Design Iteration Quotes

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

Musk took an iterative approach to design. Rockets and engines would be quickly prototyped, tested, blown up, revised, and tried again, until finally something worked. Move fast, blow things up, repeat. “It’s not how well you avoid problems,” Mueller says. “It’s how fast you figure out what the problem is and fix it.

Even in engineering-driven Silicon Valley, the buzzwords of the moment call for building a “lean startup” that can “adapt” and “evolve” to an ever-changing environment. Would-be entrepreneurs are told that nothing can be known in advance: we’re supposed to listen to what customers say they want, make nothing more than a “minimum viable product,” and iterate our way to success. But leanness is a methodology, not a goal. Making small changes to things that already exist might lead you to a local maximum, but it won’t help you find the global maximum. You could build the best version of an app that lets people order toilet paper from their iPhone. But iteration without a bold plan won’t take you from 0 to 1. A company is the strangest place of all for an indefinite optimist: why should you expect your own business to succeed without a plan to make it happen? Darwinism may be a fine theory in other contexts, but in startups, intelligent design works best.

I like to remind people that creativity also isn't a spark; it's a slog. Every artist, inventor, designer, writer, or other creative in the world will talk about his work being an iterative experience. He'll start with one idea, shape it, move it, combine it, break it, begin anew, discover something within himself, see a new vision, go at it again, test it, share it, fix it, break it, hone it, hone it, hone it, hone it. This might sound like common sense, but it's not common practice, and that's why so many people are terribly uncreative - they're not willing to do the work required to create something that's beautiful, useful, desirable, celebrated. No masterpiece was shaped or written in a day. It's a long slog to get something right. This knowledge and willingness to iterate is what makes the world's most creative people so creative (and successful).

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.

three platforms: Amazon, Google, and Facebook. Registering, iterating, and monetizing its audience is the heart of each platform’s business. It’s what the most valuable man-made things ever created (their algorithms) are designed to do.

The intelligence we will create from the reverse-engineering of the brain will have access to its own source code and will be able to rapidly improve itself in an accelerating iterative design cycle. Although there is considerable plasticity in the biological human brain, as we have seen, it does have a relatively fixed architecture, which cannot be significantly modified, as well as a limited capacity. We are unable to increase its 300 million pattern recognizers to, say, 400 million unless we do so nonbiologically. Once we can achieve that, there will be no reason to stop at a particular level of capability. We can go on to make it a billion pattern recognizers, or a trillion.

Embrace iteration as the road to improvement, but don't let that lull you into rolling out poorly-thought-out crap.

your first idea is unlikely to be the best, so you should be optimising for speed of iteration rather than quality of prototype.

I faced people from all walks of business who fully disregarded design (though they were completely influenced by it). I also met fine artists who drowned in their own work and the dense creative universe in their minds. Then I met designers. And instantly fell in love. Let me tell you why. Designers are familiar with critiques. They not only tolerate them but actively look out for them. They honestly believe in iterations and learn to edit down their work. They embrace simplicity and create beauty based on requirements other than their own. Design education teaches you to run away from assumptions and to have the stomach to scrap your work often. I’m bringing this up because it’s time to bridge the gap between design and business.

Everything would begin in the writer’s room. “We start with a very general idea,” said Jakub Szamałek, one of the writers. “Then we expand it, then we cut it into quests, then we work closely with quest designers to make sure it all makes sense from their perspective. And then we iterate and iterate and iterate.

Design research both inspires imagination and informs intuition through a variety of methods with related intents: to expose patterns underlying the rich reality of people’s behaviors and experiences, to explore reactions to probes and prototypes, and to shed light on the unknown through iterative hypothesis and experiment.

no formal design reviews, so there are no huge decision points. Instead, we can make the decisions fluid. Since we iterate every day and never have dumb-ass presentations, we don’t run into major disagreements. On this day Ive was overseeing the creation of a new European power plug and connector for the Macintosh. Dozens of foam models, each with the tiniest variation, have been cast and painted for inspection. Some would find it odd that the head of design would fret

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.

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.

