All right, so we're gonna talk a little bit about site reliability in the serverless age, and I'm gonna take you through some, I guess an opinionated point of view on serverless and reliability, and I don't expect everyone to agree. What I do hope for is that afterwards, we have a great conversation, you come by the Cloud Zero booth, we talk about your favorite beer or any of these other topics. So, whom I, you already know all these details, I think probably the most interesting, fun fact is I've lived in six states in three different countries and it just randomly means that I really don't feel like I'm home anywhere, but I live in Boston, I love Boston, I've been here for five years and it's where we started Cloud Zero.  All right, so what is serverless? How many times have we talked about the definition of serverless, right? Serverless still has servers, yes, we know. Well, there's five properties that I think apply to serverless, and when we think about serverless, this is really what we should think about, the attributes of what a serverless system or a serverless component is. It's event-driven, the infrastructure is invisible to us who are building systems on top of it, it automatically scales with usage, it's fault-tolerant and high availability built in, and we never pay for idle. So that's how I define serverless, that's how Cloud Zero defines serverless and how a lot of folks that I've been talking to on the Amazon side and on the Google side also apply a lot of these attributes to serverless. And it's gonna be very useful for the purpose of this conversation. So, I wanted to share something new that nobody has ever seen before, it's a live image from the serverless data center. It's not a lot going on here, I don't know. When I saw this tweet, I absolutely, I think I laughed for about five minutes. Of all the jokes that have been made about serverless, this is by far so far my favorite. If you come up with a better one than this, it'll appear in the next talk, please do. So let's talk seriously though about what does serverless mean. So remember those five attributes that I was just talking about? Well, that doesn't mean that it's just functions as a service, Lambda, Google Cloud Functions, Azure Functions, that's not what serverless is all about, whoop. Okay, sorry. It's a whole lot more than that. In fact, from my point of view, serverless is actually better thought of as a spectrum. And I kind of got this idea from a conversation a long time ago from speaking with Ben Kehoe at iRobot, where I think he first kind of presented this idea that serverless was a spectrum. And so I started to think about, well, what does that really mean if I were to go and take a lot of the services? So this is obviously the AWS focused. A lot of my talk today is AWS focused apologies to the Google and Azure folks in the room. I would love to work with you on a broader cloud talk, but I'm gonna focus on AWS. So if I went and I took all the kind of major AWS services and I popped them into a spreadsheet and I assigned a score to each one, they get a one or a zero or a half based on how well they apply to those five attributes. I basically get a spectrum or a chart like this. Now the interesting thing is, serverless by my definition really starts about here. If I'm using ECS Fargate, it applies to a lot of those attributes, Kinesis and then more serverless as I get farther and farther to the left side. One interesting thing is we've got services like SQS and S3. What were the first services ever announced by AWS? I think back in 2004 they talked about SQS and then S3 and then EC2 came out and then for a decade we were all confused and didn't think about this amazing serverless thing because nobody had really talked about it yet, but we were busy focused on EC2, 2014 Lambda comes out and suddenly we're having this conversation again. But it's a spectrum. And so there's a lot of components that go on because who would build a system on nothing but functions of service? No, I can't do that. I need a database, I need storage, I need all these other elements in my environment in order to build a system. And the key kind of tenant or maybe better framework to think about it is is that the cloud is really a computer and the operating system is the cloud provider and serverless is the native code for that operating system and computer. So serverless is a spectrum. Okay, so let's talk about reliability but first the setup, everything fails all the time. How many people would absolutely stand up and applaud if their CTO or CIO stood up and said everything that we're building is going to fail, right? It's a good understanding of how cloud changes everything. When we first started moving things in AWS some people attempted to do the lift and shift and they discovered how painful that was. They had to re-architect a lot of things. They had built a monolith and maybe they didn't know about it. They built a snowflake and maybe they didn't know about it but everything fails all the time, right? So if everything fails all the time and I'm building serverless systems and I'm building an AWS, well then what exactly does reliability mean if everything fails all the time, right? So I sat down and spent some time thinking about it and I was like, oh well, let's see what the dictionary says. It's kind of okay, let's see what the industry says. And at the end of the day I thought, well what are we really trying to do as practitioners, as people building systems? What are we ultimately trying to do? Well we're trying to build systems that delight our customer. And so in my mind, reliability is the trustworthiness of a system and its ability to delight the customer, right? If I know that every time I go to that system it's going to deliver a delightful experience on a trustworthy basis, then it's a reliable system from the customer's perspective and really the customer's perspective is really all that matters.  