Hello, how's everybody going? Day two we're at the end almost But you're stuck with me for 30 minutes. So and we should probably get started because I have a lot of slides So cool. So we're talking about a case study on building cloud native platforms My my boss kind of coined it a love letter to skipper. So you're warned but But just a quick quick little thing about me. I'm the operations leader previous next I oversee the the hosting platform and infrastructure and everything from the operations angle and how we deploy code How we yeah, everything on the back end For this talk, I really have three main objectives It's to talk about the journey that we've been on over the past Pretty much decade to this point the lessons we've learned along the way and Then I also want to just embed a couple of little cloud native Concepts in there at the same time teach you a little something at the same time that you can take away And then hopefully we'll have some Q&A. Otherwise, we can do it outside afterwards as well. So Cool, so right at the beginning. So in the beginning we had virtual machines So and those were just the box, right? Anyway, it's what silicon valley. That's your box Like we just had this one VM with every layer of your stack in it varnished Apache MySQL And then if you're really sophisticated towards the end of that journey You're also then splitting everything out and in this case, this is biased towards AWS But this is using like a CDN at the front load balance that distributed across a couple of those VMs Maybe some auto scaling groups and then some services in the back end to allow you to scale out This is kind of the state of the art at the time, right and From there we saw this as the way forward at the time and started to build a lot of tooling around that sort of last part So we were using packer to package our code into AWS and my images and then we had a whole bunch of tooling that were kind of orchestrated a bit of that and then roll out an auto scaling group and turn the knobs So the new version goes up and the old one goes down a lot of stuff. That's baked in the auto scaling now, but The biggest issue was it was so slow. It was very very slow like like 30 minutes slow And if you double push to master, oh boy Like just for dev you were waiting development teams were waiting waiting waiting, right? So and then you have to do a little bit of manual intervention And not only that we still then had with this model this kind of image packaged model We also still had other things to overcome. We had configurations like for Configuring the Drupal app decoupling our config from our Drupal code We had secrets that we didn't want developers to have for, you know, whatever they were API keys things like that And we also had storage concerns about what were we going to use we were integrating with s3 at the time natively using the S3 module but that required us to integrate that with every single Drupal site Spokenly essentially because stream wrappers and it was it was a nightmare. It was a nightmare at scale unless you had one or two sites That leads us into and everything looked really bleak, but then we go into chapter 2 Docker so Little question. Does anybody know when docker 1.0 came out? You want to take a guess? wait October 16th 2014 right I Feel old but not only that we Started experimenting after zero dot seven just after zero dot seven in that kind of end part when it was Very Experimental not very robust like that that a journey to 1.0 was all about trying to be production ready and stable and Something administrators could trust but then we started Playing around with zero dot seven because that was that was the first sort of open source release What that meant was we took that one box and then we went sweet We can carve that one EC2 instance out into multiple EC2 instances, right? So then we took our docker containers and made them VMs as well So in this scenario, you've got like a routing layer with engine X and Then each container was kind of just like our own our own VM or our own big monolithic image, right? And that gave us our Apache MySQL SSH as And and the whole thing was orchestrated with puppet as well So like the config was being it was it was pretty nuts really But it did give us a lot of decoupling from that instance because we could roll out new versions of PHP for different projects and and Isolate them and so it was It was a really nice sort of first step But we learned a lot of lot of lessons along the way there around running all these services in one image and and You really want to run each one of these services in its own discrete image So then Oncame Kubernetes, right? So that last architecture not really great for production, right? It doesn't like we did experiment with the idea of like Like having VMs get all like run out auto-scaled And then we would sort of package the container image in there and roll that out too But that was just as janky but on came Kubernetes and There was this amazing announcement at docker con 2014 with the announcement of Kubernetes by Eric Brewer Who dropped a lot of foundational information that just went over my head. I'm like, what are these things about pods? What's this at CD? What's it? Like what what is all this technology and honestly? I looked out and went that's Interesting, but I don't know right. It wasn't that like like I don't think the world knew What was actually happening at that stage when? Google was essentially coming into docker's con right and then announcing this massive project for running containers Anybody who know when Kubernetes 1.0 was I'll answer for you July 21 2015 so Not very long between the docker release cycle and and then the Kubernetes coming out right the 1.0 to the 1.0 And then we did slowly come we did come around to this And the way that we perceived Kubernetes was okay Well, we have that that HA stack that I had before and Now let's just swap out the middle. It's really just for that middle piece It's cloud front it's load balancer and then we can just do the Kubernetes in the middle Right and then maintain all our existing tooling for managing the infrastructure around the application Which then led to skipper being developed. We didn't start with YAML And looking back on that it kind of seems a bit crazy We didn't we didn't start writing YAML files and things like we never deployed Like our first application that we deployed was with skipper It was actually never like a YAML file in a in a repository and and using koot CTL to roll that out so The goals for skipper and the first commit for skipper was January 5th 2016 as you can see I've been going back into the time and Figuring out all these dates and getting very nostalgic along the way But the goal was to have this layer on top Because the YAML looked really complex that exposed a lot of really, you know complex deep features like Develop like right from the gecko. I was like well developers don't really care about what the resources are They don't care what ports being exposed, but that was what was being presented through these YAML files so So we ended up with this architecture for a command line and API and then Kubernetes in the back back end Which then led to And then there's the CLI so it's very you know, it's a bit of land that you know It's it does the job, but that led us to this guy go so Our journey into go Was based on our requirements right so so we looked at tools around we could have written this thing in PHP Right like that that was our bread and butter. We were you know, we we work in Drupal could have all this stuff could have been done in PHP But then we looked at our requirements and what will the CLI kind of needs to run on Linux boxes and and OS X and potentially Windows Now that's paid off even more because there's way more arm boxes like like my laptop That's an arm like an M1 like that just works Go also has a baked in Web server and then a bunch of frameworks around that it has a really really nice web server built in and And then and then finally Docker and Kubernetes were all written in go and That doesn't seem like much but at the time there weren't any client libraries or anything like that We were importing the Kubernetes source into the skipper project and then tracking everything from release to release to release and following how that You know essentially had how head was going and and trying to compile our API and keeping up With with releases that way. So whereas now it's much much more robust. There's multiple different languages for the clients But the time that's that's what we used But we we had some challenges along the way with the B1, right? So like the challenges didn't really go away. In fact, they kind of got a little bit more in the beginning because We don't have a Managed service for Kubernetes. We didn't have one in the past like we do now, right? so in the past we actually took Packer built our master or our controller like our main node and Then then the nodes after that. So we were packaging everything and self managing in ourselves essentially even before kubernetes The hard way came out which was this great Repository this great resource to kind of ease the pain of people Provisioning managing maintaining and running kubernetes from release to release to release So we were doing all of this ourselves because we knew the payoff Was far greater the fact that we were deploying containers and having this great developer experience through that CLI and API I Had to go deep into networking. So I don't think many of us think about it now because it's just there but in the past When doing that self-managed kubernetes You had to pick a networking solution and there weren't really too many around it at the time there were things like weave and Like all these different sort of like overlay networks that had different trade-offs and we're trying to gain market share and then we actually kind of Zagged a little bit when a lot a lot of folks ziggs like we went we were already running Ubuntu for our nodes and Ubuntu actually has this fan networking Solution I won't go into detail. I like I won't go deep in this, but it was very Easy to kind of understand like the inner and the outer networks So the network that the VMs were running on the network that the containers were running on and and so we wrote a little bit of Tooling around that which works works really really well Comfig was another thing we we did actually have to solve so Going back to the challenges of building packaging and deploying those VMs like the the config challenge didn't really go away We use the kubernetes config map API and then we then we kind of wrapped that with our CLI and API and smooth that out for developers as well as a I have a big big breakdown of all this on the skipper blog and then also The dock side has kind of a step through how to how that that flow works Um, there you go. There's a link. I'll post it later But then application so developers could do like a skipper config set get delete and then the application could load it through a library Which this was honestly game-changing because ever like