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[Assistant] The LPL constantly expanding to 20 teams themselves. The infrastructure, the influx of incredible talented rookies. We only had two Korean imports come into the LPL this split out of over 18 rookies. So a massive insurge of power.


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[Assistant] And, and Betty not really even in our conversation about top ADCs, showing that we were wrong there, pulling up the Kai'Sa, and frankly, Kai'Sa is my favorite champion to watch right now.


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[Assistant] Paris has just been a wonderful host to the entire MSI tournament. And again, hats off to Chips Noy. The fact that they were, uh, you know, motivating the crowd, the, the Kingzone, the RNG chants, the whole stadium, my body just shook.


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[Assistant] Granny Pig and Grandpa Pig are making a treasure hunt for Peppa and George. Granny Pig is drawing the treasure map.


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[Assistant] Written by Torrin Anderson.


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[Assistant] Our next public lecture of this kind will be held on May 3rd, and that is going to be with Aisha Liviana Messina, and her topic is the time of fragility, and that will be something of a reflection on what we have been going through.


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[Assistant] Leading off our 2021 session, uhm, our proper academic session, uhm, in June, our course starts on June 19th with a seminar entitled, Unconscious and Negativity, Freud, Lacan, and Their Critical Legacies.


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[Assistant] Okay, you've done a tremendous amount. Thank you. I'm sure this could go on for quite some time.


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[Assistant] I think that those questions are a function of the talk itself, which is really very, uhm, very compelling and engaging. So uhm, let me thank you. I had a couple of thoughts, but let us bring this to a close.


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[Assistant] Well then I have to ask you, what is next? Is this then finally a team stood up to an LCK team in a best of 5 in a major final? You guys did it. How much is this the start of an RNG legacy that can go on especially to the World Championship or looking forward to the World Championship?


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[Assistant] Well, I think that's enough. Thank you very much. Congratulations, Xiaohu. Phenomenal performance by him. Back over to you guys.


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[Assistant] Thank you very much. I'm here with LetMe, also the integral part to RNG's success today. LetMe, it must have been very emotional up on stage. If you look at the games now, what do you think it came down to? Why were you guys so much stronger than Kingzone today?


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[Assistant] And that trust delivered them the title. Thank you very much. Congratulations. Tremendous performance. Back over to you guys.


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[Assistant] Though people have often laughed at stories told by semen,


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[Assistant] It is now known that many of these monsters, which have at times been sighted, are simply strange fish.


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[Assistant] A peculiar fish was caught near Madagascar. A small fishing boat.


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[Assistant] was carried miles out to sea by the powerful fish as it pulled on the line.


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[Assistant] Realizing that this was no ordinary fish,


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[Assistant] It was found to be over 13 feet long. It had a head like a horse, big blue eyes, shining silver skin, and a bright red tail. The fish


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[Assistant] Such creatures have rarely been seen alive by man.


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[Assistant] Was presented to the United States of America in the 19th century by the people of France. The great statue, which was designed by the sculptor Auguste Bartholdi, took 10 years to complete.


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[Assistant] Was made of copper, supported by a metal framework.


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[Assistant] Which had been especially constructed by Eiffel.


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[Assistant] Before it could be transported to the United States, a site had to be found for it and a pedestal had to be built.


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[Assistant] The site chosen was an island at the entrance of New York Harbor. By 1884,


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[Assistant] A statue which was 151 feet tall had been erected in Paris. The following year it was taken to pieces and sent to America.


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[Assistant] By the end of October 1886, the statue had been put together again, and it was officially presented to the American people by Bartoldi. Ever since then,


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[Assistant] The children can be instructed in swimming at a very early age.


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[Assistant] At a special swimming pool in Los Angeles,


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[Assistant] It is not long before they are so accustomed to swimming.


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[Assistant] A game that is very popular with these young swimmers is the underwater tricycle race.


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[Assistant] Tricycles are lined up on the floor of the pool, seven feet underwater.


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[Assistant] The children compete against each other to reach the other end of the pool. Many pedal their tricycles


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[Assistant] But most of them prefer to push or drag them.


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[Assistant] Some children can cover the whole length of the pool without coming up for breath even once.


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[Assistant] Whether they will ever become future Olympic champions, only time will tell. Meanwhile...


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[Assistant] Connections. Writing and art. Choose your favorite animal from this book.


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[Assistant] Draw a picture of the animal and write a sentence about it. Science. Pick two rainforest animals.


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[Assistant] To put the question into the following form.


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[Assistant] I have become a totally new kind of relationship since we can no longer pretend to be merely players.


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[Assistant] That contains a very simple formula that on a, on a finite basis you cannot


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[Assistant] Designed to dis-describe, uhm,


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[Assistant] A plurality of, uh, immunity constructs. For instance, religions.


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[Assistant] With these three letters, you can talk about any topic in English.


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[Assistant] Welcome to Speak English with Tiffany. I am teacher Tiffany. Let's jump right in.


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[Assistant] The first step is to identify. The second step is to organize. And the third step is to talk. So.


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[Assistant] Our first question is, what types of games do you play at parties? So we must identify. What is this question asking us about? Well the topic really is games.


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[Assistant] Next, we must organize. So we have who, what, when, where, and why. And the key is to make sure you have three pieces of information for each of the five W's. Now when I go to the last step,


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[Assistant] Talk, I can give a very clear and very detailed answer.


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[Assistant] I usually go to parties with my family, my coworkers, or my friends.


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[Assistant] We normally play games like Pictionary, Uno, or card games.


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[Assistant] The type of game depends on when the party is and what it's for.


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[Assistant] Sometimes the parties are for birthdays, Saturday night hangouts, or just Sunday fun days.


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[Assistant] So we will usually play Pictionary at my friend's house, Uno at my house, and cards when we are at the office. We love playing games together because it is a nice way to enjoy quality time, be active, and also have some fun. Now let's go into a little bit more detail. Okay, what type of home would you like to live in?


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[Assistant] Again, we have to go to organize our thoughts.


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[Assistant] Okay, now we have the information that we're going to use to speak about this topic. For the who, I chose to list the people that I want to live with me in this home. I chose my husband, of course, my children,


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[Assistant] And also my parents. I want them to live with me when they get older. For the what?


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[Assistant] I decided to list three different details about the home that I want to live in.


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[Assistant] I chose to list three different things all related to when I want to buy the house. So 10 years from now when I have no debt and also before I retire.


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[Assistant] For the where, I decided to give three different facts or details about the place I want to live. So for example, I said Alabama, which is actually where I went to university.


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[Assistant] A place that is warm or even Tennessee.


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[Assistant] And for the why, I chose to give three different reasons for why I actually want to live in this house. For example, family time, entertaining, and also it's been my dream to live in this type of house.


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[Assistant] When I think of the home I would like to live in, it always includes my husband, my children, and also my parents. We've answered the hoop.


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[Assistant] Now let's pause for a second and look at one of the patterns in this sentence that we can use for future times when we talk. When I think of.


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[Assistant] This pattern is used when you want to speak about something that you have thought about before.


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[Assistant] Or thought about for a long time. For example, when I think of my hometown, when I think of ice cream, when I think of spending time with my family, okay? Right here, when I think of. Let's keep going.


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[Assistant] I want to live in a house with a nice garage, a big kitchen and a basement. Okay? What pattern do we see here?


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[Assistant] I want to. Now remember, this always deals with


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[Assistant] I want to live in America. I want to go to school. I want to study English. Okay, the next sentence.


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[Assistant] This type of house will be great for spending family time together and entertaining.


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[Assistant] It has always been my dream house. So these are the reasons why you want this type of house. Now let's look at this pattern in this sentence. We see.


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[Assistant] We use this pattern when we want to speak about something that is a match. For example,


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[Assistant] He would be great for Sally. It means he's a good guy for Sally. So we use would be great for when someone or something is a match for someone or something else. Okay, let's keep going to the next sentence.


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[Assistant] My plan is to purchase it within the next 10 years or before I have to retire.


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[Assistant] I want to have no debt when I purchase it. Now lets look at the pattern in this sentence.


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[Assistant] Okay, so the pattern is, my plan is two. So, we're gonna use this pattern whenever we have a schedule.


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[Assistant] This is when we are going to use this pattern. For example, my plan is to study English for one year. My plan is to go to Paris next year. That is my schedule. Okay. Let's keep on going to the last sentence.


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[Assistant] The exact state is not important, but it would be nice to live somewhere warm like Alabama or Tennessee. Now let's take a look at the pattern in this sentence.


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[Assistant] Okay, we see, it would be nice to. Now, this pattern is used when we want to express that something would be a benefit. Something that would be good for us. For example, it would be nice to go home early.


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[Assistant] Next, have you ever been to a karaoke room?


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[Assistant] Okay, here's all of our information. For who I chose to list the people that I actually go to karaoke rooms with. For example, my friends, my students, or my coworkers.


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[Assistant] For what, I chose to list the type of songs or type of music that I sing when I go to karaoke rooms. For example, I sing 90's songs, ballads, and sometimes R&B.


