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2022 Reading Challenge
Yevgeniy Brikman read 76 books of his goal of 52!
146%
2021 Reading Challenge
Yevgeniy Brikman read 68 books of his goal of 52!
131%
2020 Reading Challenge
Yevgeniy Brikman read 70 books of his goal of 52!
135%
2019 Reading Challenge
Yevgeniy Brikman read 81 books of his goal of 65!
125%
2018 Reading Challenge
Yevgeniy Brikman read 58 books of his goal of 52!
112%
2017 Reading Challenge
Yevgeniy Brikman read 77 books of his goal of 52!
148%
2016 Reading Challenge
Yevgeniy Brikman read 40 books of his goal of 30!
133%
2015 Reading Challenge
Yevgeniy Brikman read 37 books of his goal of 30!
123%
Yevgeniy Brikman
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Yevgeniy (Jim) Brikman is the co-founder of Gruntwork, a company that provides DevOps as a Service. He's also the author of two books published by O'Reilly Media: Hello, Startup and Terraform: Up & Running . Previously, he worked as a software engineer at LinkedIn, TripAdvisor, Cisco Systems, and Thomson Financial and got his BS and Masters at Cornell University. For more info, check out ybrikman.com. Yevgeniy (Jim) Brikman is the co-founder of Gruntwork, a company that provides DevOps as a Service. He's also the author of two books published by O'Reilly Media: Hello, Startup and Terraform: Up & Running . Previously, he worked as a software engineer at LinkedIn, TripAdvisor, Cisco Systems, and Thomson Financial and got his BS and Masters at Cornell University. For more info, check out ybrikman.com. ...more
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Yevgeniy Brikman
I used to think that experts wrote books. Now I realize that writing books is what makes you an expert.
I find writing to be an incredible learning ex…moreI used to think that experts wrote books. Now I realize that writing books is what makes you an expert.
I find writing to be an incredible learning experience. Every time I write, whether it's a blog post or a book, I walk away knowing something new.(less)
I find writing to be an incredible learning ex…moreI used to think that experts wrote books. Now I realize that writing books is what makes you an expert.
I find writing to be an incredible learning experience. Every time I write, whether it's a blog post or a book, I walk away knowing something new.(less)
Yevgeniy Brikman
Glad you found them useful!
I used to read only a few books per year too. I had to change my habits to read more; the more I did so, the more I enjoyed…moreGlad you found them useful!
I used to read only a few books per year too. I had to change my habits to read more; the more I did so, the more I enjoyed reading, which would motivate me to change habits even more to get in even more reading time.
Here are a few of the things I do:
1. I always read before bed. You'd be amazed how many books you can get through with just 20 min per night... And it becomes a habit you really look forward to, easily expanding to 30 min per night, then 45 min per night, etc.
2. I always read when I travel. I read on airplanes (rather than watching crappy movies on tiny screens), on trains & metros if I'm commuting, etc.
3. I listen to audiobooks all the time too. Whenever I'm doing I workout, I have an audiobook going, which makes workouts much more enjoyable. I also listen to audiobooks while out on walks, when doing chores, when shopping for groceries, when commuting by car, and so on.
4. I always try to have a book handy. I like paper books best, but if I don't happen to have one, I always have ebooks and audiobooks on my phone. You'd be surprised when you find yourself reading: e.g., while waiting at the doctor's or dentist's for an appointment; when standing in line; when in the bathroom (TMI?); etc. I used to mostly look at email and social media on my phone during these moments, but now, any time I have to kill more than a few minutes, I prefer to read instead.
HTH!(less)
I used to read only a few books per year too. I had to change my habits to read more; the more I did so, the more I enjoyed…moreGlad you found them useful!
I used to read only a few books per year too. I had to change my habits to read more; the more I did so, the more I enjoyed reading, which would motivate me to change habits even more to get in even more reading time.
Here are a few of the things I do:
1. I always read before bed. You'd be amazed how many books you can get through with just 20 min per night... And it becomes a habit you really look forward to, easily expanding to 30 min per night, then 45 min per night, etc.
2. I always read when I travel. I read on airplanes (rather than watching crappy movies on tiny screens), on trains & metros if I'm commuting, etc.
3. I listen to audiobooks all the time too. Whenever I'm doing I workout, I have an audiobook going, which makes workouts much more enjoyable. I also listen to audiobooks while out on walks, when doing chores, when shopping for groceries, when commuting by car, and so on.
4. I always try to have a book handy. I like paper books best, but if I don't happen to have one, I always have ebooks and audiobooks on my phone. You'd be surprised when you find yourself reading: e.g., while waiting at the doctor's or dentist's for an appointment; when standing in line; when in the bathroom (TMI?); etc. I used to mostly look at email and social media on my phone during these moments, but now, any time I have to kill more than a few minutes, I prefer to read instead.