All our projects had like settings dot local dot PHP, which was very cumbersome and Very hard to debug if somebody had some bespoke configuration locally or not and what this meant was Really, it's just that like database line, right? It's like get the config or this is the default and that was it That was it. That's your local configuration dev staging production That's all set the skipper and that smoothed out a lot of stuff where it's like, oh, this config needs to be set for CI local dev staging prod All that was kind of solved in one fell swoop with this Storage Storage Storage was interesting because really the solution at the time was deploying NFS server on to the cluster and run NFS yourself and mount some blocks and have some mounted block storage on there It's all good. That's not really ha and While we were going down the networking rabbit hole. We didn't really want to be storage Server experts, you know at the same time like we were seeing all these extra things that we were having to manage so and We also saw the pain that we had previously with like S3 module in in Drupal. So we went down the path of mounted views file systems with S3 and There are so many these are the three that we had to pick at the time and and you had like S3 FS like the first one Which is posits compliant, but it's slow Noticeably slow You had Rio FS which we landed on at the time because it was faster because it didn't adhere to all those posits compatibility rules and Then we were starting to things were getting dire and we were starting to look into goofy Which was like a go version of Rio FS because we were running into some issues with caching and things like that we were It was it was a it was a very interesting time I and I still remember where I was standing when the EFS got announced for Australia, and then we immediately got Started working getting on to that. So We also built a bunch of integrations into Like with pubes so you could extend Kubernetes to make all of this mounted volume stuff automated and And yeah, we've written a bunch of provisioners and now we're using the community one Um database images so everything's running up on your infrastructure, but then we sort of had this issue of okay Well, how to look how does local development work now? because we By doing our CLI now API integration, we didn't we lost drush integration So you couldn't just go drush at you know at dev and sink it down So we had to come up with a why we had a constraint We had to come up with a really interesting way to do it and we went well Why don't we package that database into an image and then folks can just Dock or pole and pull down the image and we can sanitize it and the more we thought about it the more we realized This is an amazing idea because not only is it sanitize You can also have policy around who can access and pull that image down See I now gets a Completely sanitized version of the database. So just had this flown affected where at first we weren't sure but then after really like Just diving in a little bit. Oh, wow. Yeah, this is it's really powerful and we've invested in this ever since There's a quick example for folks who want to come back to the slides and see a configuration But but we essentially wrote this tool called MTK which Stands for my spell toolkit and you can kind of replace fields strip out databases No data certain databases and and that's the repository there if you want to check it out for yourself So we're in a pretty good spot at this stage And we were migrating projects onto our onto the new infrastructure and they were all running on skipper v1 at the time But at the same time that we were doing all this work There's also this massive cloud native wave coming because not only were we building tools on top of Kubernetes Everybody else was building tools on top on top of Kubernetes Everybody was building solutions and products on top of Kubernetes people are getting, you know Startups will pop it up everywhere. I'm trying to be the next thing on top as well. So This is massive massive massive influx of projects and I mean this is the screenshot of you go to the cloud native foundation Which is the governing body that kind of shepherds and and assists with all these projects. This is the latest screenshot, right? It's nuts It's very confusing and it's only gotten bigger, right? so But at the same time like I said, there's all these there's all these things being built on top of Kubernetes And there's all these new patterns and models and there's a lot of maturity starting to happen in the Kubernetes project around process so We saw this and went okay, we want to build a v2 of skipper and why would we build a v2 of skipper? Well geez yep first commit 2018 so four years ago from that years ago We did we fell in love with reconciliation loops so Back at that graph where we have our CLI our API there's an I could have put a graph in but there's this in the back end too which is looping and enforcing state so instead of us our API just going deploy the app and that's it It this is also in the background going is the app deployed the app deployed is the app deployed And that sounds really silly, but it's actually extremely powerful because you're Keeping The state of the cluster like you're keeping it all in sync. So there's no drift There's nobody going in and manually changing a deployment or something and then it's it's off And this is really powerful because not only is it just for like deployments, but then it also fed into everything so So cloud want to provision a cloud front. We wrote it We wrote a reconciliation loop for that want to provision some database credentials We wrote a reconciliation look for it. We were able to write these small little loops of code But then just become this big tree and really embody that like Unix philosophy of like one Do one thing and do it well, and that's what each one of our loops were doing in kind of this Orchestra being orchestrated by our platform If you want to learn more about this approach I did a talk at container camp And I kind of compare this approach to like HTTP like a HTTP request in some ways. So Just a self plug there The other side the other thing that we learned was around our API, which was a HTTP server and we wanted to We wanted to leave our options open Let's put it that way in terms of like what do we want to build on top of this API We have a CLI right now. We want to leave the door open moving forward and that's what led us to GRPC so, I mean a lot of developer folks are very Familiar with the idea now of having like a like a definition file and then generating or building against that definition Just like Brands's Graph Q Adam Bramley's GraphQL talk yesterday very similar in some ways But yeah with GRPC you can create this definition file for a service So like the hello service and then you can tell it the inputs and the outputs And then you can generate server stubs and client stubs in whatever language. So we created go ones. There was no this PHP It's Java. There's a whole bunch of them So that left the door open for us to build fun cool stuff on top if we wanted to moving forward It also meant that our APIs were very well-defined. So we have a serve we have a server For kind of each core concepts of its environment like deploy or delete or create. That's a server config That's a server like set get delete. It was this really nice way to really elegantly define our API and Then if we ever needed to make changes you could see it right then and then what what we were changing what the input in the output was We invested heavily into terraform. So Previously we're using some puppet and some terraform But we wanted to go all in on managing our entire infrastructure with terraform, especially with Kubernetes in the mix We wanted to manage Everything that was running on cluster that wasn't managed by the skipper like it wasn't a skipper application so it was like log aggregation and and security tools and things like that we wanted that to be managed all in this one repository the same thing that's bringing up the Cluster should also provision the stuff on the cluster that's used for operations as well So and terraform took a really long time to mature towards having a provider having a plug-in a provider for Kubernetes so we wrote our own and And we still use it to this day. So it's um It's a pretty simple provider But it covers a lot of the API set because we've deployed a lot of stuff onto Kubernetes that Needs specific API. So there's a massive amount of the Kubernetes API surface that we cover with this provider We are looking at and starting to use the Kubernetes one more like the official Sorry, Hashicorp terraform provider more, but we're still using this a lot With v2 we really focused on monitoring and we invest a lot of our time into monitoring too. So I I aggregating all the cloud front data, but then also picking up all the PHP FPM engine X stats and then the logs we Really, we really hit this point where that we're happy with in our monitoring Where you can go from the top of the stack all the way down to the logs in sort of one dashboard and get this Kind of really nice overview of the health of your application And we have a great blog about it. I'm just going through them PHP. So Or more more specifically Alpine Linux. So We started with Debian images like Ubuntu Debian style container images, but they were just much too heavy much too big that which When you're looking at packaging shipping your application, you really want to trim it down to exactly what needs to run, right? so when you're Shipping an engine X image. You just want engine X and exactly what it needs, right? Or you want as close to as what engine X needs to be able to run. So there was this big as a part of the Native Wave Alpine Linux rows and popularity and kind of became a de facto standard for For your images so and What that meant for us is we kind of hit this bit of a bit of a challenge around okay Well, we want Alpine Linux But we also want to support multiple different versions and then so you kind of have this matrix of like Alpine versions and and then PHP versions you can support so we support like 7.4 8.0 8.1 8.2 I think and you know, we've we just run the gamut and then we bump our Alpine Linux version so The problem that we found was Alpine Linux only ships one version of PHP per Alpine Just like release right so so if you wanted like 8.1 You got to pick that specific release and that didn't really work for us So we've got a repository here We package our own PHP and then we keep up with the PHP release cycle so new version comes out Packaging gets updated our images get which are just below then get rebuilt nightly And then the new version gets picked up and it'll just flow through people to play their apps again Security patches are applied happy days cool So yes, that's that's where we've been and Then this is sort of what we're looking to next so and what where our focus is so a Lot of this talk has been about that journey that we've been on right and a big focus that we're on right now Is around stability like