Iterative Process Quotes

Science is often described as an iterative and cumulative process, a puzzle solved piece by piece, with each piece contributing a few hazy pixels of a much larger picture. But the arrival of a truly powerful new theory in science often feels far from iterative. Rather than explain one observation or phenomenon in a single, pixelated step, an entire field of observations suddenly seems to crystallize into a perfect whole. The effect is almost like watching a puzzle solve itself.

Developing and implementing IT governance design effectiveness and efficiency can be a multidirectional, interactive, iterative, and adaptive process.

This book is about entanglements. To be entangled is not simply to be intertwined with another, as in the joining of separate entities, but to lack an independent, self-contained existence. Existence is not an individual affair. Individuals do not preexist their interactions; rather, individuals emerge through and as pare of their entangled intra-relating . Which is not to say that emergence happens once and for all, as an event or as a process that takes place according to some external measure of space and of time, but rather that time and space, like matter and meaning, come into existence, are iteratively recon figured through each intra-action, there by making it impossible to differentiate in any absolute sense between creation and renewal, beginning and returning, continuity and discontinuity, here and there, past and future.

Voltaire wrote, “The perfect is the enemy of the good.”188 Steve Jobs told the Macintosh team that “real artists ship.”189 New ideas are never perfect right out of the chute, and you don’t have time to wait until they get there. Create a product, ship it, see how it does, design and implement improvements, and push it back out. Ship and iterate. The companies that are the fastest at this process will win.

Our techniques were the products of experiential learning; they were developed by agents in the field, negotiating through crisis and sharing stories of what succeeded and what failed. It was an iterative process, not an intellectual one, as we refined the tools we used day after day.

There must be a trick to the train of thought, a recursive formula. A group of neurons starts working automatically, sometimes without external impulse. It is a kind of iterative process with a growing pattern. It wanders about in the brain, and the way it happens must depend on the memory of similar patterns.

It was the Scrum Master’s job to guide the team toward continuous improvement—to ask with regularity, “How can we do what we do better?” Ideally, at the end of each iteration, each Sprint, the team would look closely at itself—at its interactions, practices, and processes—and ask two questions: “What can we change about how we work?” and “What is our biggest sticking point?” If those questions are answered forthrightly, a team can go faster than anyone ever imagined.

Growth is an endlessly iterative process. When we learn something new, we don’t go from “wrong” to “right.” Rather, we go from wrong to slightly less wrong. And when we learn something additional, we go from slightly less wrong to slightly less wrong than that, and then to even less wrong than that, and so on. We

The reality is that while the Internet allows content to be written iteratively, the audience does not read or consume it iteratively. Each member usually sees what he or she sees a single time—a snapshot of the process—and makes his or her conclusions from that.

Youth is as easily wasted as a fine wine consumed by a drunken man. There is no poetry in aging, and Javert lived out the process in its most hideous iteration.

Learning is an iterative process. It takes time, context-specific experience, and feedback to learn.

Building habit-forming products is an iterative process and requires user behavior analysis and continuous experimentation.

involving into an iterative process of simplifying the 'complexity', and then transforming this 'simplicity into newer complexity' while integrating the unsolved domain for an unprecedented success.

Differences: Scrum: - Timeboxed iterations prescribed. Kanban: - Timeboxed iterations optional. Can have separate cadences for planning, release, and process improvement. Can be event - driven instead of timeboxed.

as well as for the team making restaurant reservations, experimenting along the way paid off. The iterative process, where small changes are made in response to customer feedback, allowed them to optimize their strategy on the fly.

Get over “blame” and “credit” and get on with “accurate” and “inaccurate.” Worrying about “blame” and “credit” or “positive” and “negative” feedback impedes the iterative process that is essential to learning. Remember that what has already happened lies in the past and no longer matters except as a lesson for the future.

It’s often the case that teams working in agile processes do not actually go back to improve the user interface of the software. But, as the saying goes, “it’s not iterative if you only do it once.” Teams need to make a commitment to continuous improvement, and that means not simply refactoring code and addressing technical debt but also reworking and improving user interfaces. Teams must embrace the concept of UX debt and make a commitment to continuous improvement of the user experience.

Global corporations have the human capital, the financial resources, the technology, the international footprint, the power of markets and the profit motivation to build a better world. NGOs will be essential partners...Governments will be essential partners...By engaging together through an iterative process, we will achieve "A Better World.

The process of arriving at the right answer by eliminating the negative hypothesis in an iterative manner does not work when dealing with fairy tales.

Iterative development, when accompanied with reasonable end-of-iteration reviews—product, technical, process, team—is also self-correcting.

Managing your small failures (iterations) and major failures (pivots) as part of the entrepreneurial development process to save you from a fatal failure that has been the hallmark of most entrepreneurial journeys.

Growth is an endlessly iterative process. When we learn something new, we don’t go from “wrong” to “right.” Rather, we go from wrong to slightly less wrong. And when we learn something additional, we go from slightly less wrong to slightly less wrong than that, and then to even less wrong than that, and so on. We are always in the process of approaching truth and perfection without actually ever reaching truth or perfection. We

Growth is an endlessly iterative process. When we learn something new, we don’t go from “wrong” to “right.” Rather, we go from wrong to slightly less wrong. And when we learn something additional, we go from slightly less wrong to slightly less wrong than that, and then to even less wrong than that, and so on. We are always in the process of approaching truth and perfection without actually ever reaching truth or perfection. We shouldn’t

The secret to modern F1 is not really to do with big ticket items; it is about hundreds of thousands of small items, optimized to the nth degree. People think that things like engines are based upon high-level strategic decisions, but they are not. What is an engine except many iterations of small components? You start with a sensible design, but it is the iterative process that guides you to the best solution. Success is about creating the most effective optimization loop.

