Automation Team Quotes

I began looking for these four: Smart. It doesn’t mean high IQ (although that’s great), it means disposed toward learning. If there’s a best practice anywhere, adopt it. We want to turn as much as possible into a routine so we can focus on the few things that require human intelligence and creativity. A good interview question for this is: “Tell me about the last significant thing you learned about how to do your job better.” Or you might ask a candidate: “What’s something that you’ve automated? What’s a process you’ve had to tear down at a company?” Humble. I don’t mean meek or unambitious, I mean being humble in the way that Steph Curry is humble. If you’re humble, people want you to succeed. If you’re selfish, they want you to fail. It also gives you the capacity for self-awareness, so you can actually learn and be smart. Humility is foundational like that. It is also essential for the kind of collaboration we want at Slack. Hardworking. It does not mean long hours. You can go home and take care of your family, but when you’re here, you’re disciplined, professional, and focused. You should also be competitive, determined, resourceful, resilient, and gritty. Take this job as an opportunity to do the best work of your life. Collaborative. It’s not submissive, not deferential—in fact it’s kind of the opposite. In our culture, being collaborative means providing leadership from everywhere. I’m taking responsibility for the health of this meeting. If there’s a lack of trust, I’m going to address that. If the goals are unclear, I’m going to deal with that. We’re all interested in getting better and everyone should take responsibility for that. If everyone’s collaborative in that sense, the responsibility for team performance is shared. Collaborative people know that success is limited by the worst performers, so they are either going to elevate them or have a serious conversation. This one is easy to corroborate with references, and in an interview you can ask, “Tell me about a situation in your last company where something was substandard and you helped to fix it.

Every change that is made to an application’s configuration, source code, environment, or data, triggers the creation of a new instance of the pipeline. One of the first steps in the pipeline is to create binaries and installers. The rest of the pipeline runs a series of tests on the binaries to prove that they can be released. Each test that the release candidate passes gives us more confidence that this particular combination of binary code, configuration information, environment, and data will work. If the release candidate passes all the tests, it can be released. The deployment pipeline has its foundations in the process of continuous integration and is in essence the principle of continuous integration taken to its logical conclusion. The aim of the deployment pipeline is threefold. First, it makes every part of the process of building, deploying, testing, and releasing software visible to everybody involved, aiding collaboration. Second, it improves feedback so that problems are identified, and so resolved, as early in the process as possible. Finally, it enables teams to deploy and release any version of their software to any environment at will through a fully automated process.

1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience. For example, managers of software teams must spend at least 20% of their time coding. Solar roof managers must spend time on the roofs doing installations. Otherwise, they are like a cavalry leader who can’t ride a horse or a general who can’t use a sword. Comradery is dangerous. It makes it hard for people to challenge each other’s work. There is a tendency to not want to throw a colleague under the bus. That needs to be avoided. It’s OK to be wrong. Just don’t be confident and wrong. Never ask your troops to do something you’re not willing to do. Whenever there are problems to solve, don’t just meet with your managers. Do a skip level, where you meet with the level right below your managers. When hiring, look for people with the right attitude. Skills can be taught. Attitude changes require a brain transplant. A maniacal sense of urgency is our operating principle. The only rules are the ones dictated by the laws of physics. Everything else is a recommendation

Change always starts at home. The only person you can actually change is yourself. No matter how functional or dysfunctional your organization, you can begin applying XP for yourself. Anyone on the team can begin changing his own behavior. Programmers can start writing tests first. Testers can automate their tests. Customers can write stories and set clear priorities. Executives can expect transparency. Dictating practices to a team destroys trust and creates resentment. Executives can encourage team responsibility and accountability. Whether the team produces these with XP, a better waterfall, or utter chaos is up to them. Using XP, teams can produce dramatic improvements in the areas of defects, estimation, and productivity.

Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience. For example, managers of software teams must spend at least 20% of their time coding. Solar roof managers must spend time on the roofs doing installations. Otherwise, they are like a cavalry leader who can’t ride a horse or a general who can’t use a sword. Comradery is dangerous. It makes it hard for people to challenge each other’s work. There is a tendency to not want to throw a colleague under the bus. That needs to be avoided. It’s OK to be wrong. Just don’t be confident and wrong. Never ask your troops to do something you’re not willing to do. Whenever there are problems to solve, don’t just meet with your managers. Do a skip level, where you meet with the level right below your managers. When hiring, look for people with the right attitude. Skills can be taught. Attitude changes require a brain transplant. A maniacal sense of urgency is our operating principle. The only rules are the ones dictated by the laws of physics.

five commandments: 1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience. For example, managers of software teams must spend at least 20% of their time coding. Solar roof managers must spend time on the roofs doing installations. Otherwise, they are like a cavalry leader who can’t ride a horse or a general who can’t use a sword. Comradery is dangerous. It makes it hard for people to challenge each other’s work. There is a tendency to not want to throw a colleague under the bus. That needs to be avoided. It’s OK to be wrong. Just don’t be confident and wrong. Never ask your troops to do something you’re not willing to do. Whenever there are problems to solve, don’t just meet with your managers. Do a skip level, where you meet with the level right below your managers. When hiring, look for people with the right attitude. Skills can be taught. Attitude changes require a brain transplant. A maniacal sense of urgency is our operating principle.

Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience. For example, managers of software teams must spend at least 20% of their time coding. Solar roof managers must spend time on the roofs doing installations. Otherwise, they are like a cavalry leader who can’t ride a horse or a general who can’t use a sword. Comradery is dangerous. It makes it hard for people to challenge each other’s work. There is a tendency to not want to throw a colleague under the bus. That needs to be avoided. It’s OK to be wrong. Just don’t be confident and wrong. Never ask your troops to do something you’re not willing to do. Whenever there are problems to solve, don’t just meet with your managers. Do a skip level, where you meet with the level right below your managers. When hiring, look for people with the right attitude. Skills can be taught. Attitude changes require a brain transplant. A maniacal sense of urgency is our operating principle. The only rules are the ones dictated by the laws of physics. Everything else is a recommendation.

I became a broken record on the algorithm,” Musk says. “But I think it’s helpful to say it to an annoying degree.” It had five commandments: 1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience. For example, managers of software teams must spend at least 20% of their time coding. Solar roof managers must spend time on the roofs doing installations. Otherwise, they are like a cavalry leader who can’t ride a horse or a general who can’t use a sword. Comradery is dangerous. It makes it hard for people to challenge each other’s work. There is a tendency to not want to throw a colleague under the bus. That needs to be avoided. It’s OK to be wrong. Just don’t be confident and wrong. Never ask your troops to do something you’re not willing to do. Whenever there are problems to solve, don’t just meet with your managers. Do a skip level, where you meet with the level right below your managers. When hiring, look for people with the right attitude. Skills can be taught. Attitude changes require a brain transplant. A maniacal sense of urgency is our operating principle. The only rules are the ones dictated by the laws of physics. Everything else is a recommendation.

It felt like fate when I first encountered the automated trading system that promised to transform small investments into substantial wealth over time. The marketing was aggressive, bombarding my social media feeds with images of people lounging on exotic beaches, driving fancy cars, and celebrating their newfound financial freedom. WhatsApp info:+12723328343 As a recent college graduate struggling to make ends meet, I was desperate for a way out of my financial rut, and the allure of easy money was too tempting to ignore. On a whim, I decided to take the plunge. I borrowed from my meager savings and even took out a small loan to fund my excitement. The rush I felt when signing up was like nothing I had ever experienced—an intoxicating thrill, like hopping onto a rollercoaster at full speed. At first, everything seemed to be going exactly as promised. My investment seemed to grow almost overnight, doubling and tripling in value.  My skepticism began to fade, replaced by a sense of confidence and hope for the future. I even shared my success with friends and family, excitedly telling them about the platform that was going to change my life. I imagined a future free from financial worries, a life of luxury and freedom, all thanks to this “revolutionary” trading system. But soon, a familiar sense of unease began to settle in. What had been an impressive surge in profits suddenly plateaued, and I found myself facing unexpected hurdles when trying to withdraw my funds. Pop-up messages about my “account needing an upgrade” and “market tightening” explained away the issues, but the discomfort grew. Still, I convinced myself that success required patience and continued to hold out hope that the system would recover. As weeks turned into months, my investment continued to dwindle. The once-promising account balance plummeted, and each attempt to reach customer support went unanswered. The promises of easy wealth had turned into an unsettling nightmare. Email info: Adwarerecoveryspecialist@auctioneer. net Desperate for answers, I began scouring the internet for any information or advice. That’s when I stumbled across reviews of ADWARE RECOVERY SPECIALIST , a service that seemed to specialize in helping people like me recover lost funds from fraudulent platforms. I felt a glimmer of hope as I read about others who had managed to retrieve their investments with the help of ADWARE RECOVERY SPECIALIST. Perhaps, after all, there was still a way out of this mess. I reached out to their team, and to my relief, they were able to assist me in recovering a portion of the money I thought I had lost for good. ADWARE RECOVERY SPECIALIST gave me the guidance and support I needed to navigate this complicated process, helping me regain control of a situation that had seemed hopeless. Their professionalism and expertise allowed me to salvage what I could, and for that, I am incredibly grateful.