HTH!(less)
Yevgeniy Brikman
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Terraform: Up & Running: Writing Infrastructure as Code
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Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
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Startup Essentials: A Curated Collection of Chapters from the O'Reilly Business Library
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An excellent read for all product managers and founders that teaches you the proper way to be constantly talking with your customers and doing product discovery. At this point, most people building products know the importance of getting input from c
An excellent read for all product managers and founders that teaches you the proper way to be constantly talking with your customers and doing product discovery. At this point, most people building products know the importance of getting input from customers—of validating product ideas, doing user research, doing user testing, and so on—but not how to do it effectively, and that's precisely what this book teaches you. It's short and to the point, with no wasted pages or business speak. Here are some of the key insights for me: Defining continuous discovery A key idea in this book is that product discovery is not something you do once, just to launch the product, but something you do continuously. The book defines continuous discovery as follows: - The team building the product... - Has touchpoints with customers at least once per week... - Where they conduct small research activities... - In pursuit of a desired outcome. Since product teams make decisions every single day, the idea of continuous discovery is to infuse those daily decisions with customer input. The structure of discovery The process for doing discovery is: 1. Define a clear business outcome. What business need are you trying to achieve? 2. Discover and map out the opportunity space. Here, you explore customer needs: the pain points and desires—the opportunities—that, if addressed, could drive your desired business outcome. 3. Discover solutions to address those opportunities. Come up with solutions to achieve the desired outcome. These steps should be visualized in an opportunity solution tree (OST) (you can find an example image here). The root (top) of the tree is the business outcome you want as per item (1). This branches out into a series of opportunities and sub-opportunities you discover in (2). A key insight is that you only focus on the customer needs in (2) that could help you achieve your business needs in (1): this is how you ensure that would you build achieves both business and customer needs! You will then pick a small subset of the most promising opportunities to focus on, branching out just these opportunities into possible solutions for them in (3). Finally, for each solution, you'll further branch those out into a series of assumption tests that you can use to figure out which of the solutions is most likely to create the business & customer value you want. Instead of "whether or not" decisions, use a "compare and contrast" mindset One of the most common mistakes product teams make is to get caught up in "whether or not" decisions: e.g., "should we stop everything to fix this problem?" or "should we stop everything to build this feature?" This is a trap that makes you myopic and leads to poor decision making, as you're essentially asking, "is this valuable" whereas what you should really be asking is, "is this the most valuable thing we could do?" Instead of framing decisions as "whether or not" decisions, you should shift to a "compare and contrast" mindset. Instead of "should we solve this customer need?" you should ask "which of these customer needs is most important for us to address right now?" Instead of jumping at the first idea you have, ask "how else might we address this opportunity?" Visualizing your options using an OST helps you avoid the "whether or not" trap. By its very nature, the OST is a branching tree which encourages you to flush out a variety of opportunities (branches) and a variety of possible solutions (sub-branches). This allows you to use your limited time and energy on the most valuable opportunities and solutions available, rather than the first ones that come to mind. You also want to use the "compare and contrast" mindset when ranking opportunities: for example, instead of going through each opportunity and asking, "how many customers does this affect?", which would require a huge amount of data gathering, look at all your opportunities, and ask, "which of these opportunities affects the most customers?" It's usually far easier to rank opportunities against each other than it is to evaluate them in isolation. Focus on product outcomes when managing by outcomes Many businesses these days try to manage by outcome, using system such as OKRs to set objectives for teams to achieve, and letting those teams figure out how to achieve those outcomes. This is generally a good thing, but only if you pick the right types of outcomes to focus on! There are three general types of outcomes: - Business outcomes: track business progress. E.g., revenue, retention, stock price. - Product outcomes: track how the product drives business value. E.g., percent of satisfied customers. - Traction outcomes: track usage of specific features. E.g., Usage of a certain part of the website. The recommendation: in most cases, you should manage by product outcomes. Although you certainly want to track business outcomes, they are not effective tools for managing by outcomes. That's because (a) they are lagging indicators, so they are too slow to use in a product team's iterative feedback loop and (b) they aren't something the product team can influence directly—e.g., you can't force a customer to buy or the stock price to go up! Similarly, it's useful to track traction outcomes, but you don't usually want to use them to manage by outcome. That's because traction metrics make an assumption that one specific feature is what really matters, but it may turn out that customers don't care about that feature, or that feature isn't tied to their overall success. If you assign a product team a traction metric as the outcome to achieve, then their hands are tied: they end up obsessing over a specific feature that may ultimately have no impact on the customer or business outcomes we care about. There are some exceptions where traction metrics are useful: e.g., for a junior product manager, improving a traction metric can be a good way to learn and ramp up; also, for a highly mature, proven product, where you know with very high confidence that the traction metric is tied to customer outcomes, focusing on that metric can be worthwhile. For the majority of product teams, you are better off focusing on product outcomes. These tend to be leading indicators and they are outcomes the product team has some direct control over. Moreover, there is enough flexibility across the product where the team can explore and find the right things to focus on to affect those product metrics, rather than being tied to any one specific feature as with traction metrics. Start with learning goals, then move on to performance (SMART) goals At a high level, there are two types of goals you can set: 1. Performance goals (SMART): one option is to set performance goals, which should be specific, measurable, achievable, relevant, and time-bound (SMART). Example: increase page views by 10% by the end of Q2. 