Hello everyone. My name is Roman Shapochnik and I work at Pivotal. I also happen to be a co-organizer of this dev room, which by the way was my sneaky plan to get the stock accepted, because I really think that today I will be talking about something that's quite interesting. Before I do that, let me introduce myself a little bit more. My involvement with Big Data goes all the way back to the original Hadoop team at Yahoo, which I joined in 2010, and since then I managed to work at Cloudera, Pivotal, basically companies that do data, do Hadoop. And with Hadoop it was interesting, because when it first appeared everybody felt like it would totally replace traditional data warehouse systems, and for a good reason, because Hadoop had a lot going for it. It was open source, it was scalable, it was developer friendly. It was basically a really good system, and for some time it really seemed like it was going to happen. But recently an interesting trend started to appear. More and more enterprises started using Hadoop in addition to data warehouse systems, not replacing them, but basically augmenting them with Hadoop. And at first this could look like a very strange architecture, because why would you have two different systems kind of doing similar things? Why not replace it with just Hadoop and have the promised enterprise Data Lake? So today we will be talking about why those architectures actually make sense and what drives those types of architectures. All right, all right. Oh, okay, awesome. So yeah, so basically the first time I was actually starting to see this type of architectures that they were building, I went like, huh, what are you doing? So this is actually the slide that got publicly presented so I can steal it without writing my own. That was at the IBM conference, and the guy was from Seagate. So not even the customer I was dealing with. And he was explaining how Seagate is dealing with sort of the internet of things, because they basically manufacture a lot of hard drives, and they want to collect information from those hard drives. So they basically had a whole bunch of Hadoop clusters kind of on the periphery, aggregating all of the information, but then all of a sudden they actually had a traditional enterprise data warehousing right in the middle, kind of actually taking care of the important data points. And I was like, what? Why not Hadoop? And then we had a couple of more customers like that at Pilotel, and the architecture that emerged throughout that group of customers was basically this. And I would love if during the Q&A you could challenge me on this, and maybe you can tell me how wrong it is, but that's actually what I'm seeing in the field. So almost all of these guys who I will be talking about, they're building backend architectures to essentially support data-driven applications. And what I mean by that is that they basically have to optimize the relationship that they have with their customers and the devices or whatever it is that they're managing. So there's typically like a bunch of either web or IUT traffic coming into a data center and sometimes even multiple data centers, and that's on the left, right? And on the right you basically have a bunch of users using the application from mobile and desktop technologies and whatnot. So the question then becomes what do you build in a data center as a complete end-to-end architecture as opposed to just building a data science piece or a data management piece, right? And what we typically see is that, again, some of it I'm just throwing names here. You can use different projects within the Apache ecosystem, but the name gives you a flavor for what typically gets used. So typically like all of these events get represented as Kafka queues. So the saying goes that whatever it is on the outside of the data center must look like a Kafka queue inside of a data center. And then you basically have some kind of an ETL 2.0, and what I mean by that is that it's not ETL that basically takes data from one existing place, you know, does something to it and puts it into a different place. It's actually ETL that does it on the data that's in flight.  So NIFI, the presentation right before me was a good example of something like that. Pivotal has the spring cloud data flow that can do some of that too. But basically what gets done here is that you essentially split the traffic that's coming into your data center into multiple different things. So first of all, you basically get some of that traffic right straight into HDFS. And you just dump it there and you leave it there and something will happen to it later on. Then some of that traffic is actually getting into the in-memory data grid, and that's the thing that actually connects your application to the data that's in flight right now. You know, whatever the center is producing right now, you will actually get it through that thing. But then, and that's actually interesting, then you actually get some of that data into a more of a traditional MPP type of a solution, and the good news is that you actually have an open source one and green plan today. And the split of all three here is interesting because what happens then is within all of these systems you basically have people who are typically, you know, either data scientists or data engineers who essentially maintain data models based on the raw data that gets into all of these systems, right? And you only get as much usability out of the data as the kind of data model, interesting data models that you can then feed back into the application you get. And these are the guys who are maintaining it, but even those guys basically sort of a lot of times, at least in the customers that I see, kind of push those data models back into this middle layer, which is the traditional MPP. So the question is why? And I think before we can answer that question, again, let me repeat the fact that what we're building here is not just a data science piece. We're not just doing it for, let's say, you know, life sciences trying to analyze human DNA or something, right? We're building an end-to-end architecture for essentially a modern way application, you know, the way you get it from Uber or from Netflix or from Airbnb. And for all of these guys, you know, the data that they're collecting must manifest itself in the application. That's the end game for all of them, right?  You know, Netflix must recommend a new movie for you. And all of these guys, again, building this end-to-end architecture. So can we just do it with Hadoop? Well, again, like I'm saying, hopefully during the Q&A, you will try to reconvince me that maybe we can. But here's what I've seen so far in, again, talking to the customers, because it's a pretty pragmatic presentation that I'm trying to do here. So first answer is like, yes, absolutely. But then, you know, the customer turns around and asks you, but what do you mean by Hadoop? What is Hadoop these days, really?  You know, we can totally use HDFS. Maybe you're also talking about a scheduling framework, something like Yarn, that's fine. But then there are all these other questions, right? A lot of the customers that are building those types of applications right now, they are very traditional enterprises. So they do a lot of what's known as BI, you know, business intelligence. And BI is typically done through the tooling that actually expects a SQL database on the other end. So the most, you know, traditional example of the tooling like that is a Tableau. So, you know, very well-established sort of UI for doing BI. And there's absolutely nothing that you can, like, hook up Tableau right away to in Hadoop ecosystem. I kind of have to build additional bits and pieces into that. That one is actually also interesting because a lot of them are telling me, like, this is awesome that all of the big data community is talking about machine learning in Scala and on Spark. And that's basically great for the five people that know how to do it. And all the rest of us who are still stuck with SQL and, you know, stored procedures, we actually wanted to be given to us. We want to contribute to the machine learning as much as the guys who actually do Scala and Java and whatnot. So I actually tend to call it democratized BI and machine learning. If you can open up it to the people who have the old-school skill sets, then the first thing that you're doing, you're basically getting them into the game, right? You cannot really put that barrier in front of them and say, like, you've got to learn Scala, you've got to learn Spark, you know, to be productive. You kind of have to invite them over and, you know, extend the hand and say, like, yeah, you can be productive, you know, and I'll show you how. And maybe gradually, you know, you will build out your skill set. And the other two, like, once you do that, the other two then becomes, you know, the question is, like, oh, so now I have not just five guys within my company who know how to do Spark and Scala. I have hundreds of people who actually are very comfortable doing BI, but what happens if I open up that system to all of these guys to kind of hammer on it once? Can it actually handle the load? And the last one is, again, if you're building an application, you kind of have to have something that you would be able to hook up in a traditional in-memory scale-out, you know, layer. And a SQL database is typically a good option. Again, you can do HBase or something like that, but then you are sort of assembling bits and pieces in a very ad hoc manner instead of just having a central piece of your architecture that can do a lot of, that can answer a lot of these questions as opposed to, you know, trying to do just one thing, like many of the Hadoop ecosystem projects do. So to summarize what I was trying to say, I guess after I worked with Hadoop for, well, I guess it would be 10 years by now, I can really vouch for these. So Hadoop is really great for Hadoop and Hadoop ecosystem. It's really great for elastic storage capacity. I mean, HDFS is pretty well-debugged and awesome at this point. It has all the bells and whistles like high availability. You can configure it, you know, to be pretty full-tolerant.  HDFS is great. It's great for lending data. It's great for discovery. It's great for trying to figure out what's inside of your data. And that is actually a job for a couple of people in the enterprise. And here's why, because all of these enterprises are really bound by regulations and just internal, you know, rules of what data can be exposed and how. And if all you get is just raw streams of data that is coming in, you typically want to reduce the number of people who can actually explore that data. You want that exploration process to happen within a very limited group of people so that later on, you know, you can actually open it up, but you can open up in a meaningful way. So maybe you will be masking some of the data fields. Maybe you will be transforming it into something else. Maybe you will be putting a particular eckles in place, you know, to actually make it more accessible. But the initial exploration, you know, the fact that all of a sudden the log files that are streaming through your application contain, you know, personal identifiable information, that cannot be overlooked. And, you know, if you want to do sophisticated machine learning, Hadoop ecosystem is great. So complimentary today to that traditional MPP, which stands for massively parallel processing, if you're wondering what MPP is. It's actually really great for schema on write, which is basically once you know what the data is look like, you know, what the data it is that you're dealing with, you actually want it to be available to as many people as possible. And then you create all these views and tables, you know, that a very traditional BI person would expect. But then you are making the data known. You actually have an API between you, who is maintaining the data infrastructure and the people who will be analyzing and creating the data model. That is a very well understood API.  Transactionality sometimes comes into picture because it's easier to do transactions on MPP into some customers is actually meaningful. It's interesting. And the last one, for those of you who cannot actually see it, I'll read the bottom one. So that democratized machine learning, you can all of a sudden use R and you can all of a sudden use your traditional sort of SQL like tools, which I will cover in a bit. So what do you do it with? There's maybe a few choices in the open source. You know, the one that I will be getting you through today is called Green Plum Database. So Green Plum Database is actually a proud member of the Postgres family of relational databases. It's kind of like a long timeline but a couple of points I wanted to call out. You know, Postgres actually got created long time ago, like really long time ago in 86. So Green Plum Database got created based on Postgres 8. You know, around 2005. Interestingly enough, that was exactly the year when Hadoop got created. Now Hadoop got created around 2005 as a sub-project of NACCH. 2006 is when sort of Yahoo got really interested in it. And the rest is history, but the point that I wanted to make is that we open source Green Plum Database in 2015 and we even rebased it on Postgres 8.4. So it's still not Postgres 9 and we're working on that, at least it's close enough to really sort of the kind of Postgres that you would expect. So very quickly, Green Plum Database is an MPP project. You know, it's something that has been in development for more than 10 years. If you want to participate, your best option is www.greenplum.org. You know, all of the community aspects are on that website. If you're wondering what Green Plum is and what it does, think of it as a sharded Postgres.   