the ecosystem is stabilizing kubernetes as a project as Stabilized like the API's that we use are all 1.0 Essentially like they're all stable API's now. We're not chasing API changes in the kubernetes Release cycle as much anymore. So but then that all flows down to the tools that are built on top as well So that's a big focus for a try it now Security is a really big focus for us as well. So we've over the past sort of six months, I guess so the last half year we've Baked in the ability for static analysis tools to so after the applications packaged in CI We spit out a manifest you use a little bit of bash and then chuck it through Like trivy, which is an open source image scanning tool or tool of your choice And then you can scan those images and pick that up in pipeline. So there's any issues any major issues But then we're also looking into other like dynamic application security testing tools like stack or good things like that to To scan routinely scan Environments for for customers as well, but not only that This whole time we had a very strong focus on Drupal So and But that's not as much the case anymore. Well, I mean we still focus on Drupal a lot But there's all these kind of Additional services around around the outside of Drupal now. So so I mean Adam Bramley did a talk last year about Drupal and open search And that's all powered by this like the right-hand side of this of this diagram We have Drupal and then you route specific parts through a proxy to open search to then just go straight there and query from your decoupled applications Know that you might have like a very specific path. That's a completely separate application. That's not that's not Drupal And then knowing that you might have some stuff in the background That you can ping and talk to and then execute an operation against all these things Where we're slowly building out as new application types as we as we go So we've focused on Drupal now we've now we've got PHP and then we've got like a basic deployment type for these sort of extra services to test them out like node and go And things like that to kind of really dial in what what's the best way to do this? So so that's where that's where we're heading and On that note I'm a little bit for question EFS so AWS is EFS solution and then we use the we've got our EFS provisioner That we wrote so that automatically provisions EFS instances under the hood. Sorry. I'm standing the mic But we are migrating to like the official EFS CSI driver So while it looks stable looks stable on the outside zone We really just kind of all the bugs and memory leaks and things that we've seen in the issue queue Those are only really just subsided and gone into main. So yeah You pushed a branch. Yeah, cool. So So that CLI part runs in your CI CD of choice. So for us at circle CI or actions It could be whatever you want. That's a big reason why we did the CLI as well So and it's really boiled down into like three commands essentially skip a package Which under the hood is running a series of Docker build commands. It's all it's talking to the Docker API on that pipeline. Yep Do you see ICD? Yeah, yep, yep, you need you need a docket aim and running running in that pipeline, which is pretty common in in like I mean if you're rolling it yourself, it's a bit different. I guess But if it's like circle CI, it's like one line to get a docket aim and and access to one of those Yeah, or even get up actions. So, yeah Yeah, and that and then after that packaged and pushed and during that time talking to the API as well And it's pushing those images That's all done on CI and then you can go skip a deploy. So if you push to mainline A pretty common patterns to skip a deploy to dev with that version and then it Rolls out the upgrade wait, wait, wait stops and then you've got skipper exact which then you can run Like your drush commands or whatever steps you would normally run locally You can then run skipper exact as many times as you want or run one script in the remote environment That does the whole lot in a in a stepped out way like through a make file or a bash script or whatever you want in that remote environment and then executed against that and That's it. You've rolled out the environment in three steps No, no, no, it's it's definitely not it's actually I guess that's one challenge that we had was around how to how does somebody connect to a remote environment. So we have a The skipper exact command Is kind of a wrapper around SSH in some ways and we have an SSH server written and go And it actually spins up a new independence container or pod in Kubernetes with very similar configuration to your FPM instance Running a CLI image and that's your session for the whole for the whole Execution and then when when you exit your session it gets terminated and cleaned up Oh, yeah, you get so that and that kind of to pull at that thread a bit more than that also means that you have dedicated resourcing right So if somebody else comes in and runs SSH they get a new pod with X amount of resource and depending on how you allocate your resource for pods then that means Those they're not clashing with each other as much like you're not competing for memory Like somebody's running a massive batch process compared to somebody else who's running, you know Like just the deploy command to prod something like that Yeah, yeah, exactly they can exact or shell and get on to prod But then we do have our back controls over prod and non-prod environments and how that works. Yeah. Yeah Yeah Yeah, we we looked into it we did look into it. I'm trying to think what we looked into it for because oh Sorry, the question was have we considered canico for our image building and We did look into it because that was the docker Damon list kind of build build process There was also IMG, which was another one and the whole goal that these projects were trying to solve was do you really need a Docker Damon to build images like you really just packaging tab tab balls and you know with metadata on top and pushing them We ran Most of our issues at the time and why we didn't really pursue one of those tools was for multi Like multi arch builds and things like that for the most part. So as we're going down that path It's it's still a bit Still a bit tricky like I'd probably invest more time into like build acts and those kinds of tools As they as they're maturing because they're pretty pretty sophisticated tools. Yeah. Yeah, canico is still great


Gweithio am ymddangos i chi.  Fynol yng Nghymru gyda'r tanfodol CIM, ond mor wych yn cyflawni'r tanfodol CIM, ond yma gweithio i'n gweithio gael gyrraedd, yng nghymru a'r ddechrau yn y mae'r gweithio'r gweithi. all y gallu ei wneud yw'r cymorth o'r cwrdd Cymru yn ei ddweud o'r cymorth Cymru, ac mae'n gweithio ar y cyfnod ymweld i'r cyflwyffol. Felly, rwy'n gweithio i'r gweithio'r James Patrick, yng nghymru sy'n cymorth hynny. Hei, rhaid i'n gweithio'r gweithio'r gweithio'r gweithio, y gweithio gyda'r pan ddora'r bocs. Felly, rhaid i'n James Patrick. Felly, rhaid i'n gyfrifio'r copa, rhaid i'n gwneud. I was an intelligence analyst, and for the last few years I've been building up a company which specializes in publicly available electronic information, but done ethically and done right. So, what is PAEI? Well, it's publicly available electronic information where public is defined as information published in HTML, PDF or other common formats on the internet, which can be gathered lawfully and ethically by a person or algorithm using the internet or via application programming interface, which is API or API, and those queries that are made to third party platform providers. I can't stress enough the lawfully and ethically piece, and this does warrant a little bit of explanation. Lawfully means that you are complying with platform terms of service when you're accessing information, so you can't just go and scrape information from Facebook. You have to do it through the authorized protocols and channels. The same goes for Twitter and the same goes for everything else. And ethically is something that I introduced into the thinking around this after the Cambridge Analytica scandal. And bi ethically, for me, what that means is actually ensuring that the information that we get doesn't contain individual private information, PII. Because you don't actually need it to get a really good understanding of discourse or a topic or a population or things that people care about. You don't need to intrude into somebody's private life or individual psychology. When we talk about ethically, what we mean is actually using all this information in a way which is socially beneficial and not destructive. So the types of projects using PAEI. So I've used it for, and we use it for quite a lot, communications and engagement strategy development. Who do we need to speak to? Where can we get hold of them? How do we speak to them? How do they take in their information? Health equity needs assessments, which is something that we do quite a lot. Prevention strategies, and this could be reduction of serious youth violence or mitigating the impacts of dis and misinformation on elections. And then social movement design.  Interventions that actually loops back into the prevention strategies, but it's also how can we reach people with a health intervention or something else beneficial? How can we relay information about where people can be vaccinated, for example? Campaigns, I think that's fairly self-explanatory, and evaluations. So some of the interesting stuff that's being done around this is we're actually combining PAEI work with traditional research. So we're actually getting a really hyper-honest view of what people genuinely think. And if there's something that is universally true, it's that if you give people a mask, they will tell you the truth. And social media is a really powerful tool for that because it provides everyone with a mask effectively. So the first thing that I want to talk about is self-isolating communities. And this is quite a potent example of precisely what it is that we're talking about. And back on the 12th of February in 2020, I picked up on something that was clear in the social data and wrote to the WHO. And what I picked up on was that there was this coronavirus outbreak which was spreading rapidly from Wuhan. And it had been designated a new name. But at the time, the WHO were actually using these internal terms, NCOV 2019 and 2019 NCOV. Whereas the general discussion amongst everyone in the world across social media, they were referring to it as coronavirus. So the WHO was actually excluding itself from public discourse at a time when it was needed the most. So highlighted this and wrote to them and showed them just really simply the scale of that conversation, which is huge. Looked at Facebook data. There were just 75 posts that we picked out that had 49.5 million interactions on them. Of those, 48 million were talking about coronavirus. While the WHO had trapped itself in to speaking to about 750,000 people talking in these obscure medical names. And what they actually had done is they'd segmented