This is joint work with my co-authors Axel Lege, also from Erisa Inrieren and Kloes Trane from Olbo University, about the quantitative linear time branching time spectrum. So let me start with an overview, a kind of an upshot of what we want to do. So the idea is to generalize process equivalences and pre-orders as they are well known from the quantitative world to a quantitative setting. So for example, trace equivalence, we are going to generalize this to trace distances, simulation pre-order to a simulation distance by simulation equivalence to a by simulation distance and so on. But the problem is, the huge problem in this context is that once you start to consider quantities, there is a host of different ways of actually measuring your distances. Depending on your application, you might want to measure point-wise distances in your systems. You might want to accumulate your differences over time, if you are considering energy consumption for example. Once you accumulate, you might want to discount the future, so you can do some discounted accumulating distances or you can do some limit average stuff. All this has been considered in different papers. You can do something which is called maximum lead distance, specifically for time systems. This is useful. So there is a host of those quantitative ways of measuring things and we thought instead of continuing to write papers on special ways of measuring things, why not try to consider all those things under one umbrella. So what I am going to tell you about is how to do all this at the same time. Two ideas for this, when you want to apply your things in a quantitative setting, what is easiest for let's say systems engineers or somebody to come up with how to measure things is to define distances between traces of the system, between executions. Like imagine a hybrid system, you have two different ways where your variables can evolve and then the control engineers have ways to measure distances between executions. And what we want to do here is to convert those linear distances, those trace distances to branching distances which work by simulation distance, like simulation distance, ready simulation distance, this kind of stuff. So here is the outline of the talk. I am going to first recall for you the linear time branching time spectrum of different pre-orders and equivalences. Afterwards I will tell you how to apply games to go from linear distances to branching distances and then we are going to conclude and see what we did. Here is what is called the linear time branching time spectrum. And by the way, not sure that the year 2000 and 2001 is correct there, but this is something which Rob van Glebek basically wrote his thesis about. So you see here different notions of equivalences and pre-orders which are all used by people to classify processes. So you have some processes and then you are interested in are they doing the same thing? And whether or not the answer, what the answers to this question depends on your notion of doing the same thing, right? So there is different notions of doing the same thing. One of them is bi-simulation equivalence. This is rather well known. The other one also rather well known is trace equivalence or linear equivalence. But then there is also readiness, there is simulation equivalence or similarity as it's called. There is possible futures, ready simulation. There are some others which are not in this, here are in this spectrum here, which were in van Glebek's spectrum. There is also some which are not in van Glebek's spectrum. The upshot is that people are using a lot of different notions of equivalences of processes and what van Glebek did, the contribution of van Glebek, is to collect most of them into this spectrum where the arrows mean implication. So bi-simulation equivalence is the finest one. Trace equivalence is the causest one. Of course, most of them also have a pre-order version. So instead of considering simulation equivalence, you can also consider simulation pre-order. So the question now then is not anymore. Does one process simulate the other and the other the first? But does T simulate S only, right? So this is not equivalent, this is pre-orders. So it becomes rather big already the spectrum. And what I want to do next is to reorganize this using games. So let me recall or introduce, by the way, one thing I forgot. There are some extra arrows there too, it's even more complicated. There is also an arrow from nested simulation pre-order to simulation equivalence. There is a lot of implications there and it's a rather messy thing. So I want to use games for re-ordering it.  Usually it's formulated as a bi-simulation game, but the simulation game is easier. So we will play the simulation game. In this game there are two players and they are trying to find out whether the state T simulates the state S in this. So this is two transition systems with labels here. Player one will be the red player, player two will be the blue player. The red player is trying to prove that those two states are not similar, that T does not simulate S. The blue player is trying to prove the simulation relation. How does the game work? So they both, they have some marks, so player one has a red mark, player two has a blue mark or a pebble, if you want, or some other objects. And they are moving them around in this transition graph. First player one moves her pebble. There is only one way to move it, so that it always moved along transitions. And now player two has to match the label on this transition by matching his pebble. The player two has now the choice between true transitions, which both have the same labels. So player two has to match with the transition with the same label. Player two chooses to go to the right. And now player one can finish the game and proving that T does not simulate S by moving her pebble to the left. Because then player two doesn't have an answer to this move anymore. So player two lost the simulation game and this is the proof that T does not simulate S. If it would have been the case that player two could always answer to any move which player one can make, then player one would have lost the game and thus have proven that T indeed simulates S. But this is not the case here. So let me sum up this simulation game. Here's the algorithm for the game. Player one chooses an edge from the state, then player two chooses a matching edge and the game continues from this new configuration. Now, if player two can always answer the move of player one, then there is a simulation relation. And if player two at some point cannot answer, then there is no simulation relation. The interesting thing is now that this game can be modified in different ways to explain all those relations in the spectrum, which I showed you here. The spectrum is slightly re-ordered and there are some new relations in it too. But what I was playing with you was the simulation pre-order game. We can also play a bi-simulation game. You've heard about this probably already. But let me explain to you how to go up the hierarchy one step at a time. So if we wanted to play a simulation equivalence game, how would we play this? What is simulation equivalence? Two states are simulation equivalent of similar. If one simulates the second and the second simulates the first. So what do we do in the game? We let player one choose in the beginning which state to play from. Either to play from S or from T. This is the simulation equivalence game. Okay, another game, what about ready simulation? So first, I don't know if you know what ready simulation is. This is maybe not so much used in the community anymore. But ready simulation means that the players, so let me just declare the game, the players are playing a game which is like the simulation game. Only that at any point during the game, player one, player one is here, can choose to switch to the other side of the game. And then play one transition, which player two has to answer. If player two can answer the transition, then player two has one. If player two cannot answer, then player one has one. This is the ready simulation pre-order game. Now we can also play ready simulation equivalence games by combining those two features. So letting player one choose in the beginning and at some point in the game, letting player one choose to finish the game by switching to the other side. We can also play, what is more that, there is the nesting. Two nested or just nested simulation pre-order. Here's the nested simulation pre-order game. Let the game go on and at some point player one can choose to switch. And then let the game go on. So not only for answer, but just let it go on. This is nested simulation. Of course, if you have two nested or nested simulation, you can also have three nested simulation where player one can switch twice. So there is, but by equipping the simulation game with three different features, we can explain all the hierarchy. Switching in the beginning, switching during the game, and switching during the game and stopping. This is the three features which are necessary to have all those relations. I should explain here that I'm not actually sure how new this is, what I just told you here. It appears to, I mean, some people tell me that this is kind of folklore knowledge. I didn't really find it written down anywhere. It's, I don't know, maybe it's obvious for us it wasn't. Now, the next step, there is some space on the right here. And you remember that the linear time branching time hierarchy was a bit more complicated than this. There was more things in this. How do we get the other things? Well, let me first put them there. And let's start again in