Selling is crucial to your success because without the sale, you do not make any money. The great thing about writing a book to position yourself is that the book does a lot of the selling for you. People read the book and come to you for more answers. If you have products created to match the theme of your book, your platform (website) will do the selling for you. Automate as much of the process as you can with opt-in boxes, video sales landing pages and special offers. Make it as easy as you can for your fans and followers. Once your products are created, simply write about them, talk about them, and create articles from the content and say, “Yes” to interviews. The buzz created will point people back to your site where your automatic sales team is ready to take orders 24 hours a day.

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.

This is what happened when I cofounded LinkedIn. The key business model innovations for LinkedIn, including the two-way nature of the relationships and filling professionals’ need for a business-oriented online identity, didn’t just happen organically. They were the result of much thought and reflection, and I drew on the experiences I had when founding SocialNet, one of the first online social networks, nearly a decade before the creation of LinkedIn. But life isn’t always so neat. Many companies, even famous and successful ones, have to develop their business model innovation after they have already commenced operations. PayPal didn’t have a business model when it began operations (I was a key member of the PayPal executive team). We were growing exponentially, at 5 percent per day, and we were losing money on every single transaction we processed. The funny thing is that some of our critics called us insane for paying customers bonuses to refer their friends. Those referral bonuses were actually brilliant, because their cost was so much lower than the standard cost of acquiring new financial services customers via advertising. (We’ll discuss the power and importance of this kind of viral marketing later on.) The insanity, in fact, was that we were allowing our users to accept credit card payments, sticking PayPal with the cost of paying 3 percent of each transaction to the credit card processors, while charging our users nothing. I remember once telling my old college friend and PayPal cofounder/ CEO Peter Thiel, “Peter, if you and I were standing on the roof of our office and throwing stacks of hundred-dollar bills off the edge as fast as our arms could go, we still wouldn’t be losing money as quickly as we are right now.” We ended up solving the problem by charging businesses to accept payments, much as the credit card processors did, but funding those payments using automated clearinghouse (ACH) bank transactions, which cost a fraction of the charges associated with the credit card networks. But if we had waited until we had solved this problem before blitzscaling, I suspect we wouldn’t have become the market leader.

They got comfortable using a new tool called Bandito, developed by Prakash’s team, which allowed them to test various headline options, blurbs, and images to determine which ones would propel the story to social media virality. Bandito even went beyond Chartbeat’s headline automator in that after determining which headline package worked best, Bandito applied the insight without needing to get the human editor’s approval first.

Do not be tempted to think, like many leaders, that you can keep piling on the initiatives and still maintain your team’s focus. You may find yourself in the middle of the “Ship is Adrift” syndrome (Adherence Enemy #1). Instead, consider automating, streamlining or outsourcing areas that are not core to your team’s operation.

Having automated tests that are reliable: when the automated tests pass, teams are confident that their software is releasable.

all team members as well as passers-by can see the latest information at a glance: count of automated tests, velocity, incident reports, continuous integration status, and so on. This

To mitigate these types of problems, we strive to reduce the number of handoffs, either by automating significant portions of the work or be reorganizing teams so they can deliver value to the customer themselves, instead of having to be constantly dependent on others.

Agile software development needs teams to be motivated. But repetitive tasks are boring, not motivating, so they should be automated. Many

Why I was successful In 2014, I was 29 years old. I was selling against companies that had been in the QuickBooks integration business for five years or more. Some competitors had millions of dollars in venture capital. Their websites were the equivalent of a five-star hotel. These competitors had large sales and marketing teams that could easily show the value of their solution. The companies had a team of programmers. I had my pajamas, a corded phone, a cookie-cutter website, and a laptop computer. I signed up about three hundred new accounts because I was the first person to pick up the phone and I spoke English. I could answer questions on what my software can automate. If there was a problem, I called the customer and we did a screenshare. You need to talk to customers on the phone and you cannot email customers to death. Many customers later told me they reached out to competitors and received no response to sales or support inquiries. These customers said they chose my company because I was responsive. Potential customers want to speak to someone in their area who understands their language. You need to connect with them. Many people signed up for Connex because they liked me over the phone. We attract many small business owners. I had similar interests and I owned a business, just like them.

a disciplined team can slow down the pace of its descent toward monolithic hell. Team members can work hard to maintain the modularity of their application. They can write comprehensive automated tests. On the other hand, they can’t avoid the issues of a large team working on a single monolithic application. Nor can they solve the problem of an increasingly obsolete technology stack. The best a team can do is delay the inevitable.