2. Learning goals: another option is to set learning goals, where you are trying to discover an approach or strategy that might work. These tend to be more open ended. Example: find opportunities that may increase engagement. The research suggests that, when faced with a new outcome, and one that is complex, most teams perform better by setting learning goals first, and only later, setting performance (SMART) goals. That is, give your team some time to do discovery work initially (e.g., figure out opportunities to increase engagement), before picking a specific performance metric to improve (e.g., increase page views by 10%). Without that initial discovery work, you'll struggle to know what performance metric is worth improving (e.g., is it page views or time on site or DAUs), and the team will struggle to know how to improve that metric, leading to worse outcomes all around. Ask customers about past behavior, not future predictions When doing discovery, you will spend a lot of time interviewing customers. If you do it the wrong way—ask the wrong questions—you'll get information that is very misleading. In particular, if you ask questions where someone has to predict how they might behave in the future or to explain their preferences, this often leads to people thinking about their "ideal" selves and making up answers that are not reliable: e.g., you ask someone what they would pick on a menu, and they say salad, but when you observe what they actually pick, they go for the burger; or you ask someone what criteria they use to pick out jeans, and they say it's all about fit, but when you observe their actual behavior, they always buy jeans online, where you can't check fit at all, and so the real criteria is all about convenience, selection, and price. The solution: ask customers about what the actually did in the past. E.g., Ask "what did you pick on the menu last time you were at that restaurant?" or "tell me about the last time you bought jeans." This lets you learn from actual behavior, rather than perceived or imagined behavior. Note that even when asking about past behavior, customers may jump to generalizations: e.g., "I usually solve this by..." or "In general, what I do is..." These are predictions or interpretations and likely not to be reliable! Gently guide them back to specific past behavior: "OK, but in this specific instance, what exactly did you do?" Vary the scope of your questions You might ask customers a question specifically about the product you're building: "tell me about your experience last time you used our product XXX to watch movies." This will reveal pain points with your product. However, you may want to broaden the scope: "tell me about the last time you watched movies." This will tell you about your direct competitors. Or you could go even broader: "tell me about the last time you did something for entertainment." This tells you about the market category you're in. "You'll want to tailor the scope of the question based on what you need to learn at that moment in time. A narrow scope will help you optimize your existing product. Broader questions will help you uncover new opportunities. The broadest questions might help you uncover new markets." Create experience maps A key part of developing a product is understanding the full customer experience. This includes your product, but also everything happening with the customer around your product. To avoid missing this critical context, you should draw an experience map: 1. Define the scope. This depends on the product problem you're trying to solve. If you're developing a totally new product, you'll want the full experience around it; if you're working on a single new feature, you might zoom in more. Example: if you're building a brand new video streaming app, the scope might be, "how do customers entertain themselves with video?" 2. Draw the customer's experience, not your product. Don't diagram your product, screen by screen. Instead, draw the process as the customer perceives it. Example: with the video streaming app, the experience might start with the customer finishing dinner, and looking for a way to relax at night; after that, they might choose to put on the TV; then, they might find your app. Even at this point, don't draw your product screens, but focus on what the customer is trying to do: e.g., how do they choose what to watch? Where do they hear about new content? Who are they watching with? What issues do they hit along the way? And so on. 3. No artistic skill is required. This isn't an art project. Use stick figures, boxes, and arrows. 4. Update the map based on customer interviews. As you talk to customers, you'll want to ask them about the full experience, and to update your experience map based on what you learned. You may have to do some work to "execavate" the full story: ask them to "Start at the beginning—what happened first?" Or say, "Where were you? Set the scene for me." Then prompt them to go further, with "What happened next?" or to fill in gaps with "Wait, what happened right before that?" Find out who else was involved with "Who was with you?" and uncover problems with "What challenges did you hit?" and "How did you solve those?" Discover opportunities from interviews To fill out your OST, listen for opportunities during customer interviews. These are needs or pain points. A few key points: 1. Record opportunities as problems and not solutions. Customers often express a specific solution they want, and it's your job to dig in, and identify the underlying problem. For example, a customer might say, "I wish I had a way to search by voice." This is actually a solution (a feature request)! Dig in and ask, "What would that do for you?" The response might be "I don't have to spend tons of time typing out movie titles." Ah, now you understand the underlying problem! Voice search is one way to solve it, but there are many other options worth exploring too, and it's your job to figure those out. A good way to detect opportunities that are solutions in disguise is to ask, "is there more than one way to solve this?" If there's only one solution, then this isn't an opportunity, but that very solution! 2. Record opportunities from the customer's perspective, not your company's. No customer would ever say, "I wish I had more streaming-entertainment subscriptions." But they might say, "I want access to more compelling content." Always record opportunities from the customer's perspective: sanity check by asking, "would a real customer have said this, or are we just wishing someone would say this?" 3. Break big opportunities down into smaller ones. You'll sometimes hear opportunities from customers that, at first, seem very difficult to solve: e.g., "Is this show any good?" In these cases, you'll want to break the large opportunity down into smaller sub-opportunities (adding them as child nodes in the OST): e.g., the sub-opportunities may be "Who is in this show?", "Are my friends watching this show?", "Is this a genre of show that I like?", and so on. Usually, these sub-opportunities (a) will feel a lot more solvable and (b) give you the ability to deliver value over time, rather than trying to boil the whole ocean at once. Flushing out assumptions Go through your story map and: 1. Each time you see a step where you believe a user will do something, this is an assumption! Make these explicit across 3 dimensions: (a) desirability assumptions, where you assume the user wants to do what you're asking, (b) usability assumptions, where you assume the user understands what they need to do and can figure out how to do it, and (c) feasibility assumptions, where you assume you can build what is required for each step of the map. For example, if a step in your map has a user coming to your product to watch sports, you are making (a) the desirability assumptions that users want to watch sports, and to watch them using your product, (b) usability assumptions that users can figure out how to watch sports in your product, and (c) feasibility assumptions that you're able to get sports content into your product. 