Between concentric pavement ripples glide errant echoes originating from beyond the Puddled Metropolis. Windowless blocks and pickle-shaped monuments demarcate the boundaries of patternistic cycles from those wilds kissed neither by starlight nor moonlight. Lethal underbrush of razor-like excrescence pierces at the skins of night, crawls with hyperactive sprouts and verminous vines that howl with contempt for the wicked fortunes of Marshland Organizers armed with scythes and hoes and flaming torches who have only succeeded in crafting their own folly where once stood something of glorious and generous integrity. There are familiar whispers under leaves perched upon by flapping moths. They implore the spirit again to heed the warnings of the vines and to not be swayed by the hubris of these organizing opportunists. One is to stop moving at frantic zigzags through gridlocked streets, stop climbing ladders altogether, stop relying on drainage pipes where floods should prevail, stop tapping one’s feet in waiting rooms expecting to be seen and examined and acknowledged. Rather, one is to eschew unseemly fabrications and conceal oneself beneath the surface of leaves—perhaps even inside the droplets of dew—one is, after all, to feel shameful of the form, of all forms, and seek instead to merge with whispers which do not shun or excoriate, for they are otherwise occupied in the act of designating meaning. Yet, what meaning stands beyond the rectitude of angles and symmetry, but rather in wilds among agitated insects and resplendent bogs and malicious spiders and rippling mosses pronouncing doom upon their surroundings? One is said to find only the same degree of opportunism, and nothing greatly edifying that could serve to extend beyond the banalities of self-preservation. But no, surely there is something more than this—there absolutely must be something more, and it is to be found! Forget what is said about ‘opportunism’—this is just a word and, thusly, a distraction. The key issue is that there are many such campaigns of contrivance mounted by the taxonomic self-interest of categories and frameworks ‘who’ only seek primacy and authority over their consumers. The ascription of ‘this’ may thusly be ascribed also with that of ‘this other’ and so it cannot be ‘that precisely’ because ‘this’ contradicts another ‘that other’ with which ‘this other’ surely claims affiliation. Certainly, in view of such limiting factors, there is a frustration that one is bound to feel that the answers available are constrained and formulaic and insufficient and that one is simply to accept the way of things as though they are defined by the highest of mathematics and do not beget anything higher. One is, thusly, to cease in one’s quest for unexplored possibility. The lines have been drawn, the contradictions defined and so one cannot expect to go very far with these mathematical rules and boundaries in place. There are ways out: one might assume the value of an imaginary unit and bounce out of any restrictive quadrant as with the errant echoes against the rippling pavement of this Puddled Metropolis. One will then experience something akin to a bounding and rebounding leap—iterative, but with all subleaps constituting a more sweeping trajectory—outward to other landscapes and null landscapes, inward through corridors and toward the centroid of circumcentric chamber clusters, into crevices and trenches between paradigms and over those mountain peaks of abstruse calculation.

START DESIGNING YOUR LIFE. Treat the next month of your life as a design project. Do field research on yourself, looking for unmet needs in your own daily routine. Generate ideas about what changes in your behavior might be viable, feasible, and desirable. What improvements can you quickly prototype, test, and iterate? Be intentional about choosing actions you can take right now that might add more joy and meaning to your own life—and the lives of the people around you. How might you work within constraints? Keep iterating. Try this out for a month and ask yourself what’s working and what’s not. How can you continue to create more positive impact? As our IDEO friend and colleague Tim Brown writes, “Think of today as a prototype. What would you change?

The range function at can be used to create a list of numbers ranging from a starting number up to the number just before the ending number. That may sound a little confusing. Let’s combine the range function with the list function to see exactly how this works. The range function doesn’t actually create a list of numbers; it returns an iterator, which is a type of Python object specially designed to work with loops. However, if we combine range with list, we get a list of numbers.

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.

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.

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.

Do you have a fleeting glimpse that may become a goal? How can you make framing more social? There are more ways than stars in the universe. Most require that we also make framing more tangible. For instance, Jeff Bezos has created a culture at Amazon in which “working backwards” is an assumption. For any new initiative, employees begin by writing a press release and FAQ that explain the finished product to the customer. [65] No product is built without conversations and iterations around these tangible artifacts of a customer-centered frame.

