we can go. I am very excited to greet Julia here, Julia Erdogan. She wrote her doctor degree about my childhood and my youth, about the history of hacker culture in Germany, both East and West Germany. And I was somewhat active in the beginning times of the CCC and read the book with great pleasure. I didn't know all of the histories and was very excited. And after I read it, I thought that would be a great topic for Congress. And for one reason was surprised that Julia didn't hold this. And I was very excited to hear that she agreed to give this talk today here. So have fun with Julia and we are listening and waiting for your questions in the end. Hello. Yeah, thanks for asking me to do this. Yeah, it's a good question why I never did this. I always wanted to and now I have the opportunity. So thank you for the invitation and also the book review. I'm glad if it gets along well with the hackers. Yeah, I am a historian. And I was thinking about what I actually want to present today, which is a bit difficult, because I spent a lot of time with all of the matter and hadn't. And there's I can't tell everything. And but it's also a bit complicated and and to explain the conflicts within and and of course the history of the hackers and hacks is very relevant. And it had a lot of influence. And what I looked at was how did hackers influence the use of computers and how did this also influence into politics. So for the talk today, since this is also some sort of anniversary, which is always in five and 10 years, which is when we like to think about history again. And that's also when we historians get asked for our opinions. And so this is why the talk is called 40 Years of Tuvat. I will diverge a bit from what I originally wrote into the talk description and talk more about how the KS Computer Club got to bring itself into the context of history and what myths and stories it had to tell. And that's why I say myths. There are two myths of how the KS Computer Club got founded. One of them is the meeting in the Tuvat Congress and the other is the BTX hack, which is also one of the myths of how the CCC got founded. So the myth doesn't mean that it is completely freely invented and not true. It's more about what function does a myth fulfill and what is the creation point of this community. And now if we want to have a good myth, then we really need to look at all of the things that happened and choose only the the truth, all of the things that were relevant, which is not only historically relevant, but also ahistorically. So there are not only things that happened or didn't happen, but maybe also things that happened that aren't relevant. But in the end, there's always some truth in the core of the story. And it's also ahhistoric in some sense because it references a point in time, but it also has influence over more time, not just that point of where it originated. And myths and also legends and anecdotes, which can be grouped, there are lots of them in hacker culture. And so let me give an example here, which is a citation from Vau Holland, when he said hacker is somebody that can use a coffee machine to heat his sausage water. So the first person is going to say now, well, didn't he say that differently? And wasn't it the water for the potato puree? Or was it the heating plate to use toast? Well, maybe Vau Holland never said any of these words. Maybe he said all of them. Maybe he just warmed up some sausages somewhere sometime if it was in the club, if it was during some sort of Congress. And we don't really know, but we also don't really care. It's not that relevant. What's relevant is what he meant to say about hackers. And also what this citation says about Vau Holland himself. And once we get to know Vau Holland a bit, I never got to actually know him myself. But of course, by learning so much about him, I learned a lot as well about his personality. Yeah, it definitely checks out that he might have said this. And it doesn't really matter when exactly and how exactly he said it. So, okay, with this example, let's jump in to the myths and the history of the KS Computer Club. And that's also very interesting, because in the 80s, the history for the hackers was very much important. Very much especially for Vau Holland, who started building an archive of and which is still in the basement of the KS Computer Club, 120 folders, where Vau Holland collected a lot of material about the history of the club, about hacking, but also about communication history and lots of subversive use of technology. And while I was there for my research, all of this historical knowledge that was very heavy in the 80s has gotten lost a lot over the years. As an historian, I was of course asked what I'm doing there. And when I mentioned this archive, then somebody said, what that's in these folders, or maybe they didn't even know what folders I was asking about. So there is actually a massive treasure of history about the 80s and 90s history of the CCC. It unfortunately stops with them and it hasn't been completed further. And so all of this consciousness about history that was present in the 80s is not only expressing itself by building this archive and saving this history, but also going into the public. And they do this using the so-called hacker bible or rather hacker bibles, which is actually two. And one of them is from 85 and run from 88. So that means not even two years after the club got its name, or rather four years after this tour meeting, the case computer club, we said we need to think about the history of the club a bit more. And that doesn't even really fit a good anniversary. It's only four years. But for us historians, these collections of sources are really, really valuable. And then we can find a lot of things without having to look for a long time. But also in the 80s, all of these books really were also there to further the closeness of the community, to remind oneself where the history comes from. But it also, of course, is about a lot about how the hackers represent themselves to the outside and to the public. And in these years, there was starting to rise a bigger interest in this club. And within the 80s, the CCC is actually asked for a lot as experts that know this technology, but also that know the influence of technology on society. This is the time where home computers are rising. This is where online communication is gathering a lot of traction. And also, there is a lot of manipulation and misuse of technology. And where the hackers seen is diversifying massively with crackers and everything else. So we also want to explain who are actually the good guys and what are our intentions and where we're coming from. And that's why we tell that stories to to separate ourselves from that history. All right. So where is Chaos Computer Club coming from? It's supposed to be founded in 1981 after the Tageszeitung Taz, the paper Taz, was calling for a meeting in the Taz main building on 17 September 1981. That inner security can only be ensured using computers is believed by everyone in power today. That computers don't go on strike is slowly realized by medium sized companies too. That use of computers makes telephony even more beautiful. German post believes it has to prove now with the extreme text system and field test that the personal computer is now being imposed on video saturated BMW drivers becomes clear through the ad campaigns that are currently launched. And this is where it's now being received. So the hackers saying embedded computers can despite all that be used for useful applications that do not require large centralized organizations is what we believe in order for computer freaks to be no longer a story about uncoordinated. We do something and meet on and so on. So this is the relevant part. There's a call for action to be active. This is some sort of inception of community in the original in the source that I brought that you see here. There's also the information added. This is how it started. So this makes it easier for us historians how to use this source. And it is true that the computer club started. But it's not 100 percent true because obviously very important contacts were formed that influenced the later founding of the cost computer club. But those contacts were also formed through other means and some of the attendance also knew each other beforehand. And especially the founding fathers and mothers. This is especially true. And while Holland himself tried to found a club two years earlier. He requested if it was possible to found a computer club Europe which had to be denied because it sounded like a computer club that was already existing in Darmstadt is in historical sciences very unscientific to ask what's what if. But if the cost computer wasn't founded. We may not be in the House Communication Congress today. But on the European Communication Congress. I'm really happy to see that chaos prevailed. And I think that chaos is the better option. So there were earlier attempts to build a community. The contacts were already there. Not only from this meeting and additionally on this day the word cost computer club isn't being named. So it's just a loose network that exchange exchanges with each other. But the point is this meeting as a starting point for the cost computer club was very clever and also a great symbol because at that time it was chosen to fulfill two different needs. First of all it was supposed to talk to people who are interested in IT. And at the same time the International Funk Ausstellung a computer affair was taking place in Berlin. And the second point is the connection to the autonomous scene which tried to reactivate during these days. And a lot of these computer freaks were from this scene. And you can also tell by the language they use. The two words Congress was referring to tunics a meeting in 1978 which after the German Herbst the rise of the new German left which tried to oppose the new dogmatic and tried to try to oppose the retreat to the private sector. So it was supposed to create a new network and to make more of an impact on society. And this is what the hackers of the cost computer club to be prepared for. It didn't have a name yet. It was just a loose network. This group was meeting on the tish of the commune 1 in the touch main building which gives it even more symbolic meaning. Because this place and especially this especially this table already had a meaning of societal change. And in the preface for the first hacker Bible this is being a reference. This is what we are saying. We are inconvenient. And as the German post says we show atypical user behavior. We believe this is necessary as our environment becomes more step by it is required to endow it with life. It all began with the founding of the chaos computer club about four years ago. And as I said it wasn't founded four years ago. And we want to emphasize among all the the touch on whose premises the CCC was founded at the time. None of the editors understood it. But one of one women from the touch IT department is somehow involved since its inception. So this already shows both where it comes from and that it is required for hackers to do their part. The call to action again. And the connection to the political and for the call to action that is foundational for the cause computer club. Not only media who were asking the hackers questions and there's also a lot of letters in the archive. I've looked at a lot of letters