, and Matplotlib by Fabio Nelli

Amazon Web Services, backpropagation, centre right, computer vision, data science, Debian, deep learning, DevOps, functional programming, Google Earth, Guido van Rossum, Internet of things, optical character recognition, pattern recognition, sentiment analysis, speech recognition, statistical model, web application

Google Trends http://www.google.com/trends/explore Statistics on search volume (as a proportion of total search) for any given term, since 2004. Likebutton http://likebutton.com/ Mines Facebook’s public data—globally and from your own network—to give an overview of what people “Like” at the moment. Miscellaneous and Public Data Sets Amazon Web Services public datasets http://aws.amazon.com/datasets The public data sets on Amazon Web Services provide a centralized repository of public data sets. An interesting dataset is the 1000 Genome Project, an attempt to build the most comprehensive database of human genetic information. Also a NASA database of satellite imagery of Earth is available.

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You can find a more complete list and details of the open data available online in Appendix B.DataHub ( http://datahub.io/dataset ) World Health Organization ( http://www.who.int/research/en/ ) Data.gov ( http://data.gov ) European Union Open Data Portal ( http://open-data.europa.eu/en/data/ ) Amazon Web Service public datasets ( http://aws.amazon.com/datasets ) Facebook Graph ( http://developers.facebook.com/docs/graph-api ) Healthdata.gov ( http://www.healthdata.gov ) Google Trends ( http://www.google.com/trends/explore ) Google Finance ( https://www.google.com/finance ) Google Books Ngrams ( http://storage.googleapis.com/books/ngrams/books/datasetsv2.html ) Machine Learning Repository ( http://archive.ics.uci.edu/ml/ ) As an idea of open data sources available online, you can look at the LOD cloud diagram ( http://lod-cloud.net ), which displays the connections of the data link among several open data sources currently available on the network (see Figure 1-3).

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Google Books Ngrams http://storage.googleapis.com/books/ngrams/books/datasetsv2.html This source searches and analyzes the full text of any of the millions of books digitized as part of the Google Books project. Musical Data Million Song Data Set http://aws.amazon.com/datasets/6468931156960467 Metadata on over a million songs and pieces of music. Part of Amazon Web Services. Index A Accents, LaTeX Advanced Data aggregation apply() functions transform() function Anaconda Anderson Iris Dataset, see Iris flower dataset Array manipulation joining arrays column_stack() and row_stack() hstack() function vstack() function splitting arrays hsplit() function split() function vsplit() function Artificial intelligence schematization of Artificial neural networks biological networks edges hidden layer input and output layer multi layer perceptron nodes schematization of SLP ( see Single layer perceptron (SLP)) weight B Bar chart 3D error bars horizontal matplotlib multiserial multiseries stacked bar pandas DataFrame representations stacked bar charts x-axis xticks() function Bayesian methods Big Data Bigrams Biological neural networks Blending operation C Caffe2 Chart typology Choropleth maps D3 library geographical representations HTML() function jinja2 JSON and TSV JSON TopoJSON require.config() results US population data source census.gov file TSV, codes HTML() function jinja2.Template pop2014_by_county dataframe population.csv render() function SUMLEV values Classification and regression trees Classification models Climatic data Clustered bar chart IPython Notebook jinja2 render() function Clustering models Collocations Computer vision Concatenation arrays combining concat() function dataframe keys option pivoting hierarchical indexing long to wide format stack() function unstack() function removing Correlation Covariance Cross-validation Cython D Data aggregation apply() functions GroupBy groupby() function operations output of SPLIT-APPLY-COMBINE hierarchical grouping merge() numeric and string values price1 column transform() function Data analysis charts data visualization definition deployment phase information knowledge knowledge domains computer science disciplines fields of application machine learning and artificial intelligence mathematics and statistics problems of open data predictive model process data sources deployment exploration/visualization extraction model validation planning phase predictive modeling preparation problem definition stages purpose of Python and quantitative and qualitative types categorical data numerical data DataFrame pandas definition nested dict operations structure transposition structure Data manipulation aggregation ( see Data aggregation) concatenation discretization and binning group iteration permutation phases of preparation ( see Data preparation) string ( see String manipulation) transformation Data preparation DataFrame merging operation pandas.concat() pandas.DataFrame.combine_first() pandas.merge() procedures of Data structures, operations DataFrame and series flexible arithmetic methods Data transformation drop_duplicates() function mapping adding values axes dict objects replacing values remove duplicates Data visualization adding text axis labels informative label mathematical expression modified of text() function bar chart ( see Bar chart) chart typology contour plot/map data analysis 3D surfaces grid grids, subplots handling date values histogram installation IPython and IPython QtConsole kwargs figures and axes horizontal subplots linewidth plot() function vertical subplots legend chart of legend() function multiseries chart upper-right corner line chart ( see Line chart) matplotlib architecture and NumPy matplotlib library ( see matplotlib library) mplot3d multi-panel plots grids, subplots subplots pie charts axis() function modified chart pandas Dataframe pie() function shadow kwarg plotting window buttons of commands matplotlib and NumPy plt.plot() function properties QtConsole polar chart pyplot module saving, charts HTML file image file source code scatter plot, 3D Decision trees Deep learning artificial ( see Artificial neural networks) artificial intelligence data availability machine learning neural networks and GPUs Python frameworks programming language schematization of TensorFlow ( see TensorFlow) Digits dataset definition digits.images array digit.targets array handwritten digits handwritten number images matplotlib library scikit-learn library Discretization and binning any() function categorical type cut() function describe() function detecting and filtering outliers qcut() std() function value_counts() function Django Dropping E Eclipse (pyDev) Element-wise computation Expression-oriented programming F Financial data Flexible arithmetic methods Fonts, LaTeX G Gradient theory Graphics Processing Unit (GPU) Grouping Group iteration chain of transformations functions on groups mark() function quantiles() function GroupBy object H Handwriting recognition digits dataset handwritten digits, matplotlib library learning and predicting OCR software scikit-learn svc estimator TensorFlow validation set, six digits Health data Hierarchical indexing arrays DataFrame reordering and sorting levels stack() function statistic levels structure two-dimensional structure I IDEs, see Interactive development environments (IDEs) Image analysis concept of convolutions definition edge detection blackandwhite.jpg image black and white system filters function gradients.jpg image gray gradients Laplacian and Sobel filters results source code face detection gradient theory OpenCV ( see Open Source Computer Vision (OpenCV)) operations representation of Indexing functionalities arithmetic and data alignment dropping reindexing Integration Interactive development environments (IDEs) Eclipse (pyDev) Komodo Liclipse NinjaIDE Spyder Sublime Interactive programming language Interfaced programming language Internet of Things (IoT) Interpreted programming language Interpreter characterization Cython Jython PVM PyPy tokenization IPython and IPython QtConsole Jupyter project logo Notebook DataFrames QtConsole shell tools of Iris flower dataset Anderson Iris Dataset IPython QtConsole Iris setosa features length and width, petal matplotlib library PCA decomposition target attribute types of analysis variables J JavaScript D3 Library bar chart CSS definitions data-driven documents HTML importing library IPython Notebooks Jinja2 library pandas dataframe render() function require.config() method web chart creation Jinja2 library Jython K K-nearest neighbors classification decision boundaries 2D scatterplot, sepals predict() function random.permutation() training and testing set L LaTeX accents fonts fractions, binomials, and stacked numbers with IPython Notebook in Markdown Cell in Python 2 Cell with matplotlib radicals subscripts and superscripts symbols arrow symbols big symbols binary operation and relation symbols Delimiters Hebrew lowercase Greek miscellaneous symbols standard function names uppercase Greek Learning phase Liclipse Linear regression Line chart annotate() arrowprops kwarg Cartesian axes color codes data points different series gca() function Greek characters LaTeX expression line and color styles mathematical expressions mathematical function pandas plot() function set_position() function xticks() and yticks() functions Linux distribution LOD cloud diagram Logistic regression M Machine learning (ML) algorithm development process deep learning diabetes dataset features/attributes Iris flower dataset learning problem linear/least square regression coef_ attribute fit() function linear correlation parameters physiological factors and progression of diabetes single physiological factor schematization of supervised learning SVM ( see Support vector machines (SVMs)) training and testing set unsupervised learning Mapping adding values inplace option rename() function renaming, axes replacing values Mathematical expressions with LaTeX, see LaTeX MATLAB matplotlib matplotlib library architecture artist layer backend layer functions and tools layers pylab and pyplot scripting layer (pyplot) artist layer graphical representation hierarchical structure primitive and composite graphical representation LaTeX NumPy Matrix product Merging operation DataFrame dataframe objects index join() function JOIN operation left_index/right_index options left join, right join and outer join left_on and right_on merge() function Meteorological data Adriatic Sea and Po Valley cities Comacchio image of mountainous areas reference standards TheTimeNow website climate data source JSON file Weather Map site IPython Notebook chart representation CSV files DataFrames humidity function linear regression matplotlib library Milan read_csv() function result shape() function SVR method temperature Jupyter Notebook access internal data command line dataframe extraction procedures Ferrara JSON file json.load() function parameters prepare() function RoseWind ( see RoseWind) wind speed Microsoft excel files dataframe data.xls internal module xlrd read_excel() function MongoDB Multi Layer Perceptron (MLP) artificial networks evaluation of experimental data hidden layers IPython session learning phase model definition test phase and accuracy calculation Musical data N Natural Language Toolkit (NLTK) bigrams and collocations common_contexts() function concordance() function corpora downloader tool fileids() function HTML pages, text len() function library macbeth variable Python library request() function selecting words sentimental analysis sents() function similar() function text, network word frequency macbeth variable most_common() function nltk.download() function nltk.FreqDist() function stopwords string() function word search Ndarray array() function data, types dtype (data-type) intrinsic creation type() function NOSE MODULE “Not a Number” data filling, NaN occurrences filtering out NaN values NaN value NumPy library array manipulation ( see Array manipulation) basic operations aggregate functions arithmetic operators increment and decrement operators matrix product ufunc broadcasting compatibility complex cases operator/function BSD conditions and Boolean arrays copies/views of objects data analysis indexing bidimensional array monodimensional ndarray negative index value installation iterating an array ndarray ( see Ndarray) Numarray python language reading and writing array data shape manipulation slicing structured arrays vectorization O Object-oriented programming language OCR, see Optical Character Recognition (OCR) software Open data Open data sources climatic data demographics IPython Notebook matplotlib pandas dataframes pop2014_by_state dataframe pop2014 dataframe United States Census Bureau financial data health data miscellaneous and public data sets musical data political and government data publications, newspapers, and books social data sports data Open Source Computer Vision (OpenCV) deep learning image processing and analysis add() function blackish image blending destroyWindow() method elementary operations imread() method imshow() method load and display merge() method NumPy matrices saving option waitKey() method working process installation MATLAB packages start programming Open-source programming language Optical Character Recognition (OCR) software order() function P Pandas dataframes Pandas data structures DataFrame assigning values deleting column element selection filtering membership value nested dict transposition evaluating values index objects duplicate labels methods NaN values NumPy arrays and existing series operations operations and mathematical functions series assigning values declaration dictionaries filtering values index internal elements, selection operations Pandas library correlation and covariance data structures ( see Pandas data structures) function application and mapping element row/column statistics getting started hierarchical indexing and leveling indexes ( see Indexing functionalities) installation Anaconda development phases Linux module repository, Windows PyPI source testing “Not a Number” data python data analysis sorting and ranking Permutation new_order array np.random.randint() function numpy.random.permutation() function random sampling DataFrame take() function Pickle—python object serialization cPickle frame.pkl pandas library stream of bytes Political and government data pop2014_by_county dataframe pop2014_by_state dataframe pop2014 dataframe Portable programming language PostgreSQL Principal component analysis (PCA) Public data sets PVM, see Python virtual machine (PVM) pyplot module interactive chart Line2D object plotting window show() function PyPy interpreter Python data analysis library deep learning frameworks module OpenCV Python Package Index (PyPI) Python’s world code implementation distributions Anaconda Enthought Canopy Python(x,y) IDEs ( see Interactive development environments (IDEs)) installation interact interpreter ( see Interpreter) IPython ( see IPython) programming language PyPI Python 2 Python 3 running, entire program code SciPy libraries matplotlib NumPy pandas shell source code data structure dictionaries and lists functional programming Hello World index libraries and functions map() function mathematical operations print() function writing python code, indentation Python virtual machine (PVM) PyTorch Q Qualitative analysis Quantitative analysis R R Radial Basis Function (RBF) Radicals, LaTeX Ranking Reading and writing array binary files tabular data Reading and writing data CSV and textual files header option index_col option myCSV_01.csv myCSV_03.csv names option read_csv() function read_table() function .txt extension databases create_engine() function dataframe pandas.io.sql module pgAdmin III PostgreSQL read_sql() function read_sql_query() function read_sql_table() function sqlalchemy sqlite3 DataFrame objects functionalities HDF5 library data structures HDFStore hierarchical data format mydata.h5 HTML files data structures read_html () web_frames web pages web scraping I/O API Tools JSON data books.json frame.json json_normalize() function JSONViewer normalization read_json() and to_json() read_json() function Microsoft excel files NoSQL database insert() function MongoDB pickle—python object serialization RegExp metacharacters read_table() skiprows TXT files nrows and skiprows options portion by portion writing ( see Writing data) XML ( see XML) Regression models Reindexing RoseWind DataFrame hist array polar chart scatter plot representation showRoseWind() function S Scikit-learn library data analysis k-nearest neighbors classification PCA Python module sklearn.svm.SVC supervised learning svm module SciPy libraries matplotlib NumPy pandas Sentimental analysis document_features() function documents list() function movie_reviews negative/positive opinion opinion mining Shape manipulation reshape() function shape attribute transpose() function Single layer perceptron (SLP) accuracy activation function architecture cost optimization data analysis evaluation phase learning phase model definition explicitly implicitly learning phase placeholders tf.add() function tf.nn.softmax() function modules representation testing set test phase and accuracy calculation training sets Social data sort_index() function Sports data SQLite3 stack() function String manipulation built-in methods count() function error message index() and find() join() function replace() function split() function strip() function regular expressions findall() function match() function re.compile() function regex re.split() function split() function Structured arrays dtype option structs/records Subjective interpretations Subscripts and superscripts, LaTeX Supervised learning machine learning scikit-learn Support vector classification (SVC) decision area effect, decision boundary nonlinear number of points, C parameter predict() function regularization support_vectors array training set, decision space Support vector machines (SVMs) decisional space decision boundary Iris Dataset decision boundaries linear decision boundaries polynomial decision boundaries polynomial kernel RBF kernel training set SVC ( see Support vector classification (SVC)) SVR ( see Support vector regression (SVR)) Support vector regression (SVR) curves diabetes dataset linear predictive model test set, data swaplevel() function T TensorFlow data flow graph Google’s framework installation IPython QtConsole MLP ( see Multi Layer Perceptron (MLP)) model and sessions SLP ( see Single layer perceptron (SLP)) tensors operation parameters print() function representations of tf.convert_to_tensor() function tf.ones() method tf.random_normal() function tf.random_uniform() function tf.zeros() method Text analysis techniques definition NLTK ( see Natural Language Toolkit (NLTK)) techniques Theano trigrams() function U, V United States Census Bureau Universal functions (ufunc) Unsupervised learning W Web Scraping Wind speed polar chart representation RoseWind_Speed() function ShowRoseWind() function ShowRoseWind_Speed() function to_csv () function Writing data HTML files myFrame.html to_html() function na_rep option to_csv() function X, Y, Z XML books.xml getchildren() getroot() function lxml.etree tree structure lxml library objectify parse() function tag attribute text attribute

Docker in Action by Jeff Nickoloff, Stephen Kuenzli

air gap, Amazon Web Services, cloud computing, computer vision, continuous integration, database schema, Debian, end-to-end encryption, exponential backoff, fail fast, failed state, information security, Kubernetes, microservices, MITM: man-in-the-middle, peer-to-peer, software as a service, web application

Containers that link to this implementation will have their messages logged to /var/log/mailer.log. That’s not very interesting or useful in a real-world situation, but it might be handy for testing. An implementation that sends email would be better for operational monitoring. The next implementation example uses the Simple Email Service provided by Amazon Web Services to send email. Get started with another Dockerfile. Name this file mailer-live.df: FROM dockerinaction/mailer-base:0.6 ADD ["./live-impl", "${APPROOT}"] RUN apt-get update && \ apt-get install -y curl netcat python && \ curl "https://bootstrap.pypa.io/get-pip.py" -o "get-pip.py" && \ python get-pip.py && \ pip install awscli && \ rm get-pip.py && \ chmod a+x "${APPROOT}/${APP}" USER example:example CMD ["mailer@dockerinaction.com", "pager@dockerinaction.com"] This Dockerfile includes one new instruction, ADD.

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You may provision a Swarm cluster however you like. Because of the wide variety of provisioning options and the rate of change, we recommend you follow an up-to-date guide to provision a Swarm cluster on your favorite infrastructure provider. Many people deploy test clusters with docker-machine on cloud providers such as DigitalOcean and Amazon Web Services. The examples in this chapter were created and tested using Play with Docker (https://labs.play-with-docker.com/). On the Play with Docker site, you can experiment with Docker and learn about it for free. The cluster was created using the Play with Docker template that provisions three manager and two worker nodes.

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Docker running three containers on a Linux system Index [SYMBOL][A][B][C][D][E][F][G][H][I][J][K][L][M][N][O][P][Q][R][S][T][U][V][W][Y][Z] SYMBOL /counter endpoint, 2nd # (hash sign) A -a flag, 2nd abstractions access control access to devices active option ADD instruction, 2nd adminer interface adminer key, 2nd adminer service, 2nd, 3rd affinity agents, 2nd all-in-one images All-in-One pattern - -all-tags option Amazon Web Services (AWS), 2nd anti-affinity Apache Cassandra project apache2 process api service, 2nd, 3rd, 4th, 5th, 6th, 7th apk package manager AppArmor profile, 2nd app-image, 2nd app-image-debug stage application artifacts applications configurations and, separating config resource deploying applications managing config resources directly deploying to Swarm clusters cluster resource types defining application and its dependencies by using Docker services using docker stack command apt package manager, 2nd apt-get tool, 2nd apt-get update, Debian ARG instruction ARG VERSION instruction artifact confidentiality artifact integrity attachable network AUDIT_CONTROL capability auto tag automated resurrection and replication automated rollout autoscaling services availability control - -availability option AWS (Amazon Web Services), 2nd aws program AWS_ACCESS_KEY_ID AWS_DEFAULT_REGION AWS_SECRET_ACCESS_KEY B backoff strategy backoff-detector container bandwidth overhead base.version label, 2nd best practices bind mounts blind mount points block sequences (lists) block style bridge network, 2nd build command Build Plus Multiple Runtimes pattern, 2nd Build Plus Runtime pattern builder stage, 2nd BUILD_ID tag, 2nd BusyBox init C -c flag CA (certificate authority) CAIID (content-addressable image identifier) CAP drop - -cap-add flag - -cap-drop flag CAP_NET_ADMIN Cassandra client tool (CQLSH), 2nd cass-shared volume CD (continuous delivery), 2nd CentOS certificate authority (CA) CERT_PRIVATE_KEY_FILE variable CFS (Completely Fair Scheduler) cgroups Chef Inspec tool chroot system call, 2nd CI (continuous integration), 2nd CID (container ID) file - -cidfile cleanup, containers and CLI (command-line interface) CLIENT_ID environment variable Cloudflare CMD instruction, 2nd command line, working with registries from command-line interface (CLI) command property commit subcommand Completely Fair Scheduler (CFS) Compose declarative service environments with collections of YAML primer load balancing, service discovery, and networks with - -compose-file option composite key confidentiality, artifact config command Config property config.common.yml file ConfigID configs key Configuration Image per Deployment Stage pattern configurations applications and, separating config resource deploying applications managing config resources directly distribution and management secrets and container ID (CID) file container networking custom DNS configuration externalizing network management firewalls, lack of network policies, lack of NodePort publishing Container Structure Test (CST) tool ContainerConfig.OnBuild containers, 2nd, 3rd, 4th building environment-agnostic system environment variable injection read-only filesystems building images from committing new images configuring image attributes packaging “Hello, World” preparing packaging for Git reviewing filesystem changes cleanup and conflicts between, eliminating container state and dependencies flexible container identification creating and starting durable automatically restarting containers using PID, 2nd filesystem abstraction and isolation for isolation interactive, running listing, stopping, restarting, and viewing output of PID namespace and shipping containers virtualization different than content-addressable image identifier (CAIID) context context switch continuous delivery (CD), 2nd continuous deployment continuous integration (CI), 2nd COPY instruction, 2nd, 3rd copy-on-write pattern, 2nd corporate storage networks cost, of distribution CPU resources cpus option - -cpuset-cpus flag - -cpu-shares flag - -cpu-shares value CQLSH (Cassandra client tool), 2nd create command CST (Container Structure Test) tool curl command, 2nd current state custom DNS configuration custom image distribution infrastructure D DAB (Distributed Application Bundle) DAEMON Tools daemons databases db-data volume Debian Debian Linux Buster Debian Stretch base image debian:buster-slim debian:stretch platform declarative service environments with Compose collections of services YAML primer default stage depends_on key deploy command deploy key deploy property DEPLOY_ENV variable, 2nd desired state - -detach (-d) flag detached containers dev tag - -device flag devices, access to diaweb container diff command diff subcommand, 2nd DigitalOcean Distributed Application Bundle (DAB) distribution image source-distribution workflows manual image publishing and method of, choosing image distribution spectrum selection criteria private registries consuming images from registry performance of registry image, using publishing with hosted registries private hosted repositories public repositories Distribution registry - -dns flag DNS service, Cloudflare - -dns-search flag Docker containers and “Hello World” example importance of in larger ecosystem overview of problems solved by getting organized improving portability protecting computer use of, when and where.

Working in Public: The Making and Maintenance of Open Source Software by Nadia Eghbal

Amazon Web Services, Apollo 11, barriers to entry, Benevolent Dictator For Life (BDFL), Big Tech, bitcoin, Clayton Christensen, cloud computing, commoditize, commons-based peer production, context collapse, continuous integration, crowdsourcing, cryptocurrency, David Heinemeier Hansson, death of newspapers, Debian, disruptive innovation, Dunbar number, en.wikipedia.org, eternal september, Ethereum, Firefox, Free Software Foundation, Guido van Rossum, Hacker Ethic, Hacker News, Induced demand, informal economy, information security, Jane Jacobs, Jean Tirole, Kevin Kelly, Kickstarter, Kubernetes, leftpad, Mark Zuckerberg, Menlo Park, Neal Stephenson, Network effects, node package manager, Norbert Wiener, pirate software, pull request, RFC: Request For Comment, Richard Stallman, Ronald Coase, Ruby on Rails, side project, Silicon Valley, Snapchat, social graph, software as a service, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, tacit knowledge, the Cathedral and the Bazaar, The Death and Life of Great American Cities, The Nature of the Firm, TikTok, Tragedy of the Commons, transaction costs, two-sided market, urban planning, web application, wikimedia commons, Yochai Benkler, Zimmermann PGP

Cloud services have made it easier to upgrade infrastructure without interruptions, if they charge for anything at all. Just as anyone can store their photos on iCloud or their files on Google Drive and pay for more storage as needed, upgrading software infrastructure can be as simple as a few clicks on Fastly, Cloudflare, or Netlify. The biggest providers, such as Amazon Web Services and Microsoft Azure, have made hosting costs very cheap or free. That doesn’t mean it can’t get expensive, however. Donald Stufft, who maintains the Python package manager PyPI, estimates that its infrastructure costs two to three million dollars per year in hosting, donated by Fastly.204 As more developers have adopted PyPI, the project’s costs have grown significantly.

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According to Stufft, in April 2013, PyPI used 11.84GB of bandwidth.205 By April 2019, that figure had increased to 4.5PB.206 On a smaller scale, open source developer Drew DeVault estimates that he spends $380 each month on server hosting for his projects, which he pays for with user donations.207 Werner Vogels, chief technology officer for Amazon and an architect of Amazon Web Services, describes how the marginal cost of physical infrastructure can become significant at scale: Under the covers these services are massive distributed systems that operate on a worldwide scale. This scale creates additional challenges, because when a system processes trillions and trillions of requests, events that normally have a low probability of occurrence are now guaranteed to happen and must be accounted for upfront.208 Distributed denial-of-service attacks, or DDoS attacks, are one way in which the cost of physical infrastructure is still made visible.

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Like dependencies, the reputation of a developer is a dynamic property, based on their past and expected future value, viewed through the eyes of others. The practice of paying maintainers to work on open source projects is not new. Donald Stufft, for example, who maintains Python’s packaging tools, was hired by Hewlett Packard Enterprise,253 and then Amazon Web Services, to improve and maintain Python’s packaging.254 A company could just contribute their own employees to a given open source project, and they often do, but some also hire and pay maintainers. The maintainer’s reputation translates into value for the company, whether it’s brand association or having a direct line to someone with influence on the project.

Open for Business Harnessing the Power of Platform Ecosystems by Lauren Turner Claire, Laure Claire Reillier, Benoit Reillier

Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, basic income, benefit corporation, Blitzscaling, blockchain, carbon footprint, Chuck Templeton: OpenTable:, cloud computing, collaborative consumption, commoditize, crowdsourcing, data science, deep learning, Diane Coyle, Didi Chuxing, disintermediation, distributed ledger, driverless car, fake news, fulfillment center, future of work, George Akerlof, independent contractor, intangible asset, Internet of things, Jean Tirole, Jeff Bezos, Kickstarter, knowledge worker, Lean Startup, Lyft, Mark Zuckerberg, market design, Metcalfe’s law, minimum viable product, multi-sided market, Network effects, Paradox of Choice, Paul Graham, peer-to-peer lending, performance metric, Peter Thiel, platform as a service, price discrimination, price elasticity of demand, profit motive, ride hailing / ride sharing, Sam Altman, search costs, self-driving car, seminal paper, shareholder value, sharing economy, Silicon Valley, Skype, smart contracts, Snapchat, software as a service, Steve Jobs, Steve Wozniak, TaskRabbit, the long tail, The Market for Lemons, Tim Cook: Apple, transaction costs, two-sided market, Uber and Lyft, uber lyft, universal basic income, Y Combinator

Amazon also launched Amazon Prime the same year, a membership service offering buyers free and fast delivery. Both FBA and Prime are traditional product lines leveraging Amazon’s excellent logistics capabilities. They have been instrumental in supporting the e-commerce business. Amazon also branched out early on into services supporting digital businesses with Amazon Web Services (AWS), a cloud computing service launched in 2006. AWS is now the largest cloud service in the world and generated $12.2 billion in 2016. Amazon’s first foray into consumer electronics started with the Kindle in 2007, supported by Fire OS, an Android based operating system. Building on the success of Kindle e-book readers, Amazon later launched the Fire Tablet, Fire Phone, Fire TV Stick, and more recently Amazon Echo.

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Index Accor Group 3, 201, 211 add-on platforms 77, 88–9, 92, 102, 105 advertising 37, 43, 45, 67–8, 93, 97, 99–100, 142, 144, 183 AdWords (Google) 67, 69, 183, 184 Airbnb 1–2, 3, 4, 15, 24, 168, 188; brand 51–2, 153, 169–70; business model 45; credibility 158–9, 163; feedback 35, 52, 160, 179–80; insurance 164; market sizing 79; match 47, 52, 83; value propositions 83, 95, 123 Alibaba 1, 15, 28, 34, 123, 124, 132, 208 Amazon 8, 12, 13, 14, 15, 125; business model 48, 59, 63; conflict resolution 163; ecosystem 28, 57–8, 59–61, 62, 70; feedback 35, 160; home automation 212 Amazon FBA (Fulfilment by Amazon) 58, 61–2 Amazon Marketplace 26–7, 48, 57, 58, 59, 60, 61–2, 84, 111 Amazon Prime 58, 61, 62 Amazon Web Services (AWS) 58 analytics 50–1, 52, 110, 123, 162, 187, 208, 213; see also data capture Android operating system (Google) 58, 67, 185–6 anticompetitive behaviour 173, 182, 186 APIs (application programming interfaces) 52, 183, 197 Apple 5, 8, 13, 14, 28, 193; App Store 32, 38, 63, 64–6; business model 54, 63, 66; ecosystem 63–6, 70, 132; iPhone 63, 65, 123; iTunes 63, 65 application programming interfaces see APIs Ariba 96 Artfire 143 attract 6, 15, 24, 47, 167; ignition 75, 91–6; maturity 75, 121–2, 131; pre-launch 74; scaling-up 75, 105, 107, 110–14, 117 audience builders 24 AWS see Amazon Web Services balance 48, 50, 106, 111–12, 114, 117, 124, 144–7, 150 Bezos, J. 57, 58 big bang launch strategy see event strategy BlaBlaCar 7, 154, 157, 158, 159; insurance 164; pricing strategies 147, 150, 156; scaling-up 112, 115 BorrowMyDoggy 99 bottlenecks 51, 73, 75–6, 86, 98, 99, 100, 101, 108, 112, 116 bowling pin strategy 54, 95, 109 brand 14–15, 51, 76, 118, 153–70 bundling strategies 61, 131, 182 business architecture 22, 66, 74, 80, 91 business models 6–7, 11, 22, 27, 59, 62, 63, 68, 132; platform businesses 6–7, 11, 22, 23, 27–8, 41–54, 70, 87, 138, 146, 177, 199, 200, 212; traditional businesses 62, 73, 91, 193, 211 card platforms 6, 23, 80, 126 catalyst businesses 23–4 Chesky, B. 1, 2, 169 218 Index Choudary, S. 25 communications platforms see Snapchat; WeChat; WhatsApp communities 44–5, 51, 78, 94, 112, 118, 125, 170 compensation strategy see dollar strategy competing platforms 131, 140, 149 competition 22, 25, 121, 130–2, 140, 173, 193 competition authorities 122, 131, 173-5, 186, 197 complements 31, 38, 130, 131 conflict resolution 126, 163, 167 connect 4, 45–6, 62; ignition 98; maturity 97; pre-launch 85; scaling-up 115, 117 consistency 159–60, 203 contributions 78, 82-3, 94, 118 control 52, 53, 123, 140–1, 162 core transactions 49, 52, 85–6, 98–101, 125, 168 correction activities 162 Craigslist 34, 95, 167 credibility 156, 158–9, 195 critical mass 31, 35–6, 37, 46, 73, 80, 121 cultural meme 97 customer acquisition 93, 107, 127; see also platform participants customer experience 36, 48, 53; maturity 108, 111; pricing strategies 139, 140–1; scaling-up 162 data capture 187; see also analytics dating platforms 13, 36, 48, 50, 95, 166, 176 delay strategy 195 Deliveroo 95, 111, 133, 170, 208 demand coordinators 24–5 denial strategy 194 denigrate strategy 194 deter strategy 194–5 Didi Chuxing 15, 132 digital platforms 4, 5, 12, 17, 73, 157, 167 direct network effects 34–5 direct platforms 80 disintermediation 126, 147–8 dispute resolution see conflict resolution dollar strategy 196 dynamic capabilities 196, 199, 200, 201 eBay 4, 15, 22, 48, 49, 53–4; brand 169, 170; business model 21, 27; connect 4, 85; feedback 159, 160; ignition 54; network effects 33, 34; pricing strategies 129, 140, 144, 146; scaling-up 108, 112, 115, 208; value propositions 83, 112 economic value 4, 14, 197 economies of scale 31, 32–33 ecosystems 6, 13, 17, 26–8, 180–1, 186, 207, 211; Amazon 26–7, 57–8, 59–61, 62, 70; Apple 63–6, 70, 132; Facebook 28, 70; Google 66–7, 68–9, 70, 132, 180–1, 185–6; Microsoft 70, 130, 132 enablers 51, 52, 87; ignition 101; maturity 128; pre-launch 87; scaling-up 118 Etsy 84, 87, 94, 110, 132, 146, 210 Evans, P. 14, 24, 25 event strategy 96 ex ante regulation 173, 188 ex post regulation 173 externalities 31–3, 65, 175, 180 Facebook 8, 13, 14, 52, 124, 131, 163, 168; ecosystem 28, 70; ignition 95, 96; network effects 35; pricing strategies 149; scaling-up 107, 166; search results 124 fast follower strategy 199 fear barriers 168–9 feedback 32, 34–6, 47, 77, 98, 100, 101, 108, 110, 111, 125, 160, 165, 188 Fulfilment by Amazon see Amazon FBA gaming platforms 35, 69, 97, 145 Gawer, A. 14, 25 Gebbia, J. 1, 169 Google 7, 13, 14, 67, 163, 180–6; AdWords 67; Android operating system 67, 185; brand 169; business model 50–1, 68; ecosystem 66–7, 68–9, 70, 132, 180–1, 186; home automation 212; search results 125; YouTube 67, 69, 131, 182 Google+ 69, 182 Index governance 51, 76, 140, 165, 167; prelaunch 87; trust framework 155, 157, 162, 164–70 Gumtree 132, 143 Hailo (MyTaxi) 93, 185 HiGear 163 home automation 212 Honda 27 hotels 1, 3, 7, 79, 179–80 IBM 14, 26, 194 ignition 73, 74, 91–102; eBay 53–4; Facebook 95, 96 indirect network effects 34–5 indirect platforms 80–1 innovation 140, 170, 174; maturity 127, 128; regulation 174, 186, 199 Instagram 70, 99, 107, 110, 133 insurance 164 intervention 175 intimacy 156 IT infrastructure 52 key enablers 51, 52 Kickstarter 78, 109, 210 La Belle Asiette 93–4, 99 labour laws 177 Launchworks 21, 157, 206, 208 leakage 49, 126, 147–8 leverage 61, 62, 70, 88, 124–5 linear businesses 6, 12, 41–2, 44, 52 LinkedIn 49, 70, 100, 107, 143, 150, 159 liquidity 33, 52, 143; ignition 99, 101; scaling-up 117 listing fees 144 Lyft 15, 132, 174, 176, 202 management principles 6, 7, 207 management rules 7, 17 market failures 167, 175–6, 187 market makers 24–5 marketplace platforms 12, 15, 48, 26, 59, 61, 85 marketplaces 6, 26, 64, 49, 59, 84, 85, 208 219 market power 37, 173–6, 180–3, 186, 197 market sizing 79–80 Mastercard 5, 23, 26, 80, 134 match 75; ignition 97–101, 115; maturity 124; platform participants 47–8, 52, 84–5, 95; pre-launch 86; scaling-up 114–15 maturity 73, 75–6, 121–2, 127–8 media companies 25 membership fees 139, 143–4 meshed communities 94–5 Microsoft 13–14, 23, 28, 63, 65, 70, 125, 143; ecosystem 70, 130, 132; home automation 212 minimum viable product see MVP monetization 50, 67, 86, 100, 116, 142, 147 MSP (multisided platforms) 25, 46 multihoming 36–7, 149, 176, 177 multisided markets 5, 23–4, 46 multisided platforms see MSP MVP (minimum viable product) 91, 100–1 MyTaxi 93 negative externalities 32, 180 Netflix 26, 197 network effects 33–5, 35, 48, 52, 68, 99, 133, 176; positive 48, 115, 126, 133, 142, 149 networks 31, 33–5 North Star metric 86, 100, 119 on-boarding processes 107–8, 168 Onefinestay 200–1 online platforms 11, 50, 51, 162 OpenTable 93 operating systems 5, 6, 25, 34; Android 58, 67, 185–6 optimize 50, 76; ignition 100, 101; maturity 128, 130; pre-launch 74, 86; scaling-up 116 over-regulation 174 Parker, G. 23, 25–6 partners 45, 79, 128 payment platforms 6, 24, 48 PayPal 95, 131, 187 performance metrics 100, 117, 127 220 Index personalization 124 piggybacking 95 platform balance 111, 117, 128, 144–6 platform businesses 4, 5–7, 11–14, 205, 207, 212, 214; definitions 21, 24–8 platform business models 4–7, 21–2, 27, 45–7, 146, 199, 205, 207, 212, 214 platform development 73, 77, 88; ignition 73, 75–6, 91–101; maturity 73, 75–6, 122, 125, 126, 128, 130; pre-launch 50, 73, 74, 75–6, 77–8, 82–7, 87, 169; scaling-up 73, 75–6, 91, 105–18, 169, 176 platform disruption 193–9, 201–3, 210–11; traditional businesses 132, 193–4, 206, 198–201 platform ecosystems see ecosystems platform fit 75–6, 91, 92, 100, 105, 169 platform management 6–7, 17, 161, 168 platform owners 79, 84, 95, 100, 105 platform participants 35, 47, 48, 51, 78–80, 113, 142, 165; connect 98; ignition 91, 101; match 35, 84, 97; maturity 121–2; pre-launch 78–9, 86; trust framework 51, 158, 160–1 platforms 11–12 Pokémon Go 97 Porter, M. 6, 42–3 positive externalities 31, 32, 65 positive network effects: match 48; maturity 111, 114; pricing strategies 126, 142, 149; scaling-up 117 pre-launch 73, 74, 75–6, 77–8 premium services 142–3 price discrimination 137–8, 143 price elasticity 37, 137 pricing 50, 81, 116, 126–8, 137, 150 pricing friction 141–4 pricing models 139, 141–4, 146 pricing strategies 23, 138–9, 140, 145–50 producer acquisition 108–9 producer retention 108, 121 producers 78, 107–8 Quora 110, 159 Reddit 49, 78, 84, 94, 209, 210, 213 regulation 167, 173–9, 186–9 reliability 156 retention 47, 107–8, 111, 122 risk management 163 Rochet, J. 23–4 rocket model 46, 51, 73, 82, 116; ignition 73, 74, 91; maturity 73, 122; pre-launch 73, 74; scaling-up 73, 162 Ruby Lane 143, 166 SAP 123 scaling-up 73, 105–6, 116 Schmalensee, R. 24 search results 48, 50, 52, 67, 98, 114, 124, 180–1 self-driving cars 67, 211–12 sharing economy 8, 16, 164, 208–11 single homing 36–7 Snapchat 16, 110 Spotify 123 stakeholders 74, 79, 82, 210 start-ups 15 Stootie 98 strategic enablers 51, 101, 118 substitutes 38 talent platforms 205–6 Taobao 124–5 TaskRabbit 108, 148 taxi market 7, 24, 80, 93, 131, 174, 176, 178, 196 Tesco 27 Tinder 13, 96, 166 tipping point 36 Tirole, J. 7, 23–4 traditional businesses 4, 11, 13, 25, 41, 46, 79, 82, 137, 176; platform disruption 38, 193, 198 traditional business models 33 41, 53, 62, 132, 193, 211; ignition 91; maturity 132, 193 transactional platforms 85, 111 transaction fees 139, 143–4, 146 transaction metrics 111, 115, 116 Transport for London (TfL) 177–8 travel platforms 3, 11; see also Airbnb; Lyft; Uber Index trust 45, 49, 51, 84, 87, 99, 106, 115, 121, 130, 147, 153 trust framework 153, 155, 157, 165, 188 Twitter 69, 96, 159, 163 tying strategies 182–3 Uber 11, 15, 22, 52, 108, 177; business model 24, 79; price strategies 81, 140; regulation 140, 162, 167, 174, 177 UberEATS 133 under-regulation 174 unfair competition 3, 174, 180–1, 186 unicorns 15–16 Upwork 47, 88, 126, 159, 169, 206 user acquisition 93, 97, 107–10, 122; see also platform participants user retention 107–8, 111, 122 221 value chains 6, 42–3, 52–3, 202–3 value creation 11, 50, 78, 139, 174, 193, 211 value propositions 57, 63, 82, 112, 196, 197; maturity 112; pre-launch 82; scaling-up 112, 133, 142 Van Alstyne, M. 23, 25–6 VIP strategy 96 viral loops 110 Visa 5, 14, 23, 26, 80–1, 134 WeChat 6, 97, 132 WhatsApp 70, 110 YouTube (Google) 67–9, 131, 182, 183, 186 Zalando 12, 102, 200