One of the most important workflows Jobs inaugurated was daily informal product reviews in the studio of Apple’s design chief, Jony Ive. Here Jobs would finger foam prototypes and discuss minute details of product design. Thus, “there [were] no huge decision points,” said Ives. “Since we iterate[d] every day and never ha[d] dumb-ass presentations, we [didn’t] run into major disagreements.”7

Getting the specification of the thing to be defined is one of the most difficult parts of the design, so much so that the HCD principle is to avoid specifying the problem as long as possible but instead to iterate upon repeated approximations. This is done through rapid tests of ideas, and after each test modifying the approach and the problem definition. The results can be products that truly meet the needs of people. Doing HCD within the rigid time, budget, and other constraints of industry can be a challenge: Chapter 6 examines these issues.

The iterative approach begins with a goal and almost immediately leaps into concept designs, bench tests, and prototypes. The mantra with this approach is build and test early, find failures, and adapt. This is what SpaceX engineers and technicians did on the factory floor in El Segundo, and it allowed them to capture basic flaws with early prototypes, fix their designs, and build successively more “finished” iterations.

In an early-stage startup or an unproven product it’s usually more important to be able to iterate quickly on product features than it is to scale to some hypothetical future load.

MVP Testing. Prototyping and prototype testing should proceed in iterative loops until a dominant design emerges. Based on test feedback, designers should reject some prototypes and refine others, producing higher-fidelity versions. Once they converge on a single, favored solution, it’s time for minimum viable product testing. An MVP is a prototype—a facsimile of the future product. What distinguishes an MVP from other prototypes is how it is tested. Rather than sitting across a table, getting verbal feedback from a reviewer, you put a prototype that seems like a real product in the hands of real customers in a real-world context. The goal is to quickly but rigorously test assumptions about the demand for your solution—and gain what Eric Ries calls “validated learning”—with as little wasted effort as possible.

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.

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.

A good example is overflow detection on arithmetic, or providing bignums instead of just letting 32-bit integers wrap around. Now, implementing those is more expensive but I believe that providing full-blown bignums is a little less error-prone for some kinds of programming. A trap that I find systems programmers and designers of operating-systems algorithms constantly falling into is they say, "Well, we need to synchronize some phases here so we're going to use a take-a-number strategy. Every time we enter a new phase of the computation we'll increment some variable and that'll be the new number and then the different participants will make sure they're all working on the same phase number before a certain operation happens." And that works pretty well in practice, but if you use a 32-bit integer it doesn't take that long to count to four billion anymore. What happens if that number wraps around? Will you still be OK or not? It turns out that a lot of such algorithms in the literature have that lurking bug. What if some thread stalls for 2 to the 32nd iterations? That's highly unlikely in practice, but it's a possibility. And one should either mitigate that correctness problem or else do the calculation to show that, yeah, it's sufficiently unlikely that I don't want to worry about it. Or maybe you're willing to accept one glitch every day. But the point is you should do the analysis rather than simply ignoring the issue. And the fact that counters can wrap around is a lurking pitfall

But leanness is a methodology, not a goal. Making small changes to things that already exist might lead you to a local maximum, but it won’t help you find the global maximum. You could build the best version of an app that lets people order toilet paper from their iPhone. But iteration without a bold plan won’t take you from 0 to 1. A company is the strangest place of all for an indefinite optimist: why should you expect your own business to succeed without a plan to make it happen? Darwinism may be a fine theory in other contexts, but in startups, intelligent design works best.

Finally, unlike Plan-and-Document, in Agile you revise code continuously to improve the design and to add functionality starting with the second iteration.

Business Thinking Design Thinking Market analysis What might be Definitive Iterative Focus groups Observation Spreadsheets Scenarios Individual responsibility Collaboration Permanent jobs Temporary projects

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?

Here are the benefits you can expect from using this style of pseudocode: Pseudocode makes reviews easier. You can review detailed designs without examining source code. Pseudocode makes low-level design reviews easier and reduces the need to review the code itself. Pseudocode supports the idea of iterative refinement. You start with a high-level design, refine the design to pseudocode, and then refine the pseudocode to source code. This successive refinement in small steps allows you to check your design as you drive it to lower levels of detail. The result is that you catch high-level errors at the highest level, mid-level errors at the middle level, and low-level errors at the lowest level—before any of them becomes a problem or contaminates work at more detailed levels. Pseudocode makes changes easier. A few lines of pseudocode are easier to change than a page of code. Would you rather change a line on a blueprint or rip out a wall and nail in the two-by-fours somewhere else? The effects aren't as physically dramatic in software, but the principle of changing the product when it's most malleable is the same. One of the keys to the success of a project is to catch errors at the "least-value stage," the stage at which the least effort has been invested. Much less has been invested at the pseudocode stage than after full coding, testing, and debugging, so it makes economic sense to catch the errors early.