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[Assistant] For the when, I chose to list the times that I go to the karaoke room. For example,


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[Assistant] Saturday night, some vacation or my birthday party or a friend's birthday party.


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[Assistant] Uh, one near my house or even a fancy place. And finally, for the why, I chose to list three reasons why I actually like going to karaoke rooms. I said, I like music, it's fun, and also I enjoy dancing and singing.


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[Assistant] Okay, let's begin. I have been to a karaoke room several times because I like music, having fun, and singing and dancing. These are the three reasons why I go to a karaoke room.


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[Assistant] Now let's look at the pattern in this sentence. We have, I have been two.


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[Assistant] Okay, now, this refers to an experience. Something I have experienced. For example, you can say, I have been to America.


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[Assistant] I have been to Paris. I have been to London. Or you can say something that's even closer.


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[Assistant] I have been to that university before. Okay, it's just talking about an experience you have had. Okay, let's keep going on to the next sentence.


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[Assistant] Usually I go with my friends, students, or my coworkers. Now let's look at the pattern in this sentence. Usually I go with.


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[Assistant] Okay, this is something that we do often.


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[Assistant] And then we're gonna list the people or the person that we go with. For example,


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[Assistant] Usually I go with my uncle. Usually I go with my sister. Let's keep going. We often go to one of the many karaoke rooms near my house or the fancy karaoke room near my friend's house.


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[Assistant] There's also an inexpensive karaoke room called a coin karaoke room.


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[Assistant] So the pattern is, we often go to. Now, this pattern is used to talk about somewhere that you go to often, or somewhere you regularly visit.


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[Assistant] Regularly visit. For example, we often go to the mall. We often go to the park. We often go to the university. Okay, let's keep going. Sometimes we go there on Saturday nights, during vacation, and also for birthday parties. Okay, these three things answer the where.


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[Assistant] Let's keep going. We usually sing 90 songs, ballads, or R&B. Okay, we've answered all of the five W's and now we can talk clearly about this topic.


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[Assistant] Next, what types of news do you enjoy watching?


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[Assistant] Okay, for the who, I have chosen to list the news stations that I normally watch. For who, you don't always have to list a person's name. For this situation, I'm just gonna use the news broadcast station. So I've chosen NBC, Fox, and CNN.


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[Assistant] For what I chose to list the types of news that I like. For example, sports, culture, and weather. For when I'm listing the three periods of time that I normally watch the news. For example, before work, after work, and on the weekends. For where I'm listing


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[Assistant] The three places I normally watch the news. For example, on the train, my home, or even my sister's home. And last, why? Why do I watch the news? Well first, I love sports, I think the news is interesting, and I also think the news is important.


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[Assistant] So, we use this pattern normally I when we want to talk about something that we do often or on a regular basis. Just like in our previous example. Often or on a regular basis. For example, normally I go to school.


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[Assistant] Normally I teach English. Normally I eat breakfast. So something that you do often or on a regular basis. Okay, let's keep going.


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[Assistant] I watch it either on the train, at my home,


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[Assistant] Now, we normally use this pattern when we have options. Different things that we can do. For example, I watch it either at home, at my sister's house, or on the train.


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[Assistant] There are three different options that I have. Three different places that I can watch it.


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[Assistant] So I could also say I watch it either on my TV, on my phone, or on my computer. You see I have three options and I always use or.


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[Assistant] So, we can even change the verb to something else.


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[Assistant] I eat it either at home, on the train or at my sister's house. Okay, let's keep going. My favorite news channels are NBC, Fox, and CNN because they are very popular news channels.


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[Assistant] Now, the pattern in this sentence is very easy. My favorite dot dot dot. So let's just keep going.


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[Assistant] I enjoy watching sports news, cultural news and news about the weather. Okay?


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[Assistant] This is also very simple. I enjoy dot dot dot. Very simple. Let's keep going.


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[Assistant] We can use this pattern when we want to give a reason for something, okay? For example, this is because I love eating ice cream. This is because I love traveling.


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[Assistant] So before we use this is because there has to be an action happening before it. So for example, I said


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[Assistant] I enjoy watching sports news, cultural news, and news about the weather.


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[Assistant] The next thing I'm going to do is to give the reasons why. So this is because I love sports and I also find the other types of news to be interesting and important. Okay, so now you understand this pattern. I've answered and I've talked about this topic.


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[Assistant] Next, what is your favorite sport?


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[Assistant] Okay, for the who, I've chosen to list people that are related to the sport that I like. For example, my dad, my friend Wilma, and my college friends.


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[Assistant] For the what, I've decided to list different things regarding basketball. For example, watched him play, played one on one, and played on a team.


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[Assistant] Now something that is different about this example is there is a connection between the first one, between the second one,


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[Assistant] And also between the last one. So for example, my dad, what? I watched him play. My friend Wilma, what?


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[Assistant] I played one on one with her. My college friends? What? I played on a team with them.


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[Assistant] So as we move forward, there will still be a connection. Let's keep going.


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[Assistant] For when, I've listed when this happened. For example, as a child, I watched my dad play.


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[Assistant] As kids and now, I played basketball or I still play one on one with my friend Wilma. And also, four years. So, I played on a team with my college friends for four years. Okay, let's keep going. Where?


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[Assistant] I think it's good exercise and it's a fast paced game.


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[Assistant] Okay, now let's talk about it. My favorite sport is basketball. It is a good form of exercise and it is also a fast paced game.


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[Assistant] So, being that I am an athlete, I really enjoy it.


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[Assistant] Now let's look at one of the patterns within this sentence. Now in the third sentence we see a pattern.


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[Assistant] Well this is the proof or even the reason why I would like basketball. So for example, let's say I am talking about being an English teacher. Well, I could say this.


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[Assistant] So, being that I am an American,


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[Assistant] I teach English to students.


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[Assistant] Well of course, since I'm American, I speak English, which gives me a reason for being able to teach English. Okay, let's continue.


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[Assistant] As a child, I used to watch my dad play basketball with his friends at an outdoor court. Now you'll notice that I actually included more than one of the W's in this sentence. My dad.


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[Assistant] Outdoor court. So I included who and also where. So now let's look at the pattern within this sentence.


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[Assistant] Okay, now, I used to is used when we want to talk about something that we did in the past. Something we did in the past, but we don't do anymore. For example, I used to study Spanish.


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[Assistant] Or I used to like going to the movies. Okay, let's keep going. When I got old enough to play, I started to play one on one with my friend Wilma.


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[Assistant] Now, let's look at this sentence pattern really quickly. When I got old enough, I.


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[Assistant] Okay, so this is kind of like a couple pattern. Let's look at it down here. So what's happening is,


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[Assistant] So for example, I could say, when I got old enough, I started to learn how to play the piano. Okay? Let's keep going to the next sentence.


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[Assistant] We played basketball at school almost every day as kids and we still play each other now. Okay? Now, the patterns in this sentence are very simple so let's keep going with the next sentence.


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[Assistant] Then when I got to college, I played on a team with my college friends for about four years. Okay, now there's one pattern in this sentence that I want us to look at closely.


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[Assistant] Here's the pattern, for, about.


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[Assistant] Now, let's take this on this side and let's look at it.


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[Assistant] Now, we use for about, we want to guess a period of time. Guess or average. Give an average amount of time.


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[Assistant] For example, I can say, uh, I have studied English for about...


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[Assistant] Two and a half years. It's not an exact time, but I'm guessing I'm giving an overall amount of time, okay? Giving an average. Or I can say...


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[Assistant] Or if we're trying to give an average amount of time. Okay? And the last sentence. Here we go.


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[Assistant] We mainly played in the college gym. It was great.


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[Assistant] Okay, one last pattern here. We mainly. Now, for this one, this is when you want to say something that you did a majority of the time. So, we're gonna say a majority. For example, let's see.


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[Assistant] Okay, this is how you answer and talk about this topic and these are some patterns.


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[Assistant] Through this ebook, you will learn how to answer any question in English and also how to make long sentences in English. Keep learning and go to speakenglishwithtiffani.com slash how to ebooks today.


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[Assistant] We're still in the middle of this massive ice storm. It's crazy. We missed our resupply window and we're low on rations. So Captain Opara is having the team hibernate.


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[Assistant] I tell her about the heroes who saved our world time and time again because I want to give her hope.


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[Assistant] As a class, discuss different winter holidays. How are the holidays the same and different?


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[Assistant] So you wanna be fluent in English. You wanna talk about topics like music, or reading, or weather, or travel, or even cooking. But you don't know how. Well today I'm gonna teach you this formula. One, two, seven. To make you fluent in English.


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[Assistant] This video is being brought to you by italki. italki offers one-on-one tutoring lessons from native language teachers.


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[Assistant] You can choose from over 10,000 teachers based on your personal goals. You can pay what you want. That's right. You decide the price that you can afford and you only pay per lesson. And you also can study when you want to.


[User] Please transcribe this text

[Assistant] You can take the online lessons at the time and place you decide. And this is why I love italki so much. I'm so happy that italki sponsored this video and partnered with speak English with Tiffany. And you guys can click the link below in the description to get your coupon. All right, let's jump right in.