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Index Accor Group 3, 201, 211 add-on platforms 77, 88–9, 92, 102, 105 advertising 37, 43, 45, 67–8, 93, 97, 99–100, 142, 144, 183 AdWords (Google) 67, 69, 183, 184 Airbnb 1–2, 3, 4, 15, 24, 168, 188; brand 51–2, 153, 169–70; business model 45; credibility 158–9, 163; feedback 35, 52, 160, 179–80; insurance 164; market sizing 79; match 47, 52, 83; value propositions 83, 95, 123 Alibaba 1, 15, 28, 34, 123, 124, 132, 208 Amazon 8, 12, 13, 14, 15, 125; business model 48, 59, 63; conflict resolution 163; ecosystem 28, 57–8, 59–61, 62, 70; feedback 35, 160; home automation 212 Amazon FBA (Fulfilment by Amazon) 58, 61–2 Amazon Marketplace 26–7, 48, 57, 58, 59, 60, 61–2, 84, 111 Amazon Prime 58, 61, 62 Amazon Web Services (AWS) 58 analytics 50–1, 52, 110, 123, 162, 187, 208, 213; see also data capture Android operating system (Google) 58, 67, 185–6 anticompetitive behaviour 173, 182, 186 APIs (application programming interfaces) 52, 183, 197 Apple 5, 8, 13, 14, 28, 193; App Store 32, 38, 63, 64–6; business model 54, 63, 66; ecosystem 63–6, 70, 132; iPhone 63, 65, 123; iTunes 63, 65 application programming interfaces see APIs Ariba 96 Artfire 143 attract 6, 15, 24, 47, 167; ignition 75, 91–6; maturity 75, 121–2, 131; pre-launch 74; scaling-up 75, 105, 107, 110–14, 117 audience builders 24 AWS see Amazon Web Services balance 48, 50, 106, 111–12, 114, 117, 124, 144–7, 150 Bezos, J. 57, 58 big bang launch strategy see event strategy BlaBlaCar 7, 154, 157, 158, 159; insurance 164; pricing strategies 147, 150, 156; scaling-up 112, 115 BorrowMyDoggy 99 bottlenecks 51, 73, 75–6, 86, 98, 99, 100, 101, 108, 112, 116 bowling pin strategy 54, 95, 109 brand 14–15, 51, 76, 118, 153–70 bundling strategies 61, 131, 182 business architecture 22, 66, 74, 80, 91 business models 6–7, 11, 22, 27, 59, 62, 63, 68, 132; platform businesses 6–7, 11, 22, 23, 27–8, 41–54, 70, 87, 138, 146, 177, 199, 200, 212; traditional businesses 62, 73, 91, 193, 211 card platforms 6, 23, 80, 126 catalyst businesses 23–4 Chesky, B. 1, 2, 169 218 Index Choudary, S. 25 communications platforms see Snapchat; WeChat; WhatsApp communities 44–5, 51, 78, 94, 112, 118, 125, 170 compensation strategy see dollar strategy competing platforms 131, 140, 149 competition 22, 25, 121, 130–2, 140, 173, 193 competition authorities 122, 131, 173-5, 186, 197 complements 31, 38, 130, 131 conflict resolution 126, 163, 167 connect 4, 45–6, 62; ignition 98; maturity 97; pre-launch 85; scaling-up 115, 117 consistency 159–60, 203 contributions 78, 82-3, 94, 118 control 52, 53, 123, 140–1, 162 core transactions 49, 52, 85–6, 98–101, 125, 168 correction activities 162 Craigslist 34, 95, 167 credibility 156, 158–9, 195 critical mass 31, 35–6, 37, 46, 73, 80, 121 cultural meme 97 customer acquisition 93, 107, 127; see also platform participants customer experience 36, 48, 53; maturity 108, 111; pricing strategies 139, 140–1; scaling-up 162 data capture 187; see also analytics dating platforms 13, 36, 48, 50, 95, 166, 176 delay strategy 195 Deliveroo 95, 111, 133, 170, 208 demand coordinators 24–5 denial strategy 194 denigrate strategy 194 deter strategy 194–5 Didi Chuxing 15, 132 digital platforms 4, 5, 12, 17, 73, 157, 167 direct network effects 34–5 direct platforms 80 disintermediation 126, 147–8 dispute resolution see conflict resolution dollar strategy 196 dynamic capabilities 196, 199, 200, 201 eBay 4, 15, 22, 48, 49, 53–4; brand 169, 170; business model 21, 27; connect 4, 85; feedback 159, 160; ignition 54; network effects 33, 34; pricing strategies 129, 140, 144, 146; scaling-up 108, 112, 115, 208; value propositions 83, 112 economic value 4, 14, 197 economies of scale 31, 32–33 ecosystems 6, 13, 17, 26–8, 180–1, 186, 207, 211; Amazon 26–7, 57–8, 59–61, 62, 70; Apple 63–6, 70, 132; Facebook 28, 70; Google 66–7, 68–9, 70, 132, 180–1, 185–6; Microsoft 70, 130, 132 enablers 51, 52, 87; ignition 101; maturity 128; pre-launch 87; scaling-up 118 Etsy 84, 87, 94, 110, 132, 146, 210 Evans, P. 14, 24, 25 event strategy 96 ex ante regulation 173, 188 ex post regulation 173 externalities 31–3, 65, 175, 180 Facebook 8, 13, 14, 52, 124, 131, 163, 168; ecosystem 28, 70; ignition 95, 96; network effects 35; pricing strategies 149; scaling-up 107, 166; search results 124 fast follower strategy 199 fear barriers 168–9 feedback 32, 34–6, 47, 77, 98, 100, 101, 108, 110, 111, 125, 160, 165, 188 Fulfilment by Amazon see Amazon FBA gaming platforms 35, 69, 97, 145 Gawer, A. 14, 25 Gebbia, J. 1, 169 Google 7, 13, 14, 67, 163, 180–6; AdWords 67; Android operating system 67, 185; brand 169; business model 50–1, 68; ecosystem 66–7, 68–9, 70, 132, 180–1, 186; home automation 212; search results 125; YouTube 67, 69, 131, 182 Google+ 69, 182 Index governance 51, 76, 140, 165, 167; prelaunch 87; trust framework 155, 157, 162, 164–70 Gumtree 132, 143 Hailo (MyTaxi) 93, 185 HiGear 163 home automation 212 Honda 27 hotels 1, 3, 7, 79, 179–80 IBM 14, 26, 194 ignition 73, 74, 91–102; eBay 53–4; Facebook 95, 96 indirect network effects 34–5 indirect platforms 80–1 innovation 140, 170, 174; maturity 127, 128; regulation 174, 186, 199 Instagram 70, 99, 107, 110, 133 insurance 164 intervention 175 intimacy 156 IT infrastructure 52 key enablers 51, 52 Kickstarter 78, 109, 210 La Belle Asiette 93–4, 99 labour laws 177 Launchworks 21, 157, 206, 208 leakage 49, 126, 147–8 leverage 61, 62, 70, 88, 124–5 linear businesses 6, 12, 41–2, 44, 52 LinkedIn 49, 70, 100, 107, 143, 150, 159 liquidity 33, 52, 143; ignition 99, 101; scaling-up 117 listing fees 144 Lyft 15, 132, 174, 176, 202 management principles 6, 7, 207 management rules 7, 17 market failures 167, 175–6, 187 market makers 24–5 marketplace platforms 12, 15, 48, 26, 59, 61, 85 marketplaces 6, 26, 64, 49, 59, 84, 85, 208 219 market power 37, 173–6, 180–3, 186, 197 market sizing 79–80 Mastercard 5, 23, 26, 80, 134 match 75; ignition 97–101, 115; maturity 124; platform participants 47–8, 52, 84–5, 95; pre-launch 86; scaling-up 114–15 maturity 73, 75–6, 121–2, 127–8 media companies 25 membership fees 139, 143–4 meshed communities 94–5 Microsoft 13–14, 23, 28, 63, 65, 70, 125, 143; ecosystem 70, 130, 132; home automation 212 minimum viable product see MVP monetization 50, 67, 86, 100, 116, 142, 147 MSP (multisided platforms) 25, 46 multihoming 36–7, 149, 176, 177 multisided markets 5, 23–4, 46 multisided platforms see MSP MVP (minimum viable product) 91, 100–1 MyTaxi 93 negative externalities 32, 180 Netflix 26, 197 network effects 33–5, 35, 48, 52, 68, 99, 133, 176; positive 48, 115, 126, 133, 142, 149 networks 31, 33–5 North Star metric 86, 100, 119 on-boarding processes 107–8, 168 Onefinestay 200–1 online platforms 11, 50, 51, 162 OpenTable 93 operating systems 5, 6, 25, 34; Android 58, 67, 185–6 optimize 50, 76; ignition 100, 101; maturity 128, 130; pre-launch 74, 86; scaling-up 116 over-regulation 174 Parker, G. 23, 25–6 partners 45, 79, 128 payment platforms 6, 24, 48 PayPal 95, 131, 187 performance metrics 100, 117, 127 220 Index personalization 124 piggybacking 95 platform balance 111, 117, 128, 144–6 platform businesses 4, 5–7, 11–14, 205, 207, 212, 214; definitions 21, 24–8 platform business models 4–7, 21–2, 27, 45–7, 146, 199, 205, 207, 212, 214 platform development 73, 77, 88; ignition 73, 75–6, 91–101; maturity 73, 75–6, 122, 125, 126, 128, 130; pre-launch 50, 73, 74, 75–6, 77–8, 82–7, 87, 169; scaling-up 73, 75–6, 91, 105–18, 169, 176 platform disruption 193–9, 201–3, 210–11; traditional businesses 132, 193–4, 206, 198–201 platform ecosystems see ecosystems platform fit 75–6, 91, 92, 100, 105, 169 platform management 6–7, 17, 161, 168 platform owners 79, 84, 95, 100, 105 platform participants 35, 47, 48, 51, 78–80, 113, 142, 165; connect 98; ignition 91, 101; match 35, 84, 97; maturity 121–2; pre-launch 78–9, 86; trust framework 51, 158, 160–1 platforms 11–12 Pokémon Go 97 Porter, M. 6, 42–3 positive externalities 31, 32, 65 positive network effects: match 48; maturity 111, 114; pricing strategies 126, 142, 149; scaling-up 117 pre-launch 73, 74, 75–6, 77–8 premium services 142–3 price discrimination 137–8, 143 price elasticity 37, 137 pricing 50, 81, 116, 126–8, 137, 150 pricing friction 141–4 pricing models 139, 141–4, 146 pricing strategies 23, 138–9, 140, 145–50 producer acquisition 108–9 producer retention 108, 121 producers 78, 107–8 Quora 110, 159 Reddit 49, 78, 84, 94, 209, 210, 213 regulation 167, 173–9, 186–9 reliability 156 retention 47, 107–8, 111, 122 risk management 163 Rochet, J. 23–4 rocket model 46, 51, 73, 82, 116; ignition 73, 74, 91; maturity 73, 122; pre-launch 73, 74; scaling-up 73, 162 Ruby Lane 143, 166 SAP 123 scaling-up 73, 105–6, 116 Schmalensee, R. 24 search results 48, 50, 52, 67, 98, 114, 124, 180–1 self-driving cars 67, 211–12 sharing economy 8, 16, 164, 208–11 single homing 36–7 Snapchat 16, 110 Spotify 123 stakeholders 74, 79, 82, 210 start-ups 15 Stootie 98 strategic enablers 51, 101, 118 substitutes 38 talent platforms 205–6 Taobao 124–5 TaskRabbit 108, 148 taxi market 7, 24, 80, 93, 131, 174, 176, 178, 196 Tesco 27 Tinder 13, 96, 166 tipping point 36 Tirole, J. 7, 23–4 traditional businesses 4, 11, 13, 25, 41, 46, 79, 82, 137, 176; platform disruption 38, 193, 198 traditional business models 33 41, 53, 62, 132, 193, 211; ignition 91; maturity 132, 193 transactional platforms 85, 111 transaction fees 139, 143–4, 146 transaction metrics 111, 115, 116 Transport for London (TfL) 177–8 travel platforms 3, 11; see also Airbnb; Lyft; Uber Index trust 45, 49, 51, 84, 87, 99, 106, 115, 121, 130, 147, 153 trust framework 153, 155, 157, 165, 188 Twitter 69, 96, 159, 163 tying strategies 182–3 Uber 11, 15, 22, 52, 108, 177; business model 24, 79; price strategies 81, 140; regulation 140, 162, 167, 174, 177 UberEATS 133 under-regulation 174 unfair competition 3, 174, 180–1, 186 unicorns 15–16 Upwork 47, 88, 126, 159, 169, 206 user acquisition 93, 97, 107–10, 122; see also platform participants user retention 107–8, 111, 122 221 value chains 6, 42–3, 52–3, 202–3 value creation 11, 50, 78, 139, 174, 193, 211 value propositions 57, 63, 82, 112, 196, 197; maturity 112; pre-launch 82; scaling-up 112, 133, 142 Van Alstyne, M. 23, 25–6 VIP strategy 96 viral loops 110 Visa 5, 14, 23, 26, 80–1, 134 WeChat 6, 97, 132 WhatsApp 70, 110 YouTube (Google) 67–9, 131, 182, 183, 186 Zalando 12, 102, 200

The Smartphone Society by Nicole Aschoff

"Susan Fowler" uber, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, algorithmic bias, algorithmic management, Amazon Web Services, artificial general intelligence, autonomous vehicles, barriers to entry, Bay Area Rapid Transit, Bernie Sanders, Big Tech, Black Lives Matter, blockchain, carbon footprint, Carl Icahn, Cass Sunstein, citizen journalism, cloud computing, correlation does not imply causation, crony capitalism, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, degrowth, Demis Hassabis, deplatforming, deskilling, digital capitalism, digital divide, do what you love, don't be evil, Donald Trump, Downton Abbey, Edward Snowden, Elon Musk, Evgeny Morozov, fake news, feminist movement, Ferguson, Missouri, Filter Bubble, financial independence, future of work, gamification, gig economy, global value chain, Google Chrome, Google Earth, Googley, green new deal, housing crisis, income inequality, independent contractor, Jaron Lanier, Jeff Bezos, Jessica Bruder, job automation, John Perry Barlow, knowledge economy, late capitalism, low interest rates, Lyft, M-Pesa, Mark Zuckerberg, minimum wage unemployment, mobile money, moral panic, move fast and break things, Naomi Klein, Network effects, new economy, Nicholas Carr, Nomadland, occupational segregation, Occupy movement, off-the-grid, offshore financial centre, opioid epidemic / opioid crisis, PageRank, Patri Friedman, peer-to-peer, Peter Thiel, pets.com, planned obsolescence, quantitative easing, Ralph Waldo Emerson, RAND corporation, Ray Kurzweil, RFID, Richard Stallman, ride hailing / ride sharing, Rodney Brooks, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Shoshana Zuboff, Sidewalk Labs, Silicon Valley, single-payer health, Skype, Snapchat, SoftBank, statistical model, Steve Bannon, Steve Jobs, surveillance capitalism, TaskRabbit, tech worker, technological determinism, TED Talk, the scientific method, The Structural Transformation of the Public Sphere, TikTok, transcontinental railway, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, upwardly mobile, Vision Fund, W. E. B. Du Bois, wages for housework, warehouse robotics, WikiLeaks, women in the workforce, yottabyte

Recently, Amazon got into the business of lending money, originating $3 billion in loans to small sellers who use its “marketplace” platform. In 2017 it bought Whole Foods for $13.7 billion, and in early 2018 it signed a joint-venture deal with investment guru Warren Buffett and J. P. Morgan to reconfigure the health-care market. This is on top of Amazon’s wildly successful venture Amazon Web Services (AWS), which provides on-demand, pay-as-you-go cloud-computing platforms for individuals, small and large businesses, and even governments, and generates half of the company’s profits.8 AWS customers include Netflix, CapitalOne, Condé Nast, and the Central Intelligence Agency, which paid Amazon $600 million for cloud space.

…

“Abrams, Jenna,” 109 ACLU (American Civil Liberties Union), 21, 22, 96, 148–49, 153, 176n28 Acxiom, 72, 77 addiction to social media, 65–69 Admiral, 78, 79 Adolfsson, Martin, 84 AdSense, 52 Advanced Research Projects Agency Network (ARPANET), 80 advertising: data collection and, 76–77, 83–84; on Facebook, 47–48; on Google, 52; political, 149 AdWords, 52 Afghanistan, politics in, 95–96 age discrimination, 149 AI Now, 129, 157 Airbnb, 42, 119, 120, 148 al-Abed, Bana, 90 al-Abed, Fatemah, 90 Aldridge, Rasheen, 91 algorithm(s): for consumer categories, 77; in dystopian future, 129–30, 131; in politics, 109–10; recommender, 67; search, 51–52, 53 algorithmic accountability, 151, 157–59 “algorithmic management,” 32–34 Alibaba, 4, 42 Allard, LaDonna Brave Bull, 104 aloneness, 7 Alphabet, 41, 55, 76, 122, 150 Alpha Go Zero program, 122 alter-globalization activists, 91 Amazon: acquisitions by, 41; and CIA, 81; in Europe, 150; marketplace model of, 150; as monopoly, 53–54; and new capitalism, 118–19; as new titan, 38–41, 44, 45; power of, 54–55; Rekognition software of, 149; tax evasion by, 49; warehouse workers at, 31–32, 33, 34; working conditions at, 46 Amazon Shopping, 30 Amazon Web Services (AWS), 41 American Academy of Pediatrics, 9 American Association of People with Disabilities, 55 American Civil Liberties Union (ACLU), 21, 22, 96, 148–49, 153, 176n28 American Dream, 120–21 American Library Association, 55 amplification, 91 analog networks, in cognitive mapping, 143 Android operating system, 39, 41, 53, 69, 70–71, 72 “angel investor,” 120 Ant Financial, 42 antidiscrimination laws, 149 antitrust laws and practices, 43–44, 52–53, 55, 150 AOL, data collection from, 81 app(s): top ten, 38 “app dashboard,” 69 app jobs, 30–34, 35, 137 Apple: data collection from, 81; in Europe, 150; low-paid workers at, 147; as new titan, 42; refusal of “right to repair” by, 155; supply chain of, 28–29; tax evasion by, 49–50; working conditions at, 46 appropriation, frontiers of, 73–76 appwashing, 34, 35, 146 “Arab Spring,” 97 Ardern, Jacinda, 93 ARPANET (Advanced Research Projects Agency Network), 80 artificial intelligence, 121–22, 123, 130 aspirational story, 120 assembly workers, 28–29 Athena, 42 AT&T, 29, 42, 43, 71 attention, 7 Audible, 41 Australia, justice in, 21 automation, 128, 131 automobiles, analogy between smartphones and, 2–3, 161, 162 autonomy, 117, 124 AWS (Amazon Web Services), 41 bad behavior, 138–39 Baidu, 42 Bannon, Steve, 105 Barlow, John Perry, 124 Bellini, Eevie, 24 Berners-Lee, Tim, 40 Beyoncé, 61, 107 Bezos, Jeff, 38, 44, 49, 54, 118–19 Bharatiya Janata Party (India), 93 big data, 76, 83–84, 145 Binh, Huang Duc, 95 black(s): police violence against, 17–23, 35, 169n6, 169n13 Blackberry, 6 “black-box algorithms,” 151 Black Lives Matter (BLM), 89–90, 100–102, 104, 107, 111 Blades, Joan, 91 blockchains, 124 body cameras worn by police, 22 book publishers, 172n40 boredom, 11 bots, 109 Boyd, Wes, 91 boyfriend, invisible, 24 Boyle, Susan, 63 Brazil, politics in, 97 Brin, Sergei, 38, 41, 54, 119, 148 Brown, Michael, Jr., 22, 89–90, 91, 101–2 Buffett, Warren, 41 Bumble, 23, 91 Bumblehive, 81 Burke, Tarana, 108 Bush, George W., 99 Calico, 41 California Consumer Privacy Act, 150 “CamperForce,” 31–32 Canales, Christian, 21 cancer, 7 candidates, social media use by, 103–5 Capital G, 41 capitalism: chameleonesque quality of, 116–17; crony, 105; and frontiers, 72–79; maps of, 143–44; neoliberal, 69, 102, 112–13, 117–18, 144–45; smartphones as embodiment of, 161–62; spirit of, 115–18; surveillance, 8 capitalist frontiers, government and, 80–81 carbon footprint, 82, 175n77 Carnegie, Andrew, 37, 54, 57 Castile, Philando, 20, 35 CatchLA, 62 caveats, 14–15 celebrities, in digital movements, 91 celebrity culture, 64–65 censorship, 95 Center for American Progress, 55 Center for Responsive Politics, 56 Central Intelligence Agency (CIA), 81, 95, 96, 138, 175n74 centrism, 112 Chadaga, Smitha, 89 change agent, 12 Chan, Priscilla, 56 Chan Zuckerberg Initiative (CZI), 56 Charles, Ashley “Dotty,” 109 Charlottesville, Virginia, 106–7, 111 Chesky, Brian, 120 children, 8, 9, 11–12, 84 China, 4, 42, 94–95 Chronicle, 41 CIA (Central Intelligence Agency), 81, 95, 96, 138, 175n74 Cienfuegos, Joaquin, 20–21 City of the Future, 124 Clinton, Bill, 43, 91, 99 Clinton, Hillary, 50, 107 Clooney, George, 91 “the cloud,” 82 cloud storage facility, 81 coercive relationships, 137–38, 152–54 cognitive maps and mapping, 143–59; algorithmic accountability in, 151; antidiscrimination laws in, 149; coercive and unjust relationships in, 152–54; defined, 143; digital and analog networks in, 143; digital commons in, 156– 59; ecological externalities in, 145; economic implications in, 151–52; internet access for poor and rural communities in, 149–50; lacunae in, 145; low-paying jobs in, 146–49, 153–54; monopoly in, 150; political advertising in, 149; power in, 145; and principles of smartphone use, 152–59; privacy in, 150–51; selfish or immoral behavior in, 154–56; sociotechnical layers of, 145 collateral damage, 96 collective action, 155–56 Comcast, 29, 71 comic books, 11 commodification of private sphere, 144 Communications and Decency Act, 51 communities, on-line, 64 community broadband initiative, 149–50 Community Legal Services, 171–72n32 Compass Transportation, 147 Computer and Communications Industry Association, 55 “connected presence,” 6 connection, 44, 63–64, 143, 162 connectivity, 122 Connolly, Mike, 94 consumer(s): vs. producers, 28–29 consumer categories, 77 consumer scores, 77–78 consumption, 83, 138–39 content algorithms, 51–52 content creation, 74–79 contingent workers, 30 convenience, 29–30, 35 Cooperation Jackson, 155 cop-watch groups, 20–21 “The Counted,” 19, 169n6 “creative monopoly,” 44 creativity, 117 credit scores, 78–79 creepin”, 65 critiques, 7–8 crony capitalism, 105 CrushTime, 24 Cruz, Ted, 92 “Cuban Twitter,” 95 Cucalon, Celia, 48 Cullors, Patrisse, 100–102 cultural capital, 62 “custom breathers,” 69 “customer lifetime value score,” 78 “cyberspace,” 82 CZI (Chan Zuckerberg Initiative), 56 Dakota Access Pipeline Protests, 103–4, 110 dashboard apps, 132 data brokers, 72, 77 data centers, energy use by, 82 data collection, 70–72, 83–84, 137, 150–51, 156–59 data mining, 76–79 data ownership, 135–36 “data smog,” 72 Data & Society, 151, 179n20 data vendors, 76 datification, 156–57, 161 dating apps, 23–27, 35 decentralization, 101–2, 120 decommodification, 156–57 DeepMind, 41, 79, 122, 157 Deliveroo, 32–33, 153 Department of Homeland Security, 149 Desai, Bhairavi, 146 Descartes, René, 67 Diallo, Amadou, 19 Diapers.com, 41 dick pics, 25 Dick’s Sporting Goods, 91 digital-analog political model, 104, 110–12, 145, 162 digital commons, 139–41, 156–59 digital divide, 28–29, 35 “digital exclusion,” 29 digital frontier, limits of, 82 digital justice, 17–23, 152, 157, 169n6, 169n13 digital networks, 143 digital platforms, 44 “digital redlining,” 29 “digital well-being,” 69 disconnection, 132–33 discrimination in housing, 47–48 divides, 17–36; built-in, 28–34, 35; related to justice, 17–23, 169n6, 169n13; related to sexuality, 23–27, 35 division of labor, 74–75 Dobbs, Tammy, 129 documentation, 62 domestic violence, 25 DoorDash, 33–34, 146–47 dopamine driven feedback loops, 67 double standard, 25–27, 35 drone warfare, 95 “dual economy,” 12–13 dumbness, 7, 9 “dumb phone,” 1, 8, 132, 167n15 dystopian future, 125–26, 128–30, 131 eBay, 147, 148 Echo Look device, 84 ecological externalities, 145 ecological limit of digital frontier, 82–83, 175n77 economic crisis (2008), 98–100, 117–18 economic divide, 13–14 economic implications, in cognitive mapping, 151–52 economic nationalism, 105 “ecosystems,” 40 Eddystone, 78 education, 13 Edwards, Jordan, 18 Egypt, 92, 97 elderly Americans, 63–64, 84 Electronic Frontier Foundation, 55, 124 Elliot, Umaara, 90 Ellison, Keith, 45 emails: vs. phone calls, 6; scanning of, 70–71 Emerson, Ralph Waldo, 133 employment relationships, 137 encryption, 81, 175n74 energy consumption, 151–52, 154–55; by physical elements of digital life, 82, 175n77 entrepreneurship, 120–21 environmental justice, 154–55 Epsilon, 72 Equal Credit Opportunity Act, 78 Estrada, Joseph, 90–91 Europe, American tech titans in, 150 European Union (EU), 49, 52–53, 150–51 experience economy, 62, 83 exploitation of labor, 75 externalization of work and workers, 31 Facebook: acquisitions by, 41; addiction to, 66–67, 69; and antidiscrimination laws, 149; credit ratings by, 79; data collection by, 71–72, 76, 81, 84; demographics of, 84; discrimination in advertising by, 47–48; employees organizing at, 148; in Europe, 150; Free Basics by, 41–42, 50; low-paid workers at, 147; managing impressions on, 63; as monopoly, 53–54, 172n46; and new capitalism, 124, 136; as news source, 50–51; as new titan, 38–39, 40, 41–42, 44–45; power and influence of, 56; tax evasion by, 49; time spent on, 60 Facebook Live, 84 Facebook Pixel, 72 “Facebook Revolution,” 97 facial recognition software, 129, 149 Fair Housing Act, 48 fake news, 50–51, 56 Federal Bureau of Investigation (FBI), 149 feedback loop, 66–67 “Feminist Five,” 108 feminist movement, 107–8 Ferguson, Missouri, 89–90, 101–2 Fields, James Alex, Jr., 106 filter bubbles, 53, 109–10 financial crisis (2008), 98–100, 117–18 fintech companies, 78–79 flexibility, 117 FlexiSpy, 25 FOIA (Freedom of Information Act) requests, 149 food delivery apps, 32–33 food pictures, 62 forced arbitration, 148 Ford, Henry, 37, 124 Fordlandia, 124 Ford Motor Company, 37, 38, 44 Fowler, Susan, 126–27 FoxConn, 28 framing of analysis of cell phones, 12–14 Free Basics, 41–42, 50 Freedom of Information Act (FOIA) requests, 149 “free market” competition, 98 free speech, 138 Friedman, Patri, 124 frontier of social media, 59–86; addiction to, and fears about, 65–69; and big data, 82–86; characteristics of, 59–65; and government, 80–81; and privacy, 69– 72; and profit, 72–79 Fuller, Margaret, 133 Galston, Bill, 45 gamification, 23–24, 146 Garza, Alicia, 100–102 Gassama, Mamoudou, 115 Gates, Bill, 56, 130 General Data Protection Regulation (GDPR), 150–51, 156 Geofeedia, 96 geopolitics, 95–96, 144 Germany, 3 Ghostbot, 24 gig economy, 137 Gilded Age, 1–2, 11, 54 global divides, 28–29 globalization, 98 Global Positioning System (GPS), 5 Gmail, 38, 71 Goodman, Amy, 104 Google: addiction to, 69; advertising on, 76; companies owned by, 41; data collection by, 70–71, 76, 81, 150; employees organizing at, 147–48; in Europe, 150; immoral projects at, 154; and intelligence agencies, 81; lobbying by, 55; low-paid workers at, 147; molding of public opinion by, 55; as monopoly, 52–54; and new capitalism, 119–20, 123, 136; as new titan, 38, 39, 40, 41, 43–45, 56; for online search, 52; power and influence of, 55–56; research funding by, 55; “summer camp” of, 55; tax evasion by, 49, 50; use by children, 84; working conditions at, 47 Google Calendar, 71 Google Chrome browser, 53 Google Drive, 71 Google Earth, 123 Google Fiber, 41 Google Images, 131 Google News, 50 Google Play, 72 Google Search, 38, 41, 53 Google Shopping Case, 52–53 Google Translate, 10 government: and capitalist frontiers, 80–81; and Silicon Valley, 124; surveillance by, 137–38, 144, 148–49 government tracking, 94–95 GPS (Global Positioning System), 5 Grant, Oscar, III, 18 Gray, Freddie, 22, 96 Great Pessimism, 118, 119 Great Recession, 118 Greece, 97 greenhouse emissions, 82 Green New Deal, 103, 151–52, 154, 179n22 Greenpeace, 157 Grindr, 23 gun violence, 90, 91, 110, 111 GV, 41 habitus, 62 Happn, 23, 24 Harari, Yuval Noah, 129–30, 132 Hearst, William Randolph, 50 Herndon, Chris, 56 Heyer, Heather, 106 Hirschman, Albert, 133 household expenses, 13 housing, discrimination in, 47–48 human, fear of becoming less, 67–68 iBeacon, 78 IBM, 149 ICE (Immigration and Customs Enforcement), 21, 148 ICT (information and communication technologies) sector, energy used by, 82 ideals, 120 “identity resolution services,” 77 ILSR (Institute for Local Self-Reliance), 149–50, 153 Immigration and Customs Enforcement (ICE), 21, 148 immoral behavior, 138–39, 154–56 impressions, management of, 62–63 income discrepancy, 12–13 independent contractors, 33–34, 137, 153 India, 4, 42, 60, 93 individualism, 117, 118 information and communication technologies (ICT) sector, energy used by, 82 injustice, 17–23, 169n6, 169n13 In-Q-Tel, 96 insta-bae principles, 61–62 Instacart, 30, 146 Instagram: addiction to, 69; content on, 60, 61, 62; creepin’ on, 65; data collection from, 72; demographics of, 61; microcelebrities on, 60; as new titan, 38, 41; time spent on, 60; value of, 74 Institute for Local Self-Reliance (ILSR), 149–50, 153 insurance companies, data mining by, 79 intelligence agencies, and capitalist frontiers, 81 internet: access for poor and rural communities to, 149–50; access to high-speed, 5, 29; creation of, 80; early days of, 40; government shutdown of, 94; smartphone connection to, 10 Internet Association, 45 Internet Broken Brain, 66 Internet.org, 41 Internet Research Agency, 109 internet service providers, and privacy, 71–72 inventions, new, 11 investors, 120–21 invisible boyfriend, 24 iPhone, 6, 42 Iron Eyes, Tokata, 104 Jacob’s letter, 127 Jacobs, Ric, 127 Jaffe, Sarah, 91 James, LeBron, 92 Jenner, Kris, 59 Jigsaw, 41 Jobs, Steve, 2, 6, 42 Joint Special Operations Command, 95 Jones, Alex, 111 Jones, Keaton, 109 justice, 17–23, 152, 157, 169n6, 169n13 Kalanick, Travis, 126, 127 Kardashian, Kim, 59 Kardashian, Kylie, 59 Kasky, Cameron, 90 Kattan, Huda, 60 Kellaway, Lucy, 1 Kennedy, John F., 88 Kerry, John, 56 Keynes, John Maynard, 117 Khosla, Sadhavi, 93 King, Martin Luther, Jr., 97 King, Rodney, 19 Klein, Naomi, 104 Knight First Amendment Institute, 93, 175n14 Knoll, Jessica, 108 Kreditech, 78–79 Kristol, Bill, 45 Kurzweil, Ray, 123 Lanier, Jaron, 67, 70, 135 Lantern, 95 law enforcement, surveillance by, 96–97, 137–38, 176n28 “leaners,” 29 Lean In (Sandberg), 107 Lehman Brothers, 98 Levandowski, Anthony, 123 LGBTQ community, 10, 64 “lifestyle politics,” 133 “like” button, 66 Lind, William, 105 “listening tour,” 56 Liu Hu, 94 live chats, 5 livestreaming of police violence, 20–21 lobbying, 55, 56 location data, 71 logistics workers, 31 Loon, 41 Loop Transportation, 147 Losse, Katherine, 124 love, 23–27, 35 low-income households, access to high-speed internet by, 29 low-paying jobs, 146–49, 153–54 Luxy, 23 Lyft 30, 31, 33, 146, 153 Lynd, Helen, 2, 3 Lynd, Robert, 2, 3 Lynn, Barry, 56 machine learning, 76, 121–22, 174n52 “machine zone,” 8 Macron, Emmanuel, 93 mainstream media, bypassing of, 91 Ma, Jack, 42 Makani, 41 mamasphere, 64 March for Our Lives, 104 March of the Margaridas, 108 Marjorie Stoneman Douglas (MSD) High School, 90 marketification, 161 Markey, Ed, 152, 179n22 marriage, expectations and norms about, 24–25 Marshall, James, 17–19 Marshall, Tanya, 17–19 Martin, Trayvon, 22, 100 Marx, Karl, 67 mass shootings, 90, 91, 110, 111 Match.com, 23, 24 Match Group, 24 Matsuhisa, Nobu, 62 Maven, 147–48 McDonald, Laquan, 18 McInnes, Gavin, 106, 107, 111 McSpadden, Lezley, 101 meaning, sense of, 64–65, 162 Medbase200, 77 men’s work, 75 mental health of children, 8 meritocracy, 121 Messenger: data collection from, 72 Messenger Kids, 84 metadata, 72 #MeToo movement, 108 microcelebrities, 60 microchoices, 69 Microsoft: and City of the Future, 124; data collection from, 81; employees organizing at, 148; in Europe, 150; immoral projects at, 154; as new titan, 42, 43, 55, 56 Middleton, Daniel, 60 “Middletown,” 2–3 Middletown: A Study in Contemporary American Culture (Lynd and Lynd), 2–3 military, 80–81, 95 mind-body divide, 67–68 miners, 28 Minutiae, 84 misogyny, 25 Mobile Devices Branch of CIA, 81, 95 Mobile Justice MI app, 21, 169n13 Modi, Narendra, 93 Mondragon, Elena, 22–23 monetization, 59, 79 money accounts, mobile, 10 monopolies, 43–46, 52–53, 150 Morgan, J.