But basically the way to think like the mental model, a very sort of simplified mental model is it's a traditional Postgres database, but every table that you create has a column that you need to designate and that column is used for sharding. So basically whatever the hash of the value in that column, depending on the value of that hash, your data will end up in different segments on different hosts. You still get a view as though it is a database that's just available on a single system. It's a traditional Postgres database. So anything that works with Postgres would work with Green Plum.  It's a shared nothing architecture.  And a lot of times we sort of do the high speed interconnects for pipelining.  Green Plum is a thoughtless software solution. So if you've seen Green Plum in the hardware incarnation, you know, you might have seen some of the networking that was specifically optimized for it. It helps, but you don't have to do it. So you can just launch Green Plum on Amazon or Azure or in your own data center. That's just fine. So a couple of other points that I wanted to make is that Green Plum actually has a concept that is now in Postgres known as foreign data wrappers, FDWs. In Green Plum, it's a different, slightly different one because when Green Plum got it, Postgres didn't actually have FDWs. But Green Plum can connect to a lot of data sources, right? So Green Plum can actually represent as an external table something that's on S3 or in, you know, files on your file system or even in HDFS itself. And that is exactly how we're connecting it to the Hadoop ecosystem. So that connection to the HDFS is the Hadoop ecosystem connection. Interesting cool things that are sort of part of Green Plum. ORCA is the first query optimizer, you know, that is basically planning queries knowing that it does it for an MPP type of a system and it's optimizing the query to run for long. It's actually a pretty sophisticated piece of software based on a scientific research that's been done for at least five years. It's a standalone component, so it can be plugged into any kind of database. It's easier to plug it into, well, conceptual, at least it's easier to plug it into Postgres, you know, family of databases, but you can actually plug it into any database because it's implemented as a separate standalone service that does the query planning for you. Speaking of the democratized machine learning, so Green Plum actually comes with the machine learning library called Medlib. And it's an interesting one because it does machine learning as though you are doing queries on tables via traditional SQL. So for example, if we want to do a linear regression and basically just train our model, instead of writing any kind of code, we would just invoke one of the pre-canned functions that Medlib as a library gives us. Now the invocation would look like a select statement, you know, that's the training of the model, so we're basically passing tables here. And what we get is we get the output that is the model that is trained. And of course, you know, the way we would then use that model is again through the same select, you know, similar select statement. So all we're doing is we're essentially calling out functions that are being given to us through the SQL interface by the Medlib that got installed on our cluster. So again, the flow between Medlib and the rest of the system is pretty simple actually. It's kind of like, you know, the MapReduce without exposing to you the MapReduce part, right? So you essentially have a client which could be, like I said, anything, you know, could be Jupyter, you know, Apache Zeppelin if you're doing machine learning. P-SQL works just fine, so you can run all of these examples in P-SQL. So the SQL client talks to the database server and then the interesting stuff begins on the master. So you basically end up with a stored procedure that gets called on the master. A whole bunch of the same stored procedures get called on the segments, you know, then the aggregation happens if it needs to happen. So it's kind of like fanning out and then aggregating. There is a way for Medlib to also do a little lessing of the data and basically run algorithms that need to converge. So if the convergence needs to happen, then the convergence would be happening through the master. So that's the only bottleneck that you would have because, you know, the segments will not be able to talk through each other. But the convergence still could happen. So if you're converging to a reasonably small value or small in size, right, in how you represent it, then it works pretty well and that's how actually we do some of the graph algorithms within Medlib. Medlib is interesting because it's not just Greenplan. We actually have people from the Postgres community who are using Medlib to do machine learning within the Postgres installation itself.  And just, again, for you to understand what's going on, the pieces that you will be dealing with are basically at the very bottom. You have a C API that Postgres exposes to anything that happens to be a function. Then you have a whole bunch of low-level abstractions, you know, that's just mechanics of how Medlib itself is implemented. And then a whole bunch of traditional machine learning functions that have been implemented, you know, on top of these abstractions. But essentially the idea is simple. You know, the Postgres segment gives you the data in form of tuples, right, you know, think of it as an array of data that you can read through. And then these functions basically do all of the work. And if there's anything that needs to happen between different functions sort of running in different segments, then that would happen through the abstractions that are provided here.  So Python is one of them. There's a integration between Medlib and Python. So you can actually do a lot of traditional machine learning, you know, talking to the back end, which is a cluster. Another example of this layer is R. So a lot of times we would actually have people who would call Medlib functions through the R interface because we have that integration provided to them where a Medlib function would actually look like a R function. Now, of course, again, anything at this level, you don't want a lot of data to being transferred back and forth. So you're basically just programming a cluster and you're telling a cluster what to do. So that's Medlib. It's a pretty useful piece. You know, a standalone piece. It's actually an Apache piece right now. So it got transferred into the Apache software foundation. It's an incubating project. If you're interested, you know, we are more than welcome you as a contributor. You know, there's quite a lot of interesting stuff to do. Green Plum itself is not an Apache project. It's under the Apache license, but it's a standalone project. Go to www.greenplum.org. So then the only question remaining is how do we put it all together? And since I don't really have too much time left, I'll just say that the answer to that is Apache Big Top. And as Olaf was pointing out, Apache Big Top basically is the... Essentially, what DBM was to Linux, right?   So Big Top is trying to do that with big data. So, you know, we have Hadoopika system, but not just Hadoopika system now. We also have, you know, things like Green Plum, which I was talking about today. There's Big Top and then a whole bunch of distributions that use Big Top to create products that they give to their customers. So far, we've laid the groundwork in the Big Top community to integrate Green Plum into the Hadoopika system to allow you the kind of architecture I was talking about today. So the basic functionality, packaging, deployment, and Docker orchestration is there. So you can actually get the RPM and DBM packages for Green Plum, you know, from Big Top community. You can deploy them on your cluster using puppet code that is provided. There are Docker containers, you know, for that stuff as well. There's basic integration with Medlib. So Christian, who is sitting here, did a demo, I think at last FOSDA maybe, or some other conference, where we basically demonstrated how you can use Medlib through Apache Zeppelin, which is a really nice tool similar to Jupyter, you know, from the Python community. It's very well known in the Apache Big Data ecosystem. You know, if you're doing machine learning and you're trying to do this, you know, notebooks, right, for data scientists, it's a pretty useful tool. We're actually interested in, you know, some of the juju deployment. So the basic rudimentary capabilities are there, but if you're interested, you know, talk to me after this presentation. And the HDFS integration is there. You can get data in and out of HDFS as external tables, but it's slow and we're trying to optimize it now. So again, talk to me after the presentation. Here's the stuff that we're interested in doing. And if you want additional items on this list, be our guest.  I'm particularly interested in this one and this one. So Postgres 9 is one of the big deals that we have to kind of like accomplish. I don't think we'll accomplish it the same way we did it with Postgres 8, which, you know, Haike, for those of you who know him, you know, one of the sort of core Postgres guys, kind of like just did this huge rebase of the entire code base on Postgres 8. Postgres 9 is too big of a chunk for us to bite that way. So we'll probably just backport features from Postgres 9, you know, the feature at the time. And that's actually where your feedback would be extremely useful, because we need to know which features to prioritize, you know, for backporting. So for example, we know that binary JSON is super interesting, right? And the only reason we know is because, you know, people talk to us and told us so. And this one is where I think the real interesting integration between Hadoop ecosystem and Green Plum will begin. So we want to make it a full sort of fleshed member of the Hadoop ecosystem. So all of the tools that exist within that ecosystem can actually benefit from Green Plum and vice versa. So that's it. That's all I have. You know, let's try to build it together if it sounds interesting at all to you. And I'll leave you with this quote and open up for questions. Could you just pick up? I cannot really. So that's what I'm saying. We basically have to rely on that ETL that was in the middle. So remember, I was showing you the architecture. We do, but in a different way. So the question is how do we deal with unstructured data? So these are the architectures that I'm seeing. So the way you deal with unstructured data is either here. So your ETL basically puts some structure on that data, right? And that is when you know how to do it. So you know that certain fields can be extracted in flight right away. So then you extract those fields and you put it directly into Green Plum database in form of tables. That's fine. Another way is you have your unstructured data at the HDFS level. And these guys, you know, they're basically building exploratory data models, right? The data models that are constantly being tweaked. But then they export it and make them available to a bigger audience within the enterprise by essentially, again, syncing up those data models as tables at the level of Green Plum database. So that then you can actually pick them up, you know, through MATLAB and, you know, all these other tools. So that's how we deal with unstructured data. Right. And PXF, you know, one of the features that I was talking about integration-wise is, again, in the direction of helping us deal with unstructured data as quickly as possible, so to speak. Yes. Right. So there is a support. So again, I obviously couldn't go into great details about the architecture of the Green Plum. But the question is, do we support failover? Yes, we do. So you can have basically two masters, you know, in different configurations. And yes, you would basically have two nodes. It's similar to how HDFS does HA, if you know that. You know. Oh. No, it isn't. Right. Correct. So again, like I'm saying, it's kind of like with HDFS where you have two name nodes, you know. That's the same approach. Yes. Okay. So Green Plum is similar to SiteSDB. So Green Plum and Citus take slightly different approaches to how to deal with Postgres, and you get different sort of design constraints based on that. So Citus made a decision to be essentially a plugin into a Postgres, which makes it super easy to instantiate a Citus cluster.  But then, on the flip side, you're being constrained by whatever it is that is given to you by Postgres. So for example, Green Plum actually invests a lot in optimizing the interconnect. Citus doesn't really have an ability to do those types of optimizations. So it is similar, but performance-wise, we're typically faster on the sort of the benchmarks that I've seen. It doesn't mean that we're always faster, because again, it depends on the workload. But we do get more ability to optimize, because we don't depend on Postgres as much as Citus does. So that would be the quickest way to answer it. Let's take the rest of line. Please, we don't have time for another question. So thank our speaker again. Thank you.