the bottom of the hierarchy. We saw the simulation game. Now there is another feature we can add to the game, which is blindness or partial information. So now let's play a simulation game where player one is blind. So player one is playing something, but doesn't see the answer of player two. And then has to play something again. And player two is answering and player one just doesn't see what player two is doing. So then player one has no way of adapting to the strategy of player two, right? So player one might as well just have played all our actions in the beginning, up front. So player one might as well just have played a trace. So what I've just proven to you is that the simulation pre-order game when player one is blind is the same as the trace inclusion game. Now I've explained trace exclusion using a game. Actually, all the ones on the right hand side can be explained by the same game as on the left hand side, where player one is blind. Now, technically, the definition is slightly complicated. You can read this in the paper because it's a bit difficult to find out what you mean by being blind when the players can switch states all the time, can switch sides in the game. But that's the essence here is that you have those four features of switching, three different switchings, and blindness, which explain all those equivalences and pre-orders in the hierarchy. Now I would like to keep track of time because I did not actually talk about quantities yet. Yes, very good, yes. Well, maybe we should consider continuing running ahead because it's lunch afterwards. I don't want to upset people who are hungry. Okay, but to introduce time, let's revisit the simulation game and reformulate it in a slightly different way. So remember the simulation game, you have those plays, no, those rounds where player one plays some edge and player two tries to match it. And then in the end, if player two can always match, then the answer is yes. Let's choose it more in an even-fished-precie way where player two doesn't actually try to match. Now what we are doing is player one chooses and player two chooses, player one chooses, player two chooses, and once they are done, which might be after an infinite number of steps, then they compare the chosen traces. And then if the two traces they match if they are the same, then player two has one and there is a simulation. If they are not the same, player one has one. Now if you add to this information that player one is the one which wants to spoil the game, and player two is the one who wants to win the game, then you get precisely the simulation game again. So this is just a slight reformulation of the simulation game where the notion of who wins the game is first done after the game is actually being played by comparing the two traces. Once you compare the two traces, you might, of course, also do this using a distance instead. So what we did here before was to say we have those two traces, and if they are equal, then there is a simulation. If they are not, then there is no simulation. Let's go back to the beginning. I told you we want to introduce a quantitative setting by the idea that it's easy to measure or easy to define distances on system traces. Now assume we have such a distance. Assume we have a way to measure distances of traces which might come from an application. Hence a metric, or technically a hemimetric, taking two traces as input and giving that distance as output which is a real number, positive real number, non-negative real number, or infinity. Well, if we can then measure those distances and traces, then we can precisely play this Irinfisch-Fressé simulation game. Choose those edges. Once you are done, measure the distance between those two traces which you have chosen. This is what we call the quantitative simulation game. Using the four features in the hierarchy I was talking about, you can also define quantitative simulation equivalence game, quantitative trace equivalence game, and so on, and so on. So we have a full linear time branching time spectrum of distances. So this is just the same spectrum as before. But now all of those are distances. And additionally, we have this spectrum of distances for any trace distance which the application might come out with. So every time somebody thinks of a way to measure differences between traces, we can bring back a spectrum like this now. This is it. Now you have seen the quantitative linear time branching time spectrum. Let me just try to finish with some further results. Here's a transfer principle. The thesis of Bangladesh is organized in a very beautiful way, because every time he introduces a new type of pre-order equivalence, he's always putting some counter examples to differentiate them from the others. We have a transfer principle, which is saying that whenever you have a counter example which separates two different qualitative notions, which separates, let's say, three nested simulation pre-order and two nested ready simulation equivalence, then you can use the same counter example to prove that the two corresponding distances are topologically inequivalent. So basically what it means as an application is that, and now I would like to go back here, all those distances for any trace distance are topologically inequivalent. By the way, what does topologically inequivalence means? It means that I have no chance of computing one of them using one of the others. I really have to consider all of them separately. I cannot transfer any information in this diagram. Here's another thing, which is maybe slightly more interesting from a practical point of view. If the trace distance I start with, coming from the application possibly, has a recursive characterization, then all distances in the spectrum can be described as fixed points. How does it work precisely? If I can decompose my trace distance through some complete lattice, so a function f which goes to a complete lattice L, and then the g which computes the actual distance, and this function f has a recursive formula so that I can, so maybe now I should use this the other way. So that f on two traces can be computed using the first element of sigma, the first element of tau, and the distance between the rest of the two traces. If I have such a recursion formula, then I can describe all the quantitative linear time branching time spectrum distances as least fixed points of some function is using this recursion function f. Also we should notice that all trace distances which I know, which encompass all of them I was showing you in one of the first slides, they can all be described like this. So essentially what does give us is fixed point characterizations of all spectrum distances for all known trace distances. So let me conclude. What have I done here? What have we done in the paper? We show how to convert any distance on system traces or executions to any type of branching distance in the LTBT spectrum. One of the reviewers was writing us the nice sentence, in doing this they avoid many future papers on many possible variations. Just for that, this paper deserves to be. Another way you can think about this is that we are adding an extra dimension to the linear time branching time spectrum, because you can view the standard equivalences and pre-orders as discrete distances, which are either zero or infinity. Now on the other side, you have the trivial discrete distance, which is always zero, where everything is equivalent. And then we are adding our stuff into the space in between, because everything we are doing using quantities is to differentiate between things which have distance infinity in the discrete spectrum. What is more to be done here? Well, the first thing to notice is that, yeah, fine, it's very nice. You have all those fixed point characterizations here. But what about algorithms? Do they give any algorithms? Usually, you use fixed point stuff for having some algorithms. Well, the point is it depends on the lattice L. If your lattice is too complicated, then your fixed point characterization will gain you nothing. It's a nice definition. It kind of gives you something to work with, but it doesn't give you an algorithm. So what needs to be done and what we are doing is an application of these two different scenarios. Now we have a general framework, now we better show that it can be used for something. Application to different types of transition systems, applications to different types of trace distances. How does it work in concrete cases? Do we get approximations algorithms? When you want to compute something, the question is almost always, can I compute it, but can I approximate it? And for the papers we've written, it works OK. Applications to real-time and hybrid systems are slightly further out in the future, slightly more complicated, because time is a bit more complicated to deal with. For example, because of the additivity of time. Once you have two time transitions after each other, then you can also add them to get a new time transition that adds some extra complications. What is much more complicated is to add probabilities into the equation. When you talk about quantities, people sometimes assume, actually, that you talk about probabilities. But there is nothing probabilistic in this. And adding probabilities is much more complicated, because people have actually done this. There is papers by Luca del Faro, and Marie Stollinger, and others on probabilistic systems, and distances of probabilistic systems. The framework I've shown you here doesn't apply to those on the nose. There is some things which have to be done. And this is something which we are considering currently. Thank you very much.