themselves off and put themselves in a little box where they were just having a conversation with themselves around what to call this thing. And the conversation kept moving with their ability to get information out as quickly as possible. So we wrote to them, gave them some advice and we used timelines and some other data to show them where that conversation was happening, when it was happening. But also who was talking about it and we picked up some really interesting facts in there. Like one of them being at the time, one of the biggest places where the conversation around coronavirus was taking place was Spanish language, US outlets run by Russia. And we just gave them some simple advice based on what we were seeing, which was if you adapt and use Spanish language to communicate using this specific term, you're going to increase your reach substantially. Now, I can't imagine that during a public health crisis, anyone thought for a minute, because I certainly didn't, that they were going to reply to us or adopt the advice. But a day after sending them the report, we actually got a reply saying that they had adopted the advice and put it in place and we're going to be using coronavirus. And it just rapidly accelerated their ability to communicate with the global population. So that is the very practical thin end of the PAEI wedge, if you like. Behind it sits these key principles, which I've developed over the last couple of years, doing large projects in particular across Europe, where what we've been looking at is miss and disinformation, the impact on elections and the impact on populations for pushing conspiracy theories or creating dissent or all of these other things. And I've actually given names to a couple of these things, so that it's easier for people to understand them. And those names are disinfornomics and emotioneering. And these are really key principles in particular, if you find yourself dealing with, say, for example, anti-vax or certain types of extreme behaviors and political ideologies. So emotioneering effectively is controlling trust and distrust. That's all it is. That's all it means. And the way that it works is psychological. So brains absorb information from this range of sources to create like a balanced input, which tells us about the world, tells us what's going on and allows us to go about our business. But when a crisis situation occurs or we feel at risk or we get frustrated because not everyone is directly exposed to risk. So actually, your brain increases the weight it puts on everyday frustrations. It can trigger the fight, flight or freeze response. Now, in extremis, I've got commendations hanging on my wall here for tackling armed suspects and bringing people out of fires and dealing with all manner of horrible. And that does involve that reflex directly. But as I said, you can layer it down and some people can have their fight, flight or freeze reflex triggered by a news story or by the washing machine not working properly. So it's that psychology of crisis situation, which is really key. And to help us through what our brains do is they restrict the amount of information that we actually take in and they kind of filter it so that our brains can make focus decisions about how to keep us safe from the perceived harm. And what also happens during that process is biases or fears or notions. The brain tends to amplify those as well to provoke a stronger response. So what people who create and spread disinformation do is they either amplify feelings of crisis threat or frustration in your day to day life and they can do that by using bots to create a swell around certain news stories, or they can hyper risk of war, or they can hype or amplify a conversation about something which is not strictly relevant, like the release of somebody's emails and use it to destroy opponents. They can also actually create and manufacture crisis situations, especially at the hostile foreign state level, or they can prolong them so they can actually draw them out. And the other thing that they do is they control the information which relates to that crisis situation. So flood the media with certain stories or flood social media with certain stories, which is what troll farms are used for. They use to distort public perception of an issue. And this is, of course, incredibly powerful, not least because we are 24 seven connected in a way that we've never been before. And all of that information is suddenly on tap so it's flowing through everyone all the time in our palms in our pockets. It's pinging through the night. It's pinging through the day. Now disinfonomics is revenue driven amplification. Now disinformation itself is the creation sharing or amplification of deliberately false information and disinformation actors. They use these alternative media outlets and social media outlets as a direct access route because it is far easier in a way that's never existed before. And if you think about it in the past when we were thinking about things like this and propaganda effectively, they would fire flyers out of a cannon. You don't need to do that because you can fire a piece of information at the whole world without leaving your living room. The other key aspect to this is that these outlets and social media platforms because of laps regulation, they're not bound by the same standards as traditional press or some of those other operators. And the action is actually combined with a strategic objective so you can geographically identify an audience and target them directly. So an example of that would be negatively impacting the ability of a government to control COVID-19 spread by creating myths about mask wearing or getting people to behave in certain ways which evade public health measures can actually directly put pressure on public services. So you could treat it as a critical infrastructure attack. The problem that comes and has grown from this is that the fact checking industry, which has swung up over the last few years, is actually creating this self perpetuating cycle of amplification. And it's had the effect of removing the need for disinformation actors to do as much heavy lifting so they don't need to be in the message boards anymore. They don't need to be operating the trial farms as extensively as they did or using bots as they once had to to extend their reach and gain the algorithms of social media. In fact, all they do now is they rely on fact checking services to go and find obscure pieces of information which are then turned into reports which are circulated, which turn into press releases which go to the mainstream media. And in part, this process is driven by the collapsing circulation of the mainstream media as well. So they're constantly hunting for digital content because it's cheaper. And what it means is that disinformation has got like a direct access route into traditional media, which it never had before. And this has just come about for a fundamental misunderstanding of the types of people that are implementing disinformation programmes. So it's actually a cycle. So at the very top, the people are seeking out the disinformation to create their products, their outputs, their reports to go and ask for people to crowdfund their activities. They create those reports, they're circulated, they go out into a group of people. That's actually turned into revenue generation by a second layer, which is the media who are obviously looking for circulation. And as they see the revenue stats go up, they think, blimey, this is a really good way for us to make money. So they go and actively seek more and you just get this perpetuating loop of disinformation getting bigger and bigger. So as I say, it's by misunderstanding the nature of this problem and focusing on a really narrow solution, which is correcting information, which doesn't work because once people have made their mind up, they've made their mind up anyway. We've actually made it easier for dissident groups and extremists and hostile foreign states to do their work. It's unintended consequences, which is one of those things if you open Pandora's box, you should always be afraid of what's going to come out of it. So in practice, when you look at something where disinfonomics and emotion hearing has all been happening, you can actually see how it sets public health campaigns up to fail. So we'll take a good example here, which is there's this well-known trope for vaccine dissuasion, which is about the presence of gelatin in vaccines. And actually, because of the way that the algorithms work and because of the ways that the backlinks work and because of the ways that people are using SEO to spread disinformation as well. If you Google it, instead of just being taken to the NHS or reliable health services, you're also taken to a fact check and directly to the piece of disinformation and actually backlinks you out to the source material for it as well. So what we're doing is we're actually connecting people with disinformation while also asking people for donations. It's just a really, really powerful example of how that stuff works. When we look at how we can analyse PAEI to actually do some of the lifting for us to come back from this cliff edge, it's a really rapid process. And you can actually use it to get from a huge amount of data to targeted messaging design really quickly. And that's something I've been working on for the last couple of years and it's just impactive and it's effective and it's fast. So in this example here, what we actually did is started off with a whole view of France and we were looking at lockdown reluctance. So PAEI isn't just social data. It's also anything that you can map, anything which you can access. So it could be ONS statistics on deaths or suicides. It could be the index of multiple deprivations. You can layer it, you can map it, you can geographically understand a population, you can add demographic data to it. So in this case, we mapped out lockdown hesitancy or reluctance. And in 24 hours actually brought this massive volume of millions and millions of tweets and social media posts into alignment with that geographical case data. And we were able to completely understand when that audience is reacting to stuff, what hashtags they're using, what keywords. Keywords are important because if you understand the keywords in a conversation, you can understand that if you construct your content from that lexicon of popular keywords, the algorithm is actually going to lift your content to the front of the queue. So it's almost like SEO in the way that people gain that as well. Again, you can geographically prioritise because there's a lot of geofencing data which is available. You can extract device usage. So are people using Android devices or iPhone? You can see what domains they're sharing. So you can actually make decisions around where to focus PR efforts, but also to understand what kinds of information they digest, so preferences. When you've got a massive volume of data, what you can also do is take the colour scheme. So you can actually start to build on these principles of