But if the customer is happy, well you're at least getting one thing, probably the most important thing, right? Now that's not sustainable if you're managing a dumpster fire. But we want to definitely delight the customer, right? Okay, so we have an idea of what service is. We have an idea of at least what I think is reliability. So how do those forces tie together to things like DevOps and Site Reliability Engineering? And there's a lot of different points of view on this. I kind of pulled out the pieces that I think that line up with at least how we operate. And a lot of times I see DevOps as more of a culture, Site Reliability Engineering is a practice. We've got some points of view in terms of what DevOps culture means. We've got some points of view about what we should be focused on if we're in the Site Reliability Engineering practice. And by the way, DevOps wasn't a happy merger between development and operations. It was a hostile takeover, right? There wasn't a, oh everything is perfectly fine. Let's glue these two organizations together that have been at war with each other since the dawn of humankind, right? And so after that hostile takeover, that culture that drove the invading hordes, us engineers, into the operation space, made us suddenly realize, wait a second, we have to develop some sort of process now that we're in charge of this whole thing. And that's where we get practices like what's written down in the SRE book. Okay, so we've got those two pieces. So how does serverless affect those forces? So on the DevOps side, some things, in my point of view, are just absolutely required, right? So we talked about eliminating organizational silos, DevOps taking over ops. The silos have to go away, it's absolutely required. But I'm gonna be building serverless. I have to accept failure is normal. Everything fails all the time. I'm now operating on that operating system called AWS. I have to embrace failure all the time. Their CTL says that it's gonna happen 100% of the time, embrace it. We need to adopt this religion that meantime to recovery is more important than meantime before failure, right? If I'm focused on trying to make something never fail, I will fail. But if I focused on trying to be able to respond to an event, respond to an activity or something like that, I will ultimately do what's most important, which is delight my customer, right? We're always trying to optimize feature velocity, but now in serverless worlds, things are happening almost faster than we can keep up, almost certainly. We were trying to measure everything, but now we really should be trying to measure what's important. And then there's something new that shows up, which is an alignment between well-architected systems and their cost, right? This has been happening for, probably been coming for a long time. We haven't really thought deeply about it, but when we had servers in our basement, we didn't think too much about the cost. CFO purchased them every three years and there was a sunk cost and we didn't think about it. Then we went to EC2 and we had a bunch of things running and we really didn't know how much those things cost. Sometimes we thought about it. When serverless, I'm aligning my usage directly with what my customers are doing, directly with how much money I'm paying, and so good architectures cost less. So I would argue that there's a direct relationship between cost and a well-architected system and that we need to consider cost as a first-class metric, an operational metric that I monitor not on a monthly basis. I heard an earlier talk that people talking about finding their bill at the end of the month and discovering something that's happened. I used to give a talk where I talked about the most popular IDS in AWS was the billing report at the end of the month. People are using their bills as an intrusion detection system. Please don't do that one. So I think cost is a first-class metric and it's important. And so a couple of these are blank because I think they just kind of continue on, but then there's a couple things that change here as well. So availability, what does availability mean in a world where my provider is really responsible for that? I need to think deeper and more thoughtfully about the SLAs and the SLOs associated with the system I'm building. Efficiency becomes cost efficiency. Change management becomes change tracking. I just wanna understand what's happening. I've got more teams than I know what to do with and they're all building things and it's all very interdependent. There's this tricky word called observability. I'm gonna talk about that in a second, but it gets to monitoring what's important. Instead of planning