How's it going everyone Caleb here with late in sports cards? We're gonna be working on a tops 2022 definitive baseball Full case break here. This is gonna be a random division style this time and this is gonna be break number 46 Thank you everyone for joining the results are right here We'll get right into it then Yeah, it would be like the Michael K show they do like Yeah Or just like you there's like a player that like everyone struggles to pronounce. Oh, yeah That would be awesome as well Have like a sound bite of it. Yeah Here we go guys, let's get it going 2022 definitive collection Believe I did just in case I didn't the results are right here one more time. Let's do it Thanks for joining everyone. See what we got for you guys Nice the Browns lost their center in the neck and neck surgery Backup it sounded like I don't think treaders. I don't think it's treaders the center anymore I Guess There's a red in there in the middle There's a red in the middle can't hide it from you guys red in the middle Andrew McCutcheon 11 of 30 for the Phillies nice patch Exactly Jerry in fact Jerry spitting facts great stuff man We were just talking about some of the cool things you can find earlier nice McCutcheon there Phillies Kevin Bishio 26 of 50 blue James Hatch autograph of Frank Thomas 31 of 40 Dude on real man. Wow. What is going on tonight?  There's no way Are You like serious man Wow Holy cow and at least is John a Logo man one of one patch auto Ronald Acunha, Jr. John a What is happening man? That's a crazy car Geez man Look at the logo man. Look at the beautiful signature as well one of one of course Massive car John a and at least Seriously unreal man Seriously unreal God dude that card is so cool with the red patch as well on the red one of one And then the blue logo man with the blue bravest jersey just like The perfects perfect colors on this thing it just pops Unreal man will make that Sam's not nagging that he said At least on the Joe Carter here and Paul Molliter dual patch auto Yep, yep, I don't even know what to say man this frame autograph Aaron judge for a 15 Very nice there at least Seriously, this is getting crazy Rookie autograph of Gavin sheets will make that judge Nice one there on the Gavin sheets. Yeah central and big poppy 18 to 25 awesome ending there another one AL East Nice poppy 18 of 25 Beautiful definitive collection autograph David Ortiz What else can this case possibly have inside? What else could possibly be in here with that thing? There's a book This is not skies come on baby red 7 of 30 Louise Castillo Tramansini 17 of 50 Orioles At least there another one for the AL East another big poppy David Ortiz 7 of 40 nice another one at least This is insane for us man. My hands are shaking. Just like I Just crazy. It's been every break for the last two hours and 45 minutes There hasn't been one that we haven't done good Jumbo patch autograph of branding Crawford 16 at 25 See this right as it's open. This is just getting unreal. That's a nice patch there on the Crawford for four color All right, so we are gonna see this together It says the player on the back. We're not gonna look at it though. Come on Wow, Aaron Nola two of five Great one there. It does have the powder blue Jersey that's sick Aaron Nola There you go And at least having a break some really unique cards from definitive just gravy for John right there. Yeah Big big patch Exactly it is if you guys can see it better now. You can see it is a powder blue Jersey On the patch auto great pitcher there very very nice card And we've got gold frame Autograph patch match admin very nice. There he is Nice one one of 30 And it wouldn't be baseball without at least one yellow shot a while I'm breaking so here it is Patch auto 21 of 50 Christian Yelich. Everybody can stop holding their breath. Yeah, there he is now. I know central It had to come up eventually glad everyone was I know everyone was getting worried Shane that's hilarious because I was actually thinking about just being like yeah, I picked all these boxes That's that's funny That's funny and it's gonna be a ready Freeman on the defining images autograph braves nice one there Very nice, I Mean this is really quite the box here five of ten gold frame autograph purple Bernando Tati's jr Crushed it man. I know West with a nice hit there very nice will make that Calling a no Tani in this last box. I mean that's it seems like we're on that kind of Toss Tati's is like the worst mag card like how does that happen? How does that happen? I'm not saying that any other products, right empty? It doesn't make any sense empty guys Right dog. Yep. I think everyone would agree with that We flip this around here nine of 15 Rafi devours can't I At least What another one? This time three of five Peter Lonzo that'll be part of the helmet Oh holy cow And at least again Unreal definitive helmet relic card and check it out. You've got some of his number That's so I mean could you really have like a better part of the helmet could not stop dog.   Oh my god West AL West Look at that thing. Look at the tag Dude, that is like this is going on three tag on that thing. What is going on? Yeah, that is the laundry tag. Right. That's it.  Oh my god Dual autograph rod crew Joe Maurer Dual twins there. That's 18 at 35 Al central. Yeah, this is so exciting. Sam has now just resorted to standing He's not sitting He's standing Disgusting patch there on the gold frame patch auto two or five Tom Glavin. It's gonna be a Mets Tom Glavin Nice and at least Having a break rookie autograph AL East by the Bruhan 35 of 50 nice Honestly, Dan, I don't know why there should not be any spots left like zero And autograph of Carl Yastremski 22 of 25. Wait till you guys see this recap AL East Wait till you see the recap. Holy cow guys We're gonna do the patch cards here Raphael Devers Red Sox Trey Mancini Orioles Louise Castillo Reds Kevin Vigio blue jays on Andrew McCutcheon and Pete Alonso such a sick card and at least and at least you crushed it as well by the way Confirmed crushed it three of five. It's a battery. Excuse me. That's a helmet relic Let's see what we got in here guys Alrighty, we've got autographs Carl Yastremski Vital Bruhan raise Tom Glavin Nets It's gonna be Joe Mauer Rod Crue Twins Carlton Fisk there. That's gonna be a Carlton Fisk Red Sox Braves Freddie Freeman auto Christian Yelich for the Brewers That's gonna be Matt Shatman on the gold frame auto athletics. This was a autograph patch book card of Aaron. Nola And Nola it's another big hit for that at least don't see the books too often Continuing on we've got a Brandon Crawford 16 to 25 Giants David Ortiz AL East patch auto another David Ortiz auto 18 to 25 Gavin Sheets rookie auto dual patch autograph Joe Carter and Paul Molliter Patch autograph with Frank Thomas. We still have some huge mag cards to show you guys Gold frame auto of Aaron judge AL East Yankees That's like probably the worst of the mag cards and it's still just incredible Tatees five of ten nice gold frame autograph NL West How about these two beauties Hoseil to pay on the right Ronald Acunha junior on the left a couple of massive one-of-ones We've got the logo man on the left and the Incredible laundry tag on the right Congrats on that on the left there and at least And on the right AL West That's unreal. Thank you guys so much. Appreciate it. Hope you did enjoy it. We'll be on to WWE select next tonight should be awesome. Thanks guys


But seeing this many badass women on a marketing conference stage in one day, I feel the same way I did when they were doing all those flips and fight scenes on Wonder Woman. This is the coolest girl boss for the win. So I am Heather Fizziak. I'm director of organic search at VML. It's a big global digital ad agency headquartered in Kansas City. And today we're going to talk about how agencies, especially the big, giant, creative beast agencies, can build data-driven content systems. And although the research that went into this is centered around the big agency environment, I believe that these learnings can apply no matter what size agency you work at. So from anchor to asset, let's rock and roll. So a little bit of backstory. I started at VML in mid-2014, and I mostly worked at small, medium-sized agencies. I did a very brief stint at a big agency. And they were the largest agency in Kansas City by a margin, but they had 2,600 employees in 29 offices around the world. So this was like the big leagues for me. And I remember the first day I walked into VML, the walls were literally lined with hundreds and hundreds of creative awards. We're talking canned gold lions and webbies and addies and other awards I can't pronounce. And they were on the ad age agency A list, top 10, two years running, they were on the Forrester Leaders list. I mean, they've got creative achievement written all over them. It was completely intimidating. And they were working on these giant brands. These are amazing brands to work on. We had Bridgestone, we had Wendy's, we had Electrolux, we had Express. And not only did these brands have big budgets, they had big complexity and big visibility into every marketing