…

“Abrams, Jenna,” 109 ACLU (American Civil Liberties Union), 21, 22, 96, 148–49, 153, 176n28 Acxiom, 72, 77 addiction to social media, 65–69 Admiral, 78, 79 Adolfsson, Martin, 84 AdSense, 52 Advanced Research Projects Agency Network (ARPANET), 80 advertising: data collection and, 76–77, 83–84; on Facebook, 47–48; on Google, 52; political, 149 AdWords, 52 Afghanistan, politics in, 95–96 age discrimination, 149 AI Now, 129, 157 Airbnb, 42, 119, 120, 148 al-Abed, Bana, 90 al-Abed, Fatemah, 90 Aldridge, Rasheen, 91 algorithm(s): for consumer categories, 77; in dystopian future, 129–30, 131; in politics, 109–10; recommender, 67; search, 51–52, 53 algorithmic accountability, 151, 157–59 “algorithmic management,” 32–34 Alibaba, 4, 42 Allard, LaDonna Brave Bull, 104 aloneness, 7 Alphabet, 41, 55, 76, 122, 150 Alpha Go Zero program, 122 alter-globalization activists, 91 Amazon: acquisitions by, 41; and CIA, 81; in Europe, 150; marketplace model of, 150; as monopoly, 53–54; and new capitalism, 118–19; as new titan, 38–41, 44, 45; power of, 54–55; Rekognition software of, 149; tax evasion by, 49; warehouse workers at, 31–32, 33, 34; working conditions at, 46 Amazon Shopping, 30 Amazon Web Services (AWS), 41 American Academy of Pediatrics, 9 American Association of People with Disabilities, 55 American Civil Liberties Union (ACLU), 21, 22, 96, 148–49, 153, 176n28 American Dream, 120–21 American Library Association, 55 amplification, 91 analog networks, in cognitive mapping, 143 Android operating system, 39, 41, 53, 69, 70–71, 72 “angel investor,” 120 Ant Financial, 42 antidiscrimination laws, 149 antitrust laws and practices, 43–44, 52–53, 55, 150 AOL, data collection from, 81 app(s): top ten, 38 “app dashboard,” 69 app jobs, 30–34, 35, 137 Apple: data collection from, 81; in Europe, 150; low-paid workers at, 147; as new titan, 42; refusal of “right to repair” by, 155; supply chain of, 28–29; tax evasion by, 49–50; working conditions at, 46 appropriation, frontiers of, 73–76 appwashing, 34, 35, 146 “Arab Spring,” 97 Ardern, Jacinda, 93 ARPANET (Advanced Research Projects Agency Network), 80 artificial intelligence, 121–22, 123, 130 aspirational story, 120 assembly workers, 28–29 Athena, 42 AT&T, 29, 42, 43, 71 attention, 7 Audible, 41 Australia, justice in, 21 automation, 128, 131 automobiles, analogy between smartphones and, 2–3, 161, 162 autonomy, 117, 124 AWS (Amazon Web Services), 41 bad behavior, 138–39 Baidu, 42 Bannon, Steve, 105 Barlow, John Perry, 124 Bellini, Eevie, 24 Berners-Lee, Tim, 40 Beyoncé, 61, 107 Bezos, Jeff, 38, 44, 49, 54, 118–19 Bharatiya Janata Party (India), 93 big data, 76, 83–84, 145 Binh, Huang Duc, 95 black(s): police violence against, 17–23, 35, 169n6, 169n13 Blackberry, 6 “black-box algorithms,” 151 Black Lives Matter (BLM), 89–90, 100–102, 104, 107, 111 Blades, Joan, 91 blockchains, 124 body cameras worn by police, 22 book publishers, 172n40 boredom, 11 bots, 109 Boyd, Wes, 91 boyfriend, invisible, 24 Boyle, Susan, 63 Brazil, politics in, 97 Brin, Sergei, 38, 41, 54, 119, 148 Brown, Michael, Jr., 22, 89–90, 91, 101–2 Buffett, Warren, 41 Bumble, 23, 91 Bumblehive, 81 Burke, Tarana, 108 Bush, George W., 99 Calico, 41 California Consumer Privacy Act, 150 “CamperForce,” 31–32 Canales, Christian, 21 cancer, 7 candidates, social media use by, 103–5 Capital G, 41 capitalism: chameleonesque quality of, 116–17; crony, 105; and frontiers, 72–79; maps of, 143–44; neoliberal, 69, 102, 112–13, 117–18, 144–45; smartphones as embodiment of, 161–62; spirit of, 115–18; surveillance, 8 capitalist frontiers, government and, 80–81 carbon footprint, 82, 175n77 Carnegie, Andrew, 37, 54, 57 Castile, Philando, 20, 35 CatchLA, 62 caveats, 14–15 celebrities, in digital movements, 91 celebrity culture, 64–65 censorship, 95 Center for American Progress, 55 Center for Responsive Politics, 56 Central Intelligence Agency (CIA), 81, 95, 96, 138, 175n74 centrism, 112 Chadaga, Smitha, 89 change agent, 12 Chan, Priscilla, 56 Chan Zuckerberg Initiative (CZI), 56 Charles, Ashley “Dotty,” 109 Charlottesville, Virginia, 106–7, 111 Chesky, Brian, 120 children, 8, 9, 11–12, 84 China, 4, 42, 94–95 Chronicle, 41 CIA (Central Intelligence Agency), 81, 95, 96, 138, 175n74 Cienfuegos, Joaquin, 20–21 City of the Future, 124 Clinton, Bill, 43, 91, 99 Clinton, Hillary, 50, 107 Clooney, George, 91 “the cloud,” 82 cloud storage facility, 81 coercive relationships, 137–38, 152–54 cognitive maps and mapping, 143–59; algorithmic accountability in, 151; antidiscrimination laws in, 149; coercive and unjust relationships in, 152–54; defined, 143; digital and analog networks in, 143; digital commons in, 156– 59; ecological externalities in, 145; economic implications in, 151–52; internet access for poor and rural communities in, 149–50; lacunae in, 145; low-paying jobs in, 146–49, 153–54; monopoly in, 150; political advertising in, 149; power in, 145; and principles of smartphone use, 152–59; privacy in, 150–51; selfish or immoral behavior in, 154–56; sociotechnical layers of, 145 collateral damage, 96 collective action, 155–56 Comcast, 29, 71 comic books, 11 commodification of private sphere, 144 Communications and Decency Act, 51 communities, on-line, 64 community broadband initiative, 149–50 Community Legal Services, 171–72n32 Compass Transportation, 147 Computer and Communications Industry Association, 55 “connected presence,” 6 connection, 44, 63–64, 143, 162 connectivity, 122 Connolly, Mike, 94 consumer(s): vs. producers, 28–29 consumer categories, 77 consumer scores, 77–78 consumption, 83, 138–39 content algorithms, 51–52 content creation, 74–79 contingent workers, 30 convenience, 29–30, 35 Cooperation Jackson, 155 cop-watch groups, 20–21 “The Counted,” 19, 169n6 “creative monopoly,” 44 creativity, 117 credit scores, 78–79 creepin”, 65 critiques, 7–8 crony capitalism, 105 CrushTime, 24 Cruz, Ted, 92 “Cuban Twitter,” 95 Cucalon, Celia, 48 Cullors, Patrisse, 100–102 cultural capital, 62 “custom breathers,” 69 “customer lifetime value score,” 78 “cyberspace,” 82 CZI (Chan Zuckerberg Initiative), 56 Dakota Access Pipeline Protests, 103–4, 110 dashboard apps, 132 data brokers, 72, 77 data centers, energy use by, 82 data collection, 70–72, 83–84, 137, 150–51, 156–59 data mining, 76–79 data ownership, 135–36 “data smog,” 72 Data & Society, 151, 179n20 data vendors, 76 datification, 156–57, 161 dating apps, 23–27, 35 decentralization, 101–2, 120 decommodification, 156–57 DeepMind, 41, 79, 122, 157 Deliveroo, 32–33, 153 Department of Homeland Security, 149 Desai, Bhairavi, 146 Descartes, René, 67 Diallo, Amadou, 19 Diapers.com, 41 dick pics, 25 Dick’s Sporting Goods, 91 digital-analog political model, 104, 110–12, 145, 162 digital commons, 139–41, 156–59 digital divide, 28–29, 35 “digital exclusion,” 29 digital frontier, limits of, 82 digital justice, 17–23, 152, 157, 169n6, 169n13 digital networks, 143 digital platforms, 44 “digital redlining,” 29 “digital well-being,” 69 disconnection, 132–33 discrimination in housing, 47–48 divides, 17–36; built-in, 28–34, 35; related to justice, 17–23, 169n6, 169n13; related to sexuality, 23–27, 35 division of labor, 74–75 Dobbs, Tammy, 129 documentation, 62 domestic violence, 25 DoorDash, 33–34, 146–47 dopamine driven feedback loops, 67 double standard, 25–27, 35 drone warfare, 95 “dual economy,” 12–13 dumbness, 7, 9 “dumb phone,” 1, 8, 132, 167n15 dystopian future, 125–26, 128–30, 131 eBay, 147, 148 Echo Look device, 84 ecological externalities, 145 ecological limit of digital frontier, 82–83, 175n77 economic crisis (2008), 98–100, 117–18 economic divide, 13–14 economic implications, in cognitive mapping, 151–52 economic nationalism, 105 “ecosystems,” 40 Eddystone, 78 education, 13 Edwards, Jordan, 18 Egypt, 92, 97 elderly Americans, 63–64, 84 Electronic Frontier Foundation, 55, 124 Elliot, Umaara, 90 Ellison, Keith, 45 emails: vs. phone calls, 6; scanning of, 70–71 Emerson, Ralph Waldo, 133 employment relationships, 137 encryption, 81, 175n74 energy consumption, 151–52, 154–55; by physical elements of digital life, 82, 175n77 entrepreneurship, 120–21 environmental justice, 154–55 Epsilon, 72 Equal Credit Opportunity Act, 78 Estrada, Joseph, 90–91 Europe, American tech titans in, 150 European Union (EU), 49, 52–53, 150–51 experience economy, 62, 83 exploitation of labor, 75 externalization of work and workers, 31 Facebook: acquisitions by, 41; addiction to, 66–67, 69; and antidiscrimination laws, 149; credit ratings by, 79; data collection by, 71–72, 76, 81, 84; demographics of, 84; discrimination in advertising by, 47–48; employees organizing at, 148; in Europe, 150; Free Basics by, 41–42, 50; low-paid workers at, 147; managing impressions on, 63; as monopoly, 53–54, 172n46; and new capitalism, 124, 136; as news source, 50–51; as new titan, 38–39, 40, 41–42, 44–45; power and influence of, 56; tax evasion by, 49; time spent on, 60 Facebook Live, 84 Facebook Pixel, 72 “Facebook Revolution,” 97 facial recognition software, 129, 149 Fair Housing Act, 48 fake news, 50–51, 56 Federal Bureau of Investigation (FBI), 149 feedback loop, 66–67 “Feminist Five,” 108 feminist movement, 107–8 Ferguson, Missouri, 89–90, 101–2 Fields, James Alex, Jr., 106 filter bubbles, 53, 109–10 financial crisis (2008), 98–100, 117–18 fintech companies, 78–79 flexibility, 117 FlexiSpy, 25 FOIA (Freedom of Information Act) requests, 149 food delivery apps, 32–33 food pictures, 62 forced arbitration, 148 Ford, Henry, 37, 124 Fordlandia, 124 Ford Motor Company, 37, 38, 44 Fowler, Susan, 126–27 FoxConn, 28 framing of analysis of cell phones, 12–14 Free Basics, 41–42, 50 Freedom of Information Act (FOIA) requests, 149 “free market” competition, 98 free speech, 138 Friedman, Patri, 124 frontier of social media, 59–86; addiction to, and fears about, 65–69; and big data, 82–86; characteristics of, 59–65; and government, 80–81; and privacy, 69– 72; and profit, 72–79 Fuller, Margaret, 133 Galston, Bill, 45 gamification, 23–24, 146 Garza, Alicia, 100–102 Gassama, Mamoudou, 115 Gates, Bill, 56, 130 General Data Protection Regulation (GDPR), 150–51, 156 Geofeedia, 96 geopolitics, 95–96, 144 Germany, 3 Ghostbot, 24 gig economy, 137 Gilded Age, 1–2, 11, 54 global divides, 28–29 globalization, 98 Global Positioning System (GPS), 5 Gmail, 38, 71 Goodman, Amy, 104 Google: addiction to, 69; advertising on, 76; companies owned by, 41; data collection by, 70–71, 76, 81, 150; employees organizing at, 147–48; in Europe, 150; immoral projects at, 154; and intelligence agencies, 81; lobbying by, 55; low-paid workers at, 147; molding of public opinion by, 55; as monopoly, 52–54; and new capitalism, 119–20, 123, 136; as new titan, 38, 39, 40, 41, 43–45, 56; for online search, 52; power and influence of, 55–56; research funding by, 55; “summer camp” of, 55; tax evasion by, 49, 50; use by children, 84; working conditions at, 47 Google Calendar, 71 Google Chrome browser, 53 Google Drive, 71 Google Earth, 123 Google Fiber, 41 Google Images, 131 Google News, 50 Google Play, 72 Google Search, 38, 41, 53 Google Shopping Case, 52–53 Google Translate, 10 government: and capitalist frontiers, 80–81; and Silicon Valley, 124; surveillance by, 137–38, 144, 148–49 government tracking, 94–95 GPS (Global Positioning System), 5 Grant, Oscar, III, 18 Gray, Freddie, 22, 96 Great Pessimism, 118, 119 Great Recession, 118 Greece, 97 greenhouse emissions, 82 Green New Deal, 103, 151–52, 154, 179n22 Greenpeace, 157 Grindr, 23 gun violence, 90, 91, 110, 111 GV, 41 habitus, 62 Happn, 23, 24 Harari, Yuval Noah, 129–30, 132 Hearst, William Randolph, 50 Herndon, Chris, 56 Heyer, Heather, 106 Hirschman, Albert, 133 household expenses, 13 housing, discrimination in, 47–48 human, fear of becoming less, 67–68 iBeacon, 78 IBM, 149 ICE (Immigration and Customs Enforcement), 21, 148 ICT (information and communication technologies) sector, energy used by, 82 ideals, 120 “identity resolution services,” 77 ILSR (Institute for Local Self-Reliance), 149–50, 153 Immigration and Customs Enforcement (ICE), 21, 148 immoral behavior, 138–39, 154–56 impressions, management of, 62–63 income discrepancy, 12–13 independent contractors, 33–34, 137, 153 India, 4, 42, 60, 93 individualism, 117, 118 information and communication technologies (ICT) sector, energy used by, 82 injustice, 17–23, 169n6, 169n13 In-Q-Tel, 96 insta-bae principles, 61–62 Instacart, 30, 146 Instagram: addiction to, 69; content on, 60, 61, 62; creepin’ on, 65; data collection from, 72; demographics of, 61; microcelebrities on, 60; as new titan, 38, 41; time spent on, 60; value of, 74 Institute for Local Self-Reliance (ILSR), 149–50, 153 insurance companies, data mining by, 79 intelligence agencies, and capitalist frontiers, 81 internet: access for poor and rural communities to, 149–50; access to high-speed, 5, 29; creation of, 80; early days of, 40; government shutdown of, 94; smartphone connection to, 10 Internet Association, 45 Internet Broken Brain, 66 Internet.org, 41 Internet Research Agency, 109 internet service providers, and privacy, 71–72 inventions, new, 11 investors, 120–21 invisible boyfriend, 24 iPhone, 6, 42 Iron Eyes, Tokata, 104 Jacob’s letter, 127 Jacobs, Ric, 127 Jaffe, Sarah, 91 James, LeBron, 92 Jenner, Kris, 59 Jigsaw, 41 Jobs, Steve, 2, 6, 42 Joint Special Operations Command, 95 Jones, Alex, 111 Jones, Keaton, 109 justice, 17–23, 152, 157, 169n6, 169n13 Kalanick, Travis, 126, 127 Kardashian, Kim, 59 Kardashian, Kylie, 59 Kasky, Cameron, 90 Kattan, Huda, 60 Kellaway, Lucy, 1 Kennedy, John F., 88 Kerry, John, 56 Keynes, John Maynard, 117 Khosla, Sadhavi, 93 King, Martin Luther, Jr., 97 King, Rodney, 19 Klein, Naomi, 104 Knight First Amendment Institute, 93, 175n14 Knoll, Jessica, 108 Kreditech, 78–79 Kristol, Bill, 45 Kurzweil, Ray, 123 Lanier, Jaron, 67, 70, 135 Lantern, 95 law enforcement, surveillance by, 96–97, 137–38, 176n28 “leaners,” 29 Lean In (Sandberg), 107 Lehman Brothers, 98 Levandowski, Anthony, 123 LGBTQ community, 10, 64 “lifestyle politics,” 133 “like” button, 66 Lind, William, 105 “listening tour,” 56 Liu Hu, 94 live chats, 5 livestreaming of police violence, 20–21 lobbying, 55, 56 location data, 71 logistics workers, 31 Loon, 41 Loop Transportation, 147 Losse, Katherine, 124 love, 23–27, 35 low-income households, access to high-speed internet by, 29 low-paying jobs, 146–49, 153–54 Luxy, 23 Lyft 30, 31, 33, 146, 153 Lynd, Helen, 2, 3 Lynd, Robert, 2, 3 Lynn, Barry, 56 machine learning, 76, 121–22, 174n52 “machine zone,” 8 Macron, Emmanuel, 93 mainstream media, bypassing of, 91 Ma, Jack, 42 Makani, 41 mamasphere, 64 March for Our Lives, 104 March of the Margaridas, 108 Marjorie Stoneman Douglas (MSD) High School, 90 marketification, 161 Markey, Ed, 152, 179n22 marriage, expectations and norms about, 24–25 Marshall, James, 17–19 Marshall, Tanya, 17–19 Martin, Trayvon, 22, 100 Marx, Karl, 67 mass shootings, 90, 91, 110, 111 Match.com, 23, 24 Match Group, 24 Matsuhisa, Nobu, 62 Maven, 147–48 McDonald, Laquan, 18 McInnes, Gavin, 106, 107, 111 McSpadden, Lezley, 101 meaning, sense of, 64–65, 162 Medbase200, 77 men’s work, 75 mental health of children, 8 meritocracy, 121 Messenger: data collection from, 72 Messenger Kids, 84 metadata, 72 #MeToo movement, 108 microcelebrities, 60 microchoices, 69 Microsoft: and City of the Future, 124; data collection from, 81; employees organizing at, 148; in Europe, 150; immoral projects at, 154; as new titan, 42, 43, 55, 56 Middleton, Daniel, 60 “Middletown,” 2–3 Middletown: A Study in Contemporary American Culture (Lynd and Lynd), 2–3 military, 80–81, 95 mind-body divide, 67–68 miners, 28 Minutiae, 84 misogyny, 25 Mobile Devices Branch of CIA, 81, 95 Mobile Justice MI app, 21, 169n13 Modi, Narendra, 93 Mondragon, Elena, 22–23 monetization, 59, 79 money accounts, mobile, 10 monopolies, 43–46, 52–53, 150 Morgan, J.

Binge Times: Inside Hollywood's Furious Billion-Dollar Battle to Take Down Netflix by Dade Hayes, Dawn Chmielewski

activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, Amazon Web Services, AOL-Time Warner, Apollo 13, augmented reality, barriers to entry, Big Tech, borderless world, cloud computing, cognitive dissonance, content marketing, coronavirus, corporate raider, COVID-19, data science, digital rights, Donald Trump, Downton Abbey, Elon Musk, George Floyd, global pandemic, Golden age of television, haute cuisine, hockey-stick growth, invention of the telephone, Jeff Bezos, John Markoff, Jony Ive, late fees, lockdown, loose coupling, Marc Andreessen, Mark Zuckerberg, Mitch Kapor, Netflix Prize, Osborne effect, performance metric, period drama, Phoebe Waller-Bridge, QR code, reality distortion field, recommendation engine, remote working, Ronald Reagan, Salesforce, Saturday Night Live, Silicon Valley, skunkworks, Skype, Snapchat, social distancing, Steve Jobs, subscription business, tech bro, the long tail, the medium is the message, TikTok, Tim Cook: Apple, vertical integration, WeWork

“Fortunately, we’ve had the experience with Game of Thrones, we’ve had the experience of ESPN+ with the big pay-per-view events that we’re doing with UFC exclusively on our platform,” he told the Verge. “We’re getting big bursts. We’ve built capabilities to sustain that, both in terms of processing the transactions that all come in—in a very, very short period of time—as well as the streams.” Within a matter of hours, the BAMTech team offered Mayer a preliminary diagnosis: a flaw in Amazon Web Services’ real-time customer analytics software. Mayer urged Inzerillo to fix it, adding, “This is not good.” Word leaked out to the press that a “third-party vendor” was to blame. “At midnight, the system was humming along fine. Everyone was going through as many checks as possible. Clearly, everyone was streaming the first episode of Mandalorian, it was all on point,” said one West Coast–based tech executive.

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Mayer contacted Amazon CEO Andy Jassy—who at the time headed the company’s cloud computing business, which powers a number of streaming services, including Netflix—to ask what had gone wrong. Jassy offered to take the fall for the Disney glitch but said the real problem was with Disney’s own app. BAMTech had hacked Amazon Web Services’ search function to deliver movie and TV recommendations—not an intended use. Inzerillo developed a manual work-around that got the service up and running within a matter of hours and kept the Disney+ app functioning until BAMTech offered an updated version of the app weeks later. About one-third of people who had trouble getting through the front door couldn’t remember their passwords, a problem Disney helped create by requiring consumers to use the same login as when they bought theme park tickets, booked travel through the Disney Vacation Club, or shopped its online stores, said the tech executive.

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(He would pass the reins to Andy Jassy over the summer while remaining involved in the company as executive chairman.) He revealed that more than 175 million Prime members had streamed shows and movies over the previous year, with overall streaming rising more than 70 percent year over year. “Two of our kids are now ten and fifteen years old,” he said, alluding to Prime Video and Amazon Web Services, “and after years of being nurtured, they’re growing up fast and coming into their own.” Chapter 22 Paciencia y Fe Lin-Manuel Miranda grabbed the microphone from Robert De Niro and jumped around the stage like an unleashed labradoodle. “Well, what’s up, In the Heights opening night?!”

The Nature of Software Development: Keep It Simple, Make It Valuable, Build It Piece by Piece by Ron Jeffries

Amazon Web Services, anti-pattern, bitcoin, business cycle, business intelligence, business logic, business process, c2.com, call centre, cloud computing, continuous integration, Conway's law, creative destruction, dark matter, data science, database schema, deep learning, DevOps, disinformation, duck typing, en.wikipedia.org, fail fast, fault tolerance, Firefox, Hacker News, industrial robot, information security, Infrastructure as a Service, Internet of things, Jeff Bezos, Kanban, Kubernetes, load shedding, loose coupling, machine readable, Mars Rover, microservices, Minecraft, minimum viable product, MITM: man-in-the-middle, Morris worm, move fast and break things, OSI model, peer-to-peer lending, platform as a service, power law, ransomware, revision control, Ruby on Rails, Schrödinger's Cat, Silicon Valley, six sigma, software is eating the world, source of truth, SQL injection, systems thinking, text mining, time value of money, transaction costs, Turing machine, two-pizza team, web application, zero day

A failure can also result when the “desired” state is computed incorrectly and may be impossible or impractical. For example, a naive scheduler might try to run enough instances to drain a queue in a fixed amount of time. Depending on the individual jobs’ processing time, the number of instances might be “infinity.” That will smart when the Amazon Web Services bill arrives! Controls and Safeguards The United States has a government agency called the Occupational Safety and Health Administration (OSHA). We don’t see them too often in the software field, but we can still learn from their safety advice for robots.[13] Industrial robots have multiple layers of safeguards to prevent damage to people, machines, and facilities.

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Just getting access to log files can be a challenge. Don’t even bother trying to figure out why some socket is being held open for too long. Containerized applications, even more than ordinary ones, need to send their telemetry out to a data collector. Virtual Machines in the Cloud At the time of writing, Amazon Web Services is far and away the dominant cloud platform. Google Cloud is gaining traction thanks to an attractive pricing model, but it has a long way to go before its workload approaches AWS. The world can change pretty quickly, though. While advanced cloud features definitely help with lock-in, compute and storage capacity is more fungible.

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Back in ​Force Multiplier​, we saw how Reddit suffered from overeager automation. The Governor pattern discussed in ​Governor​, aims to reduce the harm when automation goes the wrong way. Let’s consider an example from a real outage that affected many people and companies. On February 28, 2017, Amazon Web Services’ S3 service in the US-East-1 region went down. Tens of thousands of companies suffered outages due to their own hard dependencies on S3. Large parts of the Net pretty much went dark. Operators went nuts. Users hammered status sites until those crumbled too. (At least, they hammered status sites that weren’t themselves hosted on S3!)

Rentier Capitalism: Who Owns the Economy, and Who Pays for It? by Brett Christophers

"World Economic Forum" Davos, accounting loophole / creative accounting, Airbnb, Amazon Web Services, barriers to entry, Big bang: deregulation of the City of London, Big Tech, book value, Boris Johnson, Bretton Woods, Brexit referendum, British Empire, business process, business process outsourcing, Buy land – they’re not making it any more, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, cloud computing, collective bargaining, congestion charging, corporate governance, data is not the new oil, David Graeber, DeepMind, deindustrialization, Diane Coyle, digital capitalism, disintermediation, diversification, diversified portfolio, Donald Trump, Downton Abbey, electricity market, Etonian, European colonialism, financial deregulation, financial innovation, financial intermediation, G4S, gig economy, Gini coefficient, Goldman Sachs: Vampire Squid, greed is good, green new deal, haute couture, high net worth, housing crisis, income inequality, independent contractor, intangible asset, Internet of things, Jeff Bezos, Jeremy Corbyn, Joseph Schumpeter, Kickstarter, land bank, land reform, land value tax, light touch regulation, low interest rates, Lyft, manufacturing employment, market clearing, Martin Wolf, means of production, moral hazard, mortgage debt, Network effects, new economy, North Sea oil, offshore financial centre, oil shale / tar sands, oil shock, patent troll, pattern recognition, peak oil, Piper Alpha, post-Fordism, post-war consensus, precariat, price discrimination, price mechanism, profit maximization, proprietary trading, quantitative easing, race to the bottom, remunicipalization, rent control, rent gap, rent-seeking, ride hailing / ride sharing, Right to Buy, risk free rate, Ronald Coase, Rutger Bregman, sharing economy, short selling, Silicon Valley, software patent, subscription business, surveillance capitalism, TaskRabbit, tech bro, The Nature of the Firm, transaction costs, Uber for X, uber lyft, vertical integration, very high income, wage slave, We are all Keynesians now, wealth creators, winner-take-all economy, working-age population, yield curve, you are the product

My biggest thanks go to my family: Agneta, Elliot, Oliver and Emilia. Living with you is the biggest privilege of all. You are all awesome, and I love you with all my heart. Uppsala, March 2020 Abbreviations ABP Associated British Ports ABPI Association of the British Pharmaceutical Industry AWS Amazon Web Services BAT British American Tobacco BIA BioIndustry Association BIG British Infrastructure Group BNOC British National Oil Corporation CAA Civil Aviation Authority CAGR Compound annual growth rate CAP Common Agricultural Policy CCT Compulsory competitive tendering CGT Capital gains tax CHPI Centre for Health and the Public Interest CMA Competition and Markets Authority CRC Community Rehabilitation Company CRE Commercial real estate CSU Commissioning support unit DEFRA Department for Environment, Food and Rural Affairs DHSC Department of Health and Social Care DRC Democratic Republic of Congo DTI Department of Trade and Industry ECU European Currency Unit FBA Fulfillment By Amazon FSA Financial Services Authority GDP Gross domestic product GLC Greater London Council GNP Gross national product GOS Gross operating surplus GVA Gross value-added HMRC Her Majesty’s Revenue and Customs HR Human resources IFA Intangible fixed asset IP Intellectual property IPPR Institute for Public Policy Research IWGB Independent Workers’ Union of Great Britain KPI Key performance indicator LSE London Stock Exchange LSEG London Stock Exchange Group MHCLG Ministry of Housing, Communities and Local Government MoD Ministry of Defence NAO National Audit Office NFC National Freight Corporation NIC National Infrastructure Commission NIDP National Infrastructure Delivery Plan OE Oxford Economics OECD Organisation for Economic Co-operation and Development Ofcom Office of Communications Ofgem Office of Gas and Electricity Markets OFT Office of Fair Trading Ofwat Water Services Regulation Authority ONS Office for National Statistics Opec Organization of the Petroleum Exporting Countries OSI Oxford Sciences Innovation OUI Oxford University Innovation PFI Private Finance Initiative QE Quantitative easing R&D Research and development REIT Real estate investment trust ROSCO Rolling stock company RPI Retail price index SIC Standard industrial classification SMEs Small and medium-sized enterprises SPC Supplementary protection certificate SPV Special purpose vehicle TfL Transport for London TOC Train operating company TRIPS Agreement on Trade-Related Aspects of Intellectual Property Rights UKCS UK Continental Shelf VAT Value-added tax WTO World Trade Organization Preface Few people in the United Kingdom, let alone further afield, have heard of Arqiva.