neural efficiency and present information to people in a way they're already familiar with. And you can actually structure it and lay it out so certain types of wording appear at certain points in an infographic and call to actions are positioned in a certain place and there are conventions around the amount of words to use. And that's really informative because it means that you're cutting through and using a lot of pre-existent psychology to do some of the lifting work in terms of getting your message across. So psychology is your friend and foe. If you get it right, it's really powerful if you get it wrong, you are out of the loop. Just very quickly, conscious versus subconscious. Our conscious mind is processing only a mining fraction of what our unconscious is processing. In fact, it's about 99.99% occurs without us even knowing about it. And we make tens of thousands of decisions every day, whether we know it or not. So people have been studied. There are people who have had different types of injury to the brain, which have stopped them from processing emotions or makes it more difficult for them to make choices. But that's a small part of the population. And for the most part, it's the unconscious that's doing the lifting, which is why understanding how to structure and colour information breaks down those barriers much more quickly. Two core elements of psychology, which are really impactful in particular in designing social campaigns. On the one side, you've got mood recall, which is our emotional link to a type of information or a sight or a sound or a smell. So if we present people with familiar information and we know that they're reacting to it in a certain way, we can predict the response to it. And collective common sense is an influencer of group behaviour. So effectively, we hear or see something which catches our attention, makes us curious. We compare it to other information that we already know. We share it. We attach evaluation to it as a group. And then we define common sense and that defines our conversations. So if using PAI analysis, we can actually see what information people are comparing against. We can see what information they are hearing, seeing and sharing. We can see how it's being valued. So that allows us to work to redefine that collective common sense for a positive outcome. We can capture emotion from everything which is publicly available. As I said, without going down to the level of an individual. And while people say measuring emotions is notoriously difficult, since the explosion of social media, there have been massive studies which have collected huge amounts of data on emotional responses. And there are now Python packages and algorithms which can be deployed against subsets of tweets and Facebook posts to give you a really balanced view of nine core emotions, positive and negative and sentiment, of course, so that you can understand and get to grips with how people are responding to information and what emotion certain types of information is relaying. So we turn this into actionable insights. We digest it. We break it down. We use AI analysis. And we make some really, really exciting discoveries using this stuff. So emotion analytics is just an incredible tool. Action equals ownership. And this is all about trust cap. You can implement behavioral change campaigns with expert partners. You can use the principles of emotion hearing to identify your intervention points. You can send your strategic communications around psychology to harness mood recall and collective common sense. And a key lesson to learn from disinfonomics is actually, if you occupy information space first, what happens is that if disinformation subsequently springs up, they end up amplifying you. So you're flipping that entire system on its head and evading all of the nonsense which stems from the fact-checking environment. Now, trust capital is really key. And this is to do with messengers, networks and their personal relationships.  So personal trust is organic and it's subjective. And it's much more difficult to convince a person that their personal truth is wrong. Which is why if you look at Facebook family products, for example, they're a really logical place to put campaigns because they have access to an older demographic. But that older demographic tends to inform the younger demographic by Facebook family products, which include WhatsApp and Instagram. And that's how that actually works to define and redefine collective common sense. I want to give you a quick case study now. So NHS England, they identified there was a problem in a certain area which was to do with vaccine hesitancy. And they attributed it to cultural reasons and the prevalence of certain vaccine myths. And they'd done some intervention work and done some restructuring around it, some training. People were concerned about vaccine safety. There was a lack of information. They were worried about infertility. And there was this key sort of lack of trust in authority and government. But what they'd done is they'd kind of referred to this stock bag of ways of fixing it. And they didn't understand the Eastern European community at all in this area, despite the fact it was quite significantly large. So, actually, we investigated this using PAI. And we went out and looked at not just English language or commonplace social media websites, but social media websites which exist across the world and in different languages because the tech that we use facilitates access to that. And we were able to uncover some really exciting findings. So, for example, with the Polish community, one of the most commonly spread nationalist tropes is that the Jews are trying to take over the world. And this was actually being pedalled that they were spreading mutations of the virus through vaccines. And then secondary to that was the introduction of microchips to monitor people. But then in the local community based in the UK, this was obviously filtering down through trust networks, but also there was this huge barrier to trust arising from the media rhetoric around Brexit and the way that people were being treated and some of the racist abuse which was taking place on the street. And it had reduced the trust of people in all government agencies, which obviously included the health service. So, there were these stories that perpetuated about the safety of the AstraZeneca vaccine. The final sort of thing that came out was that there had actually been an intervention by the Catholic Church against the AZ and Johnson and Johnson vaccines, saying that they were morally questionable. And that had a huge impact on trust. And you could see this in that conversation. So, the things that we picked up, if we thematically look at them, the common themes were, they believed that there was a causal link between vaccines and thrombosis being true, that vaccines contained aborted fetal cells, that mutations were emerging from the vaccines, that the coronavirus test was a medical experiment, that vaccines were being used to sterilise communities. And this is an example of how they tapped into mood recall, because in the Roma community, there were forced sterilisations within living memory, and this had been tweaked and targeted by disinformation actors. So, we actually went through, did a full extract of PAI. We dissected this audience. We extracted the way that they digested information. We extracted the colour schemes which are familiar to them. And we used it to design some outputs. And so, we knew that the do it for us, do it for the NHS narrative wouldn't work. We knew that presenting fact checks wouldn't work. And we knew that it was best to avoid discussions about vaccine efficacy and problems with vaccines to sort of separate things out and disconnect from the trust arguments and the pre-existing disinformation to avoid amplifying it. So, the concepts when they were built out is we could use certain types of authority figure in strong clip language using these colour schemes to prevent things in certain ways. We could use certain types of authority figures. There's a massive trust in the Eastern European community of doctors in white coats to actually tell the stories about how to engage with these processes and what's actually true. And use a variety of community messages and family-centred messaging to actually take people straight through to book in for their vaccinations. We're at 30 minutes. There's quite a lot of information I've bombarded you with. And I am more than happy to answer any questions that anybody has. That's brilliant. Thanks very much, James. That was really insightful and thought-provoking presentation. So, we're now going to have a short Q&A session. So, let's see. First question is, you say we get information from balanced sources, but on social media, is it balanced information when we're fed what the algorithms show us? When you do the extract through developer portals through the API, you don't deal with the algorithms. You deal with information in its totality. So, you actually capture everything. If you're looking at it through front-facing, so just searching social media manually, your individual algorithm is going to give you a tailored result. And what we have found as well is that some of the traditional social listening services which exist, because they seem to be linked to personal accounts or business accounts in some places, they also produce distorted results. Okay. Next question. Recent studies on how we absorb information tell us that the range of sources we listen to are the only ones that conform to the way we see the world. And that crisis accelerates this, but don't you think that achieving a balanced and truthful social discourse is a battle we can't win at all? I mean, you can win that. It's very easy for people to sort of wave a white flag or just go, there's no way that I can compete with this, but in almost every circumstance where we've gone in and seen what people's existing understanding is, there's actually a huge knowledge gap which is what the real barrier is. There seems to be a lack of distinction a lot of the time between social media and private messaging as well. So there's a huge spectrum of stuff, and actually it's not just about understanding what's going on on Twitter or what's going on on Facebook. When I do an analysis, you look at every conceivable platform, every conceivable distortion, and that can be from mum's net to gap. You capture all of it in volume data and then you can actually dissect and cut through. You can win. You just have to understand the audience, the segments within those audiences and actually where you can communicate with them. Next question. Are there any common themes on why disinformation is generated and spread? I guess you could look at that on quite a few international members on the call today. Are there any trends that you've identified? I think people get bogged down when they talk about disinformation, looking at individual disinformation topics, and they forget that it's strategic. One of the key things is understanding who it benefits. Some