To begin the discussion of the Tipping Point, I’ll start with a prominent strategy, “Invite-Only,” that is often used to suck in a large network through viral growth. Another method to tip over a market is with a “Come for the Tool, Stay for the Network” strategy. Take Dropbox, for instance, which is initially adopted by many people for file backup and keeping files synced up between work and home computers—this is the tool. But eventually, a more advanced and stickier use case emerges to share folders with colleagues—this is the network. And if that doesn’t work, some products can always just spend money to build out their network, with a strategy of just “Paying Up for Launch.” For many networked products that touch transactions like marketplaces, teams can just subsidize demand and spend millions to stimulate activity, whether that’s in paying content creators for your social network, or subsidizing driver earnings in rideshare. If the hard side of the network isn’t yet activated, a team can just fill in their gaps themselves, using the technique of “Flintstoning”—as Reddit did, submitting links and content until eventually adding automation and community features for scale. In the end, all of these strategies require enormous creativity. And to close out the Tipping Point section of the book, I introduce Uber’s core ethos of “Always Be Hustlin’”—describing the creativity and decentralized set of teams, all with its own strategies that were localized to each region. Sometimes adding the fifth or one hundredth network requires creativity, product engagements, and tactical changes. In the goal of reaching the Tipping Point, teams must be fluid to build out a broad network of networks.

Phase 1: Discovery 1. Define the problem statement What is the challenge that will be solved? The problem statement is defined at this step and becomes the foundation of the project. Here is a sample problem statement: The company has more than one hundred thousand email addresses and has sent more than one million emails in the last twelve months, but open rates remain low at 8 percent, and sales attributed to email have remained flat since 2018. Based on current averages, a 2 percentage-point lift in email open rates could produce a $50,000 increase in sales over the next twelve months. It’s important to note that a strong and valid problem statement should include the value of solving the problem. This helps ensure that the project is worth the investment of resources and keeps everyone focused on the goal. 2. Build and prioritize the issues list What are the primary issues causing the problem? The issues are categorized into three to five primary groups and built into an issues tree. Sample issues could be: •​Low open rates •​Low click rates •​Low sales conversion rates 3. Identify and prioritize the key drivers. What factors are driving the issues and problem? Sample key drivers could include: •​List fatigue •​Email creatives •​Highly manual, human-driven processes •​Underutilized or missing marketing technology solutions •​Lack of list segmentation •​Lack of reporting and performance management •​Lack of personalization 4. Develop an initial hypothesis What is the preliminary road map to solving the problem? Here is a sample initial hypothesis: AI-powered technologies can be integrated to intelligently automate priority use cases that will drive email efficiency and performance. 5. Conduct discovery research What information can we gain about the problem, and potential solutions, from primary and secondary research? •​How are talent, technology, and strategy gaps impacting performance? •​What can be learned from interviews with stakeholders and secondary research related to the problem? Ask questions such as the following: •​What is the current understanding of AI within the organization? •​Does the executive team understand and support the goal of AI pilot projects?

The dimensions impacting your team’s structure the most, and the ones you must most carefully consider include: The maturity of the market: the less mature the market (read: the more unique your product category is), the more evangelizers you need, as opposed to the next point: The maturity of your product: the less mature the product, the more hand-holding the customer needs. The maturity of your deployment process: the easier it is for the customer to get on-boarded and become self-sufficient, the more streamlined your organization can be (fewer roles, less complexity and fewer people touching the customer). The size of your customer base: the more customers you have to manage, the more important it is to move quickly towards automating activities in order to control costs. The extent of the Land-and-Expand strategy: the more potential for expansion there is with your customers, the more involved you want to be with them over time and the more quickly you would want to move commercial responsibilities over from Sales to the Customer Success organization.

The secret to surviving and thriving in the automation revolution is in what computers can’t replace: human creativity, empathy, and critical thinking—especially in unpredictable environments.