My name is Stassar and Donald Harris. I'm a squalor with Charlie Company, 1st, 5th, and 3rd here, and I'm from Rotary, Virginia. This month we've been working a lot with our Polish and Canadian counterparts. I'm just kind of training off tactics and techniques and seeing how each other work and then trying to put those together into a cohesive unit. In Italy, we don't have very many shooting ranges, so here we get to go out into the woodland a lot more and fire off more rounds and get to do more things. This whole training area here is very helpful in all aspects of that. The back blast area, it's a 90 degree, 30 meters, 30 meters, 90 degrees, 30 meters back. So when you prepare to fire, you're going to turn around, actually physically check your back blast area and call back blast area clear. I think right, I won't tell you to fire. Go ahead. Back blast area clear. Check your back blast area. Back blast area clear.  Back blast area. Cock it. Require your target. Press the safety and fire. Cock it back safe. Alright, go to a good front position. Shoot it before it lights up and firing.


harmless, but as we will see, it was not. And in fact, their instantiations of ring LWE, they just leak exact equations in the secret. So it's like learning without errors. And in particular, you can just use linear algebra to recover the secret. So this is kind of the conclusion. You can recover the entire secret with near certainty. Although it remains a legitimate question whether these evaluation at one attacks apply. But the main conclusion is that there is no, it's not a threat to ring LWE as it may have appeared to be. OK. So to talk about this, I will have to explain to you a little bit what ring LWE is. So to start with that, I will talk about LWE because ring LWE is a ring version of this. So LWE is about solving linear systems of equations. But the equation, and this is modulo p. So over the integer modulo p over the field fp. But these equations are not exact equations because otherwise one could just use the techniques of linear algebra like Gaussian elimination. But these equations are given with some errors.  And every equation, so the bi that you are given is not the correct bi. It's not the correct outcome. The bi that you are given is the correct outcome plus some error ei that is sampled from some known distribution. And that is considered to be very small. It's part of the problem that the linear part of your system, this will be important in a second, that the linear part is generated uniformly at random. So this is part of the problem. And it's also part of the model that an attacker or a person that wants to solve this problem can ask for new equations indefinitely. So this is LWE. This was introduced by Riegel about 10 years ago. And it's, so yeah, I rewrote this approximate version into an exact version by including this error vector here. This is the shape that I will use, continue using. Okay, so two of the features of LWE and two of the reasons why this problem is being appreciated a lot is that it came along with a hardness proof, so to speak. So if you can come up with an algorithm for solving LWE, you can also come up with algorithms. This is a quantum reasoning. So quantum algorithms to solve certain well-known version of lattice problems, shortest vector type problems. Okay, so this is one reason why it's being appreciated. Another reason is that it turns out to be a very versatile building block for a lot of crypto. So fully homomorphic encryption is one of the main applications. But as I said already, there's this quantum element in there, so it's believed to be quantum secure. So for PQ crypto, it's also being studied. There is one main drawback about LWE and that's a key size issue. So if you want to hide the secrets, which we think of, say, as a vector of length n, let's ignore the size of P for now. So this is something of length n that you want to hide. Well, you have to use this entire linear system as a public key. And so you need like a public key of size n squared, at least, m, so the number of rows has to be strictly bigger than the number of columns. The system has to be over-determined for the problem to be well-defined. But let's think of this as size n squared. You need size n squared to hide something of size n. And so people have tried to solve this, and this is then what the goal is of ring LWE. So let's first start with something which I call ring-based LWE. So it's on purpose that I don't write ring LWE there. This is not what I'm going to say now is not exactly ring LWE. But the idea behind using rings in LWE is as follows. Instead of thinking of our secret as a vector of length n, we will think of it as a polynomial of degree at most n minus 1. So s0, s1 up to sn minus 1. We will encode it as 0 plus s1 times x plus s2 times x squared and so on. This is just a matter of language. It's two ways of writing down the same thing. But of course, when you think of polynomials, you can try to multiply these polynomials. Now, if you multiply two polynomials of degree at most n minus 1, you will end up, most likely, with a polynomial exceeding that degree. And so you have to do it modulo some polynomial to get back to the size you want. And this is a parameter in the system. This is this f of x, modulo which we are computing. So this is the integer modulo p. So here we also have integer modulo p. But now we also work modulo f of x. Now, once you have set up this multiplicative thing, so you have given the secret key space a ring structure. This is basically what happened.  So if you take a random such polynomial and you multiply the secret with this polynomial, you reduce the outcome modulo f of x, then in terms of the original vector notation, this will turn out to behave like a matrix multiplication. And the idea is to use such matrices. Now, it's very important to be aware of the fact that these matrices are not random. But this is the whole clue. To encode this entire matrix, which is of size n by n here, it suffices to save this polynomial you'll multiply with, which is of size n. And you save a factor n. This is the idea. So just to give you an idea of the flavor, if you would work modulo x to the n minus one, modulo this polynomial, then the matrix of multiplication with a looks like this. So it's what is called a circulant matrix. So you have this first column. And then the other columns are obtained from this first column by shifting them cyclically. And so obviously it suffices to know the first column to know the entire matrix. That's the idea. Now, this is kind of a bad example because of some potential threat that is around. And these are exactly this evaluation at one attacks I wanted to talk about. So I will try to be very brief. This is just a standalone thing. If you don't get the details of this, this is not important. The most important thing is that you know that this threat is around. As soon as f of one is zero modulo p, for instance, if it's exactly zero, as is the case with x to the n minus one, this is exactly zero if you evaluated it one. But if you have zero modulo p, then you have a ring homomorph. So we have this secret key space. But then we have a map from the secret key space to a much smaller space, fp, by evaluating these polynomials at one. So this is a well-defined map. And whatever the map is, one can use it to convert samples, ring-based LWE samples to, you can evaluate everything at one, and this behaves well. This condition is needed for this to behave well. And basically because you are now in a much smaller space, you can examine things exhaustively. I will just go quickly over this, but you can examine things exhaustively and try to discover non-uniform behavior. And if you can play this game in the right way, you can probably extract information about s evaluated at one. So this is the sum of, this is, so recall, s is here, s evaluated at one is just the sum of the digits. So you can hope to play this game to extract some information about the sum of the digits. This is this class of attacks. It's not the main point of this talk, so I will not talk about these attacks any further. It's just important for you to know that they are around. And so one safety measure one could take is to work with irreducible f of x. Like it already excludes a certain kind of versions of this attack, but yeah. So for instance, x to the n minus one is excluded with this because this has a factor x minus one, but the actual condition is that f of one has zero modulo p and this is not entirely excluded by this irreducibility condition. In any case, from now on, we assume that f of x does not have non-trivial factors. So what is ring LWE then? Well, we had this idea of replacing the linear part of our system with the matrix of multiplication. So the direct analog of LWE in this ring-based world would read like this. So we have this constant factor equals this matrix of multiplication times a secret plus some error. Now the direct analog of LWE would be as follows. Take this error, take this EI independently and choose them at random and choose them small. This is what happens in LWE. So if we want to play this game literally, this is what we will do. So this is what is written here. Take them independently from the same distribution. This is like an ornable distribution with a small standard deviation. I've represented this distribution as a sphere. Just to, this symbolizes the fact that in every direction, the distribution is the same. There's no preferred direction. But this is not what ring LWE is. So this is important in particular. So the people that introduced ring LWE also gave like a security proof of this or a hardness statement. But this hardness proof does not apply to this version. And in particular, also evaluation that one attacks are known to work in certain special cases in this setup. This was by Eisenrager Lauter and Stange. So this setup is sometimes called poly LWE. So what is ring LWE then? Well, let's have a look at the samples again. So you see that I left some blank space here. This is because there's something to be filled in there. So let's do that. So we will plug in an N by N matrix that arises as a product of two N by N matrices. So I have a blue one and a red one. So the blue one is the canonical embedding matrix. I will not define what is this. But the basic point is that the ring in ring LWE does not necessarily have a canonical representation. You might have two polynomials defining essentially the same ring. And the previous version, the poly LWE one, is not robust to these kind of changes. And so there exists a sort of canonical representation of your ring. And this is where you generate the errors. And then this matrix pulls these errors back to the original setting. This is what this B inverse does. If you have never seen this before, just there is a matrix there. And then there's another matrix. And this is in multiplication by the derivative of