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If it hurts, do it more frequently, and bring the pain forward.
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Jez Humble (Continuous Delivery: Reliable Software Releases Through Build, Test, and Deployment Automation)
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Automation is cost cutting by tightening the corners and not cutting them.
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Haresh Sippy
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If automating everything makes people lazier and lazier, and laziness leads to stupidity, which it does for most people, judging by the current content circulating the social networks everywhere, except North Korea, where they don’t have any internet to speak of - at some point the Japanese robots, for which a market niche is currently being developed, with no concerns on how they should be designed to act in society or outside it - will have no choice, but to take everything over, to preserve us from ourselves…
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Will Advise (Nothing is here...)
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Well...what you did in Rosewater County was far from insane. It was quite possibly the most important social experiment of our time, for it dealt on a very small scale with a problem whose queasy horrors will eventually be made world-wide by the sophistication of machines. The problem is this: How to love people who have no use?
In time, almost all men and women will become worthless as producers of goods, food, services, and more machines, as sources of practical ideas in the areas of economics, engineering, and probably medicine, too. So - if we can't find reasons and methods for treasuring human beings because they are _human beings_, then we might as well, as has so often been suggested, rub them out.
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Kurt Vonnegut Jr. (God Bless You, Mr. Rosewater)
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In highly automated systems, the operator is often at the mercy of the system design and operational procedures.
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Nancy G. Leveson (Engineering a Safer World: Systems Thinking Applied to Safety (Engineering Systems))
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Releasing software is too often an art; it should be an engineering discipline.
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David Farley (Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation)
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Robotics, however, is much more difficult. It requires a delicate interplay of mechanical engineering, perception AI, and fine-motor manipulation. These are all solvable problems, but not at nearly the speed at which pure software is being built to handle white-collar cognitive tasks. Once that robot is built, it must also be tested, sold, shipped, installed, and maintained on-site. Adjustments to the robot’s underlying algorithms can sometimes be made remotely, but any mechanical hiccups require hands-on work with the machine. All these frictions will slow down the pace of robotic automation.
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Kai-Fu Lee (AI Superpowers: China, Silicon Valley, and the New World Order)
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Automated decision-making shatters the social safety net, criminalizes the poor, intensifies discrimination, and compromises our deepest national values. It reframes shared social decisions about who we are and who we want to be as systems engineering problems. And while the most sweeping digital decision-making tools are tested in what could be called “low rights environments” where there are few expectations of political accountability and transparency, systems first designed for the poor will eventually be used on everyone.
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Virginia Eubanks (Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor)
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As black-box technologies become more widespread, there have been no shortage of demands for increased transparency. In 2016 the European Union's General Data Protection Regulation included in its stipulations the "right to an explanation," declaring that citizens have a right to know the reason behind the automated decisions that involve them. While no similar measure exists in the United States, the tech industry has become more amenable to paying lip service to "transparency" and "explainability," if only to build consumer trust. Some companies claim they have developed methods that work in reverse to suss out data points that may have triggered the machine's decisions—though these explanations are at best intelligent guesses. (Sam Ritchie, a former software engineer at Stripe, prefers the term "narratives," since the explanations are not a step-by-step breakdown of the algorithm's decision-making process but a hypothesis about reasoning tactics it may have used.) In some cases the explanations come from an entirely different system trained to generate responses that are meant to account convincingly, in semantic terms, for decisions the original machine made, when in truth the two systems are entirely autonomous and unrelated. These misleading explanations end up merely contributing another layer of opacity. "The problem is now exacerbated," writes the critic Kathrin Passig, "because even the existence of a lack of explanation is concealed.