Technology has also given us greater automation that allows us to take large tasks and turn them into more manageable tasks. The pressure then comes from executive management to be “fully informed and fully engaged” in every aspect of our operation, after all, it’s easier than ever to do so! This of course misses the point that those advancements free the leader from the “small” things and allows them to focus on things with bigger impact like, team member development, strategy and other proactive measures. We’re missing out on the big picture if we are constantly focused on the small things.

Amazingly, the transformations are not primarily based on automation. Instead, the incredible improvements come from modifying policies around the system of work and the policies that control work in process, ensuring that there are effective cross-functional teams, subordinating everything to the constraint, and managing handoffs well.

Wage Garnishment Majority of students complete their education with student loan debt. Once you have graduated from college and stepinto the real world, you realize it isn’t as easy as it seemed. Student loan is one of the most difficult loans to repay and it also cannot be discharged into bankruptcy. Thus it has to be repaid!One thing that should always be kept in mind is to never skip your loan payments. If this happens and happens consecutively for months it will open doors to many other problems. It will put your loan in default; your entire loan amount and interest will become due immediately. It will adversely impact your credit score. We discuss Wage Garnishment with The Student Loan Help Center team, let’s see what they said about it. So What is wage garnishment? Wage garnishment happens when your loan is in default (you can consult The Student Loan help center if you want) i.e you have not paid the loan for consecutive 270 days. Now Wage garnishment is one of the legal consequences of going into default. Through this method the government starts deducting 15% of your income. That means you in hand income willreduce with only 85% coming in your bank account. However the amount of wage that can be garnished for private loandiffers from state to state since every state is not allowed to garnish the wages. How to avoid? As discussed before, wage garnishment happens only when your loan is in default. The department of education sends you one letter when you are in default. The best way to avoid this problem is to avoid going to default. There are numerous measures you can adopt right from very beginning to keep your loan repayment on track. For eg, starting to pay interest in your grace period, automating the process of monthly payments to get some discount from bank etc. Now what if you are in default or going in default, then the best option would be to consider forbearance or deferment which will stop your wages from being garnished. How can it be challenged? If you have just received the notice from Department of Education then you are given one opportunity to get a hearing and object to wage garnishment. You can challenge wage garnishment on following grounds: Your income Your employment Procedures followed to start the garnishment etc Also your wage garnishment cannot begin before the notice of 30 days. During this time period you request a hearing garnishment will be put on hold and if 30 days are over garnishment will not stop if you have won the hearing. One of the Best Student Loan consolidation services in USA is The Student Loan Help Center in Florida for all kind of Student Loan consultation you can contact any time.

There are various incremental improvements to the way software is delivered which will yield immediate benefits, such as teaching developers to write production-ready software, running CI on production-like systems, and instituting cross-functional teams.

Implementing functional test automation will get the team to work closer and prepare the system for the use of executable specifications later.

Algiz Technology provide enterprise it teams best MSI Packaging, Application Virtualization tools including the MSI packager, with the most advanced software packaging tools for deployment with a complete suite of automated customization, testing, MSI packaging and management reporting capabilities.

DECISIONS Useful: Graphical Presentation Monitor Key Indicators Effective Measurements Wisdom Knowledge The Goal: Strategic Thinking Predictive Value Experience and Judgment Automated Exception Notification Information Structured: Voluminous Grouped and Summarized Relationships Not Always Evident Raw Data: Massive Fragmented Meaningless Data EVENTS Figure 1-01. The Pyramid of KnowledgeToyota, this begins with genchi genbutsu, or gemba, which means literally “go see it for yourself. ” Taiichi Ohno, a founding father of Lean, once said, “Data is of course important in manufacturing, but I place the greatest emphasis on facts. ” 2 A direct and intuitive understanding of a situation is far more useful than mountains of data. The raw data stored in a database adds value for decision-making only if the right information is presented in the right format, to the right people, at the right time. A tall stack of printout may contain the right data, but it’s certainly not in an accessible format. Massive weekly batch printouts do not enable timely and proactive decisions. Raw data must be summarized, structured, and presented as digestible information. Once information is combined with direct experience, then the incredible human mind can extract and develop useful knowledge. Over time, as knowledge is accumulated and combined with direct experience and judgment, wisdom develops. This evolution is described by the classic pyramid of knowledge shown in Figure 1-01. BACK TO CHICAGO So what happened in Chicago? We can speculate upon several possible perspectives for why the team and its change leader were far from a true Lean system, yet they refused any help from IT providers: 1. They feared wasteful IT systems and procedures would be foisted on them.