f at x. It's some multiplication matrix. And the reason that this is there is that in actual ring LWE, the authors propose to pick secrets from something called the dual ring. Let's call it the dual ring. But you can reformulate everything in the original version. But then you have to put this matrix there. So this is basically witnessing the fact that your actual secret space is not the ring that I mentioned. But you can reconvert everything into secrets, living in that ring. But then this is the price you have to pay. Oh, sorry, push the wrong button here. So this is where we were. So there are two matrices. One for going back from this canonical world to the world we are working in. And this is to compensate for the fact that we are sampling our secrets in the ring. And then in this version, there is some hardness statement that was proven. Now we had this sphere before. Now I draw this ellipsoid. Because this factor, this matrix we put in front here, this might completely skew this spherical distribution. So the EI are still thought of as being sampled independently from the same distribution. So they are spherical. But then by applying this matrix to these guys, we will completely skew this. And so the errors in each coordinate, the resulting errors might not be independent. So some might be bigger than others. So this is represented by this ellipsoid here. And it not only skews it, it also scales it up. So this is also very important. So this, as we will see in a second, this matrix, this product of these two matrices is like a big matrix. On average, it scales things up a lot. So this, yeah, thank you. So this is what I will explain now. So let's have a better look at these matrices. So we have these two matrices. Well, one can prove that the determinant of this multiplication by f prime of x matrix has a quantity called the discriminant. And if you know what the discriminant of a polynomial is, it's basically that, but in absolute value. So it's important to know that this, in practice, is a huge quantity. It's a very big quantity in general. So this is what the determinant of this guy is. So this scales things up by a lot. On the other hand, the B inverse scales down. And the B scales things down by a factor square root of delta. So the determinant of B is the square root of delta. So the determinant of B inverse is one over the square root of delta. And so if you do the math, on average, you have one over square root of delta times delta. So you get square root of delta.  And I put square root of delta to the power one over n here. And that's because there are n directions. So the determinant kind of takes the product of everything if you want. So in the average direction, your errors are being scaled up by this matrix, multiplication by this quantity. But this is on the average. This is very important. So we still have this extreme skewness. So in some directions, the scaling might be completely different than in other directions. OK, so what did the people from the provably weak instances of Ring LWE do? Well, for convenience, they ignored the fact that we actually have to sample our secrets from this dual space. So they sampled secrets in the original ring. It's not completely clear to us how harmful this is. You can try this. But basically, if you naively remove this matrix, because this matrix expresses the fact that we have to pick secrets from the dual space, if you naively remove this matrix, then you have a problem. But because recall, the determinant of this guy is one over the discriminant. So it scales down. And the discriminant is a huge guy, so you scale down massively. And you have a risk that your errors will become just almost nothing, and that if you round your equations and you get an exact linear system and you can solve it using linear algebra. So this is definitely not a good idea. So what is the remedy they took? Well, the remedy is that to compensate for the scaling down by one over square root of delta, they scaled up again by square root of delta. So this compensates. But this is only on average. So maybe a look ahead. So remember that this matrix had determined delta. So from the ring LWE point of view, a more natural choice would have been to scale up by delta instead of square root of delta. But they only scaled up by square root of delta, which is OK, which takes care of things on average. So this is like the version they took. So this is what I say. This factor compensates on average. But remember this queerness. It might be that this B inverse scales things down in one direction much more than in other directions. And then if you scale everything up, like in every direction with the same scale, in some directions, this compensation factor might be far insufficient. And this is indeed what happens. So as we will see on the next slide, in some directions, so for instance, in the last so many coordinates, you're scaling up by square root of delta to the one over n is far insufficient. And your errors will still be very small there. So this is a graph showing this for a concrete example they gave, so they gave this example. I ignore the size of the Gaussian from which it starts. It's something like 8, in that case. It's something all of 1, say. But they were modulo this polynomial and modulo this prime. So remember that f of 1 is indeed 0 modulo p, which they needed for the evaluation at one attack. But for us, this is not relevant. But the crucial observation is that for this polynomial, this ellipsoid is extremely skew. So in some directions, this will be very big. In other directions, this will be very, very small. And this graph illustrates this. So of course, this is a three-dimensional ellipsoid. But now we are in 256 dimensional space, so we cannot draw the ellipsoid. But we can represent it like this. So for instance, in the coordinate corresponding to the constant term, so this is one dimension, say, we have a distribution. This is an empirical thing, but you can make this work in theory also. You have a distribution with basically standard deviation 400. But in another direction, so like in the term corresponding to x to the 200, we basically have 0. Well, this is because this scale is so small. So let's zoom in maybe. For instance, in the 210th dimension, we have ellipsoid is such that in that direction, the standard deviation is something like 0.3. And so now have a look at this line at height 1 half here. So this is, if we will round or samples, then everything that goes below 1 half will become identically 0. And so indeed, if we see, if we round our samples, then the last so many guys. So this is three times the standard deviation. So with 99.9% certainty, your errors will indeed be below that brown line. And so definitely below this black line. And so if you round, like with very, very high probability, you will round this down to 0, and you will get exact equations in these last coordinates. So let's have a look here. So these guys here, if you elaborate this, and then you look at the last so many coordinates, and you round them, then they will become 0 with very, very high probability. And this gives you exact equations in the secret. So the last so many equations of the linear system will be exact equations. And so if you have enough samples, you will be able to just break the system using linear algebra. So this is the summary. So evaluation at 1 allowed these people to recover S of 1 as we announced. And they had to use about 20 samples with a success rate of about 20%. But after rounding the last n over 7 equations, roughly become exact. And so if you have 7 or 8 samples, you have basically enough equations to apply linear algebra. And this then will allow one to recover the secret exactly. And similar remarks apply to their other examples. OK, so let me complete with some thoughts. So currently, evaluation at 1 is not a threat contrary to what they claim, but it's still an interesting question. So if you remedy their examples, if you scale things up properly, then they are not going to examples anymore. So the work has to be redone, but it's still not entirely clear whether it's possible or not. If it would be possible, it would be very interesting. So you might say the problem is in the fact that they used this non-dual, that they removed this matrix A, so that they sampled the secrets from a non-dual thing. But this skewness is not really a phenomenon that is specific to this non-dual world. So we had this matrix was very skew, but this one can also be extremely skew. And so to us, it's more a matter of insufficient scaling than of dual versus non-dual. Of course, the fact that they made this mistake was a bit because of the confusion there. But still, this is a related question. If we would have scaled by this instead of by the square root of that to compensate for the removal of this factor, do we then get an equally hard or a provably hard version of ring LWE or not? Another interesting remark is that if you have a look here, so these errors are very small, but these are very big. And if you now scale things up properly so that to make these big, then these will become huge. And because we are working modulo p, things will start wrapping around modulo p, and the first errors will become indistinguishable from uniform, which for most LWE applications or ring LWE applications is a problem. And so these polynomials are useless for most applications. That's maybe an interesting observation. And then the last thing is that the cyclotomic case, which are the main candidates. So cyclotomic polynomials are the main candidates for finding their way to cryptographic practice, I guess. They seem naturally protected to this kind of geometric growth. I will not go into the details of this, but for these polynomials, this insufficient scaling would have probably still been sufficient. I will stop here. All right, questions. What happens to your work if you just look at PLWE and you get rid of the B matrix entirely as well? Yeah, then we cannot conclude anything. So, yeah, Polly LWE is a bit more secure against insufficient scaling if you want them, because you don't have this matrix B that skews everything. But there's no security proof for it. Have you observed the geometrical growth for other rings like the entry prime rings? No, we have not investigated that. For the x to the n plus x plus one, for instance. So if all your roots are roots of unity, then you don't have this geometric growth, basically. As soon as your zeros of your defining polynomial are not roots of unity, then this B matrix is essentially a Van der Monder matrix, or the inverse of a Van der Monder matrix. And so you have all these exponents there, and as soon as you plug in roots that are not roots of unity, you have some exponential growth in that matrix, and this is what is reflected in these graphs. So it would be interesting maybe for x to the n plus x plus one. All right, let's thank the speaker again.