2. Conduct a pre-mortem. At the start of a project, imagine it is six months in the future, your product or initiative launched, but it was a failure. What went wrong? Identifying your leap of faith assumptions Rank assumptions on a 2d chart with two axes: - X-axis: evidence. The left side is assumptions for which you have strong evidence and the right side is assumptions for which you have weak evidence. - Y-axis: importance. On top are assumptions which are more important for your product to succeed and on the bottom are assumptions which are less important for your product to succeed. Remember that you are placing assumptions relative to each other, so the exact spot on the 2d chart doesn't matter; all that matters is the location on the chart relative to other assumptions. The assumptions that end up in the top right quadrant—the ones that are important, but for which you have weak evidence—are the "leap of faith" assumptions you should focus on! Testing assumptions with simulation tests You want to create assumption tests that help you move assumptions from "weak evidence" to "strong evidence." The best product teams can do 15-20 such tests per week! How? By testing just the assumption in a simulation test, rather than testing an entire idea. Here's how: 1. Identify the right spot in the experience map. At what moment in time does this assumption come into play? E.g., If we are testing the assumption that a user will watch sports on our platform, the moment in time might be when they sit on their couch and turn the TV on. 2. Define a hypothesis. If the assumption is true, what do we expect the user to do? E.g., If we are testing the assumption that a user will watch sports on our platform, the hypothesis is that at least X% of users will open our product after sitting down on the couch. 3. Run a simulation test. Create a minimal simulation of solely this exact part of the experience. This might be as simple as a one-question survey: e.g., "Please select all the sports you've watched in the last month" or "When was the list time you watched a sporting event?". Most of the learnings will come from failed tests: where users do not behave as you hypothesized. These simultation tests allow you to find these problems quickly—to "fail fast." ...more |
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4.5 stars The good - I *loved* the characters in this book. Bring up the name of literally any character in this book—Emmett, Billy, Duchess, Woolly, Sally, Ulysses, and Professor Abacus Abernathe—and a clear, rich image pops into my head of someone wh 4.5 stars The good - I *loved* the characters in this book. Bring up the name of literally any character in this book—Emmett, Billy, Duchess, Woolly, Sally, Ulysses, and Professor Abacus Abernathe—and a clear, rich image pops into my head of someone who feels real, alive, unique, and interesting. - Interesting plot. At some level, this is the story of cross-country roadtrip, but it's really a half-dozen stories focused on the wonderful characters. Emmett, trying to start a new life; Billy, trying to be like the heroes he reads about; Duchess, trying to balance the books of his life; and so on. - A brief glimpse of life in America in the 1950s: we get glimpses of struggling farmers; soldiers coming back from WWII; the divide between rich & poor, men & women, and whites & nonwhites; the growth of Texas and California; the ever increasing ubquity of cars & road trips; and gamblers, whorehouses, the homeless, and more. - I enjoyed the writing too. It felt simple in parts—almost like a young adult story—but it worked well. The not so good - The ending struck me as a bit abrupt and a bit odd. ...more |
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An excellent read for all product managers and founders that teaches you the proper way to be constantly talking with your customers and doing product discovery. At this point, most people building products know the importance of getting input from c
An excellent read for all product managers and founders that teaches you the proper way to be constantly talking with your customers and doing product discovery. At this point, most people building products know the importance of getting input from customers—of validating product ideas, doing user research, doing user testing, and so on—but not how to do it effectively, and that's precisely what this book teaches you. It's short and to the point, with no wasted pages or business speak. Here are some of the key insights for me: Defining continuous discovery A key idea in this book is that product discovery is not something you do once, just to launch the product, but something you do continuously. The book defines continuous discovery as follows: - The team building the product... - Has touchpoints with customers at least once per week... - Where they conduct small research activities... - In pursuit of a desired outcome. Since product teams make decisions every single day, the idea of continuous discovery is to infuse those daily decisions with customer input. The structure of discovery The process for doing discovery is: 1. Define a clear business outcome. What business need are you trying to achieve? 2. Discover and map out the opportunity space. Here, you explore customer needs: the pain points and desires—the opportunities—that, if addressed, could drive your desired business outcome. 3. Discover solutions to address those opportunities. Come up with solutions to achieve the desired outcome. These steps should be visualized in an opportunity solution tree (OST) (you can find an example image here). The root (top) of the tree is the business outcome you want as per item (1). This branches out into a series of opportunities and sub-opportunities you discover in (2). A key insight is that you only focus on the customer needs in (2) that could help you achieve your business needs in (1): this is how you ensure that would you build achieves both business and customer needs! You will then pick a small subset of the most promising opportunities to focus on, branching out just these opportunities into possible solutions for them in (3). Finally, for each solution, you'll further branch those out into a series of assumption tests that you can use to figure out which of the solutions is most likely to create the business & customer value you want. Instead of "whether or not" decisions, use a "compare and contrast" mindset One of the most common mistakes product teams make is to get caught up in "whether or not" decisions: e.g., "should we stop everything to fix this problem?" or "should we stop everything to build this feature?" This is a trap that makes you myopic and leads to poor decision making, as you're essentially asking, "is this valuable" whereas what you should really be asking is, "is this the most valuable thing we could do?" Instead of framing decisions as "whether or not" decisions, you should shift to a "compare and contrast" mindset. Instead of "should we solve this customer need?" you should ask "which of these customer needs is most important for us to address right now?" Instead of jumping at the first idea you have, ask "how else might we address this opportunity?" Visualizing your options using an OST helps you avoid the "whether or not" trap. By its very nature, the OST is a branching tree which encourages you to flush out a variety of opportunities (branches) and a variety of possible solutions (sub-branches). This allows you to use your limited time and energy on the most valuable opportunities and solutions available, rather than the first ones that come to mind. You also want to use the "compare and contrast" mindset when ranking opportunities: for example, instead of going through each opportunity and asking, "how many customers does this affect?", which would require a huge amount of data gathering, look at all your opportunities, and ask, "which of these opportunities affects the most customers?" It's usually far easier to rank opportunities against each other than it is to evaluate them in isolation. Focus on product outcomes when managing by outcomes Many businesses these days try to manage by outcome, using system such as OKRs to set objectives for teams to achieve, and letting those teams figure out how to achieve those outcomes. This is generally a good thing, but only if you pick the right types of outcomes to focus on! There are three general types of outcomes: - Business outcomes: track business progress. E.g., revenue, retention, stock