Science is often described as an iterative and cumulative process, a puzzle solved piece by piece, with each piece contributing a few hazy pixels of a much larger picture. But the arrival of a truly powerful new theory in science often feels far from iterative. Rather than explain one observation or phenomenon in a single, pixelated step, an entire field of observations suddenly seems to crystallize into a perfect whole. The effect is almost like watching a puzzle solve itself. Varmus

I find that most people serve practical needs. They have an understanding of the difference between meaning and relevance. And at some level my mind is more interested in meaning than in relevance. That is similar to the mind of an artist. The arts are not life. They are not serving life. The arts are the cuckoo child of life. Because the meaning of life is to eat. You know, life is evolution and evolution is about eating. It's pretty gross if you think about it. Evolution is about getting eaten by monsters. Don't go into the desert and perish there, because it's going to be a waste. If you're lucky the monsters that eat you are your own children. And eventually the search for evolution will, if evolution reaches its global optimum, it will be the perfect devourer. The thing that is able to digest anything and turn it into structure to sustain and perpetuate itself, for long as the local puddle of negentropy is available. And in a way we are yeast. Everything we do, all the complexity that we create, all the structures we build, is to erect some surfaces on which to out compete other kinds of yeast. And if you realize this you can try to get behind this and I think the solution to this is fascism. Fascism is a mode of organization of society in which the individual is a cell in the superorganism and the value of the individual is exactly the contribution to the superorganism. And when the contribution is negative then the superorganism kills it in order to be fitter in the competition against other superorganisms. And it's totally brutal. I don't like fascism because it's going to kill a lot of minds I like. And the arts is slightly different. It's a mutation that is arguably not completely adaptive. It's one where people fall in love with the loss function. Where you think that your mental representation is the intrinsically important thing. That you try to capture a conscious state for its own sake, because you think that matters. The true artist in my view is somebody who captures conscious states and that's the only reason why they eat. So you eat to make art. And another person makes art to eat. And these are of course the ends of a spectrum and the truth is often somewhere in the middle, but in a way there is this fundamental distinction. And there are in some sense the true scientists which are trying to figure out something about the universe. They are trying to reflect it. And it's an artistic process in a way. It's an attempt to be a reflection to this universe. You see there is this amazing vast darkness which is the universe. There's all these iterations of patterns, but mostly there is nothing interesting happening in these patterns. It's a giant fractal and most of it is just boring. And at a brief moment in the evolution of the universe there are planetary surfaces and negentropy gradients that allow for the creation of structure and then there are some brief flashes of consciousness in all this vast darkness. And these brief flashes of consciousness can reflect the universe and maybe even figure out what it is. It's the only chance that we have. Right? This is amazing. Why not do this? Life is short. This is the thing we can do.

A sound idea is a form of energy. It can not be destroyed. It evolves from inspiration, to a function of preparation, then determination - till the ideator's dream becomes actualized in real life At the very least, success is a second iteration of the original, unscripted Idea So your idea refinement process needs to be test-driven Test Determine on time if investment in terms of effort and time is worth it Work Smart Fail early, fail often, Success lies on the paths yet to be treaded, Open your mind, Think Disruption, Be Flexible Be AGILE I think this is an idea worth sharing

Just as reproductive sex amplifies genes and their expressed traits, perpetuating the iterative cycle of life, shopping and purchasing are the acts required for innovations to capture resources and reproduce the flow of an innovation’s expressed capability. This economic process of selective value informational feedback amplifies successful innovations.

Executives, project leaders, and development teams must embrace a different view of the new product development world, one that not only recognizes change in the business world, but also understands the power of driving down iteration costs to enable experimentation and emergent processes. Understanding these differences and how they affect product development is key to understanding APM.

Visionaries are especially afraid of a false negative: that customers will reject a flawed MVP that is too small or too limited. […] The solution to this dilemma is a commitment to iteration. You have to commit to a locked-in agreement—ahead of time—that no matter what comes of testing the MVP, you will not give up hope. Successful entrepreneurs do not give up at the first sign of trouble, nor do they persevere the plan right into the ground. Instead, they process a unique combination of perseverance and flexibility.

On the day the company starts, there is very limited customer input to a product specification. The company doesn’t know who its initial customers are (but it may think it knows) or what they will want as features. One alternative is to put Product Development on hold until the Customer Development team can find those customers. However, having a product you can demonstrate and iterate is helpful in moving the Customer Development process along. A more productive approach is to proceed with Product Development, with the feature list driven by the vision and experience of the company’s founders.

iterative and concurrent. An iterative server iterates through the following steps: I1. Wait for a client request to arrive. I2. Process the client request. I3. Send the response back to the client that sent the request. I4. Go back to step I1. The problem with an iterative server occurs when step I2 takes a long time. During this time no other clients are serviced. A concurrent server, on the other hand, performs the following steps: C1. Wait for a client request to arrive. C2. Start a new server instance to handle this client’s request. This may involve creating a new process, task, or thread, depending on what the underlying operating system supports. This new server handles one client’s entire request. When the requested task

In order to find and eliminate a Constraint, Goldratt proposes the “Five Focusing Steps,” a method you can use to improve the Throughput of any System: 1. Identification: examining the system to find the limiting factor. If your automotive assembly line is constantly waiting on engines in order to proceed, engines are your Constraint. 2. Exploitation: ensuring that the resources related to the Constraint aren’t wasted. If the employees responsible for making engines are also building windshields, or stop building engines during lunchtime, exploiting the Constraint would be having the engine employees spend 100 percent of their available time and energy producing engines, and having them work in shifts so breaks can be taken without slowing down production. 3. Subordination: redesigning the entire system to support the Constraint. Let’s assume you’ve done everything you can to get the most out of the engine production system, but you’re still behind. Subordination would be rearranging the factory so everything needed to build the engine is close at hand, instead of requiring certain materials to come from the other end of the factory. Other subsystems may have to move or lose resources, but that’s not a huge deal, since they’re not the Constraint. 4. Elevation: permanently increasing the capacity of the Constraint. In the case of the factory, elevation would be buying another engine-making machine and hiring more workers to operate it. Elevation is very effective, but it’s expensive—you don’t want to spend millions on more equipment if you don’t have to. That’s why Exploitation and Subordination come first: you can often alleviate a Constraint quickly, without resorting to spending more money. 5. Reevaluation: after making a change, reevaluating the system to see where the Constraint is located. Inertia is your enemy: don’t assume engines will always be the Constraint: once you make a few Changes, the limiting factor might become windshields. In that case, it doesn’t make sense to continue focusing on increasing engine production—the system won’t improve until windshields become the focus of improvement. The “Five Focusing Steps” are very similar to Iteration Velocity—the more quickly you move through this process and the more cycles you complete, the more your system’s Throughput will improve.

