All right. Good morning cyber traders. Welcome back on this Thursday the 7th of October. How's everybody doing this morning? Let me get a little see if everybody can hear me loud and clear. Good to see you. Good to see you all All right. Good. Good. Good. Good. Good morning, Gene. There you go grant.  Let me tell you so far. It's been a pretty decent week not a terrible week We don't have any really big winners that we had like the following week But we're getting a little bit of a rally in the market We're up about 300 points looks like they raised the debt ceiling and they just no less kicking the can down the road Which the market kind of likes, you know, I don't know if you call it a rally You know because I still don't see the market really going anywhere, but let me tell you Bitcoin is obviously doing pretty well We're up what 52,000 or something like that. I know we talked about it yesterday. I know Josh and You know our Bitcoin instructors out there Josh and Richard really really doing pretty well with that one You know holding holding tight there But but you know what overall a couple of stocks are really going up and down I don't really still see anything exciting. Listen when we get past 35,000 Then then 3550 You know right around that then we'll talk that we're at somewhat a rally But until then I'm still seeing a little bit of a foul still flag. I'm not really buying it I don't really see too much going on remember We're still stuck right around here around these April price ranges But there are a couple of good stocks that are moving pretty well Let me tell you with it's not a Head fake today, right Brian? I'm sure it looks like that way But anyway, there were a couple of stocks that didn't move pretty well today that I have to omit We had this one stock big that did really well a lot of you guys got in here early this morning I didn't have it on the watch list because it didn't because I put the watch list What time did I put it out there the watch list came in around? 751 I remember there are three times When you get program trading you obviously get one around seven o'clock, you know 730 around that time But the real program trading kicks in at 8 a.m. 830 and then 9 a.m. So right now we just broke the 9 a.m. I know there might be a couple of things that might be kicking in right now But this one exactly at 8 a.m. Was a great great runner You could see it right here when programs your trading kicked in around 8 a.m. Stock ran from 4 7 of $5 all left about 520 a lot of you guys sort on the watch is because watch was kind of stunk There wasn't that much out there and then all of a sudden things started making a big pop and some of you guys jumped in You know right around 550 a little shake 580 did a shake just kept breaking higher highs You know 6 6 20 6 40 all the way to about 830 and now she's starting to make a higher lows But you could have chased this stock you didn't need a lot You didn't need to make that big dollar. You could at least maybe 20 30 40 cents It's always great to start the morning on a positive note, but we'll talk a little bit about some of those stocks Let's talk about what happened yesterday. There's some of you here that missed out a little bit So the stock any was a really really nice mover yesterday We had a nice little six dollar runner right in pre-market She did a nasty shake here at 9 30 and then finally build a nice little support levels here and started breaking out Went up about to about 7 that stock did pretty well G grew that was another one. We traded, you know back on the 29th. This was a great stock right from $1.70 to $6 She had a little bit of a move right out of the gate She got halted a couple of times nice move $3 and 29 cents ran too high around 588 Nice move. You could see it got halted several times Then she started building a foul still flag on this one and then boom the thing just came crashing down Let me tell you yesterday Yesterday Morning yesterday afternoons of phase three wasn't the greatest, but we had some really good conversations going on We had a couple a little bit of spots made to push at the last minute but Yesterday's class and the day before the phase three was really really good. I have to omit And you should go back because it was great about those big movers that we traded that we saw these iceberg orders these big block orders get executed And we saw we seen what happened and if we didn't have our finger on the trigger We would have got crushed and actually the grom was one of them We had the rally we watched it. You got to go watch the video Remember if you want to be a student you have access to it talk to the education advisor, but listen This is the whole point If you didn't see if we were not hawking the stock right here You know and didn't have that game plan and did not get out That Winner would have been a huge loser. Okay, and you know the big thing about here teaching at Cybertree University The trading room is where we trade we work to get like a team The classroom is where we teach you how to stop losing money because that's everybody's biggest problem Our goal is not to teach how to make money We teach you how to stop losing it and yesterday's lesson is a you know a class you do not want to Not watch you have to go there and see that because you've all been there You've all done that you saw how I had my finger on the trigger hit that button And the second I hit it boom that thing ran from 5 30 5 38 all the way down to 4 38 You lost a dollar like that a dollar and a thousand chairs thousand dollars You could really hurt some of you, you know when it comes down to that So It was and the only reason why we got done is i'm not saying i'm a genius or you know I got lucky because I hate being lucky lucky. You don't want to be lucky in this business You got to have skill But you got to know where those orders are and you saw it and you saw him get executed And it was game over but anyway it was a really fun stock to trade another one that that was was very slow was btbt It was nice the day before You know btbt you could see it right here Um was nice, but this one moved pretty decent it ran from a price of 7 80 all the way up to about 9 40 nice move nice little trend right there So we did pretty well on that one too, but not not the greatest day, but it was a nice overall day Now what are we trading this morning? What's going on? So anyway, we know about the that we know about the oops. Let me fix this. Yes, we could see a little better And let me change two charts. So we all about be uh a big so we trade the stock Let me look at the long-term chart. You can see it here. We trade this back in august It went into september became a good not only a day trade it became the swing trade remember to be a good day trader I'm sorry to be a good swing trader. You have to be a good day trader It's what happens over the course of the day which trickles into a swing trade and for all my options trader friends out there Listen, you want to be a good options trader? How do you know what option to do to trade if you don't even know what the stock is doing first? So this is how you would basically could monetize the stock like big when we traded it And this is the stock literally just went all the way up to about 12 dollars from a two dollar stock What is that almost a thousand uh 500 percent on this on a day trade on a swing trade on a day trade You know, it also it was up a lot. So um, but anyway, she's moving pretty nicely She's testing some resistance levels over here. Uh, I popped my nose over here on level two. I mean level four And uh, there was a couple of big iceberg orders that started showing up You could see him right here. It's got some 18 000 34 000 And that was really kind of where it kind of died out There was nothing here holding it early in the morning Which prevented it other than this buyer came in which kind of started the rally But now all orders are coming in so we need to see how it How it's going to pan out when the market opens up right now the stock is pretty much flat But I mean trading at 14 million shares that is a lot Another stock that came on my watch list early this morning to 8 30. It had a nice little move This stock right here pbts Move pretty nice at a dollar 20 shove it to a dollar 45 The reason why I like this stock if you look back on the long-term chart a lot of you remember this stock We traded also the stock in july the stock ran from a dollar 50 to 280 Cheap stock very inexpensive inexpensive, you know, so we know the stock can move But she's testing some resistance levels. She's been having a really nice Trend the last couple of days, but she did back off right here, which I don't really not a fan of She's up, you know, seven percent 800 000 shares. I mean like listen, there's not that much out there. I'm very very limited What I see out there. I thought this one might be interesting also This is going on the b-list not on the a-list It's up seven percent 66 000 shares five dollar stock across just been up the last couple of days When I looked here on the long-term chart I mean it's kind of spotty. I don't know maybe on the c-chart maybe on the c-list not even on the b-list And uh, this is the other one that's just getting crushed down 36 percent It looks like it's trying to come up, but we all know about pharmaceutical stocks I always like to kind of bring this up Pharmaceutical stocks do not come back. Okay, so be very very careful when you look at these stocks, you know, some people like I know that everybody's got a couple of pharmaceuticals If not one in their portfolio because they heard that they got the next you know First it's you know the first first was a cancer drug now. It's like the next best thing for covid so um So I put it on the watch list to see how she trends out But be very careful because she's breaking 52 week lows Other than that, I mean going through the watch list. I mean seeing what's going on I mean jane looks like it just showed up right here up 18 percent You know the stock just moved at nine o'clock this morning. So this came on the came on the The next program trading time zone Looking down the watch list. I mean, I'm not seeing anything that really jumps out on me As I'm working my way down that list. I mean look we're already at 7 percent. It's a little garbage I did see the cei I mean listen be careful of the stock the stock is very volatile You gotta you know, it's been a fun stock to trade for some of us here But, uh, please don't fall in love with this. There's a huge short squeeze. She's coming up to some support levels here but You know, but don't think like it's going back to five hollers. Okay Everything else as I was working down. I was going to put the neo on there, but we know neo neo is a fast moving stock It's got some support levels here. It's a little expensive. We traded here in the past If you're not a category three trader stay away from it now That's about it. Um, a couple of stocks that some of you are shooting out here in the cyber group room I don't know if anybody here on youtube wants to tell me if there's anything you want me to