price. - Product outcomes: track how the product drives business value. E.g., percent of satisfied customers. - Traction outcomes: track usage of specific features. E.g., Usage of a certain part of the website. The recommendation: in most cases, you should manage by product outcomes. Although you certainly want to track business outcomes, they are not effective tools for managing by outcomes. That's because (a) they are lagging indicators, so they are too slow to use in a product team's iterative feedback loop and (b) they aren't something the product team can influence directly—e.g., you can't force a customer to buy or the stock price to go up! Similarly, it's useful to track traction outcomes, but you don't usually want to use them to manage by outcome. That's because traction metrics make an assumption that one specific feature is what really matters, but it may turn out that customers don't care about that feature, or that feature isn't tied to their overall success. If you assign a product team a traction metric as the outcome to achieve, then their hands are tied: they end up obsessing over a specific feature that may ultimately have no impact on the customer or business outcomes we care about. There are some exceptions where traction metrics are useful: e.g., for a junior product manager, improving a traction metric can be a good way to learn and ramp up; also, for a highly mature, proven product, where you know with very high confidence that the traction metric is tied to customer outcomes, focusing on that metric can be worthwhile. For the majority of product teams, you are better off focusing on product outcomes. These tend to be leading indicators and they are outcomes the product team has some direct control over. Moreover, there is enough flexibility across the product where the team can explore and find the right things to focus on to affect those product metrics, rather than being tied to any one specific feature as with traction metrics. Start with learning goals, then move on to performance (SMART) goals At a high level, there are two types of goals you can set: 1. Performance goals (SMART): one option is to set performance goals, which should be specific, measurable, achievable, relevant, and time-bound (SMART). Example: increase page views by 10% by the end of Q2. 2. Learning goals: another option is to set learning goals, where you are trying to discover an approach or strategy that might work. These tend to be more open ended. Example: find opportunities that may increase engagement. The research suggests that, when faced with a new outcome, and one that is complex, most teams perform better by setting learning goals first, and only later, setting performance (SMART) goals. That is, give your team some time to do discovery work initially (e.g., figure out opportunities to increase engagement), before picking a specific performance metric to improve (e.g., increase page views by 10%). Without that initial discovery work, you'll struggle to know what performance metric is worth improving (e.g., is it page views or time on site or DAUs), and the team will struggle to know how to improve that metric, leading to worse outcomes all around. Ask customers about past behavior, not future predictions When doing discovery, you will spend a lot of time interviewing customers. If you do it the wrong way—ask the wrong questions—you'll get information that is very misleading. In particular, if you ask questions where someone has to predict how they might behave in the future or to explain their preferences, this often leads to people thinking about their "ideal" selves and making up answers that are not reliable: e.g., you ask someone what they would pick on a menu, and they say salad, but when you observe what they actually pick, they go for the burger; or you ask someone what criteria they use to pick out jeans, and they say it's all about fit, but when you observe their actual behavior, they always buy jeans online, where you can't check fit at all, and so the real criteria is all about convenience, selection, and price. The solution: ask customers about what the actually did in the past. E.g., Ask "what did you pick on the menu last time you were at that restaurant?" or "tell me about the last time you bought jeans." This lets you learn from actual behavior, rather than perceived or imagined behavior. Note that even when asking about past behavior, customers may jump to generalizations: e.g., "I usually solve this by..." or "In general, what I do is..." These are predictions or interpretations and likely not to be reliable! Gently guide them back to specific past behavior: "OK, but in this specific instance, what exactly did you do?" Vary the scope of your questions You might ask customers a question specifically about the product you're building: "tell me about your experience last time you used our product XXX to watch movies." This will reveal pain points with your product. However, you may want to broaden the scope: "tell me about the last time you watched movies." This will tell you about your direct competitors. Or you could go even broader: "tell me about the last time you did something for entertainment." This tells you about the market category you're in. "You'll want to tailor the scope of the question based on what you need to learn at that moment in time. A narrow scope will help you optimize your existing product. Broader questions will help you uncover new opportunities. The broadest questions might help you uncover new markets." Create experience maps A key part of developing a product is understanding the full customer experience. This includes your product, but also everything happening with the customer around your product. To avoid missing this critical context, you should draw an experience map: 1. Define the scope. This depends on the product problem you're trying to solve. If you're developing a totally new product, you'll want the full experience around it; if you're working on a single new feature, you might zoom in more. Example: if you're building a brand new video streaming app, the scope might be, "how do customers entertain themselves with video?" 2. Draw the customer's experience, not your product. Don't diagram your product, screen by screen. Instead, draw the process as the customer perceives it. Example: with the video streaming app, the experience might start with the customer finishing dinner, and looking for a way to relax at night; after that, they might choose to put on the TV; then, they might find your app. Even at this point, don't draw your product screens, but focus on what the customer is trying to do: e.g., how do they choose what to watch? Where do they hear about new content? Who are they watching with? What issues do they hit along the way? And so on. 3. No artistic skill is required. This isn't an art project. Use stick figures, boxes, and arrows. 4. Update the map based on customer interviews. As you talk to customers, you'll want to ask them about the full experience, and to update your experience map based on what you learned. You may have to do some work to "execavate" the full story: ask them to "Start at the beginning—what happened first?" Or say, "Where were you? Set the scene for me." Then prompt them to go further, with "What happened next?" or to fill in gaps with "Wait, what happened right before that?" Find out who else was involved with "Who was with you?" and uncover problems with "What challenges did you hit?" and "How did you solve those?" Discover opportunities from interviews To fill out your OST, listen for opportunities during customer interviews. These are needs or pain points. A few key points: 1. Record opportunities as problems and not solutions. Customers often express a specific solution they want, and it's your job to dig in, and identify the underlying problem. For example, a customer might say, "I wish I had a way to search by voice." This is actually a solution (a feature request)! Dig in and ask, "What would that do for you?" The response might be "I don't have to spend tons of time typing out movie titles." Ah, now you understand the underlying problem! Voice search is one way to solve it, but there are many other options worth exploring too, and it's your job to figure those out. A good way to detect opportunities that are solutions in disguise is to ask, "is there more than one way to solve this?" If there's only one solution, then this isn't an opportunity, but that very solution! 