To be entangled is not simply to be intertwined with another, as in the joining of separate entities, but to lack an independent, self-contained existence. Existence is not an individual affair. Individuals do not preexist their interactions; rather, individuals emerge through and as part of their entangled intra-relating. Which is not to say that emergence happens once and for all, as an event or as a process that takes place according to some external measure of space and of time, but rather that time and space, like matter and meaning, come into existence, are iteratively reconfigured through each intra-action, thereby making it impossible to differentiate in any absolute sense between creation and renewal, beginning and returning, continuity and discontinuity, here and there, past and future.

I’ve repeated this process with virtually every major classic-rock artist and band that I love. I am now fully versed in the postsixties work of the Kinks, even the double-album rock operas that go on for forty-two hours. I enjoy at least one Doors album, An American Prayer, that was completed and released seven years after Jim Morrison died. I will defend not only both Page & Plant albums, but also the Page & Coverdale record. I own albums by every iteration of Crosby, Stills, Nash and Young, and will argue that Crosby & Nash is in fact better than CSNY (though not CSN). I’m still not crazy about nineties Springsteen, but I will listen to Human Touch and Lucky Town when I don’t feel like playing Darkness on the Edge of Town or The River for the ten thousandth time. Come to think of it, Lucky Town is in fact much better than most people (even myself) give it credit for.

Clients, I soon learned, could be very demanding; I still had limited direct contact with them, which suited me just fine. From what I could gather, they would routinely be completely unable to articulate their requirements, at which point, in desperation, the designers would create some artwork for them based on the few vague hints they had managed to elicit. After many hours of work, involving a full team of staff, the work would be submitted to the client for approval. At that point, the client would say, "No. That's exactly what I don't want." There would be several tortuous iterations of this process before the client finally declared his or herself satisfied with the end results. Inevitably, Bob said, the artwork that was signed off on at the end of the process was virtually identical to the first piece of work submitted, which the client had immediately dismissed as unsuitable.

My fear is that much of the antiracist literature is an iteration of the same process of maintaining and reaffirming whiteness. Little in the mainstream antiracist narrative focuses on challenging the idea of “white people” itself. Rather, it takes the category as an unassailable truth, with the emphasis placed instead on making white people nicer, through a combination of begging, demanding, cajoling, and imploring. “Whiteness” was a concept popularized by convincing one group of people it would make their lives better, and demonstrating it through the brutal dehumanization of another group. Now all “whites,” even those with little power in any other quarter of their lives, had the power of life and death over these “others.” This is a “truth” that’s had close to five hundred years to really embed itself. The question I pose is this: Does telling “white” people that racial equality means that their lives have to literally get worse (“but thems the breaks”) really seem up to the challenge of uprooting this centuries-old pernicious promise?

How, then, to proceed? My method is: I imagine a meter mounted in my forehead, with “P” on this side (“Positive”) and “N” on this side (“Negative”). I try to read what I’ve written uninflectedly, the way a first-time reader might (“without hope and without despair”). Where’s the needle? Accept the result without whining. Then edit, so as to move the needle into the “P” zone. Enact a repetitive, obsessive, iterative application of preference: watch the needle, adjust the prose, watch the needle, adjust the prose (rinse, lather, repeat), through (sometimes) hundreds of drafts. Like a cruise ship slowly turning, the story will start to alter course via those thousands of incremental adjustments. The artist, in this model, is like the optometrist, always asking: Is it better like this? Or like this? The interesting thing, in my experience, is that the result of this laborious and slightly obsessive process is a story that is better than I am in “real life” – funnier, kinder, less full of crap, more empathetic, with a clearer sense of virtue, both wiser and more entertaining. And what a pleasure that is; to be, on the page, less of a dope than usual.

Product development has become a faster, more flexible process, where radically better products don’t stand on the shoulders of giants, but on the shoulders of lots of iterations. The basis for success then, and for continual product excellence, is speed. Unfortunately, like Jonathan’s failed gate-based product development framework, most management processes in place at companies today are designed with something else in mind. They were devised over a century ago, at a time when mistakes were expensive and only the top executives had comprehensive information, and their primary objectives are lowering risk and ensuring that decisions are made only by the few executives with lots of information. In this traditional command-and-control structure, data flows up to the executives from all over the organization, and decisions subsequently flow down. This approach is designed to slow things down, and it accomplishes the task very well. Meaning that at the very moment when businesses must permanently accelerate, their architecture is working against them.

Product development has become a faster, more flexible process, where radically better products don’t stand on the shoulders of giants, but on the shoulders of lots of iterations. The basis for success then, and for continual product excellence, is speed.

Growth is an endlessly iterative process. When we learn something new, we don't go from "wrong" to "right." Rather, we go from wrong to slightly less wrong. And when we learn something additional, we go from slightly less wrong to slightly less wrong than that, and so on. We are always in the process of approaching truth and perfection without actually ever reaching truth or perfection.

