Hey, everyone. Thanks for coming. My talk is estimating security risks through repository mining. My name is Tomasz Lange, I work at Intel. I maintain Zen and a bunch of tools that are built on top of Zen.  So in this project, I come to branching out from what I usually do. Briefly of what we'll look through in this presentation, we'll look at the motivation of why I am doing this, what the problem statement is, and what we are playing to test, what the experimental design and tools are, results, threats to validity, discussion summary, pretty standard stuff. So to begin, why do we want to estimate security risk? I'm pretty sure you guys are familiar with XKCD comic. Complexity is increasing. All of the projects that we are looking at, especially in cloud scale, have a ton of open source components. And estimating and kind of figuring out which one of those components is going to be the next lock for J is super hard, even if you have an S-bomb, right? Even if you have a list of all the components of your project, figuring out which one of those is going to have an issue is really hard. And doing that manually doesn't scale. As I said, we do secure architecture code review. And it's really hard, even, to just look through the first layer of dependencies for some of our projects. And then the dependencies of dependencies usually fall off the cliff. But to the rescue, there is this new project called OpenSSF Scorecard. How many of you have heard of it? It's pretty new. All right, see a bunch of fans. That's awesome.  It gives you the score. 9.4 awesome, you're good. It even color codes it. So it's really, really awesome, easy to understand, exactly what we needed. Perfect. It does this magic by factoring in a bunch of data from repositories that kind of makes sense. Do you have binary artifacts, ship with your repository? That's bad. Do you have CI tests? Is the project well-maintained? Are changes reviewed? How many different contributors your project has? It all makes sense, right? Not all of these factors are weighted the same into the final score. But all of these kind of make sense. Some might not be as relevant, like, for example, having the CII best practices badge on your repository, whether that actually contributes to security risk is questionable. But it's probably not weighted as significantly into it. So the problem statement is, does this actually work? How would we actually know that a project with a high score could actually be considered low risk? There is pretty much no evidence pro or contra released with OpenSSF scorecard. So what can we actually do to try to figure out where we are? Pretty much two things, if you just want to rely on OpenSSF scorecard by itself, is wait and see. More projects will deploy it. Over time, we'll see if there is any correlation with vulnerabilities and CVs popping up. But if you want to check, for example, just with the current set of projects that have an OpenSSF score and existing CVs, whether there is a correlation, that's not exactly good because a lot of projects don't actually care to use CVs. So the ones that actually do have CVs will probably be correlated more with, does this project have a bug bounty versus whether it's actually lower high risk? So you could have this selection bias. So we can't really rely on CVs as a objective metric for testing this. So what else can we do? So if we can't measure it directly, we need to find a proxy. And if we just rationally think about it, if a project is well maintained, it has code reviews, it has static analysis and fuzzing, we would reasonably expect it to have fewer bugs. And we can measure bugs in repositories using static analysis tools. We have been doing that for CC++ for many years, right? So we have pretty good tools. And while bugs don't actually correlate with security risk, the factors that the OSSF scorecard considers should correlate with both if it actually works, right? So let's figure out how to actually do this test. We will find the most popular C&C++ repositories on GitHub. We will run the scorecard on them, if not already installed. And then we will run static analysis.  And then we will perform linear regression analysis to see if there is actually a correlation between those. It's pretty straightforward stuff. We built some tools to actually help automate this, so we don't have to manually run this on our own laptops or systems. And I've been a big fan of CI and GitHub action, so I figured, hey, let's just run this every month on GitHub actions. It's not really designed for that, but hey, we can do this. Why not? So we'll search and run the analysis, collect the results, and publish everything on GitHub pages at the end. And the whole thing back to back runs on GitHub, and you just get the data set at the end. I spend quite a lot of time digging through GitHub API for GitHub actions. There is a lot of gotchas and a lot of limitations, and it's a pain in the neck. But it actually works now pretty well. It's reasonably easy to extend the framework to pop in new checks. So if you have your own favorite static analysis tools or you want to do your own analysis, you can pop in your own checks here. Pretty much what we are running right now is the OSSF scorecard. And for static analysis, I'm using the Clang scan build, with the ZT verifier enabled on top that helps reduce false positives this can build could have. I'm also running the Clang tidy cognitive complexity analysis and a bunch of other metadata collection, like lines of code and all the different GitHub metrics that you can think of, like how many stars this repository has, forks, et cetera. The whole thing is open source, you can go up on GitHub right now and fork it, run your own stuff. Some limitations on the data collections, right now we are looking at only repositories with 400 plus stars. Right, we wanted to actually only care about reasonably mature projects. So 400 is kind of a number out of a head, but it works for us. Dependencies, that was actually a pretty big roadblock for static analysis. You actually have to be able to build these projects and how do you automatically build just a random project off GitHub, like the hardest part is actually finding the dependencies for them. So, well, figured let's just grab for any apt or apt-get line in the repository and anything that comes after that, we will just try to install it. The whole thing is in Docker, so who cares? So we will just try to install everything. It takes a while, but hey, it's running on GitHub, I don't care if it takes three weeks, it will finish eventually. Well, we are having a six hour limit, but it works pretty well. And three build systems, AutoTools, Mason and CMake. GitHub API rate limit is a bottleneck, right? So we are running all these scans in parallel, but GitHub is actually limiting per token that you have for GitHub accounts, 5,000 requests per hour, which we can actually hit with the scorecard quite easily. And also disk space is an issue, so if you have a project that just generates a lot of files, then you might actually run out of this space. It not works all the time, but it works pretty well. So as I said, it runs monthly, and out of like 4,000 repositories that meet our criteria, we can actually automatically build 2,000. So it's actually not bad, right? Just considering that I'm just wrapping for I get installed and installing all that comes after, being able to automatically build half of CC++ repositories actually impressively wasn't expecting that. And yeah, you can go up and grab all the data sets. We get a summary there, how many repositories we built, how many bugs we found, and how many complex functions there are. So let's look at what we were set out to study. Does OSSF scorecard works as a predictor for bugs? And yay, we actually find that for each increase in the OSSF scorecard, we actually see a reduction in bugs. Hey, this is fantastic, right? You have a point increase in the OSSF score and you have a reduction of seven, eight, nine bugs. Again, each month you see a little variance in the data set, depending on when we actually timed out on the GitHub API request. So you have a little bit of variance on what projects we are actually able to build in each month, but it's kind of consistent, right? So hey, awesome, ship it. But if you actually look a little closer, if you check this chart, do you actually see a pattern here at all, right? That red line at the bottom is actually our linear regression model. Does not really seem to fit too well, right? So while the results were statistically significant, that's just really an artifact of the actual number of observations we have. You actually have to look at this metric called R square to actually see how well your regression model is explaining the data you have and what we see here is that it absolutely does not explain the data we have. The closer R square is to one, the more it fits the data and well we see that it does not fit the data at all. So yeah, practically no connection between bugs and the OSSF score as is. So that's not good, that's not what we expected. So let's look at some of those sub metrics, right? Let's just focus on the ones that we would absolutely want to see some correlation there, like is the project maintained? Well, we actually, based on the data we have, we actually see an increase in bugs. So the better maintained the project is, we actually see two more bugs. It's statistically significant. Absolutely does not explain the data at all, right? Those numbers are tiny, so don't take anything out of this. It's practically just noise, but it's still funny, right? Same thing for CI, do you have a CI? You see an increasing bugs versus what you would expect, but none of those results are actually statistically significant, so yeah, this just does not make sense at all. Figured, well, maybe it's the scan build is not a good statistical analysis tool, so let's take a look at some other stuff. So I tried it with Facebook infer. Again, we see a reduction of bugs with Facebook infer, but it's not statistically significant and our square is bad. I've tried it with this other more experimental tool called Binab's Inspector, which I'm pretty sure none of you guys have heard of. It's a Ghidra-based reverse engineering framework that runs Z3 on the disassembled binary, so it's not even source code based or compiler based. It's this crazy experimental tool and yeah, none of it makes sense. It has a really high false positive bug detection rate, so there's really no wonder that the data just is bad. So let's look at some of those other metadata that we collected, can we find anything that explains the bugs or correlates with bugs, right? Lines of code, that was my go-to, like all right, more code you have. Statistically significant, tiny increasing bugs, but it has a pretty bad R square, right? It does not explain why we have bugs, right? More code you have, you would expect to be a good explanation for the bugs and it's not. Same for comments, well, no surprise there. It's actually more statistically significant than lines of code, which, okay, sure. Size against statistically significant, bad R square, R square is bad all around. If you thought that you can rely on the social network of GitHub as a project, has more stars, more people looking at it is going to have fewer bugs because of that, yeah, don't rely on that. Same for watches, how many people are actually watching conversations on a project? No relations to bugs, it's not helping. Forks, issues, issues actually looks pretty good, right? Number of issues reported, increase of bugs 0.6, 0.46, so that looks pretty good, statistically significant result, but bad R square, it's not explaining all the bugs that we find, so yeah, none of these makes sense. So what is going on here? Can we find anything that explains bugs? Interestingly, if you start looking at number of functions as a metric, we start to see significant results with relatively decent R square, right? Closer it is to one, the better, but hey, 27% is not terrible compared to what we found so far. And same for number of cognitively complex functions. So cognitively complex functions pretty much just means like, is this function readable by a human? It's actually defined, there's a paper, you can find it online that actually explains how the cognitively complex score is calculated, we have a threshold of, for every bad coding practice you get a point, and if you reach 25, your function is considered cognitively complex, so we only count the ones that are over that threshold, and yeah, it's actually a pretty good estimate, right? For every 10 cognitively complex functions, you will get a bug. So hey, that kind of makes sense that it's still significant and it's a good R square. Interestingly though, percent of functions cognitively complex has a really good estimate, right? Each percent increase in the number of functions that you have in your code base that's cognitively complex will add a bug. That's pretty awesome find, right? But it has a bad R square on itself. So maybe just doing a plain linear regression model is not good here. Maybe if we do a multiple linear regression where we actually combine these variables into a single model, they might be able to find whether these variables remain statistically significant and just throw out the variables that are not. So what we actually find is the percent of functions cognitively complex remains statistically significant. It goes down a little bit. What we see is that for each percent, we get a 0.8 bug, and the R square is the best so far. Same for just number of functions plus number of cognitively complex functions. We see pretty good results so far. If we combine all three of those, everything remains statistically significant, and we see a 0.5 increasing bugs for every percent. So yeah, that percent of functions cognitively complex in your project seems to be a pretty good vector and easy to look for data point in any project. You kind of see where you are at. If you look at this model with the other data that we gathered with Facebook infer, for example, what we see is a reasonably okay R square, right? 0.11, 10% of the variance is explained, not really great, but it's at least not close to zero. Interestingly, number of functions no longer statistically significant, so that is not a good variable in this model, so that could be thrown out. So again, it's the complexity, the cognitive complexity seems to be the key here that explains bugs found by Facebook infer. Yeah, bNabs, all the data is crap, so don't rely on that. Now here's some fun. Let's look at the charts of what we find here. Number of functions and bugs cross-reference. Can you spot anything here that looks weird, right? That project there with over 60,000 functions and over a thousand of those are cognitively complex, like what are they are smoking? Here, number of complex functions and scan build bugs, we have some here with over 14,000 complex functions, like all right, that must be a bug or that must be some special project. Or the one here on the left and the top, right? Very few complex functions, a ton of bugs, all right? Now here's my favorite. On the right, 100% of the functions are cognitively complex. That must be some underhanded C repository that people love and it just has a ton of stars, but it's all C, macros and fantastic. Anyway, the point here is that we have a ton of outliers in this data set, right? So do we actually want to include that when we are trying to kind of figure out where we are? Interestingly, there is a statistical method to actually filter out outliers from your data set. It's called Cook's Distance. And with that, we can actually identify 41 repositories out of those 2,000 repositories that have an outweighed effect on our model. And if we actually take just these 41 repositories out, we actually see a 37, 0.37 R square. So right, we jumped 10% in our explanation with the model for the data that we saw. So that's pretty good, right? So we have some outliers in repositories that we scanned. It may be an artifact of just those repositories are what they are, or it might be a bug on our side or in the tools that we used, who knows, but we can kind of control that, filter those out, throw it away, and we still have statistically significant results for all the variables we had. Interestingly now, percent of functions coding thinly complex goes down to 0.27. So that means every four, five percent increase in complexity will add a bug to your project. So that's still pretty good correlation there. OSSF scorecard, even on the filtered data set, still has a good estimate, statistically significant results, but a terrible R square. So OSSF scorecard, even with the filtered data set, no bueno. So R just complex functions more buggy in general, right? At this point, that's reasonable questions to ask. And what we see is that it's actually only 3.34% of the functions that were cognitively complex had a bug. That's a relatively low number, right? Compared to non-complex functions, we see less than 1% of non-complex function. But in terms of total number of bugs, what we see is that 44.7% of those bugs were found in cognitively complex functions. So that means that while only 11.8% of functions were cognitively complex out of the total, they had almost half the bugs that we found. And that's a pretty good indicator that if you want to fix your repository, make it better, focus on cognitively complex functions first, because you have fewer of them, and they likely are the problematic part of your project. So again, we may have screwed up the analysis that we did here.  We again used ScanBuild, which is a very conservative static analysis tool. It's pretty good, but it doesn't detect all the bugs. And it does have false positives. We might have had bugs in our data collection and analysis. We only built 2K repositories, and it's only CNC++. So better that is. What we find here is applicable to other projects. Who knows? And again, the only built repositories that actually had dependencies available on Debian. So that might have been a question. Oh, you've got complex functions. Can you please repeat the exact definition of it? The cognitively complex functions. I suggest you look it up. The definition of it is involved. There is a bunch of factors into it. But it boils down to, is this function readable by a human? That's pretty much the gist of it. If a human were to try to read it and understand what that function is doing, will he have a good time? That's effectively the definition of cognitively complex. And then linear regression modeling might not have been the best tool here. Again, the data set we saw on those charts don't necessarily fall in a straight line. So maybe some other modeling would better fit it. I haven't found one that did, but it might be. So some discussion points, right? So where are we? What can we getter from all of this? Like even if all the bugs we found were false positive, we didn't care about bugs. We didn't set out to really find bugs. We wanted to figure out how can we estimate risk? So if we have tools that find a ton of bugs but they are all false positive and they all seem to fall into functions that are also really hard to read by humans. That just kind of means that we have code that is both hard to reason for machines and hard to reasons for humans. Is that code going to be less risky or more risky? Make your own call. If we have our own model that predicts a high buck count for a project and then we have the open SSF scorecard that says this is good, which one do you trust? Seems complexity correlates with bugs. So why would security risk? How are you defined that be different? Another discussion point, scan bill is free. It's out there for a long time. It's very conservative. Why do we even have repositories where we find bugs with it? We should find zero bugs with scan build. Yet we found a ton of them. So I actually turned part of this project into a GitHub action. You can actually throw this on your project and it will actually run scan build on it and you can just run this on every PR and not merge code that has bugs in it. And yeah, in the end it's a pretty hard undertaking to actually correctly estimate security risk. And we absolutely do need automated tools like the open SSF scorecard. But without data, how can we actually trust that those results are good? We get a low score with open SSF scorecard. Definitely go and check out. We'll probably find issues with those repositories. But if you get a high score, at this point it's probably not a good idea to give those projects a pass. So that's not good. And I really would like to see when new security tools are coming out and making claims of simplifying security for the masses, to please show your data. Please back your claims with something that we can reasonably verify and not just guess. If you think we made an error, please publish your data. All are all up on GitHub, including the tools and the scripts. So please take a look at your own scans. I hope this is just a starting point for a discussion and just another point that can be added to the scorecard later. And please don't sue us. Thank you. That was my talk. Any questions? So you explained that you selected your data for repositories that already had 400 stars. And I understand why you did that. There are good reasons for doing that. But I wonder if that self-selected for projects where the variance within that set is very small. And then if you extended that to projects with 40 stars or 10 stars or whatever, then all of those other things that you found were not significantly significant in your set, you would see them having much more of an impact because then you're looking at repositories that we would expect to be less well maintained or have more variance. So did you look at any of those kinds of projects at all? Did you do any kind of sampling of? Yeah, the answer briefly is yes. That could absolutely have an effect on the data that we collected. It could have a selection bias on its own, right? The same way as the fact that we used only C and C plus projects and the ones that had dependencies available on Debbie and all of those would factor into the threats to validate it to the findings we had. We started with projects that had 1,000 stars and then we slowly went down to 400. That sort of already takes three weeks to churn through. So if we wanted to have a cron job that kicks in every first day of the month and have a monthly scan, it was kind of the sweet spot of 400 repositories that actually finishes within a month. So that's kind of why we picked that number. If you wanted to test it with 40 stars, just fork it, change the number to 40 and see where you at. It's doable. I haven't tested that data set myself. Yeah, I mean, rather than doing everything, you do like a random sample of some of the other stuff to see if it does provide value. But thank you. So what is your recommendation for scorecards like OpenSSF? One of the things that comes to mind is if projects enable certain compiler options that engineer way classes of issues, one would be to check if you find less bugs. If you enable a particular Clang option that doesn't allow some attack primitives to exist or bugs to exist in the code, that should be a plus by default, I guess. The other thing is, should we ding people for using memory unsafe languages and then plus one them for using memory safe languages in projects? Is that another viable option? And of course, this is only for memory safety bugs, right? There are logical bugs that are hard to find, probably more easier to exploit as well. Fantastic ideas. I have no answer, but I would love to see the data when you actually do that study and make a judgment call on what the recommendation would be. Could be, either way. Until we see the data, I can't make that judgment, right? And people like to say Rust is going to be the be all solution for security. It's still very easy to have buggy Rust code. So just because you have Rust, until you show me data that actually shows some correlation, I don't know how to make that call, right? My question is, what do you consider as a bug? Like, are you considering only memory safety issues? Are you considering logical bugs? And how do you define it? So the scan build list of bugs that it finds are defined and are documented. The only bug that we disabled specifically was that stores, right, when you actually move some value into a variable that will be discarded immediately after. That was the only bug that scan build also finds by default. We consider that to be noise. So we disable that one. But all the other scan build bugs, you can go up on their documentation. It finds buffer overflows, unsafe function calls, like string copy, basic things. So it's a very conservative. But these are typical memory safety issues, right? Like, when you said give me the data, I mean, when there is a particular thing that is going to protect a buffer overflow from happening at all, like the compiler is going to ensure that this won't happen, the data will show, right? I mean, we can run the numbers. And probably you can run the numbers on projects that have these compiler options enabled. That could be an interesting vector to including the OpenSSF scorecard. But again, we need some data on that as well to see which compiler option actually has a observable effect on the. So I think for you, since you've run the data, it'll also be good for you to say, hey, look, we found this compiler option in this project. We find less bugs, right? And this is how our data is. Because people need some signal on what is the right thing to do here. And there is a feedback loop. I think that might come from you there. Yes. So my recommendation is definitely use OpenSSF scorecard, install it on your repository. Again, over time, we will be able to see if we find a correlation with CVEs and OpenSSF scorecard scores. So definitely don't take away the message that don't use it. If we wanted to measure it right now, we couldn't do that. Because that will take time to actually see whether there is a correlation. We have a question behind you. Could you please share that paper on the complex functions? Yeah, absolutely. Actually, if you Google for ClangTidy cognitive complexity, the first thing that comes up on Google will be linking to that paper as well. It came from Synapsys, which is a static analysis company. They defined it originally, and then ClangTidy ported it. We actually found the cognitive complexity scoring itself to have bugs sometimes when it runs into some funky C macros. So again, everything has bugs. Thank you. Yep. Yes? Were they discarded for reasons of the tool chain or discarded for reasons of the repositories? Again, they were discarded because of this statistical analysis method called Coug's distance. So that actually takes into account the regression model itself and sees which of the observations had an outweigh effect on the model. And it effectively removes these observations one by one and see how far the model changes. And based on that, we can remove it. So it wasn't manually picked. I didn't go through. I don't know what these 41 repositories are. I relied on the statistical analysis itself to point out what are the outliers based on the model itself. So I have absolutely no idea what these projects actually are or whether they are bugs based on the statistical analysis tools or whether these are actually two observations. It could be that we actually have repositories that are as such. And if we remove it, then it negatively affects our model. So we actually see an improvement which is actually not good. But based on the charts that we manually looked at, it's probably reasonable to remove repositories that are 100% cognitively complex. I mean, we don't necessarily want to make conclusions based on some random repository that is probably underhanded C-contest contestants. That's not necessarily going to be applicable to something else. Again, I'm making a guess. I have no idea what these repositories are. When I hear 100% complexity, I think of every Kodak package I've ever read, those things are terrible. And every single device has 1,000 of them. Thank you. Appreciate it. It's two questions. So the first one is whether you consider at some point using maybe data from OSS Fuzz, which likely has less false positives than maybe a static analysis. So OSS Fuzz is actually included in the scorecard as a metric. But the only part of OSS Fuzz is considered here is whether the project has a OSS Fuzz harness. Whether the OSS Fuzz harness is good or not, that is not factored into the scorecard. And just because a project is in OSS Fuzz does not mean it is going to be well-fussed. It's going to be continuously fuzzed. But again, my day job, I do a lot of fuzzing. And it's actually really easy to find projects that are in OSS Fuzz and still find a lot of bugs in them by tweaking the fuzzing harness. So it factors the OSS Fuzz that could be included here probably would be code coverage, would be interesting to include. So not just whether it's fuzz, but how well it's fuzzed should be probably included in the score somehow. Right. And I also wanted to know whether you examined any of the bugs close-up or whether you found any interesting bugs from ScanBuild or any of the other analyzers. Nope. You didn't check them at all? Nope. Perfect. Thank you. If you grew up on the website, but I checked this, we have a sorted list of the repositories based on the highest number of bugs. So you actually have the top 100 repositories with the most bugs for each monthly scan. So you can actually have this kind of wall of shame. If you see your company on the wall of shame for bugs, remove it. Any other questions? We have time. All right. If no other questions, there is one more. No? OK. Well, then thanks. Check out the project and hope to see more studies like this in the future. So thanks.


Welcome back to the Trade Hacker Mindset. We are going to be continuing with our discussion points from the book Trading in the Zone by Mark Douglas. In this episode, we're gonna be talking about the impact of beliefs on trading. Trading the markets can be difficult to master and seemingly just out of reach. Professional traders have a secret.  It requires the Trade Hacker Mindset. All right, so let's jump into our discussion of the impact of beliefs on trading. We know that if there are conflicting beliefs within your mind, this is going to impact how you trade. If the external environment can express itself in an infinite combination of ways, then there's really no limit to the number or types of beliefs available to be acquired about the nature of our existence. In other words, there is a lot that we can learn about yet to make a general observation about the nature of humanity, I would say that we certainly don't live our lives in a way that's consistent with that statement. If it's true that it's possible to believe almost anything, then why are we always arguing and fighting with each other? Why is it not okay for all of us to express our lives in a way that reflects what we have learned to believe? There has to be something behind this relentless attempt to convince others of the validity of our beliefs and to deny the validity of others beliefs. Think about this, every conflict, no matter how small or how big, how little, how insignificant or how significant, whether it's between two individuals, entire cultures, societies, countries, the result of these conflicts are over conflicting beliefs. In some cases, we are so intolerant that we're willing to kill other people just to get our point across in the way of a war between nations. And these beliefs are not only in our subconscious, right?  We consciously have these conflicting beliefs to a real high degree, to a major extent, in some cases. So really, each individual belief has to have some quality of either awareness or self-awareness that causes it to function as it does. As humans, we all want to be believed, right? It doesn't matter what the belief is, the experience of being believed feels good to us. And I think these positive feelings are universal, right? It doesn't matter what culture you're in, what country, what society, meaning they apply to everyone. And on the flip side of that, conversely, no one likes to be disbelieved. It doesn't feel good. When you hear somebody say, I don't believe you, then you have kind of a negative feeling that resonates inside you. And to some degree, none of us like to have our beliefs challenged. The challenge feels like an attack, like somebody is attacking us and we always become defensive if somebody attacks our core beliefs. When we express ourselves, we like to be listened to. If we sense that our audience, whether it's one person or a group, if they're not paying attention, how does it feel? Not good, right? I mean, again, this is kind of a universal response. Conversely, why is it so difficult for us to be good listeners? Because to be a good listener, we actually have to listen without thinking about how we are going to express ourselves the moment we can either politely or rudely interrupt that person who's speaking. So what is this compelling force behind our inability to listen without wanting to interrupt? Don't we like being with other people with similar beliefs because it feels comfortable and secure? Don't we avoid people with dissimilar or conflicting beliefs because it feels uncomfortable or even threatening? The bottom line is the implication is the moment we acquire a belief, it seems to take on a life of its own causing us to recognize and be attracted to its likeness and repelled by anything that's the opposite or contradictory. Considering the large number of divergent beliefs that exist in the world, if these feelings of attraction and comfort or being repelled or threatened or universal, then each belief must somehow be conscious in its existence. And this conscious structured energy must behave in characteristic ways that are common to everybody. In the book, Trading in the Zone, Mark Douglas talks about, there are three basic characteristics about beliefs that you really need to understand to effectively install these five fundamental truths of trading that we've talked about in previous, so these three basic characteristics about beliefs are, number one, beliefs seem to take on a life of their own and therefore they resist any force that would alter their present form. Number two, all active beliefs demand expression. And number three, beliefs keep on working regardless of whether or not we are consciously aware of their existence in our mental environment. So let's talk about this first one, beliefs resist any force that would alter their present form. Now, we may not understand the underlying dynamics of how beliefs maintain their structural integrity, but we can observe that they do so even in the face of extreme pressure or force. Throughout history, there are many examples of people who believe in some issue or some cause that was so powerful that they chose to endure torture and death and in all kinds of stuff just because of their beliefs, right? We've all heard these, it could be the Bible, it could be some historical event where we've seen these types of situations happen. Now, this does not mean that beliefs can't be altered. It means that we have to understand how to work with them. Beliefs can be altered, but not in a way that most people may think. Once a belief has been formed, it can't be destroyed. In other words, there's nothing we can do that would cause one or more of our beliefs to cease to exist or just evaporate as if it never existed at all. Each individual belief is really a component of what we consider to be our identity. So if somebody tries to attack you, then your natural reaction is going to be to fight or to defend yourself, right?  Well, our beliefs make up our entire system, so doesn't it make sense if somebody's challenging your beliefs, your natural reaction is to resist that and fight back and defend what you believe? So the most effective way to work with your beliefs is to deactivate them. It's to make them softly inactive or non-functional. So after you deactivate the belief, the original structure of the belief remains intact, so technically it hasn't changed. The difference is that belief no longer has any energy.  And without energy, it doesn't have the potential to act as a force on our perception of information or on our behavior. In his book, Mark Douglas talks about how growing up, he was taught to believe in Santa Claus, right? A lot of Americans are. And so this is a good example of, you know, think about how passionate or how big of a belief that might have been when you were a kid, but once you found out that Santa wasn't real and now today as an adult, you know that that belief, you know, it's not gone, it still exists, but now it's just inactive. It's non-functional. And these beliefs exist as concepts, but they have no energy to them. So now today, if you're sitting here right now as an adult and somebody comes up to you and says, hey, Santa Claus is at the door, how would you react to that? How would you interpret that information? You would probably treat it as pretty irrelevant or as a joke, right?  So now as an adult, you basically have two contradictory distinctions about the nature of the world that exists in your mental system. One, Santa Claus exists. Two, Santa Claus doesn't exist, right? The difference between them is the amount of energy that each of these contain. The first has virtually no energy and the second has energy. So from a functional perspective, there's no contradiction or conflict. If it's possible to render one belief inactive, then it's possible to really deactivate any belief despite the fact that all beliefs seem to resist any force that would alter their present form. Now, the secret to effectively changing our beliefs is in understanding and consequently believing that we really aren't changing our beliefs. We're simply transferring energy from one concept to another. One that we find more useful in helping us fulfill our desires or goals that we're trying to achieve. All right, so now let's talk about the second characteristics of beliefs and that was all active beliefs demand expression. I've already mentioned beliefs fall into two basic categories. They're either active or inactive. The distinction between the two is simple. Active beliefs are energized. They have enough energy to act as a force on our perception of this information and inactive belief is just the opposite. So when I say beliefs demand to be expressed, it also depends on the level of energy or passion that you have around that belief. Sometimes you may want to hold back expressing on your beliefs. And this might happen for several reasons. I mean, think about a scenario when you're with a person that's saying something that you completely disagree with or even think of it as completely absurd. Will you express your truth or will you hold back? Well, that depends on the beliefs that you have about a specific situation. If your beliefs dictate that speaking up would be inappropriate and those beliefs have more energy than the ones that are being contradicted, then you might hold back and not argue openly. I think we've all been in situations where somebody said something that we completely disagreed with but we just kind of nodded our head and yep, okay, yep, mm-hmm, yep, okay, okay, see you later, that kind of thing. And so it depends on the amount of energy.   So what happens when one or more of our beliefs are in conflict with our goals or dreams or desires specifically when it relates to trading? The implications of such a conflict have a profound effect on our trading. As we've already talked about in previous episodes, beliefs create distinctions in how the external environment can express itself. Remember, these distinctions by definition are boundaries and in trading it's kind of a boundary-less environment and when we think outside of the boundaries of our beliefs this is commonly referred to as creative thinking, right? And as far as I'm aware of there's no real consensus among artists or inventors or what we call creative communities as exactly where creativity is generated from. But what we do know is that creativity seems to be pretty limitless. It doesn't have boundaries. If there are any limits on the way that we can think we certainly haven't found them yet. Just think about how quickly things move today with technology and think about 20 years ago what you're seeing today and things that you probably couldn't even conceive of 20 years ago. Well, these were all born in the minds of people who are willing to think outside the box, willing to think outside these boundaries. And whether you think of yourself as a creative person or not we all have the potential to think creatively and we all have the potential to encounter a creative experience which is basically anything new or outside the boundaries imposed by our beliefs. It could be a new sight, a new smell, a new taste something we've never seen before but from the environment's perspective it was always there. And sometimes these creative experiences can happen we can expect them but sometimes they can happen as a surprise. But regardless when we experience them we can kind of be confronted with a major psychological dilemma, right? A creative occurrence whether in the form of a thought or an experience can cause us to be attracted to or desire something that is in direct conflict with one or more of our past beliefs. Remember in past episodes when we talked about that example of the boy who encountered a dog who bit him and that was kind of his first experience with the dog well, remember that that experience was real for that boy but other experiences with dogs going forward were actually the result of how his mind processed that information. So the end result is that he experiences fear every time he encounters a dog. And so in this example since this happened to a boy when he was kind of a young toddler type age then it's not out of the realm of possible that going forward he's gonna have this belief and he's gonna have these sayings in his mind like all dogs are dangerous. And think about that just with that use of the word all the boy's belief is structured in a way that assures that he will avoid all dogs. He has no reason to question this belief because every experience that's confirmed and reinforced this validity. However, the boy along with everybody else is susceptible to a creative experience. Under normal circumstances the boy would probably do everything possible to make sure that he does not encounter a dog. But what if something unexpected or unintended occurs? Let's say the boy is walking with his parents and as a result feels safe and protected. Now suppose he and his parents come to a corner and can't see what's on the other side they encounter a scene where several children about the same age of the boy are playing with some dogs and they look like they're having a lot of fun. Now this would be considered a creative experience. The boy's confronted with this indisputable information that what he believes about the nature of dogs isn't true. So what happens now? Well, first of all, this experience it wasn't at the boy's direction, right? This came up unexpected. He didn't make a decision to willingly expose himself to this information that contradicted what he believed to be true. And second, this experience of him seeing other children playing with dogs and not getting hurt that's gonna kind of throw his mind into a state of confusion. And so after he processes this information and starts to accept the possibility that not all dogs are dangerous, several scenarios might start to be possible. Seeing other children his own age having such a great time playing with dogs could cause this boy to decide that he wants to be like the other children and have fun with dogs too. And so if that's the case then this creative encounter has caused him to become attracted to express himself in a way that he formally didn't believe was possible which is interacting with dogs. In fact, the notion was so impossible that it wouldn't have even occurred to him to consider it. Now he not only considers it, but he might desire it. But the question is, will he be able to express himself in a way that's consistent with this desire? And the answer to this question is it's a matter of the amount of energy that is flowing to each belief, right? Is the energy of him being scared of dogs more powerful? Or is the energy of his desire to be with the other kids and play and have fun with dogs more powerful? And that's the balance. And that's the kind of the way that beliefs work and in one working its way over another. So this is an example of what you call an active contradiction. When two active beliefs are in direct conflict with each other, both demanding to be expressed. So in this scenario, the boy has to find a way to, if he has this desire to wanna play with the dogs and have fun with the other children, he has to find a way to deactivate his thought that all dogs are dangerous, right? He's gonna have to take that all out of the equation and understand maybe that some dogs are dangerous, but some aren't. And the key thing is that these beliefs, when they get in this direct conflict, beliefs just don't necessarily deactivate themselves, right? Something has to happen or we have to consciously take steps to deactivate them. And all of our beliefs, they start when we're first born and all the way up until today and these new awarenesses and new discoveries, they instantly and effortlessly become dominant forces within our belief system.  Well, what we've just described with the example of the boy and the dog is the exact same psychological dilemma that virtually every trader has to resolve. So in navigation trading, we always talk about that we trade based on statistics and probabilities, not hype or emotion. And so let's say that you think you have a firm grasp on the nature of probabilities. And as a result, you know that the next trade is simply another trade in a series of trades that has a probabilistic outcome. Yet you find you're still afraid to put that trade on or you're still susceptible to several of the fear-based trading errors that we've discussed in previous episodes. Remember, that underlying cause of fear is the potential to define and interpret market information as threatening. So what is the source of this potential interpretation that market information is threatening? It's our expectations. It's our expectations of what we think should happen. When the market generates information that does not conform to what we expect, which is just the up and down ticks, they seem to take on a threatening quality. So consequently, we experience fear, stress, anxiety. And what is the underlying source of our expectations? It's our beliefs. So regardless of what you now understand about the nature of beliefs, if you're still experiencing negative states of mind when you trade, you can assume that there's a conflict between what you know about probable outcomes and any other number of beliefs in your mental environment that are arguing or demanding expression for something else. Keep in mind that all active beliefs demand expression. Even if we don't want them to, to think in probabilities, you have to believe that every moment in the market is unique or more specifically that every edge has a unique outcome. When you believe at a functional level that every edge has a unique outcome, you will experience a state of mind that is free of fear, stress, anxiety, or any negatively charged energy when you trade. And remember, a unique outcome is not something that we've already experienced. By definition, it's unique. When you believe, when you truly believe that you don't know what's going to happen next, then what exactly are you expecting from the market? If you said, I don't know, then you're absolutely right. If you believe that something will happen that you don't need to know exactly what that something is to make money, then where's the potential to define an interpret market information as threatening and painful? There isn't. Every moment in the market is really as well as in everyday life, it has elements of what we know and it has elements that we don't know or can't know because we haven't experienced it yet. Until we actively train our minds to expect a unique outcome, we will continue experiencing only what we know. And everything else will just pass us by. When you truly believe that you don't need to know, then and only then will you be thinking in probabilities and you will have no reason to block or discount or distort or deny or defend or attack anything the market is offering about its potential to move in any particular direction. If you are not experiencing the quality of mental freedom implied by what I just said and it's your desire to do so, that's a big one, you've got to have the desire to have that mental mind state, then you have to take an active role in training your mind to believe in the uniqueness of each moment and you must deactivate any other beliefs that argue for something different. Now, as I'm saying this personally, I can tell you my mind thinks this way, I would say probably 90% of the time, but there are still times and whether it's just because I'm not focused, I'm distracted or for whatever reason, there's 10% of the time when I don't fall into this category where I don't think in probabilities, where I start to feel anxious or stressed or something when I'm in a position or I'm getting ready to take a trade and so it's a continual process for me to continue to think in probabilities, to continue to think in this unique probabilistic outcome to get those negatively charged thoughts out of my mind and so it's a constant process. It's not one of those things where you can just check a box and say, okay, I'm gonna think this way, I'm done. It just doesn't work that way. It's a journey and that's why we talk about our trading journey and becoming better traders and learning over and over and over. Now, I've gotten to a place where I'm very consistently profitable and have been for the last 10 years, but that doesn't mean I'm not gonna have periods of time when I fall out of my mental state that I need to be in, but the fact that I'm aware of that and the fact that I am constantly thinking about it is gonna help me get back on track a lot quicker than somebody who doesn't even have this awareness of the mindset stuff that we're talking about here. Okay, so let's touch on the third fundamental characteristic about beliefs. Beliefs keep on working regardless of whether we are consciously aware of their existence in our mental environment. In other words, we don't have to actively remember or consciously have access to any particular belief or for that belief to act as a force on our perception of the information or on our behavior. So I know it's hard to believe that something we can't remember can still have an impact on our lives, but when you think about it, much of what we learn throughout our lives is stored in an unconscious or subconscious level. So if I asked you to remember each specific skill that you had learned so that you could drive a car with confidence, chances are you couldn't probably remember each specific skill that you learned at the time that you learned it when you turned 16, right? Now, my kids are younger, but some of you might have kids who are 16 or you've tried to teach them how to drive and you were probably shocked at how much they didn't know or how much you had to teach them because you just kind of forgot what it took to learn to drive because it's been so long. Or think of an example of somebody who drives while under the influence of alcohol. On any given day or night, there are probably thousands of people who have had too much to drink and they have no idea about the awareness of how they got from point A to point B. Kind of scary, I know, but think about that. Because your driving skills are so innate because you've learned them, you don't even think about it, people can go from point A to point B on a very unconscious level because they've done it so many times. And their driving skills are now in their subconscious, they don't even have to think about it. It's almost like you don't have to think to breathe.  It just happens. So how this characteristic applies to our trading is pretty interesting. The trading environment, think about this, the trading environment offers us a huge, massive amount of unlimited opportunities to accumulate wealth, right? But just because the money's available and we can perceive the possibility of getting it, that doesn't necessarily mean that we have an unlimited sense of self-valuation. In other words, there could be a huge gap between how much money we desire, how much money we perceive is available and how much we actually believe we are worth or deserve. Everyone has a sense of self-valuation. The easiest way to describe this sense is to list every act of belief, both conscious and subconscious, that has the potential to either argue for or against accumulating or achieving greater and greater levels of success and wealth. Then take these lists of what you've listed that's for gathering wealth, for creating success in trading and take your list of what's against accumulating or what's against achieving your goals and then right next to those, how much energy you think each one has. If you have more positively charged energy arguing for success than negatively charged energy arguing against them, then you have a positive sense of self-valuation. And if not, if you have more energy towards the negatively charged, working against wealth accumulation, then that means you have a negative sense of self-valuation. Now, it's not that simple, right? I mean, there's so many complex little dynamics and beliefs within our mind that we're not even gonna be aware of, not even gonna think to write down and it could take years of hardcore mental mindset work to organize and sort these out. What you need to know is that it's almost impossible to grow up in any social environment and not acquire some negatively charged beliefs that would argue against your success or argue against you accumulating vast sums of money in your trading. Most of these self-sabotaging beliefs have long been forgotten and operated a subconscious level but the fact that we may have forgotten them doesn't mean that they have been deactivated. Think about things that you feel guilty about. You know, these can have an adverse effect on your sense of self-worth because usually guilt is associated with being a bad person and most people believe that bad people should be punished and certainly not be rewarded, right? Maybe this came in your culture or society or religion or just your family dynamics but a lot of people have experienced these things growing up. You know, some people have grown up or believe that making money is in some ways a wrong, right? It's a bad thing. You know, the saying that the love of money is the root of all evil. You know, those kinds of concepts in our head can create this dynamic of self-sabotage when it comes to trading. You know, this comes in all levels. I mean, you might have become a consistently successful trader but your issue might be you're trying to break through to that next level. I know this is something that I've been thinking a lot about is I have a very comfortable level of risk that I take and a very consistent, comfortable level of amount of money that I can average on a daily basis when I'm day trading, not on a daily basis but you know, if I look back over a week or a month there's some pretty consistent averages and it's because I'm very comfortable with the position sizes that I'm using. And I've been looking at and evaluating myself and doing all kinds of things to figure out, okay, why do I make different decisions when I increase my position size? Because I'd like to scale up my position size. You know, if I can easily make 5,000 a week day trading, why can't that be 10,000 or 25,000? It's just adding another zero. It's just adding another, you know, multiple of the contracts that I'm using but it's a kind of a glass ceiling. It's a mental block that I have and I know that a lot of traders have. So you might be in a position where you're still trying to become, you're trying to get the mindset to become consistently profitable and some of you might be in a situation where you're consistently profitable but you haven't broken through to that level of gaining the amount of financial success that you want yet. And a lot of this has to do with our self-worth or our self-value that we have and it comes from all of the beliefs and thoughts and things that have been pumped into our heads since we were very young. So, you know, I'm not implying that you have to deactivate every single belief that would argue against your positive sense of self-valuation because you don't but you have to be aware of the presence of such beliefs and you have to take specific steps in your trading regimen to compensate when they start expressing themselves. So I hope this episode was helpful. In the next couple of episodes, we are going to dive deep into thinking like a trader. We're gonna break that up into two different sections. In the meantime, if you wanna be around a group of like-minded traders, go to community.navigationtrading.com. We've got hundreds of traders interacting on a daily basis, not only about the mindset stuff that we talk about here but also sharing trade ideas with the sole purpose of helping each other become better traders. Look forward to seeing you on the inside and we'll see you in the next episode.


July 4th, 2010. Full Metal Alchemist Brotherhood concludes. If you've been paying attention, you would know that this is pretty much the highest rated anime on my anime list and pretty much everything else. And it's probably going to stay here for a long, long time. On August 13, 2010, Crunchyroll officially licenses its first movie, Makoto Shinkai's 5cm per second. Crunchyroll was mostly used for fan subs and illegal uploads of licensed titles until the 2010s, when they fully committed themselves to removing illegal material from the site, and only streaming content to which they had legal rights. They have since become the de facto number one anime streaming site. On July 8th, 2012, Sword Art Online premiered for the first time, and although it didn't make it to any of the top anime lists, it's noteworthy in that it became quickly one of the most popular anime of all time. And some could say single-handedly revitalized the isekai-slash-stuck-in-a-virtual-reality-MMORPG genre. Since then we've had other isekais such as Rising of the Sword Hero, that time I re-incarnated as a slime, Overlord, No Game, No Life, Konosuba, Re Zero, and Countless Others. On October 5th, 2012, JoJo's Bizarre Adventure airs for the first time, spawning perhaps the most meaned anime series of all time, particularly on the internet. I think the best way to explain this is just by showing you the third highest upvoted post on Reddit, with over 254,000 upvotes. On July 20th, 2013, The Wind Rises is released, of which Hayao Miyazaki stated would be his last film. And then in 2017, to everyone's surprise, Miyazaki came back out of retirement to announce the film How Do You Live? Rumor to be released sometime early 2020. Studio Ghibli executive Toshio Suzuki explained that this is a film that Miyazaki made especially for his grandson, in memory of him before he dies. November 23rd, 2013, the Studio Ghibli film Tale of Princess Kaguya is released. On November 27th, 2013, the Hollywood adaptation of the Japanese manga Old Boy, starring Josh Brolin, Elizabeth Olson, and Samuel L. Jackson, was released worldwide. It did poorly. Often compared to its very successful 2003 South Korean adaptation, this movie was the first notable movie of the decade that was an adaptation of Japanese manga or anime. January 16th, 2014, The Wind Rises is nominated for an Academy Award for Best Animated Feature, but loses to Frozen. On May 28th, 2014, Edge of Tomorrow, starring Tom Cruise, is released with overwhelmingly positive reception. The movie was adapted from the popular Japanese light novel, All You Need Is Kill, released back in 2004. July 4th, 2014, Netflix officially licenses Knights of Sidonia. It's an anime you may never have heard of, but this was historic, being Netflix's first ever anime. Since then, there's been a large struggle between the big streaming services, Netflix, Amazon Prime, Hulu, and others to dominate their own corner of the anime industry for worldwide audiences. On July 18th, 2014, an adaptation of the anime Kite, starring India Isley, Callan McAuliffe, and Samuel L. Jackson. I don't know why he loves anime so much. Does Samuel L. Jackson like anime? Yes, I do. Hint I, too. was released and was met with very poor reception. January 15th, 2015, The Tale of Princess Kaguya is nominated for an Oscar for Best Animated Feature, but loses to Big Hero 6. On July 30th, 2015, a little-known game came out, maybe you've heard of it. It's called Fate Grand Order. It's a gacha game spin-off of the Fate Stay Night series. A gacha game is where, like a Japanese capsule toy vending machine, a core mechanic revolves around using virtual currency to draw, at random, an item, character, a weapon, or whatever, depending on the game. Of course, if you don't get what you want, you can always roll again, and if you run out of virtual currency, you can use real currency, harmless, right? Well, it turns out it's pretty popular. Fate Grand Order has earned 3.5 billion in revenue. 3.5 billion. That's more than every Zelda game combined. More than every Elder Scrolls combined. More than Fortnite, more than Minecraft. Though it has already been popular in Japan for a while, it basically paved the way for gacha games worldwide, including Final Fantasy Brave Exvius, Fire Emblem Heroes, Azur Lane, Conti Collection, Girls Front Line, and of course our sponsors for this video, Raid Shadow Legends. Sorry, no, I'm just kidding. July 3rd, 2016, Makoto Shinkai's Your Name is released, and quickly becomes not only the highest-grossing anime of all time, but also the highest-grossing Japanese film, period. January 23rd, 2017, with news that the 2020 Olympics will be hosted in Tokyo, Japan unveils the official Olympic ambassadors, including Goku, Naruto, Luffy, Sailor Moon, and others. March 23rd, 2017, the last episode of Naruto Shippuden, the message, airs. After a 12-year run starting in September of 2005, the saga of the legendary ninja Naruto comes to a close. Naruto proved itself to be one of the most successful anime series not only in Japan, but worldwide. Sure, the adventures of Boruto premiered the following month on April 5th, but it's just not the same. On March 31st, 2017, the Hollywood adaptation of Ghost in the Shell was released, famously starring Japanese actress Scarlett Johansson as Major Makoto Kusanagi. It was met with only average reviews, with many saying that it wasn't faithful to its source material. On August 25th, 2017, the Netflix adaptation of Death Note was released, with mixed reviews. It did have a little indifference. On July 20th, 2018, Mirai No Mirai premieres in Japan with a critical acclaim. On January 16th, 2019, a Twitter thread was published about anime voice actor Vic Miniana, probably most famous for voicing Edward Elric along with Kurzweber, Junpei Iori, Obito Uchiha, and Broly.  But a lot of people believed it. On February 5th, 2019, Rooster Teeth published an announcement that Miniana was no longer part of the cast for the show Ruby, and that the company was ending all associations with Miniana. On February 11th, Funimation announced one of his roles will be recasted, and they will no longer engage with Miniana in future productions. On January 23rd, 2019, Mirai No Mirai is nominated for an Academy Award. However, it's beaten by Spider-Man Into the Spider-Verse, the best animated feature. It's notable that this is the first non-Miyazaki film ever nominated to the honor. And there's actually more information about that in this video. On January 31st, 2019, the Hollywood adaptation of Battle Angel Elite was released in the United States and met with mostly positive success. It had notable cast members such as Rosa Salazar, Mahershala Ali, Jennifer Connelly, and Michelle Rodriguez. On May 3rd, the movie Detective Pikachu is released, a Hollywood spin-off based in a reimagined Pokemon universe. On May 27th, 2019, the Attack on Titan episode, Hero, was released, which currently maintains the top spot for the highest rated episode in TV history. If you'd like to watch another one of our videos, we covered that one as well. July 21st, 2019, Netflix buys the streaming rights to Evangelion. What that means is that before Netflix, if you wanted to watch Evangelion legally and you didn't watch it on Toonami back in 2006 and you didn't want to buy the $300 box set, you were pretty much out of luck. This is the first time that Evangelion has streamed legally ever, which introduced a whole new demographic to the legendary anime for the first time, unless you found a bootleg copy. On July 18th, 2019, a fire broke out at Kyoto Animation's first studio. The effects of this tragedy was felt worldwide. Out of the 71 employees in the anime studio at the time, 36 people were confirmed deceased. And if you'd like to know more about the Kyoto Animation arson attack, check out this video. Wuthering With You by Makoto Shinkai is released on July 19th, 2019. It was Shinkai's next big project after your name. So far, it has made $176.3 million, making it the highest grossing movie in Japan in 2019. The 12th highest grossing movie in Japan of all time and the 45th highest grossing movie of the year worldwide. And the movie hasn't even been released in the United States yet. September 15th, 2019, Ash Ketchum becomes a Pokemon master after 1,000 episodes, 22 years, and 22 movies. November 3rd, 2019. After 11 years of being the best-selling manga in the world since 2008, one piece is beaten in manga sales by Demon Slayer. Well, that was a lot to cover. And I'm sure I didn't cover everything. I mean, I'm probably missing your favorite anime. Well, let me know in the comments below if I miss anything that should have been on the list. Of course, there were some things that we just couldn't pin a date on, such as the general widespread success of anime. For more anime on the air now than there's ever been in history, countless remakes and sequels, anime being a staple of internet meme culture, all-time high attendance to anime conventions, and celebrities openly celebrating anime when it used to be avoided like the plague. Celebrities such as Elon Musk, Michael B. Jordan, Kim Kardashian, Billy Eilish, and of course, Samuel L. Jackson. Hint I too. Guys, it's been a great decade. Thanks so much for ringing in this new one with us here at The Asian Theory. Make sure to hit the thumbs up, smash that subscribe button, tap the notification bell, and watch more anime. Here's to 10 more years of it.


You know, I can tell you about this stuff and I can show you the data, but there's not very many visual displays Where I can show you the difference between a four and a two-year rotation. We use this demonstration I think it was developed by some Canadian researchers actually and Picked up by the USDA NRCS and has become quite widespread and we take a simple pair of men's underwear and And these happen to be of course fruit of loom. I think is the brand of choice and These are fruit of looms men briefs.  I buried these underwear they started out like that and I this spring in about early May I Buried three pairs of underwear in this block here which had been coming out of soybean and was planted to corn and I took three pairs and I put them in here and That was also coming out of soybean going into corn, but it's a four-year rotation And the reason why underwear is being used is what underwear happens to be cotton and it's all cotton Which is kind of like cellulose That's in the plants plant structure which is in plants also bound into with lignin So there's lignocellulose, but this is just the cellulose microbes love to eat cellulose And so it'll give you an idea of how fast decomposition Occurs in the soil by burying underwear. Why do we care about decomposition? And this is really the crux of this Demonstration decomposition of residues or in this case underwear is really important because what it is is recycling the material from the previous crop and making it available for the next crop and that takes Not only a a mass of microbes But also a diversity of microbes and and the way I think about it is likening it to Demolishing a building say you have an old building, right? And you want to demolish it and reclaim the materials and use as much as possible when you're making the next set of buildings And so you don't just go out there and hire a hundred bulldozers, right? And just have them let at it you hire some bulldozers you hire some guys running the wrecking balls You hire some people that can reclaim the copper hire people that can Reclaim other materials in the building maybe reclaim the glass glass and it takes a whole diversity of Labor force to demolish that building, but at the same time reclaim that material so it can be used in the next building These are the three three underwear that I planted in this Field in May two-year corn soy rotation. So here are the three that I buried in the four-year Corn pea winter wheat soy rotation take your pick which ones would you like to wear?


How's that? Have you ever wondered how the world's best photographers capture those breathtaking moments that make you pause and see the world in a whole new light? Welcome to our journey into the heart of photography where we are going to explore a concept called the decisive moment, a secret that can transform your images from just simply ordinary to extraordinary. We're going to dive into the world of the masters like Henry Cartier-Bresson and learn how to dance with time to capture your own decisive moments. I hadn't been a photographer for very long before I heard the phrase the decisive moment. It seems like this is a holy grail that is talked about in hushed tones, especially by street photographers. This is the idea that you have a scene, that everything is there and something happens. One tiny elusive intangible. You can't quite put your finger on it. You don't know what it is until you see it. Moment happens and that photograph then is elevated to the pantheon of amazing images. It's like a shadow. It's around you but it's always just out of reach. Henry Cartier-Bresson talked about it at length and his photography is often held up as the shining example of a photographer who instinctively understands how to capture that lightning in a bottle when I was younger. I know it all photographer. I used to think this was all about luck that it was just, ah, you know, Cartier-Bresson was quite lucky. He would just wander around taking a lot of pictures and it just all happened. But it's not. It's about not having an understanding of what the decisive moment is and having a system, yes a system in place to up the chances of you actually being able to capture it. Often we're fed a bit of a white light by certainly documentary filmmakers who make these videos about street photographers walking around. They're always taking pictures and stuff. They're always doing something and now that may be a technique that works for some people. For me, I prefer a little bit more of a methodical approach. The decisive moment is such an elusive thing that I want to think more about it as something that I'm baiting, enticing in towards me. Cartier-Bresson had this wonderful phrase, a hawk's eye and a velvet glove. I'm hoping that's correct because if it's not, I'm going to sound like an idiot, right? But you can see that he's sitting there, he's watching, hunting like the hawk. But then understanding, you can't just get charging in that the decisive moment is a flighty, gentle little thing that needs delicacy. So what I want to do is to set the stage for my images to allow life to unfold in front of me and alert these techniques, these insights on how to get better at a decisive moment. When I was a wedding photographer, I stand to be corrected, but I'm pretty sure the Cartier-Bresson didn't just kind of wander aimlessly around, just shooting random pictures on the off chance that one of them is going to be behind the sand like, like, I don't, I'm learning French, but you'd think I would like 96. Anyway, right, he, you know, he's quite clearly drawn to places where there's the possibility of a moment occurring. And in the wedding photography that I was doing back in 2008, I moved back to the UK and obviously it was just before the recession. So it was a wonderful time to come back to a new country with no contact with, you know, out in the countryside and stuff. So I did weddings and that taught me an important lesson about having places that are like a stage for things to happen upon. John Myovitz, I believe, talks about being in a street corner and standing just for a little while, watch things go. I remember I was, I was talking with somebody here while I was mentoring and they were drawn to a subway station in Chicago that had a wonderful brick thing and we were talking about still street photography and I said, you know that every couple of minutes there's going to be a flood of people down the stairs because obviously the train's going to arrive and people are going to get off it. Set up your image, think about the composition, all that kind of stuff that you can use and then see what happens when you let the people do their dance upon your image. That's kind of what we're talking about, you know, setting the stage but there is something else that you do need to learn to kind of maximize that chance once you have that stage in place. Throughout the wedding day there are a couple of, you know, fairly typical events. The father of the bride seeing his daughter in her dress for the first time of that nervousness outside while they're waiting to go in, you know, everybody's inside the church, you know, that wedding march is going to start.  There's always laughs and things like the first kiss, the first stars. All these rituals that occur at a wedding taught me about a skill. There's actually two parts to it. It's timing and anticipation but I couldn't really, I can't really make a single word, a Tim and suspension or something like that. It sounds like everything. If you can like combine the two, you know, in the comments but that is the skill that will make or break a shot. Having those two ideas in your head and the weddings was so useful to do that because you would see moments play out again and again and again so you learned to anticipate them and you also learned about the split timing in them, the very specific timing. The decisive moment is so elusive because if you take the picture too early, the moment that stand-up moment hasn't happened yet. If you take it too late then that moment has passed. So it's about just waiting until the apex or that thing happens and then it because that's the skill that Cartier-Bresson had. It's got nothing to do with, you know, lots of theoretical stuff or what have you. It is simply about being able to anticipate that moment and being ready for it and just letting it, just before it tips over the edge. You can see why it's so subtle, why it's so tricky to find, why, why Cartier-Bresson is talking about this velvet glove that it's just like, you know, you want to, you want that decisive moment just to land like a butterfly on your hand as you take it. It is so important to learn that rhythm of the air and the flow, like, you know, with that gentleman standing outside the metro station in Chicago, you know, to anticipate things. See if there are things that people do time and time again. Is there a pothole? Is there a puddle? Is there something? Is there something that, you know, is continuous, that you can go, ah, I'm getting a feeling for the dance that is all playing around me. And I've got more of an idea about the steps that are going on within that particular dance. I was talking to somebody the other day during, again, another mentoring session. We were talking about, you know, the shooting large format film. He was saying that, you know, the camera's like 300 quid, so nothing. But then the sheets of film, like seven pounds of sheets, I mean, so we decided maybe that was probably like a kind of an expensive thing to pursue. But it brought into focus about how in the modern world these techniques, these applications of encouraging the decisive moment to come to us, to play out in our pictures, has been overlooked in favor of just machine gunning everything in sight with the hope that one of them is going to be, you know, the decisive moment. You can't get a decisive moment like that because you're ignoring all of the aspects that need to be in place for it to happen. You can't just walk through a street with your motor drive bellowing away and think, well, I'll get something later on. You can't just film things in 4K and pick out a certain still image frame and expect that to be a thing. No, you need to put all these elements together. You need to be aware of everything around you. If you're out in a landscape because you can't have decisive moments in a landscape, you know, something might happen, an animal might come into things, something like that. When you are aware of the sway of the trees, of the drift, of the clouds, of the things that are all around you, then you impart that into your image. You impart that into your, the soul in your photograph. When I look at Cartier Brisson's work, that's what I think elevates. And not because he had this, this knack, if you want to call it, of capturing single little moments and stuff like that, but because everything else comes together as well. I know there was one picture that was kind of one of the first that I was shown as an idea of the decisive moment, which is a group of people by a river and the gentleman in the foreground. He's turned around and he's pouring out some wine. People are like, look at that. That's the decisive moment. Wow, that's what makes this picture great. And at the time it was kind of like, okay, all right, I don't really get it. But later on, when I saw more of Cartier Brisson's work and I've seen more of other people's work, that you kind of go, ah, okay, I understand now. I understand how all of these things is not just about that single one thing. It's about everything that comes together to work. We lose that when we just machine gun our way through photography. It's interesting that there are in photography, you know, some really huge names. And often they have a musical background attached to them. And I think I saw a word that seemed to just make sense when you talk about photography about, you know, there is a language of music and there was a language of photography. In photography, you can make a symphony of elements. It's a wonderful turn of phrase that, you know, when you have your, you being engaged with all the things in the scene, about being present, about being aware, about using the technique that I told you earlier, I alluded to. And that is using your ears. You should be so open to using your ears. There's one thing that people never really think about in photography because it's a visual medium that at a wedding, I would sit there and I go, ah, you know, there's something going on. I remember I'd been at a wedding in Bournemouth and it was a very fancy place. It was in somebody's house that everybody's modern buildings and they had a marquee in the background. And I was out the front of the house having a break because it had a long meal service. And they told me, I, do you know, the first dance is going to be like in an hour or so. And then there's all this cheering and shouting and stuff going on. And at a wedding, that cheering and shouting signifies only one thing. So, like, ran. It's great because unfortunately, the house wasn't that big, you know, and of course, they'd started their first dance just randomly. This wasn't even close to an hour. So that was a wonderful example of you need to use your ears. Listen. You as a street photographer, as any kind of photographer, if you're looking, if you're hunting for that decisive moment, you need to be open to the unpredictability of being present, of being aware, of embracing the chaos and the randomness and beauty of life as it unfolds before you. I feel the decisive moment works at a much higher level when you also understand the language of photography, of the words that you can use to create that stage on which to let that moment dance and hopefully be caught by your camera. If you want to find out more about how you can use that language in your own images to convey meaning, then check out this video over here. Thank you ever so much for watching and I'll see you again soon.