look at Just put it in the chat. But dan uh, dan a gold student here says what about marvel So marvel, I saw that nice little trend yesterday marvel didn't move pretty nicely yesterday She's kind of you know, she's breaking a new high. She's expensive She's more of a swing trader than more of a day trade dan. So But I know marvel marvel and it's not the marvel you're thinking of like marvel the superheroes. This is a marvel tech stock mark uh mark d gold student is bbi You know It had a big spike up and it came right back down mark You could put this on the b-list, but I you know me. I'm not a big fan trading stocks under a dollar br uh b dry from uh dan Dan, I don't know why we're looking at this stock stock's not doing anything It's really kind of flat You know 2200 shares not really. I mean listen, you're really this is what I'm talking about This is where you're really stretching it. You could even see on the long-term chart very spotty That the on on the on the long-term chart. We don't like when we see those gap fills right there Not really a stock that you want to trade Okay, but listen Don't be don't don't think this is the bottom, you know, like you come everyone's thinking like, you know Don't get caught into thinking like is it too late to jump in now? Should I jump in now? I'm not sold on this market. Okay, you got a death ceiling that they're gonna they're working to deal out It's not stitching stone. Listen. These politics politicians are always playing games with each other Uh, if they do do it they're kicking the can down the road But um the market does like it a little bit, but I'm still not seeing anything that's really like Not getting that trend you need to see several days before you could see that and the only way you see that Is what we're doing now is day trading. All right. So with that said everybody the market's opening up in 15 minutes Um, if you're new here to cyber trade university, please make sure you know, if you're a trial member Go to your make sure you talk to education advisor and make sure you book your appointment Make sure you they they you get maximize those benefits that you get here to Get the full experience of cyber trade university the trading room the classes everything else If you're watching youtube live, you could always join us like us and friend us And if you're watching this recording we hear live every morning at 9 a.m. And 2 30 afternoon eastern standard time and in the meantime everybody say thursday It's always a good day to try on thursday's tomorrow's friday. Be careful About tomorrow. I forgot to mention one last thing What does friday encounter? monday is Thanksgiving for the canadiens right is that what is that right and it's columbus day So the stock market is open But a lot of kids are off So i'm expecting a lot of people to be taking off on friday. I'm actually taking off Okay, so i'm going away with my kids. I got to celebrate, you know, metallian heritage columbus day You know, I got to have that, you know, I have to celebrate it. So, um So if I don't see you, um lita and all my canadian friends out there Have a wonderful and happy Thanksgiving. It's the greatest time of the year I love cooking and I know some of you guys do too. So enjoy that But be very careful uh tomorrow because when you have a long weekend a lot of people take extra long weekends And with kids off I don't expect monday monday would probably be a crazy day And it could uh, it could trickle into starting on tomorrow But let's see how we get out today, right just to prepare. Good luck. Everyone happy trading See you in a little bit in the trading room. Just be safe


This video will show you how to find Sinal. Use the Nursing Research Learning Guide from the Ray Howard Library. You can reach this in several ways, but the easiest is to go into modules in Canvas and scroll down to the evidence-based research paper and click on Nursing Research and APA Citation, Finding Research. Click on Sinal. This is the Nursing and Other Allied Health Database. This is the database you will use most often, although you may use academic search-complete, PubMed, psych articles, or eBerry as well. At large schools like the U-Dubb, there are more nursing and other resources. From home, you will use your shoreline email address and password to get into Sinal, so you need to access it through this learning guide. On campus, you won't need to sign in. Hope this helps.


This is Shepard Humphries with the Jackson Hole Shooting Experience, and I'm a little bit above average in height. I just have a little tip that might help you if you're also a bit taller. If you're ever going to be out shooting other people's guns, can't be a bad idea to have a recoil pad with you. I like the Limsaver. This is just a great brand. It's nice and mushy, which helps if you have a gun that has a lot of kick. But more importantly for our purposes in this little tip is the length right here. It's about, I would say, an inch, a little bit less than an inch. But it gives you that extra length of pull there so that if you're taller and have longer arms, it makes it a little bit more comfortable for you to get your good cheek rest and still be able to see down nicely. So, put one of these in your range bag. Why not hurt having a round in case you shoot somebody else's gun?


Welcome traders to the Tick-Mail chart here at the Meef Action Manager. We want to draw your attention to the CAD-Yen, we're on the four-hour time frame. Impulsive move off the lows here at 19th of July, subdivided into five row segments. Potential correction completed at this 87 level. If we can take out the 88 level, then we look for an immediate upside extension to test descending trend line resistance at the 89.15. However, if we hold this 87.90 or 88 level, then look for a double correction and bullish reversal patterns at the 87 level, there's an opportunity to set long positions again targeting that descending trend line resistance at 89. As always traders, plan the trade, trade the plan, most importantly, manage your risk. Until next time, thanks very much.


If you're tired of me, I'm going to introduce Gideon Litchfield, who's going to moderate the second half of this. Gideon is a senior editor at Quartz, and he's also a fellow at the Data and Society Research Institute, where he writes science fiction about the near future. Thank you very much. And I think we're going to need one more chair, right? So without further ado, I'm going to introduce Nick and Jacqueline to come up onto the audience. So Jacqueline is a historian of quantification. She is the author of How to Do Things with Numbers, Histories of Quantified Cultures and Lives, and assistant professor of English at Arizona State University, and a fellow of the Lincoln Center for Applied Ethics. And Nick is assistant professor at the University of Maryland's College Park College of Journalism, and member of the Human-Computer Interaction Lab also at the university. So thank you both very much for joining me. And our session is called Quantifying Ourselves to Feed the Algorithm. And I guess there are two ways in which we quantify ourselves or are quantified. And I want to start with you, Jacqueline, talking about the way in which we quantify ourselves, because the bit that we're then going to come to is how essentially other things, other people or other businesses quantify us and what happens to that data. So talk about quantifying ourselves. How has that evolved? And where do you think we are at now in terms of our level of self-quantification, which is probably mostly just for nerds at this point maybe, but are we all going to be super self-quantified very soon? Well, I don't know. I mean, I think the... So one of the things that I work on is the long history of quantifying human behaviors and human lives. And so I start in the 16th and 17th century with the rise of death mortality counts, essentially. And so that's very much a moment in which other people are counting other people. So people aren't quantifying themselves. But one of the things that I do is create a link between today's quantified self-movement and the early life writing. So confessional writing, other kinds of autobiographical modes, which very often took the form of receipts, record books, accounts of days, things like this that you can think of someone like Samuel Peeps and his compulsive need to write himself down. And he writes it down in three different forms before it finally becomes something like his diary. So this move to record our lives in order to see them is not new, right? But this push to... In the jawbone advertising, there's a better you out there, go find it. Or in other things that know yourself better, right? So the know thyself dictum is very old. But the know yourself better is this idea that a kind of abstraction and quantification will provide insight. And this goes back to some of the things that people were talking about earlier, right? That somehow there's a hope that the computational will provide better insight than the human mind, right? So to go back to your like, should you trust that person? So the move now is very much about... It's very grounded in sports and athletics. It's very grounded in bodies and the ways that bodies behave. But it's an interesting abstraction of bodily behavior into something that gets fed up into a cloud compared against metrics that are much larger than the individual. One of the things that I talk about is that the modern quantified self movement is very much about outsourcing the labor of counting. And this I think we'll get to when we talk about companies. But with tracking devices, much of what people are doing is providing companies with that data for free, right? And not even for free in the case of something like Nike Plus or other devices that you might buy. We are paying to get the devices in order to then give our data for free. So there's a kind of outsourcing of the labor activity that I think is particularly interesting in our modern moment. Yeah, so would you say that at this point the biggest beneficiary of our move to know ourselves better is us? Or is the companies that use that data? And do you think, and I don't know, you may have a view on this as well, Nick, but do you think that there is starting or there will start to be a backlash against the extent to which those companies that collect our data benefit from it? I think the rhetoric inside of the QS movement is very much about self-empowerment. And so I think most of the people who are utilizing it think that they are empowering themselves. My own sense as a scholar is that that's part of the packaging that makes the dispersion so powerful and possible. So I think really who's benefiting at a kind of monetary and power level are large corporations and governments or multinational entities, even as individuals feel empowered. But I would push back at that and say, I mean, certainly it is part of the rhetoric of these quantified self-services, but at a very human level there is a lot of utility to being able to optimize behavior and these products like Automatic, which is a car system that helps you optimize your driving or Spire, which is the Fitbit-like thing that watches your breathing and helps you calm down and be less stressful. I mean, we put these things on and we attach these things to our cars and whatnot because in some form we want to feel like we're in control of ourselves and we're using these techniques to exert agency in these situations. I think you're absolutely right. And the logic of self-mastery has been there since the Augustine tradition. So this is a really old impulse I think in Western culture in particular. It's interesting that you brought up the case of the Spire. So one of the things that I've been doing is getting all of the devices and wearing them around. And the Spire, it attaches, all of these devices have very gendered protocols. So the Spire, the two places where you're encouraged to wear it as a woman is either on your bra, right in the middle, on your breastbone, or right at your pelvis, on your pants, on your waist pants. And then it buzzes you when you're tense.  