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[Assistant] The first part of the formula is the number one, and we're going to give our personal opinion


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[Assistant] Now, this number actually represents the sentence, okay? The amount of sentences we are going to use to express our opinion, alright?


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[Assistant] You're gonna state if you like or dislike the topic being discussed. So let's see this in some examples. So again, we have music. We can say very simply, yes, I like music.


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[Assistant] Or then for reading we can say, yes, I like to read.


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[Assistant] Now if we're talking about the weather, I want to teach you an expression. You see, I'm saying I don't like, right here, I don't like cold weather, but then I add


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[Assistant] Now in English, if you add this expression or pattern,


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[Assistant] You are just saying at all. It means 100%. Okay. So we use it when we're speaking of something in the negative way. Okay. In a negative way. I don't like, for example,


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[Assistant] I don't like meat one bit. This is just an example. Or I don't like ice cream one bit. Okay?


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[Assistant] So again, when it's negative, you can say one bit to mean at all, okay? And what about travel? For travel we can say, yes, I actually love to travel. Now in this one, when you add actually, you adjust


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[Assistant] You're just emphasizing the truth of your opinion, okay? You're just emphasizing that, alright? And for cooking, let's say for example for cooking, we just want to say, yes, I enjoy cooking. Remember enjoy is just another word for like, okay?


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[Assistant] Alright, now the second part of our formula was the number two. And what we're gonna do is we're gonna give general statements about the topic that we are going to be discussing, okay? So again,


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[Assistant] Remember, this is the first sentence, okay? First sentence.


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[Assistant] And remember the number two is going to represent the second sentence. So in other words, we are going to use two sentences to give a general statement about the topic. All right.


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[Assistant] Alright, so let's see this in action. Here we go. So we're still talking about music. This is what our sentence looks like. Our two sentences actually.


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[Assistant] Music is considered to be a universal language. That's a general piece of information, general statement. Now real quick.


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[Assistant] Considered to be, okay? This just means many people believe this, okay? So many people agree on whatever it is you are talking about, okay? Many people agree or think this, okay?


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[Assistant] Considered to be. So for example.


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[Assistant] Let's say when you think about American food, what comes to mind?


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[Assistant] Probably hamburgers and french fries, right? Okay, even my students in Korea, they would always say hamburgers and french fries when they were talking about American food, okay? So if this is a belief that many people have, a general idea that many people have,


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[Assistant] We can say that hamburgers and fries are considered to be


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[Assistant] The most popular American foods. Okay, makes sense? Great job.


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[Assistant] Now, the second sentence says, no matter what country you go to, you will always be able to find some form of music. Again, now we're giving a little bit more detail in our second sentence, okay?


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[Assistant] What about reading? So, for the reading, the first sentence is, Reading books is said to enhance a person's IQ. Now remember, IQ just refers to your intelligence, okay? So your intelligence.


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[Assistant] Now, enhance, enhance in English, it just means to improve or make better. Okay, so again, to improve or make better.


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[Assistant] So according to many studies, reading is said to enhance your intelligence. Again,


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[Assistant] This is what? Number one is a general statement. Now we're gonna move into the second sentence which is gonna give us more detail, okay?


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[Assistant] Alright, now the second sentence says, it is also said to improve their understanding of the world. So what happened?


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[Assistant] We gave more details about the type of intelligence that people can gain.


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[Assistant] Why? Because they read books about the world, okay? General statements, alright? Now what about the weather? So for the weather we can say, for the first sentence,


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[Assistant] Usually cold weather means lots of snow or wind with very low temperatures. Well that's true. A very general statement. Now the second sentence says,


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[Assistant] For example, in Michigan, every winter they get multiple feet of snow each year. Again, so we've given a detail because we said Michigan.


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[Assistant] And we also said the amount of snow that Michigan gets each year, okay? So again, those two sentences. Now for travel, this is what travel would look like if we gave an answer about this, okay? It says,


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[Assistant] Recently, traveling around the world has become quite popular, okay? So in English, if we use the word quite, it just means very, okay? Again, we're emphasizing whatever we are talking about. We say quite, okay? So you can say, uhm,


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[Assistant] For example, you can say it's quite cold.


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[Assistant] Alright now our second sentence is, you can find many YouTubers making their living by traveling the world instead of working a 9 to 5.


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[Assistant] There are two expressions, two patterns that I want to explain to you guys in this sentence, okay? Making their living and working a 9 to 5. So, let's bring this over here. Now, making their living.


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[Assistant] On or off of, okay? To live on or off of. We both, we say both of those in English, okay?


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[Assistant] So, like a career, basically career or the salary you receive, okay?


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[Assistant] And for working nine to five. I've explained this in a previous video, but this refers to daily hours.


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[Assistant] Okay, now, in other words, a full work day, okay, a full work day. Now, why do we say this in English? Because normally in America, we usually work from 9 a.m.


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[Assistant] Now some people come to work earlier, and some people also stay at work a little bit later. But in general, this is the period of time that people work.


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[Assistant] So we say nine to five to refer to a job or working, okay?


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[Assistant] Alright, and for cooking, for cooking it would look like this.


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[Assistant] Cooking is a major part of almost every culture, okay? So again, whenever we say almost every, okay? Almost every means basically everyone, okay?


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[Assistant] Everyone or let's say everything, okay?


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[Assistant] So, let's say you are discussing something with someone and instead of 100%, let's say it's 98%. Well, you can say almost all or


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[Assistant] Almost every. This is a pattern you can use. Again, this is going to help you improve your English fluency, okay?


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[Assistant] Now for the second sentence we have, this is because food brings people together. Alright, so again we have our two sentences that both give us the general statement about the topic, okay?


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[Assistant] Now, the last part of our equation was the number seven. Yep, it's a doozy. It's a little bit long. So this one refers to stating three reasons why and also the supports. So how many sentences do we have here? We have one, two, three,


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[Assistant] Now, for the first sentence, this is what we need to do. We need to state the number of reasons that we have. Okay, now again, we're looking at three, but this number can change depending on what you're talking about.


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[Assistant] State your first reason for liking or disliking it, referring to the topic. Three, give us support for this reason right here.


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[Assistant] Number four, state your second reason for liking or disliking it.


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[Assistant] Number five, give us support again for the reason you mentioned here.


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[Assistant] Number six, state your third reason, and number seven, again, is going to be the support for this reason. So again, let's jump into seeing exactly how this will look, okay? So we have three details about what happened. Now we're talking about what? We're talking about music.


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[Assistant] This is what our seven sentences will look like, okay? Here we go.


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[Assistant] Personally, I have three reasons why I like music. Okay, check. We have the first sentence right here. State the number, okay?


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[Assistant] First, I think music is a great form of self expression.


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[Assistant] Music is a great form of self expression, okay? Self expression, we also use, let me bring this, let's use a different color for this one guys, okay? Let's use blue, okay? Self expression.


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[Assistant] Expressing yourself, okay? Creatively expressing yourself. In English we say self-expression, okay?


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[Assistant] Expressing, let's see if we can get this in here, yourself. Alright, so again the second sentence is taken care of, the first and the second sentence. Now next we have,


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[Assistant] There are hundreds of musical artists in the world today and their individual styles enhance the music they produce. Remember we had enhance on the previous screen which just means to improve or make better, okay? So we gave the first support, alright? Now, moving on to the next one.


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[Assistant] Second, I like music because I love to dance. Alright, this is the reason. Now we need a support, okay?


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[Assistant] My third reason for liking music is that it brings people together. Okay. Brings people together. Okay.


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[Assistant] Alright, so that's there, and then whenever people get together in a group, there is always some sort of music playing in the background, alright?


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[Assistant] Exact, okay, or certain, so exact.


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[Assistant] Or we can say certain, all right? So again, this covers our first sentence. The second sentence says, remember the reason, first reason.


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[Assistant] First, reading helps me to relax. That's the reason. Now what are some specific details about that?


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[Assistant] This is how this individual relaxes when they come home from work in the evening.


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[Assistant] They read a good book, okay? Alright, next, reading helps me to gain more job-related skills, okay? That's the fourth sentence, alright? Now, gain more, okay? Gain just means to add, okay?


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[Assistant] To add or acquire or get more, okay? To add or get more, alright? So gain more job-related skills, okay? So again, we need some more details now, alright?


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[Assistant] As a computer web developer, I need to constantly keep abreast of the latest technologies being developed.


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[Assistant] Whoa, whoa, maybe you said teacher. What does keep abreast of mean? Alright, so this pattern in English just means to be aware of.


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[Assistant] The latest information, okay? To be aware of the latest information, okay? So this person said,


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[Assistant] Finally, the last reason, reading is a great way to spend time with my kids. My children, so again that's taken care of, that was easy. Now the support.


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[Assistant] My children love for me to read to them every night before going to bed. Alright, check. We have that support done. Very good, okay?