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.

User-centered design exemplifies the cybernetic strategy of “building quality in” by transforming construction into conversation. Integrating QA within an iterative feedback loop is key to this strategy. At its heart, it improves quality by increasing opportunities for meaningful empathy. It

Design is iterative... and you have to be willing to change your own designs, as well as those that you inherit from other programmers.

Applying scientific principles and validated learning to identify and eliminate sources of waste Adapting company vision to reflect customer insight Emphasizing fast iterations to stay ahead of uncertainty Effectively evaluating progress using a new set of benchmarks designed for startups

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.

Trying new things constantly and then abandoning them without further study or work is not iterating. That’s flailing.

Helliwell and his team have run several iterations of the World Values Survey and the Gallup World Poll through their statistical grinders and have found that when it comes to life satisfaction, relationships with other people beat income, hands down. For example, these polls asked people if they had a friend or relative to count on when needed. Just going from being friendless to having one friend or family member to confide in had the same effect on life satisfaction as a tripling of income.

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

precisely for that purpose— it cannot be used to design other types of products. Users apply a toolkit in conjunction with their rich understanding of their own needs to create a preliminary design, simulate or prototype it, evaluate its functioning in their own use environment, and then iteratively improve it until they are satisfied. A variety of manufacturers have found it profitable to shift the tasks of custom product design to their customers along with appropriate toolkits for innovation. Results

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

Agile is a response to the need for more continuous software repair. Its central strategy is to reduce the size and increase the frequency of the design-develop-test lifecycle. Over time, Agile has discovered and incorporated several powerful techniques to remove noise from the iterative lifecycle. These

One of the most important elements of agile development is the notion that development should be incremental and iterative. In the agile approach, a software system is developed in a series of iterations, each of which adds and evaluates a few new features; each iteration includes design, test, and customer input.

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.

That’s how games work—we fail our way toward understanding through play.

The myth of creative genius is resilient: We believe that great ideas pop fully formed out of brilliant minds, in feats of imagination well beyond the abilities of mere mortals. But what the Kaiser nursing team accomplished was neither a sudden breakthrough nor the lightning strike of genius; it was the result of hard work augmented by a creative human-centered discovery process and followed by iterative cycles of prototyping, testing, and refinement.

If you’re beginning to think like a designer, you will recognize that life is never done. Work is never done. Play is never done. Love and health are never done. We are only done designing our lives when we die. Until then, we’re involved in a constant iteration of the next big thing: life as we know it.

When we use the term “prototyping” in design thinking, we do not mean making something to check whether your solution is right. We don’t mean creating a representation of a completed design, nor do we mean making just one thing (designers make lots of prototypes—never just a prototype). Prototyping the life design way is all about asking good questions, outing our hidden biases and assumptions, iterating rapidly, and creating momentum for a path we’d like to try out.

Our philosophy is that it is always possible to prototype something you are interested in. The best way to get started is to keep your first few prototypes very low-resolution and very simple. You want to isolate one variable and design a prototype to answer that one question. Use what you have available or can ask for, and be prepared to iterate quickly. And remember that a prototype is not a thought experiment; it must involve a physical experience in the world. The data to make good decisions are found in the real world, and prototyping is the best way to engage that world and get the data you need to move forward.