I could have imagined for tension, right? And so I think there's a really interesting disconnect between design and the conceptual ideas that go into our quantified self-technologies and then their actual use. We are seeing some pushback against this, right? I mean, there's various kind of attempts to subvert some of these tracking technologies. I was sort of interested in this. I saw recently on Fitbits, which is a project where basically they're designing interventions that help hack the Fitbit and make it look like you're being more active when in fact maybe you're not. And the motivation is that, well, insurance companies are now starting to give discounts for physical activity. And so if you're not the physical activity type, but you want the discount, maybe on Fitbits is the kind of thing for you. There's other projects going on like Adnausium that I'm familiar with. Helen Nissenbaum at NYU is working on this, which is essentially a browser plug-in, which will automatically click every ad in the browser for you. And this is sort of as a subversive way to create a lot of noise in the advertising network so that instead of being tracked and the advertising network knowing exactly what ads you've clicked on and what products you're interested in, it kind of just washes out and they're not able to tell anything about you. There are other versions of that like ghostry that block, they do the opposite. They try to create perfect silence. It doesn't always work. But I think you're absolutely right that there's a ton of effort right now to subvert these various tools. And coming back to the literacy point from earlier, I think that's actually a really important form of literacy that we can develop in the public is how do you actually poke and prod these systems how do you subvert them? How do you get the algorithm to treat you the way that you want to be treated? Well, there's an aspect. So in the backlash I can sort of see that there's an aspect of... There are two things pushing you to take part in this kind of self-quantification ecosystem. And one is the economic pressure, if you like, or the technological pressure, which is things like insurance companies saying if you don't provide us with data then you'll have higher premiums. And the other is the social pressure, like look all the people in my office are wearing Fitbits and I'm the only one who's not and they must think I'm a slob or that I'm not taking enough care of my body. And so I can see also, I was having a discussion with someone recently where they said I think that in the future there will be communities of people who are totally wired in and then there will be communities of people who are off the grid and they refuse to have anything to do with this kind of technology. So it seems to me that there are in fact two ways of rejection. One is to go off the grid and try and block it. And the other, as you say, is to kind of subvert it, to provide the data but find ways to do it so that they're not real data or that they're disguising something. Do you think both of those will happen? Well, there's maybe a third method as well, which is kind of sandboxing, which is something that I've tried to do when I start tracking myself. So after I think the third Fitbit that went through the wash I said I've had it. I'm not doing Fitbit anymore. And I started using the Moves app, which is owned by Facebook. So I guess Facebook now knows how much activity I do and it tracks me around the city and it can tell me how long of a bike ride I've taken and so on. But I'm very careful with that app in particular not to log in. I don't link it to Facebook. I don't log in at all. So there's really no connection to the outside world. I'm just using it for my own visualization of my activity. And I think that's an interesting strategy that is perhaps effective for not necessarily subverting but also not necessarily bowing down to the interests of these corporate actors. Realistically, I suspect that very few people will be as technologically as sophisticated as you are to do that kind of thing. So if we talk about the majority of people who end up one way or another being very closely quantified by these companies so what other ways are there in that case for them to assert more control over their data or at least have more knowledge about how it's used? I think this is a very tricky domain and I would say in the ecology that you imagined where there are the super-engagers and then the non-engagers there are a whole field of people who already exist who are being quantified by others but who don't themselves have access to these technologies. So you think about advertising firms that buy data about mortgages, etc. in the Detroit area and then you end up with targeted advertising for subprime loans to those individuals which sort of perpetuates the cycle that we originally saw in redlining. You also have immigrant populations that are moving across the US-Mexico border that are being tracked, quantified. The move to share data internationally actually is rapidly growing and the amount of NGO and international coalition efforts to quantify numbers of bodies and their behaviors in the global south for example I think should give us a lot of pause about who even knows they're being quantified. So I use my Facebook or I bought something at that store, there's that level. I have a Safeway card, there's that level. I use my app and I connect it, there's that because there's a community aspect to much of this talking, sharing, you were talking about the office peer pressure and some of it's about competing with one another. But there's a whole tier of people for whom all of those levels of access are not even possible because they are the people who are being quantified about, not the people who are doing any of the quantifying. And I think an important dimension of that is actually transparency and part of this actually is going to fall back on the corporations and the other organizations, governments and so on, various institutions that are quantifying people is to develop mechanisms and standards for what you can disclose about the data, about the algorithms. So what are the strings on the marionette that are even being pulled? So earlier this year at the Tau Center for Digital Journalism at Columbia I organized a workshop on algorithmic transparency in the media and the goal was really to start brainstorming and just writing everything down that we could possibly think of that could be disclosed about things like the Facebook newsfeed or things like automated news writing algorithms or automated curation algorithms. And something that came up over and over again was this notion of the algorithmic presence. Am I in a space where I'm being watched, where I'm being incorporated into some kind of algorithmic system? Is there a way for me to turn it on or off? Can I see what the system looks like to someone else? So kind of stepping into someone else's shoes. So that whole personalization aspect of what do I see versus what does someone else see? And, you know, I think there's certainly a lot of follow-up work to do in this space of algorithmic transparency but we're starting to see the first inklings that I think there will be a demand for this kind of information disclosure. Is there any examples that you can point to right now of how that's starting to happen? In other words, people's awareness of their algorithmic presence as you put it? People's awareness of their algorithmic presence. Well, the systems that allow them to have that insight or that visibility into where their data is being used and how they're being operated on? Yeah, I mean, I think there aren't great examples out there. There are some, I would say, prototype level design efforts. Some things that I've worked on, for instance, around online ranking of things and how could you actually communicate the idea that an online ranking is composed of a dozen different pieces of data that have been reweighted and sort of form into this index? I think there's actually a lot of user interface work that could be done to sort of figure out what are the right ways to signal to people because you don't want to overwhelm them with too much, right? You don't want to kind of just create this totally overbearing information space where now they're not interested in your product at all. These bits of information kind of need to be integrated into the individual tasks that people are doing with their applications. There's a sort of backdrop to all of this which is that a lot of the services that we're talking about whether it's Fitbits where you are notionally quantifying yourself and things like that or whether it's collection of your data by other companies. There's been this sort of implicit transaction or this implicit, I don't even know if to call it agreement or conception that your data is basically free or doesn't have value. So you agree to give up your data in return for a service and that service you're calling that service free like Facebook or Gmail or whatever else. You treat that service as free and you give up your data because you don't think of the data as having value. And that is essentially why a lot of the things we're discussing are happening because people don't treat their data as an object of value that they should or they don't perceive or they perceive the service that they're getting for it as free as not an object of value. In other words, people are not thinking about paying money, they're paying in data but they're not thinking that is actually paying. Is there something that could shift that perception for people? I think the more I talk about in places where people talk about how much money other companies pay in order to get that data, I think that helps raise awareness. Sarah Watson does some really great work on glitches and how glitches are the space where you begin to see the circulation of your data separate from yourself. She has this insanely poignant one that I think is so powerful. A man opens up his mailbox, gets a piece of mail that's addressed to him, his name, his address and in between the two of that is eldest daughter died of car crash and it was from an auto insurance company. And so what had happened was that someone bought a data set that was about people whose family members had died or been injured in car accidents and then targeted them with their advertisements. And of course in this particular instance, he's confronted not only with the knowledge that that data is out there circulating but also with a kind of, to go back to the notion of haunting, a kind of haunting by his now deceased daughter. And so she's done really good work with that. I think we recently did a thing where we turned people's, we called it data shed, so how much information their phone is sort of spewing outwardly at any given moment. We did a installation performance where we turned that into both sound and vibration feedback, so sonic and haptification. And so people could walk around and feel that even when they weren't doing something on their phone, their phone was still talking and sharing data. And that, it had a pretty, I mean it was a very initial, early stage event, but it was affecting for people, right? They were like, oh, why, like my phone is doing that now? Who's it talking to now? What's happening, right? And so that peaks a certain kind of curiosity which I think can drive literacy in some ways. But it also just kind of creeped people out. Some people were like, well, I don't, I guess there's nothing I can do about that.  