Correlation is enough,” 2 then-Wired editor in chief Chris Anderson famously declared in 2008. We can, he implied, solve innovation problems by the sheer brute force of the data deluge. Ever since Michael Lewis chronicled the Oakland A’s unlikely success in Moneyball (who knew on-base percentage was a better indicator of offensive success than batting averages?), organizations have been trying to find the Moneyball equivalent of customer data that will lead to innovation success. Yet few have. Innovation processes in many companies are structured and disciplined, and the talent applying them is highly skilled. There are careful stage-gates, rapid iterations, and checks and balances built into most organizations’ innovation processes. Risks are carefully calculated and mitigated. Principles like six-sigma have pervaded innovation process design so we now have precise measurements and strict requirements for new products to meet at each stage of their development. From the outside, it looks like companies have mastered an awfully precise, scientific process. But for most of them, innovation is still painfully hit or miss. And worst of all, all this activity gives the illusion of progress, without actually causing it. Companies are spending exponentially more to achieve only modest incremental innovations while completely missing the mark on the breakthrough innovations critical to long-term, sustainable growth. As Yogi Berra famously observed: “We’re lost, but we’re making good time!” What’s gone so wrong? Here is the fundamental problem: the masses and masses of data that companies accumulate are not organized in a way that enables them to reliably predict which ideas will succeed. Instead the data is along the lines of “this customer looks like that one,” “this product has similar performance attributes as that one,” and “these people behaved the same way in the past,” or “68 percent of customers say they prefer version A over version B.” None of that data, however, actually tells you why customers make the choices that they do.

It was an iterative process, not an intellectual one, as we refined the tools we used day after day. And it was urgent. Our tools had to work, because if they didn’t someone died.

In all, it took an astonishing 5,127 prototypes before Dyson believed the technology was ready to go in the vacuum cleaner. The creative leap may have been a crucial and precious thing, but it was only the start of the creative process. The real hard yards were done patiently evolving the design via bottom-up iteration. To put it another way, with the epiphany he had vaulted onto a taller mountain in a new landscape; now he was systematically working toward this new summit.

Therefore the most important function that software builders do for their clients is the iterative extraction and refinement of the product requirements. For the truth is, the clients do not know what they want. They usually do not know what questions must be answered, and they almost never have thought of the problem in the detail that must be specified. Even the simple answer—"Make the new software system work like our old manual information-processing system"—is in fact too simple. Clients never want exactly that. Complex software systems are, moreover, things that act, that move, that work. The dynamics of that action are hard to imagine. So in planning any software activity, it is necessary to allow for an extensive iteration between the client and the designer as part of the system definition.

While inexact models may mislead, attempting to allow for every contingency a priori is impractical. Thus models must be built by an iterative feedback process in which an initial parsimonious model may be modified when diagnostic checks applied to residuals indicate the need.

Recognize that getting a Hedgehog Concept is an inherently iterative process, not an event.

It is not just systems that can benefit from a process of testing and learning; so, too, can organizations. Indeed, many of the most innovative companies in the world are bringing some of the basic lessons of evolutionary theory into the way they think about strategy. Few companies tinker randomly like the Unilever biologists, because with complex problems it can take a long time to home in on a solution. Rather, they make judicious use of tests, challenge their own assumptions, and wield the lessons to guide strategy. It is a mix of top-down reasoning (as per the mathematicians) and bottom-up iteration (as per the biologists); the fusing of the knowledge they already have with the knowledge that can be gained by revealing its inevitable flaws. It is about having the courage of one’s convictions, but also the humility to test early, and to adapt rapidly.

The research participants described trust as a slow-building, iterative, and layered process that happens over time. Both trust-building and rumbling with vulnerability involve risk. That’s what makes courage hard and rare.

• Research has found that our brains don’t register much difference between physical pain and psychological pain. • An obsession and over investment in emotion fails us for the simple reason that emotions never last. Whatever makes up happy today will no longer make us happy tomorrow, because our biology always needs something more. • You can’t win if you don’t play. • By what standard do we measure ourselves? Our values determine the metrics by which we measure ourselves and everyone else. • Nobody else is ever responsible for your unhappiness but you. This is because you always get to choose how you see things, how you react to things, how you value things. You always get to choose the metric by which to measure your experiences • Growth is an endlessly iterative process. When we learn something new, we don’t go from “wrong” to “right”. Rather, we go from wrong to slightly less wrong... we are always in the process of approaching truth and perfection without actually ever reaching truth or perfection. • Certainty is the enemy of growth. Nothing is for certain until it has already happened -and even them, it’s still debatable. • There is little that is unique or special about your problems. That’s why letting go is so liberating.

Perhaps the most widely used technique at Amazon for these situations is the Correction of Errors (COE) process, based upon the “Five Whys” method developed at Toyota and used by many companies worldwide. When you see an anomaly, ask why it happened and iterate with another “Why?” until you get to the underlying factor that was the real culprit. This COE process requires the team who had a significant error or problem to write a document describing the problem or error, and to drill down on what caused it by asking and answering “Why?” five times in order to get to the true root cause.

Today, the principle of optimization—the process of making something, as the dictionary puts it, “as fully perfect, functional, or effective as possible”—thrives in extremity. An entire industry has even sprung up to give optimization a uniform: athleisure, the type of clothing you wear when you are either acting on or signaling your desire to have an optimized life. I define athleisure as exercise gear that you pay too much money for, but defined more broadly, athleisure was a $97 billion category by 2016. Since its emergence around a decade ago, athleisure has gone through a few aesthetic iterations.

They planned to fail early and inexpensively in the search for the market for a disruptive technology. They found that their markets generally coalesced through an iterative process of trial, learning, and trial again.

…To not speak honestly would cost me my self respect. I’m not afraid of criticism; I welcome it. What I’m passing along here is just the latest iteration of my learning process, which is to: Develop my perspectives through direct experiences and research, to write up what I learn, to stress test it by showing it to smart people, to explore our differences if and when we have them, to evolve my thinking some more, and, to do that over-and-over again, until I die.

Design contains uncertainty No matter how much data you have No matter how many insights you have Uncertainty is a natural part of the process‍ Stay flexible Test, learn Adjust your plans As needed

It’s fundamentally an iterative process of thinking up different ways to create a part, trying different materials, processes, and design options to come up with the best parts and strategies for manufacturing them.