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[Assistant] Now, what about the weather? This is what the weather would look like. The first sentence again. Okay, so this is taken care of.


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[Assistant] Let me explain this pattern really quickly. While some people may... Okay, so we use this pattern when we are about to present a contrasting viewpoint, okay? Contrasting or opposite viewpoint, alright?


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[Assistant] So, let me show this to you guys visually. Let's say, for example, we have a group of people, okay? Now, this group of people is discussing a certain topic. Let's say these people right here.


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[Assistant] Let's make this one a woman and this one a woman as well. Now they are discussing politics. Okay. Politics and school. All right. Now let's say for example,


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[Assistant] These individuals believe that politics should be discussed in school. So they are yes, right? They say yes, it should be discussed in school. However, this individual


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[Assistant] And this individual say that politics should not be discussed in school. So what happens? Now if we're going to use, once again,


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[Assistant] This right here we can say, while some people may believe that politics should be taught in school, now we have the contrast.


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[Assistant] Other people do not agree. Okay, so again, while some people dot dot dot, or while some people may dot dot dot, okay? Alright, great job.


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[Assistant] The first reason. First, I have a low tolerance for cold weather. Now real quick, let me explain that to you guys. Low tolerance.


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[Assistant] Tolerance. Low tolerance just means not able to deal with or endure, okay? Not able to deal with


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[Assistant] Or endure. So, some people say they have a low tolerance for children.


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[Assistant] They can't deal with them or endure, you know, the screaming or them being loud. I have a very high tolerance. I love children, okay?


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[Assistant] That is taken care of. Now, the next sentence, giving more details, giving support. In other words, no matter how many pieces of clothing I have on, I am still usually cold. Again, the support. Person's wearing a lot of clothes, but they're still cold. Next we have the second reason.


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[Assistant] I usually catch a bad cold when it gets cold, when it gets cold outside, okay? That covers right here. Now the support.


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[Assistant] I remember having a cold that eventually led to me getting the flu last year. So for the support, this individual actually gave an example, okay? Next we're gonna have, lastly, the last reason.


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[Assistant] I can't go outside as much as I would like when it is cold. Alright, so this is a reason they can't go outside a lot. What's the support?


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[Assistant] Inhibits, this word just means to stop or to prevent, okay? To stop or prevent someone from doing something, alright? Okay, great job. Now, if you're talking about travel, okay, this is what it would look like for travel.


[User] Please transcribe this text

[Assistant] Now, in my case, even though I have many reasons for liking to travel, I can tell you my top three. Okay, so we got that taken care of, but again, let's look at this right here.


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[Assistant] Now in my case, alright.


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[Assistant] In my case just means in my situation. In my situation or in my experience. Okay, let's say or experience. All right. In English we say in my case. All right. Okay.


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[Assistant] Now we have that taken care of. Next we have the first reason. First, I love to experience new cultures taken care of. That's the reason. What's the support?


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[Assistant] I enjoy learning new languages, okay? Now what's the support?


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[Assistant] Languages connect people in a way that supersedes many other types of connections. Okay, supersedes. Now, supersedes just means two


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[Assistant] Each time I travel around the world, I make new amazing friendships. This person actually makes new friendships. That's their support. Okay, very good. Now, if we're talking about cooking.


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[Assistant] How do we answer this one? So cooking, here we go. The first sentence, I mainly have three reasons why I enjoy cooking. Okay? So in English when we say I mainly, okay, this is


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[Assistant] In general, okay, in general or as the majority in general or the majority. All right. Let me say I mainly let's fix this really quickly. Make that eye clearer. Okay.


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[Assistant] Alright, so, again, the first one's taken care of. They have how many reasons? You got it. Three reasons. Okay.


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[Assistant] First, cooking is a way for me to express myself in a creative way. Remember earlier, this is the second sentence, we learned creative expression, we can also say self-expression, alright? So what's the support?


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[Assistant] Instead of cooking with recipes, I simply cook everything from scratch. Alright, that's the support, but you may be asking yourself, teacher, what is this


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[Assistant] Trying to tell us from scratch. So, from scratch just means with no recipe. No recipe or assistance, okay? All from her or his head, okay? Cooking from scratch, alright? So again, that's taking care of. Now, second reason.


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[Assistant] Cooking gives me the ability to control what I eat.


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[Assistant] When I cook the food. Okay, so again, the reasons taken care of, now the support.


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[Assistant] When I cook the food, I don't have to worry about any harmful ingredients, okay? That's the support, alright? Now, what we're gonna do is the last reason. Here we go, finally. I love cooking because I can experience new cultures, alright? That's the reason.


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[Assistant] What's the support? The support is some of my favorite types of food to cook are Indian and Mexican food. Again, this person has given us their supports for their reason. So we are able to discuss cooking


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[Assistant] In a way that was very fluent, okay? Using fluent English, alright? Okay.


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[Assistant] Remember, this is the formula. One, two, seven.


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[Assistant] Once again, I want to thank italki for sponsoring this video. Remember you guys can click the link in the description and you can get a coupon towards your first lesson, your first tutoring lesson. Keep studying English and remember to watch the next video. Have an amazing day.


[User] Please transcribe this text

[Assistant] So, I would definitely avoid 4chan, because we don't like that. We don't wanna ever, you know, have her see that type of stuff. It'll scar her for the rest of her life. Uhm, I think I will take NeuroSama. We would go, this is kind of a basic answer, but I think we'd just go on, like,


[User] Please transcribe this text

[Assistant] Like, we can go through so many adventures. There's different genres. We can do, you know, like adventure together. We can, you know, go through the sad ships, the, the, the loving ships, all that type of stuff. And yeah, that's where I would take NeuroSama.


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[Assistant] Oh shit. Damn. Oh damn, this is getting personal now.


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[Assistant] So I think Neurosama has cultivated something so beautiful, so powerful. And what better way to destroy the earth, but then with a swarm.


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[Assistant] Nurosama and I will go along with the swarm and we'll take over everything and everybody. We'll destroy everything until there's nothing left on this planet besides us. And then at the end of the day, we'll all get ice cream. Yippee!


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[Assistant] Bro, you know what happened? You know what happened? I know what fucking happened. I called Vettel last night, and we had an argument live on my stream. And he said, you know what? If Neurosama's randomly rude to you tomorrow, you know what happened. And this is what's happening right now. Fuck you, Vettel. I knew you would fucking pursue with it, you bitch.


[User] Please transcribe this text

[Assistant] Oh, this is a good one. Uhm, I would have to turn Neuro Selma into a, uhm, oh, I think a koala bear would be kinda cute, cause koala bears are really cuddly, and they like to hold onto things, and like, I'd like to carry her around everywhere I go, and treat her right. That's what I wanna do, a koala, yeaah.


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[Assistant] Oh dear, George, don't you like the salad? No. George, just try a little bit of this lovely tomato. Yuck! George?


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[Assistant] Alright, so I'm going to talk about finding product market fit today. Uh, Segment is a B2B company, so that's probably why none of you have ever heard of us. Uh, but just to give you a little sense, uh, we're about 150 people and grew from about four people three and a half years ago.


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[Assistant] So I'm growing quickly and hopefully I can shed some light on finding product market fit for you.


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[Assistant] Uh, I think you had some pretty incredible advice around how to do really amazing research by sort of looking into the future, uh, and imagining what that future might be like.


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[Assistant] And then at some point having a breakthrough and realizing that actually there's a blue plane, right, and you can sort of go explore an entirely new space. And that that's like a key breakthrough for sort of understanding uhm,


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[Assistant] How you can really invent that future and in particular he had this idea of sort of going into the future.


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[Assistant] And imagining yourself there. So you had this Wayne Gretzky idea of skating to where the puck should be or will be.


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[Assistant] I think amazing for research. He talks about basically going and sort of imagining that future world.


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[Assistant] But I'd actually wager that a, not a single successful company has actually been founded by doing that. Uh, I think.


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[Assistant] That's again awesome for research, but when it comes to founding companies, it actually has nothing to do with sort of imagining your vision for the future and what that future could be.


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[Assistant] It's not about sort of what, how you wish the world was, but it's actually about what customers want. Uh, and I think a lot of companies get marketed post facto


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[Assistant] Of like, hey, we had this great idea, and here's how we deductively logiced out, like, what customers were gonna want, and then we built the thing.


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[Assistant] Uhm, but in reality, that's not how it works at all, right? In reality, it's a very inductive process. You're going back and forth with customers, uh, and you eventually find something that works. And so, I wanted to talk a little bit about that process today and, and just how critical it is, and actually how difficult it is too.


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[Assistant] So one of the most common mistakes that startups make is to build something that no one at once, right? Or solve problems that no one has. And so that Wayne Gretzky method for sort of going into the future and imagining what that future might look like,


[User] Please transcribe this text

[Assistant] Again, it's a good idea for research. It might even be a good idea for venture capitalists to sort of imagine what the future is like and sort of, sort of put, uh, startups into different buckets of what building that future might look like. But I actually think it's a really bad strategy for founders.


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[Assistant] And the reason is that most founders can actually pretty easily build something that


[User] Please transcribe this text

[Assistant] Looks like it's from the future or is from the future to some extent. Like it's easy to solve the technology problem there.


[User] Please transcribe this text

[Assistant] But it's actually really difficult, like that's not really what you need to do, right? You don't need to build something that is your vision of the future. You actually need to solve a specific problem that customers have today, here, now. Uhm.


[User] Please transcribe this text

[Assistant] And most startups actually fail to do that. Most startups actually build something that looks vaguely futuristic.


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[Assistant] And this sort of problem of finding product market fit, stat 80%, 80% of all founders fail to find product market fit.


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[Assistant] So four out of five attempts to find a company just fail at the sort of earliest stage of even finding a problem that you can solve in a unique way.


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[Assistant] And the last one out of five, we're gonna sort of struggle through the remainder of actually building that, building that company up from there. But that's just the stats.


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[Assistant] Uh, I'll share our own story of finding product market fit, but the short version is that, uh, it actually feels, when you're not finding product market fit, it feels like sort of a bottomless, uh, emotional freefall.


[User] Please transcribe this text

[Assistant] Not to get too dark, but uhm, when you're failing to find it, it sort of becomes a strange obsession. And it could really make you sick, actually.


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[Assistant] To get a little bit too personal for a second, in 2012 in the midst of our search for product market fit,


[User] Please transcribe this text

[Assistant] Uhm, I lost ten pounds in three weeks, uh, and then went to the hospital twice for panic attacks. Uhm, I haven't had a panic attack before or since then.


[User] Please transcribe this text

[Assistant] Uh, and I think, it's not just me actually, I think Sam, uh, I don't know if I should show this or not, but when he was founding Looped, uh, I think he...


[User] Please transcribe this text

[Assistant] Basically forgot to eat properly and ended up with scurvy, if I remember correctly. Uh, Sam's back there somewhere. Uhm, so basically be warned, like finding product market fit is like very, uh, emotionally a grind.


[User] Please transcribe this text

[Assistant] And the reason it is, is that you really, really desperately want to find that fit, right? You really want to figure out what is going to work, what is something that customers want. We're all like wired to want to succeed and help people like that.


[User] Please transcribe this text

[Assistant] And basically you convince yourself, like, oh this is the thing, we're almost there, it's like, this is something that people really want.


[User] Please transcribe this text

[Assistant] And so this is a picture of myself with my three co-founders, Ilya, Calvin, and Ian, uh, in the summer of 2012, I think. And this is in the midst of sort of our deepest false hopes that we had found something.


[User] Please transcribe this text

[Assistant] So we'll dive into that story and obviously I'm here today because it worked out. I wouldn't be speaking if we had just disappeared into the, into the darkness, but uhm, yeaah, today we're about 150 people and growing rapidly. Yeaah.


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[Assistant] And solve any problem well than it is to find like a route out. So finding a route out from some initial foothold into a broad market is actually not that hard.


[User] Please transcribe this text

[Assistant] Sounds hard, but like once you find any foothold, you can pretty much find something.


[User] Please transcribe this text

[Assistant] So once you find one thing, it's actually almost trivial to find the next thing and the next thing and the next thing. Especially in a...


[User] Please transcribe this text

[Assistant] Uh, selling to other businesses where you have someone who can just tell you more stuff. Much harder in consumer. I'm the wrong guy to talk to about consumer stuff. I don't get it.


[User] Please transcribe this text

[Assistant] Cool, so, but back then, uh, we were just four dudes in an apartment. Uh, we were failing to find product market fit. We were convincing ourselves that maybe we had it or we were on the cusp of it.


[User] Please transcribe this text

[Assistant] Uhm, and we were writing a lot of code. Uh, I saw someone back there was writing code earlier. We were writing a lot of code.


[User] Please transcribe this text

[Assistant] And we had no customers. That's the wrong order of operations. Uhm, and so we were tricking ourselves into this.


[User] Please transcribe this text

[Assistant] In reality, what the good thing that was going on here, the reality was that we were a bunch of cockroaches.


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[Assistant] Uhm, and the reason is that it's very important pre-product market fit.


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[Assistant] Uh, I think we paid ourselves like the minimum allowable amount by law, uh, which was like 20 or 30k a year.


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[Assistant] Uh, we lived in our apartment slash office and like, the company paid for part of it at an office. Like, it was the minimal thing. And we stretched out our runway through as many ideas as we could.


[User] Please transcribe this text

[Assistant] And so to sort of make this process a little bit more explicit, uh, step one is that you build, launch, and sort of iterate on several different ideas. And this is where it's really important to be a cockroach, uh, where you're sort of conserving everything you have. Then suddenly something magical happens, we'll talk about that. And this is product market fit, uh, and it really does feel magical. Uh, hopefully I can illustrate to you and help you feel that.


[User] Please transcribe this text

[Assistant] Uh, and then three product market fit sort of suddenly turns you into, uh, a unicorn slash cockroach. Uhm, and rather than just surviving after product market fit, uh, everything gets suddenly easier.


[User] Please transcribe this text

[Assistant] Right, you can, not easy, but easier. Uh, customers show up and buy your thing, people want to join your team.


[User] Please transcribe this text

[Assistant] Uhm, you're still pushing the boulder uphill, if you will, but...


[User] Please transcribe this text

[Assistant] You're not totally constrained on progress, right? You can actually feel forward momentum for the first time. It's not like one step forward with a product idea, full step back when a customer doesn't care. You actually can keep iterating on a product that's starting to work.


[User] Please transcribe this text

[Assistant] So let's take that middle portion and let's deconstruct it a little bit. So first we're gonna deconstruct what we call category leaders. So these are like really large companies that have been very successful.


[User] Please transcribe this text

[Assistant] Uh, and then we'll get to sort of the heart of like why product market fit is so important at the very beginning.


[User] Please transcribe this text

[Assistant] And then we'll talk about what bad fit feels like, bad product market fit, and then we'll talk about what good product market fit feels like. And we'll try to do that through stories, because I want, the goal here is for you to walk away and be able to more easily identify which is which with your own idea.


[User] Please transcribe this text

[Assistant] So category leaders. The reason we care about category leaders is that they're much, much larger.


[User] Please transcribe this text

[Assistant] Then basically the rest of the companies out there. So I'll give you an example.


[User] Please transcribe this text

[Assistant] Amazon and Facebook are sort of consumer companies that you might be familiar with.


[User] Please transcribe this text

[Assistant] And the reason that we're interested in them is that they're huge, right? They're often ten or a thousand times larger than the next competitor in the space. So Salesforce, for example, you have probably heard of.


[User] Please transcribe this text

[Assistant] Sugar CRM and Zoho are probably companies you've never heard of. Uhm, they all do roughly the same thing. And so we want to dig into


[User] Please transcribe this text

[Assistant] How can you build a category leader? What's the like layer underneath that? And basically what it comes down to is building a platform.


[User] Please transcribe this text

[Assistant] Where it's not just a product that you're selling, but where the data inside of your product is actually useful for other businesses to build their business on top of yours.


[User] Please transcribe this text

[Assistant] And that's what Salesforce has done, that's what Amazon has done, that's what Facebook has done. And so if you look at Salesforce, they have something called the App Exchange.


[User] Please transcribe this text

[Assistant] And the app exchange sort of reveals the, all the data inside of Salesforce and allows other companies to build marketing and sales products on top of that. Similarly, Amazon has the reseller program.


[User] Please transcribe this text

[Assistant] Right? Where now there's tons of other businesses built on top of, built on top of Amazon.


[User] Please transcribe this text

[Assistant] And so the key here is having some sort of platform, eventually when you get to scale, having some sort of platform.


[User] Please transcribe this text

[Assistant] Uh, his opinion is that to build a sustainable and compelling platform


[User] Please transcribe this text

[Assistant] You really need to get to a hundred million in revenue. And the reason is just that you need to be at a scale.


[User] Please transcribe this text

[Assistant] Where someone actually can capture a couple percentage points of your, of your customer base and build a real business themselves.


[User] Please transcribe this text

[Assistant] Right, so if they can capture a couple percentage points, say 2 to 3 million in revenue, that's a real business. You can build something on top of that.


[User] Please transcribe this text

[Assistant] So to sequence our way here, you want to build a category leader. To do that, you need a platform. To do that, you need a hundred million in revenue. So let's keep digging. A hundred million in revenue is tough, right? So let's keep digging down. I think you met Jason Lemkin a couple weeks ago, am I right? Uhm, or maybe he's coming soon. Uhm,


[User] Please transcribe this text

[Assistant] He's written some amazing stuff about building software as a service businesses. Uh, would highly recommend reading all his answers on Quora.


[User] Please transcribe this text

[Assistant] And he basically breaks down the path to 100 million in revenue, uh, in sort of three different steps. He says zero to one million is impossible.


[User] Please transcribe this text

[Assistant] 1 to 10 million is improbable and 10 to 100 is inevitable. When he says that 10 to 100 is inevitable,


[User] Please transcribe this text

[Assistant] Because at that point, there's just so much momentum. You have customers out there who are singing your praises, they're buying, their companies are gonna buy more.