2. Record opportunities from the customer's perspective, not your company's. No customer would ever say, "I wish I had more streaming-entertainment subscriptions." But they might say, "I want access to more compelling content." Always record opportunities from the customer's perspective: sanity check by asking, "would a real customer have said this, or are we just wishing someone would say this?" 3. Break big opportunities down into smaller ones. You'll sometimes hear opportunities from customers that, at first, seem very difficult to solve: e.g., "Is this show any good?" In these cases, you'll want to break the large opportunity down into smaller sub-opportunities (adding them as child nodes in the OST): e.g., the sub-opportunities may be "Who is in this show?", "Are my friends watching this show?", "Is this a genre of show that I like?", and so on. Usually, these sub-opportunities (a) will feel a lot more solvable and (b) give you the ability to deliver value over time, rather than trying to boil the whole ocean at once. Flushing out assumptions Go through your story map and: 1. Each time you see a step where you believe a user will do something, this is an assumption! Make these explicit across 3 dimensions: (a) desirability assumptions, where you assume the user wants to do what you're asking, (b) usability assumptions, where you assume the user understands what they need to do and can figure out how to do it, and (c) feasibility assumptions, where you assume you can build what is required for each step of the map. For example, if a step in your map has a user coming to your product to watch sports, you are making (a) the desirability assumptions that users want to watch sports, and to watch them using your product, (b) usability assumptions that users can figure out how to watch sports in your product, and (c) feasibility assumptions that you're able to get sports content into your product. 2. Conduct a pre-mortem. At the start of a project, imagine it is six months in the future, your product or initiative launched, but it was a failure. What went wrong? Identifying your leap of faith assumptions Rank assumptions on a 2d chart with two axes: - X-axis: evidence. The left side is assumptions for which you have strong evidence and the right side is assumptions for which you have weak evidence. - Y-axis: importance. On top are assumptions which are more important for your product to succeed and on the bottom are assumptions which are less important for your product to succeed. Remember that you are placing assumptions relative to each other, so the exact spot on the 2d chart doesn't matter; all that matters is the location on the chart relative to other assumptions. The assumptions that end up in the top right quadrant—the ones that are important, but for which you have weak evidence—are the "leap of faith" assumptions you should focus on! Testing assumptions with simulation tests You want to create assumption tests that help you move assumptions from "weak evidence" to "strong evidence." The best product teams can do 15-20 such tests per week! How? By testing just the assumption in a simulation test, rather than testing an entire idea. Here's how: 1. Identify the right spot in the experience map. At what moment in time does this assumption come into play? E.g., If we are testing the assumption that a user will watch sports on our platform, the moment in time might be when they sit on their couch and turn the TV on. 2. Define a hypothesis. If the assumption is true, what do we expect the user to do? E.g., If we are testing the assumption that a user will watch sports on our platform, the hypothesis is that at least X% of users will open our product after sitting down on the couch. 3. Run a simulation test. Create a minimal simulation of solely this exact part of the experience. This might be as simple as a one-question survey: e.g., "Please select all the sports you've watched in the last month" or "When was the list time you watched a sporting event?". Most of the learnings will come from failed tests: where users do not behave as you hypothesized. These simultation tests allow you to find these problems quickly—to "fail fast." ...more |
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Yevgeniy Brikman
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3.5 stars The good - Intriguing central premise: the world is starting to cool and freeze over at an extremely rapid pace, and no one knows why. Humanity is forced to move into the warmest parts of the planet and work together to unravel this mystery, 3.5 stars The good - Intriguing central premise: the world is starting to cool and freeze over at an extremely rapid pace, and no one knows why. Humanity is forced to move into the warmest parts of the planet and work together to unravel this mystery, which leads to interesting geopolitics. - To unravel the mystery, humanity also needs to assemble the ultimate team: the world's most badass scientists, astronauts, soldiers, and other experts, with the protagonist, a super genius roboticist, as the lead. It's fairly cliche, but for the most part, it's still entertaining, and offers up plenty of competence porn. - The book is mostly focused on hard science, rather than magic, which I always appreciate. - As the mystery unravels, you get some fun twists and turns. Overall, it's an entertaining read throughout. The not so good - The idea of humanity joyfully bonding to save the world seems a bit laughable after the COVID pandemic. At this point, any global disaster book needs to include a contingent of humanity that's rooting for the world to end, and doing everything they can to interfere with the people working to save it. - The characters are pretty weak. The main protagonist is too smart, making unreasonably large mental leaps, and developing absurd technologies. There's a thin line between "competence porn" and "this is just the author advancing the plot in silly ways." The other protagonist is an astronaut who starts off super interesting, but then quickly becomes a damsel in distress, and ends up being little more than the love interest for the main character. There are a few other characters in the story who start off interesting (e.g., the other members of the ultimate badass team), but the book abandons all of them pretty quickly. - Minor spoiler: (view spoiler)[I always cringe a bit when a book has an "explainer"—that is, a bad guy who goes on a long monologue to explain everything that has been happening. And doubly so when it's an alien intelligence that can magically learn our language and communication systems just in time to deliver this monologue. (hide spoiler)] ...more |
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The good - An intriguing look at the world of nuclear submarines. It is insane that, every single day, there are gigantic metal vehicles, about 2 football fields in length, made out of titanium, weighing nearly 50,000 pounds, with no windows or visibi The good - An intriguing look at the world of nuclear submarines. It is insane that, every single day, there are gigantic metal vehicles, about 2 football fields in length, made out of titanium, weighing nearly 50,000 pounds, with no windows or visibility at all, using sonar and radar and lasers as “sight,” powered by nuclear reactors, carrying up to 1,000 nuclear warheads, floating completely silently under the ocean for months at a time, so that no one can find them… And that’s our deterrent against nuclear war. - Some fun Cold War politics, spying, intrigue, stand offs, and the like. - Some fun naval tactics and battles. The not so good - A rare case where the movie was better than the book. - It's a Jack Ryan book, but he doesn't have a particularly important role in this one. ...more |