No one is saying you can’t take a minute to think, Dammit, this sucks. By all means, vent. Exhale. Take stock. Just don’t take too long. Because you have to get back to work. Because each obstacle we overcome makes us stronger for the next one. But . . . No. No excuses. No exceptions. No way around it: It’s on you. We don’t have the luxury of running away. Of hiding. Because we have something very specific we’re trying to do. We have an obstacle we have to lean into and transform. No one is coming to save you. And if we’d like to go where we claim we want to go—to accomplish what we claim are our goals—there is only one way. And that’s to meet our problems with the right action. Therefore, we can always (and only) greet our obstacles with energy with persistence with a coherent and deliberate process with iteration and resilience with pragmatism with strategic vision with craftiness and savvy and an eye for opportunity and pivotal moments Are you ready to get to work?

Now Where Do You Find Customers? When novice entrepreneurs search for opportunities, they too often look beyond their Zone of Influence. They think the action is happening somewhere else, in some other location or industry. But seasoned entrepreneurs almost always find and create opportunities within the context of who they are, what they know, and especially who they know. In each of the examples above, the business validation process begins with potential customers in the entrepreneur’s orbit. Actual people with names. Tribes you belong to or are interested in, most of whom are already self-organized online. People you know how to reach, today. Though it’s rarely a part of their official origin stories, the biggest companies in the world—even the viral apps now worth billions—started through personal networks and real human connections. Mark Zuckerberg started Facebook in a weekend by emailing friends to use it. Version 1 did well, validating it. And Microsoft started with Bill Gates building software for a guy in Albuquerque. He had a CUSTOMER FIRST. In the beginning, founders should reach out to their friends, their former colleagues, their communities. You may think your business is unique, but trust me, it’s not. Every successful business can start this way. For example, Anahita loves her dogs and wanted healthier snacks for them. She started taking her homemade organic dog treats to her local dog park. She would sell out every time. A year later she now has a store called the Barkery, a dog bakery. Before you even think about picking a business idea, make sure you have easy access to the people you want to help. An easy way to do this is to think about where you have easy access to a targeted group of people whom you really want to help—like, say, new moms in Austin, cyclists, freelance writers, and taco obsessives (like me!). CHALLENGE Top three groups. Let’s write out your top three groups to target. Who do you have easy access to that you’d be EXCITED to help? This can be your neighbors, colleagues, religious friends, golf buddies, cooking friends, etc. The better you understand your target group, the better you can speak to them. The more specifically you can speak to their problems, the better and easier you can sell (or test products). Note how this process prioritizes communication with people, through starting (taking the first iteration of your solution straight to customers) and asking (engaging them in a conversation to determine how your solution can best fix their problem). Business creation should always be a conversation! Nearly every impulse we have is to be tight with our ideas by doing more research, going off alone to build the perfect product—anything and everything to avoid the discomfort of asking for money. This is the validation shortcut. You have to learn to fight through this impulse. It won’t be easy, but it’ll be worth it.

Here are four examples of Lead Magnets I use: A checklist that can be used to properly perform something I explained in a video. A template for determining, say, a business’s profit margin. An advanced guide that goes further into the details of a subject of one of my videos. A unique book that provides substantial value but is offered for free. For me, it is 11 Side Hustle Ideas to Make $500/Day from Your Phone. The appropriate opt-in incentive depends on your content. Here are other types of examples: A DIY carpenter could offer plans to make a corner table. A marketing YouTuber could offer scripts of what to say on sales phone calls. A landscaping expert might offer recommendations for which kinds of grass to use around the United States. YouTuber Nick True at Mapped Out Money, who makes video tutorials that teach the best practices for using the personal budgeting software YNAB, found that he gets the highest sign-up rates when he offers a checklist that relates to the video. His followers really like having a resource that they can use to put his advice into practice. Jess Dante of Love and London runs a YouTube channel helping viewers plan their trips to London by suggesting lesser-known restaurants and stores to visit. Her superstar opt-in incentive is a free London 101 Guide with everything a first-time visitor needs to know. It’s been downloaded more than 45,000 times. Where you make your call to action will also have an impact on your success building your email list. You can make your call to action in a variety of places or ways inside your videos. One of the best ways is to give a short, relevant tease of the bonus or resource you’re offering within the YouTube video and tell people where they can learn more. CHALLENGE Create a Lead Magnet. It’s time to create your first Lead Magnet using the process we’ve just outlined above. You can use your piece of content from the previous chapter as a base or start something new. Don’t spend more than two hours on the first iteration. If you want to turn it into a big thing later on, great. But start SMALL. Go to MillionDollarWeekend.com to get Lead Magnet templates! (See what I did there?)

we can always (and only) greet our obstacles with energy with persistence with a coherent and deliberate process with iteration and resilience

Many heads nodded vigorously as expected. Having worked among brilliant engineers all his career, the thought processes of his audience were a known quantity and Adan knew how to reinforce their implicit biases. Acknowledging how the old system was broken and then talking about iterating across different possible ideas until an optimal solution was identified was designed to hit all of their logical pleasure centers.

Doesn’t Kanban mean abandoning iterations and other elements of Scrum? This is a serious misconception. Kanban is the start with what you do now method; we would be the first to warn you not to drop aspects of your current process in an uncontrolled fashion. However, it would be dishonest of us to pretend that your pursuit of flow won’t at some point test your commitment to timeboxes, story points, and the like. How you and your organization deal with that will be a matter of choice.

Generally, programmers aren't thrilled about the iterative method because it means extra work for them. Typically, it's managers new to technology who like the iterative process because it relieves them of having to perform rigorous planning, thinking, and product due diligence

And yet, candor could not be more crucial to our creative process. Why? Because early on, all of our movies suck. That’s a blunt assessment, I know, but I make a point of repeating it often, and I choose that phrasing because saying it in a softer way fails to convey how bad the first versions of our films really are. I’m not trying to be modest or self-effacing by saying this. Pixar films are not good at first, and our job is to make them so—to go, as I say, “from suck to not-suck.” This idea—that all the movies we now think of as brilliant were, at one time, terrible—is a hard concept for many to grasp. But think about how easy it would be for a movie about talking toys to feel derivative, sappy, or overtly merchandise-driven. Think about how off-putting a movie about rats preparing food could be, or how risky it must’ve seemed to start WALL-E with 39 dialogue-free minutes. We dare to attempt these stories, but we don’t get them right on the first pass. And this is as it should be. Creativity has to start somewhere, and we are true believers in the power of bracing, candid feedback and the iterative process—reworking, reworking, and reworking again, until a flawed story finds its throughline or a hollow character finds its soul.

The Pseudocode Programming Process Have you checked that the prerequisites have been satisfied? Have you defined the problem that the class will solve? Is the high-level design clear enough to give the class and each of its routines a good name? Have you thought about how to test the class and each of its routines? Have you thought about efficiency mainly in terms of stable interfaces and readable implementations or mainly in terms of meeting resource and speed budgets? Have you checked the standard libraries and other code libraries for applicable routines or components? Have you checked reference books for helpful algorithms? Have you designed each routine by using detailed pseudocode? Have you mentally checked the pseudocode? Is it easy to understand? Have you paid attention to warnings that would send you back to design (use of global data, operations that seem better suited to another class or another routine, and so on)? Did you translate the pseudocode to code accurately? Did you apply the PPP recursively, breaking routines into smaller routines when needed? Did you document assumptions as you made them? Did you remove comments that turned out to be redundant? Have you chosen the best of several iterations, rather than merely stopping after your first iteration? Do you thoroughly understand your code? Is it easy to understand?

Iterative development acknowledges that we will probably get things wrong before we get them right and that we will do things poorly before we do them well

To date, most things that I’ve seen use the internet as a delivery vehicle for old style teaching. We’re taking steps in the direction — it’ll be a long, iterative process — to create new kinds of educational experiences by using this new tool of the internet.

Matz took the best of list processing from Lisp, and the best of OO from Smalltalk and other languages, and the best of iterators from CLU, and pretty much the best of everything from everyone.

Visionaries are specially afraid of a false negative: that customers will reject a flawed MVP that is too small or too limited. […] The solution to this dilemma is a commitment to iteration. You have to commit to a locked-in agreement—ahead of time—that no matter what comes of testing the MVP, you will not give up hope. Successful entrepreneurs do not give up at the first sign of trouble, nor do they persevere the plan right into the ground. Instead, they process a unique combination of perseverance and flexibility.

Summary Gaining insight from massive and growing datasets, such as those generated by large organizations, requires specialized technologies for each step in the data analysis process. Once organizational data is cleaned, merged, and shaped into the form desired, the process of asking questions about data is often an iterative one. MapReduce frameworks, such as the open-source Apache Hadoop project, are flexible platforms for the economical processing of large amounts of data using a collection of commodity machines. Although it is often the best choice for large batch-processing operations, MapReduce is not always the ideal solution for quickly running iterative queries over large datasets. MapReduce can require a great deal of disk I/O, a great deal of administration, and multiple steps to return the result of a single query. Waiting for results to complete makes iterative, ad hoc analysis difficult. Analytical databases

With Dremel, engineers could formulate queries using an SQL-like syntax, speeding up the process of iterative analysis without dealing with the overhead of defining raw MapReduce jobs.

Winners throw out the traditional product management and introduction processes they learned at existing companies. Instead, they combine agile engineering and Customer Development to iteratively build, test and search for a business model, turning unknowns into knowns. Winners also recognize their startup “vision” as a series of untested hypotheses in need of “customer proof.” They relentlessly test for insights, and they course-correct in days or weeks, not months or years, to preserve cash and eliminate time wasted on building features and products that customers don’t want.

Stoic moral training depends upon the defensibility of these schematic propositions: (a) Rational deliberative power (rational agency) is a defining feature of mature human consciousness. (b) Every agent’s rational powers, whenever exercised, operate in a particular and intricate deliberative field, in Barbara Herman’s felicitous phrase, that is laden with projects, preferences, affects, and attachments. (c) Each person’s deliberative field evolves continuously. Its initial information gathering and deliberative routines are givens (as if programmed) and, together with the initial situation, yield explicable beliefs. Initial sensibilities, sensitivities, values, aims, commitments, and preferences are also givens and, together with beliefs and deliberative routines, yield normative propositions for conduct. The circumstances in which such normative propositions are acted out or abandoned (that is, the relative strength or weakness of the will) are given, and actions follow. Each process from information gathering to action then becomes information for the next process. (d) The agent’s awareness of and reflection upon these iterated processes varies. But when awareness is high, it is fair to say rational agency is a self-transformative power: over time, its reflexive, recursive operations can transform its own powers, deliberative field, and operations—hence its norms and actions. (e) Agents can thus remake their characters over time. Note, however, that no uniform, essentially human content is specified for the deliberative field

Index design is also a largely iterative process, based on the SQL generated by application designers. However, it is possible to make a sensible start by building indexes that enforce primary key constraints and indexes on known access patterns, such as a person's name. As the application evolves and testing is performed on realistic sizes of data, certain queries will need performance improvements for which building a better index is a good solution.

Interestingly, the executives interviewed were comfortable with not having a finite task completion in the “cycle” of making strategy, because they believed that making strategy was a generative, iterative, and continuous process. Not too dissimilar from the ancient Greek notion of strategy, their

Each algorithm is a feedback loop, taking an action, observing the resulting conditions, and taking another action after that. Again, and again, and again. It's an iterative process, in which the algorithms adjust themselves and their activity on every loop, responding less to the news on the ground than to one another. Such systems go out of control because the feedback of their own activity has become louder than the original signal.