but all of them obviously and a lot of them contact the cause computer club because of this political component. They are talking about resistance. They are talking about how to hack the federal police in Germany. How to change the world. How to actually contribute. And I've talked to a lot of hackers male and female and a lot of them told me that politics was a very important part of hacking. And the relevant thing that made the CCC different from from other computer clubs was the political component. And this is why it's especially important to emphasize this event. It's not the only inception with the other more unknown is the BTX hack. The hack of the BTX screen text system. And we see the two hackers who have done that. I'm not going to repeat what everyone already knows but the two these two hacked the BTX system and were able to take about 165,000 German mark from the hamburger spark. They sent a lot of data to the site and which caused it to crash and then return arbitrary data including the access data that they could use to log in using the spark account to log in to the BTX. And they wrote a program that called the page of the cost computer club which caused the hamburger spark to have sent about 130,000 mark to the hamburger cost computer club. There's also in the Bavarian hacker posts there's a different variant that says it was actually done differently. And it's not completely clear how the hack worked and it's not really relevant. We're talking about the function of this hack as a myth and because we are in the post factual time it actually doesn't matter that much. We can just pretend that it happened like that and we will find certainly some sort of source on the internet for it. And the important thing about this hack and this is also important that we don't know exactly what it happened is also how it was staged and what happened around it. It wasn't the first time that the CTC hackers were attacking the German federal post. They had earlier mentioned that BTX was very very insecure but that they weren't reacting the way that the hackers wanted them to. They were reacting like yeah it works more or less like we wanted to. So the hackers when they were returning from a privacy meter they wanted to bring it to the public and it was a very conscious decision. So they gave the German federal post the chance to react but they didn't. So they didn't only hack the system but also made it a public event and they were contacting the Heuter Journal a German TV news show. And that was very clever. That was not everything they did. They also were offered the opportunity to attack the political system of the federal Republic of Germany. They were asked by the Privacy Protection Officer of Hamburg how it worked and they that they chose to do it alone very consciously. They could have I mean they were thinking about using the immunity of a Bundestag delegate but then they realized that it would be a too long bureaucratic process and they would they would go away and therefore they wouldn't want to use these consequences. And so that's how they got this legitimacy that they didn't have to do the hack by themselves but they also had to execute the entire thing to express how incredibly insecure the system was. That's some sort of demonstrational activity. And it was also very clever to also use this opportunity to show that well actually politics is way too static and sticky and prevents that any fast action to respond to these things. And all of these hackers were much more inclined for the direct action rather than the slow moving government. And the very smart thing about this was that they were the only two that ever knew how the hack actually went and worked. And that was also something that the financial officer of the club said back at the time that there were plenty of hacks in the time but none of them was as well insinuated and presented as the one of the Sparkhasse especially because of how these two were the only ones that knew. And now so they created this image of the well-meaning hacker. They wrote a codex and they they created this reference point. And just also because this hack was so well executed and presented and something that could be discussed for a long time. And by many people that was another reason why the hackers just gained a lot of not necessarily popularity but allege in the public. And also a legitimacy for what they did and how they acted and the relevance of computers. And so in these happenings everything was in there that made the Kerch Computer Club. The connection to the autonomous groups, the big networks, the opposition to hierarchies and the opposition to centralization and the involvement of state actors and the post. And of course anonymity and of course this publicizing the direct action, the tuva that do something and engage yourself. And also a little bit the secret of not letting everyone to look into their cards. And that is essentially what makes the club itself. And the hacker Douglas Thomas says about hacker cultures in general. This is something I want to read to you real quick. Hacking is not and has never been about machines, tools, programs or computers, although all of those things may appear as tools of the trade. Hacking is about culture in two sense. First there are sets and codes, norms and values and attitudes that constitute a culture in which hackers feel at home. And second the target of hackers activity is not machines, people or resources, but the relationship among those things. In short, hacking culture is literally about hacking culture. Or to put it a bit shorter or differently with the words of our Harland, what is it to open up a computer and to open up a culture? And this is where I'm close to the end of the talk, but I do want to use this opportunity to mention a project that we are still looking for funds and members. And it is to make a new hacker Bible. There are these tools from the 80s, but they are from the 80s. And maybe there are a few more newer things to tell. And so my colleague Christian is in contact with Fenerchifa, who back then created the Bible and published it. And if all of you are thinking of something, of topics that should definitely be in there, and of course I also talked about how important it is to maintain the various opinions in history. And but also if you really don't want to write, but I do like to tell people how to write, then even then you can go to the email address on screen now to wat at redaktionminushab3.de. And I'm very excited for your questions and discussions. Thank you Julia very much. That was an amazing talk. Thank you very much. We will hopefully have many, many questions. And so I'll say again, if you have any questions or about the last years of hacker culture in Germany, go to the IRC channel. The IRC channel is below the video player in the stream, be another tap chat, or post to Twitter or master down with the hashtag RC3CSH for chaos studio Hamburg. And while the question pad will fill itself with questions, I would like to post one question from myself personally right now. How did you get to this topic? I know that in the beginning it was weird that I not me personally, but my youth is has gotten a matter of professional research, which I would have not expected. But how does one get to thinking hacker culture is the thing that I want to research and think about? It wasn't directly. I was concerned with privacy and information security. And then in Germanistic, and as a historian, I found out, wow, there's nothing there about hackers and the Chaos Computer Club. So the only ones who were writing about it were hackers themselves. So something has to be done. And it is a great topic for a dissertation. When you start reading these sources, it is very entertaining. So I decided I have to do this. It's interesting. It's very important. And I will be able to spend the next years with this topic. All right, great. Yeah, we're excited to learn and read even more about this. So maybe one more question from me before we go to the pad. One aspect that I really found very interesting because I didn't know a lot about it was on one side the comparison to East Germany and the culture there, but also the somewhat different role that the CCC had in Germany, as opposed to other European countries, and maybe even the US, where the hacker term is from and where it also definitely there is a big hacker culture, but maybe also a bit different. Yes, definitely. It's true that the Eastern part of Germany because there wasn't freedom of expression. It's very difficult. And the infrastructure doesn't really lend itself to online hacking. So there's a great difference, but there's also the hackers of the East were interested in computers. So they were trying to get modems illegally as well and go online. So there's also the possibility to express themselves and on the infrastructure level. And why the Chaos Computer Club is so different, why it is has such a special role is very hard to grasp because I'm trying in my book to look to France, which wasn't didn't didn't oppress freedom of expression. But the hacker history is completely different. It's completely hidden in completely different than in Germany. So it's really the people involved among them, while Holland, who were able to stage it in a very special way. And especially if you compare it to the United States, the thinking about monetizing your knowledge is probably not that but explicit. I'm not really sure what's there about the hackers in America. They realized how to make a lot of money with that. Steve Wozniak and Steve Jobs realized, oh, I can make a lot of money with that. And that doesn't work together that well with the sharing of knowledge and the sense of community. And so it's, for some reason, it doesn't start in Germany. Yeah, okay. So what question do I want to continue with? Maybe the question, why do you think that chaos is a better format than European computer club? And also, who really denied the European computer club its name? So the request was a computer club in in Darmstadt, which is called Computer Club Europe. So Computer Club Europa was very close as a name. It wasn't possible to put this into the legal documents. I prefer chaos because it has the Triple C Chaos Communication Club and Chaos Computer Congress. And Warholans once said, you need the chaos because you cannot oppose a society that is as ordered as itself. All right. So when exactly was the CCC EV, the legal entity founded? And what is the difference between the Chaos Computer Club and the Chaos Computer Club EV? The EV was founded in 86. So they are a registered society. They have some, they don't have pay as many taxes. So there's a financial gain. That's one point. And the other point is that the CCC EV is, for example, this Congress is the responsible as association, not a single person who would have to pay everything if someone trips over a cable. So there's a lot of reasons why you want that. And it's also a bit difficult because for a chaos community, it's a bit difficult because it comes with a lot of bureaucratic things like you need someone who does the finances and so on. All right. Now something very important. Can you please hold up your book? Somebody asked where one can buy your book? My book is called avant-garde of the computer usage, hacker culture of Germany, the Federal Republic of Germany and GDR. And I'm asking my great boyfriend to fetch it very quickly. It