Liars luncheon so EDC you mean to tell me he was so insulted by the fans backlash and the fans criticism of the current crop of wide receivers he was so insulted and said the receivers should be so insulted by that that he not only takes for Shaw Bateman in the first round but he double dips and takes Tylen Wallace to YouTube team keep it clean what's going on is engraving here with another video whoo and this is a pic right here this is a pic right here Tylen Wallace is six feet tall but when I watch him play he's not the fastest he's not the tallest but he's explosive and he goes up and gets it he goes up and gets it he's very good at making concentrated catches and contested catches as well and I think my two favorite words to describe him when I watch film on this guy is deceptive physicality deceptive physicality because I think and he sorted to me he's built like a he built like Devon Duvenay a little bit not not as muscular not as swole as a Devon Duvenay but sort of that low to the ground physical guy but playing wide receiver and this they this this is whoo this is big this is a lot bigger than some of y'all may realize man this is a lot bigger than some of y'all may realize because this pic this pic right here and this will what a fourth round pick I believe this pic right here this puts pressure on everybody that much more that much more EDC is the wide receiver group is going to look a lot different this year than it did last year it's gonna look a lot different Hollywood is a lock to be there he's he ain't going nowhere Sammy Watkins is a lock to be there he ain't going nowhere Devon Duvenay is a lock to be there he ain't going no where but then I I wonder and I worry and this obviously ain't nothing personal cuz y'all know I'm rooting for him like crazy like crazy y'all already know what time it is with Miles Boykin love Miles Boykin and I want I really want this dude to do well man but you got to feel like this is this puts pressure on oh and Rashad Bateman obviously a lock too but this this could be pressure man and you know with with Eric DeCosta what were you seeing a pattern with Eric DeCosta and it's it's different from Ozzy Newsom and it's not a shot at Ozzy Newsom but with Ozzy Newsom he tended to hold on to players too long when it came to loyalty and and he treated the business not that this is a bad thing necessarily but in business is tough not to do it but sometimes he would hold on to guys too long but we've seen under Eric DeCosta he will get rid of somebody he takes care of business earlier rather than later whether that's getting rid of somebody whether that's giving somebody a contract extension whatever it may be on both sides of the business keeping or letting go Eric DeCosta he takes care of stuff early rather than later this pick it scares me it scares me not because I'm afraid that Tyler Wallace would do bad no Tyler Wallace he'll be just fine but it scares me for other people namely Miles Boykin and James Pro Shae now with Benjamin Victor and also Deion Kane those guys are on the team too but they signed to future reserve deals so I don't expect them to be on the active roster you never know what could happen but I don't expect it but hey it's more competition it's more competition but this pick is more so again from in my opinion I think it really puts Miles Boykin and James Pro Shae on a hot seat because you look and this is nothing coming from a fan but you look at the usage of the wide receivers look at it look how much miles boy and we know Miles Boykin he may be the best blocking wide receiver on the team but you look at the usage of a Miles Boykin in the offense and he was used a limited amount man and this was this was on Lamar it's on Lamar Lamar had a lot of opportunities where he missed him and just straight up didn't throw to him it's also on Boykin too to make the most of whatever opportunities he did get and sometimes he did now there was a few opportunities so anything that Miles Boykin did it got blown up whether it was good or bad because of because he had so few opportunities and then you look at the usage of Pro Shae too he had he was a fifth round pick last year I believe and he had one catch and I think two passes thrown his way one of them ended up being an interception the pick six I think in a Steelers game I believe but anyway with Pro Shae he was not used on offense and then he was the punt returner he was the punt returner but then boom numbers game kicked in he was on an active list for the remainder of the season toward the end of the year and they gave that to Devin Duvenay and Devin Duvenay he did a good job there he looked comfortable there too so this is why I say with Devin Duvenay he's straight he ain't got nothing to worry about again Sammy Watkins Hollywood Devin Duvenay Rashad Bateman those guys are locks and and Tyler Wallace being the fourth round pick you got to feel like that's a lot too especially since he's a fourth round pick this year but I am very just again I'm worried the most for Miles Boykin and Pro Shae man and of course there's the practice squad and whatnot but I just this this pick sends oof it's wow it's wow now one thing I know John Hall boss said that he didn't want to do it but one thing that they could possibly do with Miles Boykin to one try to maximize his potential and I'm saying that he doesn't have potential as a wide receiver because I certainly think that he does but the system that he got drafted into it just it just wasn't the best of fits to really excel and really show off the best of the best at the wide receiver position that you got but with Miles Boykin one thing that they could do John Hardball has spoken against it but he's changed his mind on stuff before they could take that Darren Waller approach with Miles Boykin he's a big guy and I know he would have to put on some pounds because and what I'm talking about is moving him to the tight end position if they did that now it would be different now but I think it could be done and again John Hardball said no no he's a wide receiver and he is that but you think about some of the things that they liked the most about him now it is much different you go from blocking cornerbacks and safeties and all that to blocking outside linebacks and defensive ends yeah that's a big difference but you just never know but I do like this pic it shows again whatever Eric DeCosta whatever he drafts in the first round oh you best believe he's he is going to double down on that Hollywood Eric DeCosta first draft Hollywood in the first round Miles Boykin in the third second draft Patrick Queen in the first round Malik Harrison in the third and now his third draft Rashad Bateman in the first and of course Odafe in the first as well but then Tyler Wallace in the fourth so oh man this is a wow this um it's a pressure move man it's a pressure move but again competition it breathes excellence man it breathes excellence so whatever receivers make this Ravens roster this year it is going to be what they feel are the best of the best in the room also another thing to think about there could be possible trades made as well the receiver room is very crowded it's very crowded so trades could be made guys could be shipped out for draft picks in the future because we know this draft is getting ready to come to an end in a little bit but people could be shipped out for draft picks from next year's draft so we'll just see how it goes man oh this uh oh this worries me man it really does man but we gonna see how everything works itself out team keep it clean I love y'all I appreciate y'all oh and here we go we out man


What's up YouTube welcome back to the living room, so we finally got our couch man, and we are on to spider-man 3 It's been a lot of fun. Thank you told him a choir. You've been an awesome spider-man nice to scribe hit us on patreon If you want more content, let's go. Let's go Let's go man. I'm ready to get some finality Harry and Peter got some business to attend to so does Mary Jane and Peter Right if they're together right, but she saw that he's spider-man, so I wonder how that's gonna change there She said go get him tiger. She said go get him tiger. That's the best part of the last movie We came depressed There he is the star baby, let's go We get to judge who's the best spider-man when this is all over. Yeah, it's all said and done We'll go through our favorite spider-man our favorite villains our favorite version of Mary Jane's aunt may use all that What's that dude's name? Yeah, I don't know. I thought it was Toby McGuire. It is I thought I just said toe for grace. I know I don't know what that was about. Oh my god That's got me tripping right now, but is that Rosemary Harris? Oh, that is JK Simmons I was about to say I thought that was the dude from that 70s show Yeah, it is From who on that 70s show Eric. Nope me. You know that 70s show them. Yeah, I don't know which one's Eric the main character The guy. Oh, yeah, I know Eric Okay But he does kind of look like Toby McGuire Right. I've always sort of got him, you know too bad Toby McGuire such an icon. Yeah 2000s icon forever. I don't really know toe for grace to be in anything else either.  What's like, what are we supposed to do right here on this one? Oh? Yeah, oh great. She's like always captured. All right. Let's get serious over here. No Great transition. It's me Peter Parker Friendly neighborhood, you know back then nothing seemed to go right for me People really like me My uncle Ben would be proud So Spidey's on the up right now basically that's good because the last couple movies have not been his movies He's still on that little moped though up of my class Dr. Connors, he's like 55 What the heck and I'm in love with the girl of my dreams that blonde girl No, oh, he was far away. She just turned around and smiled. That wasn't his fault Mary Jane Watson Miss Watson left a ticket for me Peter Parker showed up 745. He has to attend them every day probably That guy's like why let's go she's like killing it He's so happy that I feel like it's all gonna fall apart. I know He doesn't care at all She's killing it and that's ageless fire, too.  We got him Why was his face so good right there Quick little opinion Explain things to my father Still passive aggressive So here he just thinks his dad got got for no reason right Harry seems like the type of guy unwilling to have a conversation Checking understanding killed his dad, but I would at least have some questions, right? It's complicated You were wonderful It's all about diffusion keeps the sound waves from grouping you see when the sound waves they propagate it's like an Oh, no, wow, look at all those prototypes So this is what Harry's been doing. He's been trying to be like his dad, huh? But we get serious now This is a blockbuster over here She doesn't Get serious Like to sing on stage for the rest Oh I'm glad he's happy.  He's gonna be down there like With the monoculars irritated. I mean, I'll be honest though. I'd like to try one of those webs.  Well, see that would be an intergalactic type of like sludge Yeah, it looks like some type of like intelligent Oh, no, oh, no, oh, no, I don't know Rose intergalactic sludge to me right on the license plate, right? So they're gonna get pulled over. What if you like speeds past one of those things that catches the license plate? They just see the intergalactic sludge on the back Where did he hide?  I just stayed hidden. Oh, no, please don't go in there That's a kid's room with an oxygen tank. Oh, no Someone's be his daughter or something and he's we must know this child, right?  