Any discrete piece of information can be represented by a set of numbers. Systems that compute can represent powerful mappings from one set of numbers to another. Moreover, any program on any computer is equivalent to a number mapping. These mappings can be thought of as statements about the properties of numbers; hence, there is a close connection between computer programs and mathematical proofs. But there are more possible mappings than possible programs; thus, there are some things that simply cannot be computed. The actual process of computing can be defined in terms of a very small number of primitive operations, with recursion and/or iteration comprising the most fundamental pieces of a computing device. Computing devices can also make statements about other computing devices. This leads to a fundamental paradox that ultimately exposes the limitations not just of machine logic, but all of nature as well.

strategic thinking being a highly informal, iterative, and social process, there

Growth is an endlessly iterative process. When we learn something new, we don’t go from “wrong” to “right.” Rather, we go from wrong to slightly less wrong.

In particular, we can combine the chain() function with the contextlib.ExitStack() method to process a collection of files as a single iterable sequence of values. We can do something like this: from contextlib import ExitStack import csv def row_iter_csv_tab(*filenames): with ExitStack() as stack: files = [stack.enter_context(open(name, 'r', newline='')) for name in filenames] readers = [csv.reader(f, delimiter='\t') for f in files] readers = map(lambda f: csv.reader(f, delimiter='\t'), files) yield from chain(*readers)

Tactical Urbanism is used by a range of actors, including governments, business and nonprofits, citizen groups, and individuals. It makes use of open and iterative development processes, the efficient use of resources, and the creative potential unleashed by social interaction. It is what Professor Nabeel Hamdi calls making plans without the usual preponderance of planning.

New ideas are never perfect right out of the chute, and you don’t have time to wait until they get there. Create a product, ship it, see how it does, design and implement improvements, and push it back out. Ship and iterate. The companies that are the fastest at this process will win.

I’m not trying to be modest or self-effacing by saying this. Pixar films are not good at first, and our job is to make them so—to go, as I say, “from suck to not-suck.” This idea—that all the movies we now think of as brilliant were, at one time, terrible—is a hard concept for many to grasp. But think about how easy it would be for a movie about talking toys to feel derivative, sappy, or overtly merchandise-driven. Think about how off-putting a movie about rats preparing food could be, or how risky it must’ve seemed to start WALL-E with 39 dialogue-free minutes. We dare to attempt these stories, but we don’t get them right on the first pass. And this is as it should be. Creativity has to start somewhere, and we are true believers in the power of bracing, candid feedback and the iterative process—reworking, reworking, and reworking again, until a flawed story finds its throughline or a hollow character finds its soul.

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

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

The feedback cycle in business today isn’t a segmented part of the process or a period of time for interaction after which no more questions or input are allowed. Now it’s a fluid and never-ending process that involves the brand stewards — the audience — and they’re the people who feed it. Technology facilitates a fragmented process that’s hard to define and requires constant experimentation and iteration. It requires a process where it doesn’t really start and end like it used to. The needed approach from marketers today is to remove the launch mentality.

Forest Fires: the Fractal Boundary Imagine a plantation of evenly spaced trees on a very hot, dry day. As the temperature soars, the odd leaf or twig ignites, sending a whole tree up in flames. This is an essentially random process – the factors involved are beyond our powers of prediction. But once a tree is in flames, the fire easily spreads to neighbouring trees, and this process can now be modelled with iterative techniques.

Doom, meanwhile, had a long-term impact on the world of gaming far exceeding even that of Myst. The latest of a series of experiments with interactive 3D graphics by id programmer John Carmack, Doom shares with Myst only its immersive first-person point of view; in all other respects, this fast-paced, ultraviolent shooter is the polar opposite of the cerebral Myst. Whereas the world of Myst is presented as a collection of static nodes that the player can move among, each represented by a relatively static picture of its own, the world of Doom is contiguous. As the player roams about, Doom must continually recalculate in real time the view of the world that it presents to her on the screen, in effect drawing for her a completely new picture with every frame using a vastly simplified version of the 3D-rendering techniques that Eric Graham began experimenting with on the Amiga back in 1986. First-person viewpoints had certainly existed in games previously, but mostly in the context of flight simulators, of puzzle-oriented adventures such as Myst, or of space-combat games such as Elite. Doom has a special quality that those earlier efforts lack in that the player embodies her avatar as she moves through 3D space in a way that feels shockingly, almost physically real. She does not view the world through a windscreen, is not separated from it by an adventure game’s point-and-click mechanics and static artificiality. Doom marks a revolutionary change in action gaming, the most significant to come about between the videogame’s inception and the present. If the player directs the action in a game such as Menace, Doom makes her feel as if she is in the action, in the game’s world. Given the Amiga platform’s importance as a tool for noninteractive 3D rendering, it is ironic that the Amiga is uniquely unsuited to Doom and the many iterations and clones of it that would follow. Most of the Amiga attributes that we employed in the Menace reconstruction—its scrolling playfields, its copper, its sprites—are of no use to a 3D-engine programmer. Indeed, the Intel-based machines on which Carmack created Doom possess none of these features. Even the Amiga’s bitplane-based playfields, the source of so many useful graphical tricks and hacks when programming a 2D game such as Menace, are an impediment and annoyance in a game such as Doom. Much preferable are the Intel-based machines’ straightforward chunky playfields because these layouts are much easier to work with when every frame of video must be drawn afresh from scratch. What is required most of all for a game such as Doom is sufficient raw processing power to perform the necessary thousands of calculations needed to render each frame quickly enough to support the frenetic action for which the game is known. By 1993, the plebian Intel-based computer, so long derided by Amiga owners for its inefficiencies and lack of design imagination, at last possessed this raw power. The Amiga simply had no answer to the Intel 80486s and Pentiums that powered this new, revolutionary genre of first-person shooters. Throughout

Building a habit-forming product is an iterative process and requires user-behavior analysis and continuous experimentation. How