should be in the uppermost shelf. There it is. Great service. Thank you very much. So this is the book. Yeah, great. Thank you. All right. So there's another question. Is this archive from Vau? Is that somehow publicly available? There's two archives. I have to mention that. There's the Vau Holland archive that he created himself in the basement of the Chaos Computer Club Berlin. So I just wrote them a letter and Starbucks replied and said, oh, sure, just come and look at it. There's also the Vau Holland Foundation who has a different archive collecting CCC history, but more especially Vau Holland specifics things up to 2001. You have to contact the Vau Holland Foundation. It is public, but you have to be supervised. All right. Okay, now for a really technical question. Is Tuva.txt, does it exist as a file? I think we all know the scan from the printed charts except, but does the file exist? Oh, that's a very good question. Maybe the Vau Holland Foundation, you could ask, I haven't seen it personally, but they have thousands of floppy disks, boxes of floppy disks where they are stacked behind each other. Maybe you know how this looked. Maybe it's among those I can't remember. I thought somebody would want to correct the comma, which should be a dot. Yeah, good. So a bit more about the culture. So does the community have to become smaller and more local again, especially when one thinks about the sheer size that the Congress has become? Okay. Very difficult question. The point is, the opening that started in the 80s is probably not that bad. You reach a lot more people. It's a lot less restricted. It's also a lot of fun because there's so much happening. You don't know how to reduce the size. So do you tell people, oh, you want to get involved? But no, we don't do that. We already have 400 members and there's only 800 that can come to the Congress. That's the question. If you want to be there for everyone, if you want to be open, you can hardly reduce the size. I think the question is directed at the people who knew the 80s club. But on the other hand, if you remember back, everything was better. Yeah. So maybe as somebody who was a bit there in the beginning years, and then for a long time, I wasn't because I was interested in other things. And then since like five or six years, I'm back here. But I was very surprised how much similarity there still is with the first couple of Congresses. Of course, it's a very much different kind of event. The 200, 300 people that were there for the first time, as opposed to the 10,000 or maybe 17,000 in Leipzig. But the vibe and the feeling is very similar. And what happens and the people that walk around there, I think for me, I think it's an absolute win.  Final in normal people. And I'm finally reading the EV has never gotten a non-profit status. EV, by the way, is registered association.  Yeah, I'm a bit confused myself right now. I think the reasoning for the EV, the registered association, is that the legal things like have to be clear. Okay, so for a very specific question about myths, I will read it just like it is. So, for the two C3, the second Congress, there were two bomb threats, and then the event had to be cleared. Was ever resolved who posted them? And if that maybe was a smart fluke insulated by Vau? I do not know that. Unfortunately, I don't know. I can remember the clearing of the event, and I think there was a call coming in and the phone central where somebody talked something about bombs that they hid in the building. And I don't think Vau came up with this, but it doesn't really fit with what I learned about Vau. It wouldn't really fit. But I don't know who was behind that if it was the joke or if it was real. Yeah, the clearing also didn't take very long. The people that knew a lot about this, and especially some of them were in the volunteer fire brigade, and they searched the entire building and weren't able to find anything. And after a quarter hour, everything was clear again. And there's another correction. The fact that I said that the Vau Holland foundation would be a non-profit is apparently also wrong. Apparently, it has been reapproved and it is now a non-profit and can receive donations that are tax-deductible. So yeah, we don't want to not mention that. Please donate. So I'll have a look at the clock right now. I think the time slot is over right now. But if you want, you can come to the extended Q&A session in Big Blue Button. You can come there. That's where Julia and I will be for a bit more time. We would like to talk to you and ask some more questions. The link for that you will find at Hamburg.ccc.de. It's the first blog post. There's a big link to our wiki page. I will not read that URL because it's too complicated. And that's also where this Big Blue Button room is linked. The important thing is you need to be registered in the RC3 world. You will only get in there with a ticket. And I hope he will come along and we can talk a bit more about it. And I will again say thank you very much Julia for this talk, for the great answers to the interesting questions. And I hope we can greet you again with more information about the RC3 world.


Today, we're making chocolate chip cookie bowls. Yum. Okay, you guys, this is gonna be the most easiest baking I feel that you could ever do. We are doing chocolate chip bowls with ice cream. And I actually saw this on a TikTok on Instagram. And now I'm putting it on YouTube. Ha, ha, ha, ha. Okay, it is so easy. All you need, your ice cream of choice, cookie of choice, chocolate chip of choice, and your, whatever, I have chocolate sauce, okay? I love chocolate chip cookies. They are my favorite. You could even do this by starting scratch, right? You get your egg, your butter, your flour, but we want today to be easy, okay? You just find out that your daughter's friends are coming over literally at the last minute, truth story, and you wanna have something for them. This is perfect. I've got some already pre-made cookie dough. How awesome is that? All right, we're gonna make like little dough balls because we're making the chocolate chip bowl, right? And we're just gonna smash that over. This, if you don't know, is a cupcake pan. Everybody, I made sure and cleaned it. So we're just gonna smash those over like that. I'm doing one scoop. Just like when I'm cooking carrots, whenever I'm baking with chocolate chip cookies, I always have to try a few. I don't know why I do this. Please tell me I'm not the only one who does this. Look how easy that is, right? And after we do this, we're just gonna put it in the oven. I think the least is like 13 minutes. Why not like an even number? I never understood that. Or just say like 10 to 15, like 13 to 15. Who decided and made it 13? And not just like 12 to 15. I don't know. That's what good old Nestle Toll House says and I need glasses, you guys, yes, you see me. Oh no, it says 12 to 13. So I was wrong. Still sounds weird. I preheated my oven to 350. Then we're gonna bake these amazing guys for about 13 to 15 minutes. All right, see you then. I did a show called The Real and we would do Pinterest, it's called Pinterest fails. It was a fail and I had to start a fail. I think this needs to be a TikTok fail. He didn't say how many scoops. Oh my gosh, I should have known this. As a baker, the dough always rises two or three times. I blindly trusted the TikTok. I don't know what to do. And now I'm gonna have to clean my oven. Like, oh my gosh, I'm gonna give this five more minutes. Five more minutes. And then what I'm gonna do is take it out, have the cookies kind of hardened a bit and we'll see if we can salvage this, okay? Oh my gosh. I've gotta say, looking in the oven, this looked like an absolute epic fail. I should have known that the dough was going to obviously rise but also spread, but I will tell you. I only had one fail when I was baking something. It was during the pandemic and I could save it. So hopefully I can save this. Normally I like things to be very asymmetrical, like perfect. So now we're just gonna adjust pivot is I would say and kind of just go with the flow and see what we can do. I think I'm gonna go the complete opposite. When I watch the TikTok, I mean this guy looked like, just like a bachelor, like a dude. But no, he just came off as a guy that's like, okay, I don't really care how things look. I just wanted to taste good. And you know what? I'm just gonna go with it, right? So first things first, I am going to cut. Let's see. Let's cut little squares, like so. This is what my gut is saying because we want to make the little bowls, right? And then I'm praying that as I flip this over, they flip and they don't stick. Oh gosh, please, please. Jesus' name. How do you get them off? See, whoop, whoop, whoop. Boom, that actually looks okay. Look at this, that looks great. I think I'm just going to cut a little bit. There we go, beautiful. There's one, you guys. I think I saved it. I freaking saved it, you guys. Look at this, how cute. Yeah. I'm thinking of the dum, dum, dum, dum, dum, dum, dum. Yeah, baby. If you guys remember that, I hope you do. You guys remember that, right? Do you know what I'm doing? Do you know that song, Gavin? Dum, dum, dum, dum, dum, dum, dum, dum, dum, dum, dum, dum. Austin Powers. Oh dear. Araya not knowing. Juliolson twins are, and Gavin not knowing, Austin Powers. The hard part is done. And actually I learned something. This dessert is supposed to be messy and gooey and you kind of just have to go with it. Now some things that I probably would have done differently because I am naturally a rule follower. I probably would have used a little less of the cookie dough so you can have more of that bowl shagana shape. We do have it. It's here, but as you can see, it kind of just went over a little bit. But I do like this because Araya's friends are five and 11 and this will be really cute for their little hands, right? And I would have probably sprayed some like cooking spray on the back of the pan so that pulling off the cookies would have been easier. So it wasn't an epic fail. It was actually an awesome learning experience. So all we're gonna do now is just add some amazing ice cream that looks like melted a little bit because my temperature, it's been happening, but it's okay. It's still gonna be amazing. You remember like I was telling you, messy, gooey, go for it. That's what we are doing here, right? So we have some vanilla ice cream. I chose vanilla because vanilla to me is always a little bit better. It's my favorite with chocolate chip cookie dough, but maybe you'll want, let's put some, ooh, chocolate syrup over that. Oh my gosh, this looks like a kid's like dream, right? Then we're just gonna grab some chocolate chips over this bad boy. That'll be cute, leave it on the plate a little bit. I'm all about presentation. And then, ooh, look how great that looks. Oh, the best part is biting into this thing. Oh, this is messy. I didn't got some stuff on my nose.  Oh my God, this is freaking amazing. This got tacked into the messiness, man. This is amazing. 10 out of 10. It was totally worth it. We made some amazing chocolate chip bowls. Put something in the oven. Why do I still have these on? Oh my gosh. Oh, and actually, this was the one that wasn't supposed to turn out okay. And it didn't. The interesting pre-heated my onion. Oh my gosh, this is crazy. I've never seen anything like this before. That just looks bad. That is horrible.