So I think this is a kind of thorny problem. How do you get people to care enough and to then have the literacy that allows them to understand the transparency? Yes, although it also seems to me that when you inform people, when they learn about the things that are going on, they often are shocked at first, but then they accept it pretty quickly. They say, well, okay, so yes, you know, my credit card was on this database that was hacked or sure I'm using the same password everywhere and it's probably been compromised, or sure all of these things that I thought were private were set to private and not actually private. But then they go, well, you know, what I'm part of, the system, I can't fight it. They don't have a pathway. There seems to be a sort of apathy. Yeah, perhaps there is not that very salient example that would kind of get people to wake up and realize that they were losing some privacy there.  I mean, and try to just make it really get everyone's attention and say, like, yeah, you know, you want to talk about privacy, this is what the government can see about you. But I don't know that we have a lot of salient examples that would really get people to perk up and start thinking about what they're doing. And I think the reality is, you know, the value in the power really is in the aggregation of this data. You know, you think about something like 23andMe. Sure, I'm interested to see my genome sequenced and compared to other people and so on, but the scientific value of having, you know, tens of thousands of those genomes is immense and obviously could lead to all kinds of breakthroughs. So, you know, in some cases it might be worth it. I think there's an interesting, there's a case recently with respect to genomic data and privacy. I think people very often think, oh, like that ship has sailed or in order to get this information, I need to give this thing, right? There was a study done of DNA in Iceland, right? It was a very small population, which they had enough data from people who volunteered blood samples to then reverse engineer the profiles of everyone else in the nation, right? And this idea that there is the possibility of you being opted in simply by the volume of information, right? Like there is no opt-out, right, in that situation. And I think those are moments where it's very difficult and people can feel quite thwarted, right? But I think there's also to go back to this idea of statistical insight that came up earlier. I think people have a hard time feeling like they have full knowledge and authority in order to understand what's happening and so then feel thwarted in their attempts to resist. I mean, I think this issue of being watched and being, you know, data-fied is also an interesting one. So, you know, certainly we can invoke the Facebook emotional contagion study, which again did have, you know, a bit of backlash, which eventually kind of, you know, petered out. But, I mean, this is happening in a lot of different areas of online communication. So Facebook is doing it, they're reading your messages, but the New York Times is doing it, right? So, you know, if you write a comment on the New York Times, they're going to start putting your comment through an auto-verifier that decides whether or not you're going to be verified and whether or not your comment is going to be posted immediately to the site. And the way those kinds of algorithms and the way that your comments are data-fied and that decision is made is consequential to the types of conversations that will emerge. There are the kinds of discourse that are privileged, you know, are you privileging things that are written in more sophisticated language, for instance, or more readable, these can obviously have, you know, they can bias the direction of things. I'm also reminded of another example of this type of technology, sentiment analysis, where algorithms will, you know, look at your tweets and try to figure out if you're talking about this product in a positive or negative way. And what research has shown is that these methods for detecting sentiment in short messages are severely skewed toward male versions of sort of highly salient, maybe perhaps more aggressive types of expressions of sentiment. And so, you know, if you're looking to do product research on Twitter, for instance, you would want to be aware of these types of biases and what you're leaving out or what you're actually able to quantify and operationalize there. We're starting to touch now on a question that I actually want to say for the next session, which is essentially, all right, if all of this data is being collected and used and if there is nothing you can do about it, what can you at least do about the ways in which that data might be used, in which different people might be affected disproportionately? In other words, the disparate impact theory, the idea that some people, just by the nature of the data that's collected on them, are going to be more disadvantaged than others. So I don't really want to get into that right now unless you have some particular thoughts on it. I mean, the one thing that I would say is that there are ways that people can do something about it and I'll just give a quick shout out to Take Back the Tech, just put out a DIY guide to cybersecurity that is money and it's meant for sort of the average user. So there are certainly people, especially people in feminist, trans and queer spaces who are working very hard on some of these issues of how do you rein in the sort of data bleed. But I think also, you know, the question of bias or disproportionate impact, intersectionality with respect to data, I actually think it's a super important problem that hasn't been grappled with sufficiently. But I won't say more unless you...  Can you talk a bit about that? Sure. So that came out of a meeting at the Berkman Center with an amazing group of people who were there and that particular manifesto was a collaboration between myself and Jamia Wilson and Tanya Peterson. And the idea there is that data models, data as an abstraction is a way of modeling the world or behaviors or interactions. And one of the things that I feel like we see in much of the popular deployment of algorithm and algorithmic culture is an assumption of a kind of universal white, upper middle-class, male, cis, able-bodied version of things. This is certainly true, I think, when you're talking about the Fitbit spaces, right, where you're putting your data up against an aggregate and that aggregate is fundamentally limited in its scope. And so one of the things that data analysis can do very well and we see this in computational biology is deal with very complex systems. And intersectionality is perhaps the most complicated of the systems. The idea that we come from a series of overlapping different points in which power is sort of inflected over and around and through different people in different ways. And I think it would be really interesting to think about what it would mean to demand intersectional data, right, to say, yes, it's complex, we get that. I was just at an event where someone from the Internet Archive said, oh, that's too complex, we can't do it. And I thought, that's hilarious. Of all the things in all the spaces, right? So I think there might be a way of drawing on the power of complex computational systems to get at some of those things that, as you were saying earlier, drawing has a hard time with, right? Dealing with, you know, an intersectional subject position and the many of those that exist in any given room is intensely complicated, right? But we actually have tools that can handle that kind of complexity. And it would be nice to see our data models go towards that richness rather than pull away from it. And I would just sort of add to that. I think this sort of brings in maybe another dimension of algorithms and data that are sort of in different groups and also ties in a little bit to bias is, you know, we talk about algorithms as recipes and I certainly think that's an apt sort of characterization of most algorithms, but there's also this whole class of algorithms, of machine learning algorithms, which are really more like recipes for learning other recipes, right? So, you know, if you imagine writing down a recipe that described how to learn how to make a pizza, for instance, so you're not, you know, just following the steps for making a pizza, you're following steps for learning how to make a pizza. So that might involve, you know, watching the chef need the dough, watching the chef toss the dough, you know, looking at the ingredients that go into the sauce and what types of cheese and so on. And, you know, if you set that recipe for learning recipes loose in Chicago, you would learn one type of recipe for a pizza, but that would be a different type of recipe than if you set a loose in Connecticut, right? So, basically, the data that we're feeding into these machine learning algorithms can lead to very different types of recipes that are learned and that brings up the notion of needing to be aware of the types of biases that are trained into these systems and realizing, again, the sort of intersections of those. I want to come back to what you said at the beginning, Jacqueline, you know, talking about quantification as an outgrowth of journaling and of that kind of, you know, Samuel Peeps and so forth. Do we get to know ourselves better by quantifying ourselves or are we losing certain kinds of knowledge or, indeed, are there categories that are being created too narrow or too simplistic? That's a really complicated question. I mean, I think, you know, the rhetoric