disinformation relating to vaccine hesitancy comes from people who market and sell nonsense remedies or vitamin pills. So you've got an objective, you've got a strategy, you've got a delivery. For other people, you might have a hostile foreign state behind some disinformation, and actually what you need to do is deconstruct the purpose and the actual outcomes. So it could be putting pressure on public services. It could be causing money to be spent in certain ways. It could be causing dissent and protest, which puts people in conflict with authority. It's all about understanding not just that little piece of disinformation, but what it is, and it always comes down to one of two things, which is either about control or cash. You actually have to deconstruct each problem individually to understand which the root cause is before you can move through fixing it. Next question is, how could this area be regulated so that organisations and parties aren't benefiting from these troll farms? And also, is it regulated already or is it something which is fuelled by planted PR? Right, so regulation of all of this is messy, and it's because of the international nature of it. So when I, for example, look at what data we can get legally, we are dealing with civil law in the UK. We're dealing with every aspect of everything from the Regulation of Investigatory Powers Act to GDPR to the Data Protection to the Equality Act to everything else, but those are different everywhere. So trying to come up with a unified regulatory framework for social media platforms, some of which are set up by hostile actors and have all their structural arrangements buried is really, really tricky. There has been some great work since Cambridge Analytica within platforms and outside them like Facebook has completely restricted its API, so you can no longer get personal data out of it, and actually if you have people who are trying to do unethical scraping, the platform will identify, ban them, and it will do things like block IP addresses too. So technologically there's some great stuff happening, and obviously we've got the online harms legislation coming through here and a lot of work being done on that in Europe and a lot of stuff being done on that in America. They're changing the California privacy law around children too. Germany has done some amazing work on online abuse, which has actually led to Twitter to partition its algorithm and some of its databases to keep people away. There's tons going on, but it's complicated. I don't think we've ever had to confront as a global community the cross-border challenges which something like this presents, and it's caught us completely unawares, because I think in many ways there's been a mindset of thinking, the only thing that we can treat as internationally really is trade and weapons, and this kind of snuck under the radar, and it's become so big that it's very, very hard to put back in the box. Next question, someone's comment is a really interesting subject. Could this be used in prevention-led messaging for wider conditions with the same principles, and also are there any challenges in certain age groups? Okay, so you can use it anywhere. Places where we have and where we are using it, it can be anything from vaccine hesitancy to breastfeeding to reduction of knife crime across the board, you can use it. There is no restriction on that, and people don't use the internet in the same way. They don't even use the same platforms in the same way, so it's really tricky, and actually part of the big job of doing this, and actually doing this for a living, is understanding these demographics of all of these platforms as they come and go, and there are some generalised rules, so you can say that older users tend to use Facebook, millennials tend to look to things like Instagram and Reddit, and younger kids use things like Discord and TikTok, but those are like hypergeneralisations. It's much, much more complicated than that, and there are divisions, subdivisions within each platform as well as to what different users will use it for, so it's complicated. Next question on the subject of vaccinations. Which options work best to get people in the UK vaccinated in relation to the option shared towards the end of your presentation? So that actually is hypervariable on geography. When we do these analysis, we actually break them down into two component pieces. So the first is geographical or geostatistical analysis, which looks at things like vaccination rates, health inequalities, deprivation, demographics. So we identify areas and behaviours within those areas which are really clear. So you kind of start to drag out the opportunities and parts of combi. And then you actually look at the PAI in geofence terms to determine what those different behaviours are going to be, and actually every area that we've done, there are variations, and there are variations in the way that people respond to colour, to information, to structure, to static, to video, to the length of time that they will watch things, to the types of outlet they will read. It's hugely variable. But I think it's quite an important piece of work to start being able to chart that, because I think we tend to over generalise and go like, oh well, the anti-vaccination problem is specifically these three things, and therefore that must be true nationally. And in fact, it is just not. OK, thank you. One person has commented, is it all just social media data? How does this differ? You have got a wealth of publicly available. So you've got forums, you've got news media comments, you've got anything which is publicly available in terms of vaccination, population, demographic, you've got articles, you've got blogs. It's vastly different. There is a much richer picture of the population than I think we perhaps traditionally think about. OK, and I think we've probably got time for one last question. How quick is it to do a PAEI analysis? It varies on topic, but say for example, you wanted to do a PAEI analysis of social media in the immediate aftermath of a serious violent event. That can be complete within six hours, and you can implement legacy monitoring for that. If you wanted to do a detailed piece about a whole CCCG or something like that, then you would be talking seven to ten days to do it justice, because what you're actually doing is taking in every piece of available data and turning that into a complete report, actionable insights and creative design strategy in principles. OK, and I think that's all we've got time for now. So that's been great. Thanks very much, James, for your presentation. We've had some really good questions there from our viewers, and it's a shame we don't have more time to go on and on all day. In fact, one comment is that it could make a whole course out of the subject. So hopefully there's some useful tips for people to take away and some food for thought. Sadly, that's all the time we have for our webinar today. I'd like to say a big thank you to James for his excellent presentation and to the CIM charity and social marketing group for organising the event. Our next webinar express is Strong Brands have multiple personalities. How do you identify yours? With Richard Jillingwater, which will be on Thursday 3 March at 1pm. You'll find further details listed on the events page on the CIM website and you'll be able to register for the session. So on behalf of CIM, that just leaves me to thank our speaker James Patrick once again for a great presentation and to say thank you to you for joining us today. We do hope that you enjoyed the session and we look forward to welcoming you again to our webinars in the near future. Take care everybody. Goodbye.


Hey everybody, so this is my friend Jake. You may know him best by it's Jacob James on Instagram. He's my number one heckler. So I thought we should read other people's comments and cause I've seen some hilarious things. So many people have said I live for the whispers. Puzzle. What is poppin'?  So many comments. Just comment, and I oop. I wish I remembered which one was the, you look like a pigeon. Cause that one's, it's gotta be on, I think it's on YouTube. I think it's gotta be on the one, Oh, was it on the Q&A? I think it might have been, it was either the Q&A or the reaction, the viral one. Yeah, yeah, yeah. Do I look like Elijah Wood? I never thought you did. Or Daniel Radcliffe, that's another one. Well that one I've gotten since I was younger. Really? Yeah, but I actually never thought of that. But the one I've gotten the most since I was like little is Toby McGuire, Spider-Man. But that's the only one that I've ever seen myself, like that I've ever actually thought made any sense. Yeah, I can see that. One of my favorite parts about comments is like, when people have a really well or very seriously stated opinion about something. And it's just like, cause here's what Uncle Barkley says, in my opinion, this is the best cover that he has done. Like, thank you sir. I appreciate it. Like he's not laughing at all, he's just like. This is the best one. I gotta hand it to you guys though because there are so many memes and vines in here. A lot of them I have seen, but there's a lot of ones that I have not seen that everybody recommends. And that's super helpful to me. So never stop recommending things that you want to see me do because that's like, that helps feed the next thing. And I do read. I do read them. I read comments and I do read DMs, even though I don't get a chance to reply to everything because that would just take an insane amount of time. But that is super helpful when people do that. So thank you for suggesting things and also expanding my meme horizons because there's some really, really great stuff that people have turned me on to. Everybody's always talking about the clock in the oven and it's shutter speed. It's camera shutter speed. So I shoot on a Sony a6500 and I got that camera specifically because of how good all of the auto settings are. And for what I'm doing, like if I was gonna do more cinematic stuff and really kind of dive into the film process of it, then I would probably focus a lot more on the settings. But for this type of thing, man, if I can throw it on auto and know that it's gonna be in focus, it's gonna look good, like that's all I care about. So therefore it sets the shutter speed on its own. And if the light is just right coming in the window, then I don't know, man, it's just the way it reacts with the LEDs. But I prefer this just being stuck in a time loop. Just dying says how long, how long until you get one million. And the answer is tomorrow. Oop. The profile picture matches the color pretty well too. These videos are great and all, but it makes me sad that not many people watch your other content. I'm assuming Vorsky that you mean like my, my like original music, that's, that is much appreciated. I sincerely appreciate that you have noticed that. There's not a lot of it. I mean, you know, in terms of like serious content, but it's there. And anytime anybody checks that out, thank you. That's, that's very nice of you. I love