Hi everyone. I'm very excited to be here today. My name is Lalith. I'm from Israel and I'm going to talk more about it. So I wanted to begin.  So this is in the loving memory of Droll. He was a good friend of mine. He wrote more than 8752 articles. He won the Wikimedia Prize last year and he actually wrote over 2% of the entire Hebrew Wikipedia, which is a lot. So I really miss him. He taught me many things I know about Wikipedia. He gave me a warm welcome and we'll talk more about him. So I believe we all have a story to tell.  We're going to talk about local community, not just the virtual one. Of course the gender gap and women writing, but first I want to introduce myself. You all know what Bard is. It's a generative AI developed by Google. Lalith Gow is a Wikipedia editor, blogger and marketing professional from Israel. She is currently the manager. Lalith Gow is a Wikipedia editor, blogger and marketing professional from Israel. She is currently the manager of digital marketing and content creation at Clowant Health Services, Israel's largest health maintenance organization. She is also a managing editor at the Wikimedia Foundation, the non-profit organization that operates Wikipedia. In addition to her work in marketing and technology, Lalith is also a passionate advocate for social justice and inclusion. Okay, so you heard her. Yeah, that's me. Wikipedia editor, social media manager, passionate advocate. He said that actually in Hebrew it's female voice. I like it even more. So I started in 2016 writing Wikipedia. I write about many different subjects, women, art and food. So here's the situation in the world. I know it has been changed, but today we're going to be talking about Israel. So that's the situation in Israel. And now I want you to think about what could be the reason for that. I just want to say that my talk is mainly about women editors mostly and not about the article itself. So I want you to think about what could possibly be the reason for that. And here is one idea, size matters. And I'm talking about a country size, of course. And of course, community matters. Has anyone ever been to Israel? Okay, so it's going to be easier for you to answer that. How many people live in Israel? You have three options. Who thinks number one, raise your hand. Who thinks number two, please raise your hand. And number three. Okay, yeah, most of it got it right. 9.7. What is the area size? Who thinks number one? Who thinks number two? And the last one? Yeah, you got it right. That's the last one. 22,000. How long does it take to drive from north to south approximately? Two and a half hours, seven hours or 14 hours. Who thinks number one? Number two? And number three? You got that right too. Yeah, it's seven hours with traffic, I would say. So we all know that we have some historic reason for the gender gap. It used to be before that you needed to know how to write code in order to write articles in Wikipedia. It's not really like that anymore, but the gender gap still exists, right? So, yeah, that's me with my sister. I have four other siblings. I grew up in a keyboard.  Anyone know? Yeah, so according to Wikipedia, there is an international community in Israel that was traditionally based on agriculture. According to me, this is something which is really hard to explain from someone from the outside. But the main thing was and actually still is a community. Okay, so for me, it always mattered. I always wanted to feel part of something. Yeah, that's me right here. And this is something else I have in Wikipedia. I always love to write and I actually had my blog. And I kind of have that feeling that I put lots of efforts in my post, in my blog, and no one really read that. And one day someone told me, why don't you join Wikiwoman? I mean, number five website in the world, people will probably read what you write, right? So, yeah, that's what I did. I joined Wikiwoman. And today, it's not only that I write, I also feel that I'm part of something bigger. Again, this word community. I know many editors, some of them are my friends, not just WikiFriends, and we really meet. So, Wikimedia Israel, aware of the importance of us of getting together. So we meet at least twice here. We have Hebrew Wikipedia birthday and Wikipedia birthday. Okay, so you can see January 2020. Yeah, I'm here. July 2021. I'm here. And again, we always have cake. 17K. Yeah, we love the cake. Israel is, yeah, it's all about food. And so why do so many people show up? Because they can. Remember that map? So if we meet in Tel Aviv, it won't take us more than an hour or two to get there for most of us. I don't live in Tel Aviv, so we just chill out. For us, Wikiwoman, we meet actually at least three times here because we have art and feminism. I know we've talked about it before and we do it along together with Artboard. It's been going on since 2017. And we meet every March 8th, around March 8th for Women's Day. More than 20 new articles be written every year. In average, 20 to 30 women are coming to write articles together. And it's a great tradition. So one more thing about community. Do you know that girls go to bathroom together? They have those women at work that they sit at the same office every day long, but they still go to bathroom together, right?  So try to invite friends for a party. If it's a man who just probably say, I'm bringing the beers, right? Maybe he'll ask what's there to eat. And a woman, she will probably ask you who else is going to be there, right? So when I know I'm going to meet people that I know from before, it's easier for me to show up. One more thing about language. Using unbiased language can be helpful. And this was actually draw initiative. He suggested to change it. It used to be before C also for the female use of it. And now it's plural, both men and women. And this is the user. It says user in female. You added the last character and it's for female. Community manager matters in my day job. I'm a social media manager and I combine this in my Wikipedia life as well. Along with Danny, I'm the administrator of Facebook group. We also have a telegram group and a few other what's up because as well as what's up a lot. I also do that. When I need something, I like to use the wisdom of the crowd. I posted up on my social media channels and then I ask people for help. That's a cake that I wrote about here. I needed some examples and they were all in English and not just relevant, not so relevant for Israeli culture. People help me out. As you can see, 68 comments and now it's in Wikipedia. That's one more thing. And yeah, so that's us like months ago. And those are the women that's me and those are the women in Israel. Give them some love.


I told you guys one of the first four words in culture and I said cult and a lot of people don't like that, but culture originated out of cult. It is a belief. I told you guys the news of brainwashes people to believe that we're all going to die and that everything is bad. I brainwash and people hate that word but I brainwash people to believe that they're powerful. I brainwash the people to believe that I don't care who you are, where you come from, what you have, I believe you can have everything you want. You know what I believe? I believe that the people that have suffered when they're younger as adults do extremely well because they understand disappointment. And I can also tell you that I'm going to train you how to understand how to win and guess what? That's the missing factor.