is certainly that you know yourself better, in part because the idea is that by measuring yourself against others, you have a better sense, almost like a dispassionate, objective sense of who you are and how you operate. As a historian of science, you know, I have spent a lot of time thinking about how that myth of dispassionate, objective knowledge was produced, right, partly by the sort of rise and mercantilism in the imperial state in Britain in the early part of the 17th century. So I'm very reticent to say that we know ourselves better in the course of quantification. I think there are certainly things that cannot be quantified, right, so the things that computation can do are not total, right, and I think there is a real danger in our sort of long fascination with total knowledge and how quantification feeds into that. You know, things like thinking about sentiment or affect or trust, safety, you know, a full and rich good life, I think those things are awfully hard to break down into numbers, but I think there's an interesting interplay between words and numbers that I don't think we can get rid of one or the other. I don't think one stands out. I think it's an ongoing cultural interplay. Right, there's also another side to it which I'm thinking of which is dating, so anyone who's used a dating app or a dating website has done, at the very least, a limited amount of self-quantification because they've probably put down their height and their weight, and so that's already parameterizing our choices to an extent. I can imagine a dating app in the future that, you know, I don't use four-square check-ins, but let's say I did use four-square check-ins and a dating app collected all my check-ins and then kind of did some kind of comparison with someone else to see essentially how compatible we are in the places we travel, and maybe it helps me make choices or helps them make choices of who to talk to based on things like that and then maybe other things that we've quantified about ourselves to the point where the serendipitous nature and the qualitative nature of identifying who might be a good mate starts to be stripped out. Would that be bad is my question, I guess, because it would certainly be different, but would it be bad? E harmony would have you believe it's a good thing, right? I mean, they facilitate, I don't know, I don't know what their numbers are, thousands at least of marriages every year, but what's the quality of those marriages? I think they would say they're better than the sort of the, in-the-wild types of meetings that you would have. That might be right.  There was this great New Yorker cartoon last year of this woman, you know, woman and a guy getting a coffee at a coffee shop and she's kind of looking back at him annoyedly saying, how could an algorithm think that we'd be a good match, right? And I think it's really an astute observation that, you know, we quantify because it sort of facilitates these kinds of interactions, but there's sort of no guarantee of accuracy or that it will be less error prone. Well, I want to maybe suggest that, you know, algorithms are on average quite good at predicting who would be a good match, so we'll have less of those really crazy, intense relationships that drive us nuts but are kind of life-changing, but maybe fewer conflicts and marital murders, I don't know. I don't know. I mean, if we go back to the Marionette metaphor, I don't, you know, if the algorithm is just a kind of a puppet playing things, I think, you know, I definitely don't ascribe to these things are bad, you know, and I think there's an interesting thing about the tyranny part of our title, right? Insofar as I think quantifying is about state power and state control, I think it's apt, but I think people are also afraid of it, perhaps unnecessarily, and I don't want to say that I think that the quantification, especially with matchmaking, is bad because it seems to turn out well for people, but I think anytime we tip towards a single way of knowing, especially a single way of knowing that depends on models of abstraction that thus far have largely been controlled by a relatively narrow population, I think that makes things incredibly difficult, right? So if you, you know, if we have a 100,000 algorithms written by 100,000 people for 100,000 different things, that seems that kind of diversity, even in the algorithmic programming to me, seems like it would be a benefit rather than a kind of homogenizing control and command figure. Right, and that's actually something that we haven't yet mentioned in this discussion, but it is also in the background, which as a friend of mine from Silicon Valley likes to say, and he belongs to the set that he's talking about, there's a few dozen young white guys who are basically writing the rules for the entire world now, and that is introducing all sorts of biases. We have a few minutes maybe for questions. Does anybody want to ask something? Up here, and then who else? We have a microphone, thanks. They're live streaming, that's why I think they like you. One type of quantified self that actually didn't get discussed at all is the medical capacity side of it. Like I actually feel like I keep hearing about these new kind of startups that are trying to make these apps that do things like if you're diabetic, they like have a little thing that'll just track your blood sugar through your skin or something. I might have made that one up, but like stuff like that where it's very, it just, it's actual medical and it's a clear medical benefit, and I guess to me, I kind of always thought that was the longer term direction of quantified self was things that were actually just like, you know, you need to take your insulin now, or like, you know, like you're really like, your heart needs some more exercise, like you're going to be screwed, you know? And I don't know, I guess I'm just kind of curious if you guys, what you see as the evolution there, what you think about that kind of the more medically space. I mean, I think it is clear that a lot of R&D and a lot of innovation around quantified self and wearable technologies in particular, right? So wearables start in some way with the military and, you know, and hospitals. And there's a, I think there is some real benefit to that. I think it falls in the same, for me, the same line of the history of science that has from the very beginning drawn on a one-sex model, right? So the male model is the standard, and we see this in clinical trials, we see this in, you know, the way that things are adjudicated as norms, right, with respect to health. And I think, you know, the idea that these are all sort of neutral goods, I think has to be troubled a little bit by that long history, right? So there's an example right now of a device that was in Kickstarter called the Loon Cup, L-O-O-N, which is a menstrual cup that's Bluetooth enabled. And it's a perfect example, and many of the women in the audience are shaking their head like seriously, WTF. I think among the things that it's such a great encapsulation of is issues around privacy, right? So the device has to stick out of the body in order for the Bluetooth to work. It can't go through airport security because you'll be asked to remove it because they'll think it's a bomb. And the things that it measures. So it seems like a perfectly benign idea until you start to really think about it, right? So it's like, oh, well, people have irregular menstrual periods. They want to track it. People have been doing this for a very long time using pen and paper. We're just making it easier, right? But the things that it tracks, so it has an RBG sensor in it, there's not any clear diagnostic value to knowing the RBG value of your menstrual blood. The Bluetooth-enabled feature sends texts to a phone about when you need to empty your menstrual cup and I think for most women in the room that is not something that we need a phone to tell us about. But it also could potentially be used to tell your partner when you're available for sex, right? And that is a, like, unintended consequences of things I think is really dangerous, especially when we're talking about automated technologies that are positioned as things to make our lives easier but might also compromise us in certain ways. So I think there's a lot of opportunity in the bio-design space and the healthcare space. I also think it's super tricky, right? Like, the first thing we did was go out and buy one of these loon cups. It'll be coming in January so that we can figure out how you can hack it, right? And, like, what could be done with a hacked loon cup? I'll let you know. I'm looking for it here. Can you talk for a second? Mm-hmm. Yes? Jacqueline mentioned the, you know, centuries-old tradition of recording information through journaling or such. And it seems that was, you know, qualified self, I guess. And whereas now what we're dealing with is how much and when and where and maybe with whom, but there's nothing about a why. And that old way was a why. And I'm wondering if that's something that we're just in this tech area where we're all thinking about numbers. Are we gonna have something that helps us with those whys? That's a algorithm or that's a a qualified self or some other tools? So that's an interesting way of framing it. You know, when I think about something like the spiritual exercises that we have in the Jesuit tradition and stuff, that actually was not very narrative despite being written in prose, right? The process was about counting the number of times that you did something in walking through the stations of the cross and marking where you were when. And Samuel Peeps also interestingly, and this is something I'm totally fascinated by, many of the early essayists and life writers write things down as financial records first and then recopy it adding detail, recopy it adding detail, recopy it adding detail. So Peeps, for example, writes everything in a military log where it's just transactional and then literally transcribes it three different times into three different forms and what we have as his diary is the final narrative version of that. So I think there's always been this interplay between the quantitative and the qualitative or between the numerical and the prose or word based. You know, and the people who are in the QS movement and you might be able to speak to this a little bit, now real devotees think that, like they have the why, they build in the why. I think it's more in the sort of popular culture deployment that we see less of that why, but I think it's always there. I think explanation is actually a fascinating area to go to move toward in this domain because people do always want to know why, why, why. And I think the more that we can sort of open up these systems, explain connections between your data and other data, do so in sort of human readable terms, I think that's going to help certainly the user experience for these kinds of systems and hopefully make people feel more at ease with how their data is being used. Thank you. Great, well thank you both very much, Jacqueline and Nick.