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It certainly is, at least partly; and platform rentierism – in the form of Amazon Marketplace, which, like eBay, connects consumers to third-party sellers – is clearly part of the mix. Increasingly, however, Amazon is a different type of rentier, controlling a different type of asset base, and earning a different form of rent. Consider, firstly, Amazon Web Services (AWS), comfortably Amazon’s most profitable business. In establishing and growing its marquee online retail operation – the Amazon we are all familiar with – in the late 1990s and early 2000s, Amazon developed formidable proprietary computing capacity located at server farms around the world.

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Not only are the entry barriers large, then; they tend to become larger with time as a market grows and matures, conferring significant advantage on the fabled ‘first mover’, and ineluctably reducing the likelihood of the emergence of new players. Amazon’s two infrastructure rentier businesses – Fulfillment by Amazon and Amazon Web Services – are excellent examples of businesses displaying these non-natural monopoly tendencies. Replicating the vast infrastructures underpinning either one would be prohibitively costly. Writing about AWS, for example, Tom Krazit recently noted that ‘its first-mover advantage has lingered well into its second decade’ – its share of the market having held steady at around one-third for several years – precisely because ‘it takes an enormous amount of investment to create a cloud infrastructure network that can compete with AWS’.14 And AWS is not alone.

Surveillance Valley: The Rise of the Military-Digital Complex by Yasha Levine

23andMe, activist fund / activist shareholder / activist investor, Adam Curtis, Airbnb, AltaVista, Amazon Web Services, Anne Wojcicki, anti-communist, AOL-Time Warner, Apple's 1984 Super Bowl advert, bitcoin, Black Lives Matter, borderless world, Boston Dynamics, British Empire, Californian Ideology, call centre, Charles Babbage, Chelsea Manning, cloud computing, collaborative editing, colonial rule, company town, computer age, computerized markets, corporate governance, crowdsourcing, cryptocurrency, data science, digital map, disinformation, don't be evil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Dr. Strangelove, drone strike, dual-use technology, Edward Snowden, El Camino Real, Electric Kool-Aid Acid Test, Elon Musk, end-to-end encryption, fake news, fault tolerance, gentrification, George Gilder, ghettoisation, global village, Google Chrome, Google Earth, Google Hangouts, Greyball, Hacker Conference 1984, Howard Zinn, hypertext link, IBM and the Holocaust, index card, Jacob Appelbaum, Jeff Bezos, jimmy wales, John Gilmore, John Markoff, John Perry Barlow, John von Neumann, Julian Assange, Kevin Kelly, Kickstarter, Laura Poitras, life extension, Lyft, machine readable, Mark Zuckerberg, market bubble, Menlo Park, military-industrial complex, Mitch Kapor, natural language processing, Neal Stephenson, Network effects, new economy, Norbert Wiener, off-the-grid, One Laptop per Child (OLPC), packet switching, PageRank, Paul Buchheit, peer-to-peer, Peter Thiel, Philip Mirowski, plutocrats, private military company, RAND corporation, Ronald Reagan, Ross Ulbricht, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley startup, Skype, slashdot, Snapchat, Snow Crash, SoftBank, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Susan Wojcicki, Telecommunications Act of 1996, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Hackers Conference, Tony Fadell, uber lyft, vertical integration, Whole Earth Catalog, Whole Earth Review, WikiLeaks

On election day they built a call center, they built an elaborate database to know where their volunteers were, know the neighborhoods where people appeared not to have voted, so they could go knock on doors and get out the vote,” Andy Jassy, head of Amazon Web Services, told All Things Digital. “Nine Questions for Andy Jassy, Head of Amazon Web Services,” All Things Digital, November 8, 2013, https://web.archive.org/web/20170528161820/http://allthingsd.com/20131108 /nine-questions-for-andy-jassy-head-of-amazon-web-services/comment-page-1/. 136. Adi Robertson, “Jeff Bezos’ Blue Origin Partners with Boeing and Lockheed Martin to Reduce Dependence on Russian Rockets,” The Verge, September 17, 2014, https://www.theverge.com/2014/9/17/6328961/jeff-bezos-blue-origin-partners-with-united-launch-alliance-for-new-rocket. 137.

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Brad Stone, The Everything Store: Jeff Bezos and the Age of Amazon (Boston: Little, Brown, 2013); Jennifer Wills, “7 Ways Amazon Uses Big Data to Stalk You,” Investopedia, September 7, 2016, http://www.investopedia.com/articles /insights/090716/7-ways-amazon-uses-big-data-stalk-you-amzn.asp; Greg Bensinger, “Amazon Wants to Ship Your Package Before You Buy It,” Wall Street Journal, January 17, 2014. 83. Hal Bernton and Susan Kelleher, “Amazon Warehouse Jobs Push Workers to Physical Limit,” Seattle Times, April 3, 2012. 84. Dan Frommer, “Amazon Web Services Is Approaching a $10 Billion-a-Year Business,” Recode, April 28, 2016, https://www.recode.net/2016/4/28/11586526 /aws-cloud-revenue-growth. 85. Went on a buck-hunting trip with your grandson? A Republican candidate could target you for gun-rights ads. Belong to an evangelical Bible study group?

CTOs at Work by Scott Donaldson, Stanley Siegel, Gary Donaldson

Amazon Web Services, Andy Carvin, bioinformatics, business intelligence, business process, call centre, centre right, cloud computing, computer vision, connected car, crowdsourcing, data acquisition, distributed generation, do what you love, domain-specific language, functional programming, glass ceiling, Hacker News, hype cycle, Neil Armstrong, orbital mechanics / astrodynamics, pattern recognition, Pluto: dwarf planet, QR code, Richard Feynman, Ruby on Rails, Salesforce, shareholder value, Silicon Valley, Skype, smart grid, smart meter, software patent, systems thinking, thinkpad, web application, zero day, zero-sum game

Index 3D technology David Kuttler, 302 Jerry Krill, 83–85 64-bit architectures, 272–274, 281 A acquisitions, William Ballard, 279–280 Adobe, reverse image searching and, 246 AEP (American Electric Power), 128–129 Aguru Images, Craig Miller, 53–54, 56–59 algae, Craig Miller, 69, 76 Alm, Al, 50 Alving, Amy, 1–20 Amazon EC2, 36 Amazon Web Services (AWS), 245 American Electric Power (AEP), 128–129 American Public Media, 163 Amperion, 128 APL (Applied Physics Laboratory) global technical outreach, 90 Jerry Krill, 82 application development, Jeff Tolnar, 145 architecture technical review, 26 Arduinos, Craig Miller, 77 asymmetric warfare, Jerry Krill's comments on, 92 AWS (Amazon Web Services), 245 B Ballard, William, CTO of Gerson Lehrman Group, Inc, 261–283 Beyster, Bob, 51–52 Bilger, Mark, 23 Black, David, 262 Bloore, Paul, 239–260 Bodine, Greg, 30 BPLG (BPL Global), 127–128, 132 breakout engagements, Gerson Lehrman, 265 budgeting, Jeff Tolnar, 146–148 bundling hardware and software, Jeff Tolnar, 135 Burke, Thomas, 177 business development personnel, Gerson Lehrman, 266 C CA Open Space, 39 callable interface, 41 career lessons Darko Hrelic, 206–207, 211 Jan-Erik de Boer, 236 Jerry Krill, 91 Paul Bloore, 253 Wesley Kaplow, 110 career path Amy Alving, 1–6 Darko Hrelic, 205–206 Dmitry Cherches, 181–185 Jan-Erik de Boer, 237 Jeff Tolnar, 128 Marty Garrison, 152 Paul Bloore, 240–242 Tom Loveland, 176–178 Wesley Kaplow, 105–110 CATEX, Craig Miller, 51 Cherches, Dimitri background, 173 of Mind Over Machines, 173–204 ChoicePoint, 158 CIO responsibilities, of William Ballard, 268 CIO role, versus CTO role, 282 CIOs, Jeff Tolnar, 130–131 cloud computing, 35 Amy Alving, 16–17 Craig Miller, 65 David Kuttler, 302 Dmitry Cherches, 186–188, 200 Gerson Lehrman, 277 Jeff Tolnar, 142, 147–148 Marty Garrison, 160, 167 CloudShield, 13 cluster analysis, Craig Miller, 64 CommonCrawl group, 257 communication activities, of William Ballard, 269 communication, importance of, 30 competition, evaluating Amy Alving, 12–13 Darko Hrelic, 211–212, 217 Gerson Lehrman, 265 Jerry Krill, 101–102 Paul Bloore, 244–245 computing cloud computing, 35 Amy Alving, 16–17 Craig Miller, 65 David Kuttler, 302 Dmitry Cherches, 186–188, 200 Gerson Lehrman, 277 Jeff Tolnar, 142, 147–148 Marty Garrison, 160, 167 distributed computing, 242 mobile computing, 39–40 Darko Hrelic, 209–210 impact on Gerson Lehrman, 276 Jerry Krill, 96 Marty Garrison, 168 Paul Bloore, 254 Wesley Kaplow, 123 Comverge, 137 conference calls, ZipDx, 277–278 consulting, by Gerson Lehrman, 265 continuous deployment cycle, at Gerson Lehrman, 281 corporate strategy, Rick Mosca, 191–194 councils, Gerson Lehrman, 264 creativity, role in career path Jerry Krill, 85–87 Paul Bloore, 241 Wesley Kaplow, 116 Crinks, Bert, 115–116 CTO responsibilities, of William Ballard, 267–271, 282–283 Cuomo, Jerry, 25 customizing software, William Ballard's advice regarding, 270–272 cyanobacteria, Craig Miller, 79 cyber security Amy Alving, 18–19 CloudShield, 13 Darko Hrelic, 214–215 Dmitry Cherches, 201 Gerson Lehrman, 276 Springer, 235 Wesley Kaplow, 123 D Dannelly, Doug, 69 DARPA (Defense Advanced Research Projects Agency), Amy Alving, 5–6 Darwin, Charles, 70 data mining, real-time, 281–282 databases Jeff Tolnar, 141 used at Gerson Lehrman, 272 David Kuttler, 296 DAVID system, 165 de Boer, Jan-Erik, 219–237 Debevec, Paul, 57 decision-making, Jeff Tolnar, 133–134 Defense Advanced Research Projects Agency (DARPA), Amy Alving, 5–6 Delman, Debra, 161 Demand Media, 261–262 Department of Energy (DoE), 54–55, 64 deployment cycle, at Gerson Lehrman, 281 DER (distributed energy resources), 139, 142–149 DEs (Distinguished Engineers), 25 development methodology, Paul Bloore, 256–257 development resources, Jeff Tolnar, 139–140 DiData, Craig Miller, 53, 61 digital watermarking, 243 Dimension Data, Craig Miller, 52 Distinguished Engineers (DEs), 25 distributed computing, 242 distributed energy resources (DER), 139, 142–149 Dobson, David, 36 DoE (Department of Energy), 54–55, 64 domain expertise, 36 E economics of consulting with Gerson Lehrman, 265 William Ballard's advice regarding, 270–271, 279 electric co-ops, Craig Miller, 54–55 electrical transformers, Craig Miller, 68 employees, Gerson Lehrman, 265–266 Endhiran movie, Craig Miller, 58–59 energy policy, Craig Miller, 50 engineering offices, Gerson Lehrman, 266 engineers, cost of, 270, 279 ETL (extraction, translation, load), 203 evaluating competition Amy Alving, 12–13 Darko Hrelic, 211–212, 217 Gerson Lehrman, 265 Jerry Krill, 101–102 Paul Bloore, 244–245 expert network space, Gerson Lehrman, 265 expertise, use of, 274–275 external organizations, affiliations of Gerson Lehrman with, 269 extinction, Craig Miller, 78–79 extraction, translation, load (ETL), 203 F FDA, David Kuttler, 300–301 Ferguson, Don, interview with, 21–47 Fernandez, Raul, 52 ferrofluid, 241 First of a Kind project, 23 Folderauer, Ken, 114 fractal architecture, 273–274 free space optics, 84, 95 frequency variations, Craig Miller, 62–63 functional programming, 278 fundraising, Jeff Tolnar, 146 G Garrison, Marty, interview with, 151 Gayal, Amoosh, 24 Gelpin, Tim, 89 G.I.

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So that in a nutshell is what TinEye is. It was something that I wanted us to do for quite some time. We released it in March of 2008, but I'd wanted to make a web-wide search for a couple of years prior to that, but we didn't actually have the computing capacity to do it. In late 2007, Amazon was developing their AWS, their Amazon Web Services, and cloud computing platform. I was immediately taken by it because I thought, “Aha. Now we can manage to get ahold of the computing capacity we need to launch the search engine.” So we started work on it immediately, and I think it was about four or five months later when we first put TinEye on the web.

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What's the world starting to look like, and how should we position our services so we take advantage of what the world's going to be in five years' time? One future vision I have is currently counter culture. One of the things that I'm kind of known for is that I do worry about costs. I'm kind of referred to as a cheap guy when it comes to spending money on equipment. So we started TinEye completely using cloud-based services on Amazon's Web Services. Pretty soon, though, we realized that we could do it much more cost-effectively on our own equipment. So this means that we moved away from cloud computing and we do everything ourselves in-house. And that's just interesting because it seems to be swimming against the stream as far as what I see when I speak with other technology people.

Super Pumped: The Battle for Uber by Mike Isaac

"Susan Fowler" uber, "World Economic Forum" Davos, activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, always be closing, Amazon Web Services, Andy Kessler, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Bay Area Rapid Transit, Benchmark Capital, Big Tech, Burning Man, call centre, Cambridge Analytica, Chris Urmson, Chuck Templeton: OpenTable:, citizen journalism, Clayton Christensen, cloud computing, corporate governance, creative destruction, data science, Didi Chuxing, don't be evil, Donald Trump, driverless car, Elon Musk, end-to-end encryption, fake news, family office, gig economy, Google Glasses, Google X / Alphabet X, Greyball, Hacker News, high net worth, hockey-stick growth, hustle culture, impact investing, information security, Jeff Bezos, John Markoff, John Zimmer (Lyft cofounder), Kevin Roose, Kickstarter, Larry Ellison, lolcat, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Masayoshi Son, mass immigration, Menlo Park, Mitch Kapor, money market fund, moral hazard, move fast and break things, Network effects, new economy, off grid, peer-to-peer, pets.com, Richard Florida, ride hailing / ride sharing, Salesforce, Sand Hill Road, self-driving car, selling pickaxes during a gold rush, shareholder value, Shenzhen special economic zone , Sheryl Sandberg, side hustle, side project, Silicon Valley, Silicon Valley startup, skunkworks, Snapchat, SoftBank, software as a service, software is eating the world, South China Sea, South of Market, San Francisco, sovereign wealth fund, special economic zone, Steve Bannon, Steve Jobs, stock buybacks, super pumped, TaskRabbit, tech bro, tech worker, the payments system, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, union organizing, upwardly mobile, Vision Fund, WeWork, Y Combinator

As more and more people connected online, demand for new, internet-enabled services grew by the day. Second, the hurdles for entrepreneurs who wanted to launch a company were lowering quickly. Amazon Web Services, or AWS, changed the startup game entirely. Amazon started AWS in 2002 as an engineering side project; it would grow to become one of its most successful innovations in Amazon history. Amazon Web Services powers cloud computing services for coders and entrepreneurs who can’t afford to build their own infrastructure or server farms on their own. If a startup is a house, AWS is the electric company, the foundation and the plumbing combined.

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The App Store changed the model for software development entirely. All a programmer needed was an idea and facility with Apple’s mobile software code. With those two components, anyone could build and distribute their own apps and market them to millions of people instantly. Spin up a server on Amazon Web Services, blast out some code, and submit your app to Apple for review, and your work could be up and running in days. For people opening the App Store at home, it was like walking the aisles of their local Best Buy. Unfettered access to millions of games and programs on their iPhone required little more than a Wi-Fi connection and a few extra bucks.

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INDEX Page numbers followed by n refer to footnotes. 510 Systems, 108 ABC, 131 Abu Dhabi Investment Authority, 317 Abyzov, Ilya, 90 Accel, 40 Adobe, 27, 39 Advanced Micro Devices, 67 Advanced Technologies Group, 184 Agenda, 67 AIG, 33 Airbnb, xiii, 6, 65, 224 Akamai Technologies, 29, 31, 32 Albarrán, Tammy, 224, 226, 271–72, 275 Alexander, Eric, 260–61, 262, 337 Alfonso, Melene, 16 Alipay, 148 Allen & Company, 319, 320 Alltel, 100 Alphabet, 232. See also Google Alsup, William, 255–56 Amazon, 4, 11–14, 27, 35, 68–69, 92n, 111, 115, 154, 195, 231n Amazon Web Services (AWS), 34–35, 39 American Civil Liberties Union, 211 Andreessen, Marc, 69n, 75 Andreessen Horowitz, 40, 184 Android, 156, 159 AOL, 31, 69n, 229, 230 APAC, 260 Apple, 4, 36, 38–40, 59, 132, 165, 195, 233, 235, 245, 317. See also specific products the Apple problem, 153–64 App Store, 37, 39–40, 59, 155, 157–63, 235, 245 Aquafina, 60 Arab Spring, 199 Arlene Schnitzer Concert Hall, xv Arrington, Michael, 55, 58 Arro, 113 Asia, 139, 140–52, 187, 259, 260.

Modern Monopolies: What It Takes to Dominate the 21st Century Economy by Alex Moazed, Nicholas L. Johnson

3D printing, Affordable Care Act / Obamacare, Airbnb, altcoin, Amazon Web Services, Andy Rubin, barriers to entry, basic income, bitcoin, blockchain, book value, Chuck Templeton: OpenTable:, cloud computing, commoditize, connected car, disintermediation, driverless car, fake it until you make it, future of work, gig economy, hockey-stick growth, if you build it, they will come, information asymmetry, Infrastructure as a Service, intangible asset, Internet of things, invisible hand, jimmy wales, John Gruber, Kickstarter, Lean Startup, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Marshall McLuhan, means of production, Metcalfe’s law, money market fund, multi-sided market, Network effects, PalmPilot, patent troll, peer-to-peer lending, Peter Thiel, pets.com, platform as a service, power law, QWERTY keyboard, Ray Kurzweil, ride hailing / ride sharing, road to serfdom, Robert Metcalfe, Ronald Coase, Salesforce, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, Skype, Snapchat, social graph, software as a service, software is eating the world, source of truth, Startup school, Steve Jobs, TaskRabbit, technological determinism, the medium is the message, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, white flight, winner-take-all economy, Y Combinator

., a car chassis used across many different models) Industry platform: Products, services or technologies that serve as foundations upon which complementary products, services, or technologies can be built (e.g., Intel) Platform as a service: Category of cloud computing services that provides computing platform and solution stack as an online service (e.g., Amazon Web Services) Each of these instances of the word “platform” refers to an underlying product or technology that allows modular components to be built on top of it. Platform business models often use this kind of modular modification, which is why the terms frequently are conflated. Finally, probably the most common misuse of the term “platform” is when it’s used to describe an integrated suite of software products.

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Today, its app store has millions of app downloads and is an important part of Salesforce’s value proposition. Other cloud software industries have evolved in a similar manner, such as cloud data storage and so-called infrastructure-as-a-service (IaaS) companies. Both Google App Engine and Amazon Web Services have robust development platforms with thousands of available apps and integrations. Healthcare software is another great example of this trend. The first wave of new healthcare software came during the dot-com era. Like Wilson’s Dentasoft and most enterprise software at the time, these products were sold for large, up-front fees.

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And although Facebook started as a social networking platform, it has expanded to include content platforms (Instagram and Facebook Pages), messaging platforms (Messenger and WhatsApp), and development platforms, as well as a number of experiments with product and services marketplaces. Finally, Amazon has the Amazon Marketplace, Amazon Payments, Amazon Web Services, Fire TV, and the Kindle, which includes both a content platform (for books and digital content) and an Android-based development platform (for apps). The e-commerce company is also looking at expanding into services marketplaces with its Home Services marketplace. In China, the outcome has been similar, with the dominant platform ecosystems controlled by Baidu, Alibaba, and Tencent, collectively referred to in China as BAT.

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann

active measures, Amazon Web Services, billion-dollar mistake, bitcoin, blockchain, business intelligence, business logic, business process, c2.com, cloud computing, collaborative editing, commoditize, conceptual framework, cryptocurrency, data science, database schema, deep learning, DevOps, distributed ledger, Donald Knuth, Edward Snowden, end-to-end encryption, Ethereum, ethereum blockchain, exponential backoff, fake news, fault tolerance, finite state, Flash crash, Free Software Foundation, full text search, functional programming, general-purpose programming language, Hacker News, informal economy, information retrieval, Internet of things, iterative process, John von Neumann, Ken Thompson, Kubernetes, Large Hadron Collider, level 1 cache, loose coupling, machine readable, machine translation, Marc Andreessen, microservices, natural language processing, Network effects, no silver bullet, operational security, packet switching, peer-to-peer, performance metric, place-making, premature optimization, recommendation engine, Richard Feynman, self-driving car, semantic web, Shoshana Zuboff, social graph, social web, software as a service, software is eating the world, sorting algorithm, source of truth, SPARQL, speech recognition, SQL injection, statistical model, surveillance capitalism, systematic bias, systems thinking, Tragedy of the Commons, undersea cable, web application, WebSocket, wikimedia commons

. • CPU clock speeds are barely increasing, but multi-core processors are standard, and networks are getting faster. This means parallelism is only going to increase. • Even if you work on a small team, you can now build systems that are distributed across many machines and even multiple geographic regions, thanks to infra‐ structure as a service (IaaS) such as Amazon Web Services. • Many services are now expected to be highly available; extended downtime due to outages or maintenance is becoming increasingly unacceptable. Data-intensive applications are pushing the boundaries of what is possible by making use of these technological developments. We call an application data-intensive if data is its primary challenge—the quantity of data, the complexity of data, or the speed at which it is changing—as opposed to compute-intensive, where CPU cycles are the bottleneck.

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Thus, multi-machine redundancy was only required by a small number of applications for which high availability was absolutely essential. However, as data volumes and applications’ computing demands have increased, more applications have begun using larger numbers of machines, which proportion‐ ally increases the rate of hardware faults. Moreover, in some cloud platforms such as Amazon Web Services (AWS) it is fairly common for virtual machine instances to become unavailable without warning [7], as the platforms are designed to prioritize flexibility and elasticityi over single-machine reliability. Hence there is a move toward systems that can tolerate the loss of entire machines, by using software fault-tolerance techniques in preference or in addition to hardware redundancy.

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Summary | 23 [8] Haryadi S. Gunawi, Mingzhe Hao, Tanakorn Leesatapornwongsa, et al.: “What Bugs Live in the Cloud?,” at 5th ACM Symposium on Cloud Computing (SoCC), November 2014. doi:10.1145/2670979.2670986 [9] Nelson Minar: “Leap Second Crashes Half the Internet,” somebits.com, July 3, 2012. [10] Amazon Web Services: “Summary of the Amazon EC2 and Amazon RDS Ser‐ vice Disruption in the US East Region,” aws.amazon.com, April 29, 2011. [11] Richard I. Cook: “How Complex Systems Fail,” Cognitive Technologies Labora‐ tory, April 2000. [12] Jay Kreps: “Getting Real About Distributed System Reliability,” blog.empathy‐ box.com, March 19, 2012. [13] David Oppenheimer, Archana Ganapathi, and David A.

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann

active measures, Amazon Web Services, billion-dollar mistake, bitcoin, blockchain, business intelligence, business logic, business process, c2.com, cloud computing, collaborative editing, commoditize, conceptual framework, cryptocurrency, data science, database schema, deep learning, DevOps, distributed ledger, Donald Knuth, Edward Snowden, end-to-end encryption, Ethereum, ethereum blockchain, exponential backoff, fake news, fault tolerance, finite state, Flash crash, Free Software Foundation, full text search, functional programming, general-purpose programming language, Hacker News, informal economy, information retrieval, Infrastructure as a Service, Internet of things, iterative process, John von Neumann, Ken Thompson, Kubernetes, Large Hadron Collider, level 1 cache, loose coupling, machine readable, machine translation, Marc Andreessen, microservices, natural language processing, Network effects, no silver bullet, operational security, packet switching, peer-to-peer, performance metric, place-making, premature optimization, recommendation engine, Richard Feynman, self-driving car, semantic web, Shoshana Zuboff, social graph, social web, software as a service, software is eating the world, sorting algorithm, source of truth, SPARQL, speech recognition, SQL injection, statistical model, surveillance capitalism, systematic bias, systems thinking, Tragedy of the Commons, undersea cable, web application, WebSocket, wikimedia commons

CPU clock speeds are barely increasing, but multi-core processors are standard, and networks are getting faster. This means parallelism is only going to increase. Even if you work on a small team, you can now build systems that are distributed across many machines and even multiple geographic regions, thanks to infrastructure as a service (IaaS) such as Amazon Web Services. Many services are now expected to be highly available; extended downtime due to outages or maintenance is becoming increasingly unacceptable. Data-intensive applications are pushing the boundaries of what is possible by making use of these technological developments. We call an application data-intensive if data is its primary challenge—the quantity of data, the complexity of data, or the speed at which it is changing—as opposed to compute-intensive, where CPU cycles are the bottleneck.

…

Thus, multi-machine redundancy was only required by a small number of applications for which high availability was absolutely essential. However, as data volumes and applications’ computing demands have increased, more applications have begun using larger numbers of machines, which proportionally increases the rate of hardware faults. Moreover, in some cloud platforms such as Amazon Web Services (AWS) it is fairly common for virtual machine instances to become unavailable without warning [7], as the platforms are designed to prioritize flexibility and elasticityi over single-machine reliability. Hence there is a move toward systems that can tolerate the loss of entire machines, by using software fault-tolerance techniques in preference or in addition to hardware redundancy.

…

[7] Laurie Voss: “AWS: The Good, the Bad and the Ugly,” blog.awe.sm, December 18, 2012. [8] Haryadi S. Gunawi, Mingzhe Hao, Tanakorn Leesatapornwongsa, et al.: “What Bugs Live in the Cloud?,” at 5th ACM Symposium on Cloud Computing (SoCC), November 2014. doi:10.1145/2670979.2670986 [9] Nelson Minar: “Leap Second Crashes Half the Internet,” somebits.com, July 3, 2012. [10] Amazon Web Services: “Summary of the Amazon EC2 and Amazon RDS Service Disruption in the US East Region,” aws.amazon.com, April 29, 2011. [11] Richard I. Cook: “How Complex Systems Fail,” Cognitive Technologies Laboratory, April 2000. [12] Jay Kreps: “Getting Real About Distributed System Reliability,” blog.empathybox.com, March 19, 2012

The Truth Machine: The Blockchain and the Future of Everything by Paul Vigna, Michael J. Casey

3D printing, additive manufacturing, Airbnb, altcoin, Amazon Web Services, barriers to entry, basic income, Berlin Wall, Bernie Madoff, Big Tech, bitcoin, blockchain, blood diamond, Blythe Masters, business process, buy and hold, carbon credits, carbon footprint, cashless society, circular economy, cloud computing, computer age, computerized trading, conceptual framework, content marketing, Credit Default Swap, cross-border payments, crowdsourcing, cryptocurrency, cyber-physical system, decentralized internet, dematerialisation, disinformation, disintermediation, distributed ledger, Donald Trump, double entry bookkeeping, Dunbar number, Edward Snowden, Elon Musk, Ethereum, ethereum blockchain, failed state, fake news, fault tolerance, fiat currency, financial engineering, financial innovation, financial intermediation, Garrett Hardin, global supply chain, Hernando de Soto, hive mind, informal economy, information security, initial coin offering, intangible asset, Internet of things, Joi Ito, Kickstarter, linked data, litecoin, longitudinal study, Lyft, M-Pesa, Marc Andreessen, market clearing, mobile money, money: store of value / unit of account / medium of exchange, Network effects, off grid, pets.com, post-truth, prediction markets, pre–internet, price mechanism, profit maximization, profit motive, Project Xanadu, ransomware, rent-seeking, RFID, ride hailing / ride sharing, Ross Ulbricht, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, smart contracts, smart meter, Snapchat, social web, software is eating the world, supply-chain management, Ted Nelson, the market place, too big to fail, trade route, Tragedy of the Commons, transaction costs, Travis Kalanick, Turing complete, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, universal basic income, Vitalik Buterin, web of trust, work culture , zero-sum game

If you are already uncomfortable with what Google, Amazon, Facebook, and Apple know about you, think about it in the context of a centralized IoT. Having transactions pass through a small number of behemoth companies would not only be an inefficient way to route data and require regulatory constraints to the system, but it could also create an Orwellian level of control. Do we really want Amazon Web Services or any other big cloud service provider controlling all that valuable data? Not only would that company gain an unprecedented, privileged window onto the entire world of material things and human activity, but it would, in effect, put those centrally controlled companies in charge of what will be billions of machine-to-machine transactions of tokens and digital currencies.

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That would overwhelm a distributed ledger’s limited storage capacity very quickly—certainly that of Bitcoin. Rather, the data will reside off-chain, wherever the person or institution chooses to store it: locally on their own computer, smartphone, or other device; with a cloud computing service from IBM, Microsoft, or Amazon Web Service. All those options, of course, require some level of trust in the provider. So it’s interesting that some of the emerging decentralized systems for storing data over the Internet, such as Maidsafe, Storj, IPFS (the Interplanetary File System), or Sia, are also being touted as personal data management tools for identity purposes.

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There are blockchain-based offerings looking to disintermediate the business of outsourced storage and computing, for example, to break the expensive, wasteful, and environmentally harmful dominance of corporate-owned data centers. With names such as Storj, Sia, and Maidsafe, these new platforms reward you with tokens if you offer up your spare hard-drive space to other computer users in a global network of users. You could say these “cloud” services are much truer to that name than those of Amazon Web Services, Google, Dropbox, IBM, Oracle, Microsoft, and Apple, the providers with which most people associate that word. But even bigger changes are being considered, including projects to entirely re-architect the Web itself. There’s Solid, which stands for Social Linked Data, a new protocol for data storage that puts data back in the hands of the people to whom it belongs.

Bold: How to Go Big, Create Wealth and Impact the World by Peter H. Diamandis, Steven Kotler

3D printing, additive manufacturing, adjacent possible, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, Boston Dynamics, Charles Lindbergh, cloud computing, company town, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deal flow, deep learning, dematerialisation, deskilling, disruptive innovation, driverless car, Elon Musk, en.wikipedia.org, Exxon Valdez, fail fast, Fairchild Semiconductor, fear of failure, Firefox, Galaxy Zoo, Geoffrey Hinton, Google Glasses, Google Hangouts, gravity well, hype cycle, ImageNet competition, industrial robot, information security, Internet of things, Jeff Bezos, John Harrison: Longitude, John Markoff, Jono Bacon, Just-in-time delivery, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loss aversion, Louis Pasteur, low earth orbit, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Mars Rover, meta-analysis, microbiome, minimum viable product, move fast and break things, Narrative Science, Netflix Prize, Network effects, Oculus Rift, OpenAI, optical character recognition, packet switching, PageRank, pattern recognition, performance metric, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, Ray Kurzweil, recommendation engine, Richard Feynman, ride hailing / ride sharing, risk tolerance, rolodex, Scaled Composites, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart grid, SpaceShipOne, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, Stuart Kauffman, superconnector, Susan Wojcicki, synthetic biology, technoutopianism, TED Talk, telepresence, telepresence robot, Turing test, urban renewal, Virgin Galactic, Wayback Machine, web application, X Prize, Y Combinator, zero-sum game

• We will balance our focus on growth with emphasis on long-term profitability and capital management. At this stage, we choose to prioritize growth because we believe that scale is central to achieving the potential of our business model. This letter is often held up as the encapsulation of Bezos’s view on the subject, but personally, I think an answer he gave to an Amazon Web Services Live audience in 201227 was far more revealing: “What’s going to change in the next ten years?” And that is a very interesting question; it’s a very common one. I almost never get the question: “What’s not going to change in the next ten years?” And I submit to you that that second question is actually the more important of the two—because you can build a business strategy around the things that are stable in time. . . .

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But it’s the combination of long-term thinking and customer-centrism that has helped Amazon extend their reach far beyond books. Bezos has ventured into music, movies, toys, electronics, automotive parts, and well, just about everything. They have also continued to surround their original market, moving from books into ebooks and ebook readers (with Kindle), and most recently, publishing itself. Meanwhile, Amazon Web Services—their cloud business—has become a beast in its own right (worth nearly $3 billion, according to a November 2013 Business Insider analysis).28 As Morgan Stanley analyst Scott Devitt told the New York Times:29 “Amazon is marching to a different drumbeat, which is long term. Are they doing the right thing?