This is stock number 9337X. We are here at Summit Automotive in Fond du Lac, Wisconsin, your new and used heavy duty truck headquarters. Today we are checking out this super clean 2008 Ford F-250 crew cab long box. This truck has a 6.4 liter power stroke diesel. In this HD video you will be able to tell that this truck is extremely clean inside and out. I didn't see any dents or dings on the front bumper. Dark forest green over silver is a paint scheme. Comes with the 20 inch polished aluminum rims that are in really nice shape and it has brand new Edvanta LT275 55R20 tires on it. We put these tires on in our safety inspection. You can see just how clean the frame and underbody is on this truck. So brand new tires all the way around on this truck. This is a Lariat. See the front grill is in excellent condition. The hood is very clean as well. I didn't see any dents or dings on that. Do apologize for the little bit of snow that we got going on here. Basterner side rim is in excellent condition as well. Scuffs or scrapes on that and that tire is brand new as well. As you go down this side of the truck you can see just how clean the body is, how reflective and mirror like and glass like that paint is. We take these HD videos so if you are for our weight or even if you're close by just cannot make the trip down. You can still see the truck here, the truck, and have confidence in the vehicle that you're looking at before you even get here. We sell a lot of these trucks out of state so we want to give the most accurate representation possible. That's why we do the videos so that you can have confidence in the vehicle and the people that you're buying from if you're out of state. Back tires are brand new as well. Looks like the rear brakes are brand new as well. Frame and underbody very, very nice condition. Looks like that all the way underneath considering it's an 08. Trucks 10 years old already. Lower rockers are in really nice shape. Has the bed rail covers. The bed itself is in nice shape. Rear bumpers, no dents or dings on those. Does have a full towing package which includes a receiver hitch, 4 pin and 7 pin wiring. Tailgates in really nice condition as well. It does have the tailgate step assist so how that works just comes out like that. This piece goes up like so, makes it really easy to get into the bed of the truck. Put it back in, very simple, slides in like that, just hit unlock and down it goes. Very nice spray and bed liner in this truck. It does have a goose neck hitch and bed rail covers. It's nice and easy to lift up too, excuse me. As you go down to this side of the truck, just as clean as the passenger side, just did want to show that it does have the rear airbag suspension and that's what you saw on the back here. You pump those up right back here with those two little air nozzles but as you go down this side of the truck just as clean as the passenger side, this rim is in really nice condition as well and that tire is brand new too, but very clean down this side of the truck. I didn't see any dents, I didn't see any dings. Has power scope mirrors so they power fold in, power fold out, and power telescope in and out. They go in like so and they power telescope out like so and in. Power windows, power locks, power mirrors, auto headlamps, inside the Lariat package gives you the medium stone gray leather interior, there are no rips or tears in these seats. This truck has never been smoked in and both of these seats are heated. Factory floor mats throughout, carpeting is very nice and clean underneath and we'll take a quick look inside the truck where it's warm and as you can see this truck has 87,095 miles, does have a compass display right there as well as a mileage display. Another wrap steering wheels in pretty nice condition, no rips or tears on that and it does have audio controls on that side and cruise controls on this side.  And it does have an auxiliary jack right there. You have the wood grain dash trim on the dash and the doors, passenger side seat is in really nice condition as well, no rips or tears on that. Factory floor mat and carpeting is in excellent shape on that side. And finally we have the home link system for your garage doors, security systems and lighting systems as well as a power sunroof. We'll take a quick look at the back seats and then we'll check out under the hood. These seats are just as clean as the front seats. There are no rips or tears back here, very very nice condition. Like I said it does have the power slide and rear window. These seats do fold up and then it has a steel load floor. Now why is that nice? If you got tools or muddy boots or something you can put them on here not get your seats all dirty. Also, if you got kids they can put their feet on here when they're sitting down or if you have pets you can put a nice flat piece of plywood down or something so your pets have somewhere nice and flat to lay. And that folds up like that plus it's steel so it's not going to break on you. And behind the seats we do have a little bit of storage but more importantly you have the tether anchors for your child car seats back here as well. And just very clean, has factory floor mats back here. No rips or tears in the carpeting and we'll take a quick look under the hood. 6.4 liter power stroke diesel, engine bay is very clean, runs very smooth. This truck has been fully safety and inspected by our service shop. Has a fresh oil and filter change, all the fluids have been checked and topped off. It does have the motorcraft batteries in it. That's usually a good sign that the truck's been maintained at a Ford dealer consistently throughout its life. This truck's gone through our full safety inspection and is 100% ready to go. And to see more pictures of this truck or one of our other 400 new and used cars, trucks, SUVs, minivans, Wranglers, half tons, we quarter tons, you name it we've got to go to our website www.summitauto.com. Full pictures and descriptions of every single vehicle on our lot. Videos of every single used vehicle that we have all at summitauto.com. Thank you so much for checking out the video. If you like this one, want to make it yours, give us a call right now<PHONE_NUMBER> ask for one of our sales associates to make this truck yours today. Once again, that number is<PHONE_NUMBER>. Thanks for checking out the video. If you want to check out more or see what's coming in daily, go to youtube.com slash summit auto. Remember to like, subscribe, and share on this video and all those on there as well. In fact, in a second, you'll see a link to subscribe to our YouTube channel on your left. There's a link to more three quarter ton truck and one ton truck videos on your right. If you haven't been to our website on the bottom, a link to this truck on our website, click those, check us out. And we really look forward to helping you with this super clean 2008 Ford F-250 Crew Cab Long Box Power Stroke Diesel. Thanks again.


Welcome to Toffy TV, it is the Everton News Daily. Sean Dite has given his pre-match press conference ahead of Everton's game against Tottenham Hotspur on Monday night the game kicks off at eight o'clock. It was the first time Sean Dite has faced the press since the Premier League referral to an independent commission over Everton's profit and sustainability breach, allegedly. This is what Sean Dite had to say on the FFP situation at Everton. I think it covers that in the statement from what they said and the rest of it is down to the powers that be to do anything they need to do, so the club has just assured me that the statement is correct and then as I said the rest of it will go down to the powers that be to do what they have to do. In terms of planning for the future, does it impact at all on the ability to bring players injured in the summer? Not that I don't know of. I think at the minute we're really focusing on what's going on now. Of course there's background views of the future but working with the squad now, working to get more points quite obviously and to make sure we get what we all want just to be in the Premier League. Just following on from that, Dite's was asked about whether or not this gives him a better understanding of why fans have been protesting before games and his worry knows about the situation and whether this gives him a better understanding or whether this gives more credence to those fans who are unhappy with the current situation at Everton and in particular the board. This is what the Everton manager had to say on my question. I think that was not relevant to any statement coming out. I think there's been a long term thing. It's certainly from a distance. I don't think it was under the radar. I think it was apparent that fans weren't happy about different things at the club. Since I've been here I must say they've been terrific with us and the team. That's all I could ask for when I got here because it's before my time all the noise from fans about different reasons and what they felt that they wanted to put across their reasons. I could only ask for them to back the team and they have. They've been magnificent in doing so and the team responded with that so vice versa. We want that connection. We want to align with the fans and it starts well on the pitch and that's what we're trying to do. There you go. On to the things on the pitch which is the most important thing isn't it? Dite said he was quite happy with people who've been away on international duty. Everybody seems to be back relatively unscathed. He was asked about Nathan Paterson. He got some game time with Scotland during the break. It was his first minutes back on the pitch since the injury he picked up against Brighton. Also asked about James Garner who played some minutes for England under 21s as well and Dite said he was glad they got the time and it's up to them now to push on. Simon Della Sims was asked was there any specialist coaching going on with Sims and he said not really. We've just done little bits with him. He's doing well. And then inevitably the question came up about Dominic Calvert Llewman. This was Dite's response to that one. He hasn't cram in the group but he's back on the grass again and has been. So he's making progress which is good. He did have that tiny niggle on the injury. It's a slightly different area but he's making very good progress so we're happy with that at the moment. No timescale? No timescale. I don't do that anyway and certainly when we're just trying to get him back to full fitness as I call it or as close to of course. According to reports in France, André Gomez is having more than half of his wages paid by Everton while he is currently on loan at Llew. The Portuguese midfielder has made 20 appearances so far for Llew this season, scoring one goal. His boss has been delighted with his form really. Paolo Fonsecari was happy that Gomez went there. He was instrumental in taking him there. And I guess it's no surprise really that Everton have had to pay at least half of the wages given Gomez is on a lot of money. Everton are really well paid at Everton and so to go to a club like Llew and expect the same kind of wages with them taking all of it was probably a little bit. How can I put it? Unsurprised that we've had to chip in. Let's leave it at that. What it does do is I guess he's only got 15 months left on his current deal at Everton. I don't imagine he's going to get another one. And I guess because he's on so much money the report says he's the highest paid player in the Llew squad he will be I guess. It puts into question whether or not they're going to be able to afford to take him full time. They'd have to come to some sort of arrangement with the player of course for them in taking a pay cut. So it's not really that surprising this report but it is out there today and the quip have rumoured that story. Everton's midfielder Amadou Omnana has been named in the 25 best male footballers in the world aged 21 or under. That is from a report done by ESPN. The Everton midfielder is in good company fitted alongside the likes of Jude Bellingham and Pedrie. He's done well this season coming out of France at Llew funnily enough coming here and having to settle into the Premier League. I think he's had some games when he's been excellent. Other games when he's not really got into it but you'd expect that from a young player playing in England for the first time he is. Attracting a lot of interest. He's just been away with Belgium and played both of them games. Even captain of Belgium for the last 10 or 15 minutes of their 3-2 win in GM and he's so progressing quite nicely at the moment. Apparently Everton made a bid to sign Olivier Giroux at the end of the January transfer window but that was turned down. Everton apparently offered him an 18-month contract that is according to sources in Italy and a really well paid offer on that contract which the 36-year-old turned down as well he is. Apparently just about to sign an extension to his Milan deal so the second time maybe that Olivier Giroux has turned down Everton that's what the echo will be important today anyway. That is it for the Everton Newsday to make sure you give the video a thumbs up, subscribe if you haven't and why not become a Toffy TV Premier member if you want to do that. The link is in the description or click the QR code which is on the screen now. See you later.


I actually Another quick snap lord here just another quick snap.   Are y'all see are y'all trolling me?  Good job there DK. Good work, man. Good work DK I have Kevin Muay the free card.     He had everybody every single person with the gifted. Let's go He why am I what like what do you mean? Like what do you want me to do here? What are you like? Why y'all mean for it's like 2 a.m.  Oh my god, Jamal. That's a kill shot.   I had a deep third order.    Who's that Metcalf or Chad Johnson?  Does anybody feel this way?  There's no way.  What's this motherfucker Sherman dog?     God. Damn it. Aiden.      Look at what playages. I'm quick snapping.    What happened to you Aiden? What happened to you happen to him?   Do my boys tired. Let me put the backups in come on Aiden Hutchinson. It's your time Aaron Donald. It's your time Yeah, back it up. Honestly, honestly, I like fourth and eight But I like I like being in the 14-yard line right now.   Now. They won't come out with Randy Moss this weekend So DK might be cooked.       I mean, that's a catcher.