There we go, there we go, bringing you along this journey toward my first marathon and yes it starts now, six months from now, hopefully I will be running a marathon and it starts in the gym for me with light stretching, core work, a little bit of lifting, all that stretching machine is just kicking my butt. But I already made a little progress from two days ago and then the pool. I'm really excited and actually if you have any tips for the pool let me know but I'm really excited to take better care of my body as training increases, as mileage increases, the volume of running mixed with speed work, I know I'm gonna have to do better in 2019 taking care of as the, I think everyone in Europe calls it the niggles, those little aches and pains as we say here in America, like those little niggles they can really come back to bite you if you don't take care of yourself. So anyway the pool is gonna be key for that in 2019, I'm excited to just stay as nimble as possible. Okay, 2019 here we go, the training schedule, first of all I said this two days ago. First tip for planning out your racing schedule and you probably, many of you probably already do this naturally, it's pretty basic but it's a good reminder, know your why, know your why. Why are you racing this particular race? Maybe it's to challenge yourself to go from a 10k to a half marathon. Maybe it's to inspire yourself to lose a little weight, like to get a little more fit, a little more lean. Maybe it's to raise money for a charity. Maybe it's to complete your first marathon right here, right here, that's me. Maybe it's to see a new place of the world that has a beautiful marathon distance race or an ultra race and you're gonna travel around the world to this location to experience a new culture. So tip number one, know your why and it's really important to have a really solid why. Point number one, tip number one, before you plan your 2019 training schedule, you cannot plan a training schedule if you do not know your peak races, alright. I am starting to hone in, actually I'll just call it right now. If I register and I'm accepted but I think I will be. The Pike's Peak Ascent is August 25th, that'll be my second peak race. It's here in Colorado, it's a half marathon up a 14,000 foot mountain, I'm so excited for it and then my first marathon probably will be in June, I'm honing in on the Grandma's Marathon in Duluth, Minnesota, we'll see, we'll see again, I have to contact the race director January 1st to see if I can get into the elite field and I'll talk about why I'm leaning toward that marathon over others and if I don't get into that marathon then I will definitely be doing a marathon in the fall, either the CIM out in Sacramento or potentially an East Coast marathon waiting to, I'm working on that with someone that I know. So anyway, got to know your peak races before we can dive into this and on that note, let's go out to the shed and get down to the details. Alright we're out in the shed, there's so much to cover on this topic of building a training plan for 2019 and this is not a vlog or a video about specific workouts, this is about the macro level 30,000 foot view looking down upon my 2019 calendar year with flexibility built in. Make that point clear, this is why you saw me inside there researching the marathons, researching the half marathons, by the way thank you for all the comments a couple days ago about the Grandma's Marathon which I'm interested in and then just sharing about half marathons across the United States that are coming up, I haven't registered yet, I'm working on that but peak races are like you have to have those peak races in order to work backward in your training schedule, so that is point number two, once you have chosen a peak race which we love peak races as runners because like that's where we can strive for PRs, strive for maybe podium finishes or strive, who knows maybe even to win prize money or even just simply like to say to yourself I want to improve my overall health and fitness by five to ten percent in the new year, like anyway peak races are a beautiful thing but once you have chosen the peak race you have the date on the calendar and then what I like to do is work backward from that date literally on a physical calendar and I know this is old school but this is what I do, I work backward, I count how many weeks do I have until the peak race is going to happen, so then I know in my mind okay I have for example for Grandma's it's 25 weeks from now which is a little under six months that's a lot of time that's good and then from Grandma's to the next peak race for me in 2019 it's the Pikes Peak Ascent which is nine weeks, also pretty good, not it not perfect but pretty good, so once you have your peak races chosen you can then build begin to build your training blocks and this is point number three, we can't stay at the top of the mountain forever meaning at our peak level of fitness like our body will break down and you'll get injured or you'll just burn out and so we need to have valleys and peaks in our training, valleys and peaks, connected to peak races connected to the calendar based upon how many weeks you have to work with will impact your training block, this is what I like to do, three months of aerobic development one month of anaerobic development, so four months leading up to a peak race and this is if you do the math 12 divided by four is three and that's why I love to shoot for two to three peak races in a calendar year, now we can't get into the science behind aerobic versus anaerobic but I will just say I lean toward more aerobic training versus anaerobic training and I wish I could get into the details of that like pH level in our blood and how that impacts our overall body health anyway we're not going to get into that right now but our aerobic development as especially as half marathon and marathon runners like it's so so critical and what is what is aerobic development basically our body's ability to deliver oxygen to all of our all of our muscles and as through our bloodstream so anyway we won't get into the details of that but that is what I like to do three months of aerobic development so long running long slow running sometimes like it's not too exciting just getting in the miles and I will just give you one more detail when I start my training on January 1st I will run 15 to 20 miles that first week which is you know not too much and then I will add 10 miles a week 10 miles a week 10 miles a week until I feel as though my body is reaching that limit of aerobic development where I'm like okay I'm getting a little tired here I don't want to go too far but I also don't want to you know I don't want to hold back either there's over training in running and there's under training like you all have probably experienced over training so a lot of you have already shared that with me down in the comments but what about under training huh as we don't often hear about under training so we got to walk that fine line at times and so for training block I like to lean toward four months is like the ideal amount of time for me so that is my overarching training block philosophy and then for this year leading up to grandma's I'm gonna have a mini peak in March come down a little bit and then another big peak leading up to grandma's in June and then I'll come down again and then go back up for August come down again way down and then maybe maybe a big peak in late 2019 for another marathon we'll see about that but that is my overarching kind of wave of 2019 training blocks and then drilling down just a little bit and this is a little side tip that I'm just uh and this is based a little bit upon your your flexibility with your life and I realize everybody has to work many of you have families like it's just a busy world out there but I would say don't be afraid to break out of the weekly training cycle a lot of people do a long run every week which is awesome but if your body is starting to bark at you and say hey I'm getting a little tired in this training block I need to back off a little bit don't be afraid to say okay I'm gonna do a long run on Sunday and then I'm gonna do a long run in 10 days from now rather than seven days from now but of course it is impacted a little bit by your work schedule by your life schedule but if you could squeeze it in maybe at night especially in the summertime or maybe early in the morning but I'm just always a little uh shocked at how everyone has to get in their long run every single weekend which is good if you are feeling fresh and healthy but don't I guess like like the seven like who's who made up the rules that we have to stick to a seven day training cycle don't be afraid anyway that's like a little tip that I lean toward especially in the summertime when the days are longer I'm breaking out a little bit of that seven day training cycle and bumping it up usually to like 10 days so like if it's if there's 30 days in a month that means you'll have three training cycles or three three weeks but it's really an extended week of training in in one month does that make sense and just a couple more points before we wrap up back to the calendar I like this paper calendar because it allows me to see the entire year right in front of me and I know you can do this electronically uh but I prefer to write it out with a pencil make sure you have a pencil so you can go back in a race and rather than set in stone I'm gonna run 50 miles this week no matter what I like to give a range so usually I do a 10 mile range so like you'll you just see here like here's 70 to 80 miles 80 to 90 miles 90 to 100 miles we'll see we'll see but like it's it's better to instead of feeling pressure like oh my gosh no matter what I have to hit you know 60 miles this week give yourself a range and maybe it's a 15 to 20 mile range I wouldn't recommend anything over that because then you can get maybe a little too uh relaxed but definitely don't hesitate to have a range for your mileage and then you can just go week by week month by month building your training schedule and then once you have your goal mileage laid out oh then you start plugging in the workouts and that's when it gets fun and exciting and I'm really excited to share with you like specific workouts once 2019 uh starts rolling I will share with you like specific workouts whether it's mountain running um you know fart lick running whether it's strength training running I have ideas for that for you guys um or whether it's just simply getting on the track and doing you know speed work on the track like that's important too so anyway that is how I like to do it and then I also track my running of course through Strava and by the way let's connect down below if we haven't connected already on Strava and then as backup yes I do it on a spreadsheet as well so an electronic spreadsheet just to track things but I first start here on paper so I have a general vision of what I'm looking at for 2019 I know I just covered a lot I hope that made a little bit of sense please please ask your questions down in the comments and maybe better yet if you have really specific questions email me because I'm not saying I can answer every single comment in detail but if you find my email here on YouTube on the desktop version I will respond to you it might take me two or three days but I will respond to you and um but I hope that made sense all right I love you guys keyword of the day blocks for training blocks that's right blocks like a square cube blocks and the question of the day what do your training blocks look like how do you build them what do they look like maybe even specifically in early 2019 like share as much as you and maybe you don't have training blocks and that's okay but maybe you're trying to build one maybe now because of this video you're interested in building training blocks into your annual training schedule so I'm so excited for this topic again thank you for bearing with me that was a lot to cover and I wish I could have talked all night but I gotta let you guys uh eat your breakfast or go to bed one of the two seek beauty work hard and love each other


Hello everyone, welcome back to another mid-journey tutorial going to show you how to get the video of your image transforming just like this right out of mid-journey. Let's go step by step. Okay, let's get into mid-journey. The first step is you need to make sure you're in version 3 as of time of this recording December 13, 2022. Version 3 is the only one that works with this. The next step, type in Imagine like you normally do and I'll type in Alice in Wonderland comma photo realistic. Why not? And then here it is right here. Dash dash video. When you do this, it's going to do 100% and then I'll come back when this is done and I'll show you the last step. Alright, welcome back. You'll see here that it's rendered out the four portraits as usual. The next step is you need to go over here to this add reaction, select the envelope emoticon and then hit enter and presto. It will send you the four images and here's the one we wanted. Click on that and watch Alice in Wonderland being made from scratch. That's all there's to it guys. Thanks for watching.