for how much capacity I'm gonna need, I need to understand the limits of the environment that I'm deploying in. Remember, scalability is supposedly automatic, but there are limits. And instead of provisioning systems, I'm really focused on automation and auto-scaling, configuring all those things. So there's some changes, but I'm living in a service world. These are the changes that I need to think about. So I wanna deeper dive into some of the hard stuff. Okay, so these four areas I'm gonna spend a little bit more time on. All right, so what does it mean SLA and SLO management, right? So let's ask ourselves. Do our cloud providers, do their SLAs that I've built my system, my house of cards on top of, even support the SLO or SLA that I'm advertising to my customers? How many people knew that EC2 has an SLA, but Lambda does not, right? One person, yeah. There's absolutely no promise that Lambda's gonna continue to work. And if it stops working, there's gonna be no ramifications other than you're upset, right? DynamoDB, they have an SLA. API Gateway, no SLA. CloudWatch logs, tracking all this stuff, no SLA, right? So you need to take an approach of adding those things together, thinking them through, starting to develop your own metrics and own understanding of how those systems combine to create the availability of your environment. So that's the first one. The other one is we need to really think about cost as that first class metric that I was talking about. So, wow, Lambda, it's so unbelievably cheap. I only spent $1.79 to do all this compute that used to cost me hundreds of dollars. Wait a second, why is the bill $800? Well, somebody turned on debug logging. Somebody was logging everything to CloudWatch, and now it's $800 per day. Architecture change, configuration change, dramatic effect on the overall bill. If I'm only taking a moment to look at the Lambda price or individual components of my system, I'm forgetting that Lambda and serverless and all of these components are all part of the serverless system. I have to think of it as a whole, right? Observability versus monitoring. So this term has been all over the place. I actually have been hating this term for a very long time, but then I spent some time thinking about the definition a little bit, and it's a combination of two things, and I think we've been forgetting that it is required to be a combination of two things. One is my ability to observe what the system is outputting. Is the system logging enough? Is it alerting enough? Is it presenting enough information? The other piece is actual analysis of that data, and that's the part that I think a lot of us forget. So observability is a measure of how well the state of a system can be determined from the analysis of its outputs. That's observability. If we're not doing the analysis part, we're just doing monitoring, and that doesn't really scale in a serverless world. Serverless systems are easy to observe. They produce enormous amounts of data, but they're very hard to analyze because of all of the dependencies, all of the inner workings that are tied together. It's very hard to analyze, and thus have a kind of out-of-the-box low observability, but it's key to measuring what's important. So we have to figure this one out. By the way, that is an analysis. If you go looking for the most disturbing eyeball photo, there are worse ones than this, but that one is really troubling. So how many people are here operating as the analysis system in their environment? That doesn't scale. It definitely doesn't scale in a serverless world. And the reason is because the system, remember, is changing faster than we can keep up. Next thing you know, you'll have teams that do nothing but maintain the dashboards that do nothing that track the system that's constantly changing. And sooner or later, that team might start looking the same size as the team that's actually building the solution. It's not a scalable long-term solution. We have to get out ahead of that problem and figure out how to build this into the system itself. Okay. And then the last piece I wanted to mention is capacity planning. So we think about capacity planning in a cloud-centric world and a serverless-centric world where auto-scaling is the norm, where automation is the norm, what really stands in our way of scaling up our limits, provider limits, how many things can I spin up? How many functions can I create? How many connections can I create? What type of calling pattern exists between Lambda and DynamoDB or Lambda and S3? How do those things behave? So when we think about capacity planning, we think about our ability of our system to scale, even though we've read the books and it said scaling is for free, we have to make sure that we architect to them right so that the patterns and the architecture supports the ability to do that. Sometimes it's as simple as increasing the limit, sending an email to AWS and saying, hey, can you bump up that limit? How many concurrent executions I can get? That's a changeable limit. But sometimes it's based on things that we can't control. I can only spin up 500 simultaneous concurrent invokes in US East. Then I have to wait a minute and then another 500. By the way, that's different per region and there's a lot of other little complexities like that. So you have to think about the limits.  