when people say this, I can tell this channel's about to blow. Or like, I can tell this is about to go by. I was like, dude, I went from 50 to 100,000 subscribers in like a week and a half. Yeah. I mean, I don't, I don't know what else I can do just to blow. Oh, what have you made me thinking? You could stop at five or six doors or just one. I actually, I downloaded that video. It's in the queue. What? I'm going to sing Share Lloyd by Share Lloyd. What is that? That's from a video. Did you watch the video? Wait, which one is this? Which one are we looking at? We're looking at Disgusting. I thought we were looking at the most recent one. I left a comment, it was a rather vulgar comment on that video on YouTube and it got removed within like five minutes. It was weird. What? Yeah. What did, what did you say? I don't, I don't want to repeat it. Well, I said when, when you're not and she keeps stuck. And that got removed. They got removed within like five minutes. Oh my God. I didn't remove it. That was like the perfect comment for that. I know, but I didn't. Don't you know, I only make G rated content. That's right. Yeah. Please sir, make a piano cover of not the Yattas. So, okay, here's the, I don't, I don't know why everybody wants like, it's, It's already done. It's done. It's, that's the version. And it's a great version. I don't know who did it, but like it's, it's amazing. It's, and I don't know what else, I don't know what I, what else I would do with it, but I appreciate that people want it. Actually, this one's funny only because the way it's worded. What's it? This is on the Tyler one video and says, when you try to hold a toot in, when you try to hold a toot in while in the company of family. It's just like, it's not that funny, but like the way it's worded. The toot. It's like the use of the word toot. Toot and while in the company of family. It's very, very, very properly worded. Very properly. I wonder how good you'll sound if you do ASMR. Yeah, that should be your next thing. What did, what did that do to the video? I don't think I'm going to do ASMR, but I'll update you if that does happen. And ASMR dub would be interesting. Yeah, but you can't, you can't dub whisper. Fun fact, I can't double whisper. There's no, you're not producing any vibration with your vocal cords. There's no pitch. It's just, it's a good point. It'd be a good challenge though. There's no challenge to be done. I think it gets impossible. If anyone can do it, it's Charles Cornell. I liked this one. It's one of those spam comments on Instagram. Come check out my page. I post funny memes. You won't regret it. And then model dad says, I checked it out and immediately regretted it.  Nobody credits you in repost. Three months. You've been working for him. I've been looking for him for three months. I've been doing this for three weeks, but I appreciate you seeking me out.  What do you expect, bro? Like you're just talking to somebody, but he's just like typing while he's talking. Thanks man. I appreciate it. Oh, I love it when people say totally unironically like, oh, I've been here since like, I've been here since six K subscribers. And I'm like, you've been here since last week. I left the comment in response to somebody the other day who was like, I've been following you since like, what is some number? Some 50 K. Yesterday. Yeah. Since I've been following you since yesterday. 10 K gang. Where are you at? Okay. Oh, right. Yeah. I forgot about that one. And I'm fans of Rhett and Link. I've watched their stuff for a long time. And you know, the first video I saw of theirs was the Taco Bell drive through one. And I know they were making content long before that, but that was like, I remember watching that video and I was like, this is brilliant. You know what I mean? But I never in my life thought that I looked anything like him. I didn't either.  And we have like a similar kind of hair thing going on. I guess, I don't know. It's so funny like seeing what other people think that, like who you look like. Yeah. I just never thought about that. I never would have thought that. Yeah.  The person who made that one beat, like the, I don't know. Yeah. I don't know what it's like a low five. Okay. Or something like that. I'm going to put it like right here. But that was like, dude, I saw that on Twitter. Yeah, dude. I was jamming that like unironically just chilling in my house. So good. So good. I see, I see some stuff sometimes where they're, where somebody's like, this guy's all over my beat and it's annoying. I'm like, sorry. There is this truth though. There's sometimes when I'm scrolling through YouTube and every time I scroll through, there's this one creator every, I hit not interested every time and it still pops up. And I'm like, what do I have to do to get you on my life? It's YouTube, man. It's not me. If my videos are showing up and you're recommended, I'm sorry if you don't want them there. But I have nothing to do with that. Here's one on the God and Anime one on Instagram. He has not left his bench in two weeks. Kind of true. It's true. People always ask for sheet music. I don't have any sheet music. I don't write sheet music. I have no idea how you would notate any of this. So I can't help you there. I don't know. Maybe one day I'll figure out how to make music to something. But how the heck did I get here from pimple popping videos? Oh no. By the way, the Bill Wertz references are blowing my mind. That dude is one of the most talented people I've ever seen online. And the fact that people think that anything I'm doing is remotely like his, wow. That's really cool. And I appreciate that a lot because Bill is the man. I didn't even realize that he said, I'm going to stab him. Because it was so, so violently Canadian the way he said it. That I thought, I thought, I almost thought he was saying stop for a second. Because I wasn't really listening. I was only listening for pitch. And then, and then I saw the comments about, about him pulling a knife. And I was like, oh, I never even saw it. As I was making it, that's hilarious. I wish there's two comments that I've, that we've gotten that, that I wish I could find and I can't. Somebody commented on my amazing grace cover and they were like, not funny.  Tara Stark says, can you post this on Twitter so I could retweet? Yes, I can. And I did already. Follow me on Twitter. Oh, everybody's been commenting saying stuff about Seth Everman. That dude is awesome. And I would love to do something with him. It's just like the deadpan musical humor is just absolute gold. Drama kids. I don't understand why people get so upset over sports. Also drama kids.


As you probably know, as everybody knows, I mean, over the last 10 years income inequality has become one of the main talking points of the left. Although I have to say that over the last three years, since Black Lives Matter and since the rise of the new wacky left, income inequality is taking a back seat to issues related to race and gender and trans and all that stuff. So because income inequality is more of a neomoxist thing, I mean, Piketty, who brought income inequality to the forefront in his book 10, 11 years ago, capital in the 21st century or Das Kapital in the 21st century, as I like to call it. Piketty is a neomoxist and, you know, Marxist without the intellectual aspect of Marx but just adopting the economic arguments of Marx and without the kind of philosophical arguments, which is interesting, which I think most modern Marxists are. They drop all the pretense of philosophy and just talk about the economics. But it's interesting that over the last few years, the left doesn't talk much about inequality. You don't hear it anywhere as much as you did five, six, seven years ago. But anyway, the big issue was in the United States was seeing ever-increasing, ever-increasing income inequality, inequality between the rich and the poor, the middle class and the poor, the rich and the middle class. The whole spectrum is massive divergence and increasing genicoefficience, which is a measure of inequality and increasing inequality across the entire spectrum, which they tell us is horrible and disastrous and destructive and needs to be dealt with. And I've talked about in the past about why income inequality is not a problem in and of itself, why the real question is the causes of it but not the fact that it exists is neither good nor bad. And indeed, you could argue that having inequality is a sign of at least having inequality can be a sign of freedom, although it can also be a sign of not freedom. So the way I like to think about it is freedom always results in inequality. And we'll see an example of this in China. Freedom always results in inequality and significant inequality. And the fact is that most systems of authoritarianism result in vast equality among people, although there's always a small group that are super unequal relative to everybody else. All right, so in the United States, we've been told over and over and over and over and over and over and over again that income inequality is a problem. It's growing, we're back to the late 19th century, I wish, in the sense of because of economic freedom, because of the Reagan Revolution and all the deregulation that appeared out of nowhere, I wish I could see it, but all that deregulation, all those lowering taxes, all of this has resulted in massive increases in inequality. Piketty has written a book and several articles about this and what's his name? Stiglitz, who's an overpriced winner in economics, has also written about this and many other American economists have got on their train and they are pushing the biggest problem in the American economy is income inequality, at least until Black Lives Matter showed up and then it became all these other things. But it turns out that even by their own standards, by their own measures, by their own data, this is just not true, not true. So one of the problems with most of these studies is that they look at income inequality, the differences in income, before taxes and transfer payments, and we talk about this in the book, equal is unfair, this is easily observable, so that you're just looking at wages or income.  