It wasn't like that wasn't a good reason for what it was doing That's a truth you and the truth sitting in prison having three meals a day together I live in the presence of great truth right there in that bedroom Oh He done broke out Yeah, we're not supposed to know who that guy is right because I don't know him at all. I don't think so promise Rose how we got our bad guy. We got a motive and everything. Oh, geez. Let's do bad things. Oh, that could be it Yeah, he's got a good incentive to rob spider-man's about to run into him probably was he at Don't tell me that's the bait. Oh, it's amaze new crib. Sam Jay. I'm gonna ask her to marry me. Oh The day and you said yes, right wanted to say yes, but I said no I wasn't ready Neither was he so we took our time look forward to it a man has to be Understanding and put his wife before himself. Can you do that Peter? Yeah? Think I can and then you have my bless He wasn't very confident was he I hope you've considered a proper proposal your uncle had it all planned He took me to the beach one Sunday. It was a knockout in his bathing suit We were very good swimmers beautiful day, and he said let's swim to the island Perfect spot by no tree and we lay down and looked up at the sky. Then he said Oh, it's your eyes, man. He said open and he was holding this ring. Oh Dazzling, I thought it was the son. Oh, this is so sad married 50 years come August Dang now Peter has to feel guilty so Make it very special for Mary Jane do something. She'll never forget give her this That's a good sentiment though. I've always loved that one passing those down like that. Mm-hmm. That's a sweet They even watch that step though for an old lady. I like her new house. Oh, it's cute. Yeah, it is There goes Harry who's old pal Harry. Mm-hmm. Hey, whoa, what's he on a hoverboard? Is my man on a snowboard? What are you sure that's Harry? I Shoot slow You know focus snap. He just slice them up real quick. Dang Has he been injecting himself with that green stuff absolutely Heavy Hooked up right now. He's gonna start hearing them giggles No, that's gonna be a lot of money. He lost it. Oh now you just borrowed his hoverboard. Oh, he's oh God No way the precision the precision did he not catch it? I think Harry caught it. Oh Nice, that's a better catch and when Frodo did it He better put it in a better pocket. He's like really trying to get him right now. Well, Tony Hawk underground, right? He had that little snowboarding game used to stand on back in the day. Oh SFX. Yeah, it's annoying that He has a sword. Oh my It goes some more of them. Oh Nicked him. Oh That was Harry. I'll just go get a lot more of those. I know. Oh No, I Love the sound effect of it Knocked him out. I know now look at him doing all bad.  Did he no way not for real no way look at his shirt all sliced up I know he barely got that Harry just came out do his dad in no type of way his costume wasn't as tough as Glider wasn't as cool. No, he's glad it was pretty cool. That's I kind of like a hoverboard. He could buy a Walmart Yeah, but I can't do that Oh, yeah That's who killed uncle Ben. No, I thought the guy who killed uncle Ben thought the window That wasn't that guy. That's who they just said. Yeah, but we're missing something because that is not the same guy Yeah, it wasn't the same guy not even at all The other guy had frost tips he caught my dog with the overhand right literally Dang He's about going into some like radioactive waste or something. Oh, no Take some high voltage. We got a little fluctuation on one. There's a change in the silicon mass. It's probably a bird It'll fly away when we fire it up.  It's not a really expensive experiment. That was like the one that Mysterio had That guy run. Oh, oh, no egg beater on it. They're about to make a mom It looks like it's about to like tornado him Yeah, now we can't get out. Oh my god, that would probably hurt, right? Oh That sands going into his like fibers. It's about to recreate his like DNA. Yeah, exactly All that sand smacking him up doing that. Oh, no Becoming sanshrew. How do you destroy that a water Pokemon? I think he's dead.  Oh, wow There's been some memory impairment right now. He can't remember the accident. Good Only time will tell. He's awake if you want to see him. Maybe I should wait. It's fine. Just keep it brief I want him to rest. So he's saying Harry ain't gonna remember anything. Yeah, sorry term What maybe he'll forget that that spider-man killed his dad. You think he could forget that? maybe He's like Peter. Hey, buddy. Remember when we were in science together? Yeah, he did too. Why do you say like that? Mary Jean does got there. It's kind of genius spider-man gets to redo that moment all over again Until we're not, you know until he remembers something. Oh, no Harry's on that medicine, huh? I know. So who's this guy gonna be Sandman? Maybe we'll play some Metallica What do you think this is an Iron Man movie? It does look like it's alive. Yeah, that's the shoulders right there. His head's not floating. He just fell apart. It's his first shot But he's thirsty. This was like the worst villain you could be because this is like they could just keep coming back I mean, yeah, but look how thirsty you would be. So what do you have to do?   Yeah, I can't do that You was just at the wrong place at the wrong time.  Oh, no He can't cry This is kind of crazy. Oh, he gave himself a t-shirt. That's respectful. He's transforming back. Okay. Let's go That was wild. Let's go.  I was right there on the first row. That's a critic. This is something you're gonna have to get used to Leave me. I know spider-man gets it. Oh, here we go. You can't see biscuit reference. Oh, was it really? I don't know Duty calls peter parker go get him tiger.  Oh, she's leaving Honestly forgot all about that thing. I know it's after peter I'm just playing I give me a jane a hard time, but it'd be hard to be her.  Is that same man? It's my coffee What is that thing doing in my background?  That would be so scary. Oh, no Was that the girl in this class? Oh my gosh. Yeah, the one he was working on. Oh, there he is right there That's Gwen. What? What's she doing up there? I don't know. I just saw her last night. She said she had a modeling cake Who are you? It's Brock sir. Edward Brock jr. I work at the daily bugle. Oh, no And I'm dating that that's his daughter right there and he's just keeping it cool like that. Oh my god Oh You're so needed right now. Oh my god That girl's an athlete though. Yeah, she has incredible strength. She's got hit with a 300 pound.  She's falling Dang, she's doing a hard fall.  Just kidding. They got a guy That's kind of amateur. Have you noticed the stuff makes you look a little bloated? He just he just got dissed by his replacement. I'm right dazzling Just a little sub the boys in the ad department and I whipped up the relation at the bugle's been pretty good Though not as good as the new york times or several others smaller Oh my gosh, they got them on blood pressure medicine Oh, he's had the same assistant all three movies. Thank you Who is this dude still something called nice and easy. What's on you? It's called go away He's so different than that Sunday show Pete you better get in there new guy. He's trying to sell some spidey photos. Thanks Parker, you're late. Maybe too late. Rocker here. Beach to it. It's Brock sir. If I got you this What you got me this I didn't see there. How'd you get that high climbed nearly fell off a flagpole flagpole Which one do we use? I like Bernstein's better cheaper too. Congratulations, son. We'll use your shot I'll pay you 50 bucks jj. I'm your man. I know more about what makes a good picture than any photographer in this town See photography. It's not just about no offense. Uh flagpoles It's about lighting composition drama.  Johnson quit remember you fired him whatever There's a staff job. Mr. Jameson. I think I deserve it. He's right Jonah. He's been with us for years. He's right Jonah Anybody care about what I want get out Are we still mad about that suit these bullets tickle He could literally go home and stick his hand in a cookie jar and take a selfie of it and troll his boss And get fired This movie's got a lot of dynamics so far. He's having like a job competition with this guy. It's like training camp Is that about to be Stan? Yep. I guess one person can make a difference enough said Oh I miss those. I love Stan cameos Oh Mary Sure catch a little homecoming present Hey, we were pretty good in the backyard, weren't we? We're terrible and we tried out for the varsity the cheerleaders Right. Hey, do you have any girlfriends? I don't know you don't burger. That's what I know of sir Dang pretty cool path. It's okay. It's like I'm not earned for money Maybe I can turn this no girlfriend thing around. You came out like a goofball a lot happier You know, I always appreciated how you helped me through high school. Oh, no He just looks like such a villain. Right. It's the main thing. Hey, uh, let's watch Get something eat. Oh still got the moves. Yeah Why did his dad look like he was gonna say something? Oh, she's about to get replaced too, huh? Oh same song the pitch is so different though Why are we stopping? Oh, gosh. Oh, no not the mix up. We tried to reach here. Oh, Mary Jane, honey Sit on let if you'd like we could uh, we could say you became ill Absolutely dang that was kind of terrible, wasn't it? What did she do for them to be cheering? She just used to it got fired. No, she's got to be jealous of peter now Dang, they replaced her name so quick He just was like the biggest dork in the world. Just that smile He's so hype. Can't you see that gorgeous smile? This is front page stuff Okay, I've got to practice. I'll see you later. Okay. How about tonight? Not tonight Why not? What about that amazing amazing night that we had? We had coffee. No Hey beautiful one more Everything okay. Yeah, I'm fine. You're not still mad at me. Are you? No, peter. I'm proud of you He messed up a shot. She messed up the shot. So he was assuming that she was mad Give him a good show. Don't worry about that review.  Promise Why is there always got to be problems? What's his agenda? Isn't that that guy from that prison break? He fits.  Do you think he just turned back into sand though? I don't know. Where'd his clothes go because then you can find him, right? Well, his clothes appeared from sand That sucked She hopped out that car. Do you see that?  Uh-uh Dang that hurdle. He's like stop stop. Maybe you're right.  She's just happy Man, I'm getting this movie. I'm zoned into it Hey, Pete said you're in a play. I'll come again tonight Now this is embarrassing, but I once wrote you a play in high school I was he about to make a little cameo. They love me. Why'd he film himself so much? Does he beat that director?  Oh, no Peter park, you know law show damn mine. He said I hope Pete's getting a shot at this Uh-oh, dang. I'm so mad at Toby McGuire right now. That was crazy He was so brazen about that and he knows that she's in the crowd too There you go. He's gonna take that money. Dang. He knew exactly where to go Yeah, but what does he think he's gonna do just drop that off at his daughter's door as a sand monster You know How do you beat him? I don't think he'd make the gas pedal do that. Just saying I'm the sheriff around these parts. Okay Oh I love how he just says uh-oh Right, that's how I felt too. Then he has the legless slash hairy move Slash Sean white dang do that over and over again Like you just keep chipping away at him You can have enough time Dang, they're all right. Did he make off with the money though? Yeah He had it That would be so uncomfortable in my in your shoe like that. We'll get the little strap holding his leg now Why would you do that today? Mary Jane's gonna say no because peter obviously has no like awareness or timing very socially awkward today She may not show Did Pete just check another girl out just a minute ago? Is that the same guy? Is that supposed to be peter? The same guy that was um the usher at Mary Jane's Uh, they are What was that? I think it is Um, I have a request my girl you want to pop the question. He was saying it's tiny.  It's cute some champagne. How'd that get in there? She might break out with you peter. Look at you. Wow boy. She's acting in class. So you came in there swinging I wouldn't even came you to you'd have been with a newspaper girl I know exactly how you feel I have been through this it happens to me all the time I see spider-man posters in the window kids running around with me on their sweaters big halloween item I don't know. I guess I've become something of an icon of myself. I'm just a nerdy kid from queens. Do I deserve this? Hello, this is mary jane watson. Oh, it's so nice to finally meet you.  