What Ibarra calls the “plan-and-implement” model—the idea that we should first make a long-term plan and execute without deviation, as opposed to the “test-and-learn” model—is entrenched in depictions of geniuses. Popular lore holds that the sculptor Michelangelo would see a full figure in a block of marble before he ever touched it, and simply chip away the excess stone to free the figure inside. It is an exquisitely beautiful image. It just isn’t true. Art historian William Wallace showed that Michelangelo was actually a test-and-learn all-star. He constantly changed his mind and altered his sculptural plans as he worked. He left three-fifths of his sculptures unfinished, each time moving on to something more promising. The first line of Wallace’s analysis: “Michelangelo did not expound a theory of art.” He tried, then went from there. He was a sculptor, painter, master architect, and made engineering designs for fortifications in Florence. In his late twenties he even pushed visual art aside to spend time writing poems (including one about how much he grew to dislike painting), half of which he left unfinished. Like anyone eager to raise their match quality prospects, Michelangelo learned who he was—and whom he was carving—in practice, not in theory. He started with an idea, tested it, changed it, and readily abandoned it for a better project fit. Michelangelo might have fit well in Silicon Valley; he was a relentless iterator. He worked according to Ibarra’s new aphorism: “I know who I am when I see what I do.

Part of the process is Apple’s overall corporate strategy: What markets does it target, and how does it target them? Part of it is keeping abreast of new technology developments and being receptive to new ideas, especially outside the company. Part of it is about being creative, and always learning. Part of it is about being flexible, and a willingness to ditch long-held notions. Part of it is about being customer-centric. And a lot of it is trying to find the simplest, most elegant solution through an iterative, generate-and-test design process. Innovation at Apple is largely about shaping technology to the customer’s needs, not trying to force the user to adapt to the technology.

PROVEN PATTERN #1: BITS RATHER THAN ATOMS Google and Facebook are largely software businesses that focus on electronic bits rather than material atoms. Bits-based businesses have a much easier time serving a global market, which in turn makes it easier to achieve a large market size. Bits are also far easier to move around than atoms, so bits-based businesses can more easily tap into distribution techniques like virality, and their ability to be highly networked provides more opportunities to leverage network effects. Bits-based businesses tend to be high-gross-margin businesses because they have fewer variable costs. Bits also make it easier to design around growth limiters. You can iterate more quickly on software products (many Internet companies release new software daily) than on physical products, making it faster and cheaper to achieve product/market fit. And

With the backing of Soviet first secretary Leonid Brezhnev, Erich Honecker now took on the mantle of leadership, ruling with his small clique, including the minister of state security, Erich Mielke. Ulbricht retained the official title of head of state, but this new leadership was now closely aligned with Moscow. The ailing and somewhat bitter Ulbricht would suffer a stroke and pass away in August 1973. The Wall was his legacy, and by 1976 construction of a fourth-generation Wall began. “Grenzmauer 75” would be state-of-the-art in design and construction, a world away from the prefab first version of 1961. The new iteration was speedily installed, comprising L-shaped reinforced concrete sections

Tips on Web Design and Site Marketing Web content is king, which is why we have devoted an entire chapter to it later in this book. It is what draws visitors and ultimately what converts them to customers. So, try to make your web content as engaging as possible. Make sure the content is interactive, unique and educational. Ensure that visitors have the option of plugins while encouraging them to visit as many pages on your site as possible if they want to obtain vital information. The images you use on your website should be both enticing and descriptive in nature. In today’s world, social media is all pervasive. In order to encourage visitors to share your web content, you can include icons of social media platforms on your website. In some select cases, consider integrating social media feeds, like Facebook or Instagram, onto your website so that they can automatically show the latest postings. A "Call-to-Action" can help convert visitors to your site into customers. Always try using a very clear and concise "Call-to-Action" language. Understand what type of conversion you are looking for, and try to provide multiple levels of conversion. For example, a plastic surgeon may provide Schedule an Appointment as a call to action, which will attract only the segment of web visitors who have reached their decision stage. By adding conversion points for visitors who are at earlier stages of their decision making, like signing up for a webcast or your newsletter can help you widen your conversion points and provide inputs to your email marketing. To raise the average amount of time a visitor spends on your website and to minimize the bounce rate, ensure that your website offers a user-friendly and attractive design. This way you will increase the number of links you have on your website and boost its SEO ranking (Tip: While Google’s algorithm is not public, our iterative testing shows that sites with good usability analytics metrics like time on site and bounce rate play favorably in Google’s algorithm, other things remaining constant). Ensure you observe due diligence when designing a website that will enable visitors to navigate in different languages. For example, you may need a lot more space for your menu, as there are languages that use up more space than the English language.