Okay, some weird, crazy projections on the future. So let's think about costs and architectures. Come back to this chart for a second, right? So that was already a big problem. A lot of times we're not thinking about the necessary costs, we're just focused on the individual things or the parts that I built and we forget that somebody can turn on debug logging and cause this to occur. So that's a problem. But could it be worse? Well, of course, of course it could be worse. So follow through this, right? Step one, three invokes. Step two, 100 files. Step three, invoked 100 times from an S3 bucket. Step four, 1,000 records written at DynamoDB. DynamoDB Stream invokes a lambda function. Say I set my batch size to 100, invoked 100 times. That writes 100, 1,000 files to that S2 bucket, problem. This is both costly and catastrophic. Now you might be asking, well, I built this system, I would never create that thing. Why on earth would anybody do that? Obviously this makes no sense, right? Well, what if I'm trying to break up these services? What if there are two different teams working on this? Or if you're only responsible for a small part of that system, what if those parts of the system are coming from a third party? Think about how easy it is if you're capturing events off an S3 bucket, how easy it is for somebody else to go dump a million files into it. Have you configured the filters right? Have you configured all the details? What are the limits? What are the connections? How does data flow? That's a big problem. This is where people get bit all the time when they're trying to build serverless. They have recursive functions, they have cost overruns, they have architecture that just doesn't seemingly scale, and they're wondering, wait a second, maybe this thing isn't for me. And I'll tell you, the thing is for you, but we have to change our thinking. We can't lift and shift into serverless. It's impossible. We could do lift and shift into EC2, and it was mildly painful. It's impossible to lift and shift into serverless. But it gives us a lot of power, which is why we're doing it. But who's responsible? Who's gonna solve this challenge? We have to think about all those elements together. So that's my prediction. That a new tribe among us here is gonna emerge. People who understand the ramifications of architecture and connections and cost and how those things tie together. So Simon Wardley was talking a little bit about this. It's serverless confi tweeted about it. He actually put out a survey to about 300 people with some real data, one data point, but real data. I highly recommend you go check it out. That basically says this tribe is already starting to emerge. So we kind of call this Finn DevOps. It's a DevOps engineer. It's a developer. It's an operations person who cares about cost, who understands the ramifications of cost and architecture and how they're intertwined. They know intimately what the cost of a user or a transaction is. They care about that. And they track the dependencies and data flows like they're life dependent on it. They're trying to solve that observability problem in a mountain of data. So I think those people are starting to merge now. They're already out there. I fully expect to start seeing job descriptions and things like that emerging in 2019 where people start to ask questions. So just by a show of hands, how many people here care deeply about their AWS bill? About a third of you. That's wonderful. When I come back here next year, I want 100% of you all to raise your hand because a well-architected system is a cost-effective system. So thank you very much. I really appreciate the time. I really enjoyed that talk. And this is the first time I had heard Finn DevOps as a term. I kind of feel like I'm doing this already but I don't think a lot of other developers are or are thinking about it. How do you sell this idea through to developers who maybe haven't thought of it or are actively disinterested in it? So really, it's a really tough problem. So when I was at Veracode, I brought AWS in to Veracode. We didn't have AWS, we're all on-prem. And the first thing I did was ask the CFO if I could go spend a little money. And he said, sure, go, don't spend more than $3,000. And somehow, magically through that experience, it forced me to start thinking about cost. But as, but obviously you can't create a movement with just one person. I had to involve other people, other teams in the process. And it was really tough because I think as engineers, a lot of us are just kind of have it ingrained in our mind that the infrastructure that we're running our code on is a sunk cost or we're just so far removed from it. And that's really tough. And so what I think is missing in what just as a way of example, Cloud Zero's trying to do is to actually make the cost in real time something that's visible to you.  