If you've got a picture of my kiss with spider-man, could you bring it to class? I'd really love it After all who gets kissed by spider? Every day she does Well, it was lovely to meet you. Oh poor girl. I can't even imagine that happening in real life though That would be so disrespectful Oh, no, no, no He's trying though. He's in my science class. It's not her best subject She's her lab partner. He saved her life. She thinks you're a genius and she had her polished fingernails all over you Or didn't you notice and she gave spider-man the key to this video. I'll never forget that She's just a girl in my class. Let me ask you something when you kissed her. Who is kissing her spider-man or peter? What do you mean? You know exactly what I mean. That was our kiss. Why would you do that?  Do you want me? Well, are you sleeping on the web tonight? Why would I want to push you away? I love you. She's a girl in my class mj.  Yeah, he doesn't even know she got fired That's how un-present he is. He's just so unaware like bro Yeah, but she didn't do all that foolishness today. I wish you'd pick up the phone Man, you shouldn't have picked up. I'm mad at peter right now. Mm-hmm Originally, we thought that this man, Dennis Carradine was your husband's killer. We were wrong Turns out mr. Carradine was only an accomplice. It was the other guy. I don't know What are you talking about? They said the other guy's the one who killed uncle ben Right, but I just remember it being the other guy. There was two of them You got this wrong two days ago. He escaped. He confessed his guilt to a cellmate. Oh, he's playing in the back Oh, uncle ben is just chilling. No wait. Yeah, why did you do that? It was him, but I didn't remember him. It never showed him. They just added the sin just now Right. That's why he wasn't in the car. He just pulled off and left him.  We're doing our job Oh Those walls are crazy.  I told you that I'm not accusing you of anything. Yeah, peter doesn't regret that I'm fine. I don't need your help. Dang and peter. We can all use a friend. He's trying to make himself lonely What's up with you? Oh because you just found out what happened with uncle ben's killer Yeah, he's got more people to push out of windows He's like in uniform and everything ready to go Is this an alien? You mean that intergalactic sludge? Yeah, it looks like it. Oh, no Oh my gosh. Why is it wrapping around him like that? Why did it choose him? Is it trying to attack spider-man?  Whoa, his suit looks so cool though. Yeah, that is pretty clean. I don't know. I like that one a lot more Look at the foggy eyes. Wow I'm so confused about this so he can spider-man's better now. I don't know that was weird to me Whoa, he's so much more coordinated This is something else You don't ask a lot of questions though. No, I'm a physicist not an amazing test. Can we do that now? It seems to like you it has the characteristics of a symbiote Which needs to bond to a host to survive sometimes when they bind they can be hard to unbind Brilliant Marco there's a sandstorm Oh, no, he's gonna use that thing.  I would use it too Especially at night make it a little harder to see him I mean if he would have just chose black from the beginning, I know he could have done that but he didn't Follow the trail not this guy He's getting really up in the action. Love the new outfit is exactly what I need to scoop Parker. Give me some of that web action See it jump It's probably like a $3,000 camera. Why did he just jump in there like that? I'm gonna show you a wrecking Oh, he's got that extra the backup cam Flint Marco What do you want from me? Remember Ben Parker the old man you shut down in cold blood. What does it matter to you anyway? Oh my gosh. Yeah, but when does he sand? Right now. Oh my man on the belt sander. Oh, he landed at water. What does that do? Yeah, see if he turned into mud What if he turns into concrete though? That's dangerous. He might would have hit him if he didn't scream the whole time Bro, he's about to release the hole. Yeah, there you go Is that it? There's no way that's it. Dang Pete. That was kind of cold. He said good riddance Oh, yeah, I put it in front of the face.  He probably gets tired of getting badgered for money every time he walks in the house Yeah, he is looking pretty follow up boy His hair almost looks purple doesn't it? I went for that color But he only has it when he has that thing on because now it's brown Did you say you went for that color remember?  Yeah It's not playing me. He said did you guys say you went for that color last night? I don't even bit him Spider-man doesn't kill people. He does.  Yeah symbolic for something right don't give up mary jane Oh, so she got hired. Yeah, she doesn't look happy about it though. He looked kind of like shady She's about to call harry. Yep, because she's wearing a green skirt I That is pretty good though. Why is he doing that though? MJ's always turning up boy. He said i'm not doing anything. He's just making a masterpiece. We're having a guest Can you get some food right away, sir? He doesn't have one drop of paint on him. I'd be a mess He seems so much happier.  Listen. I'm really sorry for what happened earlier. I shouldn't have yelled about that door It was nothing to be angry about. It's not a big deal But if you feel that bad about it can buy me pizza sometime One more if it's a woman you're calling then you say for a good woman. I'm good man. That's it Are you calling Mary jane? I think that would be so good. None of your business go Oh, she's still making Thanks. Bye. Bye Oh, I can't believe this harry's house He put only two scoops in it and said I hope you love peppers You're annoying Our skirt was like it's made out of grass. Ooh, he's like nice moves He should go show where the vase he painted Because that thing was fire So they're making omelettes. Oh, yeah kind of looks like it. There's some scrambled cheese eggs. Yeah, definitely That omelette's busted harry Can you do that? Yeah, you know, I can you do it with both hands like us. Yeah, you can do and I know it Don't be touching. There she goes face touching always when she's got a man I ain't gonna say nothing in this movie. Do you do you MJ? No, it's okay, please He's so nice in this one All right, he's so much cooler in this one. I like him this way. He's not just like, uh, I just want to kill peter parker. I mean spider. Well, he's been wanting since we first met him He's like a spider-man peter parker. They're all better than me.  Don't forget Well, that was short lived Yeah, you say, yeah, I swear on my father's grave spider-man will pay. Oh, yeah, he said that at the funeral Remember me. Yes father. I was right about her about peter about everything You know what you must do make him suffer Burst we attack his heart. He said that in the first one. Yep It's a lot more dramatic in the first one too. But at least he's like talking to someone in this one not to himself I was not expecting that you're gonna do something for me Man, everything was cool. So she went over there and mess with him. Mm-hmm Dang There's a phone call for you. It's mary jane. She called back. Really? Yeah. She's so happy for him She could dunk on him. Hey For real. I'm so glad you're okay. What a public spot.  Basically. He's like my beloved There's something I have to tell you. It's not working you and me. I don't want to see you anymore Wait, why is she breaking up with him? What are you talking about? Did Harry make her? I don't know Oh, Harry doesn't make her. She doesn't know Please don't say that. I can do better. I can change Love each other. I love you when people have problems. They they work it out. They talk to each other I think she is being enforced. Look, you see. Oh my gosh, definitely be enforced I fall in love with someone else. I mean after an astronaut spider-man, who else? I know.   So can you please go do something about it? That she's lumber. Oh, he said they're talking to him.  Not exactly what you had in mind for yourself singing waitress What are you talking about? Well, she was fired from the show. She was fired.  Don't be defending mary jane. Oh, yeah, she did. She did that message I'm just fine. You can defend her. I'm the other guy. Whoa. She came to me one afternoon troubled She needed someone and I was there for I've always loved her B. You know that look at his face I don't believe you. I just thought you should know. Punch him in his face, peter. No, also mom. Yes, please. How's the pie? What in the world? No shot, that's such a sad move. He must like hit under the arch of the window right there I don't know man. I'm kind of pissed for peter. How are you feeling? I'm mad for him too Because things seem to be going right until mary jane had to go over there No, everything was going cool until peter had to kiss that girl Oh, yeah, true. When he kissed that girl it all went downhill.   That version might right They two old bandmates. What did you do to her? I did what you failed to do. I was there for her Mary jane and I we understand each other. She knows me very well And when she kissed me it was just like she used to kiss me that taste There you go. Why does peter have the same haircut as me? Oh my gosh Harry's too funny in this. I know he's a good villain actually.   He loved me. He despised you for an embarrassment to him. Dang Lucky little goblin jr. Gonna cry no filter version of him. Oh my No shot What So peter parker just basically doesn't really care anymore, right? Look, he's getting eyeballs now two completely different type of women Mugged them up. Okay three different types My son thought the world of this guy. This is the guy that gave the key to the city too. Bro. All we do is change the suit Oh, yeah, it's just right right here till jade space. He got the position. Look how big it is I never thought he'd really do that. You gotta see it as it is. It's funny You should say that I was looking through some old photos and it looks very uh, similar I gotta get back to work. You're trash, bro. Your picture is a fake. Parker.   You won't forgive miss your religion. What? We're just horsing around He got breaking glasses. If you do this, I will lose everything. You should have thought of that earlier What are you doing, peter? Tell us if you're an editor. Tell him to check his source next time Oh, it's a fake. Jonah's about to be pissed. The traffic department confirms it Pack your things. Get out of my building I was just you're fired. You know, we're gonna have to print a retraction Put it a retraction in 20 years They would never ever ever print that retraction on the front page though. I know we'll be on the back page Are you at a specimen you left me, Parker? Chemistry is not unlike the chondritic meteorites of the 70s You know what, Parker? Parker? Come smoke. It amplifies characteristics of its host Hey, give me another cookie I love this part You got any with nuts? Um, I have some nuts.  That's not the position I hired you for Peter, these are incredible You got to have these, Jonah If you want the shots, I'll take the staff job Double the money Not the hair flip When she's hurting right now I know because he's cracking me up He's finally got some swag to him Right But it's like too much Like what's the guns?  Oh my gosh What is this? Why has this part not been memed? Okay Oh, he's in a blood monster Right So when he killed Uncle Ben, Penny was young Maybe a baby at the time?  It's like they traded swag or something This is jazz Oh yeah, Mary Jane works there Oh, we went to make her jealous Who is this guy? Hot legs Just slipping people 20s Isn't that your old girlfriend? Do you want to go someplace else? I'll be okay Mary Jane, show us what you got She's not catching on What's the point of bringing you there? I'll be right back All right You just leave in the middle of her performance No, he didn't This is for you Thanks Bro, I'm so confused right now like What is happening? Did I fall asleep on this couch? That alien thing really took a hold of him Do you see her hair? Do you see her hair? Mary Jane didn't catch it She's like, who is this guy?  Yeah It was still all about Peter She apologized to Mary Jane You all right, Mary Jane?  Is everything okay here, Paul?  Take him out of here Take your hand off of me Oh Oh no The lawsuit Yeah, bro, man Tripping, Peter I don't know I don't know Uh-uh What's he missing about his suit? Did he have it on? What do you think, babe? His speaker lost his mind? Yeah, but it's that thing There's always like the classic Spider-Man contemplating everything by itself On a ledge scene, huh? Of course he's where that guy is I come before you today Humbled and humiliated To ask you for one thing What is it, bro?  