And if you take those into account in a variety of different ways, income inequality has not increased. Arguably, it's actually decreased by 3% over the last few decades. But now there's a new book coming out, new book coming out, let's see, what's it called? The new book is called The Myth of American Inequality, pretty straightforward title, Myth of American Inequality, it's coming out September 15th. It was written by Phil Graham, the former senator from the state of Texas, and Ely, something Ely, who is an assistant, used to be, he served twice as assistant commissioner at the Bureau of Labor Statistics, so a stat guy. And they have a new book coming out, looking at the statistics of inequality. And one of their findings is, which is really interesting, one of the findings is that once you take into account taxes and transfer payments, that the bottom 60% of income, the bottom 60% of income, so anywhere from zero to 60%, the people that fall into that level of income, there's very little difference between them, very little difference between them. That the welfare state, the war on poverty, has to a large extent equalized income of the bottom 60% of Americans. So if you look at government transfer payments, the bottom 20%, they have surged by 269%, 270%, between 1967 and 2017, while middle income households saw their real earnings after taxes rise by only 154% during the same period. So you can see welfare payments were rising at a far, far higher rate, far, far, far higher percentage than earnings of middle income. So what's happened is, the poor have caught up to the middle fast. And this is, we'll see in a minute, the kind of consequences this has. So here's some stats. This is from a editorial in the Wall Street Journal that the two authors wrote. And Phil Graham is one of the last good senators that I remember, particularly in economic issues. He was very good. He was good within the context of what is available, right? But I don't know that any senator today is as good as Phil Graham used to be. See you real. In 2017, right, among working age households, the bottom 20%, so the poorest 20%, earned only $6,940, $41 on average. That's low. But that's because only 36% of them were employed. But once you take into account transfer payments and taxes, those households had an average income of $48,806. So they earned from working, $6,941. Now you can see, if your household income is $6,941 and we measure income inequality off of that number, wow. Other people are making $60,000. That's a huge gap. But if it turns out that you get all this welfare in and you get $48,806, well, that is a lot more reasonable. That's how people live. You can't really live in $6,900. But of course they're only making $6,900, that's an average, because only 36% of them are employed. Now, if you take the second quartile, so this is 20 to 40%, quintile, not quartile, quintile. Quintiles are 20% gaps, right? If you take the second quintile, average age working households earned $31,811, 85% of them were employed. And you can see the income inequality is huge here. They made $31,000 off of $6,900 for the lower quintile. But once you take into account transfers and taxes, they had income of $50,500. So they went from 31 to 50. That 50,000 is only 2,000, less than 2,000 more than what the bottom 20% were. Only 3.5% more than the bottom quintile. And then if you took the middle quintile, the one that covers it's 40 to 60, so it covers true middle, right? Middle class. The middle quintile earned 66,453, and 92% of them were employed. Not surprising. But after taxes and transfers, they kept only 61. So notice, this is the first quintile where household income actually went down once you took into account taxes and transfers. The bottom 40%, their income goes up once you take into account taxes and transfers. 40 to 60 is the first one when it comes down, and it comes down to 61,000. So once you take into account taxes and transfers, right? Once you take into those account, into them account, 48,000, 50,000, 61,000, almost the same. Come on, people, quintiles are not that hard to follow. One, two, three quintiles. That shouldn't even challenge your crow. Epistemology. Three, three you can keep track of. 20% lowest, 20% second lowest, 20% middle. Now, once you take into account household size and you look at average income per capita, per capita, then actually the lowest quintile has a higher per capita income than the second quintile once you take into account transfers. So the people who are not working are making more money than the people who are working. 32,574 per capita versus 33 per capita. So you can see that the people who are not working at the very lowest quintile, only 9% of them are working. I'm making in America, sorry, only, what is it? Only 36% of them are working. Only 36% of them are working. That quintile is making as much money in terms of income as people who are working 92%, 85%. What you get here is a real inversion. What you get here is a situation where for many Americans, it's worth it not to work. It's worth it to be on the door. It's worth it to receive a variety of different benefits, transfer payments, because the cost of them in terms of income is zero and potentially working is less beneficial than receiving welfare as a consequence. You get high numbers of young people who are poor by definition almost, not working, not participating in the workforce. So the bottom quintile, the bottom quintile has 2.4 times as many working age members, I'm sorry, the second quintile has 2.4 times more working age people working than the bottom. People are working harder, longer, more people are working and yet they're making the same amount of money.  People are working hard, barely surviving, somehow scraping it together in that second quartile, quintile, and yet other people who are not working are just where they are. Now imagine if you're a working class American who's in that second or third quintile and you look at the people who are not working in America and you're seeing them basically the same place you are and all they do is complain because their welfare checks are not large enough because supposedly there's systemic racism because of all kinds of things.  I think a lot of the angst that exists out there, you know, partially there's high suicide rates among working class Americans, the high opioid use among working class Americans and the attractiveness of somebody like Donald Trump to working class Americans has to do with this sense of something is deeply wrong in America. If I work hard all day and other people are not working at all and we're making the same amount, I'm being taxed, my taxes going to pay for them but they're not working and they can work. I mean it's frustrating, demoralizing. It explains the low workforce participation rate that we're seeing as welfare increases as government programs and this is government programs at the federal, state, local level increase, why work? And notice that in 1967 before the war on poverty was announced, in 1967, 68% of working age people in the bottom quintile worked. In 2017 it was 36%, I don't know what it is today. So the lesson many people learnt from welfare from the war on poverty is I don't need to work. I can get money without working. Now you could have predicted this. This is not rocket science. This is not a surprising result in Centerv's mind. This is not a surprising result in Centerv's matter but there you go. It is what it is. It is a result. This is what happens. The welfare state, as I've said, over and over and over again in debates and is destructive. It's destructive to the character of people. It's destructive to the self-esteem of people. It's destructive to their incentives.  But the handing of people checks, the giving them of checks, the demoralizing impact of that, not only is demoralizing to people receiving the checks, they're not ambitious. They don't, particularly the weaker ones but it's primarily demoralizing to the people whose money is being taken away from them to provide those checks who themselves are having a hard time earning a living. People just above some kind of poverty line and it's demoralizing to that middle class. So what we're seeing in America today is not an increase in inequality but an increase in equality at the bottom and that increase of equality at the bottom is actually eroding self-reliance. It erodes worker pride. It erodes labor force participation. It is a government generated income inequality and government generated income equality always and everywhere results in disaster and always and everywhere is a sign of a lack of freedom in this case through the welfare state. Now, one way within the context of a welfare state you could solve this, not that I'm for this because I'm against a welfare state on principle is to make it a requirement to work if you're gonna get wealthy, if you're gonna get any supplement first work, if you're capable of, if you're, but the tragedy is you've got working-age people who are just not working and there's no cost to that. There's no penalty to bear and that has vast social consequences and it has vast political consequences and I think to shift to populism both on the left and the right are big consequence of the fact that we have such an expansive welfare state. Thank you for listening or watching the Iran Book Show. If you'd like to support the show, we make it as easy as possible for you to trade with me. You get value from listening, you get value from watching, show your appreciation. You can do that by going to iranbrookshow.com slash support by going to Patreon, subscribe star locals and just making a appropriate contribution on any one of those, any one of those channels. Also, if you'd like to see the Iran Book Show grow, please consider sharing our content and of course subscribe, press that little bell button right down there on YouTube so that you get an announcement when we go live and for those of you who are ready subscribers and those of you who are ready supporters of the show, thank you. I very much appreciate it.


Yo, what's good? Y'all should boy down from the down show and today I'm doing something different today I'm gonna be doing my top 10 favorite Christmas movies all time. Let's go ahead and get started So before you get started make sure you like and subscribe and comment and uh, yeah Let's go ahead and get started number 10. We got the Santa Claus Tim Allen 1994 All right, next up we got number nine the Scrooge with Bill Murray as the Scrooge I head it off Number eight we have Huller Express Tom Hanks 2004 number seven we have how the Grinch stole Christmas Jim Carrey year 2000 All right, number six We have The Muppet Christmas Carol 1992 All right next we have number five Elf Will Ferrell All right, number four. We have Nightmare Before Christmas. Definitely one of my favorites We have Our Charlie Brown Christmas Yes Number two we have Christmas story. Okay Number one Home Alone first one. That's an easy one for me. Everybody knows that's number one Now I do have some honorable mentions because I felt like they didn't deserve to be ranked They are classics from like all-time classics We have Frosty the snowman the cartoon from 1969 and we are ruled off the Red Nose reindeer 1964 so Let me know your thoughts. Did you like my list? What do you think about it? Maybe you don't know if you have a better list than me, please do me a favor going to comments right now I just want to know your list This is just for a chance for everybody to kind of see what movie they need to see if I haven't seen a Christmas movie that you Think I need to watch please send in my way put into my comments right now Make sure you like to subscribe and that's it y'all. I just want to give y'all my top 10 real quick I want you to take this time to spend time with your family Other than that, I thank you so much all 273 of my subscribers. I love y'all. I appreciate y'all Hope you stay safe keep healthy eat right eat good. Take care of your mind. Everybody. Peace