Many companies create their own metrics to map business metrics to ML metrics. For example, Netflix measures the performance of their recommender system using take-rate: the number of quality plays divided by the number of recommendations a user sees.4 The higher the take-rate, the better the recommender system. Netflix also put a recommender system’s take-rate in the context of their other business metrics like total streaming hours and subscription cancellation rate. They found that a higher take-rate also results in higher total streaming hours and lower subscription cancellation rates.5

Ries wrote that startups operate in an environment of “extreme uncertainty” in which it is impossible to know whether the product they have developed is one that consumers will value. “We must learn what customers really want,” he advised, “not what they say they want or what we think they should want.” The only way to do that is to “experiment.” Create a “minimum viable product,” put it in front of consumers, and see what happens. With lessons learned, change the product, ship again, and repeat the cycle. Ries called this the building phase, as multiple iterations gradually build the final product. I would call it the planning phase, as the design of the product evolves following the dictum “Try, learn, again.” Semantics aside, the only real difference is the method of testing.

It should come as no surprise that without a solid and shared definition of the goal, the project team’s ability to deliver the desired results are limited at best.

Like every tokamak, ITER has central solenoid coils, large toroidal and poloidal magnets (respectively around and along the doughnut shape). The basic specifications are a vacuum vessel plasma of 6.2 meter radius and 830 cubic meters in volume, with a confining magnetic field of 5.3 tesla and a rated fusion power of 500 MW (thermal). This heat output would correspond to Q ≥ 10 (it would require the injection of 50 MW to heat the hydrogen plasma to about 150 million degrees) and hence would achieve, for the first time on Earth, a burning plasma of the kind required for any continuously operating fusion reactor. ITER would generate burning plasmas during pulses lasting 400 to 600 seconds, time spans sufficient to demonstrate the feasibility of building an actual electricity-generating fusion power plant. But it is imperative to understand that ITER is an experimental device designed to demonstrate the feasibility of net energy generation and to provide the foundation for larger, and eventually commercial, fusion designs, not to be a prototype of an actual energy-generating device.

An architecture that scales well for a particular application is built around assumptions of which operations will be common and which will be rare — the load parameters. If those assumptions turn out to be wrong, the engineering effort for scaling is at best wasted, and at worst counterproductive. In an early-stage startup or an unproven product it’s usually more important to be able to iterate quickly on product features than it is to scale to some hypothetical future load.

It is an iterative process that goes something like this: * Screen it * Discuss it * Get the feeling that you don’t know what you’re doing * Weep openly * Tear it apart * Correct it * Re-board it * Rebuild it * Screen it again * Repeat as necessary

Starting the design process focused on graphic design instead of interaction design is a common and costly amateur mistake. However, to deliver a professional quality experience, the execution stage must finish strong in this area. One of the economic advantages of conceptual model-based IxD is that, by the time high-fidelity mockup generation becomes appropriate, the foundation will be so stable that the graphic design work will require far fewer iterations.

Even in engineering-driven Silicon Valley, the buzzwords of the moment call for building a “lean startup” that can “adapt” and “evolve” to an ever-changing environment. Would-be entrepreneurs are told that nothing can be known in advance: we’re supposed to listen to what customers say they want, make nothing more than a “minimum viable product,” and iterate our way to success. But leanness is a methodology, not a goal. Making small changes to things that already exist might lead you to a local maximum, but it won’t help you find the global maximum. You could build the best version of an app that lets people order toilet paper from their iPhone. But iteration without a bold plan won’t take you from 0 to 1. A company is the strangest place of all for an indefinite optimist: why should you expect your own business to succeed without a plan to make it happen? Darwinism may be a fine theory in other contexts, but in startups, intelligent design works best.

Once you've successfully realized the iterated future state, you must have two things firmly in place to sustain it: (1) someone formally designated to monitor value stream performance to assess how it's performing, facilitate problem solving when issues arise, and lead ongoing improvement to raise the performance bar, and (2) key performance indicators to tell whether performance is on track or not (value stream management)

As it happens, the term “stereotype” offers a useful entry point for thinking about the default settings of technology and society. It first referred to a practice in the printing trade whereby a solid plate called a “stereo” (from the ancient Greek adjective stereos, “firm,” “solid”) was used to make copies. The duplicate was called a “stereotype.”47 The term evolved; in 1850 it designated an “image perpetuated without change” and in 1922 was taken up in its contemporary iteration, to refer to shorthand attributes and beliefs about different groups. The etymology of this term, which is so prominent in everyday conceptions of racism, urges a more sustained investigation of the interconnections between technical and social systems.