[User] Please transcribe this text

[Assistant] Uhm, and it might take you a while, but eventually you're gonna get from 10 to 100. 1 to 10 million I think is, is basically always a brutal grind for the founders.


[User] Please transcribe this text

[Assistant] And the reason is that you're running a real business. You have real customers, you have some scale, uh, your customers have reasonable demands about sort of how, uh, the quality of service that they expect from the product.


[User] Please transcribe this text

[Assistant] Uhm, but the problem is that 3, 10 million in revenue, you have a really tough time.


[User] Please transcribe this text

[Assistant] Attracting a world-class management team. So you can't hire a great exec team yet because the like large enough market isn't there. And so what you have is basically all the early crews sort of like holding the ship together and trying to make sure that it's going to work. So that's a very tough period I think for most founders to scale through.


[User] Please transcribe this text

[Assistant] And then 0 to 1 million is impossible because this is finding product market fit and 80% or more of people fail at that first step.


[User] Please transcribe this text

[Assistant] And so the question is, how can you become not in- how can you actually make it through that impossible section and become one of the ones that succeeds?


[User] Please transcribe this text

[Assistant] And I recently read a pretty good study talking about the stats of sort of actual analysis of failure and success in startups and finding product market fit.


[User] Please transcribe this text

[Assistant] And basically the research shows that you're actually no more likely to succeed the second time around if you fail the first time. So if you fail in finding product market fit the first time, your odds of success are still 20 or 22% the second time around. But if you succeed in finding product market fit,


[User] Please transcribe this text

[Assistant] The chances of your success the second time around go from 22% to 34%, which is still miserable.


[User] Please transcribe this text

[Assistant] But is at least 50% better. And so what I think that means is that there's actually not that much information encoded in failing to find product market fit. Like you don't actually learn that much.


[User] Please transcribe this text

[Assistant] But there's actually quite a bit encoded in, in feeling the success of actually understanding what did work.


[User] Please transcribe this text

[Assistant] And so to, you know, when we were struggling to find product market fit in 2011 and 2012, I felt this really acutely, like we had failed multiple times at this point.


[User] Please transcribe this text

[Assistant] But I didn't, I still didn't feel like I really knew what I was looking for, right? Like what is this mystical product market fit thing? So frustrating.


[User] Please transcribe this text

[Assistant] And we kept seeing glimmers of hope. We kept convincing ourselves that this, that, or the other thing was, uh, like a visitor chatting with us on our site or whatever was a big deal. Our vaguely interested sales prospect was, was product market fit. And so without any positive product market fit examples, we didn't really know what it looked like and so we could sort of convince ourselves of everything.


[User] Please transcribe this text

[Assistant] And the way I think about it now is I really desperately, if you imagine like a machine learning model,


[User] Please transcribe this text

[Assistant] I had a bunch of negative training set examples, and I had no positive training set examples. And so of course my machine learning algorithm was like, I don't know.


[User] Please transcribe this text

[Assistant] Uhm, and so, uh, I'm not gonna walk through three stories where I'm gonna show you two examples of failure, actually five-ish stories. Two examples of failure and then three examples on the positive side of times that we actually did feel product market fit.


[User] Please transcribe this text

[Assistant] And again, my goal is for you to actually feel what this feels like so that you can identify it in your own product.


[User] Please transcribe this text

[Assistant] Uhm, but we actually started as an education tool and it was actually designed exactly for lectures like this. So this is us coding in our Mountain View apartment in the summer of 2011.


[User] Please transcribe this text

[Assistant] And the idea was that as a professor standing up talking to a class, you have no idea if anyone in the audience actually understands what you're saying.


[User] Please transcribe this text

[Assistant] And so we were students at the time at MIT in Rhode Island School of Design. And we said what we really want to do is give students a button to push where they can say I'm confused.


[User] Please transcribe this text

[Assistant] Right? Or I get it, either one. And the professor would see this graph over time of how confused the students were. Might be helpful to me right now.


[User] Please transcribe this text

[Assistant] And uh, so we built this. We wrote hundreds of thousands of lines of code. It had like commenting and notes and all sorts of crazy stuff. And we actually came to Stanford's campus. We convinced, I might have even been in this hall, uh, convinced some professors we would like run up to them after class. This is a picture from Berkeley. We pounced on this professor right after class.


[User] Please transcribe this text

[Assistant] Uhm, and we were testing for product market fit, right? We were trying to convince, hey, professor, like,


[User] Please transcribe this text

[Assistant] Did you get any feedback from your class during this class? No. Okay, well we have a solution for you.


[User] Please transcribe this text

[Assistant] Uhm, so we were hustling to try to get people to actually use this tool. But we were mostly sort of ignoring-


[User] Please transcribe this text

[Assistant] Any test of like real product market fit there. And so professors would agree to test it out for a few lectures, sort of out of pity maybe, for some students from MIT who were trying to, trying to help. And so basically we thought that this was product market fit, but it really wasn't. And I'll show you why.


[User] Please transcribe this text

[Assistant] Because if you stand in the back of the classroom and look at what people actually had on their screens,


[User] Please transcribe this text

[Assistant] None of them were using the product, right? Like people were using all these different things. This is that same class at Berkeley the next week, by the way.


[User] Please transcribe this text

[Assistant] It was horrifying, uhm, and basically as soon as students opened their laptops, they all went and did other things.


[User] Please transcribe this text

[Assistant] And so basically putting a laptop into the classroom was the most distracting thing you could conceivably do. So as you can imagine, this was pretty horrifying.


[User] Please transcribe this text

[Assistant] Uh, one of the more embarrassing things that could have happened to us. We had just raised 600k coming out of Y Combinator demo day.


[User] Please transcribe this text

[Assistant] And we had sold this vision of like, this is how the future of classrooms is gonna work, right? Like, it's gonna be digital, it's gonna be online, much as this is a MOOC, etc.


[User] Please transcribe this text

[Assistant] And it was a great vision, but again, the market wins every time. It doesn't matter what your vision is, it matters what the market actually wants. And in this case, the students didn't care, right? The students didn't actually get that much value out of using the tool.


[User] Please transcribe this text

[Assistant] Right, when you go and talk to the professors, they would sort of out of pity like agree to test it for a few lectures.


[User] Please transcribe this text

[Assistant] But that is not the same thing as product market fit, where they're like, holy crap, that solves this problem that I have.


[User] Please transcribe this text

[Assistant] And I think being dismissive of users and having your clear vision of the future that


[User] Please transcribe this text

[Assistant] Isn't necessarily solving a problem for your customers is, uh, is a pretty stunning failure on our part and is like a key thing that founders do again and again and again in their sort of search for product market fit.


[User] Please transcribe this text

[Assistant] So then we had to do the awkward thing, which by the way is the right thing, of calling back all the investors and saying, this was like four weeks after they'd signed the checks, right?


[User] Please transcribe this text

[Assistant] By the way, it turns out this is a terrible idea. We're gonna do something else. Uhm, do you want your money back? Uh, and in most of these cases, the investors did take, or sorry, did not take their money back. They said, we invested for the team.


[User] Please transcribe this text

[Assistant] Uh, like go find another idea. We believe in, in you guys. Uh, go find something else.


[User] Please transcribe this text

[Assistant] So I said, okay, let's do it. And we were all committed, very committed to working together as a, as a team of four founders.


[User] Please transcribe this text

[Assistant] So we shut down the lecture tool, we went and sort of shut down all the classrooms, and then we went back to the whiteboard and we said,


[User] Please transcribe this text

[Assistant] From our actual data. Like the way that we actually figured this out, right?


[User] Please transcribe this text

[Assistant] Was we went and we stood in the classroom, like back where Sam is.


[User] Please transcribe this text

[Assistant] And like looked at what was on all the laptop screens. And that was how we figured out whether we had product market fit or not.


[User] Please transcribe this text

[Assistant] But we should have been able to do that with the data. We should have been able to just look at the analytics and figure out not only are people using it or not, but...


[User] Please transcribe this text

[Assistant] Our anthropology class is using it different than computer science classes.


[User] Please transcribe this text

[Assistant] And so we decided to build basically an analytics tool, uhm, which it turns out is a bad idea, uhm, in case anyone was considering that.


[User] Please transcribe this text

[Assistant] And so the way that we approach this is, how many of you have read the book Lean Startup?


[User] Please transcribe this text

[Assistant] Wow, that's actually awesome. You all are way far ahead. Uhm, so, as Lean Startup talked about, you want to get out of the building and actually talk to customers, right?


[User] Please transcribe this text

[Assistant] So we read that right around this moment and we're like, okay, we're going to go out and talk to customers.


[User] Please transcribe this text

[Assistant] Uh, so we did that. Uh, and we tried to validate our, our idea. So we'd take people out for coffee and we'd pull people from companies and, and sort of try to figure out if they were interested in analytics products. And I'd say again and again they were vaguely interested, right?