Hey, my name is Cody Askins. I was a six-figure agent my first year. I've grown a million-dollar agency, million-dollar lead generation company, multiple seven-figure companies in the insurance business focusing on helping agents. And we're launching a coaching program so that we can have one-on-one access together. That's right. You and me focus spending time together diving into your business thinking, hey, how can we scale you? How can we make you great? How can we help you make some more mochio-denero, baby? Hey, I'm here to help. Again, my name is Cody Askins. We're building a massive following in the insurance industry to help agents. And I want to help you.


I'm super happy to be here. I've been using, you know, Ruby in Rails for ages and feels great to be speaking in RailsConf. So, as Collab did yesterday, you should follow me on Twitter. I don't know. Like you followed him, so I think you can just follow me just as well. So, yes, I'm actually Italian. I can speak Italian, I can cook Italian dishes. I think that makes me pretty much Italian. I started doing Ruby in 2010. In the last couple of years, I've been doing mostly JavaScript. I got really interested in machine learning and I climbed a lot. So, I'm not a good climber, I just climbed a lot. So, I think we're trying to organize like a climbing session tomorrow. So, if anyone is interested, just like follow me on Twitter and then hit me up. Okay, cool. Yes, right now, I'm working at a company in London which is called Erlang Solutions and they were very kind to send me here. So, I'm going to tell the world about how awesome they are because they're the first company who actually believed in Erlang as a technology in 1999, which seems like a century ago. I guess it is. And of course, they love Elixir. They love all sort of technologies which run on the beam. So, React, RabbitMQ, all this sort of stuff. Okay, that was like my business blob. Okay, so my goal today is to do some live coding. So, let me just first ask you, oh no, something, VLC is crashed, clear. Can you guys in the back see all this? Could it be bigger? A little bit bigger? More? Good. I'm just gonna keep going. No? Is it okay? Less? Okay, I'm just gonna go for this but if you can't see it, let me know. But before I start, I just want to show you my desktop background. I think it's really nice because there's this obvious Photoshop in the front. It's just such a high ranger quality and then you have these two guys in the back. They're not even looking at each other. What's the point? I don't know. I hope you're familiar with the movie. Like if you're not familiar with the movie, raise your hand. Keep it raised, please. Well, okay, I can get it off now. Anyways, so what I wanted to do today, first of all, there's this amazing web M which I showed you before which is this guy playing the flute. But there's also trained CSV file and this is actually like real historical data of the Titanic passengers. So there's 892 lines, minus one for the header. So it's like 891 passengers. And for each one of these passengers we have this information. So the passenger ID, whether they survived their passenger class, their name, their sex, the age, the number of siblings as spouses, the number of parents and children, which ticket they bought, the fare, so how much they pay for it, the cabin, and where they embarked on the ship. So what we're going to do today is to, well, I have to admit it, like mainly the main goal is just like to make sure that the movie didn't lie to us. So that was like my main like scientific goal when I started on this adventure. So what we're going to do is to use some Python libraries. I think the guy who spoke before me, he already explained like why Python is the way to go when you're doing machine learning. And I think he's right. So they're just like some really amazing libraries and I hope today I can show you just like how good they are. So yeah, let's start. I'm going to create the file which is called visualize.py and we're going to use this library which is called pandas, which is an amazing name because everyone loves pandas and it's like one of the best libraries I've ever seen for just like dealing with CSV files, for example. And also we're going to import matplotlib.pyplot which is a visualization library. There's this concept in pandas of data frames. So whenever you're loading your CSV, it will just be converted into a pandas data frame. So I'm just going to call it like df and then you use pandas, you just do read CSV, it has the file and he will automatically read it off. So for example, if we wanted to see like a little bit more about, you know, like the distribution of the survivor rates, we just do print.df, survived, this is like automatically created by the library and then we can just do value counts. Okay. Feel free to interrupt me like there's like anything like unclear. Okay.  But it would be nice if you could actually see this, right? So to do that, we just do plot.  And I'm just going to set some opacity. And then in the end, actually I should create the graph first. So I'm just going to go figure and it's plt.figure and here, just pass a fix size. 18, six. Okay, good. And here I'm just going to do plt.show. So hopefully this is going to work. And here it is. Okay. So in two lines, we basically transform like this information into this graph. And of course as humans, we can't really reason too well with these numbers. So I want to see the percentages. And in order to do that, I just have to write here normalize true. And if I do that and run Python, you'll see this is what happens. So we can see that actually on our dataset, there's like a 30% people which survived and 60% unfortunately did not. Okay. But what's better than the single graph is to have loads of graphs. And in order to do that, we're going to use these subplots of grid, which basically creates a grid of subplots. Does that make sense? I don't know. But the only thing we need to do is just like to set a title for this thing. And we're going to say this is survived. Okay, cool. And basically like this structure just means like this is a rectangle, two rows, three columns, and this is the first cell, right? So if I run this, you'll see that it basically just like puts it like up there. I really hate the fact that like by default, the graphs like are not full screen. So I have like some really nice code, two lines of code which will do that. So I'm just going to paste them in. Okay. Okay, cool. So now we can keep making more graphs and I'm just going to use like the good old like copy paste. So what we want to do now is to take a look at, we want to see if there is a relationship between the survival rate and the age. So there is this tool, which is called a scatter plot, which will show you this. And the only thing we need to do, let me just like remove these two lines. Okay, so we take a look at the survival rate and we compare it to the age. And I'm going to pass a little bit of opacity here as well just because otherwise the dots would just get like too clumped together. And here I'll say this is the age with regards to the survival rate. Okay? And if I print this, we'll see that actually, this is quite unexpected, at least like for me when I started doing this, like there is no like apparent like age connection between the survival rates. So you can see like the main like lumps of people are between 20 and 40, both on the left-hand side and the right-hand side. As I said before, one means survived and zero means passed. And if we take a look at it a bit more closely, you can see that like older people, like have passed a little bit more and there's like younger people here which might have survived. But other than that, I think the distribution like still like doesn't allow us to, you know, like any conclusion. Okay. Something else we might want to take a look at is just like the distribution of the passenger classes. So here I'm just going to do this, change this to two or two. And here instead of survive, just going to passenger class and same here. Okay? And if I run this, we'll see that most of the passengers were in the third class and we have people in the first class and people in the second class.  Something we might want to look at is the relationship between the age of the passengers and the class like they were able to buy. And in order to do that, I'm going to do something which will probably make you very, very afraid if you work with HTML at some point, like tables, call span, anyone? Yeah? Yeah, good call span is rocks. Anyways, so we're going to use this feature of Python which is the list comprehension. So I'm just going to say it's basically one, two, three dot each, right? And for each one of those, we're going to want to display the age when the passenger class is equal to a certain number. So when you're using the square brackets which we're doing just like to filter, like you want to extract the age but only for the rows which passenger class was x, okay? And for each one of those, I'm going to create a new graph and this case is going to be called the kernel density estimation. You can look it up on Wikipedia afterwards what it actually means, but it looks pretty. So that's why I've added it. And here the title is going to be age with regard to P-class. And I'm just going to add a little legend to the graph so that you can actually tell what is going on. Second, third, okay? And if I run this, you'll see this is a really nice graph, right? Anyone agrees with me? Anyone disagrees, most importantly? Okay, so we can see that for the third class passengers, they're like way younger, that their average age is 20 years old and for the second class passengers, they get around 30 years old and the first class passengers, they're around 40. So as they get older, they get richer and therefore they can buy more expensive tickets. Since I was really interested in understanding more of the movie, I've actually figured out that there is like a glaring, historical mistake, which is that I discovered that the ship actually made two stops. So it started in Southampton, but it actually made other two stops and we can take a look at the embarked column. And I'll just put it in one, two. I think this is okay. And if I run this, you'll see that 70% of our data set embarked on Southampton in England, but then the ship made a pit stop in France, in Cherbourg, and then it actually made another stop in Ireland, in Queenstown. So yeah, if you're like Titanic fans, I think this is like a good piece of trivia. No, like at parties especially, I think it's quite cool. But I think it's quite nice, when you have these 15 lines of Python code and you can actually drill down the data set and try to put some of these values in correlation and try to just see what's going on. And instead of building a prototype and taking at least a week or something, here using these libraries, it just takes you five minutes, I think. I think it's pretty powerful. Something which is missing from these graphs is the gender of the passenger. And the reason why that is missing is because I think it's quite important. So I think it deserves a dashboard on its own. So I'm going to do some nice copy-paste. Gender, pie, copy everything, delete this. And actually I don't really need most of this stuff. I'm going to comment this stuff and just leave this, okay? So