and budget decision they made. It was fast-paced. It was a lot of moving pieces. It was like I was chugging from this fire hose of creativity. And I was completely out of my element. I do not have a creative bone in my body. My job is a technical field. I still explain to people that what I do is get stuff found on the internet. I feel like they made some kind of clerical error when they hired me. But they did. They hired me to take over this one-person SEO practice that got brought in after the fact to do some retroactive technical cleanup if they got brought in at all. So I had my work cut out for me to make a name for search in a creative agency. So I already quit my other job. So I figured I should buckle down and do the one thing I knew how to do. I started optimizing. And you guys, it kind of worked. So the first two years we got 80% revenue growth both years in a row. This year we're pacing for another 40, 50%. And we did a great job of becoming a part of VML's DNA. And we did it by becoming easy to understand, seamless to integrate, and most importantly, impossible to ignore. We built this dream team. I call this my dream team slide. Peace by peace. We were scrappy and lean like a startup. We did this PR road show inside and outside the agency to educate people on what it is that we do. We inserted ourselves into conversations where we were not asked to be, but we knew we could help. We started putting out points of view, thought leadership pieces. We started selling projects or partnerships instead of projects. It was a complete mentality shift at this agency. We even won a little award for our global search work on Hills Pet Nutrition.  It was almost like this meteoric growth was too good to be true. And that's because it was. We hit the ceiling. We made these massive promises to these amazing clients that search was a good investment for them to ship their dollars to. That we could help their customers through search. That we could get them real, measurable, tangible results. Our word is everything. So we were getting some small wins, you know, the technical, the SEO 101 basics. But we know that this only takes clients so far. We knew we had gone about as far as we could go with that. But we were facing some tough questions. Are the partnerships we sold to these clients worth it? Technical SEO, easy. Check it off a list. We started building great relationships with developers. Nailed it. On page SEO, you got it. I will optimize your title tags and my descriptions all day. Content, these big international enterprise brands have a shockingly low amount of content on their sites. So this is a huge opportunity, right? Crap. We consistently struggled to get content for our clients' websites and to convince people that this was a priority. The best we could hope for was some thin, weak blog content or some fluffy content from a questionable influencer that we might get asked to optimize after the fact. But what baffled me is that we knew content marketing budgets were growing. According to this PQ Media Study, content marketing is going to grow to $313 billion with a B dollars by 2019. Or this study from the Content Marketing Institute, I believe, shows that 39% of B to B marketers believe their content budgets are going to increase next year. Where I did my own research for an executive research project, and 72% of respondents said that they believed that their clients' budgets were going to increase for content at least slightly, if not dramatically. So if the demand for content is there, and the money exists, why can't my word-winning creative shop create content for some of the best brands in the world? We sucked at it. Again and again, we would cross off these technical SEO items, and we would hit a wall with getting copy, and our ability to get results stopped in its tracks. It wasn't getting scoped in as a dependency. We couldn't get the resources. Whatever the case, there was always some obstacle between us and getting words on a client's website. But we had to figure this out. If we don't figure this out, we can't get the results we promised our clients. We lose our scopes, and people could lose their jobs. Content marketing seems so simple, especially the way we talk about it in this industry. But in the agency world, it was the one thing that threatened to kill my team. I cannot go back to my boss, to my clients, to my CEO, to the people I hired, and tell them, well, we can't figure out content. I guess we should all pack it up and go home. This is what keeps me up at night. I have this world-class SEO team, and we can't deliver results because we can't deliver content. Give me a break. Unacceptable. So we started a research, and one comforting and yet alarming fact is that we were not alone. In fact, agencies everywhere reported having trouble with almost every part of the content marketing workflow. None of us had it figured out. Here's a study from ad age. Top problems, creating quality, engaging content, lack of budget, lack of time. Content marketing institute, content creation again, strategy development, prioritization, my own research, client maturity about content, selling the value, proving the ROI, working in silos, bad scoping, and others. More and more and more. Different studies, different methodologies, different numbers, but the story remains the same. We have big content problems to solve for. And if there is a problem, I am an engineer at heart. I'm going to solve it. Thanks, Vanilla. So we can't solve the world in a day. Let's focus on a couple of items here. First, proving the value of content so you can get the investment and the buy-in that you need to move forward in the agency world. Creating a process that is flexible and adaptable enough for the agency environment and sourcing that content so you can create it affordably well and at scale. A couple of assumptions about agencies. We live for chaos. We are fueled by creativity and chaos and process and rigid structure is often seen as the trade-off to that brilliant creativity. There's never, ever, ever a one-size-fits-all approach in the agency world for every agency or every client. There's just too much unpredictability. There's always some exception to the rule. And we know that content formats are going to continue to evolve and change. Cindy just said that the URL may not even be necessary next year. Who knows? But we're seeing this just in the three years I've been at VML. We're putting search technology in cars. We're putting in wearable devices. We need a system that's flexible enough to adapt to the new forms of technology. And we found four main problems in the research, and the first was territorialism. So in the agency world, where everybody's vying for a seat at the table, and everybody wants to get a bigger piece of the budget pretty much. And so it creates territorialism, which then begets winners and losers inside the agency.  So everyone thinks they own content, especially creative, but our research shows that not all content belongs in the creative department. So we'll talk about how to overcome that. Next was ownership. Everyone thinks they own content and has an opinion, but truthfully, no one department owns content. So what happens is everybody ends up going rogue and doing their content marketing in silos, so we need some sort of ownership to see this process through and connect the dots. Speaking of process, the next problem is there is no process. So I sat down with a dozen subject matter experts in different disciplines, and I interviewed them for 60 to 90 minutes each, and I asked them, what is content to you? What does it work like in your discipline? How do you know if it's working? Where do you come in? What does the perfect system look like? And I compared the data on the commonalities and the anomalies so we could develop a process that works for agencies and not just for search. Last sourcing, we need to be able to create high quality content affordably and at scale, but without sacrificing the creativity that an agency is known for. So proving the value, let's dive in. This was my Eureka moment. So not everyone actually agrees on what content means. Looking back, this seems so simple and obvious, but to me this was mind blowing. Creative thinks about content completely differently than digital does. There is a fundamental misalignment in definition. So if you ask 100 different marketers, which I did, to define content in their own words, you will get 100 different definitions, which I did. So this is the top 25 words that came up in the content marketer definitions. And these are good, right? These are pretty solid words. They're not wrong. They're pretty great. But the problem is every definition from one to the next varied so widely that there was no consistency and understanding of what it meant. That's a big deal. So as I dug down deeper, I found that the true dichotomy lies between campaign and continuous. So Creative thinks of these big burst campaigns. It's a big effort crammed into one massive short term campaign. And digital thinks of continuous content for the consumptive audience. And they think about