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Italic numbers refer to charts/graphs Aabar, 127 Abundance (Diamandis and Kotler), xi–xii, xv, 34, 54, 136, 137, 146, 162, 274 AbundanceHub.com, 158, 162, 210, 277 Abundance360Summit, 278 Academy of Achievement, 129 activists, xiii, 180 in crowdfunding campaigns, 201–3, 212, 230 additive manufacturing, 30, 31, 33, 41 AdhereTech, 47 AdSense, 139 Advanced Research Projects Agency Network (ARPANET), 27 advertising, 241, 242 in crowdfunding campaigns, 212–13 crowdsourcing platforms for, 151, 152–54, 158 advocates, in crowdfunding campaigns, 200–201, 205 AdWords, 241 aerospace industry, 112, 117, 133 skunk methodology used in, 71–73, 75 3–D printing and, 34, 35–37 see also space exploration affiliate marketing, 199–200, 205 Ahn, Luis von, 154, 155–56 Airbnb, 20, 21, 66 Airbus, 249 airlines, 43, 124, 125, 126, 127, 260 AI XPRIZE, 54 algorithms, 43, 51, 52, 66, 85, 220, 227 crowdsourcing projects and, 158, 159, 160, 161, 227 machine-learning, 54–55, 55, 58 Netflix Prize for, 254–56 PageRank, 135 Align Technology, 34–35 Amazon, 50, 76, 97, 128, 129–34, 157, 195, 254 drone proposal of, 61, 133–34 Amazon Web Services, 131, 132 America Online (AOL), 76, 143 Anderson, Chris, 10–11, 54, 123, 224, 229, 242 Anderson, Eric, 95, 96, 97, 98, 99, 100, 179, 202, 203–4 Andraka, Jack, 65 Andreessen, Marc, 27, 33 Android, 16, 135, 176 AngelList, 172, 173–74 Anheuser-Busch, 145 Ansari XPRIZE, 76, 96, 115, 127, 134, 246, 248–49, 253, 260, 261, 262, 263, 264, 265, 266, 267, 268 anti-aging projects, 66, 81, 136, 139 Apollo Program, 96, 100, 118, 139 Appert, Nicolas, 245 Appirio, 227–28 Apple, 18, 28, 62, 72, 111, 128 applications (apps), 13, 13, 15, 16, 28, 45, 47, 150, 158, 176 Arduino, 43 ARKYD Space Telescope campaign, 172, 174–75, 179–80, 186, 187, 188, 193, 195, 207, 209, 242 early donor engagement in, 203–5 hype created in, 205 launch of, 200, 208 recruiting of activists in, 201–3, 212 ”space selfie” reward offered in, 180, 189–90, 196, 208 Arnaout, Ramy, 227 artificial intelligence (AI), x, 22, 24, 41, 44, 52–59, 61, 62, 63, 66, 81, 135, 146, 160, 162, 216, 228, 275, 276, 295n crowdsourcing projects and, 167, 295n entrepreneurial opportunities in, 54, 56–59 Google’s development of, 24, 53, 58, 81, 138–39 Association of Space Explorers, 102 asteroids, ix–x, 180, 228–29 mining of, 95–96, 97–99, 107, 109, 179, 221, 276 Asteroid Zoo, 228 astronomy, 219–21, 228, 247, 267 Autodesk, 48–49, 51, 63, 65 automation, 47–48, 56 automobile industry, 29, 222–23 3–D printing in, 32 see also Local Motors; Tesla Motors autonomous cars, 43–44, 44, 48, 62, 66, 135, 136, 137, 262 autonomy, 79, 80, 85, 87, 92 Babson School of Business, 14 BackRub (research project), 135 Bacon, Jono, 237 Bad Girl Ventures, 19 bake-offs, in incentive competitions, 264 Barnett, Chance, 173 Barrie, Matt, 149–50, 158, 163, 165, 166, 167, 207 Barry, Dan, 35, 61, 62 Bass, Carl, 48–49, 50, 51 Baxter (robot), 60–61 Beland, Francis, 250 Bennett, Jim, 255 Berns, Gregory, 108 Beth Israel Deaconess Medical Center (Boston), 227 Better Blocks, 240–41 Bezos, Jeff, xiii, 73, 97, 115, 126, 128–34, 138, 139, 167 on risk management, 76–77 thinking-at-scale strategies of, 128, 129, 130–33 Bezos, Mark, 128 Bianchini, Gina, 217, 219, 224, 233 Big Think, 49, 121 biotechnology, 63–65 see also genomics; synthetic biology BlackBerry, 176 Blakey, Marion, 110 Blastar (video game), 117 blogs, in crowdfunding campaigns, 177, 205, 206 blue ellipticals, 219–20 Blue Origin, 97, 133 Boeing, 127, 249 Boston Dynamics, 61 Boston Globe, 227 Brand, Stewart, 26 Branson, Richard, xiii, 73, 84, 86, 99, 100, 111–12, 115, 123–28, 138, 139 space tourism projects of, 96–97, 115, 125, 127 thinking-at-scale strategies of, 125–27 Briggs, William, 47 Brin, Sergey, 81, 128, 135 British Admiralty, 267 British Airways, 124, 125, 126 British Medical Journal, 109 British Petroleum (BP), 250, 251 Brooks, Rodney A., 60 Brown, Dan, 152 brute force, 51 Burchard, Brendon, 210 Business Insider, 132 Business World, 144 buzz marketing, 240–41 Bye, Stephen, 45 Calacanis, Jason, 139 Calico, 139 Callaghan, Jon, 62 Caltech, 27 camel racing, 59–60 cameras, 3–4, 152 see also digital cameras Cameron, James, 250 campaign managers, in crowdfunding, 192, 194 Canadian Space Agency (CSA), 102 Cane, Daniel, 57 Capp, Al, 71, 72 CAPTCHA, 154, 155, 167, 295n CastingWords, 145 celebrity (the face): in crowdfunding campaigns, 192, 198, 207 in incentive competitions, 273 cell phones, 49, 135, 163 see also smartphones CFM International, 34 challenge/skills ratio, 91 “charge-coupled device” (CCD), 4–5 Chen, Michael, 35, 36, 37 China, 17, 18, 62 Chinese National Space Administration, 102 Chrome, 135, 138 Chung, Anshe, 144 Cinematch, 254, 255 Cisco, 46 Clarke, Arthur C., 52, 53, 100 cloud services, 39, 45, 50, 51, 56, 57, 63, 65, 66, 132, 216, 227–28 CNN, 48 cognitive biases, 246 cognitive surplus, 215 CoheroHealth, 47 Colgate Palmolive, 154 Comedy Central, 95 communities, online, 22, 182, 215–42, 243 building member base in early days of, 233–34 case studies of, 219–28 collaborative structures of, 217, 227, 228, 236, 237, 255 contests and competitions in building of, 224, 225–27, 232, 237, 240; see also incentive competitions DIY, see DIY communities driving growth in, 239–41 engagement strategies in, 224, 227, 235, 236–38, 239, 241 exponential, see exponential communities Law of Niches in, 221, 223, 228, 231 managing of, 238–39 monetization of, 241–42 passion as important in, 224, 225, 228, 231, 258 rate of innovation in, 216, 219, 224, 225, 228, 233, 237 rating systems in, 226, 232, 236–37 reputation economics in building of, 217–19, 230, 232, 236–37 self-organizing structures of, 217, 237 see also crowdfunding, crowdfunding campaigns; crowdsourcing Compaq, 117 computers, x, 7, 26, 72, 76, 135 see also artificial intelligence (AI); supercomputers Comsat, 102 constraints, power of, 248–49, 259 contract research and manufacturing services (CRAMS), 65 Coolest Cooler campaign, 210–13 corporate sponsorship, 246, 246 Cotichini, Christian, 257 Cotteleer, Mark, 33 Coulson, Simon, 150 Craigslist, 11, 257 creative assets, crowdsourcing of, 158 Creative Commons license, 224 Credit Suisse, 56 Cretaceous Period, ix CrossFit, 229 Crowdfunder, 172, 173, 175 crowdfunding, crowdfunding campaigns, xiii, 22, 103, 144–45, 147–48, 167, 169–213, 216, 242, 243, 247, 258, 270 advertising in, 212–13 building perfect team for, 191–94 building your audience in, 199–203 case studies in, 174–80 celebrity face of, 192, 198, 207 choosing idea for, 184–85 costs in, 195 data-driven decision making in, 207–10, 213 emergence of, 170–71, 170 engagement strategies in, 203–6, 207 feedback in, 176, 180, 182, 185, 190, 199, 200, 202, 209–10 fundraising targets in, 185–87, 191 global focus in, 209 how-to guide to, 181–213 launching with super-credibility in, 190, 199, 203, 204 length and schedule for, 187–89 pitch videos in, 177, 180, 192, 193, 195, 198–99, 203, 212 planning, materials, and resources in, 194–95 promotions and contests in, 207 reward-based, see reward-based crowdfunding setting rewards in, 189–91, 189 seven benefits of, 181–83 telling meaningful story in, 195–98 types of, 172–75 week-by-week execution plan for, 206–7 see also specific crowdfunding campaigns Crowdsortium, 162–63 crowdsourcing, xiii, 18, 22, 57, 85, 103, 143–67, 193, 223, 237, 240–41, 243, 245, 256, 275 in advertising, 151, 152–54, 158 AI as potential threat to, 167, 295n in automotive production, 223–24, 238 best practices of, 163–67 building communities for, see communities, online clear roles and communication in, 165–66 collaboration in, 144, 165–66, 167, 217, 227, 228, 236, 237, 255, 260–61 competitions and, 148, 152–54, 159, 160, 223, 224, 226–27, 232, 237, 240, 259; see also incentive competitions of creative and operational assets, 158–60 definition of, 144 in designing incentive competitions, 257–58 dual-use, 154–56 Freelancer.com case study in, 149–51, 158 growing interconnectedness and, 146–47, 147 incentive competitions and, see incentive competitions industry websites on, 162–63 of micro- vs. macrotasks, 156–58 most common uses for, 156–67 in product development, 18, 19, 223–25, 226–27 in retail and consumer products industry, 159–60 in scientific research, 145–46, 220–21, 227, 228–29 in software development, 144, 159, 161, 226–27, 236 of testing and discovery insights, 160–62 traffic data garnered by, 47 Crowdsourcing.org, 162 Csikszentmihalyi, Mihaly, 89, 92 Cube, 32 CubeSats, 36–37 Culver, Irv, 72 Cummins Engine, 222 Curiosity rover, 99 customer-centric business, 84, 116, 126, 128, 130, 131–32, 133, 138 Daily Show, 95 DARPA Grand Challenge, 262 Dartmouth Summer Research Project, 59 data mining, 42–44, 47–48, 256 AI’s role in, 55–59 behavior tracking and, 47 see also information data sets, preparing of, 164 Da Vinci Code, The (Brown), 152 debt funding, 172, 173, 174 deceptive phase, exponential, 8, 8, 9, 10, 24, 25–26, 29, 41 of AI, 59 in robotics, 60 of 3–D printing, 30, 31 Deep Learning (algorithm), 58, 59 Deepwater Horizon oil rig, 250 Defense Department, US, 71, 72 DeHart, Jacob, 143, 144 DeJulio, James, 151–52, 153, 166 Dell, 50 Deloitte Center for the Edge, 106 Deloitte Consulting, 33, 39, 159, 160, 245, 274 Deloitte University Press, 56 dematerialization, exponential, 8, 8, 10, 11–13, 14, 15, 20–21, 66 democratization, exponential, xii, 8, 10, 13–15, 21, 33, 59, 276 in bioengineering, 64–65 infinite computing and, 51–52 demonetization, exponential, 8, 8, 10–11, 14, 15, 138, 163, 167, 223 in bioengineering, 64–65 infinite computing and, 52 D.

Chaos Engineering: System Resiliency in Practice by Casey Rosenthal, Nora Jones

Amazon Web Services, Asilomar, autonomous vehicles, barriers to entry, blockchain, business continuity plan, business intelligence, business logic, business process, cloud computing, cognitive load, complexity theory, continuous integration, cyber-physical system, database schema, DevOps, fail fast, fault tolerance, hindsight bias, human-factors engineering, information security, Kanban, Kubernetes, leftpad, linear programming, loose coupling, microservices, MITM: man-in-the-middle, no silver bullet, node package manager, operational security, OSI model, pull request, ransomware, risk tolerance, scientific management, Silicon Valley, six sigma, Skype, software as a service, statistical model, systems thinking, the scientific method, value engineering, WebSocket

For one thing, it took eight years to fully extract themselves from the datacenter. More relevant to our interests, the move to horizontally scaled cloud deployment practices did not coincide with the boost to uptime of the streaming service that they expected.3 To explain this, we have to recall that in 2008, Amazon Web Services (AWS) was considerably less mature than it is now. Cloud computing was not yet a commodity, and not nearly the no-brainer, default deployment option that we have today. Cloud service back then did hold a lot of promise, and one of those promises was that instances4 would occasionally blink out of existence with no warning.

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The framework consists of four primary functions: Generatr Identifies the object to inject the failure on and calls Slingr. Slingr Injects the failure. Trackr Logs details about the experiment as it occurs. Experiment description Provides documentation on the experiment along with applicable input and output parameters for Lambda functions. ChaoSlingr was originally designed to operate in Amazon Web Services (AWS). It proactively introduces known security failure conditions through a series of experiments to determine how effectively security is implemented. The high-level business driver behind the effort was to improve the company’s ability to rapidly deliver high-quality products and services while maintaining the highest level of safety and security possible.

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Index A above-the-line systems, Putting the Principles into Practice, Conclusion accidental complexity, Accidental Complexity adaptive capacity, Engineering Adaptive Capacity adoptionfour considerations of, Adoption obstacles to, Obstacles to Adoption obtaining management buy-in, Adoption organization-wide progression of, Who Bought into Chaos Engineering organization-wide spread of, How Much of the Organization Participates in Chaos Engineering perquisites to, Prerequisites After phasedefined, Creating Foresight summary of, Hypothesize Agile movement, Economic Pillars of Complexity Applied to Software alerting, Monitoring and alerting Allspaw, John, Human Factors, People in the Loop-Conclusion Alquraan, Ahmed, Fault Injection in the Network Alvaro, Peter, Human Factors, The Substitution Myth, The Experiment Selection Problem (and a Solution)-Conclusion Amazon Web Services (AWS)Christmas Eve 2012 outage, Chaos Monkey Is Born early days of, Management Principles as Code Antifragility, Antifragility application logiccontinuous verification for, Correctness fault injection testing, Fault Injection in Applications mismatch with business logic, Example 1: Mismatch Between Business Logic and Application Logic-Example 1: Mismatch Between Business Logic and Application Logic Aschbacher, Nathan, Evolution, Let’s Get Cyber-Physical-Conclusion automated canaries, Types of CV Systems B backup and restorein older systems, Design Patterns Common in Older Systems security Game Days, Security Game Days Barker, James, Moving your organization forward Beck, Kent, Economic Pillars of Complexity Before phasecognitive interviews, Effectively Partnering Internally-Understand Operating Procedures defined, Creating Foresight discussion of scope, Discuss Scope experiment design, Designing the Experiment-Tool Support for Chaos Experiment Design goal of, Steps of the Chaos Engineering Cycle hypothesis creation, Hypothesize-Hypothesize role determination, Hypothesize summary of, Hypothesize below-the-line systems, Putting the Principles into Practice, Conclusion blind resiliency testing, Blind Resiliency Testing Blue Teams, Security Chaos Engineering and Current Methods bootcamp concept, The hypothesis, All You Need Is Altitude and a Direction bootstrapping, Reboot everything Borg, Scope of Tests at Google “breaking stuff”, Breaking Stuff, Chaos Engineering and Resilience Brooks, Frederick, Confronting Complexity bullwhip effect, Contemplating Complexity bureaucratic organizations, Safely Organizing the Chaos bus factor, People outages business concernsbenefits of collaborative mindset, Open Minds, Open Science, and Open Chaos-Conclusion Chaos Maturity Model (CMM), Chaos Maturity Model-Putting It All Together return on investment (ROI), ROI of Chaos Engineering-Conclusion business continuity plans (BCPs), Business continuity plans business logiccontinuous verification for, Correctness mismatch with application logic, Example 1: Mismatch Between Business Logic and Application Logic-Example 1: Mismatch Between Business Logic and Application Logic C cache inconsistency, Avoid Cache Inconsistency Cahoon, Jason, Principles in Action, Google DiRT: Disaster Recovery Testing-Conclusion call graph tracing, Observability: The Opportunity canary testing, Types of CV Systems capacitycapacity management, Capacity management role in fault tolerance, Getting to Basic Fault Tolerance Capital Oneachievements of, A Capital One Case Study approach to Chaos Engineering at, Principles in Action case studyblind resiliency testing, Blind Resiliency Testing chaos experiments in CI/CD, Chaos Experiments in CI/CD designing experiments, Things to Watch Out for While Designing the Experiment evangelism, Evangelism team structure, Team Structure tooling, Tooling transition to Chaos Engineering, Transition to Chaos Engineering case studies (see use cases) Chaos Automation Platform (ChAP)application of Advanced Principles, The Advanced Principles in ChAP benefits of, ChAP as Continuous Verification demonstrating ROI for, Alternative ROI Example limiting blast radius in, Minimize Blast Radius overview of, Tool Support for Chaos Experiment Design, CV in the Wild: ChAP running experiments, ChAP: Running Experiments selecting experiments, ChAP: Selecting Experiments Chaos Community Day, Community Is Born, The Future of “The Principles” Chaos Engineering (see also Principles of Chaos Engineering)applying to databases, Applying Chaos Engineering-Fault Injection in the Filesystem applying to sociotechnical systems, Humanistic Chaos-If You’re Not Failing, You’re Not Learning benefits of, Safety, Conclusion, Degree of Variation characteristics of, Fast Evolution, The Systemic Perspective, What Chaos Engineering Is-Antifragility conflicting guidance on, Principles in Action, Human Factors core philosophy of, Google DiRT: Disaster Recovery Testing defined, Formalizing the Discipline, What Chaos Engineering Is, Chaos Engineering and Resilience evaluation of, Chaos Maturity Model Failure Mode and Effects Analysis (FMEA) and, FMEA and Chaos Engineering foundational models for, Safety, Economic Pillars of Complexity Applied to Software goals of, Formalizing the Discipline, Breaking Stuff, Chaos Engineering and Resilience, ROI of Chaos Engineering, The Rise of Cyber-Physical Systems history of, Introduction: Birth of Chaos-Fast Evolution Human and Organizational Performance (HOP) and, HOP Meets Chaos Engineering-Chaos Engineering and HOP in Practice in IT and cloud-dominated ecosystems, Let’s Get Cyber-Physical role in complex systems, Setting the Stage, Encountering Complex Systems steps of experimentation, What Chaos Engineering Is, Tooling, Steps of the Chaos Engineering Cycle-Hypothesize team structure, Team Structure Chaos Kong, Going Big, Automation of the platforms Chaos Maturity Model (CMM)Adoption, Adoption-Obstacles to Adoption benefits of, Business Factors, Chaos Maturity Model development of, Chaos Maturity Model mapping the properties, Putting It All Together Sophistication, Sophistication-Automation of the platforms Chaos Monkeyas low-hanging fruit of Chaos Engineering, Vary Real-World Events birth of, Chaos Monkey Is Born, Adoption initial use of, Automation of the platforms popularity of, Why Do We Need Chaos Engineering?

The Psychology of Money: Timeless Lessons on Wealth, Greed, and Happiness by Morgan Housel

airport security, Amazon Web Services, Bernie Madoff, book value, business cycle, computer age, Cornelius Vanderbilt, coronavirus, discounted cash flows, diversification, diversified portfolio, do what you love, Donald Trump, financial engineering, financial independence, Hans Rosling, Hyman Minsky, income inequality, index fund, invisible hand, Isaac Newton, It's morning again in America, Jeff Bezos, Jim Simons, John Bogle, Joseph Schumpeter, knowledge worker, labor-force participation, Long Term Capital Management, low interest rates, margin call, Mark Zuckerberg, new economy, Paul Graham, payday loans, Ponzi scheme, quantitative easing, Renaissance Technologies, Richard Feynman, risk tolerance, risk-adjusted returns, Robert Gordon, Robert Shiller, Ronald Reagan, side hustle, Stephen Hawking, Steven Levy, stocks for the long run, tech worker, the scientific method, traffic fines, Vanguard fund, WeWork, working-age population

Just like Heinz Berggruen, but with Microsoft and Walmart instead of Picasso and Matisse. Not only do a few companies account for most of the market’s return, but within those companies are even more tail events. In 2018, Amazon drove 6% of the S&P 500’s returns. And Amazon’s growth is almost entirely due to Prime and Amazon Web Services, which itself are tail events in a company that has experimented with hundreds of products, from the Fire Phone to travel agencies. Apple was responsible for almost 7% of the index’s returns in 2018. And it is driven overwhelmingly by the iPhone, which in the world of tech products is as tail-y as tails get.

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But CEO Jeff Bezos said shortly after the disastrous launch of the company’s Fire Phone: If you think that’s a big failure, we’re working on much bigger failures right now. I am not kidding. Some of them are going to make the Fire Phone look like a tiny little blip. It’s OK for Amazon to lose a lot of money on the Fire Phone because it will be offset by something like Amazon Web Services that earns tens of billions of dollars. Tails to the rescue. Netflix CEO Reed Hastings once announced his company was canceling several big-budget productions. He responded: Our hit ratio is way too high right now. I’m always pushing the content team. We have to take more risk. You have to try more crazy things, because we should have a higher cancel rate overall.

Chaos Monkeys: Obscene Fortune and Random Failure in Silicon Valley by Antonio Garcia Martinez

Airbnb, airport security, always be closing, Amazon Web Services, Big Tech, Burning Man, business logic, Celtic Tiger, centralized clearinghouse, cognitive dissonance, collective bargaining, content marketing, corporate governance, Credit Default Swap, crowdsourcing, data science, deal flow, death of newspapers, disruptive innovation, Dr. Strangelove, drone strike, drop ship, El Camino Real, Elon Musk, Emanuel Derman, Fairchild Semiconductor, fake it until you make it, financial engineering, financial independence, Gary Kildall, global supply chain, Goldman Sachs: Vampire Squid, Hacker News, hive mind, How many piano tuners are there in Chicago?, income inequality, industrial research laboratory, information asymmetry, information security, interest rate swap, intermodal, Jeff Bezos, Kickstarter, Malcom McLean invented shipping containers, Marc Andreessen, Mark Zuckerberg, Maui Hawaii, means of production, Menlo Park, messenger bag, minimum viable product, MITM: man-in-the-middle, move fast and break things, Neal Stephenson, Network effects, orbital mechanics / astrodynamics, Paul Graham, performance metric, Peter Thiel, Ponzi scheme, pre–internet, public intellectual, Ralph Waldo Emerson, random walk, Reminiscences of a Stock Operator, Ruby on Rails, Salesforce, Sam Altman, Sand Hill Road, Scientific racism, second-price auction, self-driving car, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, Social Justice Warrior, social web, Socratic dialogue, source of truth, Steve Jobs, tech worker, telemarketer, the long tail, undersea cable, urban renewal, Y Combinator, zero-sum game, éminence grise

Until AdGrok’s very end, search terms like “goldman sachs” and “fuck you” (I had written a post about the ever-elusive goal of “fuck-you money”) would be the most popular terms that led to clicks to our site.* It irritated MRM to no end. But hey—I didn’t see fifty thousand people a day lining up to use the product we had built. We’d take eyeballs wherever we could find them. As a result of the Amazon Web Services near fiasco, plus several more outages and server meltdowns, MRM suggested we run a “chaos monkey” from time to time. This was a software tool created and open-sourced by Netflix, meant to test a product or website’s resiliency against random server failures (such as we’d just witnessed with the blog).

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In tandem, the popularity of accelerators like Y Combinator, plus a general acceptance of entrepreneurship as a career, meant lots of very skilled engineers and product people were skipping the corporate trajectory and building exciting products. The emergence of turnkey, on-demand computation like Amazon Web Services, plus off-the-shelf Web-development frameworks like Ruby on Rails, meant that new ideas were easier than ever to test. Many entrepreneurs chose to build shovels rather than dig for gold, creating more complex software building blocks to underpin the innovation, such as back-end services like Parse, accelerating the startup explosion in an almost exponential way.

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On the technical side, it means understanding the company’s “stack”; that is, the pile of interrelated user interface and back-end server technologies that power the product. It might even be as detailed as line-by-line code reviews with the startup’s engineers. You can fake a lot in a startup these days, what with Amazon Web Services and all sorts of off-the-shelf back-end components that let any even minimally competent duffer set up a Web app that does something. Intelligent planning for growth is rare among early startups, but it’s the name of the game at a large, rapidly scaling tech company. Waiting for a team to grow from technical adolescence to mature talent was too long even for a larger company.

Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale by Jan Kunigk, Ian Buss, Paul Wilkinson, Lars George

Amazon Web Services, barriers to entry, bitcoin, business intelligence, business logic, business process, cloud computing, commoditize, computer vision, continuous integration, create, read, update, delete, data science, database schema, Debian, deep learning, DevOps, domain-specific language, fault tolerance, Firefox, FOSDEM, functional programming, Google Chrome, Induced demand, information security, Infrastructure as a Service, Internet of things, job automation, Kickstarter, Kubernetes, level 1 cache, loose coupling, microservices, natural language processing, Network effects, platform as a service, single source of truth, source of truth, statistical model, vertical integration, web application

If there is hardware but it needs to be configured, using an automated deployment process should restore services, including the backed-up data, within days, if not hours. One option to avoid the cost of having a cold standby cluster sitting around is the use of a public cloud offering such as Amazon Web Services (AWS). This allows for quick provisioning of a cluster of likely shared machines, which are then configured similarly to the lost local cluster. Obviously, the real difficulty here is to move the data during a recovery process, but this can be avoided by, for example, using Amazon S3 for backup data or having the cloud provider offer a high-bandwidth interconnect that allows you to copy the backup data when needed.

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Solutions in the Public Cloud Our discussion of public clouds is different from that of private clouds. In contrast to private cloud Hadoop services, there are thousands of examples in which large organizations and enterprises are successfully running Hadoop in the public cloud. In the coming chapters, we focus our discussion on the three largest public cloud providers in the market: Amazon Web Services (AWS) Microsoft Azure Google Cloud Project (GCP) This chapter looks at the portfolios of our three cloud providers through the lens of Hadoop. We cover the key categories for each: instances, storage, and possible life cycle models. Next, we offer advice on how to use the provider portfolios to implement clusters and big data use cases.

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Although we focus on Cloudera Director here, we strongly encourage you to look at the similar offerings from other vendors, such as Hortonworks Cloudbreak or MapR Orbit, and to evaluate which is most appropriate for your use case. Cloudera Director As of late 2017, Cloudera Director can be used to provision clusters running Cloudera Distribution, including Apache Hadoop (Cloudera Distribution Hadoop [CDH]), on each of the three major public clouds (Amazon Web Services, Google Cloud Platform, and Microsoft Azure). Director advertises the following key features relevant to our discussion on automated provisioning: Automated provisioning of Cloudera Manager and CDH clusters and supporting databases Deployment of secure, Kerberos-enabled clusters (with a preexisting KDC) Deployment of highly available services Scaling clusters: expansion and contraction of clusters by adding and removing worker nodes Ongoing management: master instance migration Cloudera Director has two components: a command-line client and a server, as shown in Figure 17-3.

The Mesh: Why the Future of Business Is Sharing by Lisa Gansky

"World Economic Forum" Davos, Airbnb, Amazon Mechanical Turk, Amazon Web Services, banking crisis, barriers to entry, Bear Stearns, bike sharing, business logic, carbon footprint, carbon tax, Chuck Templeton: OpenTable:, clean tech, cloud computing, credit crunch, crowdsourcing, diversification, Firefox, fixed income, Google Earth, impact investing, industrial cluster, Internet of things, Joi Ito, Kickstarter, late fees, Network effects, new economy, peer-to-peer lending, planned obsolescence, recommendation engine, RFID, Richard Florida, Richard Thaler, ride hailing / ride sharing, sharing economy, Silicon Valley, smart grid, social web, software as a service, TaskRabbit, the built environment, the long tail, vertical integration, walkable city, yield management, young professional, Zipcar

They leverage hundreds of billions of dollars in available information infrastructure—telecommunications, mobile technology, enhanced data collection, large and growing social networks, mobile SMS aggregators, and of course the Web itself. They efficiently employ horizontal business to business services, such as FedEx, UPS, Amazon Web Services, PayPal, and an ever-increasing number of cloud computing services. All the Mesh businesses rely on a basic premise: when information about goods is shared, the value of those goods increases, for the business, for individuals, and for the community. Mesh businesses are legally organized as for-profit corporations, cooperatives, and nonprofit organizations.

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Not all large companies can adopt a Mesh strategy as thoroughly and successfully as Netflix. Many can, will, and have adopted aspects of the Mesh where they’re able to perceive the competitive advantages. Here are five ways: 1. Provide services or platforms that enable and encourage Mesh businesses. We’ve already discussed several, such as Amazon Web Services, PayPal, and FedEx. Like many other companies, the streaming music service Pandora extends its service through an iPhone app. The iPad, Kindle, and Kno are strong new platforms for distributing e-books, blogs, video, and magazines. These services are likely to be reshaped in even more favorable ways by the Mesh as it grows.

Little Bets: How Breakthrough Ideas Emerge From Small Discoveries by Peter Sims

Alan Greenspan, Amazon Web Services, Black Swan, Clayton Christensen, complexity theory, David Heinemeier Hansson, deliberate practice, discovery of penicillin, endowment effect, fail fast, fear of failure, Frank Gehry, Guggenheim Bilbao, Jeff Bezos, knowledge economy, lateral thinking, Lean Startup, longitudinal study, loss aversion, meta-analysis, PageRank, Richard Florida, Richard Thaler, Ruby on Rails, Salesforce, scientific management, Silicon Valley, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, systems thinking, TED Talk, theory of mind, Toyota Production System, urban planning, Wall-E

Critics ridiculed the company, calling it “Amazon.bomb” or “Amazon.con.” Some Wall Street analysts and investors even called for Bezos to resign. However, the ultimate outcome has been that Amazon’s exploratory mentality has spawned continual breakthroughs, such as Amazon stores, which allows small vendors to sell products on its site, as well as Amazon Web Services (AWS), which includes Elastic Compute Cloud (EC2), permitting third parties to rent storage space on the company’s servers. Third-party vendors now account for roughly 30% of Amazon’s sales, a key source of the company’s impressive growth. Chris Rock, the Google founders, and Jeff Bezos and his team are examples of people who approach problems in a nonlinear manner using little bets, what University of Chicago economist David Galenson has dubbed “experimental innovators.”

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Abilities, 36–41 Academy Awards, 79, 145 Acoustics, 80–83 Active users, 131, 13–40 Adobe, 19, 89 Advertising, Internet, 4–5 AdWords, 4–5 Affordable loss principle, 28–32, 33 Afghanistan, 25–26, 105 Agile development, 84–91, 148, 151, 152, 194 Agilent Technologies, 19 Agriculture, 99 Alcoholism, 141–42 Al Jabouri, Najim Abdullah, 104, 105 Al-Qaeda, 25–26, 27, 92, 94 Amazon, 5–7, 45, 84, 107, 153 Amazon Auctions, 6 Amazon Web Services (AWS), 7 Ambition, 35 American Psychologist, 141 Animation, 29–32, 41–54, 59–62, 69–76, 105–106, 142–46, 148–49, 155, 158–59, 186–87 humor and, 73–76 plussing, 69–73 prototyping, 59–62 small wins, 142–46, 148–49, 150 See also Pixar; specific animated films Ansari, Daniel, 67–68 Apple, 12, 29, 30, 108, 156 Architecture, 13, 45–46, 54, 55, 77–83, 84, 117, 154, 161, 162 imposition of constraints, 77–84 prototyping, 54–55, 63, 80, 81 See also specific architects and buildings Ariel Ultra, 113–14 Army, U.S., 22–28, 91–95, 103–105, 149–51, 155 counterinsurgency methods, 27, 91–95, 103–105, 149–150, 151, 155 Ballmer, Steve, 156 Bangladesh, 98–102 Barnholt, Ned, 19–22 Bayles (David) and Orland (Ted), Art & Fear: Observations on the Perils (and Rewards) of Artmaking, 169 Beck, Kent, 84 Becket, Welton, 79 Beethoven, Ludwig van, 8, 13, 181–82 Begum, Sufiya, 98–102 Belkin Corporation, 111–13, 153 Belsky, Scott, Making Ideas Happen, 172–73 Berkowitz, Aaron, 67–68 Bezos, Jeff, 5–6, 7, 8, 13, 35, 45, 107, 110–11, 115, 153, 159 Biaj, Iraq, 93–94 Big bets, 20 little bets vs., 19–33 Bird, Brad, 42–44 Blank, Steve, 102–103 Bleeding edge user needs, 136 Blocking, 70 Body language, 123 Boston, 109 Brafman, Ori, 138, 139–40 Click, 138 Sway, 138 Brain, 15, 47, 66, 183–84 capabilities, 47–48 improvisation and, 66–68 Brereton, Kevin, 115 Brin, Sergey, 4–5, 8, 115 Brown, Tim, Change by Design, 171 Bug’s Life, A (film), 59 Bush, George W., 125, 127 Business Land, 112 Calculators, 21–22 Canon, 85 Cars (film), 106 Catmull, Ed, 30–32, 41–45, 60, 62, 75, 121, 142–46, 148–49, 150 CAT scans, 30 Central planning approach, 15–17, 24–26 Challenges, 38, 48, 49, 78 growth mind-set and, 35–49 Chance opportunities, 123, 124, 129 Chandler Pavilion, Los Angeles, 79, 80, 83 Change, 160–61 Chanos, James, 109 Chesborough, Henry, Open Innovation, 166 Chicago, 55–58 Child, Julia, 115 Child development, 160–61 Christensen, Clayton, 107 The Innovator’s Dilemma, 166 The Innovator’s Solution, 166 Clinton, Hillary, 56, 125, 126 CNBC, 125 Cockburn, Alistair, 84 Cold War, 23–26 Collins, Jim: Built to Last, 166–67 Good to Great, 120–21, 166 How the Mighty Fall, 166 Columbia University, 36 Comedian, The (film), 165 Comedians, 1–3, 12, 13, 32, 109, 128, 131, 133, 135, 140, 141, 151, 154–55 Competition, 17, 156 Computer Land, 112 Computer Point, 112 Computers, 22, 29, 83, 107–108, 110, 112–13, 142–46 animation, 29–32, 41–45, 59–62, 69–76, 105–106, 142–46, 148–49, 186–87 software, 83–91, 112, 136–37, 143–46, 149, 151–52 See also Internet Conceptual innovation, 7, 180–81 Confidence, and growth mind-set, 35–41 Consolidating gains, 149 Constraints, imposition of.

Remix: Making Art and Commerce Thrive in the Hybrid Economy by Lawrence Lessig

Aaron Swartz, Amazon Web Services, Andrew Keen, Benjamin Mako Hill, Berlin Wall, Bernie Sanders, Brewster Kahle, carbon tax, Cass Sunstein, collaborative editing, commoditize, disintermediation, don't be evil, Erik Brynjolfsson, folksonomy, Free Software Foundation, Internet Archive, invisible hand, Jeff Bezos, jimmy wales, John Perry Barlow, Joi Ito, Kevin Kelly, Larry Wall, late fees, Mark Shuttleworth, Netflix Prize, Network effects, new economy, optical character recognition, PageRank, peer-to-peer, recommendation engine, revision control, Richard Stallman, Ronald Coase, Saturday Night Live, search costs, SETI@home, sharing economy, Silicon Valley, Skype, slashdot, Steve Jobs, the long tail, The Nature of the Firm, thinkpad, transaction costs, VA Linux, Wayback Machine, yellow journalism, Yochai Benkler

Everything it does is aimed to drive sales of its products more efficiently. One of the techniques that Amazon uses mirrors the technique of the Internet generally: Amazon has opened its platform to allow others to innovate in new ways to build value out of Amazon’s database. Through a suite of tools called Amazon Web Services (AWS), Amazon enables developers to build products that integrate directly into Amazon’s database. For example, a developer named Jim Biancolo used AWS to build a free Web tool to track the price difference between new products and used products (plus shipping). And a company called TouchGraph used AWS to build a product browser that would show the links between related products.

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To enable this competition to happen, Netflix shared “a lot of anonymous rating data.” The company 80706 i-xxiv 001-328 r4nk.indd 137 8/12/08 1:55:20 AM REMI X 138 also increasingly offers through RSS feeds access to ranking information about its users’ choices. Amazon does this through its Amazon Web Services. And Google does this perhaps most of all, through Google APIs that encourage what has come to be known as the Google mash-up. Don Tapscott and Anthony Williams describe one example of the Google mash-up in their book, Wikinomics. In May 2005, Paul Rademacher was trying to find a house in Silicon Valley for his job at Dreamworks Animation.

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., 195. 80706 i-xxiv 001-328 r4nk.indd 318 8/12/08 1:56:29 AM INDEX ABC, 213 Academy Awards, 45–46 access, 43–49, 67, 106, 255, 261, 291 advertising, 2, 48–49 blogs and, 63, 291 classified, 187 Dogster and, 187 Wikia and, 204–5 Wikipedia and, 161–62, 203–4 Alcoholics Anonymous (AA), 148 Alexa, 236–37 Alexander, Alastair, 208 Allman, Eric, 163–64 amateur creativity, 33, 90, 103 deregulation of, 254–59 in music, 24–29, 32–33 Amazon, 48–49, 125–26, 129–30, 141, 142, 239–40 Little Brother and, 132, 136 Amazon Web Services (AWS), 126, 138 Anderson, Chris, 129 Andover.net, 199 anime music videos (AMVs), 77–78, 79, 80 AOL, 153–54 Apache, 164–65, 183, 241–42 Apple, 88, 142, 165 iPod, 41, 46, 47, 88 iTunes, 12, 13, 41–42, 134 Armstrong, Edwin Howard, 30 Armstrong, Louis, 104 80706 i-xxiv 001-328 r4nk.indd 319 astronomy, 170–71 Atmo, 73 AudioMulch, 12 Awesometown, 227 Bainwol, Mitch, 114 Baker, Stewart, 89 Barish, Stephanie, 80 Barlow, John Perry, 67 barter economies, 180 Baumol, William, 230 Beam-it, 135 Beatles, 74, 75, 255 Becker, Don, 180 Behlendorf, Brian, 164, 183, 242–43 Benkler, Yochai, 50, 58, 59, 62, 118, 140, 146, 169, 172, 176, 178 Berners-Lee, Tim, 58, 221 Bezos, Jeff, 125, 129, 136 Biancolo, Jim, 126 BigChampagne Online Media Measurement, 110 BIND, 164 Blair, Tony, 73–74, 273 blip.tv, 249 Blockbuster, 123–24, 129, 142 blogs, 57, 58–62, 63–65, 103, 139, 291 value of, 92–93 books, 42, 99–100, 268, 269, 291, 292 access to, 43–44 8/12/08 1:56:29 AM 320 books (cont.)

Zucked: Waking Up to the Facebook Catastrophe by Roger McNamee

"Susan Fowler" uber, "World Economic Forum" Davos, 4chan, Albert Einstein, algorithmic trading, AltaVista, Amazon Web Services, Andy Rubin, barriers to entry, Bernie Sanders, Big Tech, Bill Atkinson, Black Lives Matter, Boycotts of Israel, Brexit referendum, Cambridge Analytica, carbon credits, Cass Sunstein, cloud computing, computer age, cross-subsidies, dark pattern, data is the new oil, data science, disinformation, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, Electric Kool-Aid Acid Test, Elon Musk, fake news, false flag, Filter Bubble, game design, growth hacking, Ian Bogost, income inequality, information security, Internet of things, It's morning again in America, Jaron Lanier, Jeff Bezos, John Markoff, laissez-faire capitalism, Lean Startup, light touch regulation, Lyft, machine readable, Marc Andreessen, Marc Benioff, Mark Zuckerberg, market bubble, Max Levchin, Menlo Park, messenger bag, Metcalfe’s law, minimum viable product, Mother of all demos, move fast and break things, Network effects, One Laptop per Child (OLPC), PalmPilot, paypal mafia, Peter Thiel, pets.com, post-work, profit maximization, profit motive, race to the bottom, recommendation engine, Robert Mercer, Ronald Reagan, Russian election interference, Sand Hill Road, self-driving car, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, software is eating the world, Stephen Hawking, Steve Bannon, Steve Jobs, Steven Levy, Stewart Brand, subscription business, TED Talk, The Chicago School, The future is already here, Tim Cook: Apple, two-sided market, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, vertical integration, WikiLeaks, Yom Kippur War

In parallel, a new concept emerged—the cloud—and the industry embraced the notion of centralization of shared resources. The cloud is like Uber for data—customers don’t need to own their own data center or storage if a service provides it seamlessly from the cloud. Today’s leader in cloud services, Amazon Web Services (AWS), leveraged Amazon.com’s retail business to create a massive cloud infrastructure that it offered on a turnkey basis to startups and corporate customers. By enabling companies to outsource their hardware and network infrastructure, paying a monthly fee instead of the purchase price of an entire system, services like AWS lowered the cost of creating new businesses and shortened the time to market.

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The case against Amazon is probably strongest, and it provides a framework for understanding the larger issues. For Amazon.com, freedom from antitrust scrutiny has allowed the company to integrate vertically, as well as horizontally. From its original base in retail for nonperishable goods, Amazon has expanded horizontally into perishables, with Whole Foods, and into cloud services, with Amazon Web Services. Amazon’s vertical integration has included Marketplace, which incorporates third-party sellers; Basics, where Amazon private-labels bestselling commodity products; and hardware, such as Alexa voice-controlled devices and the Fire home video server. In a traditional antitrust regime, Amazon’s vertical-integration strategy would not be allowed.

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Accel Partners, 54, 58 Acxiom, 226 addiction, 100–101, 106–7, 162, 206, 240, 246, 250, 253, 254, 257, 268, 269, 281 advertising, 17, 47, 68–69, 85–86, 105, 120, 125, 257, 283–84 Facebook and, 11–12, 47, 59–61, 63, 68–77, 85, 103, 119, 128–29, 130, 132, 143, 148, 173, 184, 185, 202, 207–9, 211, 217–19, 237–38, 258, 265, 270, 281 Google and, 47, 67–69, 86, 104, 173, 283–86 political, microtargeting in, 237–38 YouTube and, 103, 283–84 Albright, Jonathan, 125 Alcorn, Al, 34 algorithms, 66, 94, 98, 122, 128, 129, 169, 264 extreme views and, 92 of Facebook, 4, 9, 11, 66, 74, 76, 81, 87, 91, 128–29, 143, 166, 232, 235, 243, 270, 274, 277, 281 of Google, 66, 235 of YouTube, 92–93, 139, 274 Allen, Paul, 25 Altair, 34 Altavista, 42 Alter, Adam, 272 Amanpour, Christiane, 147 Amazon, 16, 27, 36, 38, 68, 137, 224, 262, 283 Alexa, 137, 262, 268, 271, 281 Echo, 236 market power of, 46 monopoly power and antitrust issues, 47–48, 136–40, 225–26, 246, 247, 261, 262, 284 Web Services, 40, 41, 137, 138 words associated with, 231 American Academy of Pediatrics, 273 Anderson, Chris, 109 Anderson, Fred, 14 Andreessen, Marc, 27, 36, 37, 43, 58 Android, 138, 204, 271, 282 angel investors, 48 anonymity, 37, 55, 92, 101, 123 antitrust and monopoly issues, 46–48, 136–41, 162, 220, 223, 224, 227, 234, 238, 246, 247, 250, 261–63, 277, 279, 281–83, 285–87 Amazon and, 47–48, 136–40, 225–26, 246, 247, 261, 262, 284 AT&T and, 224–25 Facebook and, 47–48, 100, 136–41, 162, 225–26, 234, 246, 247, 261, 263, 284 Google and, 47–48, 136–40, 162, 225–26, 234, 246, 247, 261–63, 282, 284 Microsoft and, 46–47, 154–55, 286 Apache, 40 Apple, 14, 25, 29–30, 34, 35, 38, 49, 50, 83, 118, 249 iPhone, 38, 84, 100, 105–6, 271 Jones and Infowars and, 228–29 market power of, 46 Onavo and, 140 privacy and, 38, 158, 271 Silver Lake and, 29–30 words associated with, 231 approval, need for, 98 Apture, 107 Arab Spring, 64, 243 ARPANET, 34–35 artificial intelligence (AI), 69, 84–85, 88, 98, 128, 151, 158, 219–20, 223, 236, 252, 260, 261, 263, 264, 267–69, 272 of Facebook, 10, 11, 69, 85, 87, 91, 95, 108, 203, 219–20, 230, 261 of Google, 108, 219–20, 253, 261 Associated Press, 124 AT&T, 224–25 Atari, 34 Atlantic, 195, 231 Attention Merchants, The (Wu), 120 attorneys general, 120, 172, 227 authoritarianism, 278, 279 automobiles, 158, 223 banks, 231–32 Bannon, Steve, 181, 182, 197, 199 Bast, Andy, 109 Belgium, 247 Bell Labs, 225 Berners-Lee, Tim, 36, 161 Bezos, Jeff, 27 Black Lives Matter, 8, 243, 250, 275 Blacktivist, 131 Bloomberg, 50 Blumenthal, Richard, 128 Bodnick, Marc, 14, 18, 59 Bogost, Ian, 195–96 Bolton, Tamiko, 161–62 Bono, 13, 18, 28–29, 30, 59–61, 159 Booker, Cory, 128 Bosworth, Andrew “Boz,” 160, 165 memo of, 204–6 bots, 90, 116, 124, 126, 174, 177, 227 bottomless bowl, 97 Box, 41 brain, 88 brain hacking, 81–82, 118, 141, 148 Brand, Stewart, 177 Brazil, 280 Brexit, 8–9, 96, 180, 196, 198, 244 Breyer, Jim, 58 Bricklin, Dan, 34 Brin, Sergey, 27 bro and hipster cultures, 49–50 Brotopia (Chang), 50 bullying, 87, 101–2, 205, 253, 269, 273, 280, 281 Bushnell, Nolan, 34 Business Insider, 141 BuzzFeed, 204 California, 227–28 environmental regulations in, 201 secession movement, 114, 115 Cambodia, 215, 246 Cambridge Analytica, 78, 180–98, 199, 202–4, 207, 208, 210, 213, 216–18, 251, 259 Cambridge University, 181 Candy Crush, 191, 269 capitalism, 200, 201, 220, 238, 262 Castor, Kathy, 210–11 cell phones, 36, 225 see also smartphones censorship, 252 Center for Humane Technology (CHT), 157, 166–67, 173, 188, 272 Chancellor, Joseph, 181, 186 Chang, Emily, 50 Chaos Monkeys (García Martínez), 71, 72, 73 Chaslot, Guillaume, 92 Chicago School antitrust philosophy, 137–39, 285, 286 children, 214, 240, 253–54 technology and, 106, 156, 166, 237, 255, 268, 269, 272–73, 279–80 China, 162, 205, 215–16 Cisco, 46 CityVille, 191 Clayton Act, 136 Clinton, Bill, 60 Clinton, Chelsea, 167 Clinton, Hillary, 5, 11, 117, 121, 124, 125, 130, 166 Facebook Groups and, 7–8 Clinton Foundation, 130 cloud, 38, 40, 41, 104, 249, 250 Amazon Web Services, 40, 41, 137, 138 CNBC, 118, 161 CNN, 130–31, 147, 161, 193, 229, 231 Coca-Cola, 141 Cohen, Michael, 209–10 Cold War, 32 Columbia University, 54 Common Sense Media, 119, 156–57, 167, 227, 272 Compaq Computer, 27, 35, 152 competition: lack of alteratives to Facebook and Google, 92, 100, 141, 223, 280 see also antitrust and monopoly issues computers, computing, 22, 25, 31–36, 108, 225 in classrooms, 273 cloud, see cloud minicomputers, 33, 46 PCs, 17, 22, 25, 29, 33–36, 40, 42, 45, 46, 273 Congress, 121–33, 135–36, 152, 154, 163, 200, 208, 222, 226, 238 House Energy and Commerce Committee, 209–10, 227 House Intelligence Committee, 123, 127–28, 132, 133, 167, 227 Senate Commerce Committee, 209 Senate Intelligence Committee, 12, 111–12, 127, 132 Senate Judiciary Committee, 128, 131, 132, 136, 209 Zuckerberg’s testimony before, 209–12, 216, 217 Conservative Political Action Conference (CPAC), 174 conspiracy theories, 92–95, 115, 119, 121, 214, 228, 229, 234, 242, 243, 274 Pizzagate, 124–26, 130 Constitution, 12, 259 Fourth Amendment to, 201 content vendors, 283 Conway, Ron, 48 Cooper, Anderson, 81 copyrights, 281 Cosgrave, Paddy, 108–9 Cow Clicker, 195–96 Cox, Chris, 144 Cox, Joseph, 229–30 Cruz, Ted, 185 Currier, James, 47 Cyprus, 125 Czech Republic, 125 Dalai Lama, 31 dark patterns, 96 data and data privacy, 155, 158, 159, 203, 217, 220, 234–36, 238, 253, 258–59, 263–65, 269, 271–72, 277–84 Apple and, 38, 158, 271 Big Data, 158 browsing history, 218–19 Cambridge Analytica and, 78, 180–98, 199, 202–4, 207, 208, 210, 213, 216–18, 251, 259 combined sets of, 68, 285–86 and data as currency, 285 data ownership, 222, 237, 247, 259, 264 data portability, 247 Facebook’s banning of data brokers, 208 Facebook’s continuing threat to, 246 Facebook user data, 4–5, 9, 62, 72, 75–76, 78, 87, 131–32, 141–42, 174, 180–98, 202–4, 210–11, 216–19, 223, 258–59 Facebook user privacy settings, 97 fiduciary rule and, 226–27, 247, 260–61 Global Data Protection Regulation, 221, 222, 224, 259–60 and Internet of Things, 262, 268, 271 and internet as barter transaction, 285 internet privacy bill of rights, 221–24, 226–27 metadata, 68–69, 211, 217–20, 264 passwords and log-ins, 249–50, 271 “price” of data, 285–86 regulations and, 201 and value of data vs. services, 286 Zuckerberg’s attitudes regarding, 4–5, 55–56, 60, 141–42 Data for Democracy, 90, 122, 127 Dead & Company, 74 Defense, U.S.

Node.js in Action by Mike Cantelon, Marc Harter, Tj Holowaychuk, Nathan Rajlich

Amazon Web Services, business logic, Chris Wanstrath, create, read, update, delete, Debian, en.wikipedia.org, Firefox, Google Chrome, machine readable, MITM: man-in-the-middle, MVC pattern, node package manager, p-value, pull request, Ruby on Rails, SQL injection, web application, WebSocket

Figure 12.1 illustrates how you can use cloud hosting to automate the creation and destruction of an application’s servers. Figure 12.1. Creating, starting, stopping, and destroying cloud servers can be fully automated. The downside to using cloud servers is that they tend to be more expensive than VPSs and can require some knowledge specific to the cloud platform. Amazon Web Services The oldest and most popular cloud platform is Amazon Web Services (AWS; http://aws.amazon.com/). AWS consists of a range of different hosting-related services, like email delivery, content-delivery networks, and lots more. Amazon’s Elastic Compute Cloud (EC2), one of AWS’s central services, allows you to create servers in the cloud whenever you need them.

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Index [SYMBOL][A][B][C][D][E][F][G][H][I][J][K][L][M][N][O][P][Q][R][S][T][U][V][W][X] SYMBOL 404 status code, 2nd 500 status code, 2nd A absolute paths vs. relative paths Accept header acceptance testing defined overview Soda installing overview using with Sauce Labs using with Selenium Server Tobi Accept-Encoding header add command add-ons for flow control adduser command administration panel middleware after function, 2nd afterEach function, 2nd Ajax (Asynchronous JavaScript and XML) Amazon S3 Amazon Web Services ANSI (American National Standards Institute) ANSI escape codes Apache application dependency application/x-www-form-urlencoded content type, 2nd, 3rd arguments, command-line argv property, 2nd ascii encoding ASI (automated semicolon insertion) asynchronous I/O Asynchronous JavaScript and XML.

Building Microservices by Sam Newman

airport security, Amazon Web Services, anti-pattern, business logic, business process, call centre, continuous integration, Conway's law, create, read, update, delete, defense in depth, don't repeat yourself, Edward Snowden, fail fast, fallacies of distributed computing, fault tolerance, index card, information retrieval, Infrastructure as a Service, inventory management, job automation, Kubernetes, load shedding, loose coupling, microservices, MITM: man-in-the-middle, platform as a service, premature optimization, pull request, recommendation engine, Salesforce, SimCity, social graph, software as a service, source of truth, sunk-cost fallacy, systems thinking, the built environment, the long tail, two-pizza team, web application, WebSocket

Starting in 2007 with a monolithic Rails application, by 2009 Gilt’s system was unable to cope with the load being placed on it. By splitting out core parts of its system, Gilt was better able to deal with its traffic spikes, and today has over 450 microservices, each one running on multiple separate machines. When embracing on-demand provisioning systems like those provided by Amazon Web Services, we can even apply this scaling on demand for those pieces that need it. This allows us to control our costs more effectively. It’s not often that an architectural approach can be so closely correlated to an almost immediate cost savings. Ease of Deployment A one-line change to a million-line-long monolithic application requires the whole application to be deployed in order to release the change.

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The more logic that creeps into the client library, the more cohesion starts to break down, and you find yourself having to change multiple clients to roll out fixes to your server. You also limit technology choices, especially if you mandate that the client library has to be used. A model for client libraries I like is the one for Amazon Web Services (AWS). The underlying SOAP or REST web service calls can be made directly, but everyone ends up using just one of the various software development kits (SDKs) that exist, which provide abstractions over the underlying API. These SDKs, though, are written by the community or AWS people other than those who work on the API itself.

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But Amazon also knew that small teams can work faster than large teams. This led famously to its two-pizza teams, where no team should be so big that it could not be fed with two pizzas. This driver for small teams owning the whole lifecycle of their services is a major reason why Amazon developed Amazon Web Services. It needed to create the tooling to allow its teams to be self-sufficient. Netflix learned from this example, and ensured that from the beginning it structured itself around small, independent teams, so that the services they created would also be independent from each other. This ensured that the architecture of the system was optimized for speed of change.

Designing Reactive Systems: The Role of Actors in Distributed Architecture by Hugh McKee

Amazon Web Services, business logic, fault tolerance, Internet of things, microservices

When programs run in a computer system, the actual work is handled by what is called a thread or thread of execution. Just as each computer system has a limited number of cores and a limited amount of memory, there is also a limited number of threads. A typical computer system has a small number of cores, and common small or medium-sized servers may have 4 to 16 processing cores. (Just looking at Amazon Web Services instances, you can see it’s possible to provision cloud instances with 128 processor cores and 2 terabytes of in-memory RAM.) For memory (RAM), this varies from a few gigabytes to terabytes. A typical server has 8GB to 64GB, but this number keeps going up as the cost of memory decreases in light of technological improvements.

Seeking SRE: Conversations About Running Production Systems at Scale by David N. Blank-Edelman

Affordable Care Act / Obamacare, algorithmic trading, AlphaGo, Amazon Web Services, backpropagation, Black Lives Matter, Bletchley Park, bounce rate, business continuity plan, business logic, business process, cloud computing, cognitive bias, cognitive dissonance, cognitive load, commoditize, continuous integration, Conway's law, crowdsourcing, dark matter, data science, database schema, Debian, deep learning, DeepMind, defense in depth, DevOps, digital rights, domain-specific language, emotional labour, en.wikipedia.org, exponential backoff, fail fast, fallacies of distributed computing, fault tolerance, fear of failure, friendly fire, game design, Grace Hopper, imposter syndrome, information retrieval, Infrastructure as a Service, Internet of things, invisible hand, iterative process, Kaizen: continuous improvement, Kanban, Kubernetes, loose coupling, Lyft, machine readable, Marc Andreessen, Maslow's hierarchy, microaggression, microservices, minimum viable product, MVC pattern, performance metric, platform as a service, pull request, RAND corporation, remote working, Richard Feynman, risk tolerance, Ruby on Rails, Salesforce, scientific management, search engine result page, self-driving car, sentiment analysis, Silicon Valley, single page application, Snapchat, software as a service, software is eating the world, source of truth, systems thinking, the long tail, the scientific method, Toyota Production System, traumatic brain injury, value engineering, vertical integration, web application, WebSocket, zero day

This might run afoul of the EU’s General Data Protection Regulation (GDPR)8 compliance rules, which went into effect on May 25, 2018. Again, here are some pitfalls to avoid: Don’t reinvent the wheel; if you have a Data Science or other team concerned about RUM, it might already have a data pipeline9 that we can utilize. Similarly, Google, Amazon Web Services (AWS), and others offer solutions for message queueing and streaming. Don’t rely on third-party APIs as a conduit for monitoring third-party health. Third parties generally do an excellent job of performing what they’re paid to do; however, their reporting APIs are not necessarily their core competency.

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Of course, you need to put constraints in place to enforce security boundaries and help prevent mistakes, but providing the ability to define and execute delivers the most value. Figure 10-7. Traditional ticket-driven request fulfillment versus full self-service For example, consider the Elastic Compute Cloud (EC2) service from Amazon Web Services (AWS). The ability to press a button and get a running virtual machine was interesting. However, the ability to control your own destiny by making your own machine images (AMIs) and configuration was revolutionary. It empowered individuals and allowed teams to decouple and move at their own pace.

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Replies Site reliability is operational reliability, scalability, and efficiency. This includes business continuity (disaster recovery, high availability). The operational site becomes a product in and of itself and may include their own CI/CD for internal tooling. The automation tends toward custom tools; for example, Python with Boto library, Ruby with AWS [Amazon Web Services] SDK, and Go language rather than the use of high-level tools like Terraform and Ansible, as they are considered inefficient. Though this is not absolute, just a trend. SRE is programming the operations to create reliable and efficient infrastructure. DevOps focuses on breaking down cultural silos and increasing efficiency or velocity of deployment (CI/CD) pipeline, from development to delivery; this includes building and pre/post artifact testing (testing before and after the artifact is built), thus CT, or continual testing.

Puppet 3 Cookbook by John Arundel

Amazon Web Services, cloud computing, continuous integration, Debian, defense in depth, DevOps, don't repeat yourself, Free Software Foundation, GnuPG, Larry Wall, place-making, Ruby on Rails, web application

There are plenty of cloud service providers around, with Amazon's EC2 being one of the best-known and oldest-established. In this recipe I'll show you how to manage cloud instances using EC2, but the general principles and most of the code will be adaptable to any cloud provider. Getting ready… You'll need an Amazon Web Services (AWS) account if you don't already have one. Of course if you're using an EC2 instance to try out the recipes in this book, you'll already have everything you need, but otherwise you can sign up for an AWS account here: http://aws.amazon.com/ You'll need the AWS access key ID and secret access key corresponding to your account.

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How it works… puppet config print will output every configuration parameter and its current value (and there are lots of them). To see the value for a specific parameter, add it as an argument to the puppet config print command: ubuntu@cookbook:~/puppet$ puppet config print noop false See also The Generating reports recipe in this chapter 251 Index A B a2ensite command 148 Amazon EC2 tab 188 Amazon Web Services (AWS) 188 Apache servers about 144 managing 144, 145 Apache virtual hosts creating 145-147 custom domains 149 docroots 149 working 148, 149 APT package framework 106 arguments passing, to shell commands 84, 85 array iteration using, in templates 96, 97 arrays about 45 creating 45 creating, with split function 46 hashes, using 46 using 45 working 45 arrays, of resources using 58 audit metaparameter 140 Augeas tool about 87 automatically editing config files with 89, 90 auto_failback setting 171 automatic syntax checking Git hooks, used 29 AWS Management Console URL 188 Blueprint installing 214 running 215, 216 C case statements about 50, 52 default value, specifying 52 examples 50 working 51 CentOS 8 changes parameter 90 check_syntax.sh script 32 circular dependency about 245 fixing 248 class inheritance extra values, adding using +> operator 78 using 75, 76 cloud computing 188 command output logging 237, 238 working 238 community Puppet style about 34 indentation 34 parameters 35 quoting 34 symlinks 36 using 34 variables 35 conditional statements comparisons 48 elsif branches 48 example 47 expressions, combining 48 working 47 writing 47 config files building with snippets 91- 93 editing automatically with Augeas 89-91 adding lines to 88, 89 configuration data importing, with Hiera 206-208 configuration settings inspecting 251 contain_package assertion 220 cron Puppet, running from 18, 19 cron jobs distributing 126-128 cross-platform manifests writing 79 custom facts creating 200, 201 D Dean Wilson example, URL 231 debug messages logging 239 resource, checking 240 variable values, printing 240 decentralized Puppet architecture creating 14, 16 definitions creating 59 using 59 working 60 dependencies using 61- 64 dependency graphs drawing 245-247 directory trees distributing 135, 136 DocumentRoot parameter 150 dotfiles 121 DOT format, graphs expanded_relationships.dot 247 254 relationships.dot 247 resources.dot 247 dry run mode using 236, 237 dynamic information importing 83 E EC2 instances AWS example, creating 188-193 managing 188 ENC.

WTF?: What's the Future and Why It's Up to Us by Tim O'Reilly

"Friedman doctrine" OR "shareholder theory", 4chan, Affordable Care Act / Obamacare, Airbnb, AlphaGo, Alvin Roth, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, AOL-Time Warner, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, behavioural economics, benefit corporation, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, Blitzscaling, blockchain, book value, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, carbon tax, Carl Icahn, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, congestion pricing, corporate governance, corporate raider, creative destruction, CRISPR, crowdsourcing, Danny Hillis, data acquisition, data science, deep learning, DeepMind, Demis Hassabis, Dennis Ritchie, deskilling, DevOps, Didi Chuxing, digital capitalism, disinformation, do well by doing good, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, Filter Bubble, Firefox, Flash crash, Free Software Foundation, fulfillment center, full employment, future of work, George Akerlof, gig economy, glass ceiling, Glass-Steagall Act, Goodhart's law, Google Glasses, Gordon Gekko, gravity well, greed is good, Greyball, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, independent contractor, index fund, informal economy, information asymmetry, Internet Archive, Internet of things, invention of movable type, invisible hand, iterative process, Jaron Lanier, Jeff Bezos, jitney, job automation, job satisfaction, John Bogle, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Zimmer (Lyft cofounder), Kaizen: continuous improvement, Ken Thompson, Kevin Kelly, Khan Academy, Kickstarter, Kim Stanley Robinson, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Ellison, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, machine readable, machine translation, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, Network effects, new economy, Nicholas Carr, Nick Bostrom, obamacare, Oculus Rift, OpenAI, OSI model, Overton Window, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, post-truth, race to the bottom, Ralph Nader, randomized controlled trial, RFC: Request For Comment, Richard Feynman, Richard Stallman, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, Ronald Coase, Rutger Bregman, Salesforce, Sam Altman, school choice, Second Machine Age, secular stagnation, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart contracts, Snapchat, Social Responsibility of Business Is to Increase Its Profits, social web, software as a service, software patent, spectrum auction, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, stock buybacks, strong AI, synthetic biology, TaskRabbit, telepresence, the built environment, the Cathedral and the Bazaar, The future is already here, The Future of Employment, the map is not the territory, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Davenport, Tony Fadell, Tragedy of the Commons, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, two-pizza team, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, web application, Whole Earth Catalog, winner-take-all economy, women in the workforce, Y Combinator, yellow journalism, zero-sum game, Zipcar

Yet for Instagram to exist and thrive, every phone had to include a digital camera and to be connected to a communications network, and that network had to be pervasive and data centers had to provide hosting services that allow tiny startups to serve tens of millions of users. (Instagram had perhaps 40 million users when it was bought; it has 500 million today.) Add up the employees of Apple and Samsung, Cisco and Huawei, Verizon and AT&T, Amazon Web Services (where Instagram was originally hosted) and Facebook’s own data centers, and you see the size of the mountain range of employment of which Instagram itself is a boulder on one small peak. But that’s not all. These digital communications and content creation technologies have made it possible for a new class of media company—Facebook, Instagram, YouTube, Twitter, Snap, WeChat, Tencent, and a host of others around the world—to turn ordinary people into “workers” producing content for their advertising business.

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I didn’t make that mistake when I gave another version of the talk at an Amazon all-hands meeting in May 2003. The first-generation web services that the e-commerce giant rolled out in 2003 were all about access to their in-house product catalog and its underlying data, and had little to do with the infrastructure services that were launched in 2006 under the name Amazon Web Services (or AWS) and that sparked the great industry transformation that is now called “cloud computing.” Those services came about for entirely different reasons, but I like to think that I planted the seeds of the idea with Jeff that if Amazon was to prosper in the years ahead, it had to become far more than just an e-commerce application.

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As Jeff described in a short 2008 interview with Om Malik: “Four years ago is when it started, and we had enough complexity inside Amazon that we were finding we were spending too much time on fine-grained coordination between our network engineering groups and our applications programming groups. Basically what we decided to do is build a [set of APIs] between those two layers so that you could just do coarse-grained coordination between those two groups.” (That is, “small pieces loosely joined.”) This is important: Amazon Web Services was the answer to a problem in organizational design. Jeff understood, as every network-enabled business needs to understand in the twenty-first century, that, as HR consultant Josh Bersin once said to me, “Doing digital isn’t the same as being digital.” In the digital era, an online service and the organization that produces and manages it must become inseparable.

The Metaverse: And How It Will Revolutionize Everything by Matthew Ball

"hyperreality Baudrillard"~20 OR "Baudrillard hyperreality", 3D printing, Airbnb, Albert Einstein, Amazon Web Services, Apple Newton, augmented reality, Big Tech, bitcoin, blockchain, business process, call centre, cloud computing, commoditize, computer vision, COVID-19, cryptocurrency, deepfake, digital divide, digital twin, disintermediation, don't be evil, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, game design, gig economy, Google Chrome, Google Earth, Google Glasses, hype cycle, intermodal, Internet Archive, Internet of things, iterative process, Jeff Bezos, John Gruber, Kevin Roose, Kickstarter, lockdown, Mark Zuckerberg, Metcalfe’s law, Minecraft, minimum viable product, Neal Stephenson, Network effects, new economy, non-fungible token, open economy, openstreetmap, pattern recognition, peer-to-peer, peer-to-peer model, Planet Labs, pre–internet, QR code, recommendation engine, rent control, rent-seeking, ride hailing / ride sharing, Robinhood: mobile stock trading app, satellite internet, self-driving car, SETI@home, Silicon Valley, skeuomorphism, Skype, smart contracts, Snapchat, Snow Crash, social graph, social web, SpaceX Starlink, Steve Ballmer, Steve Jobs, thinkpad, TikTok, Tim Cook: Apple, TSMC, undersea cable, Vannevar Bush, vertical integration, Vitalik Buterin, Wayback Machine, Y2K

When consumers own these servers, in the form of consoles or gaming PCs, it doesn’t matter that they’re unused or offline. However, data-center economics are oriented toward optimizing for demand. As a result, it will always be expensive to rent high-end GPUs with low utilization rates. This is why Amazon Web Services gives customers a reduced rate if they rent servers from Amazon in advance (“reserved instances”). Customers are guaranteed access for the next year because they’ve paid for the server, and Amazon is pocketing the difference between the cost and the price the customer is charged (AWS’s cheapest Linux GPU reserved instance, equivalent to a PS4, costs over $2,000 for one year).

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In addition, the ostensible core of Facebook’s Metaverse strategy is not Oculus, nor VR and AR, but the Roblox and Fortnite-like Horizon Worlds integrated virtual world platform (which is built on Unity). And Roblox has the exact consumers who threaten Facebook’s future—not those disengaging with the social network, but those who never even adopted it. If Facebook is the most aggressive investor in the Metaverse, and Google the most poorly positioned, Amazon sits somewhere in the middle. Amazon Web Services has nearly a third of the cloud infrastructure market and, as discussed throughout this book, the Metaverse will demand unprecedented computing power, data storage, and live services. AWS, in other words, benefits even if other cloud providers take a greater share of future growth. However, Amazon’s efforts to build Metaverse-specific content and services have been largely unsuccessful and arguably less of a priority compared to more traditional markets, such as music, podcasting, video, fast fashion, and digital assistants.

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See Google Altberg, Ebbe, 110 Amazon, xiv Amazon content via the Apple App Store, 184–85, 197 business model, 164 Fire OS, 213 Fire Phone, 143 gaming and, 178–79, 278, 281n investment in AR/VR hardware, 143, 277–78 market capitalization of, 166 positioning for the Metaverse, 274, 277–78 recommendation engine, 288 see also Bezos, Jeff Amazon Game Studios, 277 Amazon GameSparks, 107–8, 117 Amazon Go, 157 Amazon Lumberyard, 278 Amazon Luna, 96, 131, 277–78, 282 Amazon Music, 197, 277 Amazon Prime, 179, 185, 197, 277–78 Amazon Prime Video, 185, 277 Amazon Web Services (AWS), 84, 99, 277–78 AMC Entertainment, 28 American Cancer Society, 9 American Express, 172, 188 American Tower, 243, 244 America Online (AOL), 13, 15, 61, 130, 165, 273, 283 Andreessen Horowitz, 233 Android, 25, 61, 143, 212–14 Amazon Fire Phone, 143 backwards “pinch-to-zoom” concept, 149–50, 151 game development for, 131 gaming and, 32, 92, 133 Google Cardboard viewer for, 142 Google’s approach to, 184, 212–15, 275 progressive closure of, 213 Samsung’s approach to, 213 the 30% standard, 188, 190–91, 204–5 Animal Crossing: New Horizons, 30–32, 247 AOL Instant Messenger, 61 Apple Audio Interchange File Format (AIFF), 122 dominance of, 189 investment in AR/VR hardware, 143–44 lawsuit from Epic Games, 14n, 22–23, 32n, 134, 186, 284 lawsuit from the European Union, 184 market capitalization of, 166, 186–87 moral stance on pornography, 261 patents of, 143–44, 150 “There’s an app for that” ad campaign, 26, 150, 243 Apple App Store, 26, 132, 165, 309 categories of apps in, 183, 185–87 control over competing browsers, 194–95 control over payment rails, 201–4, 243–44 economics of, 186 as hindering the development of the Metaverse, 192–95, 197–99, 243–44, 309 policies on blockchain, crypto mining, and cryptocurrency trading apps, 200–201 the 30% standard, 120, 172–80, 183–84, 186–92, 197, 201, 203–4, 286 user identity and control, 299 Apple iOS, 60–61 Animoji, 159 “App Tracking Transparency” (ATT), 204–5 AssistiveTouch, 153 control over its NFC chip, 199–200 Face ID authentication system, 159 FaceTime, 65, 83 the home button, 148–49, 244 iCloud storage, 124, 200 “iPad Natives,” 13, 249 iPads, xi, 294 iPhones, 64, 131, 146, 242–44 Metal, 142, 175, 196 multitasking, 149, 244 Newton tablet, 145 “pinch-to-zoom” concept, 149–50, 151 Safari, 194–96, 209 Siri queries to Apple’s servers, 161 “slide-to-unlock” feature, 150–51 WebKit, 39, 194 Apple Music, 184, 197, 255 Apple News, 256 Apple Watch, 152, 161 application programming interfaces (APIs) authentication, 138 Discord APIs, 135 Instagram’s Twitter integration API, 287, 300 proprietary APIs and gaming consoles, 174–77, 287 in United States v.

Pegasus: How a Spy in Your Pocket Threatens the End of Privacy, Dignity, and Democracy by Laurent Richard, Sandrine Rigaud

activist lawyer, Airbnb, Amazon Web Services, centre right, Charlie Hebdo massacre, Chelsea Manning, citizen journalism, Citizen Lab, corporate governance, COVID-19, David Vincenzetti, Donald Trump, double helix, Edward Snowden, food desert, Jeff Bezos, Julian Assange, Kevin Kelly, knowledge worker, lockdown, Mohammed Bouazizi, NSO Group, offshore financial centre, operational security, Stuxnet, Tim Cook: Apple, unit 8200, WikiLeaks, Yom Kippur War, zero day

“Some of the biggest hosting companies are in Germany, as well as some of the cheaper ones,” Claudio says. “Pretty common destination when you want to set up infrastructure on a large scale.” Claudio and Donncha were also able to report that NSO had recently—and ill-advisedly—started using Amazon Web Services as a host for at least seventy-three of their servers. A forensic find on Prashant Kishor’s cell phone, just four days before publication, provided Claudio and Donncha new and disturbing detail. Pegasus had exfiltrated nearly 100 megabytes of data from the iPhone in the previous week alone—right around the time Kishor was meeting with the Indian prime minister’s chief political opponent.

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For your reference, the terms that appear in the print index are listed below. Africa Agalarov, Emin Akhbar al-Youm Aktay, Yasin Algeria Aliyev, Heydar Aliyev, Ilham corruption and Israel and Aliyeva, Mehriban Aliyev family Al Jazeera English Al Maktoum, Mohammed bin Rashid al-Qaeda in Yemen al-Sisi, Abdel Fattah Amar, Ali Amazon Amazon Web Services Amesys Amnesty International (see also Amnesty International, Security Lab; Amnesty Tech) targeting of Amnesty International, Security Lab digital forensic analyses and Forensic Methodology Report forensic tool Megalodon and methodology of peer review and Amnesty Tech Androids Animal Politico Ankara, Turkey Anonymous Ansbacher, Uri Apple cybersurveillance policies of R&D facility in Tel Aviv vulnerabilities and Apple iMessage (see also SMS messages) Apple Music Apple Photos Arab Spring Aramco Araud, Gérard Arenal Romero, Joaquin Arfi, Fabrice Aristegui, Carmen Aristegui, Teresa Aristegui Noticias Armenia Assange, Julian Assiri, Yahya attack vectors (see also exploits) SMS messages as Azano Matsura, Jose Susumo Azenco Azerbaijan Armenia and as client state of NSO corruption in cybersurveillance in elections in human rights in Israel and journalists in. see also Ismayilova, Khadija oil in opposition parties in political prisoners in POWs and targets in United States and in world government rankings Azeri national liberation movement Bahamas Bahrain Baku, Azerbaijan Bataclan theater, attack on BBC Be’er Sheva Belgium Benbenisti, Isaac Ben-Gurion, David Bennett, Naftali Bergman, Ronen Berkeley Research Group (BRG) Bezos, Jeff Biermann, Kai BlackBerrys Blair, Cherie Blair, Tony Boeing Boisseau, Frédéric Bouachrine, Taoufik Bouazizi, Mohamed Boudot, Martin Bouvart, Arthur Bredoux, Lénaïg Breham, Joseph British Virgin Islands Brown, Gordon Bull SA Burj Khalifa Burrough, Bryan Cairo, Egypt Calderón, Felipe Canada Carrasco, Jorge Carroll, John Cartel Project Carter, Ash Casa Blanca story Cash Investigation Caspian Sea Cengiz, Hatice Charbonnier, Stéphane “Charb” Charles, Prince Charlie Hebdo, attack on China Chouf TV Cisco Citi Citizen Lab.

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See Azano Matsura, Jose Susumo Mubarak, Hosni Mukhtarli, Afgan Munzinger, Hannes Murdoch, Rupert Murillo Karam, Jesus Muslim Brotherhood Mustafayeva, Leyla National Flag Square national security, vs. privacy NEC Neryia, Avishai Netanyahu, Benjamin “network injection attack” Nevada News of the World New York Times Nexa Technologies Nicolino, Fabrice North Korea Novalpina NSO Group Technologies LTD (see also Pegasus) Aliyev government and Azano and bar on infecting mobile phones with US numbers clients of. see also Pegasus end users; specific countries company image competition and corporate governance regime of countermeasures of customers of demise of domain names of employees of financial woes of Francisco Partners and “Governance, Risk and Compliance Committee” Guarnieri answers questions about. see also Pegasus Haaretz and heads of state targeted by Human Rights Policy of Hungary and Israel and Khashoggi assassination and lawsuits against letter from lawyer for license of origin story of publicity campaign to defend regulatory regime and report presented to before publication request-for-response letter to resellers of sales agents of stated values of Transparency and Responsibility Report uses Amazon Web Services as host using process names as camouflage value of vetting program WhatsApp’s lawsuit against Ó Cearbhaill, Donncha background of closing in on NSO development of forensic tool and digital forensic analyses and discovery of Pegasus and Forensic Methodology Report hacktivism and in Ireland Ismayilova and joins Amnesty Tech meeting in Berlin Megalodon and methodology of Mobile Verification Toolkit (MVT) and Radi’s phone and safety concerns Security Lab offline and Technical Analysis of Forensic Traces and Network Measurements Obama, Barack Obermaier, Frederik Obermayer, Bastian Obermayer, Suzanne Obiang, Teodorin oil Olea, Xavier Olioll Olum operating systems, NSO licensing and Oracle Orbán, Viktor Organized Crime and Corruption Reporting Project (OCCRP) Orient XXI Orlando, Florida, nightclub shooting in Orwell, George overregulation Palestinians “Palladium” Panama Panama Papers Panama Project (see also Panama Papers) Panyi, Szabolcs Paradise Papers Pasha Construction Pasha Holdings Pashayevs family Patrucic, Miranda PayPal pedophiles Pegasus development of discovery of earliest system end users of. see also specific countries evolution of heads of state targeted by Khashoggi’s killing and licensing of marketing of offered to Israel’s prospective allies SEDENA’s disappointment with STDi purchases rights to resell targets of. see also specific individuals and groups trickling sales of Pegasus Anonymizing Transmission Network Pegasus Project backlash against conference gaining momentum ground rules identity verification and lawyer for libel laws and maintaining secrecy of and NSO official confirmations of attacks reported publication of security protocols Pegg, David Pelloux, Patrick Peña Nieto, Enrique Pennzoil Perrault, Gilles, Our Friend the King Perrin, Edouard Pethő András Photobucket photos (see also Apple Photos) Pièces à Conviction Pineda, Cecilio Pineda, Marisol Plenel, Edwy Poland politicians (see also specific individuals) Premières Lignes Priest, Dana privacy, vs. national security Proceso process names Puebla, Mexico Putin, Vladimir Qatar Qualcomm Querétaro, Mexico “rackable servers” Radi, Omar after February 20 Movement arrests of conviction of digital forensic analyses of his phone interviews of in jail personal story of targeting of trial of Radio France Radio Free Europe/Radio Liberty (RFE/RL) Radu, Paul Raimondo, Gina M.

Work in the Future The Automation Revolution-Palgrave MacMillan (2019) by Robert Skidelsky Nan Craig

3D printing, Airbnb, algorithmic trading, AlphaGo, Alvin Toffler, Amazon Web Services, anti-work, antiwork, artificial general intelligence, asset light, autonomous vehicles, basic income, behavioural economics, business cycle, cloud computing, collective bargaining, Computing Machinery and Intelligence, correlation does not imply causation, creative destruction, data is the new oil, data science, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, Demis Hassabis, deskilling, disintermediation, do what you love, Donald Trump, driverless car, Erik Brynjolfsson, fake news, feminist movement, Ford Model T, Frederick Winslow Taylor, future of work, Future Shock, general purpose technology, gig economy, global supply chain, income inequality, independent contractor, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, job polarisation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, knowledge economy, Loebner Prize, low skilled workers, Lyft, Mark Zuckerberg, means of production, moral panic, Network effects, new economy, Nick Bostrom, off grid, pattern recognition, post-work, Ronald Coase, scientific management, Second Machine Age, self-driving car, sharing economy, SoftBank, Steve Jobs, strong AI, tacit knowledge, technological determinism, technoutopianism, TED Talk, The Chicago School, The Future of Employment, the market place, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, Turing test, Uber for X, uber lyft, universal basic income, wealth creators, working poor

Uber, for instance does not own the cars; they do not have to pay for fuel; they are not responsible for car insurance or maintenance or anything like that. Even in the core of the business, they do not own massive computer servers or anything. Instead, they rent them out from platforms like Amazon Web Services. Effectively, the Uber model has been to try to own as little as possible. But what they do own is the technological platform that connects riders with passengers, and that is the source of their value extraction. The problem for Uber (and likeminded companies) is that this model has not been very profitable.

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And as companies with extensive data extraction are the most likely to benefit from the virtuous cycle, it means a further consolidation of power, data and resources in the hands of the few companies that already dominate the platform economy. This is all the more important because AI is a general purpose technology.10 AI’s impact will be felt across the economy—so those who control AI, who can actually do AI, are going to have major power and influence in the economy. For instance, for economic impacts we might think about Amazon Web Services or Google Cloud building up AI that they can rent out to other businesses that are too data-poor to build their own AI. They will effectively rent out the basic infrastructure of the digital economy. Likewise, we can imagine companies becoming the sole provider of a particular service. Facebook, for example, already uses such dominance in the attention economy to shape otherwise powerful media companies.

Mastering Pandas by Femi Anthony

Amazon Web Services, Bayesian statistics, correlation coefficient, correlation does not imply causation, data science, Debian, en.wikipedia.org, Internet of things, Large Hadron Collider, natural language processing, p-value, power law, random walk, side project, sparse data, statistical model, Thomas Bayes

The conversion from analog to digital media coupled with an increased capability to capture and store data, which in turn has been made possible with cheaper and more capable storage technology. There has been a proliferation of digital data input devices such as cameras and wearables, and the cost of huge data storage has fallen rapidly. Amazon Web Services is a prime example of the trend toward much cheaper storage. The Internetification of devices, or rather Internet of Things, is the phenomenon wherein common household devices, such as our refrigerators and cars, will be connected to the Internet. This phenomenon will only accelerate the above trend.

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Velocity of big data From a purely technological point of view, velocity refers to the throughput of big data, or how fast the data is coming in and is being processed. This has ramifications on how fast the recipient of the data needs to process it to keep up. Real-time analytics is one attempt to handle this characteristic. Tools that can help enable this include Amazon Web Services Elastic Map Reduce. At a more macro level, the velocity of data can also be regarded as the increased speed at which data and information can now be transferred and processed faster and at greater distances than ever before. The proliferation of high-speed data and communication networks coupled with the advent of cell phones, tablets, and other connected devices, are primary factors driving information velocity.

Mastering Blockchain: Unlocking the Power of Cryptocurrencies and Smart Contracts by Lorne Lantz, Daniel Cawrey

air gap, altcoin, Amazon Web Services, barriers to entry, bitcoin, blockchain, business logic, business process, call centre, capital controls, cloud computing, corporate governance, creative destruction, cross-border payments, cryptocurrency, currency peg, disinformation, disintermediation, distributed ledger, Dogecoin, Ethereum, ethereum blockchain, fault tolerance, fiat currency, Firefox, global reserve currency, information security, initial coin offering, Internet of things, Kubernetes, litecoin, low interest rates, Lyft, machine readable, margin call, MITM: man-in-the-middle, multilevel marketing, Network effects, offshore financial centre, OSI model, packet switching, peer-to-peer, Ponzi scheme, prediction markets, QR code, ransomware, regulatory arbitrage, rent-seeking, reserve currency, Robinhood: mobile stock trading app, Ross Ulbricht, Satoshi Nakamoto, Silicon Valley, Skype, smart contracts, software as a service, Steve Wozniak, tulip mania, uber lyft, unbanked and underbanked, underbanked, Vitalik Buterin, web application, WebSocket, WikiLeaks

Centralized, decentralized, and distributed network designs Figures 1-3 through 1-5 illustrate the differences between centralized, distributed, and decentralized systems in the form of databases that store information. Figure 1-3. In a centralized database, like PayPal, all nodes connect to a single, central node that is controlled by one entity Figure 1-4. In a distributed database, like multiple databases hosted on Amazon Web Services (AWS), each node can maintain a replicated copy of the same data, each node knows the identity of other nodes, and all nodes are controlled by one entity Figure 1-5. In a decentralized database, like Bitcoin’s Blockchain, each node can maintain a replicated copy of the same data, each node may not know the identify of other nodes, and all nodes are controlled by many entities who may be anonymous Bitcoin Predecessors The internet’s ubiquity has been disruptive and changed many industries.

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Blockchain as a Service The concept of Blockchain as a Service (BaaS), where vendors provide easy-to-implement solutions that can be customized, is likely to grow as use cases for the technology increase. Similar to Software as a Service (SaaS) and cloud offerings, these blockchain products provide elements such as centralized management of users and distribution of nodes. Here are a few examples: Amazon Quantum Ledger Part of Amazon Web Services, the Quantum Ledger Database (QLDB) is a ledger that is secured by cryptography and uses the Ethereum and Hyperledger Fabric frameworks. However, the system is centralized, because node-based distributed blockchains are harder to set up. Its strong point is that it offers users the ability to set up an immutable and cryptographically secure ledger.

That Used to Be Us by Thomas L. Friedman, Michael Mandelbaum

addicted to oil, Affordable Care Act / Obamacare, Alan Greenspan, Albert Einstein, Amazon Web Services, American Society of Civil Engineers: Report Card, Andy Kessler, Ayatollah Khomeini, bank run, barriers to entry, Bear Stearns, Berlin Wall, blue-collar work, Bretton Woods, business process, call centre, carbon footprint, carbon tax, Carmen Reinhart, Cass Sunstein, centre right, Climatic Research Unit, cloud computing, collective bargaining, corporate social responsibility, cotton gin, creative destruction, Credit Default Swap, crowdsourcing, delayed gratification, drop ship, energy security, Fall of the Berlin Wall, fear of failure, full employment, Google Earth, illegal immigration, immigration reform, income inequality, Intergovernmental Panel on Climate Change (IPCC), job automation, Kenneth Rogoff, knowledge economy, Lean Startup, low interest rates, low skilled workers, Mark Zuckerberg, market design, mass immigration, more computing power than Apollo, Network effects, Nixon triggered the end of the Bretton Woods system, obamacare, oil shock, PalmPilot, pension reform, precautionary principle, proprietary trading, Report Card for America’s Infrastructure, rising living standards, Ronald Reagan, Rosa Parks, Saturday Night Live, shareholder value, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, the long tail, the scientific method, Thomas L Friedman, too big to fail, University of East Anglia, vertical integration, WikiLeaks

Amazon.com, for example, is now selling not only books and chain saws but business facilities in the cloud. Andy Jassy is Amazon’s senior vice president in charge of Amazon Web Services, Bloomberg BusinessWeek explained (March 3, 2011), which means that his job is to rent space to individual innovators, or companies, on Amazon’s rent-a-cloud. Although all shoppers are welcome, this Amazon, [Jassy] explains, is for business customers and isn’t well marked on the home page. It’s called Amazon Web Services, or AWS … , which rents out computing power for pennies an hour. “This completely levels the playing field,” Jassy boasts. AWS makes it possible for anyone with an Internet connection and a credit card to access the same kind of world-class computing systems that Amazon uses to run its $34 billion-a-year retail operation … AWS is growing like crazy.

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ACT Adams, Henry Adams, John Advanced Research Projects Agency Afghanistan; Soviet invasion of; U.S. war in African Americans; education of; in World War II Age of Fracture (Rodgers) Ahansal, Mustapha Ahmetovic, Belma Air Force, U.S. Alabama, University of; Creative Campus Alibaba Alice’s Adventures in Wonderland (Carroll) Alito, Samuel Allegheny College Allen, Woody All in the Family (television show) Alonzo, Amanda Alpoge, Levent al-Qaeda Aman, Peter Amanpour, Christiane Amazon; Web Services (AWS) America COMPETES Act (2007) American Association of Retired Persons (AARP) American Federation of State, County and Municipal Employees American Federation of Teachers American Interest, The Americans and the California Dream (Starr) American Society of Civil Engineers (ASCE) American Solutions American Telephone and Telegraph (AT&T) Amtrak Acela Anand, Namrata Andersen, Kurt Anderson, Chris Android Angelides, Phil anti-Federalists AOL Apollo space program Apotheker, Léo Apple; iPad; iPhone; iPod; Macintosh computers Applied Materials apps Arab oil embargo Arab world, uprisings in Argonne National Laboratory Arkansas Armey, Dick Army, U.S.; Training and Doctrine Command Asato, Cathy Asia Society, Center on U.S.

Stake Hodler Capitalism: Blockchain and DeFi by Amr Hazem Wahba Metwaly

altcoin, Amazon Web Services, bitcoin, blockchain, business process, congestion charging, COVID-19, crowdsourcing, cryptocurrency, Ethereum, ethereum blockchain, fiat currency, information security, Internet of things, Network effects, non-fungible token, passive income, prediction markets, price stability, Satoshi Nakamoto, seigniorage, Skype, smart contracts, underbanked, Vitalik Buterin

However, private and centralized blockchains where the computers form their network are owned and controlled by a sole being. These private blockchains can be implemented to fit your business needs and give you the advantages of blockchain while minimizing your data exposure to the public. Examples of platforms that offer private blockchain solutions are IBM Blockchain, Azure Blockchain, and AWS (Amazon Web Services) Blockchain solutions. In the blockchain, every node has a complete history of the blockchain's data since its birth. For Bitcoin, the data is the full history of all Bitcoin transactions. If there is an error in one node's data, it can use the thousands of other nodes as a reference to correct itself.

The Four: How Amazon, Apple, Facebook, and Google Divided and Conquered the World by Scott Galloway

"Susan Fowler" uber, activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Robotics, Amazon Web Services, Apple II, autonomous vehicles, barriers to entry, Ben Horowitz, Bernie Sanders, Big Tech, big-box store, Bob Noyce, Brewster Kahle, business intelligence, California gold rush, Cambridge Analytica, cloud computing, Comet Ping Pong, commoditize, cuban missile crisis, David Brooks, Didi Chuxing, digital divide, disintermediation, don't be evil, Donald Trump, Elon Musk, fake news, follow your passion, fulfillment center, future of journalism, future of work, global supply chain, Google Earth, Google Glasses, Google X / Alphabet X, Hacker Conference 1984, Internet Archive, invisible hand, Jeff Bezos, Jony Ive, Khan Academy, Kiva Systems, longitudinal study, Lyft, Mark Zuckerberg, meta-analysis, Network effects, new economy, obamacare, Oculus Rift, offshore financial centre, passive income, Peter Thiel, profit motive, race to the bottom, RAND corporation, ride hailing / ride sharing, risk tolerance, Robert Mercer, Robert Shiller, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Silicon Valley, Snapchat, software is eating the world, speech recognition, Stephen Hawking, Steve Ballmer, Steve Bannon, Steve Jobs, Steve Wozniak, Stewart Brand, supercomputer in your pocket, Tesla Model S, the long tail, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, undersea cable, vertical integration, warehouse automation, warehouse robotics, Wayback Machine, Whole Earth Catalog, winner-take-all economy, working poor, you are the product, young professional

In the past few years, Amazon has traded on this brand equity, leveraging it to extend into other businesses, and has expanded into other, simply better (more profitable) businesses. Looking back, Amazon’s retail platform just may have been the Trojan Horse that established the relationships and brand later monetized with other businesses. While year-to-year growth for Amazon’s retail business ranged from 13 percent to 20 percent from Q1 to Q3 2015, Amazon Web Services—the retailer’s network of servers and data storage technology—has grown 49 percent to 81 percent during that same interval. AWS also grew into a significant portion of Amazon’s total operating income, from 38 percent in Q1 2015 to 52 percent in Q3 2015.60 Analysts predict that AWS could reach $16.2 billion in sales by the end of 2017, making it worth $160 billion—more than the company’s retail unit.61 In other words, while the world still thinks of Amazon as a retailer, it has quietly become a cloud company—the world’s biggest.

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Note: Page numbers in italic refer to illustrations. About.com, 147–48, 150–51 Age of Enlightenment, 127 Airbnb, 225–27 Alibaba, 206, 206–10 Amazon, 13–62 agility of, 235 and Alexa/Echo, 9, 31, 45, 50–52, 56 algorithm of, 105, 106 Amazon Marketplace, 25, 41 Amazon Prime, 13, 14, 37, 42, 48, 48–49 Amazon Web Services, 37, 41–42 analog moats of, 91 automated warehouses of, 27, 29–30, 42, 52–54, 91 book sales of, 25–26, 45 brain-based appeal of, 176–77 and brand era, 50–52, 172–73 capitalization of, 3, 32–35, 36, 38–39, 54, 56, 188–89 and competition, 9, 25, 56, 181 consumers’ alliance with, 167–68 consumer trust in, 31, 32, 41, 56 current state of affairs, 3–4 data collection of, 32, 200 effect on retail sector, 24–25, 28–29, 57–59 and failure, 39, 40 frictionless purchases on, 32, 186–87 fulfillment infrastructure, 3, 42, 49, 54, 55, 56, 91, 166, 181, 186 future of, 165–66, 182 global reach of, 190–91, 191 and Google, 60, 188–89 growth of, 27–28, 34, 41, 53 headquarters of, 202 and hunter/gatherer strategies, 14–16, 25–26, 44, 176–77 likability of, 193 multichannel retail, 44–49 and Procter & Gamble (P&G), 194 product differentiation of, 186–87 and product searches, 8, 189 profits/revenue, 17–18, 34–35 and Quidsi, 46 and rational decision making, 170 risk taking, 35–39, 40 scalability, 24–25 sellers’ joining of, 166 stock appreciation of, 28, 28 and stores, 30, 31, 36, 43–45, 55, 56 and taxes, 34, 193 vertical integration of, 195 and Walmart, 228 Whole Foods acquisition, 44, 54, 55–56, 59 zero-click ordering, 31–32, 56 See also Bezos, Jeff analog moats, 10, 90–91, 92 Android, 155, 178 Apollo Project, 267 Apple, 63–95 algorithms of, 105 analog moats of, 90–91, 92 behavioral data of, 200 and competition, 9, 67, 90–91, 92, 179–81 computers, 69, 73–74, 132, 161, 180 Cook’s leadership of, 68–69, 87 current state of affairs, 4 and download piracy, 68 and education market, 93–95 and failure, 39 genitalia-based appeal of, 178–79 global reach of, 190, 191 headquarters of, 202 innovator status, 64, 67, 68, 161 intellectual property theft, 161, 166 iPod, 67–68, 74, 79 likability of, 193 low-cost production of, 88–89 luxury status of, 69–72, 74–76, 78–79, 80–86, 87, 88, 92 privacy issue, 63–64, 136 product differentiation of, 186 profits/revenue, 4, 69, 70, 85 risk taking, 68 secular worship of, 66–67 stores, 80–81, 82–83, 89, 91, 195–96 strategy for success, 57 and taxes, 83, 193, 197 vertical integration of, 195–96 vs.

Webbots, Spiders, and Screen Scrapers by Michael Schrenk

Amazon Web Services, corporate governance, digital rights, fault tolerance, Firefox, machine readable, Marc Andreessen, new economy, pre–internet, SpamAssassin, The Hackers Conference, Turing test, web application

In typical SOAP calls, the SOAP interface and client are created and the parameters describing requested web services are passed in an array. With SOAP, using a web service is much like calling a local function. If you'd like to experiment with SOAP, consider creating a free account at Amazon Web Services. Amazon provides SOAP interfaces that allow you to access large volumes of data at both Amazon and Alexa, a web-monitoring service (http://www.alexa.com). Along with Amazon Web Services, you should also review the PHP-specific Amazon SOAP tutorial at Dev Shed, a PHP developers' site (http://www.devshed.com). PHP 5 has built-in support for SOAP. If you're using PHP 4, however, you will need to use the appropriate PHP Extension and Application Repository (PEAR, http://www.pear.php.net) libraries, included in most PHP distributions.

Lab Rats: How Silicon Valley Made Work Miserable for the Rest of Us by Dan Lyons

"Friedman doctrine" OR "shareholder theory", "Susan Fowler" uber, "World Economic Forum" Davos, Airbnb, Amazon Robotics, Amazon Web Services, antiwork, Apple II, augmented reality, autonomous vehicles, basic income, Big Tech, bitcoin, blockchain, Blue Ocean Strategy, business process, call centre, Cambridge Analytica, Clayton Christensen, clean water, collective bargaining, corporate governance, corporate social responsibility, creative destruction, cryptocurrency, data science, David Heinemeier Hansson, digital rights, Donald Trump, Elon Musk, Ethereum, ethereum blockchain, fake news, full employment, future of work, gig economy, Gordon Gekko, greed is good, Hacker News, hiring and firing, holacracy, housing crisis, impact investing, income inequality, informal economy, initial coin offering, Jeff Bezos, job automation, job satisfaction, job-hopping, John Gruber, John Perry Barlow, Joseph Schumpeter, junk bonds, Kanban, Kevin Kelly, knowledge worker, Larry Ellison, Lean Startup, loose coupling, Lyft, Marc Andreessen, Mark Zuckerberg, McMansion, Menlo Park, Milgram experiment, minimum viable product, Mitch Kapor, move fast and break things, new economy, Panopticon Jeremy Bentham, Parker Conrad, Paul Graham, paypal mafia, Peter Thiel, plutocrats, precariat, prosperity theology / prosperity gospel / gospel of success, public intellectual, RAND corporation, remote working, RFID, ride hailing / ride sharing, Ronald Reagan, Rubik’s Cube, Ruby on Rails, Sam Altman, San Francisco homelessness, Sand Hill Road, scientific management, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, six sigma, Skinner box, Skype, Social Responsibility of Business Is to Increase Its Profits, SoftBank, software is eating the world, Stanford prison experiment, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, stock buybacks, super pumped, TaskRabbit, tech bro, tech worker, TechCrunch disrupt, TED Talk, telemarketer, Tesla Model S, Thomas Davenport, Tony Hsieh, Toyota Production System, traveling salesman, Travis Kalanick, tulip mania, Uber and Lyft, Uber for X, uber lyft, universal basic income, web application, WeWork, Whole Earth Catalog, work culture , workplace surveillance , Y Combinator, young professional, Zenefits

That group—the cleaning business—still generates 95 percent of Q’s revenues, but eventually Teran thinks Q Marketplace could become the larger part of the business. Teran’s ultimate goal is to handle every aspect of managing a physical space. He uses Amazon Web Services as a model. Instead of building and managing their own data centers, today most companies just fill out a few forms and rent computer power from Amazon Web Services. They might have no idea what kind of computers are being used, or who is running the servers. In the ultimate version of this, a law firm or ad agency could rent new offices, call Q, and never worry about the physical space again.

Digital Bank: Strategies for Launching or Becoming a Digital Bank by Chris Skinner

algorithmic trading, AltaVista, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, bank run, Basel III, bitcoin, Bitcoin Ponzi scheme, business cycle, business intelligence, business process, business process outsourcing, buy and hold, call centre, cashless society, clean water, cloud computing, corporate social responsibility, credit crunch, cross-border payments, crowdsourcing, cryptocurrency, demand response, disintermediation, don't be evil, en.wikipedia.org, fault tolerance, fiat currency, financial innovation, gamification, Google Glasses, high net worth, informal economy, information security, Infrastructure as a Service, Internet of things, Jeff Bezos, Kevin Kelly, Kickstarter, M-Pesa, margin call, mass affluent, MITM: man-in-the-middle, mobile money, Mohammed Bouazizi, new economy, Northern Rock, Occupy movement, Pingit, platform as a service, Ponzi scheme, prediction markets, pre–internet, QR code, quantitative easing, ransomware, reserve currency, RFID, Salesforce, Satoshi Nakamoto, Silicon Valley, smart cities, social intelligence, software as a service, Steve Jobs, strong AI, Stuxnet, the long tail, trade route, unbanked and underbanked, underbanked, upwardly mobile, vertical integration, We are the 99%, web application, WikiLeaks, Y2K

Soon, Amazon was more of a behemoth of data, moving into selling anything from white goods to televisions, and it is easy to sell online when you know how to leverage data relationships. Even then, for Amazon, even this was not enough. In recognising its information leadership, Amazon opened Amazon Web Services (AWS) to become the largest cloud computing firm out there. Amazon now adds server systems to AWS every day that would have been the equivalent of the complete server architecture required to run the total retail business two years ago. That’s information warfare: leveraging systems expertise to get more share of wallet, expansion of market, growth of proposition, and development of the offer into a range of services with no dependency on one.

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The Innotribe Start-up Challenge was designed to introduce the most promising FinTech and Financial Services Start-Ups to SWIFT’s community of more than 9,700 banking organisations, securities institutions and corporate customers in 209 countries. THE CURRENCY CLOUD, GLOBAL An interview with Michael Laven, Chief Executive, the Currency Cloud Cloud computing has been around for a while, but still suffers from some extreme views with some considering cloud to just be Amazon Web Services whilst others see this as a way of leveraging new forms of business models. Michael (Mike) Laven, CEO of Currency Cloud is one of the latter visionaries, and is changing the game in cross-border payments by building a cloud-based offer for currency movements and offering this as a low-cost, transparent service to consumers and small businesses.

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, Alan Greenspan, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Boston Dynamics, British Empire, business cycle, business intelligence, business process, call centre, carbon tax, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, congestion pricing, corporate governance, cotton gin, creative destruction, crowdsourcing, data science, David Ricardo: comparative advantage, digital map, driverless car, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, Fairchild Semiconductor, falling living standards, Filter Bubble, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, full employment, G4S, game design, general purpose technology, global village, GPS: selective availability, Hans Moravec, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, intangible asset, inventory management, James Watt: steam engine, Jeff Bezos, Jevons paradox, jimmy wales, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Khan Academy, Kiva Systems, knowledge worker, Kodak vs Instagram, law of one price, low skilled workers, Lyft, Mahatma Gandhi, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Mars Rover, mass immigration, means of production, Narrative Science, Nate Silver, natural language processing, Network effects, new economy, New Urbanism, Nicholas Carr, Occupy movement, oil shale / tar sands, oil shock, One Laptop per Child (OLPC), pattern recognition, Paul Samuelson, payday loans, post-work, power law, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Robert Solow, Rodney Brooks, Ronald Reagan, search costs, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Simon Kuznets, six sigma, Skype, software patent, sovereign wealth fund, speech recognition, statistical model, Steve Jobs, Steven Pinker, Stuxnet, supply-chain management, TaskRabbit, technological singularity, telepresence, The Bell Curve by Richard Herrnstein and Charles Murray, the Cathedral and the Bazaar, the long tail, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

They can search the Web and browse Wikipedia. They can follow online courses, some of them taught by the best in the academic world. They can share their insights on blogs, Facebook, Twitter, and many other services, most of which are free. They can even conduct sophisticated data analyses using cloud resources such as Amazon Web Services and R, an open source application for statistics.13 In short, they can be full contributors in the work of innovation and knowledge creation, taking advantage of what Autodesk CEO Carl Bass calls “infinite computing.”14 Until quite recently rapid communication, information acquisition, and knowledge sharing, especially over long distances, were essentially limited to the planet’s elite.

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id=USARGDPH INDEX Academically Adrift: Limited Learning on College Campuses (Arum and Roksa) Acemoglu, Daron Affinnova Aftercollege.com Agarwal, Anant Age of Spiritual Machines, The: When Computers Exceed Human Intelligence (Kurzweil) Agrarian Justice (Paine) agriculture: development of inelastic demand in Ahn, Luis von Aiden, Erez Lieberman Airbnb.com Alaska, income guarantee plan in algorithms Allegretto, Sylvia Allstate Amazon Amazon Web Services American Society of Civil Engineers (ASCE) Android animals, domestication of Apple Arthur, Brian artificial intelligence (AI) future of SLAM problem in uses of see also robots Arum, Richard ASCI Red ASIMO Asimov, Isaac Asur, Sitaram Athens, ancient ATMs Audi Australia, immigrant entrepreneurship in Autodesk automation: future of labor market effects of in manufacturing Autor, David Baker, Stephen Barnes & Noble Bartlett, Albert A.

Rocket Billionaires: Elon Musk, Jeff Bezos, and the New Space Race by Tim Fernholz

Amazon Web Services, Apollo 13, autonomous vehicles, business climate, Charles Lindbergh, Clayton Christensen, cloud computing, Colonization of Mars, corporate governance, corporate social responsibility, deep learning, disruptive innovation, Donald Trump, Elon Musk, fail fast, fulfillment center, Gene Kranz, high net worth, high-speed rail, Iridium satellite, Jeff Bezos, Kickstarter, Kim Stanley Robinson, Kwajalein Atoll, low earth orbit, Marc Andreessen, Mark Zuckerberg, Mars Society, Masayoshi Son, megaproject, military-industrial complex, minimum viable product, multiplanetary species, mutually assured destruction, Neal Stephenson, Neil Armstrong, new economy, no-fly zone, nuclear paranoia, paypal mafia, Peter H. Diamandis: Planetary Resources, Peter Thiel, pets.com, planetary scale, private spaceflight, profit maximization, RAND corporation, Richard Feynman, Richard Feynman: Challenger O-ring, Ronald Reagan, satellite internet, Scaled Composites, shareholder value, Silicon Valley, skunkworks, SoftBank, sovereign wealth fund, space junk, SpaceShipOne, Stephen Hawking, Steve Jobs, Strategic Defense Initiative, trade route, undersea cable, vertical integration, Virgin Galactic, VTOL, We wanted flying cars, instead we got 140 characters, X Prize, Y2K

Bezos declined to speak to the AP about the project, and a spokesman simply said that the company “won’t go anywhere soon.” That, at least, was true. Amazon was already big, but not yet the Goliath it is today. This was before iPhones led the smartphone revolution, before the Kindle, before Amazon Prime, and before Amazon Web Services and Alexa. At this stage in its lifespan, the retailer hadn’t even made an annual profit, despite spending the past four years as a publicly traded company. Investors loved the stock because of its incredible growth rates, the way it scarfed up market share, even entire markets, with ravenous energy.

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In the years ahead, Amazon would begin releasing physical products like the Kindle, a short-lived phone, and smart speakers for its AI assistant. It would be an early adopter of the growth in cloud computing and deploy its own proprietary servers, optimized to allow other digital entrepreneurs to scale up their own services. Amazon Web Services became a huge moneymaker that undergirded a new generation of internet companies. Amazon established its own distribution centers and began investing in robotic technology to automate deliveries, even experimenting with drones for airborne package drop-offs. This period may have been a distraction from the work of making space cheaper to access, but it would pay off in literal dividends in the years ahead.

Advances in Financial Machine Learning by Marcos Lopez de Prado

algorithmic trading, Amazon Web Services, asset allocation, backtesting, behavioural economics, bioinformatics, Brownian motion, business process, Claude Shannon: information theory, cloud computing, complexity theory, correlation coefficient, correlation does not imply causation, data science, diversification, diversified portfolio, en.wikipedia.org, financial engineering, fixed income, Flash crash, G4S, Higgs boson, implied volatility, information asymmetry, latency arbitrage, margin call, market fragmentation, market microstructure, martingale, NP-complete, P = NP, p-value, paper trading, pattern recognition, performance metric, profit maximization, quantitative trading / quantitative finance, RAND corporation, random walk, risk free rate, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, Silicon Valley, smart cities, smart meter, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, traveling salesman

Hirschman, A. O. (1980): National Power and the Structure of Foreign Trade. Vol. 105. University of California Press. Holzman, B. et al. (2017): “HEPCloud, a new paradigm for HEP facilities: CMS Amazon Web Services investigation. Computing and Software for Big Science, Vol. 1, No. 1, p. 1. Jackson, K. R., et al. (2010): “Performance analysis of high performance computing applications on the Amazon Web Services Cloud. Cloud Computing Technology and Science (CloudCom). 2010 Second International Conference. IEEE. Kim, T. et al. (2015): “Extracting baseline electricity usage using gradient tree boosting.”

Messy: The Power of Disorder to Transform Our Lives by Tim Harford

affirmative action, Air France Flight 447, Airbnb, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, assortative mating, Atul Gawande, autonomous vehicles, banking crisis, Barry Marshall: ulcers, Basel III, Berlin Wall, Bletchley Park, British Empire, Broken windows theory, call centre, Cass Sunstein, Chris Urmson, cloud computing, collateralized debt obligation, Computing Machinery and Intelligence, crowdsourcing, deindustrialization, Donald Trump, Erdős number, experimental subject, Ferguson, Missouri, Filter Bubble, financial engineering, Frank Gehry, game design, global supply chain, Googley, Guggenheim Bilbao, Helicobacter pylori, high net worth, Inbox Zero, income inequality, industrial cluster, Internet of things, Jane Jacobs, Jeff Bezos, Loebner Prize, Louis Pasteur, machine readable, Marc Andreessen, Mark Zuckerberg, Menlo Park, Merlin Mann, microbiome, out of africa, Paul Erdős, Richard Thaler, Rosa Parks, self-driving car, side project, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Steven Levy, Stewart Brand, Susan Wojcicki, tacit knowledge, TED Talk, telemarketer, the built environment, The Death and Life of Great American Cities, the strength of weak ties, Turing test, Tyler Cowen, urban decay, warehouse robotics, William Langewiesche

Instead, over the next few years, Amazon launched products as disparate as the Kindle (which immediately and repeatedly sold out, as Amazon struggled to manufacture it), Mechanical Turk (an unsettlingly named global clearinghouse for labor, which pioneered crowdsourcing but was criticized as being a sweatshop), the Fire Phone (widely reviewed as ugly, weird, and disappointing), Marketplace (where competitors to Amazon would use Amazon’s own product listings to advertise their own cheaper alternatives), and Amazon Web Services. AWS in particular was a bold stroke—a move into cloud computing in 2006, four years ahead of Microsoft’s Azure and six years ahead of Google Compute. As Bezos liked to say during the crunches of 1998 and 1999, “If you are planning more than twenty minutes ahead in this environment, you are wasting your time.”15 He was a man in a hurry.

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By Christmas 2009, Amazon had a market share in e-books of around 90 percent.34 One can tell almost exactly the same story about Amazon’s initially baffling venture into cloud computing: a crazy rush, a series of early technical problems, a loss-making price. Then within a few years, Amazon, a mere bookseller, was the dominant player in cloud computing, and analysts were pronouncing that Amazon Web Services was a more valuable business than Amazon’s online retailing operation. The opportunity had been there to take, but the titans of the industry, IBM, Google, Apple, and Microsoft, had all hesitated at the prospect of a costly battle with the upstart.35 Again and again, we see Amazon moving quickly, losing money, struggling to cope with the demand it created, and in the end, dominating a market.

Creating Development Environments With Vagrant - Second Edition by Michael Peacock

Amazon Web Services, cloud computing, continuous integration, Debian, domain-specific language, web application

Vagrant uses providers to integrate with the third-party virtualization software, which provides the virtualized machines for our development environment. The default provider is for Oracle VirtualBox; however, there are commercial providers to work with VMware Fusion and also plugins for Vagrant to work with Amazon Web Services. The entire configuration is stored in simple plain text files. The Vagrant configuration (Vagrantfile), and the configuration that defines how our Vagrant machines are configured (typically Shell scripts, Ansible playbooks, Chef cookbooks or Puppet manifests that Vagrant has built-in support for, as provisioners) are simply written in text files.

The Stack: On Software and Sovereignty by Benjamin H. Bratton

1960s counterculture, 3D printing, 4chan, Ada Lovelace, Adam Curtis, additive manufacturing, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, Andy Rubin, Anthropocene, augmented reality, autonomous vehicles, basic income, Benevolent Dictator For Life (BDFL), Berlin Wall, bioinformatics, Biosphere 2, bitcoin, blockchain, Buckminster Fuller, Burning Man, call centre, capitalist realism, carbon credits, carbon footprint, carbon tax, carbon-based life, Cass Sunstein, Celebration, Florida, Charles Babbage, charter city, clean water, cloud computing, company town, congestion pricing, connected car, Conway's law, corporate governance, crowdsourcing, cryptocurrency, dark matter, David Graeber, deglobalization, dematerialisation, digital capitalism, digital divide, disintermediation, distributed generation, don't be evil, Douglas Engelbart, Douglas Engelbart, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, Eratosthenes, Ethereum, ethereum blockchain, Evgeny Morozov, facts on the ground, Flash crash, Frank Gehry, Frederick Winslow Taylor, fulfillment center, functional programming, future of work, Georg Cantor, gig economy, global supply chain, Google Earth, Google Glasses, Guggenheim Bilbao, High speed trading, high-speed rail, Hyperloop, Ian Bogost, illegal immigration, industrial robot, information retrieval, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Jacob Appelbaum, James Bridle, Jaron Lanier, Joan Didion, John Markoff, John Perry Barlow, Joi Ito, Jony Ive, Julian Assange, Khan Academy, Kim Stanley Robinson, Kiva Systems, Laura Poitras, liberal capitalism, lifelogging, linked data, lolcat, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megaproject, megastructure, Menlo Park, Minecraft, MITM: man-in-the-middle, Monroe Doctrine, Neal Stephenson, Network effects, new economy, Nick Bostrom, ocean acidification, off-the-grid, offshore financial centre, oil shale / tar sands, Oklahoma City bombing, OSI model, packet switching, PageRank, pattern recognition, peak oil, peer-to-peer, performance metric, personalized medicine, Peter Eisenman, Peter Thiel, phenotype, Philip Mirowski, Pierre-Simon Laplace, place-making, planetary scale, pneumatic tube, post-Fordism, precautionary principle, RAND corporation, recommendation engine, reserve currency, rewilding, RFID, Robert Bork, Sand Hill Road, scientific management, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley ideology, skeuomorphism, Slavoj Žižek, smart cities, smart grid, smart meter, Snow Crash, social graph, software studies, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Startup school, statistical arbitrage, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, Superbowl ad, supply-chain management, supply-chain management software, synthetic biology, TaskRabbit, technological determinism, TED Talk, the built environment, The Chicago School, the long tail, the scientific method, Torches of Freedom, transaction costs, Turing complete, Turing machine, Turing test, undersea cable, universal basic income, urban planning, Vernor Vinge, vertical integration, warehouse automation, warehouse robotics, Washington Consensus, web application, Westphalian system, WikiLeaks, working poor, Y Combinator, yottabyte

The geopolitics of the Cloud is everywhere and wants everything: the platform wars between Google, Facebook, Apple, and Amazon, anonymized servers routing the angry tweets from street battles, Anonymous going up against Mexican drug cartels, WikiLeaks crowd-sourcing counterespionage, Tor users building on top of Amazon Web Services services, carriers licensing content, content providers licensing bandwidth, proprietary fiber networks connected trading centers, and on, and on. It might seem at first blush that these events, each perhaps pushing legal boundaries in its own way, should be understood as disruptive contaminations of a standing political order—acts of resistance to the system, even.

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That said, as the Cloud is planetary is scope, state control of its systems is guaranteed only to the extent that private providers continue to respect (by consent, force, joint venture, outright merger) the practical sovereignty of the national jurisdictions in which their servers are installed, where their offices are headquartered, how exactly their data structure economic exchanges, and how their monetization of those data is or is not taxed. That arrangement is both resilient and unstable in various measures and tracks the successes and failures of globalization itself. Today most Cloud service providers have constraining jurisdictions built into service plans. For example, Amazon Web Services segregates the serving of hosted data according to several geographic “availability zones,” allowing developers to deploy specific versions of their application to specific users in specific countries according to local laws and priorities, regardless of where Amazon might be hosting or mirroring their data.

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The platform does not care about your name or who you really are deep down, but only in the likelihood that the next presentation of object X, Y, or Z will motivate your One-Clicking and the subsequent activation of supply chain cascades that ensue. For this, Amazon does not even necessarily have to provide the User-facing front end for Cloud services. Just as UPS and FedEx moved into high-margin logistics consulting businesses, Amazon Web Services is a major provider of large-scale Cloud hosting and e-commerce for third parties (including states and their intelligence agencies). In addition to finding customers for its suppliers, Amazon also rents the pick-axes for the Cloud rush. This superterranean platform-of-platforms (from servers to warehouse to inventory delivery) allows both small and large affiliate actors to engage multiple overlapping and even opposed publics on the same shared hosting infrastructures.

Ansible Playbook Essentials by Gourav Shah

Amazon Web Services, cloud computing, Debian, DevOps, fault tolerance, information security, web application

Before using this subcommand, it is important to set an editor in the environment, as follows: # setting up vi as editor $ export EDITOR=vi # Generate a encrypted file $ ansible-vault create aws_creds.yml Vault password: Confirm Vault password: Launching this command opens up an editor specified with the editor environment variable. The following is an example of the aws_creds.yml file that you may create to store the AWS user credentials in the form of an access key and secret key. These keys are then used to make API calls to the Amazon web services cloud platform. Saving this file and exiting the editor will generate an encrypted file: You can check the type of file created and its contents by running following commands: # Check file type and content $ file aws_creds.yml aws_creds.yml: ASCII text $ cat aws_creds.yml $ANSIBLE_VAULT;1.1;AES256 64616236666362376630366435623538336565393331333331663663636237636335313234313134 3337303865323239623436646630336239653864356561640a363966393135316661636562333932 61323932313230383433313735646438623032613635623966646232306433383335326566343333 3136646536316261300a616438643463656263636237316136356163646161313365336239653434 36626135313138343939363635353563373865306266363532386537623463623464376134353863 37646638636231303461343564343232343837356662316262356537653066356465353432396436 31336664313661306630653765356161616266653232316637653132356661343162396331353863 34356632373963663230373866313961386435663463656561373461623830656261636564313464 37383465353665623830623363353161363033613064343932663432653666633538 Updating the encrypted data To update the AWS keys added to the encrypted file, you can later use Ansible-vault's edit subcommand as follows: $ ansible-vault edit aws_creds.yml Vault password: The edit command does the following operations: Prompts for a password Decrypts a file on the fly using the AES symmetric cypher Opens the editor interface, which allows you to change the content of a file Encrypts the file again after being saved There is another way to update the content of the file.

The Practice of Cloud System Administration: DevOps and SRE Practices for Web Services, Volume 2 by Thomas A. Limoncelli, Strata R. Chalup, Christina J. Hogan

active measures, Amazon Web Services, anti-pattern, barriers to entry, business process, cloud computing, commoditize, continuous integration, correlation coefficient, database schema, Debian, defense in depth, delayed gratification, DevOps, domain-specific language, en.wikipedia.org, fault tolerance, finite state, Firefox, functional programming, Google Glasses, information asymmetry, Infrastructure as a Service, intermodal, Internet of things, job automation, job satisfaction, Ken Thompson, Kickstarter, level 1 cache, load shedding, longitudinal study, loose coupling, machine readable, Malcom McLean invented shipping containers, Marc Andreessen, place-making, platform as a service, premature optimization, recommendation engine, revision control, risk tolerance, Salesforce, scientific management, seminal paper, side project, Silicon Valley, software as a service, sorting algorithm, standardized shipping container, statistical model, Steven Levy, supply-chain management, systems thinking, The future is already here, Toyota Production System, vertical integration, web application, Yogi Berra

In Linux and the Xen hyper-visor, this is called “steal time”: it is the amount of CPU time that your virtual machine is missing because it was allocated to other virtual machines (Haynes 2013). IaaS providers usually cannot provide guarantees of how much steal time will exist, nor can they provide mechanisms to control it. Netflix found the only way it could deal with this issue was to be reactionary. If high steal time was detected on a virtual machine in Amazon Web Services (AWS), Netflix would delete the virtual machine and have it re-created. If the company was lucky, the new virtual machine would be created on a physical machine that was less oversubscribed. This is a sorry state of affairs (Link 2013). Some resources are shared in an unbounded manner. For example, if one virtual machine is generating a huge amount of network traffic, the other virtual machines may suffer.

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See Oncall rules, 353 thresholds, 49 Alexander, Christopher, 69 Allen, Woody, 285 Allspaw, John automation, 249 disaster preparedness tests, 318–320 outage factors, 302 postmortems, 301 Alternatives in design documents, 278 Amazon design process, 276 Game Day, 318 Simple Queue Service, 85 Amazon Elastic Compute Cloud (Amazon EC2), 472 Amazon Web Services (AWS), 59 Analysis in capacity planning, 375–376 causal, 301–302 crash reports, 129 in monitoring, 345, 353–354 Ancillary resources in capacity planning, 372 Andreessen, Marc, 181 Anomaly detection, 354 “Antifragile Organization” article, 315, 320 Antifragile systems, 308–310 Apache systems Hadoop, 132, 467 Mesos, 34 web server forking, 114 Zookeeper, 231, 363 API (Application Programming Interface) defined, 10 logs, 340 Applicability in dot-bomb era, 463–464 Application architectures, 69 cloud-scale service, 80–85 exercises, 93 four-tier web service, 77–80 message bus, 85–90 reverse proxy service, 80 service-oriented, 90–92 single-machine web servers, 70–71 summary, 92–93 three-tier web service, 71–77 Application debug logs, 340 Application logs, 340 Application Programming Interface (API) defined, 10 logs, 340 Application servers in four-tier web service, 79 Approvals code, 47–48 deployment phase, 214, 216–217 design documents, 277, 281 service launches, 159 Arbitrary groups, segmentation by, 104 ARC (Adaptive Replacement Cache) algorithm, 107 Architecture factors in service launches, 157 Archives design documents, 279–280 email, 277 Art of Scalability, Scalable Web Architecture, Processes, and Organizations for the Modern Enterprise, 100 Artifacts artifact-scripted database changes, 185 defined, 196 Assessments, 421–422 Capacity Planning, 431–432 Change Management, 433–434 Disaster Preparedness, 448–450 Emergency Response, 426–428 levels, 405–407 methodology, 403–407 Monitoring and Metrics, 428–430 New Product Introduction and Removal, 435–436 operational excellence, 405–407 organizational, 411–412 Performance and Efficiency, 439–441 questions, 407 Regular Tasks, 423–425 Service Delivery: The Build Phase, 442–443 Service Delivery: The Deployment Phase, 444–445 Service Deployment and Decommissioning, 437–438 services, 407–410 Toil Reduction, 446–447 Asynchronous design, 29 Atlassian Bamboo tool, 205 Atomicity ACID term, 24 release, 240–241 Attack surface area, 79 Auditing operations design, 42–43 Augmentation files, 41–42 Authentication, authorization, and accounting (AAA), 222 Authentication in deployment phase, 222 Authors in design documents, 277, 282 Auto manufacturing automation example, 251 Automation, 243–244 approaches, 244–245 baking, 219 benefits, 154 code amount, 269–270 code reviews, 268–269 compensatory principle, 246–247 complementarity principle, 247–248 continuous delivery, 190 crash data collection and analysis, 129 creating, 255–258 DevOps, 182, 185–186 exercises, 272–273 goals, 252–254 hidden costs, 250 infrastructure strategies, 217–220 issue tracking systems, 263–265 language tools, 258–262 left-over principle, 245–246 lessons learned, 249–250 multitenant systems, 270–271 prioritizing, 257–258 repair life cycle, 254–255 software engineering tools and techniques, 262–270 software packaging, 266 software restarts and escalation, 128–129 steps, 258 style guides, 266–267, 270 summary, 271–272 system administration, 248–249 tasks, 153–155 test-driven development, 267–268 toil reduction, 257 vs. tool building, 250–252 version control systems, 265–266 Availability CAP Principle, 21–22 monitoring, 336 Availability and partition tolerance (AP), 24 Availability requirements cloud computing era, 469 dot-bomb era, 460 first web era, 455 pre-web era, 452–453 second web era, 465 Averages in monitoring, 358 AWS (Amazon Web Services), 59 Backend replicas, load balancers with, 12–13 Backends multiple, 14–15 server stability, 336 Background in design documents, 277–278 Background processes for containers, 61 Backups in design for operations, 36 Baked images for OS installation, 219–220 Banned query lists, 130 Bare metal clouds, 68 Barroso, L.

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See Oncall rules, 353 thresholds, 49 Alexander, Christopher, 69 Allen, Woody, 285 Allspaw, John automation, 249 disaster preparedness tests, 318–320 outage factors, 302 postmortems, 301 Alternatives in design documents, 278 Amazon design process, 276 Game Day, 318 Simple Queue Service, 85 Amazon Elastic Compute Cloud (Amazon EC2), 472 Amazon Web Services (AWS), 59 Analysis in capacity planning, 375–376 causal, 301–302 crash reports, 129 in monitoring, 345, 353–354 Ancillary resources in capacity planning, 372 Andreessen, Marc, 181 Anomaly detection, 354 “Antifragile Organization” article, 315, 320 Antifragile systems, 308–310 Apache systems Hadoop, 132, 467 Mesos, 34 web server forking, 114 Zookeeper, 231, 363 API (Application Programming Interface) defined, 10 logs, 340 Applicability in dot-bomb era, 463–464 Application architectures, 69 cloud-scale service, 80–85 exercises, 93 four-tier web service, 77–80 message bus, 85–90 reverse proxy service, 80 service-oriented, 90–92 single-machine web servers, 70–71 summary, 92–93 three-tier web service, 71–77 Application debug logs, 340 Application logs, 340 Application Programming Interface (API) defined, 10 logs, 340 Application servers in four-tier web service, 79 Approvals code, 47–48 deployment phase, 214, 216–217 design documents, 277, 281 service launches, 159 Arbitrary groups, segmentation by, 104 ARC (Adaptive Replacement Cache) algorithm, 107 Architecture factors in service launches, 157 Archives design documents, 279–280 email, 277 Art of Scalability, Scalable Web Architecture, Processes, and Organizations for the Modern Enterprise, 100 Artifacts artifact-scripted database changes, 185 defined, 196 Assessments, 421–422 Capacity Planning, 431–432 Change Management, 433–434 Disaster Preparedness, 448–450 Emergency Response, 426–428 levels, 405–407 methodology, 403–407 Monitoring and Metrics, 428–430 New Product Introduction and Removal, 435–436 operational excellence, 405–407 organizational, 411–412 Performance and Efficiency, 439–441 questions, 407 Regular Tasks, 423–425 Service Delivery: The Build Phase, 442–443 Service Delivery: The Deployment Phase, 444–445 Service Deployment and Decommissioning, 437–438 services, 407–410 Toil Reduction, 446–447 Asynchronous design, 29 Atlassian Bamboo tool, 205 Atomicity ACID term, 24 release, 240–241 Attack surface area, 79 Auditing operations design, 42–43 Augmentation files, 41–42 Authentication, authorization, and accounting (AAA), 222 Authentication in deployment phase, 222 Authors in design documents, 277, 282 Auto manufacturing automation example, 251 Automation, 243–244 approaches, 244–245 baking, 219 benefits, 154 code amount, 269–270 code reviews, 268–269 compensatory principle, 246–247 complementarity principle, 247–248 continuous delivery, 190 crash data collection and analysis, 129 creating, 255–258 DevOps, 182, 185–186 exercises, 272–273 goals, 252–254 hidden costs, 250 infrastructure strategies, 217–220 issue tracking systems, 263–265 language tools, 258–262 left-over principle, 245–246 lessons learned, 249–250 multitenant systems, 270–271 prioritizing, 257–258 repair life cycle, 254–255 software engineering tools and techniques, 262–270 software packaging, 266 software restarts and escalation, 128–129 steps, 258 style guides, 266–267, 270 summary, 271–272 system administration, 248–249 tasks, 153–155 test-driven development, 267–268 toil reduction, 257 vs. tool building, 250–252 version control systems, 265–266 Availability CAP Principle, 21–22 monitoring, 336 Availability and partition tolerance (AP), 24 Availability requirements cloud computing era, 469 dot-bomb era, 460 first web era, 455 pre-web era, 452–453 second web era, 465 Averages in monitoring, 358 AWS (Amazon Web Services), 59 Backend replicas, load balancers with, 12–13 Backends multiple, 14–15 server stability, 336 Background in design documents, 277–278 Background processes for containers, 61 Backups in design for operations, 36 Baked images for OS installation, 219–220 Banned query lists, 130 Bare metal clouds, 68 Barroso, L.

Applied Artificial Intelligence: A Handbook for Business Leaders by Mariya Yao, Adelyn Zhou, Marlene Jia

Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, artificial general intelligence, autonomous vehicles, backpropagation, business intelligence, business process, call centre, chief data officer, cognitive load, computer vision, conceptual framework, data science, deep learning, DeepMind, en.wikipedia.org, fake news, future of work, Geoffrey Hinton, industrial robot, information security, Internet of things, iterative process, Jeff Bezos, job automation, machine translation, Marc Andreessen, natural language processing, new economy, OpenAI, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, robotic process automation, Salesforce, self-driving car, sentiment analysis, Silicon Valley, single source of truth, skunkworks, software is eating the world, source of truth, sparse data, speech recognition, statistical model, strong AI, subscription business, technological singularity, The future is already here

Many of the tech giants offer to “democratize AI” by releasing open-source development tools such as Keras, TensorFlow, CTNK, and PyTorch, or offer enterprise cloud solutions and proprietary Machine Learning as a Service (MLaaS) platforms. For companies that don’t have the infrastructure or the technical knowledge, these represent excellent solutions for quickly integrating AI capabilities into your company’s workflow . If you already store data and build applications on Amazon Web Services (AWS), Microsoft Azure, Google Cloud, or Apple’s iOS platform, using tightly integrated machine learning solutions like AWS Rekognition or Apple’s Core ML can simplify work for your own developers and may be the most economical business decision. However, the mere existence of a solution does not mean that it’s definitely right for your company.

Beautiful security by Andy Oram, John Viega

Albert Einstein, Amazon Web Services, An Inconvenient Truth, Bletchley Park, business intelligence, business process, call centre, cloud computing, corporate governance, credit crunch, crowdsourcing, defense in depth, do well by doing good, Donald Davies, en.wikipedia.org, fault tolerance, Firefox, information security, loose coupling, Marc Andreessen, market design, MITM: man-in-the-middle, Monroe Doctrine, new economy, Nicholas Carr, Nick Leeson, Norbert Wiener, operational security, optical character recognition, packet switching, peer-to-peer, performance metric, pirate software, Robert Bork, Search for Extraterrestrial Intelligence, security theater, SETI@home, Silicon Valley, Skype, software as a service, SQL injection, statistical model, Steven Levy, the long tail, The Wisdom of Crowds, Upton Sinclair, web application, web of trust, zero day, Zimmermann PGP

Enter the new world of web services and cloud computing. To mitigate the risk of catastrophic system loss, I wrote a simple plug-in (see the later section “Platforms of the Long-Tail Variety: Why the Future Will Be Different for Us All” on page 165) to the home server that makes use of the Amazon Web Services platform. At set intervals, the system copies the directories I chose onto the server and connects via web services to Amazon’s S3 (Simple Storage System) cloud infrastructure. The server sends a backup copy of the data I choose to the cloud. I make use of the WS-Security specification (and a few others) for web services, ensuring the data is encrypted and not tampered with in transport, and I make use of an X.509 digital certificate to ensure I am communicating with Amazon.

…

He served on the Roundtable on Scientific Communication and National Security, a collaborative project of the National Research Council and the Center for Strategic and International Studies. 268 CONTRIBUTORS INDEX Numbers 3-D Secure protocol account holder domain, 76 acquirer domain, 76 e-commerce security and, 76–78 evaluation of, 77 issuer domain, 76 transaction process, 76 802.11b standard, 51, 52 802.11i standard, 51 A ABA (American Bar Association), 203 Access Control Server (ACS), 77 accountability, 213, 214 ACS (Access Control Server), 77 ActionScript, 93 ad banners (see banner ads) Adams, Douglas, 158 Advanced Monitor System (AMS), 254, 256 advertising (see online advertising) adware (see spyware) Aegenis Group, 66 Agriculture, Department of, 196 AHS (Authentication History Server), 77 AI (artificial intelligence), 254, 257 AllowScriptAccess tag, 94 Amazon Web Services platform, 152 Amazon.com, 102 American Bar Association (ABA), 203 AMS (Advanced Monitor System), 254, 256 analyst confirmation traps, 12 Anderson, Chris, 165 Andreessen, Marc, 165, 166 Anna Carroll (barge), 206 anti-executables, 253 anti-spyware software evolution of, 251 initial implementation, 251 intrusive performance, 254 strict scrutiny, 252 anti-virus software diminished effectiveness, 249 functional fixation, 15 functionality, 232 historical review, 248–249 honeyclients and, 141 intrusive performance, 254 malware signature recognition, 251 need for new strategies, 248 strict scrutiny, 252 zero-day exploits and, 252 Apgar score, 37 Apgar, Virginia, 37 Apple Computer, 8 artificial intelligence (AI), 254, 257 Ascom-Tech AG, 117 Ashenfelter, Orley, 164 Aspect Security, 188 Atkins, Derek, 119 ATMs, early security flaws, 36 attacks (see malicious attacks) attribute certificates, 111 Attrition.org, 55 authentication 3-D Secure protocol, 77 auto-update and, 15 CV2 security code, 76 e-commerce security, 83, 84 federated programs, 210 NTLM, 6 password security, 7 PGP Global Directory and, 127 portability of, 85 security pitfall in, 71 SET protocol, 78 WEP support, 52 Authentication History Server (AHS), 77 authoritative keys, 123 authorization We’d like to hear your suggestions for improving our indexes.

No Filter: The Inside Story of Instagram by Sarah Frier

Airbnb, Amazon Web Services, Benchmark Capital, blockchain, Blue Bottle Coffee, Cambridge Analytica, Clayton Christensen, cloud computing, cryptocurrency, data science, disinformation, Donald Trump, Elon Musk, end-to-end encryption, fake news, Frank Gehry, growth hacking, Jeff Bezos, Marc Andreessen, Mark Zuckerberg, Menlo Park, Minecraft, move fast and break things, Network effects, new economy, Oculus Rift, Peter Thiel, ride hailing / ride sharing, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley startup, Snapchat, Steve Jobs, TaskRabbit, TikTok, Tony Hsieh, Travis Kalanick, ubercab, Zipcar

With the unexpected deluge of users, they needed to think fast to keep Instagram online. Systrom called D’Angelo, the early Facebook chief technology officer and early investor in Instagram, for his advice. It was the first of several calls that day. Every hour, Instagram seemed to grow faster. D’Angelo eventually helped the company transition to renting server space from Amazon Web Services instead of buying their own. Within the first day, 25,000 people were using Instagram. Within the first week, it was 100,000, and Systrom had the surreal experience of seeing a stranger scrolling through the app on a San Francisco bus. He and Krieger started an Excel spreadsheet that would update live with each user added.

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ABC News, 211 Above Category cycling, 185 abuse, abuse content, 41, 97, 261 Academy Awards, 152 Systrom at, 191–92, 204 Accenture, 260 Acton, Brian, 125, 256, 258 Adams, William (will.i.am), 128 Adidas, xix Adobe Lightroom, 240, 243 Adobe Photoshop, 21, 23, 244 advertising, 59, 176, 256 false, 244; see also fake news FB’s business of, 75, 77, 89, 91–92, 94, 96, 105, 118–19, 125, 149–50, 163, 217, 224, 277 IG’s business of, 104, 118–21, 124, 151, 155, 163–65, 174, 175–76, 184, 225, 241, 277 mobile, 74–75 television, 215 see also brand advertising advertising agencies, 89 FB’s relationship with, 120–21, 124 Ahrendts, Angela, 147 @aidanalexander, 171 AiGrow, 246 Airbnb, xvi, 45 Alba, Jessica, 130, 250 Alexander, Aidan, 171 algorithms: FB’s use of, 91, 103, 128, 162, 163, 208, 209, 210–12, 215, 221, 224, 259 IG’s early lack of, 34, 143 IG’s use of, 81, 170, 174, 197–98, 218, 229, 230–32, 233, 251, 271 IG users’ mistrust of, 197 YouTube’s use of, 233–34 @alittlepieceofinsane, 161 Allen, Nick, 117 Allen & Company, 49 Amanpour, Christiane, 127 Amaro photo filter, 23 Amazon, 22, 28, 139, 242 Communications Decency Act and, 41 Whole Foods acquired by, 64 Zappos acquired by, 105 Amazon Web Services, 26, 79–80 American Medical Association, 244 American Society of Plastic Surgeons, 244 analytics, 90, 102, 226 IG’s use of, 100, 178, 183, 226 IG users’ access to, 275–76 Anchor Psychology, 172 Anderson, Steve, 34 early IG investment of, 11, 15 on IG board, 37, 56, 63–64 Anderson Cooper 360, 142 Andreessen, Marc, 11 Andreessen Horowitz: early investment in IG by, 11, 15, 33 investment in PicPlz by, 33–34, 36, 77 Android, 19, 33, 110, 203 IG app for, 50, 51 angel investments, 16, 17, 24, 36 anonymity, user, 41, 80 on Formspring, 40 on IG, 41, 80, 163, 173, 218, 219, 260, 261 troubling content and, 40, 163, 218–19, 260; see also bullying antitrust laws, 75, 268 Antonow, Eric, 165 AOL, 116 Apple, 10, 21, 56, 65, 147, 167, 234 Apple app store, 26, 28, 38, 115 Apple IDs, 42 apps: filter, see filter apps, photo location-based, 15 see also mobile apps; specific apps Argentina, 12 Arthur D.

Succeeding With AI: How to Make AI Work for Your Business by Veljko Krunic

AI winter, Albert Einstein, algorithmic trading, AlphaGo, Amazon Web Services, anti-fragile, anti-pattern, artificial general intelligence, autonomous vehicles, Bayesian statistics, bioinformatics, Black Swan, Boeing 737 MAX, business process, cloud computing, commoditize, computer vision, correlation coefficient, data is the new oil, data science, deep learning, DeepMind, en.wikipedia.org, fail fast, Gini coefficient, high net worth, information retrieval, Internet of things, iterative process, job automation, Lean Startup, license plate recognition, minimum viable product, natural language processing, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, six sigma, smart cities, speech recognition, statistical model, strong AI, tail risk, The Design of Experiments, the scientific method, web application, zero-sum game

Let’s look at all the decisions that have already ossified in your pipeline:  You’re using the same ML pipeline that your data scientist used for the small      POC. Is it the best pipeline for a security system rolled out to 100,000 people? Your pipeline is using Apache Spark [14], and you’ve hired an Apache Spark specialist. You’ve also hired Amazon Web Services (AWS) [11] specialists, but they don’t know anything about the competing cloud services, such as Microsoft Azure [13] or Google Cloud Platform (GCP) [12]. Your neural network is implemented using TensorFlow [91]. You have a contract with the humane society to use training pictures of the pets.

…

AI superpowers: China, Silicon Valley, and the new world order. Boston: Houghton Mifflin Harcourt; 2018. Peng T. Andrew Ng says enough papers, let’s build AI now! Synced. 2017 Nov 4 [cited 2019 Feb 15]. Available from: https://syncedreview.com/2017/11/04/ andrew-ng-says-enough-papers-lets-build-ai-now/ Amazon.com, Inc. Amazon Web Services. [Cited 2019 Jul 19.] Available from: https://aws.amazon.com Google, Inc. Build. Modernize. Scale. [Cited 2019 Jul 19.] Available from: https://cloud.google.com Microsoft Corporation. Microsoft Azure. [Cited 2019 Jul 19.] Available from: https://azure.microsoft.com/en-us/ 244 APPENDIX 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 C Bibliography 245 Apache Software Foundation.

The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism by Nick Couldry, Ulises A. Mejias

"World Economic Forum" Davos, 23andMe, Airbnb, Amazon Mechanical Turk, Amazon Web Services, behavioural economics, Big Tech, British Empire, call centre, Cambridge Analytica, Cass Sunstein, choice architecture, cloud computing, colonial rule, computer vision, corporate governance, dark matter, data acquisition, data is the new oil, data science, deep learning, different worldview, digital capitalism, digital divide, discovery of the americas, disinformation, diversification, driverless car, Edward Snowden, emotional labour, en.wikipedia.org, European colonialism, Evgeny Morozov, extractivism, fake news, Gabriella Coleman, gamification, gig economy, global supply chain, Google Chrome, Google Earth, hiring and firing, income inequality, independent contractor, information asymmetry, Infrastructure as a Service, intangible asset, Internet of things, Jaron Lanier, job automation, Kevin Kelly, late capitalism, lifelogging, linked data, machine readable, Marc Andreessen, Mark Zuckerberg, means of production, military-industrial complex, move fast and break things, multi-sided market, Naomi Klein, Network effects, new economy, New Urbanism, PageRank, pattern recognition, payday loans, Philip Mirowski, profit maximization, Ray Kurzweil, RFID, Richard Stallman, Richard Thaler, Salesforce, scientific management, Scientific racism, Second Machine Age, sharing economy, Shoshana Zuboff, side hustle, Sidewalk Labs, Silicon Valley, Slavoj Žižek, smart cities, Snapchat, social graph, social intelligence, software studies, sovereign wealth fund, surveillance capitalism, techlash, The Future of Employment, the scientific method, Thomas Davenport, Tim Cook: Apple, trade liberalization, trade route, undersea cable, urban planning, W. E. B. Du Bois, wages for housework, work culture , workplace surveillance