what I want to do now is just like to take a look at the difference of survivor rates between men and women, right? But I want to make more graphs because more graphs are more awesome. So I'm just going to do this quickly. And here I want to show this survivor rate but only when the sex is male, okay? And here I'll write men survived and then I'll do the same thing here and I'll just say sex is female and here will be women survived, okay? To make things a little bit nicer, just like create a color just for female and I'll just make it something just like different. Color, female, color, I think this looks good. And we can see here, like the graphs have pretty much the same shape. So we can see in total there's like 40% people survived. The men, they have like a 20% survivor rate and the women looks quite similar unless you look at the numbers like underneath, probably you can't really see, but if I zoom, it says one, right? So actually 70% of the women survived, like at least like in this current dataset. Like by itself, this information doesn't mean that much because you know, if you think there is a room with 100 men and one woman, sorry. Like this information by itself is not really significant. So maybe we can just like take a look if that's the case or not. And to do that, we just like take the same code we used here and just say, just like show me the sex, okay? And we can see that, oh, I think I made a mistake somewhere, I should have updated this to three. Okay, and we can actually see that, okay, there's like more men, but the difference is not that big. There's like a 45%, 65%. So I think the dataset is quite balanced in that way. Like it's not an inconsistency of the dataset. So there must be something else, right? Before we looked at the passenger distribution correlating that with the age and we can do the same thing and correlating the passenger class with their survival rate. So I'm just going to uncomment this code we had before. And here instead of the age, I'm just going to take a look at the survival rate. And I think the rest can just remain the same apart from this. And I'm just going to make the colespan larger. Every time it's a colespan, just like it's funny. And here we have the graph where you can see on the left, the passengers in the third class, they're like really like, you know, in such a bad spot. And then instead the passengers in the third class, they have a good survival rate. So I think this is quite cool because we can see like on the first row that this is like difference between the genders. And here there's a difference between the passengers class. So maybe what we can do is to try to see, oh, maybe if we try to combine the first two rows and there's like some like striking, you know, feature of the data. So in order to do that, I'm just going to do copy of this. Don't worry about the code. Like I'll put it online. I think it's already online actually. So what I'm going to do is I want to take a look at the survivor rate of all the men, but I also want to add another condition. So I'm just going to use this end and say that the P class is equal to one. And here I just say, first class men survived, okay? And then I'm going to do the same thing, but I'm just going to change this to one, ooh, to one, and this to three. And this is going to be third class men survived. And if I run this, you'll see that this pretty much confirms our like suspicions before. The first class men have a 35% survivor rate and the third class men have a little over 10%. So if you're a third class man, like you're not doing great. Instead, if we want you to do the same thing for women, I'm just going to copy this. There's a lot of copy and pasting this talk, as you can see. Well, one could say the same thing about programming in general. And so there's a color, which is female color. And here I'm just going to say women. And the last one is going to be female in like third class, okay? So third class. Okay, cool. And I think this is quite striking because you can see that in the first class women, I had to check the dataset actually because like when I first ran this, I couldn't really believe it. But like, I think like there's 78 first class women and 77 of them survived in the dataset. And instead, if you're a third class women, the distribution is like more even, it's like 50-50. But I think like, especially given my initial, like scientific goal of this exploration, I can say that the movie is actually confirmed. So there is some historical accuracy to the movie because this third class man would be Jack and this first class woman would be Rose. So it's not really a surprise that Jack perished and Rose survived.  It's like actually how things went. So we can take a look at the picture again. No? Okay, but as you can see like it's super nice to be able to do this like data slicing and just like visualize these things like in a little bit of like easy way. But what would be even cooler is to try to run some predictions, right? We all want to be wizards one day. So what are we going to do is just to create like the most basic heuristic that we can do, which is just like to predict if you're a woman, you're going to survive.  I'm sorry for all the male audience like in the room. So we're just going to create a new file. Actually, maybe I'm just going to create a call you like predict pi and import pandas as PD. And here I'm just going to create like call this data frame train because it's what machine learning called like the training set like the initial data that you use to build the model, right? So here I'm going to do read CSV, train.csv. And what I want to do is to create a new column. So I'm going to do that just by doing this. So in this way, it's going to create a new column called hip for hypothesis, but it's like a cooler hypothesis. Thank you. And I'm going to initialize the whole column to zero except that I want like that. When some condition applies, I want that column to be one. So I'm going to use this function, which is called luck. And this function takes like, well, it's not really a function, but anyways, this thing takes two arguments. The first one is the condition. And the second one is the column you want to update. So the condition is DF sex equal equal female. And the column I want to update is hypothesis. And I'm going to set that to one, okay? And so in this way, basically, we created our first guess of like how to predict the outcome that we want to predict. But now we want to check like how accurate is our prediction.  I'm just going to create a new column called result. And inside the result, I'm going to check the survival rate against our hypothesis. And here, I'm going to set the result, okay? And basically now like we can do the same thing we did before. We just like extract the result back. We run value counts on it. And I'll just run it like this for now, okay? No, DF is not defined. I think it's because I called it train. Train, sorry, okay, this should be all right. And we can see that this thing like was correct 701 times and was wrong 190 times, which in percentages means something which I can't remember. But I think it's around like 70%, 78%, okay? So if you consider that if you were only guessing, you'd have an accuracy of like 50%, you just improved your heuristics by 28%, which is like a very simple guess, okay? And you got to that gas because you took a look at the data, you tried to understand a little bit how the data works. And of course, if you use like more advanced algorithms, you'll be able to improve that number. But I think it's quite cool just like to see the huge difference of when you have like a basic understanding of the data and when you don't. So I think this is cool, but something which is cooler is to let the machine do the whole thing, right? Luckily, there's a set, like it's an ensemble of libraries, which is super well known, which is called scikit-learn. And inside scikit-learn, there's basically so many machine learning models. So what we're going to do is to use a model which is called a leaner model. So I'm going to do leaner.py. And I just want to quickly explain you like how leaner model works, right? So if you see like a graph like this, and like all these are data sets, like the data points, we as humans would guess, okay, the data is exhibiting this sort of trend, right? And I think this is quite simple, like you ask my cousin who's like five years old, you could probably do it. But the problem is that in real life, the data sets have way more features than two. So for example, if you're doing image recognition, every different pixel of the image is a different feature. So if you're analyzing images which are 50 by 50 pixels, you're facing a problem which has 2,500 dimensions. So if I'm going to ask you to solve the same problem in 2,500 dimensions, I don't really know who is able to do that. But the advantage of having a numerical approach which does the same thing is that apart from performance, of course, the numerical problem doesn't care. The numerical problem would just like solve the problem just as well. And I think that's why like most people think that machine learning is quite scary because it tries to condense this sort of human knowledge into some number somewhere. And there's not a really good explanation for us like what that means. But I don't really want to scare you with my dystopic future tales. So I'm just going to import pandas again as a PD. train.pd, read.csv, train.csv. I'm just going to show you, I said I was going to live code, but I actually lied. And there's some helper functions I wrote before because I didn't really want to bore you these details. But basically, of course, one of the things you always have to do with data is to clean it up. So for example, in this case, some rows don't have the fair information, some rows don't have the age information. So I'm just like filling them back up with like the average value. And then another thing is like most of these numerical approaches work really well with numbers, but it can't work with strings. So I'm just converting the idea of the sex to a number.  