it as new and improved copy and multimedia that's continually released over time. That is a huge difference in understanding. No wonder we're having a hard time bringing content to life. We're speaking different languages. So we had to get serious about defining this inside and outside the agency. Here's how it breaks down. So campaign thinks about aspirational as this push marketing, inspiring people to action with big short bursts, creative campaigns, it's memorable. They create a fabulous message, they push it out to the world, and they hope you like it. We can't do that in the digital world. We have to flip that thinking on its head and do the pull marketing style. We need to actually do the effort to understand what it is the consumer needs or wants. And we have to fulfill it. It's not about what they want to do. It's about what the consumers want to accomplish online. And we either fulfill it or we don't. So creative, of course, has very high standards for creativity. But for digital content, it can't just be creative for the sake of being creative. It actually has to serve a purpose and we need a balance of both for brands. Creative tends to think of single stream. They tend to design campaigns around one or just a select few channels, wherever the media dollars and platform partnerships take them. But in digital, we think about how to adapt a story to live in many different channels. And it'll live on long after the media dollars stop. And last, it can be very costly to develop a campaign in the creative world where it launches and lasts for a very short piece of time for one piece of content. But in continuous content, we need a lot more content, so we need to be faster. We need to be leaner, more agile, less expensive. Very different skill sets. So now that we've defined content clearly, so we can use this inside the agency and with our clients, how are we going to educate our clients so they know that this is the right thing to do?  Pardon my French. And sorry to the French people that I just call that French. Money talks. Money talks. So website traffic is not and has never been enough to convince enterprise brands that they need to invest in continuous content. They have way bigger fish to fry. They're thinking about profit margin and shareholder value and market share. And yet SEOs are so prone to promising rankings and traffic. The website is the least of their concerns. That was a really tough pill for me to swallow that search was not the center of the universe. So we created a few tools to help clients understand the value of what we do and how we can bring content marketing to life for them. Why it's so important? So first tool, this is a super rough content maturity model. And so it ranges down the bottom left from reactive, very ad hoc with little thought to the management and consequences of the content that they're publishing, if they publish any at all. All the way to completely strategic, fully integrated. Content is a part of their DNA. Thinking about how their efforts are going to come to life through content is their default state. I would say that most of our clients probably start in that tactical space. They're creating some content, they're doing a pretty good job, but it's in silos and they're not connecting the dots. So we use this as a tool to educate them on where they fall in the spectrum and what it's going to take to get to the next iterative step in the process. Here's another tool, growing base versus buying spikes. This is obviously completely fake data. But it does a really beautiful job of distilling down in a digestible way. The difference between the value that campaign and continuous brings to the table. So smart campaigns, they get these huge spikes in visibility, right? You get this big increase and then it drops to nothing. And then you get this big increase and it drops to nothing when the media dollars stop. And again, and again. Maybe win some awards. Now, in the foreground, you got the black part here, I'm sorry, in the background, the blue with campaign and continuous. This shows how the continuous digital content creation can support those campaigns, you get similar spikes when the campaigns launch. And then afterward, it dips down too because the media dollars have stopped and that hasn't influenced, but it doesn't drop to zero. And then the next campaign, it spikes again and then it drops, but it doesn't drop to zero, it actually drops less than before. And so on and so forth. And what you end up is with this orange line for trajectory that shows how you're continually evolving that brand over time and capturing more real estate and traffic than you ever did before. Basically, it makes the spikes more spiky, the dips less dippy, you get it. And it keeps brands relevant even when the media dollars stop. That's a good tool. This is what I call atrophy versus growth. Left axis growth, bottom axis time. So hopefully we have real data of the progress we've been able to make for a client so far using that technical and on-page SEO 101 basics. Real data show a little bit of growth. Maybe we see a little bit more coming from the efforts we've already done. But we know we've reached that point where we have to start creating content to expand their footprint online. Well, that's where that orange line could take you. We try to put realistic projections together. If you create the content we're recommending, we think we can get you here in year one, year two, year three. But the important part, I find sometimes that telling clients how content can benefit them does not work as well as scaring them with, if you don't do this, you're going to lose or miss X. So we just oppose that with what happens if they don't create content or if they don't invest in continual content creation and search optimization. It could potentially stay flat or it could even atrophy at X rate per year. That would scare me. Okay, one more tool for you. A channel ROI comparison. So there was this really good Avinash post in the last couple of weeks. I don't know if anybody follows him, but he asked us to challenge ourselves with this very hard question. Is the time and the money and the effort that we are putting into these channels proportionate to the profit or the impact that it has on the other end? It's a tough question to ask and it's a tough question to answer. But this tool does that in a very basic way. So you can choose whatever channels you like, whatever metrics you like that you can get data for, but for the sake of simplicity, I went with paid and organic search here. The gray bars are the content budget that we recommend to the client in year one, year two, year three. And it's all inclusive. Those are made up numbers. Please don't freak out. It's all inclusive, right? It's the research, the writing, the editing, the publication, the optimization. Everything's in there. Maybe it stays the same. Maybe it increases. I don't know. You do the math. Year one, year two, year three. So from that, the black line, we're gonna estimate our potential traffic growth. Yes, I know the search landscape is changing. Yes, I know this is impossible to be perfect. But we think about things like the volume of content we're planning to create. We think about the search volume potential, the competition. And we do our best to come up with realistic conservative traffic estimates. From there, we calculate the estimated organic cost per click based on their investment. From the growth, we're gonna get them. And here's the fun part. We get the same cost per click data from our Google ad words wrap. We get historical data going back as far as we can for that specific client on how much the cost per clicks have gone, costs per click, see if I know, have gone up year to year. And then we use that to project how the paid search cost per click is gonna increase in the subsequent three years. Well, right away, before I've even started investing in content, I see the gap is huge. These are also not real numbers. Please do not tweet these out as if it's like perfect math. The gap is huge, but here's what's really interesting. Paid search costs for the same result are going up, up, up. Where organic search costs are going down, down, down. I'm a client, you got my attention. You're telling me I can get the same results or better with less money? I'm listening. Now, I do not mean to diss on my paid media friends because I think there's a place for both. I think they serve different purposes at different times. But if I'm a client, I wanna know how my money is working. Now, again, clicks is not necessarily the best metric. Think about goal conversions, e-commerce revenue, whatever. You could run this for any channel, any metric and SEO and content is not always gonna win. All of these charts and graphs I will put in a link at the end so you have a base template to work from. Okay, cool, we sold content, right? How are we gonna build a system to actually bring that to life in that complicated agency environment? So I mentioned I interviewed all those subject matter experts in the different disciplines, what they care about, pain points, perfect