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Meghan O'Gieblyn (God, Human, Animal, Machine: Technology, Metaphor, and the Search for Meaning)
“
One of those was Gary Bradski, an expert in machine vision at Intel Labs in Santa Clara. The company was the world’s largest chipmaker and had developed a manufacturing strategy called “copy exact,” a way of developing next-generation manufacturing techniques to make ever-smaller chips. Intel would develop a new technology at a prototype facility and then export that process to wherever it planned to produce the denser chips in volume. It was a system that required discipline, and Bradski was a bit of a “Wild Duck”—a term that IBM originally used to describe employees who refused to fly in formation—compared to typical engineers in Intel’s regimented semiconductor manufacturing culture. A refugee from the high-flying finance world of “quants” on the East Coast, Bradski arrived at Intel in 1996 and was forced to spend a year doing boring grunt work, like developing an image-processing software library for factory automation applications. After paying his dues, he was moved to the chipmaker’s research laboratory and started researching interesting projects. Bradski had grown up in Palo Alto before leaving to study physics and artificial intelligence at Berkeley and Boston University. He returned because he had been bitten by the Silicon Valley entrepreneurial bug.
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John Markoff (Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots)
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In typical DevOps transformations, as we progress from deployment lead times measured in months or quarters to lead times measured in minutes, the constraint usually follows this progression: Environment creation: We cannot achieve deployments on-demand if we always have to wait weeks or months for production or test environments. The countermeasure is to create environments that are on demand and completely self-serviced, so that they are always available when we need them. Code deployment: We cannot achieve deployments on demand if each of our production code deployments take weeks or months to perform (i.e., each deployment requires 1,300 manual, error-prone steps, involving up to three hundred engineers). The countermeasure is to automate our deployments as much as possible, with the goal of being completely automated so they can be done self-service by any developer. Test setup and run: We cannot achieve deployments on demand if every code deployment requires two weeks to set up our test environments and data sets, and another four weeks to manually execute all our regression tests. The countermeasure is to automate our tests so we can execute deployments safely and to parallelize them so the test rate can keep up with our code development rate. Overly tight architecture: We cannot achieve deployments on demand if overly tight architecture means that every time we want to make a code change we have to send our engineers to scores of committee meetings in order to get permission to make our changes. Our countermeasure is to create more loosely-coupled architecture so that changes can be made safely and with more autonomy, increasing developer productivity.
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Gene Kim (The DevOps Handbook: How to Create World-Class Agility, Reliability, and Security in Technology Organizations)
“
A somewhat provocative example of the interconnections between the gaming industry and finance. A technologist working for a large London hedge fund hinted this to me in interview. Trained in computer science and engineering, this interviewee first worked as a network programmer for large online multiplayer games. His greatest challenge was the fact that the Internet is not instantaneous: when a player sends a command to execute in action, it takes time for the signal to reach the computer server and interact with the commands of other players. For the game to be realistic, such delays have to be taken into account when rendering reality on the screen. The challenge for the network programmer is to make these asymmetries as invisible as possible so that the game seem 'equitable to everyone.' The problem is similar in finance, where the physical distance from the stock exchange's matching engines matters tremendously, requiring a similar solution to the problem of latency: simulating the most likely state of the order book on the firm's computers in order to estimate the most advantageous strategies or the firm's trading algorithms. Gaming and finance are linked not through an institutional imperative of culture or capital - or even a strategy, as such - but rather through the more mundane and lowly problems of how to fairly manage latency and connectivity.
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Juan Pablo Pardo-Guerra (Automating Finance: Infrastructures, Engineers, and the Making of Electronic Markets)
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engage in development tasks, because the service basically runs and repairs itself: we want systems that are automatic, not just automated. In practice, scale and new features keep SREs on their toes.
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Betsy Beyer (Site Reliability Engineering: How Google Runs Production Systems)
“
Even if the total number of jobs does not fall, the current wave of automation tends to displace jobs that require some skills (bookkeepers and accountants) and increase the demand, either for very skilled workers (software programmers for the machines) or for totally unskilled workers (dog walkers, for example), which are both much more difficult to replace with a machine. As software engineers become richer, they have more money to hire dog walkers, who have become relatively cheaper over time, since there is little alternative employment for those with no college education. Even if people remain employed, this leads to an increase in inequality, with higher wages at the top and everyone else pushed to jobs requiring no specific skills; jobs where wages and working conditions can be really bad. This accentuates a trend that has taken place since the 1980s. Workers without a college education have increasingly been pushed out of mid-skill jobs, such as clerical and administrative roles, into low-skill tasks, such as cleaning and security.