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Yevgeniy Brikman
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Working Backwards: Insights, Stories, and Secrets from Inside Amazon
by Colin Bryar (Goodreads Author) |
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The first half of this book does a great job of teaching some of the principles that made Amazon successful. There are a ton of deep insights in there and they are worth reading for just about every leader (of course, not everything will apply to eve
The first half of this book does a great job of teaching some of the principles that made Amazon successful. There are a ton of deep insights in there and they are worth reading for just about every leader (of course, not everything will apply to every company: not everyone works for a hypergrowth, VC-backed company, that now has over 1 million employees). The second half of the book has stories of how these principles were used when building out specific Amazon products, which was moderately interesting, but had a bit too much of a "rah rah rah, look how great we are" marketing message, so I'd recommend skipping it. Here are some of my key takeaways: 1. Good intentions don't work. Mechanisms do. As a company, you can't rely on good intensions—e.g., "try harder" or "next time, remember to..."—as a way to solve problems. Most people already have good intentions: they are already trying hard and doing their best to remember things, but intent and personal desire just aren't enough. To really fix problems, you need to put in place mechanisms: that is, you need to create or modify the systems and processes within which people work. This book goes through some of the key mechanisms they use at Amazon, some of which I'll cover below. 2. The bar raiser. Most people aren't particularly good at interviewing. Moreover, we're all subject to various biases, such as an urgency bias, where you might be tempted to compromise on a candidate in the interest of filling an important role sooner. Making the wrong hire is extremely costly to an organization: they slow down team members; they take up management time; they do inferior work; and eventually, you have to let them go. Therefore, you're almost always better off leaving a role unfilled for a longer time than taking a risk on a rush hire. One of the mechanisms Amazon uses to deal with hiring problems like this is to include a "Bar Raiser" in every interview loop. The job of the Bar Raiser is to ensure that every hire "raises the bar": that is, they are better in at least one important way than the other members of the team they'd be joining. This way, with each hire, the team gets stronger and stronger. The Bar Raiser has the ability to veto any hire, overriding everyone else's decision, including the hiring manager, if they feel a hire doesn't raise the bar. To minimize the Bar Raiser's bias, the Bar Raiser can never be the hiring manager, and is typically someone completely outside of the immediate team doing the hiring. Moreover, the Bar Raiser is never punished because a role went unfilled for a longer period of time. 3. Single-threaded teams "The best way to fail at inventing something is by making it somebody's part-time job." Amazon only takes on a new initiative if they can assign a dedicated team to work on that initiative—and nothing else. Inventing something new is hard enough even if you dedicate 100% of your time to it; if you try to split your time across multiple initiatives, you're all but certain to fail. In addition to having each team focus on just one thing, Amazon also designs teams to be able to work completely autonomously from each other. That is, rather than trying to find optimal ways to coordinate and communicate between teams, they try to eliminate the need for any communication or collaboration entirely. Therefore, each team must have clear, unambiguous ownership of specific features or functionality which they can build and deploy with minimal reliance on others: i.e., with little to no coordination or approvals from other teams. This allows each team to go extremely quickly. Their first attempt at this was to organize around "two-pizza teams," enforcing that teams could not be larger than the number of people you could feed with two pizzas. This led to two issues. One issue was that two-pizza teams were originally designed around a single manager that everyone, regardless of role, would report to; it turned out that "general managers" who had expertise to manage every single discipline on their team (e.g., engineering, design, sales, marketing) were extremely difficult to find. The second issue was that some initiatives needed more people than you could feed with two pizzas; in fact, it turned out that the success of a team was less tied to its size and more tied to whether the leader had the appropriate skills, authority, and experience to get the job done. Therefore, the second attempt was to move to "single-threaded teams," where each team focuses on just a single thing and is part of a matrix reporting structure, where each person on the team has a solid line reporting relationship to a manager in their own discipline / vertical (e.g., Engineers reporting to Engineering Managers), and a dotted line relationship to the leader of single-threaded team. 4. Written narratives instead of slide decks Amazon does not allow presentations or slide decks in meetings or product review sessions. Instead, they require written narratives (typically 6 pages in length). They start each meeting with ~20 minutes of silence while everyone reads the narrative, and then everyone goes around the room and provides feedback. The author of the narrative never presents: they just listen and gather feedback. This is based on a few tenets. One of these tenets is that it is the ideas, not the presenter, that should matter most. With a presentation, the skills of the speaker often have a disproportionate impact, with great speakers sometimes able to sell crappy ideas, and weaker speakers sometimes failing to sell great ideas. With a written narrative, it is the ideas and reasoning that take center stage. Another tenet is that a slide deck is a far less effective medium than a written narrative for complex decisions: that is, decisions that are important ("one way doors") and involve lots of interconnected ideas, nuances, and data to explore. For these sorts of discussions, instead of a slide deck with sparse words, bullet points, and pretty images, what you really want is prose, data, numbers, and charts in a written format that makes it easier to contextualize, compare, narrate, go back and forth, and so on. The format for a written narratives will vary based on what you're discussing, but there are two sections that are particularly useful to include in almost every written narrative: - Central tenets: right at the front, define the foundational elements of the reasoning that led to the recommendations in the document. If the central tenets are in dispute, it's easier to address those directly in one place, than to debate in a dozen places all the steps/recommendations that logically followed from those central tenets. Example tenets: speed and quality are always important, but when forced to choose between the two, we will always prioritize quality; when forced to choose between building something convenient for customers or something convenient for ourselves, we always choose the former. - FAQ: a strong written narrative not only makes its case, it also anticipates counterarguments, points of contention, and anything else that is likely to be misunderstood. 