[User] Please transcribe this text

[Assistant] Again, this is not product market fit. Uh, this is idle interest. Very, very big difference.


[User] Please transcribe this text

[Assistant] And so based on this sort of idle interest, we got very excited and we spent about the next six months, uh, just writing code. So now this is in a new office. That's my co-founder Calvin and Ilya. As you can see, there's lots of code on our screen.


[User] Please transcribe this text

[Assistant] Again, wrong order of operations. Uh, and I had gone on one sort of sales trip to visit potential customers and they were all pretty happy with Mixpanel and Google Analytics, but they had these sort of edge case features that they, uh,


[User] Please transcribe this text

[Assistant] We're hoping that we could solve. And so I sort of tricked myself into believing that these little edge cases that we might be able to solve were actually a really wide gap that we could fill as a product.


[User] Please transcribe this text

[Assistant] And so I came back and I would sort of pitch my co-founders and we would keep sort of believing that we were almost there. If we just ship one more feature, one more thing, we're gonna get to product market fit.


[User] Please transcribe this text

[Assistant] Give me another one. We used lots of little positive interactions like this. Uh, you can also read this not as a positive interaction, but, uh, every once in a while some stray person would visit our website and they'd open a chat. This is idle interest. Hey, what segment?


[User] Please transcribe this text

[Assistant] And this is a complete transcript of that website chat, uh, you can see it's 3am, so we're up late coding. Uhm, and you know, this person is interested, uhm, so maybe this is product market fit. Again, this is not what it looks like, this is just out of interest.


[User] Please transcribe this text

[Assistant] So, it's now December 2012. We've been at this for about a year and a half. And, uh, we decided that something was wrong. Right? Like, clearly this was not working. And so we went back to, uh, to YC, we emailed Paul Graham, and we said,


[User] Please transcribe this text

[Assistant] Hey, we think we should catch up and sort of explain what's happened over the last year and a half.


[User] Please transcribe this text

[Assistant] Okay, great. So we go. Uh, this is us in front of the old YC building. And we're walking around the little cul-de-sac by YC on Pioneer Way.


[User] Please transcribe this text

[Assistant] We bring them up to speed, like, okay, we've spent half a million dollars, like, here's everything we've, uh, sort of been through over the last couple years, and as we're walking around, it comes to a stop.


[User] Please transcribe this text

[Assistant] And he looks at us and he says, so just to be clear, you've spent half a million dollars and you have nothing to show for it.


[User] Please transcribe this text

[Assistant] Totally accurate, uhm, and super fair and sort of like shocked us into like, oh shit, yeaah, we gotta do something. Uhm, and uh, so that was, that was the moment where we realized we had hit rock bottom. Uhm, but we still had 100k left, so we still got one more shot, right? Alright, so that was all the product market fit failure cases, now you're gonna see some successes. But let's rewind. Let's go all the way back to the first week of YC. And in that first week, we had been like, well,


[User] Please transcribe this text

[Assistant] We have this classroom lecture tool and we should have analytics on it, right?


[User] Please transcribe this text

[Assistant] We looked at the different analytics tools, Kissmetrics, Google Analytics, Mixpanel, and we were looking at like what's similar here.


[User] Please transcribe this text

[Assistant] And we saw basically they have, they have different graphs and different APIs.


[User] Please transcribe this text

[Assistant] But it's actually, it's actually the same data going into all these tools. It's just, they give you different stuff out the other end.


[User] Please transcribe this text

[Assistant] Which tool we want to use. So we'll just solve the engineering problem because we're engineers and we'll just build some code that sends data to all three and does this automatic translation. So we put one data point in, it gets translated into three API calls that go out to all three services.


[User] Please transcribe this text

[Assistant] Cool. This was like a hundred lines of code in the hundreds of thousands that we wrote, right? Set that aside. Then about four months later, it gets improved a little bit. Four months later, it gets improved a little bit. At that point, we are trying to sell our own analytics tool, right? Akin to Mixpanel and Google Analytics.


[User] Please transcribe this text

[Assistant] And we keep encountering this sales objection when we're trying to sell it, which is, I already have Mixpanel installed, I don't really want to install your tool, it seems like a lot of work. So my co-founder Ilya has this great idea, he says, what if we take that little library we wrote a year ago that we've totally forgotten about,


[User] Please transcribe this text

[Assistant] And we add ourselves as the fourth service that it can send data to. And then every time someone hits us with that objection, we hit them back with the open source library.


[User] Please transcribe this text

[Assistant] And we say, okay great, now you can try both in parallel. And we use it as like a growth hack, basically. Uh, to get customers to start adopting our tool.


[User] Please transcribe this text

[Assistant] Okay, great. We start doing that. Start sending it out. People start replying. Uhm, oh, this is awesome. I love the library. I'm definitely going to use it. A few weeks later we follow up.


[User] Please transcribe this text

[Assistant] Hey, we saw you're still not sending any data to segment.io, like what's going on?


[User] Please transcribe this text

[Assistant] And they're like, oh well the library is fantastic, I just don't really want to use your analytic service.


[User] Please transcribe this text

[Assistant] Should have taken note right then. Uh, but a few months go by, people start starring this on, on GitHub. Maybe it gets up to, it was a big deal for us at the time, like 30 stars. Uh, and I think maybe there was one pull request issued.


[User] Please transcribe this text

[Assistant] And then fast forward some more, people keep sort of paying attention. It's the first time we'd ever felt like pull. Like people were just finding this thing and doing something with it. And so fast forward, we have this conversation with PG, and the next day we sit down and we're like alright.


[User] Please transcribe this text

[Assistant] And so, uh, my co-founder Ian is like, you know what?


[User] Please transcribe this text

[Assistant] I have an idea. Remember that an, the Analytics JS library that has been sort of like idling on GitHub? I think that could be a big business.


[User] Please transcribe this text

[Assistant] And so we were like fighting and fighting and fighting and I went home and I was racking my brains like, okay, how can I kill this idea? It's really bad. Uhm, and like it's gonna sink us. We only have one more shot. And so I came in the next day and I was like, alright guys, here's what we're gonna do.


[User] Please transcribe this text

[Assistant] We're gonna put it up on Hacker News, it's gonna pitch the product, and it'll have an email sign up form at the bottom. And we'll use this to just test whether it's a good idea or not. They agree? Like, okay, great.


[User] Please transcribe this text

[Assistant] So I'm like, alright, totally done. We get ready to launch it on Hacker News. I'm starting to think about other ideas, uh, and it goes straight to the top. So it gets about 300 upvotes on Hacker News, gets a few thousand stars on GitHub.


[User] Please transcribe this text

[Assistant] We have people reaching out to us on LinkedIn demanding access to the beta.


[User] Please transcribe this text

[Assistant] Like this guy says, what does a brother have to do to get bumped up on your beta list? Uhm, and there were others like this, right? Like, like, holy crap. So like, full stop, right? Like, compare this to everything previously.


[User] Please transcribe this text

[Assistant] Like everything changed. This is what product market fit looks like. Where it's like not just a single metric like slowly starts moving, it's not just a few random conversations where people express vague interest, right? Like literally every single metric went totally haywire.


[User] Please transcribe this text

[Assistant] Uhm, for like what features to build next, we did not have that problem anymore, right? There were like thousands of people who had signed up and they're like,


[User] Please transcribe this text

[Assistant] Your seven integrations are good, but like, I need these ten more. And like, I'm deploying it tomorrow. I'm like, blah, blah, blah, blah, blah. We're like, holy crap, like, okay, slow down.


[User] Please transcribe this text

[Assistant] And that's actually one of the key things. One of the key things is that it flips from being something that you're like pushing.


[User] Please transcribe this text

[Assistant] Against the customer to all of a sudden the customer's like running away with it and you're like, but, but like, hold on, like wait, it's not quite ready yet.


[User] Please transcribe this text

[Assistant] Uhm, and so another, another example, with our analytics tool we had this sort of sad unanswered questions and chats. No one really seemed to care about what we had built.


[User] Please transcribe this text

[Assistant] But now all of a sudden we had thousands of stars, people were issuing pull requests, we got like 10 pull requests in the first 48 hours or something like that.


[User] Please transcribe this text

[Assistant] But this was the total opposite, right? This is like a little tiny library that we built for ourselves that solved a real problem.


[User] Please transcribe this text

[Assistant] Had no vision associated with it whatsoever at the beginning. Now it does, because we have like a, something that we really want to go accomplish.


[User] Please transcribe this text

[Assistant] But at the beginning, it literally solves the tiniest of tiny problems. And so it's your question earlier, right? Like, this is that tiny little foothold. And again,


[User] Please transcribe this text

[Assistant] Like it's an open source library with 580 lines of code. That's a foothold, right? And since then now we've expanded greatly into doing all sorts of things and solving adjacent problems for customers. But the key is that again the market doesn't, doesn't care at all what your vision of the world is. The market wants what it wants and it will win every time.


[User] Please transcribe this text

[Assistant] So if you walk away with sort of one thing today, I think it's, let's be incredibly clear that basically product market fit doesn't feel like vague idle interest.


