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You Have to Enjoy It a Lot

228,877 次观看 • 1 年前 •via X (Twitter)

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Good Products are Opinionated. “Every great founder I’ve seen up close, or even from afar, is highly opinionated and they’re almost dictatorial in how they run things. Also, early-stage teams are opinionated. And the products they build are opinionated. Opinionated means they have a strong vision for what it should and should not do. If you don’t have a strong vision of what it should and should not do, then you end up with a giant mess of competing features. jack Dorsey has a great phrase: “Limit the number of details and make every detail perfect.” And that’s especially important in consumer products. You have to be extremely opinionated. All the best products in consumer-land get there through simplicity. You could argue the recent success of ChatGPT and similar AI chatbots is because they’re even simpler than Google. Google looked like the simplest product you could possibly build. It was just a box. But even that box had limitations in what you could do. You were trained not to talk to it conversationally. You would enter keywords and you had to be careful with those keywords. You couldn’t just ask a question outright and get a sensible answer. It wouldn’t do proper synonym matching, and then it would spit you back a whole bunch of results. That was complicated. You’d have to sift through and figure out which ones were ads, which ones were real, were they sorted correctly, and then you’d have to click through and read it. ChatGPT and the chatbot simplified that even further. You just talk to it like a human—use your voice or you type and it gives you back a straight answer. It might not always be right, but it’s good enough, and it gives you back a straight answer in text or voice or images or whatever you prefer. So it simplifies what we looked at as the simplest product on the Internet, which was formerly Google, and makes it even simpler. And you just cannot make a product that’s simple enough. To be simple, you have to be extremely opinionated. You have to remove everything that doesn’t match your opinion of what the product should be doing. You have to meticulously remove every single click, every single extra button, every single setting. In fact, things in the settings menu are an indication that you’ve abdicated your responsibility to the user. Choices for the user are an abdication of your responsibility. Maybe for legal or important reasons, you can have a few of these, but you should struggle and resist against every single choice the user has to make. In the age of TikTok and ChatGPT, that’s more obvious than ever. People don’t want to make choices. They don’t want the cognitive load. They want you to figure out what the right defaults are and what they should be doing and looking at, and they want you to present it to them.”

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Is Traditional Software Engineering Dead? “Does this mean that traditional software engineering is dead? Absolutely not. Software engineers—even the ones who are not necessarily tuning or training AI models—these are now among the most leveraged people on earth. Sure, the guys who are training and tuning models are even more leveraged because they’re building the tool set that software engineers are using. But software engineers still have two massive advantages on you. First, they think in code, so they actually know what’s going on underneath. And all abstractions are leaky. So when you have a computer programming for you—when you have Claude Code or equivalent programming for you—it’s going to make mistakes. It’s going to have bugs. It’s going to have suboptimal architecture. So it’s not going to be quite right. And someone who understands what’s going on underneath will be able to plug the leaks as they occur. So if you want to build a well-architected application, if you want to be able to even specify a well-architected application, if you want to be able to make it run at high performance, if you want it to do its best, if you want to catch the bugs early, then you’re going to want to have a software engineering background. The traditional software engineer is going to be able to use these tools much better. And there are still many kinds of problems in software engineering that are out of scope for these AI programs today. The easiest way to think about those is problems that are outside of their data distribution. For example, if they need to do a binary sort or reverse a linked list, they’ve seen countless examples of that, so they’re extremely good at it. But when you start getting out of their domain—where you have to write very high-performance code, when you’re running on architectures that are novel or brand new, when you’re actually creating new things or solving new problems, then you still need to get in there and hand code it. At least until either there are so many of those examples that new models can be trained on them, or until these models can sufficiently reason at even higher levels of abstraction and crack it on their own… And remember: there is no demand for average. The average app—nobody wants it, at least as long as it’s not filling some niche that is filled by a superior app. The app that is better will win essentially a hundred percent of the market. Maybe there’s some small percentage that will bleed off to the second-best app because it does some little niche feature better than the main app, or it’s cheaper, or something of the sort. But generally speaking, people only want the best of anything. So the bad news is there’s no point in being number two or number three—like in the famous Glengarry Glen Ross scene where Alec Baldwin says, “First place gets a Cadillac Eldorado, second place gets a set of steak knives, and third place you’re fired.” That’s absolutely true in these winner-take-all markets. That’s the bad news: You have to be the best at something if you want to win. However, the set of things you can be best at is infinite. You can always find some niche that is perfect for you, and you can be the best at that thing. This goes back to an old tweet of mine where I said, “Become the best in the world at what you do. Keep redefining what you do until this is true.” And I think that still applies in this age of AI.”

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Full video and transcript: “So the first step to becoming indistractable is realizing that most of our distractions begin from within. We tend to blame the pings, dings, and rings, but that only accounts for 10% of our distractions. 90% of our distractions begin from uncomfortable internal states. Boredom, loneliness, fatigue, anxiety. What that means is that we have to learn how to master those internal triggers before they become our master. So whether it's too much news, too much booze, too much football, too much Facebook, you are always going to get distracted from something unless you understand what is that uncomfortable sensation you are trying to escape. Remember, time management is pain management. Step number two to becoming indistractable is making time for traction. You cannot say you got distracted from something unless you know what it distracted you from. So if you don't put something in your calendar that is traction, there's no such thing as distraction. If you have a blank open calendar, you have no right to say you got distracted, because what did you get distracted from? You have to plan your time. The third step to becoming indistractable is to ask ourselves this critical question around external triggers. Is this external trigger serving me or am I serving it? And then to systematically remove, hack back, all those external triggers on your phone or your computer, the pointless meetings, the emails that didn't need to be sent, we can systematically remove all those triggers that don't serve us. And then finally, the last line of defense against distraction is to prevent distraction with PACTS, which is where we use what's called a pre-commitment device to make sure that the last line of defense, the firewall against distraction, is that we know in advance what we will do when distraction rears its ugly head. So by following these four strategies, master internal triggers, make time for traction, hack back external triggers, and prevent distraction with packs, those strategies anyone can use to become indistractable. And of course there's a lot more about the explanation of all those strategies in my book, Indistractable.”

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“Geniuses only.” Nivi: To me, the missing ingredient in most people’s recruiting is intolerance. You should really just treat every employee in the company, including yourself, as an enemy agent that’s trying to destroy the company by bringing mediocre talent into the business. It’s unfortunately just the nature of human nature. Naval: My co-founder and I have a new criterion in our company: “Geniuses only.” It’s a harsh word, but it sets a very high bar. You can just look around for who’s not a genius. The only way you’re going to attract geniuses—whatever that term means to you—is by having a company full of geniuses. And if someone’s not a genius, then either you’re transitioning into the phase where you can no longer hire geniuses and you just need to scale up for whatever reason, or you can just show that person the door because you hired them prematurely for the kind of company you’re trying to build. Now, this is very difficult. You’re lucky if you can hire one genius a month. You as a founder have to identify them and do whatever it takes to recruit them and motivate them. So it’s inherently self-limiting. Given that a person probably isn’t going to stick around your company for more than three, four, five years—although in some great companies, people stick around for decades—at that attrition rate you’re talking about a 30 to 50 person company. But if you can even assemble a team of 10 geniuses, you’re way ahead of everybody else. At most companies—the successful ones—the founders, and maybe a few early people are at the genius level. But in the urge and the rush to scale, that gets drowned out too quickly.

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