Joining the world of shapes to the world of numbers in this way represented a break with the past. New geometries always begin when someone changes a fundamental rule. Suppose space can be curved instead of flat, a geometer says, and the result is a weird curved parody of Euclid that provides precisely the right framework for the general theory of relativity. Suppose space can have four dimensions, or five, or six. Suppose the number expressing dimension can be a fraction. Suppose shapes can be twisted, stretched, knotted. Or, now, suppose shapes are defined, not by solving an equation once, but by iterating it in a feedback loop. Julia, Fatou, Hubbard, Barnsley, Mandelbrot-these mathematicians changed the rules about how to make geometrical shapes. The Euclidean and Cartesian methods of turning equations into curves are familiar to anyone who has studied high school geometry or found a point on a map using two coordinates. Standard geometry takes an equation and asks for the set of numbers that satisfy it. The solutions to an equation like x^2 + y^2 = 1, then, form a shape, in this case a circle. Other simple equations produce other pictures, the ellipses, parabolas, and hyperbolas of conic sections or even the more complicated shapes produced by differential equations in phase space. But when a geometer iterates an equation instead of solving it, the equation becomes a process instead of a description, dynamic instead of static. When a number goes into the equation, a new number comes out; the new number goes in, and so on, points hopping from place to place. A point is plotted not when it satisfies the equation but when it produces a certain kind of behavior. One behavior might be a steady state. Another might be a convergence to a periodic repetition of states. Another might be an out-of-control race to infinity.

Linda Rubright’s definition of “Iterative Process” is “Total fail. Repeat.” Creators must be willing to fail and repeat until they find the step that arrives. Samuel Beckett said it best: “Try again. Fail again. Fail better.

Creativity has to start somewhere, and we are true believers in the power of bracing, candid feedback and the iterative process—reworking, reworking, and reworking again,

Variable Analysis 1. Define the problem. a. What problem are you trying to solve? b. Determine the primary variable • Closely inspect the point of failure or failed output • What measurable property do you want to change? • Is it possible to define the problem using a more specific variable? 2. Describe the problem: describe the problem in detail. a. What does the problem look like? b. When did the problem start? c. How often does the problem occur? d. Where does the problem first occur? e. When where don’t you see the problem? 3. Create a variable tree: develop each layer of sub-variables by understanding how the process works a. How is the process designed to control the primary variable? b. What else determines the value of the primary variable? c. Can we combine any of the sub-variables? 4. Eliminate sub-variables from the tree a. What should the value of each sub-variable be to prevent the problem from occurring? • What is the relationship between each sub-variable and the primary variable? b. Eliminate sub-variables that do not contribute to the problem • What is the actual value of the sub-variable during failure? During non-failure? • What tests could you use to eliminate variables that are difficult to measure? • What does the pattern of failure tell you? c. Expand sub-variables that have not been eliminated (iterating step 3) d. Start with sub-variables that the pattern-of-failure suggests are most likely to contribute to the problem e. Continue to expand and eliminate sub-variables until you have found the out-of-spec variable(s) that are directly in your control • Can you explain exactly how the out-of-spec sub-variable(s) contribute(s) directly to the problem? f. If you get stuck . . . • Have you eliminated a sub-variable that you should not have? • Have you missed a sub-variable? 5. Implement the solution a. Implement the solution b. Verify the solution

Growth is an endlessly iterative process.

In fact, like Norton and Ramadan, most entrepreneurs are “replicative,” that is, they take an existing product or idea and make it better. This is what Howard Head and James Dyson did. This process of incremental or iterative improvement is the basis of almost all innovation. Innovation proceeds in phases; inventors take what exists and create accretive combinations. Often an innovation comes down to having brought existing things together in a way never before seen.

adapt the concept of complex grief into its current iteration—impossible grief applies to cases where the grief-processing mechanism has been obstructed, like a clog in a drain. Family members of people who were in the towers the day they fell, who were never given remains to bury. Women who were assaulted by a classmate, a boyfriend, a friend, who are told by almost everyone that what they experienced does not qualify as assault. Impossible grief is grief that does not adhere to a social contract of justice or human rituals that have existed since the dawn of time. A death with no body, a violation by someone who is not seen as the transgressor. A woman whose relationship wasn’t recognized as legitimate at the time she lost her partner. Tina teaches people how to snare the obstruction so that grief can make its way through the proper channels unencumbered. It’s always running in your veins, but better that than a life-threatening clot.

Embracing design thinking principles fosters creativity, innovation, and problem-solving skills, empowering students to approach challenges with a fresh perspective and develop viable solutions through iterative processes of ideation and prototyping.

In the early 2010s, Nvidia—the designer of graphic chips—began hearing rumors of PhD students at Stanford using Nvidia’s graphics processing units (GPUs) for something other than graphics. GPUs were designed to work differently from standard Intel or AMD CPUs, which are infinitely flexible but run all their calculations one after the other. GPUs, by contrast, are designed to run multiple iterations of the same calculation at once. This type of “parallel processing,” it soon became clear, had uses beyond controlling pixels of images in computer games. It could also train AI systems efficiently.

Failure needs to be contextualized. It needs to be understood and accepted as part of the process. Too often failure is defined as something that either does or doesn’t happen until the end. Leaders need to communicate that failure is part of the process and that it’s expected. And then when they see failures, and they inevitably will, they need to talk about it. They need to tell stories about failures. They need to demonstrate through their actions and their narratives that failures are expected and are as much a part of the process as anything else. And that when the team fails, they adjust. And that this is exactly what we all should be doing. Our goal is to generate that failure as early as possible so we can get to the next iteration. That’s considered part of the process.

Experiments themselves are a Darwinian process we can learn from. This is what nature is doing when it throws out genetic mutations. Many fail, but a few produce organisms that adapt better, perform better, and thus survive. Rapid and constant experimentation and iteration is a basic process, fundamental to all of life. Mother Nature doesn’t weep or get embarrassed about all the millions of mutations that fail.