And if you're female, you're going to be one. And the same thing applies for the embarked information. Okay. So here, what I'm going to do is to import these utils and just do utils.clean data train. Okay. And how most of these algorithms work is that you basically you tell the algorithm, these are all the inputs and there is one output. And the inputs are called features. So what you're going to do is to extract these features. So let's say I want to use the passenger class, the age, the sex, and let's say the fare. Okay. And I'm going to extract those values. And then I want the target, which is the survived information. And I'm going to extract that as well. Okay. And since this algorithm is going to try to decide if this row is going to go into the survived bucket or the disease bucket, it's usually called a classifier. And here I'm going to use scikit-learn. So from scikit-learn, I'm going to import this linear model. And this linear model has this little thing which is called logistic regression. Okay. Which is basically the same thing that which I described before just like trying to figure out like which is like a good line to separate two datasets. And it's super simple. You take this classifier, you say, okay, fit these features against this target and then print me the scores of these features against this target. Okay. And if I run that, it's like 79%. And as you can see, I didn't tell the algorithm anything at all. It's just like, you figure out, okay, these are the inputs. This is the output that this guy asked me to figure out. I'm just going to do my best. And the model that he constructed was like a little bit better than our naive intuition. Of course, the linear model isn't always like the right answer because we know in real life, there are problems which are not linear. So if I asked you to describe this data, you wouldn't say it's this, right? I mean, unless like you have a thing for like straight lines. Ah, maybe you do something like this, which is not too bad. I'm not judging, but I would say that usually you'd go for something like this, right? So luckily, like most like machine learning experts, they recognize this hatred for straight lines as well. And they created this module, which is called pre-processing, where you can basically, you can manipulate your features and transform them into their sort of like polynomial transformation. So basically you can make them quadratic if you want to. And it will just like take your data and combine them and multiply them together and create new columns for you. So what I'm going to do is just to create one of these like transformers. Um, polynomial features. And you have to pass like the degree. So in this case, I'm going just to try with the quadratic. And now we can transform our existing features using this fit transform of features, okay? So basically we had these features and in this way we transform them into their sort of like quadratic versions. And now we do the exact same thing. We just like say, okay, fit these poly features against the target. And then print me the score of this other classifier. Poly features target, okay? And we see like this thing already like improved a little bit. Usually you can see that it's because, I think like the mental model is now the algorithm is trying to match against like data which behaves in a quadratic way. Usually if you add like more information like the algorithm will behave better. So for example, I can add the number of paradigm children and number of sibling spouses. I think this should be enough. And let me try to run it again. And you can see that even more information to the algorithm, the algorithm is able to like, you know, make better predictions, okay? And all this in like 20 lines of code. I think it's pretty cool. How much time do I have left? Six minutes. Okay, cool. So I just want to show you one more thing which is what we should call decision trees. So basically there are these algorithms which are going to build these decision trees. So for each row in our CSV is going to run through a series of questions. And then depending on the outcome of these questions we're going to classify our little row. So let me just do some good copy pastes. Predict tree pie. And no, I don't think my pace worked. Let me try again. Yes, this pattern. And instead of linear model, we're going to import tree. I think we don't need this. So I'm just going to change this to decision tree classifier. All the rest remains unchanged. And in this way, the algorithm is going to build a tree and try to match that.   Because we all know there's no such thing as a free lunch. So 98% is like way too good. It can't be, right? So basically what the algorithm did in this case is like a phenomena in machine learning which is called overfitting. So basically we're passing some data and the algorithm is trying to find a very complex solution which matches against all the data points. And that's why afterwards, when we try to ask him, oh, how are things going? And he's like, yeah, everything's great. I got it, bro. But when you see this solution, you're like, nah, I think you might be using. Luckily, the scientists recognize this problem as well. And it's quite like a big problem in machine learning, this problem of overfitting. So we have to figure out ways to make the machine learn like the generalized properties of the system, not the specific properties. So in scikit-learn, there's this little thing which is called model selection. And if you use this model selection thing, you can basically say, okay, take this model and try to hide some data from this algorithm so that the algorithm builds a model which is more generic. And I'm going to use this function which is called crossval score. You can explore later what that actually means. But I'm just going to pass the features, the target. You have to pass a scoring methodology which I'm just going to pass a accuracy. This is not good, don't do it, but I think it's just simple enough. And I'm going to say, okay, we run this process 50 times. So basically this thing is going to sub-sample randomly the dataset, hide some stuff from the algorithm, we run it again and try to see how it behaves, right? So if I print this, scores, print, scores. Whoa, whoa, whoa, that's not how Python works, I'm afraid. It'll be nice. And you see, if I try to hide that from the algorithm, the actual average of the scores is quite abysmal. And this is like what we expect because we are trying to build a solution which is like too specific, so it can't really work. The good news is that there is a way to fix this and it's basically to tell the algorithm, well, don't be too smart. So what I'm going to do is to, first of all, pass a random state to the algorithm and then basically specify a max depth. So if you imagine this as a tree, the tree will never go more than six levels, okay? And then I'm going to pass this other little thing which is called sample split which basically controls how many elements have to get into a branch before the tree decides to split into a sibling. It's not super important, but I think it's nice. And if I just rerun the same code again, you'll see the difference. You'll see that the initial performance wasn't as great, but the general average is much better, okay? So basically in this way we've created a model which is more resilient because in this way we're cheating before we're just passing all the data, but in real life you'll train the model and then score it on new data. So it has to work well for data it's never seen before. And I still think this is quite weird. So luckily there is a really nice, no wait, it's called tree. And there's a really nice utility inside the tree library which is to export to a graph is file. So actually let me just double check how I do that. Okay, gotcha. So it just passed a tree, you pass the feature names which I don't have. So I'm going to have to add that and you set the out file to a dot file, okay? So I'm just going to cut this to feature names. Feature names equals thing I just cut. I think this should work. Okay, cool. So if I, control Z, if I list there's a file which is called tree dot. If I place it just like a little bit weird, but I can convert it to a PNG file. Tree dot into a tree dot PNG. And if I open this folder, you'll see that there's a really nice graph now. And this is the actual tree that the algorithm built. This is just like visualized. And these are like all the decisions that the tree does in order to classify your row. Is if you can see like the top level decision are the most important decisions, right? And it's not a coincidence that the first decision that the algorithm makes is the passenger, man, or woman. And then the one immediately after that is, oh, is it the child? Like it's the age of this passenger, like less than 6.5. And the other immediate decision is, what is the passenger class, okay? So basically the algorithm figure out a lot of things which we figure out like on our own. But as I said before, this thing can do it like on a thousand dimensions. So yeah, that's all I have.


This is a side chair, meaning it's not an arm chair, it doesn't have arms. Much more interestingly, it's a Hitchcock chair. Now, Hitchcock chairs are both known as chairs that were made by the Hitchcock Company or Lambert Hitchcock, initially the entrepreneur in Connecticut, but more significantly there are certain genre of chairs. So lots of different painted chairs of the first half of the 19th century, sort of festooned with lots of cornucopia and sort of gold stenciling, cane seats were known as Hitchcock chair. So it's got a larger sort of import because of that, but it's extremely popular. You can still find lots of these in antique shop. What I find really interesting about it, first of all, is the decoration. And I think that's what it was meant to say. It's a decorated chair, not just a plain black chair. What I know from my own prior knowledge, of course, is that often painted decoration stands in for sort of other kinds of decoration. In earlier chairs, one would have used rich carving, which takes a lot of experience by the artisan. So here, instead of having rich depth in the carving, we have two things which stand in for that three-dimensionality. We have turnings. This is done on a lathe. These are done also mass produced, so that these parts are relatively interchangeable. So at the same time as these Hitchcock chairs are being mass produced, $1.50 a piece, usually sold in sets, someone like Eli Terri in the Connecticut clock, industry is also making cheap shelf clocks by relatively interchangeable parts, so that the gears and the clocks are made all at once and then can be fit into a variety of different clocks. So that obviously is going to cut down on costs. And also on the skill level for the chair workers assembling the chair. So much of the work is really done by semi-skilled workers rather than an older style where one person made one chair at a time. In some chair industries, they would have made some parts at the sawmill, they would have then made other parts or assembled them in a shop, and then third, they would have had women and children seating the chairs by hand in homes and then collected everything together. So in the case of Hitchcock's innovation, sort of like the Lowell Mills is that he did everything together in a factory, which really allowed him great advances in terms of scale, savings by scale. When you look in the back, on the back of the seat, it will say Hitchcock warranted. And so it's got a stencil on the back. This is the first entrepreneur to do this, that there's sort of warranted that if, you know, there's a problem with this, you can sort of return there. So again, it's this assumption, and this is a new stage, that these will be distributed throughout the United States. There will not be a face-to-face encounter between maker and consumer, so that you would need to have this sort of publicized warranty in a way that if you actually knew the craftsman 20 years earlier, you wouldn't need that sort of published, stamped warranty. What Hitchcock's great idea was to take a bit of this and a bit of that, put it together, push it forward with division of labor, and also extensive marketing, and really produce something that's a prototype of a sort of mass-produced object that bespeaks gentility to a wide section in the American public, from top to bottom, and do it at a really low price. And that really is what accounts for the popularity of the chair at the time, and I think also its significance for us to sort of look at and talk about.