You go, well, how can I really affect change? But if I can have information that tells me, oh, I made a change and now suddenly I have an alert and oh, I'm spending $30 more an hour. Oh, I did that. All right, let me go tweak with that. So what you can do today is try to encourage people to get more interactive with the bill to pay more attention to those things. And we're certainly trying to help solve that problem. So great question. It's tough one. I'm sorry, I don't think I totally solved it as like there's a silver bullet for us other than our platform, but I'm not up here today. So are there open source tools or third-party tools which can look at your model, how you're doing it and predict the cost and compare it between different cloud providers? So between different cloud providers? Yeah. I mean, so each cloud provider has, in the early days, the cost management tools that the cloud providers spread were horrible. You really had to go figure it out yourself. But now they're pretty robust. So you can get a lot of data out there. There's been a ton of blog posts recently about, okay, Lambda, Vokes versus Cloud Functions versus Azure Functions. But I think that kind of misses the point, right? You need to think of the total system cost. So I haven't seen any open source emerge to do that sort of thing, other than some of the kind of cloud APIs you can already call from your cloud provider to pull in that data. And I've seen customers trying to solve this problem today by basically writing a bunch of scripts, pulling a bunch of data and loading it into Excel and doing some magic. And obviously that's not scaling very well for them. But that in the open source world, I guess would be the state of the art. So it's actually a very similar question. And it has to do with what's your perspective in terms of once you have this analysis of being able to look at your costs, how do you prevent vendor buy-in or vendor lock-in? So if you are in AWS environment and you start using the Lambda Functions, all of a sudden now you are deeply, deeply tied to that environment. And you want to be able to be financially savvy. So how, where do you see that ROI and costs come into play? So vendor lock-in, that's one of my favorite questions. Thank you. And I've got two responses to it. So the first is vendor lock-in is a red herring. I'm sorry, it doesn't, if you're worried about vendor lock-in, you're worried about the wrong thing. It just simply isn't a problem that you should be thinking about. I know that people go, wait a second, when I'm all beholden to one operating, one cloud provider, well, I made a choice a long time ago when I started building that I was gonna build on Linux or Windows or something else. The cloud providers or operating systems just get over it and implement. At the end of the day though, it's really not just about Lambda, it's about all the services. So what I think will eventually happen, just put a bow on that though, is what stops me from building a multiple provider serverless system? Well, it's bandwidth and network connections and the ability to move data from point A to point B. That isn't anywhere where it needs to be in order to really enable that. So we see hybrid kind of serverless apps emerging where you have something like Auth0 as a service that I use that handles my authentication. And I communicate with that because it's not, doesn't require high bandwidth or high traffic. But it's a tough, it's a tough problem to solve that won't get solved until the cloud providers all have 10 gigabit ethernet connections between them, right? And so until then, it's just not worth thinking about it. But to kind of like dig into it a little bit more, just embrace it, I guess is the best way I can say it. The reality is we're here to build great stuff in a short period of time. I see people are building serverless systems on any platform, any provider in a period of time that is way shorter than I think anybody could imagine. Six months for things that normally take two years. So it's more getting more into the mindset of how quickly can I move and how quickly can I change and build versus I'm gonna be locked into one provider or another over time. I'd love to dive more into that. That's probably an entirely another talk though. But thank you.


but of course, he's a PhD student in the physics department. And my special job today, and my pleasure, is to introduce our home ground speaker, Dr. Yen Jiang. Dr. Jiang has more than 20 years of academic teaching and research experience. He moved from Hong Kong Polytechnic University to SOAS last year. But actually, he has a long-term relationship with SOAS and the University of London in general. He graduated with PhD in linguistics from UCL and SOAS in 1995. And he's also an editor and contributor of SOAS Working Papers in linguistics and phonetics in 1993, which is now called SOAS Working Paper in linguistics. And he has more than 60 publications, both in Chinese and English. And his research focuses on formal semantics competitive semantics, pragmatic, and rhetoric among many others. So I'm sure we all enjoy getting to know his topic much as you do about bill in Mandarin Chinese. OK, please join me in welcoming Yen. Thank you very much. Thank you for Ms. Lulu for her very kind introduction. And it's really a great pleasure to come to the departmental seminar to give a presentation. So I have prepared a rather detailed handout with glosses and romanization for Chinese sentences. So I hope that it will make easier for those of you who don't specifically work on the study of Chinese syntax. And so I am prepared to go through the example sentences very closely together with you so that you can see the point of this topic. I