system. And I did that to truly understand what they needed from content, not what I thought they needed or I thought the agency needed. And this stuff was so critical to get the buy-in from those people I interviewed in their departments to facilitate the change management that it's gonna take to put this in plain, a 3,000-person agency. So in the agency world, it starts with a brief, figure out what we're even doing here, channel inputs, keyword research, et cetera. You build a content playbook to define the strategy that everybody's gonna align to. You create a content calendar for the who, what, when, where of what content's gonna get created. It goes to production and then it gets published, approved, amplified. It seems so, so simple now that it's on paper. And at the risk of ruining a perfectly simple flywheel, this is what it looks like when you double-click down into that in the agency world. The agency brief step has audience personas, brand research, competitive analysis. The channel inputs stage could have seven or more inputs by itself, keyword research, social listening data, PR threats and opportunities. User experience data, web analytics, behavioral analysis and so on and so forth and so on and so forth. My point is not that this is the perfect agency flywheel for everyone. My point is you need to go figure this out in your own agency. Most people don't take the time to sit down with them and ask. So in addition to this perfect flywheel, at the top you see that editor in chief role, there's your ownership. So this person may not be a practitioner of each of these individual disciplines, but we learned that we may need to create a role like this to oversee the process, understand and bring in the right stakeholders at the right time and make sure we're connecting the dots and delivering on what we promised for our clients. So one size does not fit all. Do the interviews in your own agency. I will share the questions that I asked in my own. Warning, there's like 40. You might be surprised what you find.  Yeah, brah. It's hard. Yeah, is it actionable? Totally. And it's probably going to get smoother and easier as time goes on. Is it easy?  If you want easy, you're in the wrong job. But most of those things that you saw on that flywheel, they're happening right now in agencies today in silos. And it is wasting your client's money. And it is getting worse results. And it is getting fewer results, because no one's taking the time to do this right. Some stuff is just hard. That seems to be the theme of Moscon so far.  You have a responsibility to put in the time, use your brain, and build something better for your clients. Real talk, not every agency is going to have these resources. I get it. And Ian had a great talk on Monday about small, scrappy agencies where people wear multiple hats. Go rewatch that. I get it. And not every client's going to have these budgets to cover that many resources. I get that too. But any pieces you can add into that flywheel make it a stronger content strategy. And this is a good skeleton of a process that you can adapt for your agency's reality. So make it yours. All right, sourcing content. All right, it's a classic make versus by decision. Any MBAs? Yeah. Make versus by decision. Do we create it or do we outsource it? Well, first, we're going to consider some pros and cons of a couple different sources. I'll give you a few quick examples. But this is from my perspective. Once again, you're going to have to use the old noggin and figure it out for yourself what you think about these sources. All right, first, let's talk about in-house creative.  Like, it's going to be flawless every detail top to bottom of that campaign is going to be beautiful. And you're going to get more creativity than you could from any other source. It's unparalleled out of a creative agency. And then it's going to be completely custom. Every client, every campaign, everything is going to be custom end to end. Now, the downsides are they tend to think in single campaigns. And it can be really slow to execute because of how many resources and how much time goes into creating the perfect campaign. And it can be very costly to create that one piece of content. OK, what about outsourcing to a third-party content vendor? I was super skeptical of this, especially with the high creativity standards of the agency. But I've kind of come around now that we've found one or two that are pretty good. Content is their specialty. They do this all day, every day, and nothing else. They probably know what they're doing. They're really great at flexing to changes in demand. When things get heavier, when they need to back off, people don't lose their jobs. They have the resources to adapt to that, which is much harder to do in the agency. And they often have end-to-end systems where you can put all your necessary inputs in, keyword research, for example. And they route it through the team for writing and then to optimization and to legal for approvals and the client for approval all the way until it's ready to publish back at the agency again. That's useful. And it can be really fast. Speed to market, very high. Now, on the downside, it does create extra steps. Sometimes that's outside the agency. And so that does create some complexity, I get it. Profit margin might get squeezed. Might even go down to zero if the client goes direct to the outsourced vendor. Quality control can be a little tougher. And it may be a bit of a process to achieve the quality you want over time. And I would say this is lowercase-c creativity. They don't have the same institutional knowledge and understanding of the brand that you do. All right, last one, this one's kind of fun. So we're seeing a lot more artificial intelligence vendors coming on the scene to machine-generate content. And actually, Will Critchlow at Searchlove showed some examples of this in human writing versus artificial intelligence-generated writing. And people couldn't tell it apart. It is a scary place. But perks. It seems to be automated and relatively easy to use. You put stuff in, it does its magic, and out comes some content. It seems to be good at creating a campaign of multiple formats of content with the same message or project for different platforms. And it learns over time. It's going to continually get better and better and better. But it's still the early days of content via AI. And so it's limited by the inputs. It's a machine, we're humans. So if the things we put into the machine are not enough or are not the right things, we may be disappointed with what comes out the other end of that machine. And it's a machine, so it lacks human judgment. It doesn't know what's good or bad until we're giving it feedback in its feedback loop. And quality control is probably most difficult in this particular content vendor channel. So this is just a few sources, like I said, adapt for your own reality. But once we've done this exercise, sitting down and analyzing the pros and cons, we create a make versus buy decision model. So very simple. On the left axis, we may list out all of our different potential content vendor sources. And on the top, we list out different business criteria. I used a few examples here. Cost to create, control over the process, how fast it goes to market, how creative it is, which really matters to our clients. There's other possible criteria lists along the bottom, and I do include this in the link. And then I kept mine super simple. I went one for worse, 10 for best. Okay, and then I had it spit out a score. And that helped me to prioritize which sources or vendors would make the most sense to create content for our clients. Should we keep it in house or should we outsource it? So you may use this to choose which tool your agency uses permanently, or you have it in your toolkit so you can look real smart when you pull it out for different clients in different projects. So, very powerful little tool there. Big takeaways. You cannot assume that everyone views content the same way. Do the work to discover what your clients and your colleagues think content means and take the time to clearly redefine what you need content to mean. Stop winging it in silos. Build a documented process, or at least a skeleton of one, based on interviews that you have done around your agency. Get that buy-in. Understand what they need content to do and how they envision it working, and then you automatically have their buy-in because you built this process on their input with their interests in mind. And last, examine all of your potential content production sources. I think the most important point here is it may not be inside the agency sometimes. I hope that most of the time it is, but it's not always. And weight them from your perspective and determine which ones are the right sources to use for which clients and which projects. Help them make that smart make versus buy decision. And with that, special goodies. That is mozcon.vml.vmlconnect.com. User name is VML All Caps. Password, Mozcon 2017, MNC, or Capitalize. Everything I talked about is in there. Thank you very much. Party on.