Bro, what is about to happen right now?  I don't know that maybe it jumps on him Parker He can't get it off, can he?  Is he gonna turn into a Spider-Man? Or is he gonna turn into just like an emo version of himself?   I don't know Hey, I've never had a shower like that That was warm It's like all the heat was staying there Oh God, it's gonna be that guy Oh no Let's go Just who we needed to see When we spoke on the phone I sensed that something was bothering you Did you ever propose?  I know, like, what's that? Isn't she 80 stories up? Right Boy, there ain't time for it Throw it on Pull it out Right, he's sitting there reminiscing Getting all nostalgic He's like, oh, last time I put this on I didn't know how to dance Jennifer is in the trenches, ain't she? Yeah, she is Apparently Spider-Man's being challenged To some sort of confrontation Stop us if he can Oh, my god, now Harry's about to have to make a decision What's he gonna do?  I need your help Oh, my gosh Can't take them both No way Whoa What happened? Remember You don't deserve my help Hold on, hold up I remember earlier that when they were fighting Harry threw it right And Pete was like Oh, yeah Yeah, and he threw it and it exploded and hit his face Oh, it burned him up? Yeah, chopped him up Look She needs us Didn't Harry throw it at him, though? Yeah Get out Dang It's kind of on you, Harry I don't really feel bad for you, bro I didn't get Kintana He's got a Kintana and a burnt CD I've seen things in this house I've never spoken of What are you trying to tell me? The night your father died, the blade that pierced his body came from his glider You just not telling him this?  Right Oh, is he stuck? Oh, I thought he got stuck I can find a way to settle this You're so right I'm thinking humiliation He got them golem deep Do you remember? Do you remember what you did to me?  Tiger Let's go, MJ What did she miss though? Why does it scream like that? Peter's undefeated fighting falling off buildings Oh, if we just had Harry with a glider right now A snowboard Oh no Ooh Ow And he landed in some sand No Why is that truck up there? How many trucks did they put up there? Oh, she jumped She had a lot of trust in that web She's athletic too Hold on, Mary Jane! Anyone who knows Spider-Man can hold on to things for a long period of time Whether you're Aunt May They all practice with him You got to I'm telling Penny about this Uh-oh Did it Mysterio do that in the one we were watching? Yeah, I think so There was- he like looked like the sand creature or cyclone Picked off by Venom He just became Venom and he's already good at swinging on them things Uh-oh Oh my gosh Spidey smashing Hey, Mary Jane Oh my gosh They're beating the tar out of him I would not be watching this This could be a tragic day Could be the end of Spider-Man And y'all are just watching So dramatic Oh my god Come on, Harry Where are you at, bro? There he is Oh my gosh Dang, that bomb went in right in this temple, didn't it? Yeah Let's go Harry going ice games mode The winner games I'm here for you, brother Best moment Best moment of all three A couple minutes ago wouldn't have been so bad either What are you gonna do?  Why does she keep getting like smack with taxis? I don't know But Mary Jane's done saved her own life Like 12 times Let's go This is making me proud right here Let's go He's riding on his like thing with him Right Is it a search stuff, Harry? Oh my god The town's loving it Mary Jane would have to be like The most traumatized human being on earth right now Yeah Just break down Mary Jane, it's okay This is the last movie Somebody's gotta die right? No, they better not Oh my gosh He's gonna cause so much damage If I saw that I would be freaking out All right, now we just gotta take care of Eric No way that guy's doing that to you What you can't kill You gotta take it off Oh, you'd like that, wouldn't you? I know what it feels like Feels good The power His eyes Let it go What if he just doesn't care about that? Yeah, there you go It makes me happy No I thought Harry was about to get stabbed with one of those Oh, he doesn't like that His vibrations mess with him, huh? But that's when he was hitting that bell tower Oh, he has the movie He has to give us a flashback Cause we're kind of dumb Yeah, the ones nowadays really don't do that The sacrifice No, dude It's over Uh-uh He went out of straight G if that's the truth Use those Yeah Cut him up Ooh He needs a gong or something Oh, Sven doesn't like those vibrations for real Oh, he's about to start playing a song, Annie Nice Good thing you thought about that Right Whoa What are you doing?  I have no idea that quick Cause it stayed in his room for quite some time It chooses you Look at that throw Oh He hopped in with it You were a straight fool, my guy Weren't you at church when that happened?    I've done terrible things, too Are they about to have a therapy session over here? No, you don't get to walk away?  I don't know Besides let him go That was scary, that was like that supernatural demon I don't know, so far his Spider-Man story is a little more tragic than the Tom Holland one I mean, he lost when he started though Yeah, that's tough, too But he lost his legit best friend though And the third piece to his love triangle No, it's just easy from here on out All right, they're both gonna be sitting there thinking about Harry I'm just kidding You're terrible Ever comes our way We always have a choice My friend Harry taught me that He chose to be the best of himself It's the choices that make us who we are And we can always choose to do what's right That's so sad that Harry's gone though I know Especially in their lives, there's gonna be a huge void Well, that ain't good Yeah, she should probably focus on herself for a little bit Mid song He showed up and said, let me fix things with my woman He said, let's get out of here toots Yeah, come on toots Bang, he did that like he had some of that Venom Sudan Just a damn good movie Okay guys, that was Toby McGuire's Spider-Man So baby, what did you think about that movie? I thought I had so much fun during this trilogy It was great because it was like 2000s movies all over again And to be honest, I never got to experience them at the time I'm honestly glad I didn't because they would have been a little too mature for my age at the time But I'm glad I got to experience them as an adult And I had a lot of fun My favorite moment of the whole thing was him And Mary Jane finally getting their like togetherness at the end Even though they were dancing to No Song Because she was the one singing it Right, that was a little awkward I've said before in the very first movie This was one of my favorite movies growing up And revisiting the original movie And watching the second and the third installment Is definitely something that I've put off for way too long The second movie blew my mind It was absolutely incredible I thought it was one of the better movies I've seen it in a really long time The third one was really good I don't think it was as good as the second one Not that we really need to compare But the first and the second one was just lightning in a bottle This one was really good too I thought the suit was amazing No, I thought that one right there was hilarious Because when he was like It was funny When he had this When he was the Spider-Man When he was like this one Oh, when he was like My chemical run Yeah, he was like My chemical Spider-Man Right When he was that That was too funny That was good I'm not even gonna lie That was the best part I guess like back then That was sort of the culture He sort of had this same haircut As me a little bit Mine's a little more like Bull shaped I guess It was a lot of fun though, man We gave Mary Jane a hard time In this movie We're just poking fun at her, man It's all in good fun As Mrs. October said These 2,000 movies are just really ridiculous in that way If you guys know what I'm talking about Then you guys know what I'm talking about They're like parodies of themselves And even when they're being serious And the drama's on the TV And you're supposed to take it serious They still have a way to like Slip in as much humor as possible So they're very lighthearted in that way I just didn't realize these movies Were gonna be so funny And that really explains to me Why they try so hard to make the Tom Holland movies As funny as they are Because you notice in those movies They have a very like funny tone to them Maybe it's because the original Spider-Man's Was like borderline hilarious Right So you just had to continue it on Like Spider-Man has to be the lighthearted one Like when there's all this drama going on You have to have Spider-Man to lighten you up I would definitely classify this as like an action comedy 100% Yeah But It was good I had a lot of fun watching these 3 That was fun guys I don't really know what more I can say about Spider-Man I mean the story is what it is It's really sad to see Harry die in the end I wish that he would have never turned into the Green Goblin I wish we could have avoided that whole story arc Do you think in the comics The Green Goblin ever sided with Spider-Man Or was he always the villain? Do you think they did that? Like you know to like put it into a movie You know I really don't know I know that That would be good to know guys If you could let us know I would imagine when they do things like that You know because the whole MCU has not held very true to the comics right I would imagine that they would like test like audiences and things like that So you know I guess really all that would have to do with like audience scores On how people would receive or perceive things like that So Right You know I don't really know because I don't know how the comics are So I don't I can't really decide like what the movie is based on the comics Because I genuinely have no clue Right I'm completely ignorant to those This is a really good movie We were introduced to Venom We've met a lot of really really awesome bad guys Really awesome girls in these movies My favorite hands down was probably I mean the Green Goblin The original Oswald right Because Or Osborn Not Oswald Osborn They literally asked that man They said how was working He said work was murder Like that's hilarious This is the best line in this whole franchise So um No I think I think Uncle Ben's with great With great power comes great responsibility right That one's so iconic that people People quote that from Okay well no one I know quoted that Except for me right now Hey you're not watching good people then I guess not Anyways guys thank you for rocking with this man That's Spider-Man We're not gonna babble too much about it But we watch these movies because as you guys know We have been on the MCU journey That MCU journey has not come to a stop We are just at a pit stop right now Checking out all these movies that we caught up on So I know we have Spider-Man We have Andrew Garfield Spider-Man One and two coming up And I also know that we need to check out like Venom And we need to start getting into the X-Men movies And things like that So you guys can let us know in the comments Exactly what order would be the best order For us to put them out on our channel So we are very patient on this channel guys We want to put things out in the right order When we want to do things And we can do MCU every Monday till we like die Absolutely I'm having so much fun with it So you guys let us know the order I know there'll be disagreement about that But whatever the most people agree on So if you see a comment that you agree with Like it if you think that's the order And whichever comment gets the most likes Will be the order that we put these movies out in Thank you guys so much for hanging out with us man It really does mean the world We're right at six months And it's just been It's been so much growth And it's just been so many awesome people And we really realized making this channel At the end of the day It really just comes down to you guys man Yeah, I bet you all went Yeah 100% So we recognize you guys in the comments Some of you guys comment so much That it's almost like You know we're really making a little family here And you guys definitely know who you are man For sure So thank you guys so much It's been the October's We'll see you all in the next one