5. Working backwards: write the press release first Whenever working on a new initiative, Amazon requires that you write the press release first: before any product has been built, before the initiative has even been approved, you write up a press release to announce what you have in mind. This is a key part of the idea of "working backwards" for which the book is named: writing the press release first ensures that, right up front, you think through things from the customer perspective. This includes forcing you to think through: - The "so what?": why should a customer care about what you're building? - The value proposition: how is what you're building better than what's out there? - The messaging: how do you convey what your product is and how its better in a way that's clear and compelling? - The customer experience: how will customers use what you're building? - The must-haves: which features make the press release? These are the must-haves to build right away; everything else is a nice-to-have. You write this all up, share it with the team for feedback, and iterate on the press release over and over to refine it until you are sure that what you have is worth building (and possible to build). It's not uncommon to go through 10+ iterations of the press release before starting on a product. In fact, most product ideas never make it past this press release stage. This is a feature, not a bug: iterating on the press release is much faster than iterating on the real product, and it allows you to go through many options quickly, prioritizing only the ones you think will have the biggest benefits for your customers and company. The key ingredients of a press release include: - Heading: name the product in a way customers will understand. One sentence. - Sub-heading: describe the product and its benefits in a way customers will understand. One sentence. - Summary paragraph: the proposed launch date and location, plus a summary of the product and its benefits. - Problem paragraph: the problem the product solves, as seen from the customer's perspective. - Solution paragraph(s): how the product simply & effectively solves the customer's problem. - Quotes: a quote from a company spokesperson and a quote from a hypothetical customer describing the benefits they are getting from the product. - Getting started: describe how to get started, including links to where to get more info and purchase. - External FAQ: answers to questions you anticipate from customers and the press, such as more details on how the product works, how much it costs, where to buy it etc. - Internal FAQ: answers to questions you anticipate from the team reviewing the press release, such as TAM, economics, P&L, dependencies, feasibility, and so on. 6. Metrics Amazon groups metrics into two categories: - Input metrics: leading indicators that Amazon can control directly, such as selection (how many items they have in their product catalog), price (how much each item costs), and convenience (if the product is in stock or how long it takes to ship it). - Output metrics: lagging indicators that Amazon cannot control directly, such as orders, revenue, profit, and stock price. Both types of metrics are important. However, you should focus most of your energy on optimizing input metrics, because: - Input metrics are those that you can influence directly. - If you influence input metrics correctly, they lead to the output metrics you want. - Input metrics are leading indicators, so they are better predictors of the future, and let you identify issues far earlier than output metrics, which are lagging indicators. - Input metrics typically describe things that customers care about: e.g., product availability, prices, shipping. Output metrics typically describe things that the company cares about (e.g., revenue, profit, etc), but customers don't care about those at all. Amazon's belief is that the long-term interests of the company and its shareholders are perfectly aligned with the interests of the customers, so you're better off focusing on the metrics aligned to customer success. The key question is which input metrics should you optimize for? That is, which input metrics that, as you modify them, best lead to the outputs you desire? It can take a lot of trial and error to figure this out. Here's an example from Amazon: - Number of detail pages: they started with this as an input metric. - Number of detail page views: they realized that detail pages no one looks at aren't as valuable. - Percentage of detail page views where products were in stock: detail pages people look at, but can't buy from, aren't as valuable. - Percentage of detail page views where products were in stock and available for 2-day shipping: this ended up being the most valuable metric to optimize for. Picking the right input metrics to focus on can have a profound impact. When Amazon was focused solely on "number of detail pages," they spent a lot of time adding more and more items to their inventory, which drove up Amazon's costs, but didn't have as much of an impact on sales. The shift to "number of detail page views," got the team to dig through customer search history, find out what customers were actually looking for, and focus their efforts on stocking those specific items, which had a far bigger impact on sales. And finally, the focus on keeping things in stock and available for rapid shipping ensured the team was adding items to their inventory that would drive sales immediately. This may sound simple in retrospect, but it's very easy to pick the wrong metric, and miss these sorts of insights entirely. ...more |
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I never read this when I was younger, but after hearing many people gushing about it, I decided to give it a shot. It's definitely a children's story, but it was still a quick & enjoyable read, with some nice themes around friendship, physical and me I never read this when I was younger, but after hearing many people gushing about it, I decided to give it a shot. It's definitely a children's story, but it was still a quick & enjoyable read, with some nice themes around friendship, physical and mental health, nature, and, like most children's stories, of growing up. ...more | |
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4.5 stars The good - Legitimately funny. I actually laughed out loud at multiple parts of this book. - Several excellent characters, with Jackson Lamb as the highlight (and the cause of most of the laughing). - Exciting plot & pacing. - The world of MI5 4.5 stars The good - Legitimately funny. I actually laughed out loud at multiple parts of this book. - Several excellent characters, with Jackson Lamb as the highlight (and the cause of most of the laughing). - Exciting plot & pacing. - The world of MI5, terrorism, and intrigue, and the focus on Slough House, where they send the "washed up" spies with failing careers (who make for great antiheroes), continues to be highly entertaining. The not so good - The big reveal at the end is pretty disappointing: too convoluted and too complicated. ...more |
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Quotes by Yevgeniy Brikman
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“The best thing you can do to come up with a lot of new ideas is to learn a lot of old ideas. Because new ideas are just connections between old ideas, the more ideas you have in your head, the more connections you’ll be able to create between them.”
― Yevgeniy Brikman, Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
― Yevgeniy Brikman, Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
“Boyd’s Law: speed of iteration beats quality of iteration.”
― Yevgeniy Brikman, Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
― Yevgeniy Brikman, Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
“what kind of environment encourages new ideas? It varies from person to person, but here are the most common ingredients: Give yourself plenty of time. Keep an idea journal. Work on the problem. Get away from work. Add constraints. Look for pain points. Talk to others.”
― Yevgeniy Brikman, Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
― Yevgeniy Brikman, Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams