I'm Eileen Kellescan and I used to be Rachel's PhD student but I just started as a professor and this is my first talk as a professor so it's very exciting for me. And I'll go over the first half of slides half set of slides and after that Rachel will continue with her new findings in our research and today we'll be talking about how we can de-anonymize programmers based on their coding style. Stylometry. Stylometry is the study of language, study of style in language and when we say language we mostly think about natural language which is for example English that we speak or our native languages but there are also artificial languages for example programming languages are artificial languages and when we say stylometry we wanted to look at all kinds of languages and on the natural language side we have been looking at English or English as a second language to identify the native language of a speaker or translated text so that we can again identify the native language or the translator that has been used as well as the author and we have been looking at underground forum texts where underground forum users engage in business transactions and so on and we're still able to identify the authors from their messages and in artificial languages we wanted to see if coding style is unique for each programmer so that it becomes a fingerprint for them and we have been focusing on Python as well as C and C++ and we looked at source code and we saw that there's very high accuracy in de-anonymizing programmers for source code so we wanted to see if we can do this with binaries as well and this work the tools that we have developed and made open source are being used by many researchers or different agencies as well such as the FEI or expert witnesses they can use the scientific information in court while testifying and European high-tech crime units are using this to for example identify suspects in different online platforms and regarding artificial languages focusing on code DARPA is interested in this project as you might imagine since they are part of the Department of Defense and they might want to know the identities of malicious actors as well as uh oops as well as expert witnesses and the U.S. Army Research Laboratory which we are collaborating with and this has been an ongoing collaboration for four years now okay why would we like to do an anonymized program first of all it's out of scientific curiosity I would like to know since we learned programming on an individual basis do we end up developing a unique coding style and this can be used for software forensics or detective plagiarism but at the same time we can use this for example verification of authorship or uh this can aid in copyright investigations but at the same time such um for example security enhancing technologies can be very privacy infringing and it can be used at the same time for surveillance and to track some programmers uh Saeed Malikpur is one example where we can see security enhancing technologies can at the same time be very privacy infringing uh Saeed Malikpur he's an Iranian citizen and he was identified as the web programmer of a porn site and when he went to Iran he was arrested and he was sentenced to death and he has been in prison for years now and he couldn't get out even though he's a Canadian resident just because he was identified as the programmer of this site that is against Iranian government's uh views okay how can we use source code stylometry uh from a machine learning perspective um I'll try to go not into too many details but try to give you the basics about machine learning so that you understand the flow of how we can do anonymous programmers and we're looking at different tests such as multi-class or two-class machine learning uh tasks where we can for example the software forensics plagiarism detection copyright investigations in two-party cases as well as authorship verification which would be a one-class to open world machine learning task and in order to do this we have the traditional machine learning workflow where first of all we need training data that is representative of what we are looking for and then with this training data we extract some features that are representative of coding properties or coding style and we feed this these features into a machine learning classifier so that we can train the classifier to learn each author's coding style from the features that we extracted and after that take the test samples and use the machine learning classifier to identify who this uh who this source code sample or binary or text sample belongs to and in this case we are using random forest because uh by nature they are multi-class classifiers and they then they don't tend to overfit and when we use this classic uh machine learning workflow we see that we get very high accuracies in de-anonymizing programmers which is solving that uh if programmers would like to remain anonymous this is a serious threat to their anonymity if they want to for example contribute to open source repositories and they would still like to remain anonymous and for example with light-scale de-anonymization of 1600 programmers each with nine source code samples that are on average 70 lines of code we get 94 percent accuracy in de-anonymizing or identifying the authors of 14400 code samples and in order to do this we need to develop a method and while we are first developing the method uh we need a controlled environment to do this and for that we chose google code gem as our development data set google code gem is an annual competition where contestants from all over the world try to solve algorithmic questions uh within a limited amount of time and as they are able to provide their solutions correct solutions they get posted online uh google post them and they uh they go to higher rounds where programs become or the uh problem becomes harder and they have to implement more sophisticated functionality so that we can control for the uh difficulty of the problem as well as how advanced the programmer is okay we have our data set and we collected uh source code samples from 1600 programmers and we pre-process uh the code samples especially to get the abstract syntax trees from source code and for that we use the fuzzy abstract syntax tree parser which is able to uh even parse uh incomplete source code and uh with the abstract syntax tree that represents the grammatical structure of code uh we start extracting features, feed it into a random forest and each tree of these 500 trees in the random forest are voting for one particular programmer as the most likely programmer to have written uh a particular disputed test sample and then we do classification and when we are talking about features uh we look at different categories of features for example uh when we look at the source code sample on the left side we see how function names or variable names uh are chosen by programmers and these are higher level features that can be more easily changed uh and they are called lexical features and spaces or the formatting is also part of these lexical features but there are also syntactic features such as the grammar of natural language in this case this is the syntax the properties of this programming language and when we get to abstract syntax tree we can see how complicated this uh structure can get and based on this we extract features such as okay from the lexical side things such as uh variable names function names uh spacing by grams or uh features like this but on the abstract syntax tree side we are looking at more structured features and we abstract things from about uh 50 different abstract syntax tree nodes such as function and statement two nodes that are connected to each other with an edge and then how that the average uh depth of uh uh node is for example and these are very identifying features and they are not as trivial to change quickly and how can we use these in real world scenarios okay we uh extract these features and let's try to see how we can represent and or replicate real world cases let's say that we would like to find out who Satoshi Nakamoto is and let's say that uh we have a suspect set of size X we take the suspect set of uh source code samples from the past so that we can uh train classifiers with this training data from the suspect set extract features and then take bitcoins initial git code as the test sample and then try to see which programmer is most likely the author of bitcoins uh first git commit and when we try to replicate this scenario take 1600 programmers from Google code gem though this is not a suspect set uh with these 1600 programmers we use nine files for each of them and then uh we get 94% accuracy in correctly identifying these and we use nine fold cross validation for this uh what happens if a programmer would like to stay anonymous and knows that coding style would give them away uh obfuscation is the first thing that comes to my mind and uh off the shelf obfuscators such as tunics and it's available online many programmers use it and when we take it to obfuscate our code here we see the original sample and we can see different spacing or formatting with uh a certain abstract syntax tree uh structure as well as different function and variable names and once it is obfuscated all the function names or lexical features are refactored uh with random representations and all the comments are replaced with hexadecimal asker representations so everything is refactored spaces are stripped and so on but we see that the de-anonymization accuracy is not affected by such obfuscations at all because when we look at how the obfuscation happened we see that refactoring uh did not change that abstract syntax tree at all and it remains unchanged so our method is impervious to such off the shelf obfuscators okay what happens when we use more sophisticated obfuscators such as tigress I take about 15 lines of code and obfuscated this function virtualizer and then I end up with about 500 lines of code it looks much more cryptic and I cannot really tell what's going on easily from a higher level and uh at the same time the abstract syntax tree most importantly changes in this case okay and this affects accuracy significantly so the accuracy of being able to identify uh C programmers was 96 percent for 20 programmers and here the random chance of correctly identifying a programmer is 5 percent when we obfuscated with tigress the accuracy drops down to 67 percent okay there is a significant drop in accuracy but compared to 5 percent of random chance 67 percent is still a high threat to anonymity even when we obfuscated with such sophisticated obfuscators and another case uh another real world case would be uh authorship verification for example someone comes up and says that okay I'm Satoshi Nakamoto and in that case we can ask for their past coding samples take those samples to train a classifier where uh this person Satoshi or Mallory uh is one class and then the second class is the open world it's programmers random programmers from the open world and then I take bitcoin uh source code and try to see who it belongs to does it belong to Mallory or to the open world and based on this we can see if it belongs to this Mallory person and at the same time if we have uh training data from this person in the past in different open world scenarios okay what about executable binaries though when we compile code it goes through various transformations does coding style remain in compiled code and uh again we have a few lines of code and in binary it looks quite cryptic we cannot tell much but thanks to um improvements in reverse engineering methods we can uh generate rich feature sets even from binaries and in this case we know that malware authors would like to remain anonymous and do not have any identifying information out there in the public and there was this fun interview with the law about malware author and this used to be recent but it's 2016 September it's not recent anymore uh but when this author is asked who are you the answer is just some guy who likes programming I'm not known security researcher programmer or member of any head crew so probably best answer for this would be nobody malware authors or people that would like to remain anonymous would like to be nobody's but if in binaries coding style is embedded then that shows that that is a fingerprint identifying information for certain uh users online okay again we have our classical um machine learning workflow we need source code samples uh for controlled environment which we take from google code gem compile it and then reverse engineer it to get this assembly features assembly features the compile it to get uh the source code so that we can extract or generate abstract syntax tree as well as the control flow graph and then for 100 programmers we are left with about a million features so but a million features I cannot really tell what's going about what's going on about style of these programmers so we apply uh attribute selection methods to select the features that are most representative of style in binaries and then feed this again into a random force of 500 trees and then do a classification to de-anonymize the programmers and the features that we are talking about are for example once the code is uh once the binary is the sample disassembled we have assembly features and we take assembly token biograms or two consecutive lines and so on and from syntactic features again from the abstract syntax tree we are taking node biograms or the average depth of a certain node and so on and from control flow graphs we have similar features to abstract syntax trees and once we extract all of these we have a lot of features that we are dealing with and for that uh when we are applying dimensionality reduction the first thing we do is uh apply the information gain criterion so that we take features out of these 700,000 features uh that reduce the entropy when they are taken out of the feature set and then we are left about with 2,000 features that keep the accuracy at its highest and it's most representative of the coding style in binaries but again if I want to understand where code is in binaries I won't be able to see this from 2,000 very low level features that don't really mean much when you first take a look at them and for that I also apply correlation based feature selection which is taking the features that have the highest interclass correlation which means that for an author it has the highest correlation but it has the lowest intercorrelation with other programmers and it becomes the most identifying for individual programmers and I'm left with about 50 features now I can get a better understanding of what might be representing coding style or in binaries and when I try to analyze these 50 features even though they are very low level we still get low level properties that are representative of style that remain in binaries and we have things such as arithmetic or logic operations, stack operations as well as file input operations and variable declarations and initializations and these are not very trivial to basically a refactor or change to hide your coding style. Okay we said that we have a controlled environment we take code samples from Google Code Jam and then compile it and the reason for doing this was so that we can control for optimizations which might affect the anonymization accuracy or the anonymity of these samples and when I take 100 programmers and I apply no optimizations when compiling I get 96% accuracy again with nine samples with nine fold cross validation and when I apply optimizations and then as well as stripping symbols the accuracy keeps decreasing. With optimizations it's not affected a lot it drops to 89% accuracy but with strip symbols the accuracy is affected more but again here we see that with strip symbols we have 72% accuracy but the random chance of correctly identifying these programmers is 1% so even stripping symbols is not anonymizing these people. Okay what kind of optimizations can I apply to anonymize myself in an automated way and for that I used an open source project open LLVN and applied three different types of optimizations. First of all bogus control flow insertion where code will never reach that but it's still in the binary so it looks like a feature and what if I substitute instructions with equivalent instructions making the codes shorter or more complicated and so on or flatten the control flow to mess with the control flow features and we see that optimizations are decreasing the accuracy to 88% from 96 for 100 programmers and again this is showing that such obfuscations are not sufficient to hide coding style even in binaries coding style remains after many transformations, compilations or obfuscations. What happens if we try to increase our class size and we have 600 programmers we see that with 20 programmers having 99% accuracy and correctly de-anonymizing them with 600 programmers we get 83% accuracy where the random chance of correctly identifying these people is less than 0.2% and we see that the accuracy degrades gracefully in this case. Okay what about real world cases? This was a very controlled environment Google code jam people are implementing the source code, the functionality in a limited amount of time and it's small snippets of code and so on. For that first of all I parsed GitHub repositories and ended up taking a bunch of codes from hundreds of programmers and after that I compiled those and many of them did not compile and GitHub repositories are working progress so that's okay but it took me days. I'm left with 50 GitHub programmers and able to de-anonymize them with 65% accuracy. Okay this is one real world scenario. What about malicious programmers? I'm currently actively working on that but one case study I had in a published paper was with six malicious programmers and 10 samples. Some of these samples came from the Hacker forum, Null.io forum and once the forum was leaked I was able to find some live links to malicious code that they were selling and providing to their customers and I was able to download those and find the ones that was relevant to my training set and reverse engineer it to get the features as well as some malware orders from security reports and so on. And please, please if you have a data set with known orders of malware or if you have good automated methods for quickly reversing malware or malicious software or encrypted or packed or so on anything that can help it please comment talk to us after the talk and we see that with these six malicious programmers we have 100% accuracy but I would like to make this experiment much larger scale and for that you need help with the data set and now I will leave it to Rachel so that she can talk about more fascinating details about programmer de-anonymization. So I'm going to dig a bit deeper on to programmer de-anonymization on GitHub. When we did this experiment we got much lower accuracy than in the original Google Code Jam data set and with the experiments I'm going to show over the next couple slides I think a lot of this comes down to the fact that a lot of these repos we only had like a couple of files per author and it turns out that this sort of I think that's the thing that matters the most. There is a lot of noise where sometimes people will like link in other things and so on but I think one of the biggest issues is that when you only have like two files to train on or one file in this case and one to test as opposed to the nine files that we used for our experiments I think that makes a big difference. So we've been up till now we've only talked about situations where people are writing code individually on their own. So most people probably don't actually code that way in real life most of the time. Most code is collaborative. So when we started presenting the initial work we had a couple tweets about it, Halvar who some of you might know said I'll believe that this code stylometry stuff works when it can be shown to work on big commit GitHub histories instead of the Google Code Jam data sets and Zuko talked about hearing from an internet apple that they just allow her from contributing to open source on her own time and so we were interested from this like perspective both of you know privacy like if I want to contribute to something and I want to know if that particular commit is going to cause me problems later and also just you know to be more to validate this stuff more in the real world we wanted to do some experiments. So in this case right we're only carrying maybe who wrote a small piece of code or we want to de-anonymize some pseudonymous account on GitHub who we have like several snippets or segments of code right so we don't have these whole files nicely written and in this case we're using the same feature set that we used before we're trimming it down to about 3400 features so like quite a bit more than Eileen was using earlier on but these are very small segments and snippets so sometimes we need more features for that and you know the ultimately when we were doing this we get about 73% accuracy at identifying the author when we're talking about 100 programmers for a snippet of code that's about five lines long. So we are interested in kind of understanding when this works and when this doesn't work so what we did was we built this calibration curve and what that does is it just shows us like in general what the confidence of the classifier is relative to its accuracy for individual samples and we can see that in some cases we have pretty like high confidence and in a lot of cases we don't have such high confidence and yeah so this can help us understand even though we have 73% accuracy we can say given this you know answer that the attribution has given us should we believe it or not based on this confidence and then we can know like if we know it with high confidence we have much better belief that this is actually the programmer that we're looking for and we're also interested in you know how long these snippets need to be and how many snippets you need to train on in order to get good results. So here's a like an interesting kind of we have this sort of curve that happens where so say we have fairly large snippets that are like 38 lines of code in general with the Google Code Jam data that we were talking about before they were about 70 lines of code of the files but in this case we only have four samples for each author or programmer right and this gives us about 54% accuracy on 90 programmers but even if we have smaller samples but much more of them typically our result goes up so even when we're only looking at single lines of code and I'm a little nervous about this result because it's really preliminary but if we had like 150 samples to train on we can usually identify the author about 75% of the time so we're still trying to kind of understand what makes lines of code more attributable or not I mean certain lines of code are going to be pretty generic but other ones not so much but what happens when we want to identify accounts not necessarily individual commits right this actually works a lot better because what happens here is that the errors aren't correlated so we get close to about a hundred percent accuracy if we have four snippets meaning like the way that we the way that we analyze this is we ran like get blame on these repositories right so that's how we get the repository clipped into snippets and it turns out that the errors this 73% are not typically not that correlated so if we if we try multiple times and then we vote our results go close get close to a hundred percent not perfect and so you can see this sort of heat map here where the red area is basically over 90% accuracy and it tends to happen once you end up having more than about nine samples and more than about five account five snippets to train on right like that you're or to test on so once you have like a certain amount of training data and a certain amount of like testing data meaning the account that you're wondering around about has committed you know more than four or five times then your results get pretty good interestingly we were also one of the other things we tried was instead of saying okay we're going to identify these little snippets individually and then vote what if we merge them all to make a big sample and it turns out that's better than the initial snippets but it's not as good as doing them sort of one at a time and averaging because then the errors tend to get compounded in a single merged thing okay now for something a little bit different I'm going to talk about deep learning because it's the new hotness and so we talked about in one of the things that sort of novel about this work is using the abstract syntax tree in the past most people had just used lexical and layout features and sort of they've been doing this sort of code attribution stuff since the 70s but it gets it tended to get around 80% accuracy and not scale above like 30 programmers or so so using these AST type features allowed us to get good results but the thing is an AST itself is not a feature right a tree is not a feature you can't just feed this into a random forest and have it you know tell you who it is we manually chose these features as Eileen has mentioned we chose unigrams and bigrams and depth and so these tend to give us like very local features and very global features but what we want actually is the ability to get maybe more nuanced features than that so enter like a deep neural net so we're going to try and automatically learn a new feature representation so what we do is we first map the AST nodes into a vector and we just use this embedding layer to do that and then we create these subtree layers and they're going to be using LSTMs or bidirectional LSTMs in order to learn new structures of the AST and then we have a softmax layer to actually do the classification so little bit of background on LSTMs if you've not learned too much about them so we have our neural net here on the right it's an RNN which basically allows us to handle sequential input and actually have some memory to be able to remember information that's where those little feedback loops are and an LSTM actually adds memory cells to this to have sort of more useful memory again so we can not just have super local features so these cells have gates in them and they ask sort of what should I remember of this information that's going in what should I just ignore and what should I forget that we've already learned so that we can over time develop a richer representation of the AST so in this case we're only trying to use the AST features we're not using any of the layout or lexical features and that's why these results for the random forest are lower than what Eileen showed but you can see in this is using Python and C++ that we get you know 86 percent accuracy on 25 programmers or 73 percent accuracy on 70 programmers in Python again just using the AST so this layout and lexical stuff matters too but when we use our new AST features we do get a big jump here and so this new feature representation does seem helpful and it's nice to have a sort of a sole AST representation because it does allow us to it's as we mentioned before it's much harder to obfuscate it's easy to port and so on. Yeah so this allows us to learn better ASTs without doing this manual feature engineering and it's language independent and in our future work we'd like to actually combine these features that have been learned with the random forest and the fuller feature set to see if we get better results or if this just overlaps with some of the stuff that we're learning from the layout and lexical features. Okay so what about other languages as I mentioned basically porting this to a new language requires an AST parser which exists for almost everything and lexical and layout features that you choose for the language so so far we've done things we've done things for C++ C Python and JavaScript and we get similar accuracy so far on the Google Code Gem data set the results with just using the AST and we tend to vary more which is kind of interesting. One of the Holy Grail kind of applications of this would be able to test on to sort of train on one language and test on another language and we don't know currently like how much does your programming style change when you actually change languages. So to do this we need some sort of universal intermediate AST representation or some sort of just pairwise you know porting between two languages. There is a project to work on this it's like the Babelfish project but it doesn't really appear like ready yet for this kind of application. It's something we're planning to look into a little bit if people know about sort of generic AST representations that'd be another thing we'd love to get your feedback on. So I'm going to end the talk with a couple interesting sort of software engineering insights that we've gathered as we've done this work about like what makes programming unique which I think is kind of fun. So in general we will we started with looking at attributing groups of people with so there's another programming contest called the code forces contest which has a team competition and the teams can compete on sets of problems. We looked at we have very preliminary results with 118 teams with about 20 submissions each and they get about 67% accuracy. Now I think this is one of the hardest cases for group attribution because the way that code forces works is it gives you a big group of problems to work on as a team together. So I think people are mostly splitting those up so it's not actually group coding so I'm kind of surprised that it even works as well as it does at identifying the team. So in the future we'd like to work again with some more code repositories to get a better sense of like stuff that we know and can control for how much collaboration actually went into it. Difficult versus easy tasks. It turns out that implementing harder functionality makes programming style more unique. So when you're solving you know and we can kind of control for this because the problems in the programming contest are supposed to get harder as they move on. So if we look at the same set of 62 programmers solving seven easy problems we get 90% accuracy which is pretty good. But when we look at the same set solving seven harder problems the accuracy goes up to 95%. So that tends to matter. Also programmer skill matters. So programmers who got further in the contest which is some measure of skill perhaps were easier to attribute. So in general like the coders that advanced less far we got 80% accuracy on them on the again these are on the easy problems because they did that much. But then when we look even on the easy problems at the people who got further in the competition were able to classify them with 95% accuracy. So it's kind of interesting that as you develop programming skill your style tends to be more unique. We're also interested in how coding style changes over time. So we looked at again this competition where people are training and testing on our competing in both 2012 and 2014. So when we train on 2012 and we test on 2014 the accuracy goes down from 92% on this 2012 set to 88% when we test on the 2014 set. So it's a little bit of a drop. I'd be more interested in maybe looking at like when we look at even larger time scales than that or sort of particularly formative sort of years maybe like university and things like that and how it affects people's programming style. Lastly we're interested in coding style by country right. So one of the things that this contest does it has contestants from all over the world. So when we were reporting this to JavaScript we grabbed a bunch of JavaScript files, 84 files written by programmers in Canada and then programs programmers in China. And we were interested in just a binary classification whether we could tell whether the file had been written by a Canadian or a Chinese programmer. Now this we expected to be like particularly easy because there's like a native language difference which may show up in things like variable names and so on. And in fact it worked pretty well. So it was around it was 91.9% accuracy for this task. In the future we're planning to look at much larger set of countries and a much larger sort of set of files and see about sort of if there's actually kind of if this is a native language effect or maybe sort of an education system style culture effect and what's going on there. But I think it'll be interesting. So in future applications we're as we said we're really interested in whether this actually works to find malicious code authors and also you know what sort of anonymous contributors have to worry about when they contribute code online. We're interested in breaking this stuff. So writing better obfuscators all the obfuscators we've tried so far have not been really targeted specifically to the AST. So we think that can happen. There was some research done at the University of Washington building on our work showing that people can kind of imitate other people's style when they're given that as a task to some extent. So that's you know there's hope right.  When we and in particular like if you're going to contribute to a repository anonymously you might want to create a new account for each commit even though that's annoying. To find authors who write vulnerable code we're interested in looking at source code and understanding kind of what software engineering type stylistic features lead to vulnerabilities and also you know some people have talked to us about finding out who to recruit directly but looking at how unique their coding style is and whether that suggests something about their programmer skill. So this was not work done just by Eileen and myself alone. I have lots of students and other collaborators at Drexel University and at Princeton and at the Army Research Lab and at Goddigan in Germany who've all worked on various aspects of this project. So thanks to Bandar, Edwin, Rich, Andrew, Spiros, Arvind, Frederica, Mosfika, Dennis, Conrad, Greg, Claire, Mike and Fabian for all of their contributions to this work. This is our contact information, our code to do all of this stuff so if you actually want to try and figure out who Satoshi not going to motois and have a like actual suspect set you're welcome to try that it's not something that we're going to do we respect privacy. So but you know the code is out there and we have I think about four ish minutes for questions so if people have some questions we would love to take them and then after the talk we'll walk out the back and ask any and you can ask any more questions that you have. Thanks any questions? Yeah. How do we do this? There's seriously no one ever does Q&A. Maybe we should. I intentionally left time. So for the coding styles that you were going through for the people for the Google coding challenge, you said you were able to look at people who are going the furthest in the challenge. Did you see trends that were going along that we could later help make better coders later using that information? No, we have.  So yeah, so we have not done much analysis of sort of what makes the coding style of people who get further in the programming competition, you know, different or more attributable, but I think that that would be a really interesting direction and we'd like to look at it. Do you have anything? And one property was that more advanced programmers tend to write longer code. That's one trend. Yeah, I mean it's it's tricky because we don't know if that's like the causal thing or just sort of a side thing. But but yeah, the in general the code was longer, which helped. So yeah, I just have a comment. This is very interesting. One thing I would see this going towards is cataloging programmer reputation, which is kind of like what we've gotten into with penetration testing. So instead of going towards like a completely open source ecosystem, we're pushing for statistical testing. And this can now further be used to look at programmers and see their history with with security and then give a score to the code in that regards. So do you think there's any value to cataloging people in terms of looking at security for code? Or that's just an underlying ecosystem problem? Yeah, there is ongoing research for automatically understanding security properties of code. And they are working with similar properties. And does this answer your question? Alright, so let's give the speakers a hand.


Hey everyone, happy Thursday, July 1st. Nice way to start the month of July, plus 2434 on the day. Let's go through these. I'll show you what we did. Two mighty 90s, one in Boeing for 624, one in Tesla for 100. I did a Paris trade in the Dow versus the Nasdaq Book 205 on that.  So let's take a look at these. Show you what we did. Let's start with the mighty 90s. Let's go to Boeing. So in Boeing, we had this initial push up early in the morning, got a nice pop in volume, got short right here and caught this little flush down for plus 624. In Tesla, go in the opposite direction, took a long position. So Tesla was flushing down, got a pop in volume, got long right here and just caught this little bounce, got out after two green bars, a small winner plus 100. Also did a Paris trade in Nasdaq versus Dow, just to show you what I'm looking at here. Dow had been staying relatively strong all day, trading above yesterday's high. The Nasdaq had a nice flush kind of mid-morning and started to bounce. So when it was bouncing up towards yesterday's low, I was thinking about either taking a downside runner and just getting short Nasdaq futures here. And I thought, yeah, I'm actually going to go with the regular sized futures, but I'm going to hedge it with the Dow. So I was long Dow. So I was looking for Dow to kind of stay steady to hire. And I was looking for the Nasdaq to fall over. And that's what happened. Dow just kind of chopped around and Nasdaq rolled over and I just booked a profit. I was kind of done watching it. If I do the micros, I don't necessarily have to babysit it as much, but with the full-size futures that P&L was still fluctuating quite a bit, even though I was quote unquote hedged with the Dow. So just took a $205 profit out of that one. That was my last trade of the day. So I was done. And then on the runners, let's go to AMC. This was actually my first trade of the day. Had this initial push up and massive volume. So it started to pull back. I thought we'd get a continuation higher, but AMC just fell apart. And so I just ended up cutting out quickly, took a loss of $390, which based on the position size is bigger loss than I really wanted to take, but still was able to cut it off pretty good. My two biggest winners, Amazon, took a nice downside runner in Amazon. After the market started getting weak, Amazon broke through the lows of the day. When it bounced up to unchanged, I got short right here, cut a little heat for about five minutes, and then finally rolled over, took off half here, and then let this thing run, took off my last half down there for plus $740. And then my biggest winner, Roblox.  Had this nice massive spike in volume when it bounced, got short right here. Actually, I got short about right here and it kept bouncing. So I added right here, rolled over, took some off, kind of chopped around and kind of just blood lower, blood lower, took off some here.  So nice day of trading, nice way to start July, plus $2,434 on the day, on a little bit of a win streak here. I think that's four or five days in a row. So hope to keep that going. We'll be streaming live tomorrow, Friday, and then next week as well. We will be off on Monday in observance of July 4th, Independence Day. But otherwise, Tuesday, Wednesday, Thursday, and Friday next week, we will be streaming live as well. See you then.


This is stock number 9390A. We are here at Summit Automotive in Fond du Lac, Wisconsin. You're new and used. Half-ton truck headquarters. Today we are checking out this Super Clean 2007 Chevy Avalanche. From this HD video you will be able to tell that this truck is extremely clean, considering the age and miles on it. It has factory fog lights. Gold mist is the color. Head lights are nice and clear. This fender is in really nice shape. All the wheels are in really nice condition. Factory alloy painted alloys and it has Goodyear Wrangler 275 55R20 tires with about half the tread left. We shoot all of our videos in 1080p so if you have HD capabilities on your computer tablet or smartphone device turn them on now because it is like you're right here looking at the truck with me. 5.3 liter V8 motor, passenger side rim no scuffs or scrapes on that. One tiny little ding on that fender there but other than that very nice and clean. And down this side of the truck is in really nice condition. I didn't see any corrosion or any signs of corrosion starting. Has the roof rack. Very clean down this side of the truck. Has the factory running boards. This back rim is in excellent shape as well. No scuffs or scrapes on that. And the back tires look to be quite a bit newer than the front ones. These are probably 90% of the tread left on these tires. So lots of tread on the rear tires. As we come around to the back there is a ding in that quarter panel there. Rear bumper is not perfect but it does have the backup sensors. Full towing package on this truck. Tailgate is in really nice shape. No dents or dings on that. It has the factory hard plank tonneau cover. And you get the drop-in bed mat back here that's in really nice shape. Tailgate shuts nice and solidly as you go down this side of the truck. Just as clean if not cleaner than the other side. Didn't see any dents or dings down this side. Passing aside rear rim. It's a nice condition and that tire has a lot of tread left on it too. As you can see down this side of the truck very very clean. Has the power mirrors with the built-in directional signals. Inside the LTZ package gives you the tan leather interior. Both of these seats are power. Both of these seats are heated. There are no rips. There are no tears in the seats. Driver side lumbar factory floor mats. Carpentine's pretty nice underneath. Power windows, power locks, power mirrors. These mirrors do power fold in like so. Any of your heated seat buttons and memory driver seat button right there. Auto headlamps and as we hop inside you can see that this truck has 89,082 miles. Steering wheel is in pretty nice condition. No rips or tears on that. This does have a six disc CD changer. Has the Bose sound system, dual climate control, power pedals, backup sensors, and stability track. This one has the on-star capabilities, homelink system, and this one does have the power sunroof. Pass under side seat is in really nice condition. No rips or tears on that. And we got a factory floor mat on that side as well. We'll take a quick look at the back seats and then we will check out under the hood. Back seats are just as clean as the front seats. No rips or tears back here. Very nice condition. Does have the fixed glass rear window. That actually does come out. That back window does come out. These seats fold up like so and out. And then this whole piece comes off and you can actually put it in there and avalanches are pretty crazy if you've ever checked them out. If you've never checked them out, you gotta because they're pretty neat. Factory floor mats back here as well. And we'll take a quick look under the hood. Under the hood we have the 5.3 V8 motor and Jim Bay's very clean runs very smooth. This truck has been fully safety and inspected by our service shop. Has a fresh oil and filter change. All the fluids have been checked and topped off. And this truck is 100% ready to go. To see more pictures of this truck or one of our other 400 new and used cars, trucks, SUVs, minivans, Wranglers, half tons, three quarter tons, one tons. You name it we got to go to our website www.SummitAuto.com. Full pictures and descriptions of every single vehicle on our lot. Videos of every single used vehicle that we have. All at SummitAuto.com. Thank you so much for checking out the video. If you want to make this truck yours, give us a call right now.<PHONE_NUMBER>. Ask for one of our sales associates to make this truck yours today. Once again, that number is<PHONE_NUMBER>. Thanks again for checking out the video. If you want to check out more, go to youtube.com slash Summit Auto. Remember to like, subscribe, and share on this video and all the videos you see there. In fact, in a second, you'll see a link to subscribe to our YouTube channel on your left. A link to more half-toned truck videos like this one on your right. And if you have not been to our website on the bottom, a link to this truck on our website, click those, check us out. And we really look forward to helping you with this super clean 2007 Chevy Avalanche LTZ. Thanks again.


Good afternoon. Oh sorry. Hi everybody. This is Elena Crete with the Sustainable Development Solutions Network here in New York and we have on the line here Michael Ginsburg who is currently working on a chapter of our white paper on the research and development priorities for deep decarbonization in the United States. Michael is a PhD candidate at the Environmental Engineering School at Columbia University and has been working on this topic for a couple of months now and so Michael is going to walk us through his research to date and where the chapter stands which we are hoping to publish in early 2020 and also just pose some questions along the way and some opportunities where he's looking for resources or collaborations with the SDSN USA Network. So without further ado Michael I'll turn it over to you and we can see your first slide so please advance as you wish. Great well thanks a lot for having me and I'm sorry can you hear that in the background? Okay yeah so thanks a lot for having me I'm really pleased to share my current the current state of my research on the research and development priorities for deep decarbonization. So this this chapter will examine enabling technologies for deep decarbonization it's going to include technological roadmaps by group as well as breakthroughs required and interactions and cost sensitivities between groups in particular it will look at the impact of research and development on timing and cost of deployment of technologies and show dependencies as well as a timeline by decade through 2050. In the first part of this chapter the guiding questions are what are the key technologies that will enable 100% decarbonization by 2050 and what are the R&D breakthroughs that are required. So this section will address the R&D implications by technology group highlighting current bottlenecks and interactions such as cost sensitivities, land use, sector coupling, and timing. This part will be primarily informed by expert elicitation as well as review of the research. So these are the technology groups broken down, electric vehicles, wind energy, solar photovoltaics, carbon capture utilization and storage, geologic sequestration, biofeedstock, industrial sector, fuel cell vehicles, as well as a potential ninth category which is behavior relating to demand response. So the primary data sources that are not expert elicitation come from the evolved energy energy pathways report as well as the 2014 DDPP. So for the first group, electric vehicles, the research I've been conducting so far is tied to primarily the cost of batteries. You can see here NREL's annual technology baseline is the primary data source. So the kinds of predictions that I'm looking for here are dollars per kilowatt and future projections based on both data as well as expert elicitation. For wind energy, I've collected some input from the expert elicitation. The current bottlenecks that I've observed are wind turbine component reliability, the need to increase the life of gearboxes and generators. And one of the ways that I want to demonstrate the technological phase and current relative market penetration of different technologies is through what's called an S-curve which shows the group, the group, the subcategories of technologies that are broken down by startup expansion and mature. So for wind turbine technologies, the mature technology is geared drive, expansion is direct drive, and startup is permanent magnet drive. And so the timeline so far that I've developed is in the 2020s wind turbines with capacity over 10 megawatts will be common. The 2030s rise of offshore wind farms and 2040s recycling and reuse of wind turbines will be especially critical. In addition, one of the results of the expert elicitation is a breakdown of the research needs by type of research. So the questions that I've been posing are based on the type of funding needed by research type, how would it best be divided? So currently the findings are that basic research is 10% whereas applied research, experiments and pilots and commercial demonstration comprise the remainder at 30% each. Another important graph that I want to include is the wind energy levelized cost of energy by level of investment. So these are predictions for what can we expect the price of wind energy to be considering differing levels of research and development investment. So business as usual, today's R&D, you can see has the highest LCOE whereas medium and high R&D results in a much lower levelized cost of energy. Similar for solar photovoltaics, I've broken down into this S-curve where we have crystalline silicon as the primary technology comprising over 90%, cadmium telluride and perhaps gallium arsenide are in the middle and then perovskite solar cells are the current startup tech. The bottlenecks currently are the need for a cost-efficient grid integration, efficiency, stability and the need to extend inverter life. And then the timeline so far is PV recycling in the U.S. will be required in the 2020s. This is particularly important as we see end of life PV modules near their end of life. 2030s, we can see an increase in efficiency above 30% especially with tandem solar cells. And then into the 2040s, renewable electrification of the chemical industry as well as PV powered reverse osmosis desalination systems. So my current research in CCUS as well as direct air capture has identified that direct transformation of CO2 to useful products has a high energy requirement which translates into a high cost. So it's difficult to capture CO2 from point sources like flue gas and ambient air as well as the need to better understand the life cycle emissions impacts of CO2 capture and conversion. And the timeline in the 2020s, direct air capture at hundreds of metric tons of CO2 per year is likely and then the costs in the 2030s should fall to below $150 per ton of CO2. And the 2040s currently is working on that. As well as the S-curve for the direct air capture, we're seeing this is a very new technology with a lot of interest but there's a couple of specific technologies that have been identified that I'm going to discuss in this section. Similarly, the levelized cost of energy translates into a direct air capture cost in dollars per tons of CO2 captured. So that has a similar relationship whereas you go higher in the dollars spent and lower in the tons of CO2 cost. I'll also mention one more thing. The CCUS, the other areas I'm looking at include not just direct air capture but also capture from the flue gas of power plants and and methanation through different materials. So geologic sequestration is an area where my research is still ongoing and I need input from collaborators as well as literature recommendations. Similar for biofeedstock, this is just a graph from NREL's ATB annual technology baseline and I'd like to create the same kind of metrics that I have for wind and solar for biofeedstock. So also welcoming input here. The industrial sector, here we have a graph of a PEM electrolyzer for water splitting electrolysis and this is actually an expert elicitation showing that increased R&D results in lower capital cost of the electrolyzer. So I have, I'm seeking additional input as well in the electrolyzer in this area and fuel cell vehicles as well. So welcoming collaboration here. So the behavior and demand side transformation is an area that that I would like to include but still thinking about how to effectively do so. So once I have those specific areas fleshed out completely what I want to ask in the second part is what is the cost of R&D and ROI for next-gen technologies above the baseline 350 ppm compatible trajectory. So basically this will build on part one by considering the reduction in cost of deployment of the technologies given these given breakthroughs have occurred. So the the cost of deploying currently available technologies is shown in the energy pathways report. In the next slide I'll show you but what I would like to do is to project the reduction in cost as a percentage of GDP given achievement of the breakthroughs discussed in part one. So this is the graph that's shown in the in the report and I like how it's displayed the specific net energy system costs as well as as a percentage of GDP and what I would like to show is the reduction in these costs given technological breakthroughs so that the invest so we can see the the the ROI on the investment. And then finally just I will succinctly end the chapter with the key findings from parts one and two as well as a overall timeline or critical path to deep decarbonization that shows the dependencies between technology groups by decade. So again I want to emphasize that these are the areas that I'm currently engaged in research on and I'm welcome I welcome in particular input from the research teams at various universities who would like to share either reports or their or their expert insights. And to that effect I have created a survey which you can see at the link here. You can also take a screenshot of the of this code and or you could also contact me via email or or at my number. So thank you very much and I'm looking forward to everyone's feedback. Thank you Michael. This is definitely taken a much clearer shape since the last time we talked and since the summer so congrats on that. I can see that you still have a lot of research to do but the progress on the wind and solar and other technology sections is impressive. I do have a few questions that maybe I'll just start out with just so that we have an answer on here and then Caroline or Lena if you have any feel free two of my colleagues are with me here Michael. So one thing I just noticed so throughout this chapter we're really focusing on the comparison of cost as an enabling factor to adoption and one piece of feedback we've gotten in a few of our other studies is how to incorporate other aspects of sustainability to you know promote or advocate for certain technologies. So I'm wondering if there are other ways to include issues of equity adoption and you know positive externalities from you know reduced emissions or greener energy sources in your analysis to kind of promote those a bit clearer. Yeah that's a great that's a great point and I think in my initial in my initial draft and thinking I intended to include more of a life cycle assessment type of description of the technologies and I think that it would be it would be prudent to include perhaps a small summary of the primary for each of those technological groups. I do think that's that's an important that's an important thing to include but one of the things of feedback I received from from the team with the evolved energy was the the life cycle assessment can be maybe I don't want to say murky but it could it could be perhaps not the best way to effectively communicate to the audience that we that we're reaching towards but I as an environmental engineer myself I agree with you that LCA is important to include. Absolutely and then another question just on your industrial sector part you focused a lot on the electrolyzer I'm wondering if there are specific industrial sectors you're covering there and I'm thinking specifically kind of the technologies involved with the process emissions from cement can be a very different technology than you know electrifying steel manufacturing per se so just wondering what you're yeah thank you for that question so in the initial the table here so electrolyte electrolysis I is one of the technologies but I certainly am including additional additional technologies and one of the key reports that I've well there's two actually the reports that I've been looking at are one by Julio Dr. Julio Friedman and as well as Colin McCormick so those are both on industrial sector decarbonization through and also high heat decarbonization of high heat applications so yes it won't be just electrolysis I'm looking at a lot a lot of other technologies there great yeah and I know that a lot of a lot of organizations have been mentioning hydrogen lately which we've talked about before which would also be really interesting to understand where that stands and what are some of the challenges with bringing that to scale as well beyond just cost mm-hmm yeah for part three I just have a thought or recommendation so I think summarizing your recommendations and having very clear takeaways is always super helpful and beneficial I would add to that as well and suggest potentially if it if it's clear and makes sense to mentioning specific stakeholders or leaders in these different spaces so if I was a researcher who's obviously reading this chapter to get a taste of what are the r&d gaps and what are you know the current state of these various technologies but now I'm very curious and I want to learn more understanding which groups at which institutions are really kind of the best in class for these beyond you know a generic google search is always really helpful so if that could also accompany your recommendations that would be great yeah that's a good idea and to clarify are you referring to the stakeholders who people could go to for more information or the ones that are looking into this given that all of this is really about the cutting edge currently right right yeah and one thing that one one thing that I was wondering for this section is in some in some papers there's specific there are recommendations that are specifically designed or directed at you know government agencies for instance like this government agency should invest x millions of dollars I don't know if we want it to be that prescriptive do you have any thoughts on that um I mean it could be interesting it's kind of a it's a moving target right um but even just a small half page on that could be very interesting and then my only other point that my last point I wanted to mention too was and I'm sure you're already doing this but given that we do have a few other chapters in the white paper that are ongoing I'm thinking specifically the geographic siting chapter by grace could be a really good opportunity to align with her at least thematically to make sure these big scale transformations that you're looking into and their projection down the line what does that mean in practical terms and what are the consequences for siting but that way your chapters can complement each other so if you haven't already done so definitely reach out to her because I know this month specifically she is working on that great yeah that's definitely um top of top of mind great ladies any other questions nothing all right I think we are good Michael so thank you so much um uh as a quick reminder we're going to be having another webinar in two weeks time on the 18th right before the Christmas holiday where we're going to hear about um from Erin on the employment in a low carbon transition discussion which also might be interesting for your topic Michael you have probably the most cross cutting um theme here for the white paper so it'd be really interesting to make sure we're tying in across those um uh so I welcome you to join it and anybody else that's listening to this webinar thank you thanks that's great I will definitely uh plan to attend that and I think it's important to um yeah especially where this chapter is at the at the end of the white paper I think it's important to tie in a lot of those um of those other points absolutely so Michael we'll follow up with an email to our network and make sure to copy you and then we'll make sure also to share the survey that you put together so that we can solicit some online um online input from our network great thank you all right thank you so much all right take care bye bye


good morning everybody make sure you can hear me if you would please put in that chat section there if you can hear me I want to make sure you can hear me everybody can get the stream so it is what is today my goodness it is January the 20th and it is our day I think four of this live something like that and this is all for you guys so that you can't learn how to make money online so make sure you're putting your comments in there what's up Cameron good to see you buddy and so yeah you want to make sure you're putting comments in there so you're learning this series I'm doing is through the end of the month and it's free I'm not charging you anything for it so you might as well get something out of it you might as well ask every single question you had I know excuse me my throat in the morning is a little rough I know when I got started if I were to have had a place I could go to and get questions answered oh my god I would have been so thrilled but there just wasn't there if me I'll drink water here make sure my throat doesn't go up much better much much better so you know having having a place to go to guys and you know what I'm gonna be doing in in all of these is kind of filling you in on you know what I'm doing every single day now I've been telling you that I was working on a webinar master class whatever you want to call it and we pretty much finished it up last night as far as doing all the slides you know if you're if you're planning on doing webinars I'll tell you the best way to do it is with slides you know some people try to do it live where they're actually in front of the camera unfortunately that doesn't really work out that well and the reason it doesn't work out well is it can be distracting so you know it can be distracting in a couple ways you know maybe your video is not that great maybe your video is great and it's awesome but that takes away from the message so it's kind of hard to win when you're doing a webinar or any type of you know master class or anything like that and do it on the camera so you know I don't I don't recommend doing that now I'm using a guide from Jason Fladlin he has a book called one to many a matter of fact let me put that in here and you guys may want to read this book read this book alright and it's called one two many he's done I think over a hundred million dollars now in webinars that he's done can somebody earn online without credit cards and all yeah I mean you need credit cards credit cards are necessary you do in many cases you're gonna have to have like a PayPal account or something now as far as selling online like if you want to go to up work or if you want to go to freelancer.com or any of those sites they normally will pay you via PayPal but what you're doing on those sites is you're actually doing jobs for other people you're not running your own business now if you run your own business online and you need to collect money you know some of the sites will just pay you like through PayPal if you're using a Amazon or an Etsy or eBay or something like that if you want to have your own business and collect money from people you need to sign up for a stripe account now a stripe account I'm pretty sure and I know Cameron you're under the age for adulthood so you may have to have an adult set that up for you but you can get a stripe account and if you get a stripe account you can collect money you know via Mastercard, American Express, you know that type of thing, Visa and you're gonna need to be able to collect money from people and you just you got to be able to do that so that's the only obstacle in that so there's a couple ways to go about it but you can certainly do it so anyhow we're working on this webinar and you think wow we got it done well yes and no we got it done but that doesn't mean it's actually done let me switch screens here there's another screen that makes it easier for me as I can see real-time what's going on there we go and when I say it's done I mean like all the slides are done that's that's the first stage and we use PowerPoint and you can also use Google Slides I have it on PowerPoint right now but once the slides are actually done then that's when the work begins okay because you know webinars masterclasses are kind of interesting about half of them are the information that you're training on and then the other half or more is Q&A and overcoming objections and talking about the price and all that kind of stuff and that can be very very lengthy so you know that's something we got to work on today that's something we're gonna spend some time on getting all these questions that people ask and put those in there so we can answer those questions and then you kind of got to write your script for each you know screen you have a screen for each one or excuse me a slide for each one I don't say screen a slide for each one and then you kind of got to write your script that goes with that particular slide you don't want to put everything you're saying on each slide so yeah it's not something you want to do now I've been doing webinars for probably about four or five years now so you know I don't have tons of experience on it but I do have some experience on it and they've been successful I've made you know a lot of money doing them Cameron says my father could help me with PayPal sure yeah that's fine just you know write it through him and keep him abreast of what you're doing but yeah certainly you know your mom or dad or you know any any adult that you trust you know and I'm sure you trust your dad but I'm just saying any adult that you trust you can use to set up your account Stripe is the way to go really and I don't know Cameron I think you're in Pakistan and I don't know if they have Stripe in Pakistan that's interesting let me see here I'll look that up for you real quick and we'll see is Stripe in Pakistan and if I'm wrong about Pakistan let me know because maybe I'm looking this up for nothing Stripe is not available in Pakistan however you can still use it as a US company you know you can set up a company in the US Cameron so you know if you want to if you want to Google using Stripe in Pakistan it'll tell you how to do it because you can set up your Stripe account for Pakistan in the United States according to this stuff I'm seeing online here so if you want to do that you can certainly do that and this this training guys this daily blog or whatever it is I'm doing it's just to help you guys out with your business that's it so make sure you're putting your questions in there make sure you're participating because if you don't participate you won't learn anything and that's what we want you to do is I want you to learn something so that you don't have to work a job you should really shouldn't have to work a job in 2021 it's just unnecessary there are some other gateways that work in Pakistan Cameron I'm looking this up right now you can use jazz cash you can use telenoor you can use UBL and HBL they all give you the option of cash payments where the buyer can generate a coupon pay with coupon this is very useful option since Pakistan cash on delivery is the preferred mode you can kind of see what I'm looking at right there my ring light is showing it up there but that's that's what I found right there so check that out look that out the best check out for a website does Cartra have their own secure check out yeah Paul they they do however you've got to integrate it with some processor okay so there is a check out cart built in Cartra definitely but you've you're still gonna have to use like an authorized on that you're gonna have to use a PayPal you're gonna have to use a strike you're gonna have to integrate one of those with it but the cart is built in there and it works very very well I like it a lot no problem Cameron I'm glad that that answered your question something else I wanted to talk about guys is no matter what stage you're at in your business you want to get better you want to always get better okay it's like you know exercise and I hate to always equate things to exercise but you know exercise is really important you know I was listening to David David Goggins the other day and he was saying they were saying what's you know do you have to have physical strength to have mental strength and he says most definitely I mean you almost can't have mental strength without physical strength you you you almost can't do what you want to do if you're not healthy and strong but what I want to get to on exercise is no matter what stage you're at on exercise you always have to push yourself a little bit past it and the reason you got to push yourself a little bit past it is because you won't get any better and if you don't push yourself past it you'll never even stay strong okay so it's like if I can do you know 40 push-ups in 40 seconds in matter of fact that's what I do I do these three sets of 40 and I do them in 40 seconds you know when I first started doing them I could do like 25 you know which I thought yeah it was good but it wasn't great but now I can do 44 in 40 seconds so you know I've improved and but the only way I improved was like when I was at 25 I tried to do 26 I tried to do 27 and try to do 28 and that's the same thing with your business if you have a business and you're making x number of dollars and you're happy with it and you don't try to do anymore your business will eventually shrink okay so whatever stage you're at you've got to push yourself to do more you just do you got to push yourself to learn more you know if you've got like a great email and it's really converting you want to do an ab test and see if you can find a better email if you've got a great ad and it's making you money you got to do an ab test and try to find a different ad that may make you even more money and you got to constantly do these things because not only will it make you stronger but things change you know your audience changes you know the market changes everything changes nothing stays the same you know people say well I hate change I hate change I'm like well you're gonna hate life because that's what it's all about life is about change constant unrelenting change that's what life is about so you kind of got to just deal with that uh let's see I got a couple more comments in here is there a bank that's better for online businesses uh I don't I don't really like using banks period um the only financial institution that I well I got some savings money and some couple of different banks but I don't really use them I mean I use navy fed uh for my business uh and that's because my dad was military so you know I got the military in there for navy fed however um I don't I don't I just use it for my deposits and stuff um I don't think one is better than the other I like credit unions because they don't charge you a bunch of fees uh I hate fees oh my gosh I hate me some fees uh so I don't think I don't know I think they all suck I don't like banks I just don't like them okay let's see here tell me a website that teach graphic design for free you're on it you're on it right now it's called youtube youtube is probably the best resource to learn anything for free anything for free like if I were to go right now and I'm going to do this while we're live here and I'm going to go to youtube real quick and I'm going to put a learn graphic design let's see what I got uh design and I've got um now I've got a bunch of ads you can see all the ads here but as I scroll past the ads here I've got learn graphic design at home to eight minute video that one's got 103,000 views fundamentals of graphic design it's got 3.3 million views I got I got a figure I got a figure that's probably a good video right there become a designer you know so you can just go through youtube I mean you could get an education in anything you want on youtube I mean there's just there's millions of videos for free um you don't need to go to a website not necessary so that that would save you some money right there as I look through there's just so many of them and then what you can do uh you can go up and you can uh sort these by excuse me view count I'm gonna do that real quick I sorted by view count uh and let's see what I got here I got I mean just tons of them there's there's like this one here's 3.8 million views this one here's 2.8 million views beginner's guide to 3d see that so I mean that's all you got to do this this youtube thing guys this youtube thing is amazing I mean it's amazing um but you got to use it on every end of the spectrum now I use it excuse me the throat is bad today I use it to learn things like if I want to learn anything you know let's say I'm working in Camtasia and I want to learn how to shorten a clip I'll just type in there Camtasia shorten clip and boom there's a video on it it's that simple but I also use it to my advantage because I'm doing live videos like this I do recorded videos and when I do those videos I can help other people I can build my audience I can make money on ads I mean I make I make decent money on ads now and that I just got that monetized a few months ago and I've I've got an income off off youtube now which is pretty darn cool my wife does too we have two incomes off youtube and she started another channel so she's got two channels and that other channel will be monetized soon and we'll have three incomes off that that doesn't include the fact that we have affiliate links like in my description of this video I'm gonna have affiliate links where you can click and go buy stuff and then I make money off that and then I have you know courses I give away and people opt in for that and they like that and then they buy other stuff from me so man I mean youtube youtube is really good now I will tell you this it's a lot of work most people are never going to do it you know I went back the other day and I was looking at some comments I had from five years ago and the funny thing was that a lot of people said well yeah I'm gonna do this I'm gonna do that and I went back and looked at their channels and from five years ago they haven't done anything you know they still got you know five videos up or zero video videos up or haven't done one in five years they they did them for like two weeks and then they just quit it's so easy to be successful and here's the key to being successful don't quit just don't quit I mean if you don't quit and you keep doing things and you keep failing you'll run out of things to fail on I mean you just will you just run out after a while you know if I have an ad I'm running or if I have a webinar I'm doing whatever it is and it doesn't work I don't go darn that didn't work I go oh that way didn't work but I know they work you know I know webinars work I know ads work I mean shoot you know these these companies are not billion dollar companies because ads don't work they do work you did it wrong and a lot of us don't want to look in the mirror and go hey it's you it it's you you know it's not it's not the platform you know people say well I started a YouTube channel with the algorithm doesn't like me no that's not the case at all you did something wrong okay you just did something wrong and the simple thing to do is look at the people that you know have a big audience look at the people get a lot of views and find out what they're doing and do that I mean it it's like it's never been like this before where you could look at your competitors and see what they're doing and do the same thing I mean you didn't have access to that before and now you do you used to have to have so much money to go into business you don't have to have that anymore see here I'm getting a couple comments I know you tell people to subscribe and ring the bell is there a way to tell people to subscribe on email can it be linked to YouTube oh yes and subscribe and what ring the bell turn on all bell notifications so that you're notified every single time I do a new video and go live I've said that a few times a few hundred times maybe thousands of times but yeah do subscribe give me a thumbs up too if you don't mind and Paul thanks for reminding me hammer on you the best online business teacher that's very nice of you buddy I really appreciate you in Pakistan they all take money like $600 IDM Pakistan right right I did I did see that online there that's kind of interesting how that works but you know guys if you're not making money online it's it's because of one or two things one or two things if you want to know the two reasons you're not making money online number one you haven't done anything or you haven't done enough okay you're not doing anything you're not doing anything that's why you're not making money uh and number two you quit okay that's the only two reasons you won't make money online there are enough people out there now who have had success who've made money I have uh and just listen to me just just do what I'm telling you to do and it's really really that easy and now I'm going through extra effort by showing up every day live like this and talking to you guys and giving you this information and man just take it and apply it really it's it's not that hard if I can do it I mean anybody can do it I'm I'm not super intelligent or anything and you know what's interesting is most of the people that listen to my videos are smarter than me and they're smarter than me because they speak more than one language okay I mean I speak some Spanish I'm okay in Spanish but the point is like Cameron here is in Pakistan he speaks the language there and he understands English too and he's 12 you know what have you done um you look at and people say well I'm a time JR you don't understand it's a time issue it's never a time issue ever it's never ever ever a time issue because you decide what you're going to do with your time every day I mean you make that decision you know I I made a decision today I got up and I said I'm going to go do a live video I could have said you know I'm going to go in here and just scroll through Facebook and I'm going to watch Netflix I've got money in the bank I mean my bills are paid you know I could just say that's what I'm going to do today what are you doing with your time and you say well JR I've got so many things I gotta do every day well take some of the things that you're doing that are not getting you great results and get rid of them and replace it with doing something to make money online tell me something about digital marketing Cameron something about digital marketing uh wow that's a pretty broad question um I would tell you the best thing you could do about digital marketing is start looking at videos go through my channel you know watch 50 or 100 videos I promise you Cameron if you go through and you watch 100 videos of my channel just type in digital marketing you'll find plenty of them who're making money online or whatever and go to my channel and just listen to them you're going to come back to me and go man I know a lot of stuff I know a lot of stuff now the only reason you're going to do that is not because I'm great it's just because I tried some things about 80% of them failed about 20% of them worked and then what I did is I took that 20% and I did videos on those okay I didn't do videos on the stuff that didn't work now occasionally I'll comment and I'll say don't do this because it doesn't work but bottom line is just watch some videos man I mean it's so so so easy if you do have specific questions you know like about emails or sales pages or any of that I'll be happy to help you with that but I know me every day every day every single day 100% of the time I'm learning something new I've got a stack of books up there I've got like three in the living room and I may not read a book from cover to cover I may just thumb through and go wait a second this is an area I want to learn I hear this guy's really good I go to Amazon I look at the ratings of the books I get the book I had two books come yesterday that's not a good thing it's not a good thing yesterday that I had two books come you know why because apparently in my Amazon card I had clicked a book twice I thought it was so good and I bought it twice not smart okay so I'm gonna read but the good thing about Amazon is they say hey you can return anything I can I've got you know Amazon Prime so I will return one of the two books that I got yesterday because I only need one of the same book but it's good right it's a great book and it's about habits and I think I have you know pretty good habits however um let's see here I've got something there messed up on my screen I don't know if you guys are still seeing me or not my screen went out and I want to make sure it didn't go out for you guys did it go out for you guys I want to make sure it didn't go out let me just check here for a second uh oh it's just kind of it's kind of paused for a second so it will come back up guys so if you don't see me for a second your screen will come back up uh let's see here I'll put that in the comments video has stopped but it will come back up okay and I will not leave you until that comes back up okay and I don't want to leave you with no video uh let's see here video has stopped it will come back up were you in business before you got online seeing you okay you can't see me I get my screen for some reason is not showing anything on here so I was concerned that you guys wouldn't see me um you know yeah um Paul my situation was I used to run car dealerships new car dealerships and I went through a little divorce um and uh when I did um I had another business and that business was I was on the road I was a professional speaker and I went around I trained new car dealerships and their management teams on how to sell cars at one point I ran the number one Christopher dealership of the state of California we were number three in the nation so I took that knowledge and I put it into a course and it was six-figure selling techniques I had to make six-figure selling cars um and I taught uh salespeople how to make a lot of money selling cars and I did that for about eight nine years uh and then when I was getting ready to go for the divorce I wanted to get custody of my kids and my lawyer says there's no way no way you'll get custody because you're touring eight months out of the year you're on the road speaking in all these different cities I had been pretty much every city in the U.S. at that point so I had to go back into a car dealership and actually run the dealership again so that's that's what I ended up doing and after a few years of that I was like this isn't for me I can't know this is not my thing uh I gotta get out I gotta do my own thing so that's when I started my uh digital business so yeah to answer your question yeah I have businesses before uh Cameron says when I would be 25 I will do gaming with my education and with online business or freelance yeah there's so many things you can do Cameron there really are I mean it's like it's just an unlimited number of things you can do it's it's crazy uh and I want to I want to restart my screen here guys because mine is driving me crazy it's showing that I'm not live and I know I'm live so let me just see here make sure that comes live again I have I've got you guys here on the laptop and then I've got a iPad right here which you can see right so there's the iPad so I'm kind of I'm kind of like watching you guys watch me and let me get over to my screen here hey Cameron have you tried to make any money online yet have you tried to do any of that um Paul have you generated some money online but I've known you for a while I want to make sure you're making money and do if I can help you out in any way um I want to do so so yeah because I'm down to about six or seven minutes and then I got to go in and I got to work on webinars again uh because I got to get that thing finished today I've I've been dragging my feet a little bit I should have worked and I spent like an hour and half watching tv last night shouldn't have done that shouldn't have done that shouldn't have done that but I'm not perfect okay I worked until about 7 30 last night I started about seven in the morning something along those time um yes sir yes sir you have well that's good that's good okay cool uh and guys also we have that um a little in the chat box there's a little dollar sign there if you want to contribute to this channel if you like these videos if they're helping you out if you feel like you get something out of them uh you know you can put a dollar in there two dollars or five dollars whatever you want to do and I got messages on my watch tries me nuts I shouldn't wear this watch I got to get rid of this watch this apple watch is more irritating than anything else so anyhow that's kind of my day of course I'll work I'll go we'll have a little lunch and then I'll work out after lunch it's it's pretty mundane you know if you think the life of a digital marketer and Sonny uh Lizadero I think is her name she did a video on this the other day where she thought she'd be standing in front of you know fancy cars and you know and showing off her mansion and laying by the swimming pool and that would be your life and that's not a digital marketer's life okay digital marketer's life is sitting behind a screen the majority of the day and having your watch remind you that you need to stand up because you've been sitting for four hours that's a digital marketer's life that's what that really is and I actually have that little warning on my watch to remind me to get up and get get moving of course I work out every day too so I have to do that but it's it's mundane it's monotonous the cool thing about it is you control it you can make whatever money you want you can do whatever you want on a daily basis and that's the part I do like okay so I'm gonna go ahead and get out of here if you have any more questions you can stick them in there if they pop up before I close out um let's see you've already made a profit my god come on I mean camera I mean you shoot he's already made 45 bucks with the help of his big brother 12 okay so if you're listening to this a 12 year old can do this not not that being 12 is a negative by the way uh I was 12 we were all 12 at one point but the point is if you're not doing something quit quit making excuses don't make excuses you know everybody is good at two things in business okay one of two things in business one of two things in business okay the first thing is making money in business okay you could be good at that okay if you're not good at that thing you're going to be good at the other thing and the other thing is making excuses you're good at making excuses okay so if you haven't made money online um it it's because you're making excuses you're not doing anything you're not doing the right things I mean it's everybody's making money online why not you why not you just get in front of the mirror and ask yourself that question uh it was great to see you and others on that marketers crews working on ship yes it was Paul and we will be back we're going to be back I promise you we're going to be back and we're going to be on those cruises again we're going to be doing all that fun stuff I really enjoyed that looking forward to that but now now you need to subscribe if you haven't done so subscribe and ring the bell you got to ring the bell uh thanks sir Paul uh camera and Stella and you thank you Paul so he's in there talking to you too um excuses are the bricks that built the health failure yes they are great saying uh guys thank you so much for being here I gotta run there I gotta work on my webinar masterclass right now sorry about the screen shaking every time I move my arms I see this thing shake I gotta put my arms down here okay I gotta sit really still yeah that's what I gotta do all right don't forget to subscribe um turtle bell notifications I got a free course that's gonna be in the description opt in for that grab that free course and I will see you guys tomorrow thanks very much for being here