What is ever but he's Jason here for Jasper's case race outcome We just sold out our last filler here to give away five spots in the hip parade break In this 2024 our kind of signature series one box break number three guys So first we'll do the break randomized because their names teams distribute the hits out then at the end We'll randomize the original list top five get into the hip parade guys And we're just six full spots away guys from selling out that hip parade today guys and breaking it tonight So here we go. Let's do the break first. Let's roll it three and three six times One two three four five and six six times three and three six six Jimmy Don Luis six times one two three Four five six Six times six times Red Sox down at the temple they raise All right, so of course stick to what you got guys since it is just gonna be a little box break and it's only one card Let you quickly see your teams though. All right. We go guys See who's gonna get this hit here And it's a South for lick to ninety nine eighty four out of ninety nine Milwaukee Brewers squint of James Lassio Yo, Lassio Little South for like coming your way Now let's switch scenes And again, here's all the list of names Here's the roll three and a four Seven times guys lucky number seven one two three four five six seven Seven times seven Seven Joe just missing all man, but still be a maybe Nicholas is in Travis Eugene Jimmy and Eric Congratulations three to four seven times You five are in So again guys like I mentioned before all we're missing is those straight-up spots guys if that can sell out tonight It'll break tonight Like I said before to if we can't sell those six bucks. All right, I'll create another filler guys But we're gonna have to do one more filler and one straight-up spot Which I might be a little easier at this later hour for tonight to try to do just six straight-up spots So appreciate it guys. Thank you guys Jatsby's case breaks.com


Good day everybody Dr. Sanjay Sanyal, Professor Department Chair. So this is the supinecatever. This is the right side of the face I'm standing on the right side camera person is also on the right side This is going to be demonstration of the muscles and the structures that we can see in this dissected specimen So just to bring up to speed. This is the parotid gland that we have dissected out This is the mesetra muscle This is the buckle pad of fat. These are the facial veins in the facial artery Now let's take a look at the structures that we can see here. We can see these muscle fibers here These are the nasalis fibers. The alar part of nasalis fibers And we can see some more of the alar part of the nasalis fibers here also and we can see the Angular vein which continues as the facial vein is disappearing under that now Let's come to this point where my finger is located This is the point which is approximately 1 centimeter from the angle of the mouth This region is referred to as the modulus. The modulus is a fibromuscular Condensation of the face. It is a cartwheel shaped fibromuscular Condensation which holds up the angle of the mouth and this modulus Receives eight muscles which provides attachment to eight muscles Three muscles from the top Three muscles from below one from posterior laterally and one from medially So what are the muscles that attach to the modulus the muscle which comes from the media side? We can see a little bit of that muscle here This is the orbicularis aureus The orbicularis aureus completely encircles upper lip in the lower lip And it is responsible for percing the lips and this is attached to the modulus here The next muscle that we can see here is this one This is the zygomaticus major which comes from the zygoma and gets attached to the modulus We can see some muscle fibers coming from the top These are the levator anguli aureus and the levator labiae superioris Which also get attached to the modulus from below we can see this muscle here This is the depressor anguli aureus and deep to that We will have the depressor labiae inferioris medially the risorius laterally So we have three muscles from below three muscles from top one muscle from the media side What about the muscle from the lateral side if we look closely we can see this muscle here This is the buccinator and if I press here you can see the mouth is moving This buccinator also Gets attached to the modulus the importance of this modulus is that when there is a facial nerve paralysis All these muscles get paralyzed and the modulus this fibromuscular Convinceation drops down and because of this dropping down of the modulus There's a drooping of the angle of the mouth To continue. What are the other muscles that we can see in this dissection? We can see these muscle fibers here surrounding the island. This is the orbicularis oculi Orbicularis oculi has got a peripheral set of fibers which run from the medial canthus Medial canthal ligament all the way around and goes again back. That is the orbital part This is responsible for tight closure of the eyelid then we have another set of fibers which run under the eyelid They get attached to the medial canthal ligament and the lateral canthal ligament both above and below They are referred to as the palpiparal part. They are responsible for gentle closure of the eyelid like when a person is sleeping Let's take a look at this muscle here little bit of that is visible here This is a very extensive muscle which starts all the way from the front of the chest and the shoulder and goes up And it gets attached to the mandible and it decosates with the muscles of the chin This is the platysma. The platysma is located in the subcutaneous tissue of the neck But it is a muscle of the face. It is an axillary muscle of mastication It helps to depress the mandible against resistance. The next structure that we can see here is this Muscle. This is not a facial muscle. This is the muscle of mastication. This is the meseter The meseter takes attachment from the zygomatic arch and it descends down It's a very strong muscle and it gets attached to the lateral surface of the angle of the mandible where my finger is located Onto the meseter tuberosity. The meseter is a very powerful elevator of the mandible Therefore it helps to close the job. The superficial fibers of the meseter are responsible for protrusion of the mandible. When the meseter is contracting strongly It tends to pull the zygomatic arch down and in rare occasions It can even cause fracture of the zygomatic arch and therefore to counter the pull of the meseter down It is pulled up by the deep temporal fascia which attaches to the zygomatic arch from above The next structure that we can see here is this fat.  It is very prominent The exact function of the buckle pad of fat is doubtful though some say it is meant for suckling This buckle pad of fat is only a little bit is visible here It is like the iceberg phenomenon. A large part of it is not visible because it goes deep And it even goes under the zygomatic arch to the temporal fascia Cosmetic surgeons use this for various reconstruction. The next structure that we can see here is this This is the parotid gland. In this particular cadaver We can see this portion of the gland here. This is an accessory parotid gland Which is also referred to as Socia parotidis. This is usually located above the parotid duct or the stensons duct and we can see that and it is giving Communications to the duct. This parotid duct goes across the meseter muscle and we can see that it is going across In this particular cadaver it is going above the buckle pad of fat and we can see it is disappearing in this muscle here It pierces through the buccinator And it opens opposite the crown of the upper second molar tooth. So this is the course of the parotid duct This can be a site of stone or stricture And it is diagnosed by means of parotid Silography this parotid gland and the meseter were enclosed in a tough sheath Which is referred to as a rotidome mesetric sheath It is derived from the investing layer of the deep cervical fascia And it completely encircles both these structures and we have separated them The clinical significance of this is that when there is any enlargement of the parotid gland Like for example parotidis or parotid absis It stretches the parotid sheath And that parotid sheath is supplied by the great auricular nerve Which is the cause of feeling the pain in the parotid region in parotidis So that is all that we can see in this particular dissection. Thank you very much for watching Dr. Sanjay Sanyal signing out. David O is the camera person If you have any questions or comments, please put them in the comment section below. Have a nice day