Hello, and welcome to this presentation of the STM32 real-time clock. It covers the main features of this peripheral, which is used to provide a very accurate timebase. The RTC peripheral features an ultra-low power calendar with alarms, which run in all low power modes. Additionally, when it is clocked by the low-speed external oscillator, or LSE, at 32.768 kHz, the RTC is functional even when the main supply is off, and when the VBAT domain is supplied by a backup battery. The RTC embeds 128 bytes of backup registers used to preserve data when the main supply is off. These backup registers can be used to store secure data, as they are erased when a tamper event is detected on the tamper pins. The RTC consumes 225 nanoamps at 1.2 volts, including the LSE power consumption. The hardware calendar is provided in binary-coded decimal or BCD format to reduce software load, particularly when the date and time must be displayed. The anti-tamper circuitry includes ultra-low power digital filtering, avoiding false tamper detections. The key features of the RTC are seconds, minutes, hours, weekday, date, month, and year, provided in a binary-coded decimal format. Subseconds are provided in binary format. Add or remove one hour on the fly to the calendar in order to manage daylight savings. Two programmable alarms, which can wake up the microprocessor from all low-power modes. An embedded auto-reload timer, which can be used to generate a periodic flag or interrupt with wake-up capability. The resolution of this timer is programmable. The calendar can be calibrated thanks to a reference clock source, which is the mains at 50 or 60 Hz. A digital calibration circuit allowing compensation of the crystal accuracy with 0.95 ppm resolution. A timestamp function to save calendar contents in timestamp registers, depending on an external event. And 128 bytes of backup registers split into 32 32-bit backup registers. These registers are preserved in all low-power modes and in VBAT mode and are erased when a tamper detection event occurs on any one of the three tamper pins. All tamper pins are available in VBAT mode. Here is the RTC block diagram. The RTC has two clock sources. The RTC clock or RTC CLK is used for the RTC timer counter and the APB clock is used for RTC register read and write accesses. The RTC clock can use either the high-speed external oscillator or HSE divided by a programmable factor from 2 to 63, the low-speed external oscillator or LSE, or the low-speed internal oscillator or LSI. To be functional in stop or standby mode, the RTC clock must use the LSE or LSI. To be functional in VBAT mode, the RTC clock must use the LSE. The RTC clock is first divided by a 7-bit programmable asynchronous prescaler which provides the CKAPRE clock. Most of the RTC is clocked at the CKAPRE frequency. So in order to reduce power consumption, it is recommended to set a high asynchronous division value. The default value is 128. Then a 15-bit programmable synchronous prescaler provides the CKSPRE clock. CKSPRE must be 1 hertz in order to update the time and date BCD registers in one second increments. The sub-second register resolution is defined by the CKAPRE frequency. By default, it is 256 hertz. The SSR register resolution is increased by reducing the asynchronous prescaler value. The asynchronous prescaler can also be bypassed. In this case, the sub-second register resolution is defined by the RTC clock frequency. The RTC is initialized using a secure method. The RTC registers are right protected to avoid any possible parasitic right accesses. First, the disabled backup domain protection bit must be set in the power controller control register in order to enable RTC right accesses. Then a specific sequence must be written in the RTC right protection register. Initialization mode must be entered in order to change the clock prescaler values or the calendar value. The RTC calendar keeps running in all low power modes, in VBAT mode, and during reset. Initialization of the time and date registers is performed through their shadow registers, which are in the APB clock domain. The sub-second register cannot be initialized. The calendar sub-second, time and date registers content can be read in two different modes. When the bypass shadow registers control bit is cleared, the shadow registers are read. The advantage of this mode is that it guarantees that all three registers are consistent. When the time register is read, the date register is frozen until it is read. When the sub-second register is read, the time and date registers are frozen until the date register is read. The disadvantage of this mode is that when exiting stop, standby, or shutdown mode, the software must wait for a synchronization delay to ensure that the shadow registers are updated with the last calendar register values. This synchronization delay can be up to two RTC clock periods. When the bypass shadow registers control bit is set, the actual calendar registers are read directly. The advantage of this mode is that there is no need to wait for the synchronization delay. The disadvantage is that the read values can be false or not consistent due to synchronization issues. So they must be read twice and compared with previous read values to ensure they are correct and coherent. This slide presents the main calendar features. Daylight savings can be managed by software with automatic one-hour addition or subtraction. It is possible to synchronize the RTC clock to a remote clock by adding or subtracting an offset to the sub-second register on the fly with CKAPRE clock resolution. This feature is commonly used in RF applications. A reference clock, means at 50 or 60 hertz, can be used to enhance long-term calendar precision. The reference clock is automatically detected and used to update the calendar when it is present. When the reference clock is not available, the LSE clock is automatically used to update the calendar. This feature is not available in standby, shutdown, and VBAT modes. A timestamp function is available. The calendar values, sub-second, time, and date registers are saved in timestamp registers when an event occurs on the timestamp IO. A timestamp event can also occur when a switch to VBAT occurs. The digital calibration is used to compensate crystal inaccuracy and accuracy variations with temperature and aging. It consists in masking or adding a programmable number of RTC clock cycles, fairly well distributed in a configurable window. The calibration value can be changed on the fly, depending on detected temperature changes, for instance. A 1 hertz calibration output signal is provided to measure the crystal frequency before and after applying the calibration value. The accuracy shown here is the resolution of the digital calibration. The calibration window size is configurable between 8, 16, and 32 seconds. For a 32 second calibration window, the accuracy is plus or minus 0.48 ppm. The total correction range is from minus 480 to 480 ppm. The accuracy resolution scales with the calibration window size. Final accuracy in the application will depend on the crystal parameter precision, temperature detection precision, how often the software calibration procedure is launched, etc. In order to reach the precision of the calibration window, the measurement window must be a multiple of the calibration window. The RTC embeds two flexible alarms based on comparison with the calendar value. The alarm flags are set if the calendar sub seconds, seconds, minutes, hours, or date match the value programmed in the alarm registers. The alarms event can wake up the device from all low power modes. The alarms event can also be routed to the