When you automate a previously all-human system, it becomes entirely deterministic.

Greenfield DevOps projects are often pilots to demonstrate feasibility of public or private clouds, piloting deployment automation, and similar tools. An example of a greenfield DevOps project is the Hosted LabVIEW product in 2009 at National Instruments, a thirty-year-old organization with five thousand employees and $1 billion in annual revenue. To bring this product to market quickly, a new team was created and allowed to operate outside of the existing IT processes and explore the use of public clouds. The initial team included an applications architect, a systems architect, two developers, a system automation developer, an operations lead, and two offshore operations staff. By using DevOps practices, they were able to deliver Hosted LabVIEW to market in half the time of their normal product introductions.

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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Think big, start small, and scale fast”: Initiate the transformation with the definition of a multi-year, company-wide vision, roadmap, and business case. These plans need to be flexible and adaptable. Then, “start small” with the implementation of a pilot, and take the time to learn from this first experience. Finally, implement the broader scope in stages to manage the risks. Gradually increase the speed and scale of the transformation, and as a result, generate high impact. “IA is a business transformation, not a technology project”: The perspective of business benefits should guide the transformation. This transformation involves not only technology, but more importantly, people – with change management, and retraining – and processes – with redesigns. “IA is a journey, not a destination”: IA is not a one-off exercise; it is a never-ending transformation journey. It continually brings additional benefits to the organization by applying evolving concepts, methods, and technologies. Hence, building teams with the right skills to guide the company in this transformation is critical. “Infusing IA into the culture of the company”: Implementing IA with siloed, isolated teams does not work. Automation needs to be infused into the company. Change management, education, empowerment, and incentivization of everyone in the company is vital. Every employee should know what IA is and what its benefits are, and be empowered and incentivized to identify use cases and build automation.

some work activities are displaced, but new ones, more fulfilling and value-adding, arise. What are the new ones? Think of these activities that everyone knows are crucial, but that actually none has the time to perform – for example, spending one hour per week with each team member to coach them.

create their own OKRs for their own organization. For example, the design department might have objectives related to moving to a responsive design; the engineering department might have objectives related to improving the scalability and performance of the architecture; and the quality department might have objectives relating to the test and release automation. The problem is that the individual members of each of these functional departments are the actual members of a cross‐functional product team. The product team has business‐related objectives (for example, to reduce the customer acquisition cost, to increase the number of daily active users, or to reduce the time to onboard a new customer), but each person on the team may have their own set of objectives that cascade down through their functional manager. Imagine if the engineers were told to spend their time on re‐platforming, the designers on moving to a responsive design, and QA on retooling. While each of these may be worthy activities, the chances of solving the business problems that the cross‐functional teams were created to solve are not high.

Automated systems can allow mistakes, errors, and attacks to be propagated and multiplied in far more damaging ways than manual systems. As the DevOps comedy account @DevOpsBorat says, “To make error is human. To propagate error to all server in automatic way is #devops.” 2 Furthermore, automated tooling is fallible; and as we know so well in the security world, it can be easy for humans to begin to trust in the computer and stop applying sense or judgment to the results. This can lead to teams trusting that if the tests pass, the system is working as expected, even if other evidence might indicate otherwise.

Agile teams rely on automation heavily in order to get the speed, repeatability, and consistency that they need to keep moving forward. However automation itself comes with its own risks. The tools themselves can be the target of attack and an attack vector in themselves,

I read a post on Palestinian Refugee ResearchNet’s blog claiming that Facebook was blocking the term “Palestinian” from being used in page titles. The post included a screenshot of an attempt to create a page called “Palestinian Refugee ResearchNet,” with a warning splashed across the top that read: “Our automated system will not allow the name ‘Palestinian Refugee ResearchNet.’ It may violate our Pages Guidelines or contain a word or phrase that is blocked to prevent the creation of unofficial or otherwise prohibited Pages. If you believe this is an error, please contact our Customer Support team.” The blogger, Rex Brynen, tested several similar titles, replacing “Palestinian” with “Israeli” and “Afghan.” Both worked, so he wrote to the support team.

Then campaigns would be designed based on content our in-house team had composed—and the final, fifth step, the micro-targeting strategy, allowed everything from video to audio to print ads to reach the identified targets. Using an automated system that refined that content again and again, we were able to understand what made individual users finally engage with that content in a meaningful way. We might learn that it took as many as twenty or thirty variations of the same ad sent to the same person thirty different times and placed on different parts of their social media feed before they clicked on it to act. And knowing that, our creatives, who were producing new content all the time, knew how to reach those same people the next time CA sent something out.