And so just by way of introduction, my name is Matt White. I work at Money Desktop. I just started there this week. So far, it's pretty awesome. They do mostly Rails. They do some Sinatra, other web stuff. I've been doing web programming for about five years now, three plus of that in Rails. And I'm what I would call a TDD convert, meaning, thank you. Meaning, I started out not ever writing any tests because no one made me. We thought that was for QA, right? That's what tests are for, right, they're for QA. So that was kind of how I grew up in the Rails community. Fortunately, I moved over to Mosey where I met Mike Moore and some other people that are awesome. And they helped me see a better way. So as I give this talk, I'm going to assume that you at least know what test-driven development is. If you don't, here's a diagram that kind of gives you a summary. Otherwise, I would suggest going to Wikipedia. They got a great article on it. But sometimes other people have different ideas of what TDD is. But I think the one on Wikipedia is pretty good. Pretty good summary.  No, I mean like all the time, yeah.   You just either don't know it or you can't admit it. It's one of those two things. So, as we go along, you'll probably, you'll be able to understand my point. So, I'm talking about TDD, but what is CDD? That's the other part of my talk here, the CDD thing that you may not have heard of.  And I disagree with most of what you find there. So don't waste your time with that. If you want a good definition, I would suggest going to the Scotland on Rails talk that was given by Jim Weirich two years ago. And he has a much better way of doing it.   And so, that kind of seems maybe a little bit silly or simplistic, but that's the idea is to make it easy. This is for, like I said, I was a convert. This is kind of one way that I learned to do it the right way. And so, if you're one of those people that doesn't test all the time, or has no tests at all, then this is a great way to get into it. So, why would you use it? That's a good question. Why would I use CDD? There's a number of reasons, and these are just some of the top ones. One, it defines your app. During the demo, you'll see that CDD and TDD are totally inseparable. CDD is useless if you're not testing. And anyone who's ever written a decent amount of tests know that a well-written test suite will define your app. An easy way to evaluate a Rails project, or Sinatra, or anything else that has tests is to go look at them, and if there aren't any, well, then you got a problem. But usually the test of their well-written will define the app pretty well. So, that's a great way to make sure your app is properly defined. Another thing it does is it adds test coverage as I mentioned, is you comment it out and add your tests. You'll automatically have your coverage. It encourages simple design. TDD by default does that. If you're testing, then you gotta write your methods so that they can be tested.  It gives you confidence. You know that if you add something or take something away from your code that your app isn't gonna blow up. You got something that's verifying that it actually works so you're not always in fear that, oh shoot, is this, we're moving this line gonna break everything, or we're moving some gem or whatever. I find that as I do this CDD, I'm entering a state of flow much easier, and I'm enjoying what I'm doing. It's like you're fighting against this thing that's telling you you're failing, and then once you beat it, then it's kind of fun and cool to see that you've been able to write better software.  And one of the big reasons for that is that you're not wasting your time debugging things, and you actually know what your objective is. You'll have these tests written that have defined your app and you know where you're going and you know when you're done. Instead of working on something that's kind of beating around the bush and not actually getting to where you need to be. And I think that this technique should be a lot more common. It's something that I think is easily overlooked and just kind of an afterthought, testing in general kind of is.  Okay, it looks like everyone's raising their hand.    So now you know, now you know how it works. Now you know how it works. But the point of this is that, so here's a very scientific graph of how much you all suck. And you can see the legend up there with TDD, CDD. The line is much lower over time. You see the gap also widens, meaning that people who are using it are not already ahead of the game to begin with, but over time they're actually even further ahead because they have been able to learn more and get better at writing apps. And so you're somewhere along this continuum right now.   But the fact of the matter is that nobody writes perfect apps and as you can see, I made that point at the very end there by leveling the line out at a really low level of suck. So when do you use it? I think I kind of explained that, but just to be clear, a great time to use it is if your test coverage is weak or you have non-reusable or non-dry code. Anyone not familiar with the term dry is don't repeat yourself. So any code that has been repeated throughout the app and anytime you want to do any kind of general refactoring, it's a great technique to use. Another great time to use it, hopefully the sound is working okay, is if your boss ever reacts like this. Oh, I don't think I'm getting sound like, you know. Yeah, I've got it plugged in. Anyone have a good level reader? Sounds like it's working. I don't know, I might have to skip that one, but. Oh, I just closed it now. Yeah, I don't think it's working.  So if your boss ever tells you that or you're in a code review and you hear that, then yeah, that would be a great time to start using testing and comment driven development. So as I'm doing it, there are some tools that I find really useful. One is Autotest, we've kind of already seen that used in the demonstrations that have been going on. For some reason it looks a little bit different in Linux. It didn't always used to. I don't know what happened. It seemed to happen when I changed to 192, although when I searched for Ryan Davis comments on that, he insists it's not. So I'm not sure what the deal is. I don't want to make him grumpy though, so I'm not going to go accuse him of not updating it correctly. So Autotest is great, so you don't have to go constantly run back and forth between your tests. It'll just run them for you every time something changes. And the other one is FLOG. FLOG is really awesome. If you've never seen FLOG, this is by the Seattle RB guys. Yes, this is awesome. So basically the way FLOG works, so if this is your code here, that little square here, I don't know if you guys can see that, but basically FLOG sees it as a score and it follows this ABC Assignments Branches and Calls and it assigns a score to your code based on how many assignments, branches and calls there are. And so it's like golf.  That's why FLOG clearly still backwards is golf. So basically a high FLOG score is really bad. Oh, I forgot to sign out of. I almost got trolled by my colleagues here. There we go. Okay, so here's my crappy code that I'm going to demo now. And this is kind of a small demo. A lot of times you'll have methods that are a lot longer than this that could use some refactoring. This one, in my opinion, is too long. I usually try to aim for five lines or less. And my methods helps for better design. So this one's too long if you ask me and it needs some help, it needs some love. It's not the prettiest thing in the world. So I'm going to start out by turning on Autotest. I might already have it on, but it looks like I do, but I'm going to restart it here. So you can just run Autotest. If you don't want Autotest by default, when you first run it or turn it on, we'll run your whole test suite. And if you have a big test suite, that can be kind of annoying if you just want to run it quick. So you can run it with the dash F if you want it to not run your whole test suite. And so it just kind of sits there in the background while you're coding. And there you can see at the top it ran my test. There's one test right now, so kind of weak. But if I go and flog this code, so I just say flog. And in this case, I'm just interested in the models because that's what I'm writing tests for. So sorry if you can't see that. Is that better? A little bit better. So I just run flog dash A. A says do it against everything and not just the worst ones and show the score for everything that is. And there you can see the total is 20.5. The average per method is 6.8.  Now, according to the Jake Scruggs, what a good flog score is. Sorry if you can't see that. Zero to 10 is awesome. 11 to 20 is good enough. 21 to 40 might need refactoring. It gets worse and worse than that. 200 plus, someone please think of the children. So, and at previous jobs I've had, I've actually flogged some of the code and it has been well over 200. So I wouldn't be surprised if some of us here could flog our code and find really high scores. And there are only few cases in my opinion where your score should be over 20. They should be rare exceptions and you should be aware of those. 11 to 20, like it says, I think is good enough. In this case, I don't think it is because that's the only method in the whole class. So it can be better. Zero to 10 is what I'd like to go for but occasionally 11 to 20 would be okay. So as you can see here, I got 18.3. I'd like to get that lower. And so what I'm gonna do is I'm gonna comment this whole thing out and when I do that you would think because I have a test, right? It should fail, right? Okay, so I'm gonna save it and then all the test is gonna run its magic in the background, but it passed. Uh-oh, something's wrong.  