Zoom App Quotes

Interactive designers may [...] recommend technologies that do not date as easily as others, such as touch tables rather than apps. One strategy is to use technologies that have been in existence for a while, as component and style have been proved to last, at least for a number of years. The most effective interactive often do not seek to use the latest technology, but rather work with existing technological "gestures", such as using fingertips to zoom in, and exploit these Given that the only certainty for technology is further change, the success of any interactive is always measured by its usefulness, and its relevance to the exhibition content. The only way to mitigate against obsolescence is the richness of the interpretation—if the story is strong enough, an older technological interface can sometimes cease to matter.

They have an app or you can work with one of their body coaches on Zoom (tb12sports.com). The TB12 Sports band-training program is my staple for strength training.

If a networked product can begin to win over a series of networks faster than its competition, then it develops an accumulating advantage. These advantages, naturally, manifest as increasing network effects across customer acquisition, engagement, and monetization. Smaller networks might unravel and lose their users, who might switch over. Naturally, it becomes important for every player to figure out how to compete in this type of high-stakes environment. But how does the competitive playbook work in a world with network effects? First, I’ll tell you what it’s not: it’s certainly not a contest to see who can ship more features. In fact, sometimes the products seem roughly the same—just think about food-delivery or messaging apps—and if not, they often become undifferentiated since the features are relatively easy to copy. Instead, it’s often the dynamics of the underlying network that make all the difference. Although the apps for DoorDash and Uber Eats look similar, the former’s focus on high-value, low-competition areas like suburbs and college towns made all the difference—today, DoorDash’s market share is 2x that of Uber Eats. Facebook built highly dense and engaged networks starting with college campuses versus Google+’s scattered launch that built weak, disconnected networks. Rarely in network-effects-driven categories does a product win based on features—instead, it’s a combination of harnessing network effects and building a product experience that reinforces those advantages. It’s also not about whose network is bigger, a counterpoint to jargon like “first mover advantage.” In reality, you see examples of startups disrupting the big guys all the time. There’s been a slew of players who have “unbundled” parts of Craigslist, cherry-picking the best subcategories and making them apps unto themselves. Airbnb, Zillow, Thumbtack, Indeed, and many others fall into this category. Facebook won in a world where MySpace was already huge. And more recently, collaboration tools like Notion and Zoom are succeeding in a world where Google Suite, WebEx, and Skype already have significant traction. Instead, the quality of the networks matters a lot—which makes it important for new entrants to figure out which networks to cherry-pick to get started, which I’ll discuss in its own chapter.

When examined through the lens of Meerkat’s Law and the central framework of this book, it is obvious why the resulting networks generated by big launches are weak. You’d rather have a smaller set of atomic networks that are denser and more engaged than a large number of networks that aren’t there. When a networked product depends on having other people in order to be useful, it’s better to ignore the top-line aggregate numbers. Instead, the quality of the traction can only be seen when you zoom all the way into the perspective of an individual user within the network. Does a new person who joins the product see value based on how many other users are already on it? You might as well ignore the aggregate numbers, and in particular the spike of users that a new product might see in its first days. As Eric Ries describes in his book The Lean Startup, these are “vanity metrics.” The numbers might make you feel good, especially when they are going up, but it doesn’t matter if you have a hundred million users if they are churning out at a high rate, due to a lack of other users engaging. When networks are built bottom-up, they are more likely to be densely interconnected, and thus healthier and more engaged. There are multiple reasons for this: A new product is often incubated within a subcommunity, whether that’s a college campus, San Francisco techies, gamers, or freelancers—as recent tech successes have shown. It will grow within this group before spreading into other verticals, allowing time for its developers to tune features like inviting or sharing, while honing the core value proposition. Once a new networked product is spreading via word of mouth, then each user is likely to know at least one other user already on the network. By the time it reaches the broader consciousness, it will be seen as a phenomenon, and top-down efforts can always be added on to scale a network that’s already big and engaged. If Big Bang Launches work so poorly in general, why do they work for Apple? This type of launch works for Apple because their core offerings can stand alone as premium, high-utility products that generally don’t need to construct new networks to function. At most, they tap into existing networks like email and SMS. Famously, Apple has not succeeded with social offerings like the now-defunct Game Center and Ping. The closest new networked product they’ve launched is arguably the App Store, but even that was initially not in Steve Jobs’s vision for the phone.87 Most important, though, you aren’t Apple. So don’t try to copy them without having their kinds of products.

How long should it take for an app icon to animate up from its place on the home screen to fill the entire display? How far should you have to drag your finger on the screen for it to be possible to interpret the touch as a swipe gesture? How much should a two-finger pinch gesture allow you to zoom in on an image in the Photos app? The answers to all of these questions were numbers, and might be 0.35 seconds for the app animation, or 30 pixels for the swipe gesture, or 4x for photo zooming, but the number was never the point. The values themselves weren’t provably better in any engineering sense. Rather, the numbers represented sensible defaults, or pleasing effects, or a way to give people what they meant rather than what they did. It takes effort to find what these things are, which is appropriate, since the etymological root of “heuristic” is eureka, which (of course) comes from the Greek and means “to find.” This is where that word, “eureka,” actually figured into our development process, since good heuristics don’t come in brilliant flashes, but only after patient searches, and it wasn’t always clear to us that we had found the right heuristic even when we had. We arrived at our final decisions only with judgment and time. Heuristics are like this. They’re subjective.

The whole word is familiar with Zoom now.

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