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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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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CRM (Customer Relationship Management) is a marketing strategy that focuses on managing interactions and relationships with customers. CRM enables businesses to improve customer satisfaction, loyalty, and retention by providing personalized experiences that meet their needs. CRM is an essential aspect of modern marketing as it enables businesses to understand their customers' behavior, preferences, and needs and develop targeted marketing campaigns that resonate with them. In Go High Level, CRM (Customer Relationship Management) is a core component of the platform. The CRM functionality in Go High Level enables businesses to manage their customer interactions and relationships more effectively, improving customer satisfaction, loyalty, and retention. The CRM functionality in Go High Level includes a range of features and tools designed to help businesses automate and streamline their customer-facing processes, as well as provide them with insights into their customers' behavior, preferences, and needs. In essence, CRM is a set of practices, technologies, and strategies that businesses use to manage their customer interactions and relationships. The goal of CRM is to build stronger, more meaningful relationships with customers by providing them with personalized experiences and tailored solutions. CRM in marketing can be divided into three main categories: operational CRM, analytical CRM, and collaborative CRM. Operational CRM focuses on automating and streamlining customer-facing processes, such as sales, marketing, and customer service. This type of CRM is designed to improve efficiency and productivity by automating repetitive tasks and providing a centralized database of customer information. Operational CRM includes features such as sales pipeline management, lead nurturing, and customer service management. Analytical CRM focuses on analyzing customer data to gain insights into their behavior, preferences, and needs. This type of CRM enables businesses to make data-driven decisions by providing them with a better understanding of their customers' needs and preferences. Analytical CRM includes features such as customer segmentation, data mining, and predictive analytics. Collaborative CRM focuses on enabling businesses to collaborate and share customer information across different departments and functions. This type of CRM helps to break down silos within organizations and improve communication and collaboration between different teams. Collaborative CRM includes features such as customer feedback management, social media monitoring, and knowledge management. CRM is important for marketing because it enables businesses to build stronger, more meaningful relationships with customers. By understanding their customers' behavior, preferences, and needs, businesses can develop targeted marketing campaigns that resonate with them. This results in higher customer satisfaction, loyalty, and retention. CRM can also help businesses to improve their sales and marketing processes by providing them with better visibility into their sales pipeline and enabling them to track and analyze their marketing campaigns' effectiveness. This enables businesses to make data-driven decisions to improve their sales and marketing strategies, resulting in increased revenue and growth. Another benefit of CRM in marketing is that it enables businesses to personalize their marketing campaigns. Personalization is essential in modern marketing as it enables businesses to tailor their marketing messages and solutions to meet their customers' specific needs and preferences. This results in higher engagement and conversion rates, as customers are more likely to respond to marketing messages that resonate with them. Lead Generation: Go High Level provides businesses with a range of tools to generate leads, including customizable landing pages, web forms, and social media integrations.

They start making a list: Every developer uses a common build environment. Every developer is supported by a continuous build and integration system. Everyone can run their code in production-like environments. Automated test suites are built to replace manual testing, liberating QA people to do higher value work. Architecture is decoupled to liberate feature teams, so developers can deliver value independently. All the data that teams need is put in easily consumed APIs

here is an early version of principles established by a client adopting LeSS Huge in a product group: 1. The perfection goal is to have a releasable product all the time. Release stabilization periods need to be reduced and eventually eliminated. 2. Co-located, self-managing, cross-functional, Scrum teams are the basic organizational building block. Responsibility and accountability are on team level. 3. The majority of the teams are organized as customer-centric feature teams. 4. Product management steers the development through the Product Owner role. Release commitments are not forced on teams. 5. The line organization is cross-functional. The functional-specialized line organizations are gradually integrated in the cross-functional line organization. 6. Special coordination roles (such as project managers) are avoided and teams are responsible for coordination. 7. The main responsibility of management is improvement—improve team’s learning, efficiency, and quality. The content of the work always comes from the Product Owner. 8. There is no branching in development. And product variation is not to be reflected in the version control system. 9. All tests are automated with the exception of (1) exploratory test, (2) usability test, and (3) tests that require physical movement. All people must learn test automation skills. 10. Adoption is gradual and evolutionary. These principles are considered in every decision.

When teams decide to automate a set of existing tests that were previously designed as manual tests, the best way forward is to rewrite and redesign the tests from scratch. Keep the purpose, but throw away pretty much everything else.