Okay, there we go. So all it's doing is assert true. Not very useful. So let's get rid of that. You don't need that garbage. And so what we need to do here is look at this method and see what we wanna test. Now we don't need to test find. We're gonna trust that Rails can find properly, so that's okay. But we do need to test these little validations that are going on here. They're raising under certain conditions. So I'm gonna say, well, here we go. Raise transfer amount must be greater than zero if amount is less than zero. So if amount being transferred from account is less than zero, looks like one of the rules here of this app is that you can't transfer between accounts if the amount is less than zero. So we need to write a test that goes and verifies that. So I've cheated a little bit here so I don't have to waste your time writing all that. So I'm gonna do a little setup here, a little setup method and one easy test of that. And so here you see test transfer with amount less than zero should fail with exception. Now, I know a lot of people like to use RSpec. I'm using test unit just to keep this simple. It's already built in. You don't have to do anything extra if you're not doing testing. It's already there if you're doing Rails. And so here's this test. It's expecting an exception to be raised of runtime error and it's running this method transfer amount to with a negative value to Joe's other account here. And so we're expecting this exception to be raised and then the message on that exception should be transfer amount must be greater than zero. And if we go back and look at that, we can see that that's what we're expecting. So if I go and uncomment that and say that we saw the test failed as it was run there, now that I've uncommented that it works, magic. Okay, so now we've added test coverage to that part. So let's keep going. We see this other one here. Raise unauthorized transfer if self user ID is not equal to to account user ID. Okay, so this app is also stating that we can't transfer money to other users. So this is a very simple app that only transfers money between a user with multiple accounts. So from his first account to his second account. So I'm gonna go back and grab another test here that I previously wrote that goes and checks for that. Okay, so transfer to another user's account fails with exception. And so it's pretty much the same as the previous test. We're trying to transfer from Joe account one to John account one. So two different users. So now let me just make sure. So that's still commented there. I say this auto test should fail as I'm going back here to uncomment. So there's the failure and comment. And I save, it runs again and there we go. So now we've got test coverage on that part at least. So as we go through this, we can see that there's some things that can be moved around. You can see that this section down here, there's kind of some repetition. It's not very declarative. They're pretty much doing the same thing, just one with a positive, one with a negative. Those could easily be renamed to withdraw or deposit and it would be more declarative, right? And this one here, as we go to test that one, we would realize this is really just a validation because it's saying that if we try to transfer out more than the account actually has, it should fail. And in Rails, that's just a simple validation to this is gonna have, I'm gonna go and jump ahead here and copy all of this so we can see how much prettier it can be. Okay, so there's all my tests in place. They should all fail pretty miserably. At least one of them did. And then after some refactoring and looking at things, this is how it ended up looking using this CDD, TDD approach. Okay, so we save that, auto test will go run again and everything's happy, five test, nine assertions. Okay, so now we can see that validation up there. We can see that we renamed these, we pulled those transfers out and named them withdrawal and deposit so that made it more modular. Another thing we noticed as we were writing tests here, if we transfer and one succeeds and one fails, well we need to make sure that they both get back to the previous state, otherwise some will end up with more or less money than they deserve. So we wrapped that in a transaction here and made sure that that would roll back if one of them failed. So, and that kind of saved us there. Another thing we noticed as we were doing that is this method here, self.user.verify accounts integrity. Wanted to make sure that that was getting tested properly and so we tried to go figure out what it did as it was commented out before, right? Go and look and it's just kind of stubbed out. Figure out at some point what this does and if we need it. Kind of useless, huh? And that's the kind of thing you might notice sometimes when you're doing CDD is that you have code that either does nothing or doesn't do what you expect and so you can take it out and put it other places. So we took it out and we were left with this. So this looks a lot better. This is a lot more, it's tested. More testable, a lot cleaner, more modular. And as we were doing it, we were able to enter more state of flow and enjoyment as we were doing it. It was easier to understand what's going on because now we can just go look at the tests and see what's going on. Oh, transfer to account succeeds. So when I transfer using this method, I expect it to succeed. Okay, accounts have correct amount after failed transaction. Oh, okay, so we don't have any invalid data. We can then comment this one now. Yeah, things are a lot happier now. Things look a lot better. So, oh, I mean you need to reflog it. Yeah, that's what I was just gonna do. There we go. Flog total 21.6. So the total is about the same and that's expected but the average per method now is about half of what it was before and that big method that had 18.3 is now at 6.6. So everything's less than 10 now, much better. So the complexity of the code is much better now. So I encourage you to use this flog and there's other stuff. If you ever use metric foo, flog is part of it and there's a lot of other cool gems that are included in metric foo that you can use and get some good reports on the state of your app and whether or not it sucks or not or how bad it sucks, I should say, because it sucks, so. Okay, so you probably have all heard this quote before, perfection is achieved not when there is nothing more to add but when there is nothing left to take away and that's kind of one of the aims of CDD. Try to take out as much as we can and have it still work at a minimum level and that way our app is much more streamlined and a lot easier to work with and so that's pretty much what I have. Does anybody have any questions? It's actually the same, the growl notifier I'm using is the same as what we were seeing earlier. For some reason in Linux it shows up funny. It didn't use to, like I was saying earlier, I don't know what happened to it but it still works at least, so. Yeah, I'll look into that later. I don't know if that works because growl doesn't actually run on Linux, so. Oh, well this one's through lib.notify, on Ubuntu or any Linux flavor, so. Yeah, it used to look just like growl but I don't know what happened. Is there a chance for that? Yeah. What's it called? I don't remember offhand. If you Google auto test and there's a blog post about getting started with auto test and it's pretty long and detailed and it goes into it really well, it's on there somewhere. Thank you. No problem. Anything else? Okay. That's it.


That's why I came up with a plant paradox friendly version of everyone's favorite indulgent breakfast blueberry pancakes.  For one it's not made of wheat flour and it's not slathered in maple syrup. It's kind of a cross between a pancake and a cake and it's perfect for this time of year when blueberries are actually in season. But you can make it all year round just leave out the blueberries no matter what it's still delicious. It's got loads of flavor on its own thanks to the lemon the vanilla and the coconut and since you make it in a blender you're only going to get two dishes dirty making breakfast the blender and a pie tin. So then you're going to lightly spray a pie pin with some olive oil spray or some coconut oil spray and be ready to use that. Okay so now we're going to take all of our ingredients and we're going to put them in a blender. So we're going to take some coconut oil that we've already melted and we're going to take some vanilla extract please use the real stuff we're going to throw in some salt we're going to use aluminum-free baking powder we're going to use baking soda and about six drops of stevia I love the sweet leaf brand and a couple of pastured or omega three eggs then we're going to take some coconut yogurt plain coconut yogurt if you can't find it please feel free to substitute goat yogurt or sheep yogurt but please don't use American yogurt or Greek yogurt it's got casein A1 then we're going to add our dry ingredients we're going to take some coconut flour and some almond flour and some tapioca now I actually prefer cassava which is more coarse than tapioca but they're both from the same root and we're going to put all that in our blender okay so we'll put that in and we're just going to pulse it for a little bit do that for about 30 seconds and then take spatula scrape it down okay now the other thing once you've got that blend you're going to grate a lemon and just get a grater get kind of the coarse grater you don't have to be perfect about this please keep your fingers out of the way I don't want to have to see you in the emergency room so we're going to take that zest and then we're going to cut the lemon in half use a juicer because a lot of our lemons particularly here in California if you're coming out of the yard or full of seeds and you don't really want seeded blueberry pancakes okay so we're going to throw that and the zest back in and if you want to do this as part of the first step that's fine with me and in goes the zest don't be afraid of the zest believe it or not there's an amazing component in lemon peel called lemonine that I've talked about before it actually contains a really cool compound so it's great for you every day okay back we go turn that around great and we're ready to go and that's really all you need this is going to be fairly thin looking and that's going right into our pie pan and then what I like to do is sprinkle the blueberries on top and we've got some blueberries fresh from the farmers market they're actually in season now and we're just going to put not too many but a good handful of blueberries remember fruit is a treat you have to treat it like candy okay we're ready to throw this in the oven and away we go so you want to bake your pancake until it's golden brown around the edges and firm in the center takes about 25 minutes you know it's really the perfect amount of time to set the table wash the dishes and feed the dog so when the pancake is done you want to remove it from the oven and let it cool to room temperature before serving it'll feed two to three people easily you know it's really that easy blend bake and serve so the next time you want to feed someone you love a special breakfast feed them something that loves them back I'm Dr. Steven and Gundry and I'm always looking out for you but right now I'm going to look out for myself fantastic thanks for watching click the circular channel icon to subscribe and make sure you don't miss a single video click on the left to watch another great video and don't forget to visit my website for more of my best tips because I'm Dr. Gundry and I'm always looking out for you