specific output pin RTC out with configurable polarity. The calendar alarm sub seconds, seconds, minutes, hours, or date fields can be independently masked or not masked for the comparison. When the masks are used, periodic alarms are generated. In addition to the calendar and alarms, another 16-bit auto reload counter can generate periodic events with wake up from low power modes capability. This counter cannot be read. Depending on the software configuration, the wake up timer clock can be the RTC clock divided by 2, 4, 8, or 16, or the output of the synchronous pre-scaler. With the divided RTC clock, the wake up period can be from 122 microseconds to 32 seconds when the RTC clock frequency is 32.768 kilohertz. The resolution is down to 61 microseconds in this case. With the CK SPRE clock, the wake up period can be from 1 second to 36 hours when the CK SPRE clock is at 1 hertz. The RTC embeds ultra low power tamper detection circuitry. The purpose is to detect physical tampering in a secure application and to automatically erase sensitive data in case of intrusion. Three tamper pins and events are supported and are functional in all low power modes and in VBAT mode. The tamper 3 event detection is generated either by an event on I.O. or by an over or under voltage of the RTC power supply domain or by an over or under temperature detection. These voltage and temperature monitor detections are enabled in the PWR C2R control register. The detection can be edge or level triggered and the active edge or level is configurable for each event. Backup registers content are erased when a tamper event is detected. In addition, a tamper event prevents any read access to the backup SRAM until its erase operation is finished. A tamper event can generate a timestamp event. The tamper detection circuit includes an ultra low power digital filter. The internal I.O. pull-up can be used to detect the anti-tamper switch state. The I.O. pull-up is applied only during the pre-charging pulse in order to avoid any consumption if the tamper pin is at a low level. Pre-charging pulse duration is configurable to support different capacitance values and can be one, two, four or eight RTC clock signals. The pin level is sampled at the end of the pre-charging pulse. A filter can be applied to the tamper pins. It consists of detecting a given number of consecutive identical events before issuing an interrupt to wake up the device. This number is configurable and can be one, two, four or eight events at a programmable sampling rate from one to 128 Hz. This figure illustrates tamper detection using the internal pull-up. The internal pull-up can be applied for one, two, four or eight cycles. If the switch is opened, the level is pulled up by the resistor. If the switch is closed, the level remains low. The input voltage is sampled at the end of the pre-charge pulse. Several RTC events can generate an interrupt. All interrupts can wake the microprocessor up from all low power modes. The Alarm A interrupt is set when the calendar value matches the Alarm A value. Similarly, the Alarm B interrupt is set when the calendar value matches the Alarm B value. The wake-up timer interrupt is set when the wake-up auto-reload timer reaches zero. The timestamp interrupt is set when a timestamp event occurs. The tamper one, two and three interrupts are set when a tamper event is detected respectively on the RTC TAMP 1, RTC TAMP 2 or RTC TAMP 3 pin. The RTC peripheral is active in all low power modes and the RTC interrupts cause the device to exit the low power mode. In stop and standby modes, only the LSE or LSI clocks can be used to clock the RTC. Note that only the LSE is functional in VBAT mode. A bit is available in the MCU debug interface in order to stop the RTC counter when the core is halted for debugging. This is a list of peripherals related to the real-time clock. Please refer to these peripheral trainings for more information if needed. Reset and clock control, power control and extended interrupt controller. Thank you.


Hello and welcome to my tiktok go follow me Before you go and follow me here's my tour Wow, wasn't that great I've done quite a few videos where I've reacted to tiktok and so today I wanted to do something that a lot of people have done which is going through my Likes on tiktok and showing you the tiktoks that I've liked and my kind of humor. Hmm. Huh? Let's scroll down. I'm gonna scroll down for a while so that I can kind of react with you, you know So it can be like ooh, what did I like six months ago? I don't remember. I remember this one and I really like it I'm getting a septum fears. It seems so uncomfortable I just love all the pain and And then yep, you're right.  That looks like meekest rat pickles what you do refine your teacher. Oh, this is a dojo cat This is probably gonna be a lot of me not saying anything and just kind of giggling a little bit You know me gotta get that grapey crunch Here's a good Tucker Carlson. This dude is really funny. Hey son. Did you happen to drink my last beer? I Had one last my last beer all day It's not here. It's not here. Why is that?  It's so good Like top three favorite things in the world titties take up one and two. Yeah, there's two of them Yeah, and then chocolate milk.  Oh My god, I love this for the goong and chieftain. Maybe we have a line for him that goes um Something like you saw a big doodoo this time Yeah, George, I think that's a that's a terrible idea That's a real thing that was written by George Lucas That's so nice, you know That's so nice. Look at that cow. Look at that.  It's just too much. This is a real thing that someone made. It's too much. It's too much It's too much Oh No, I was thinking like maybe by the time I do that.   God, it's coming big big big Dude, that's not so cool though.   Oh look at these guys compilation. It's just otters Ready oh This guy's the best train guy. Yes. Yes I love how I love how passionate he is about trains you guys all know this guy I love how passionate he is. How happy these train Loco I love it. I love it. This is so funny.    So you guys should be set up. I do hate it's so accurate It's made so it's made so cheap. That's why we always replace them now as far as how long It's so accurate for a long period of time And I've also seen them break the next day if you want to sign my paperwork real quick Now ma'am, I will say this the color that you guys put on these walls. It is gorgeous You're done with paperwork. Okay.   Yeah, come on.  Look down the hallway Yeah, I can I want my brother to have a kid so I could be an uncle and just Decimate my nephew and call it. Ditch this and tell me the space fact. It just makes your brain go Oh No, Hank, I don't remember what this is the universe were infinitely big and static or infinitely old Okay, the sky would always be white with stars because an infinite universe Whether infinitely old or static would have an infinite number of stars and thus Every point in the sky Would be a star and its light would have had time to reach us It's called ober's paradox and it was one of the first ways that we proved That the universe had a start Had a beginning because like that's not like a thing that makes sense, right? It doesn't feel like it had a beginning if it was always around That's a lot That's too much and that is where i'm going to end this video I hope you guys like some of the ticktocks that I showed you Hope you got a good sense of what makes me laugh. It's mostly Anyway, thank you guys so much for watching. Hope you enjoyed if you did make sure to like the video What's that like button right in the face? Now I'll see you guys in the next video. Love you all stay cranky. Bye