A common pattern for teams that start with test automation, or development groups that start breaking down silos between testers and developers, is to take existing manual tests and automate them. Unless it’s a training exercise for an automation tool, this is almost always a bad idea.

In many organizations where automated functional testing is done at all, a common practice is to have a separate team dedicated to the production and maintenance of the test suite. As described at length in Chapter 4, “Implementing a Testing Strategy,” this is a bad idea. The most problematic outcome is that the developers don’t feel as if they own the acceptance tests. As a result, they tend not to pay attention to the failure of this stage of the deployment pipeline, which leads to it being broken for long periods of time. Acceptance tests written without developer involvement also tend to be tightly coupled to the UI and thus brittle and badly factored, because the testers don’t have any insight into the UI’s underlying design and lack the skills to create abstraction layers or run acceptance tests against a public API.

By adding the expertise of QA, IT Operations, and Infosec into delivery teams and automated self-service tools and platforms, teams are able to use that expertise in their daily work without being dependent on other teams.

High-performing teams were more likely to incorporate information security into the delivery process. Their infosec personnel provided feedback at every step of the software delivery lifecycle, from design through demos to helping with test automation.

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Broadly speaking, for effective delivery and operations of modern software systems, organizations should attempt to minimize intrinsic cognitive load (through training, good choice of technologies, hiring, pair programming, etc.) and eliminate extraneous cognitive load altogether (boring or superfluous tasks or commands that add little value to retain in the working memory and can often be automated away), leaving more space for germane cognitive load (which is where the “value add” thinking lies).

But what aren't you doing already? What more can you possibly do?’ ‘I guess he means the team stuff’, I said. ‘The bonding. The camaraderie I've never really been –‘. ‘Don't start judging yourself’, she said sharply. ‘Don't start seeing yourself in the light of those kinds of standards.’ ‘No, but it's true. There's always been the part of work I've struggled with, the unquestioning side. The feeling of joining in. I've always tried to do it at this kind of remove. Maybe what he's saying is –‘ ‘Of course you've done it at a remove. How else are you supposed to do it and still be you?’ ‘But maybe those days are gone’, I said. ‘Maybe I have to accept that. Maybe there just won't be those kind of jobs anymore - the ones where you can roll out of bed and staggering without speaking to anyone and keep your head down and just do it, you know? maybe this is what work is, now’ […] ‘Definitely. Simple tasks can be automated. They've already almost got the machine learning to do what you do. It's about what else a human can bring to the table, which is, literally, their humanity.’ It was possible, I realised, to imagine. A semi-global future in which the bulk of paid human employment would revolve not around hard skills, but around the messy, blurry business of interpersonal success. A new divide would open up, between the well liked, The easy to get along with, and the awkward, The rude, the unfriendly. I pictured the encampment on which I had lived, filled not as it was then, with migrants, unfortunates, hard drinkers, the out of luck. But instead, the abrasive, the poorly adjusted, the excessively reserved and painfully shy. (p.136-7)

While there is no formula for cognitive load, we can assess the number and relative complexity (internal to the organization) of domains for which a given team is responsible. The Engineering Productivity team at OutSystems that we mentioned in Chapter 1 realized that the different domains they were responsible for (build and continuous integration, continuous delivery, test automation, and infrastructure automation) had caused them to become overloaded. The team was constantly faced with too much work and context switching prevailed, with tasks coming in from different product areas simultaneously. There was a general sense in the team that they lacked sufficient domain knowledge, but they had no time to invest in acquiring it. In fact, most of their cognitive load was extraneous, leaving very little capacity for value-add intrinsic or germane cognitive load. The team made a bold decision to split into microteams, each responsible for a single domain/product area: IDE productivity, platform-server productivity, and infrastructure automation. The two productivity microteams were aligned (and colocated) with the respective product areas (IDE and platform server). Changes that overlapped domains were infrequent; therefore, the previous single-team model was optimizing for the exceptions rather than the rule. With the new structure, the teams collaborated closely (even creating temporary microteams when necessary) on cross-domain issues that required a period of solution discovery but not as a permanent structure. After only a few months, the results were above their best expectations. Motivation went up as each microteam could now focus on mastering a single domain (plus they didn’t have a lead anymore, empowering team decisions). The mission for each team was clear, with less context switching and frequent intra-team communication (thanks to a single shared purpose rather than a collection of purposes). Overall, the flow and quality of the work (in terms of fitness of the solutions for product teams) increased significantly.

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