But iteration without a bold plan won’t take you from 0 to 1. A company is the strangest place of all for an indefinite optimist: why should you expect your own business to succeed without a plan to make it happen? Darwinism may be a fine theory in other contexts, but in startups, intelligent design works best.

Figure 1-9. Four principles. To serve memory and use, I’ve arranged these principles and practices into a mnemonic –STAR FINDER. In astronomy, a “star finder” or planisphere is a map of the night sky used for learning to identify stars and constellations. In this book, it’s a guide for finding goals, finding paths, and finding your way. First, we can get better at planning by making planning more social, tangible, agile, and reflective. At each step in the design of paths and goals, ask how these four principles might help. Social. Plan with people early and often. Engage family, friends, colleagues, customers, stakeholders, and mentors in the process. When we plan together, it’s easier to get started. Also, diversity grows empathy, sharing creates buy-in, and both expand options. Tangible. Get ideas out of your head. Sketches and prototypes let us see, hear, taste, smell, touch, share, and change what we think. When we render our mental models to distributed cognition and iterative design, we realise an intelligence greater than ourselves. Agile. Plan to improvise. Clarify the extent to which the goal, path, and process are fixed or flexible. Be aware of feedback and options. Know both the plan and change must happen. Embrace adventure. Reflective. Question paths, goals, and beliefs. Start and finish with a beginner’s mind. Try experiments to test hypotheses and metrics to spot errors. Use experience and metacognition to grow wisdom.

Whether it’s a test framework or test cases, keep it simple and iterate on the design as your project evolves. Don’t try to solve everything upfront. Be aggressive about throwing things away. If tests or automation are too hard to maintain, toss them and build some better ones that are more resilient. Watch out for maintenance and troubleshooting costs of your tests down the road Observe the 70-20-10 rule: 70 percent small unit tests that verify the behavior of a single class or function, 20 percent medium tests that validate the integration of one or more application modules, and 10 percent large tests (commonly referred to as “system tests” and “end-to-end” tests) that operate on a high level and verify the application as a whole is working. Other than that, prioritize and look for simple automation efforts with big payoffs, always remembering that automation doesn’t solve all your problems, especially when it comes to frontend projects and device testing. You always want smart, exploratory testing and to track test data.

The iterative cycle provides opportunities to convert weakness into strength by using the knowledge derived from failure to shorten the gap between practitioners and their goals.

Finally, at the praiseworthy end of Edmondson’s spectrum, we find intelligent failures. These are hypothesis tests and exploratory tests that open up potentials, and treat failure as an opportunity to learn rather than something to be avoided.

With our Darwinian demo methodology, we had a huge advantage over artificially selecting breeders and the glacially slow accumulations of genetic improvements that drive natural selection. Working in software meant we could move fast. We could make changes whenever we wanted, and we did. We created new demos that were concretely and specifically targeted to be better than the previous one. We constructed Hollywood backlots around these demos to provide context and to help us suspend our disbelief about the often nonexistent system surrounding the feature or app that was the focus of our attention. We gave each other feedback, both as initial impressions and after living on the software to test the viability of the ideas and quality of the associated implementations. We gathered up action items for the next iteration, and then we forged ahead toward the next demo. I’ve given a name to this continuing progression of demo -> feedback -> next demo: creative selection.

It was often difficult to decide where an algorithm should end and a heuristic should take over. It usually took us many design and programming iterations to evaluate all the relevant options. The best solutions were an accumulation of small decisions carefully weighed against each other as we sought to tame the complexity of so many compounding and overlapping factors.

Design thinking is an iterative process in which we seek to understand the user, challenge assumptions, and redefine problems in an attempt to identify alternative strategies and solutions that might not be instantly apparent with our initial level of understanding. At the same time, design thinking provides a solution-based approach to solving problems.

Design Thinking is an Iterative and Non-linear Process