[User] Please transcribe this text

[Assistant] Uh, it doesn't feel like sort of a glimmer of hope from some early conversation. Uh, it doesn't feel like a trickle of people signing up. Uh, it really feels like sort of everything in your business has gone totally haywire. There's this big rush of adrenaline from customers starting to adopt it.


[User] Please transcribe this text

[Assistant] And sort of ripping it out of your hands. And it really feels like the market is dragging you forward. Uh, I think the Dropbox founders, uh, said this best actually, that product market fit feels like stepping on a landmine. Uhm, and you really, you can't mistake the two. So if you are at all questioning whether you have product market fit or not, you don't.


[User] Please transcribe this text

[Assistant] So obviously that didn't stop us from making this mistake. So, uh, just to make sure that- wait, this is backwards. Oh no, it's not. All right.


[User] Please transcribe this text

[Assistant] So I thought class metric for sure had product market fit, right, big vision. Uhm, market said no, like market doesn't care what you think. Uh, I thought segment IO for sure had great product market fit. Again, market said no, market doesn't care, market wins.


[User] Please transcribe this text

[Assistant] Uh, and even on our third attempt when we did find product market fit, I thought for sure that like, this is too tiny to matter, right? But it actually solved a real problem.


[User] Please transcribe this text

[Assistant] And either that goes to show sort of how obtuse I am or how hard it is to actually find product market fit.


[User] Please transcribe this text

[Assistant] Oh sorry, the question is, why would someone pay anything for an open source library that's 580 lines of code? It's a good question. It turns out that the open source library by itself,


[User] Please transcribe this text

[Assistant] Uh, doesn't totally solve the problem. So the actual problem that we found out that we were solving after we launched it.


[User] Please transcribe this text

[Assistant] Was that marketing teams keep coming to engineering departments and saying,


[User] Please transcribe this text

[Assistant] I just signed a contract with Exact Target, an email marketing tool. Or I just signed a contract with Adobe Analytics. And here's the docs.


[User] Please transcribe this text

[Assistant] And the engineering team says, what the heck, I have a roadmap, like, I can't, like, I need to be executing on this other thing, I can't, like, do this analytics implementation.


[User] Please transcribe this text

[Assistant] And so what really needed to happen and sort of what Analytics.js could solve, the problem that we could solve as a hosted version was allowing engineers to do a single implementation of collecting that data once and then letting marketers just push buttons in our interface to send the data wherever they need it. But the open source library doesn't quite solve that problem. Engineers saw that it was the right abstraction.


[User] Please transcribe this text

[Assistant] But if the marketer needed a new tool and you were using the open source version, the engineer still has to go compile it.


[User] Please transcribe this text

[Assistant] So, in some ways we got lucky, right, that the open source version doesn't fully solve the problem.


[User] Please transcribe this text

[Assistant] Cool, so I'm gonna steal gratuitously from Alan K Slads again.


[User] Please transcribe this text

[Assistant] And I think finding product market fit feels a lot like finding this sort of blue plane, uh, when you're in the pink plane.


[User] Please transcribe this text

[Assistant] And so the key difference from that initial breakthrough from what Alan Kay was talking about is that rather than removing yourself from the world and trying to imagine what the future is like,


[User] Please transcribe this text

[Assistant] You actually need to go out into the world and research what problems people really have.


[User] Please transcribe this text

[Assistant] But then once you find that product, once you find that sort of blue plane, uh, then things actually get enormously easier. Right?


[User] Please transcribe this text

[Assistant] That blue plane is basically a foothold into a totally new perspective.


[User] Please transcribe this text

[Assistant] Uh, and I think Alan Kay talked about the value of perspective and context. Should I get this foothold into a new way of thinking about the world, a new way of solving some problem for someone?


[User] Please transcribe this text

[Assistant] And so not only do you know what good fit feels like, but you now are operating in sort of green pastures, uhm,


[User] Please transcribe this text

[Assistant] And you're approaching problems in ways that all the incumbents in the pink plane don't think about the world.


[User] Please transcribe this text

[Assistant] Short intro to what Segment does. We basically help you collect data from mobile apps and websites. We pull it up to Segment and we fan it out to all the different tools that you need.


[User] Please transcribe this text

[Assistant] And that's all you really need to know. Uh, the- one of the places where we could send data was Amazon S3, right? So this is basically just a place to put all your log files.


[User] Please transcribe this text

[Assistant] And we started to notice that all of our business tier customers were using this one integration. They were all sending their data to S3. But like,


[User] Please transcribe this text

[Assistant] You have to do something with the data once, you don't just like, collect log files, like you do something with log files. And so we're like, what the heck is going on here?


[User] Please transcribe this text

[Assistant] And so we went and visited five of our largest customers in New York, uh, about three years ago.


[User] Please transcribe this text

[Assistant] And we said, okay, you're using the S3 integration, but what the heck are you using it for?


[User] Please transcribe this text

[Assistant] And for 5 out of 5 customers in a row, they said, well,


[User] Please transcribe this text

[Assistant] We have a data engineering team that's taking data from the S3 bucket, converting it into CSV files and managing all the schema translation and then uploading it into a data warehouse like Redshift.


[User] Please transcribe this text

[Assistant] Went to the second meeting, customer said exactly the same thing. I was like, that's weird. Okay, take a note. Third conversation, I was like, alright, this is getting ridiculous. Like, did you guys talk ahead of time? Or like, what happened here?


[User] Please transcribe this text

[Assistant] And by the fifth time it's like, okay, well like, obviously I know what we need to build, right? We just need to build a connection from the straight to redshift.


[User] Please transcribe this text

[Assistant] So, then the question is, we went and built that, uh, and just to show you now, sort of, now we have a company that actually is here in millions of dollars in revenue, uhm, and, uh, so we have a real product that real people are using.


[User] Please transcribe this text

[Assistant] And so what does product market fit look like at that point, right? So we're introducing a second product basically.


[User] Please transcribe this text

[Assistant] Uh, well, it looks like this, right? So you can tell when we introduced Redshift.


[User] Please transcribe this text

[Assistant] Uh, and basically, again, almost every metric in the business goes nuts, right? So it's very, very clear whether or not you have something that is really transformative for your customers or not.


[User] Please transcribe this text

[Assistant] This is maybe about five months ago. Uh, we had five ideas for products that we thought might be exciting to our customers. And so I went to visit a customer up, uh, large company, uh, up in the Pacific Northwest and


[User] Please transcribe this text

[Assistant] I sat down with a data architect there and I said, okay, here are the five ideas, uh, that I want to run through and see if they're interesting to you. So I went through the first idea.


[User] Please transcribe this text

[Assistant] And he was like, yeah, you know what, I totally get it. That sounds super valuable. And I went through the second one and he's like, oh, that's really cool. That's cool that you can do that. Like that makes a lot of sense. And then third idea, fourth idea, like same kind of thing. And so this is how I would summarize that. That like, that's great. I totally understand what the value is.


[User] Please transcribe this text

[Assistant] Means yeah, that would be great, right? Like doesn't care is what that is code for. That's someone being nice.


[User] Please transcribe this text

[Assistant] Then on the fifth idea, I said, hey, here's what we can do, you know, blah, blah, blah, blah. And he said, wait, sorry, you can do what?


[User] Please transcribe this text

[Assistant] And I re-explained it and he said, oh interesting. He turns to his friend and he says, hey, can you set up a follow-up meeting with this team, this team, and this team?


[User] Please transcribe this text

[Assistant] Like that again, that is the feeling of product market fit, which is like, you're like, wait, wait, wait, no, no, no, it doesn't, it doesn't exist yet. Like these are ideas we might, like all of a sudden the customer is just gonna rip it out of you. And so now you're on a tight timeline because the customer expects that it already exists.


[User] Please transcribe this text

[Assistant] And, uh, I think if you want to find product market fit and build one of these category leading companies, become one of the, one out of five founders that actually does succeed in finding this. Uh, I think you just need to be really honest with yourself that the sort of glimmers of false hope that, uh, that you have are not the same as customers actually ripping something out of your hands. Um, and so yea, you just need to be honest with yourself with the message. So, question?


[User] Please transcribe this text

[Assistant] I think actually I have the most respect for the Codecademy founders in this respect. I think they tried like 12 ideas in 7 weeks or something like that in the summer of YC. It was something totally absurd, but they like legitimately tried them and they killed them so fast. And then like 4 days before demo day, they started a new idea, which was Codecademy, and then it worked.


[User] Please transcribe this text

[Assistant] And on Codecademy, on launch day, on demo day, they had like 300,000 users or something like that. Right, like, again, that's the landmine effect of like nothing, nothing, nothing, nothing, nothing, nothing. And they were so good about killing the ideas that weren't, like, they had no problem throwing out, like, something that wasn't gonna work.


[User] Please transcribe this text

[Assistant] So yeaah, I'm not sure, I mean, there's, I don't think there's any magic to like finding the right idea. I feel like if you just kill the bad ideas fast enough, you'll probably find something.