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Abhijit V. Banerjee (Good Economics for Hard Times: Better Answers to Our Biggest Problems)
“
The talent required within the CoE is wide and ranges from business and operations excellence to risk and IT departments. According to McKinsey’s survey, the CoE of top-performing companies includes a large variety of profiles such as delivery managers, data scientists, data engineers, workflow integrators, system architects, developers, and, most critically, translators and business analysts.152 A
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Pascal Bornet (INTELLIGENT AUTOMATION: Learn how to harness Artificial Intelligence to boost business & make our world more human)
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The advance of computerization and automation technologies has meant that many medium-skilled jobs—clerks, travel agents, bookkeepers, and factory workers—have been replaced with new technologies. New jobs have arisen in their place, but those jobs are often one of two types: either they are high-skilled jobs, such as engineers, programmers, managers, and designers, or they are lower-skilled jobs such as retail workers, cleaners, or customer service agents. Exacerbating the trends caused by computers and robots are globalization and regionalization. As medium-skilled technical work is outsourced to workers in developing nations, many of those jobs are disappearing at home. Lower-skilled jobs, which often require face-to-face contact or social knowledge in the form of cultural or language abilities, are likely to remain. Higher-skilled work is also more resistant to shipping overseas because of the benefits of coordination with management and the market. Think of Apple’s tagline on all of its iPhones: “Designed in California. Made in China.” Design and management stay; manufacturing goes.
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Scott H. Young (Ultralearning: Master Hard Skills, Outsmart the Competition, and Accelerate Your Career)
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Engineering autonomy in healthcare solutions must be a human-centered process. Engineers should not conflate automation, where AI can consistently repeat a task, with AI having autonomy. Humans must remain in the loop.
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Kerrie L. Holley (AI-First Healthcare: AI Applications in the Business and Clinical Management of Health)
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Joseph A. Schumpeter, the Harvard economist who in 1943 published the iconic Capitalism, Socialism, and Democracy. The seventh chapter of that work, entitled “The Process of Creative Destruction,” is for many academics a sacred text. “The process of creative destruction,” Schumpeter writes, “is the essential fact about capitalism. It is what capitalism consists in and what every capitalist concern has got to live in.” Creative destruction is an elegantly simple idea describing the industrial mutation of old structures into new ones. The department store evolves from and “creatively destructs” the country store; the auto industry evolves from and replaces the horse and buggy business, automation makes many factory and farm jobs obsolete but creates new jobs in information technology, engineering, healthcare, and biotech.
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Ellen Ruppel Shell (Cheap: The High Cost of Discount Culture)
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The more you produce, the more you have to read. Automated code generation only makes matters worse. As Martin Fowler writes about low code quality: “Even small changes require programmers to understand large areas of code, code that’s difficult to understand.” [32] Code that’s difficult to understand slows you down. On the other hand, every minute you invest in making the code easier to understand pays itself back tenfold.
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Mark Seemann (Code That Fits in Your Head: Heuristics for Software Engineering (Robert C. Martin Series))
“
Miguel Antunes, R&D Principle Software Engineer at OutSystems, a low-code platform vendor, relayed an example of this very challenge. Their Engineering Productivity team at OutSystems was five years old. The team’s mission was to help product teams run their builds efficiently, maintain infrastructure, and improve test execution. The team kept growing and took on extra responsibilities around continuous integration (CI), continuous delivery (CD), and infrastructure automation.
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Matthew Skelton (Team Topologies: Organizing Business and Technology Teams for Fast Flow)
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A good chief executive is essentially a hard-to-automate decision engine,
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Cal Newport (Deep Work: Rules for Focused Success in a Distracted World)
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The next billion-dollar startup will only have three employees.” The culture of that startup would be “AI first,” and it would use autonomous AI agents to get work done. All marketing and sales would be automated via AI bots, and the three employees would be: The CEO, who would handle vision and purpose and lead public-facing marketing. She would also code and be involved in engineering. The Product Lead, who would interface with customers and team to manage the product roadmap and drive development The Operations Lead, who would be responsible for the outcome of the AI bots and handle finance and legal and smooth operations. We
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Salim Ismail (Exponential Organizations 2.0: The New Playbook for 10x Growth and Impact)
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The National Transportation Safety Board’s recent speed report suggests five approaches.15 First, they want to lower speed limits. Cool. Second, they want to use “data-driven approaches for speed enforcement” in combination with their third approach, automated enforcement. OK. Fourth on the list is what they call “intelligent speed adaptation.” This term refers to things like onboard warnings when the driver speeds, but also includes using technology to limit car speeds in particular locations and on specific streets. Sounding better. Last, they say we need to do better when it comes to exercising “national leadership,” which basically means we need more funding and more education. I
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Wes Marshall (Killed by a Traffic Engineer: Shattering the Delusion that Science Underlies our Transportation System)
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Digital marketing expert Angela Liberatore stresses the importance of data analytics, which lets businesses track how well their digital campaigns are doing and make changes if needed. Conversion Rate Optimization (CRO) focuses on improving the website to turn visitors into customers. Mobile marketing makes sure content works well on smartphones and tablets, as more people use these devices. Lastly, marketing automation tools help save time by automatically sending emails or posting on social media. All of these parts together create a strong digital marketing strategy that helps businesses grow, engage customers, and increase sales in today’s digital world. Digital marketing includes several important parts that work together to help businesses reach and connect with their audience online. One part is search engine optimization (SEO), which helps websites show up higher on search engines like Google. Content marketing is another part, where useful things like blog posts, videos, and infographics are made to attract and interest customers. Social media marketing uses platforms like Facebook, Instagram, and LinkedIn to increase brand awareness and build a community. Email marketing allows businesses to send personalized messages directly to their audience. Paid advertising, like Pay-Per-Click (PPC) ads, brings quick traffic to websites. Influencer marketing uses the popularity of influencers to promote products or services.
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Angela Liberatore
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s s i o n o f R a t i o n a l S o f t w a r e C o r p o r a t i o n i s t o e n s u r e t h e s u c c e s s o f c u s t o m e r s c o n s t r u c t i n g t h e s o f t w a r e s y s t e m s t h a t t h e y d e p e n d o n . We enable our customers to achieve their business objectives by turning software into a source of competitive advantage, speeding time-to-market, reducing the risk of failure, and improving software quality. We fulfill our mission with the Rational ApproachTM, a comprehensive softwareengineering solution consisting of three elements: • A configurable set of processes and techniques for the development of software, based on iterative development, object modeling, and an architectural approach to software reuse. • An integrated family of application construction tools that automate the Rational Approach throughout the software lifecycle. • Technical consulting services delivered by our worldwide field organization of software engineers and technical sales professionals. Our customers include businesses in the Asia/Pacific region, Europe, and North America that are leaders in leveraging semiconductor, communications, and software technologies to achieve their business objectives. We serve customers in a diverse range of industries, such as telecommunications
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Anonymous
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o n o f R a t i o n a l S o f t w a r e C o r p o r a t i o n i s t o e n s u r e t h e s u c c e s s o f c u s t o m e r s c o n s t r u c t i n g t h e s o f t w a r e s y s t e m s t h a t t h e y d e p e n d o n . We enable our customers to achieve their business objectives by turning software into a source of competitive advantage, speeding time-to-market, reducing the risk of failure, and improving software quality. We fulfill our mission with the Rational ApproachTM, a comprehensive softwareengineering solution consisting of three elements: • A configurable set of processes and techniques for the development of software, based on iterative development, object modeling, and an architectural approach to software reuse. • An integrated family of application construction tools that automate the Rational Approach throughout the software lifecycle. • Technical consulting services delivered by our worldwide field organization of software engineers and technical sales professionals. Our customers include businesses in the Asia/Pacific region, Europe, and North America that are leaders in leveraging semiconductor, communications, and software technologies to achieve their business objectives. We serve customers in a diverse range of industries, such as telecommunications, banking and financial services, manufacturing, transportation, aerospace, and defense.They construct software applications for a wide range of platforms, from microprocessors embedded in telephone switching systems to enterprisewide information systems running on company-specific intranets. Rational Software Corporation is traded on the NASDAQ system under the symbol RATL.1
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Anonymous
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DevOps and its resulting technical, architectural, and cultural practices represent a convergence of many philosophical and management movements (including): Lean, Theory of Constraints, Toyota production system, resilience engineering, learning organizations, safety culture, Human factors, high-trust management cultures, servant leadership, organizational change management, and Agile methods.
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Gene Kim (The DevOps Handbook: How to Create World-Class Agility, Reliability, and Security in Technology Organizations)
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Algorithmic profits Algorithmic marketing is allowing companies to do things they couldn’t do before, and some early signs show it can deliver big value, especially in financial or information services. In North America, Amazon.com grew 30 to 40 percent, quarter after quarter, throughout the United States’ 2008-2012 recession, while other major retailers shrank or went out of business. From 2006 to 2010, Amazon spent 5.6 percent of its sales revenue on IT, while rivals Target and Best Buy spent 1.3% and 0.5%, respectively. That investment and focus has yielded increasingly sophisticated recommendation engines that deliver over 35 percent of all sales, an automated e-mail/customer service systems (90 percent are automated, versus 44 percent for the average retailer) that are a key component of its best-in-class customer satisfaction, and dynamic pricing systems that crawl the Web and react to competitor pricing and stock levels by altering prices on Amazon.com, in some cases every 15 seconds.
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McKinsey Chief Marketing & Sales Officer Forum (Big Data, Analytics, and the Future of Marketing & Sales)
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intech automation is the study regarding automotive engineering for light and heavy vehicles in Brisbane Australia. Intech.edu.au is availing the courses automation.
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inetch
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Terry Guo of Foxconn has been aggressively installing hundreds of thousands of robots to replace an equivalent number of human workers. He says he plans to buy millions more robots in the coming years. The first wave is going into factories in China and Taiwan, but once an industry becomes largely automated, the case for locating a factory in a low-wage country becomes less compelling. There may still be logistical advantages if the local business ecosystem is strong, making it easier to get spare parts, supplies, and custom components. But over time inertia may be overcome by the advantages of reducing transit times for finished products and being closer to customers, engineers and designers, educated workers, or even regions where the rule of law is strong. This can bring manufacturing back to America, as entrepreneurs like Rod Brooks have been emphasizing. A
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Erik Brynjolfsson (The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies)
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We will actively manage this technical debt by ensuring that we invest at least 20% of all Development and Operations cycles on refactoring, investing in automation work and architecture and non-functional requirements (NFRs, sometimes referred to as the “ilities”), such as maintainability, manageability, scalability, reliability, testability, deployability, and security. Figure 11: Invest 20% of cycles on those that create positive, user-invisible value (Source: “Machine Learning and Technical Debt with D. Sculley,” Software Engineering Daily podcast, November 17, 2015,
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Gene Kim (The DevOps Handbook: How to Create World-Class Agility, Reliability, and Security in Technology Organizations)
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To achieve market orientation, we won’t do a large, top-down reorganization, which often creates large amounts of disruption, fear, and paralysis. Instead, we will embed the functional engineers and skills (e.g., Ops, QA, Infosec) into each service team, or provide their capabilities to teams through automated self-service platforms that provide production-like environments, initiate automated tests, or perform deployments.
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Gene Kim (The DevOps Handbook: How to Create World-Class Agility, Reliability, and Security in Technology Organizations)
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Automation is a force multiplier, not a panacea.
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Betsy Beyer (Site Reliability Engineering: How Google Runs Production Systems)
“
The fascination with automation in part reflected the country’s mood in the immediate postwar period, including a solid ideological commitment to technological progress. Representatives of industry (along with their counterparts in science and engineering) captured this mood by championing automation as the next step in the development of new production machinery and American industrial prowess. These boosters quickly built up automation into “a new gospel of postwar economics,” lauding it as “a universal ideal” that would “revolutionize every area of industry.” 98 For example, the November 1946 issue of Fortune magazine focused on the prospects for “The Automatic Factory.” The issue included an article titled “Machines without Men” that envisioned a completely automated factory where virtually no human labor would be needed. 99 With visions of “transforming the entire manufacturing sector into a virtually labor-free enterprise,” factory owners in a range of industries began to introduce automation in the postwar period. 100 The auto industry moved with particular haste. After the massive wave of strikes in 1945–46, automakers seized on automation as a way to replace workers with machines. 101 As they converted back to civilian auto production after World War II, they took the opportunity to install new labor-saving automatic production equipment. The two largest automakers, Ford and General Motors, set the pace. General Motors introduced the first successful automated transfer line at its Buick engine plant in Flint in 1946 (shortly after a 113-day strike, the longest in the industry’s history). The next year Ford established an automation department (a Ford executive, Del S. Harder, is credited with coining the word “automation”). By October 1948 the department had approved $ 3 million in spending on 500 automated devices, with early company estimates predicting that these devices would result in a 20 percent productivity increase and the elimination of 1,000 jobs. Through the late 1940s and 1950s Ford led the way in what became known as “Detroit automation,” undertaking an expensive automation program, which it carried out in concert with the company’s plans to decentralize operations away from the city. A major component of this effort was the Ford plant in the Cleveland suburb of Brook Park, a $ 2 billion engine-making complex that attracted visitors from government, industry, and labor and became a national symbol of automation in the 1950s. 102
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Stephen M. Ward (In Love and Struggle: The Revolutionary Lives of James and Grace Lee Boggs (Justice, Power, and Politics))
“
We cannot achieve deployments on demand if each of our production code deployments take weeks or months to perform (i.e., each deployment requires 1,300 manual, error-prone steps involving up to three hundred engineers). The countermeasure is to automate our deployments as much as possible, with the goal of being completely automated so they can be done self-service by any developer.
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Gene Kim (The DevOps Handbook: How to Create World-Class Agility, Reliability, and Security in Technology Organizations)
“
Don’t be afraid to provide white glove customer support for early adopters to help them through the onboarding process. Sometimes automation also entails a host of emotional concerns, such as fear that someone’s job will be replaced by a shell script. By working one-on-one with early users, you can address those fears personally, and demonstrate that rather than owning the toil of performing a tedious task manually, the team instead owns the configurations, processes, and ultimate results of their technical work. Later adopters are convinced by the happy examples of early adopters.
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Betsy Beyer (Site Reliability Engineering: How Google Runs Production Systems)
“
Which backends of this server are considered “in the critical path,” and why? What aspects of this server could be simplified or automated? Where do you think the first bottleneck is in this architecture? If that bottleneck were to be saturated, what steps could you take to alleviate it?
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Betsy Beyer (Site Reliability Engineering: How Google Runs Production Systems)
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If the steam engine freed human beings from feudal bondage to pursue material self-interest in the capitalist marketplace, the Internet of Things frees human beings from the market economy to pursue nonmaterial shared interests on the Collaborative Commons. Many—but not all—of our basic material needs will be met for nearly free in a near zero marginal cost society. Intelligent technology will do most of the heavy lifting in an economy centered on abundance rather than scarcity. A half century from now, our grandchildren are likely to look back at the era of mass employment in the market with the same sense of utter disbelief as we look upon slavery and serfdom in former times. The very idea that a human being’s worth was measured almost exclusively by his or her productive output of goods and services and material wealth will seem primitive, even barbaric, and be regarded as a terrible loss of human value to our progeny living in a highly automated world where much of life is lived on the Collaborative Commons.
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Despite Noetic Science’s use of cutting-edge technologies, the discoveries themselves were far more mystical than the cold, high-tech machines that were producing them. The stuff of magic and myth was fast becoming reality as the shocking new data poured in, all of it supporting the basic ideology of Noetic Science—the untapped potential of the human mind. The overall thesis was simple: We have barely scratched the surface of our mental and spiritual capabilities. Experiments at facilities like the Institute of Noetic Sciences (IONS) in California and the Princeton Engineering Anomalies Research Lab (PEAR) had categorically proven that human thought, if properly focused, had the ability to affect and change physical mass. Their experiments were no “spoon-bending” parlor tricks, but rather highly controlled inquiries that all produced the same extraordinary result: our thoughts actually interacted with the physical world, whether or not we knew it, effecting change all the way down to the subatomic realm. Mind over matter. In 2001, in the hours following the horrifying events of September 11, the field of Noetic Science made a quantum leap forward. Four scientists discovered that as the frightened world came together and focused in shared grief on this single tragedy, the outputs of thirty-seven different Random Event Generators around the world suddenly became significantly less random. Somehow, the oneness of this shared experience, the coalescing of millions of minds, had affected the randomizing function of these machines, organizing their outputs and bringing order from chaos. The shocking discovery, it seemed, paralleled the ancient spiritual belief in a “cosmic consciousness”—a vast coalescing of human intention that was actually capable of interacting with physical matter. Recently, studies in mass meditation and prayer had produced similar results in Random Event Generators, fueling the claim that human consciousness, as Noetic author Lynne McTaggart described it, was a substance outside the confines of the body . . . a highly ordered energy capable of changing the physical world. Katherine had been fascinated by McTaggart’s book The Intention Experiment, and her global, Web-based study—theintentionexperiment.com—aimed at discovering how human intention could affect the world. A handful of other progressive texts had also piqued Katherine’s interest. From this foundation, Katherine Solomon’s research had vaulted forward, proving that “focused thought” could affect literally anything—the growth rate of plants, the direction that fish swam in a bowl, the manner in which cells divided in a petri dish, the synchronization of separately automated systems, and the chemical reactions in one’s own body. Even the crystalline structure of a newly forming solid was rendered mutable by one’s mind; Katherine had created beautifully symmetrical ice crystals by sending loving thoughts to a glass of water as it froze. Incredibly, the converse was also true: when she sent negative, polluting thoughts to the water, the ice crystals froze in chaotic, fractured forms. Human thought can literally transform the physical world.
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Dan Brown (The Lost Symbol (Robert Langdon, #3))
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The machine, called a punch-card tabulator, had been invented in the early 1880s by an engineer named Herman Hollerith for the purpose of automating the US census.
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Nicholas Carr (The Big Switch: Rewiring the World, from Edison to Google)
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There were girls here with fire-engine-red lips, and boys with such pronounced eyeliner that it looked permanent. And as you moved back to the dancefloor, the music overwhelmed you: Yellow Magic Orchestra, Space, Ultravox, Eno, Fad Gadget, Sparks, Grace Jones, Thomas Leer, Cerrone, Psychedelic Furs and Bowie, obviously, lots of Bowie. On and on it went, a constant swirl of automated Germanic beats – hard-edged European disco, synth-led, bass-heavy … all very angular: Kraftwerk and Gina X, Giorgio Moroder and Donna Summer, and some early Roxy Music.
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Dylan Jones (Sweet Dreams: The Story of the New Romantics)
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The near-ubiquitous use of algorithmically driven software, both visible and invisible to everyday people, demands a closer inspection of what values are prioritized in such automated decision-making systems.
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Safiya Umoja Noble (Algorithms of Oppression: How Search Engines Reinforce Racism)
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A good chief executive is essentially a hard-to-automate decision engine, not unlike IBM’s Jeopardy!-playing Watson system. They have built up a hard-won repository of experience and have honed and proved an instinct for their market. They’re then presented inputs throughout the day—in the form of e-mails, meetings, site visits, and the like—that they must process and act on. To ask a CEO to spend four hours thinking deeply about a single problem is a waste of what makes him or her valuable. It’s better to hire three smart subordinates to think deeply about the problem and then bring their solutions to the executive for a final decision.
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Edgar H. Schein (DEC Is Dead, Long Live DEC: The Lasting Legacy of Digital Equipment Corporation)
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Google has also benefitted from being at the inflection point of software moving from massive client-side binaries with multi-year release cycles to cloud-based services that are released every few weeks, days, or hours.1 This confluence of happy circumstances has endowed us with some similarities to the utopian software development process. Google SWEs are feature developers, responsible for building components that ship to customers. They write feature code and unit test code for those features. Google SETs are test developers, responsible for assisting SWEs with the unit test portion of their work and also in writing larger test frameworks to assist SWEs in writing small and medium tests to assess broader quality concerns. Google TEs are user developers, responsible for taking the users’ perspectives in all things that have to do with quality. From a development perspective, they create automation for user scenarios and from a product perspective, they assess the overall coverage and effectiveness of the ensemble of testing activity performed by the other engineering roles. It is not utopia, but it is our best attempt at achieving it in a practical way where real-world concerns have a way of disrupting best intentions in the most unforeseen and unforgiving way.
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Activities that were previously considered mere sources of entertainment or spiritual growth are now being seen as engines of economic growth.
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Andrés Oppenheimer (The Robots Are Coming!: The Future of Jobs in the Age of Automation)
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In 2000 a group of computer scientists and engineers at Georgia Tech collaborated on a project called the “Aware Home.”4 It was meant to be a “living laboratory” for the study of “ubiquitous computing.” They imagined a “human-home symbiosis” in which many animate and inanimate processes would be captured by an elaborate network of “context aware sensors” embedded in the house and by wearable computers worn by the home’s occupants. The design called for an “automated wireless collaboration” between the platform that hosted personal information from the occupants’ wearables and a second one that hosted the environmental information from the sensors.
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This is where things get a little grey.
Buying Gmail accounts is not strictly illegal, but it violates Google’s Terms of Service. Google prohibits the buying, selling, or transferring of accounts without their permission.
So while there are no direct laws banning the purchase, your account may be:
Flagged
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Banned
Google employs smart detection systems to identify suspicious activities such as mass login from different locations, bot behavior, or bulk sign-ins.
TL;DR: You won’t go to jail for buying Gmail accounts, but you might lose them — so proceed with caution and use them responsibly.
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To ensure your investment pays off, here are key factors to consider:
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Understanding the landscape of buying old Gmail accounts
Buying old Gmail accounts is a phrase that appears a lot in marketing circles and on forums where people talk about email marketing social media management and account access The idea sounds appealing aged gmail accounts promise established history existing contacts and sometimes the perception of higher trust than brand new gmail accounts But before anyone considers purchasing or using older accounts it is essential to separate marketing myths practical realities legal risks and safer alternatives
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Why people consider aged gmail accounts
Many businesses and individuals think aged accounts can speed up outreach campaigns enable instant use of email lists and offer access to services that restrict new accounts Search engines and platforms sometimes treat long standing profiles differently so aged accounts are seen as useful for email marketing channel diversification or setting up linked online presence For some marketers the attraction is bulk gmail accounts or phone verified gmail accounts that appear ready to use instantly with free storage and associated google drive access Others look for accounts that seem to have older emails and activity to lend credibility when connecting with social media platforms or creating a youtube channel
The risks and legal considerations
Purchasing accounts carries significant risks that businesses should weigh carefully First there is a trust and security issue When an account is sold the buyer often does not receive accurate provenance information and the original owner may still have recovery access or could reclaim control That creates exposure for password leaks unauthorized access and data loss Second using purchased accounts for email marketing campaigns can violate google terms of service and the policies of other platforms which can lead to account suspension reputational damage and potential legal claims In many jurisdictions sending marketing messages from addresses obtained in questionable ways can run afoul of anti spam laws such as CAN SPAM or GDPR provisions about lawful basis for processing personal data
Reputation and deliverability consequences
Email reputation is central to successful email marketing Buying bulk gmail accounts or multiple gmail accounts to distribute cold outreach can backfire because deliverability relies on consistent sending practices domain reputation and recipient engagement New accounts that suddenly send large volumes often trigger spam filters and older accounts with irregular history may have hidden flags that reduce inbox placement Poor deliverability harms sender score and can lead to IP addresses and associated accounts being blacklisted Mixed country usage and inconsistent ip addresses can further confuse filtering systems so using multiple accounts from different countries ips without a clear legitimate reason increases risk
Why phone verified accounts and pva accounts are not a simple fix
Phone verified gmail accounts and pva accounts are often marketed as premium because they have an added layer of verification But that verification can create false confidence Phone verification does not guarantee long term control and sellers who supply phone numbers may reuse or recycle verification methods which weakens security Sellers may offer instant delivery and promises like mix ip more or turkey ip more or eu ip more to appeal to international needs however these tactics often violate platform policies and attract additional scrutiny from automated systems and human reviewers
Ethical alternatives that achieve the same goals
There are legitimate ways to build an effective email marketing program and robust online presence without buying older accounts Focus on creating and warming
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