title it as much about dough for a special reason because this dough is very much like the English every, or the English all, or the English even. And that, strangely speaking, has attracted quite a lot of attention among the Chinese traditional and contemporary syntactic circles. It seems that everyone wants to have a dip of finger in the topic. And every time the theory or the trendy theories change, then people want to do the topic again. So that is why there has been a steady accumulation of literature on this topic, around 200 journal papers or book chapters. And many people got their PhD degrees for working on dough, whether in English or in Chinese. But then the majority of work have been done in Chinese language. And I think that there is even some kind of gap between people working on Chinese from the English literature and people working on Chinese because the data is rich and the native speakers' intuition is important in making judgments to the extent that journal papers published in English tend not to quote a lot of Chinese literature. Giving people probably the false impression is that the authors thought up these ideas by themselves. I think that this is an unwanted outcome, as far as I can see. So I will introduce the background and part of the earlier work I did. And then if we have time, and then we talk about some recent papers on dough by some of the other colleagues and peers. I think the study of Chinese syntax is complicated in its own sense. One is that Chinese, as is well known, Chinese lacks a rich morphology. So morphological and inflectional features are almost new when we study Chinese grammar or Chinese syntax. That makes it very different from the study of syntax in romance languages. On the other hand, then Chinese grammarians have to rely a lot on the distribution. So there can be a lot of permutation. And then people result to the abstract theories on the analysis of language, for example, move WH on a language that doesn't have overt WH. Or providing a case theoretic description of Chinese in which no explicit case inflections are shown at all. But people manage to work on it. And there is also the added complexity for Chinese in the sense that politically speaking, well, only allowed, for example, in China to talk about just one Chinese language, which is Mandarin. And all the other variations are considered to be dialectal variations. But in fact, what is very prominent from phonetic point of view is that many dialects are indistinguishable from one another. But the study of syntax is very much suppressed in the past until only very recent times. For example, the study of Shahini's grammar has been very much suppressed to the extent that there is only one known Shahini's grammar written by the contemporary scholars. But before him, then it was the missionaries who wrote all the interesting Shahini's grammar in French and in English 100 years ago. But then now we can try to look at these studies again. But in the past, the results have been very much ignored in the field. And then there is also the non-homogeneous ingredients in Mandarin Chinese if we're talking about the very northern ones or the southern ones. So it's not a homogeneous language we're talking about. And finally, there are all these constraints because of the characters we use. So that makes us oblivious of any possible inflection or suffixational particles that may exist if we were to transcribe the language from the initial stage. But now we tend to ignore it because we only look at the characters and the characters don't recognize these. This is especially the problem when we look at dialects. Well, those dialects will have to follow the formal conventions of the Chinese characters without really showing their individual characteristics. But so I have given some summaries of the idiosyncrasies of Mandarin Chinese in the first page. But I don't have time to talk about them in detail. And today I talk about a special problem which is related to the adverb of scope or adverb of quantification. In Chinese there is a group of about a dozen such adverbs of scope that command our attention. And each of them is special in the sense that its sense can be polysemous in the sense that we don't just have one single unique meaning for each particle. But then for these adverbs of quantification, the context usually decides which interpretation we can obtain from it. But then on the other hand, these senses are also very logical in the sense that they are probably the nearest to what we want to characterize in terms of classical first-order logic or in some modern variations like formal semantic representations. And they are also rather different from the encoded meaning of related English adverbs. So for example, in English there is every or and then even. And then not many people would like to associate even with or. But in Chinese, then they happen to be of one encoded word or particle. Well, in general, this is not too idiosyncratic because if we look at many European languages, then we do see some similarities. And that has been worked on by some people that I'll mention later. So the problem I will talk about today is to use it in a rhetorical way, how to get even in Chinese. I talk about the data first because it will require us to get ourselves familiar, right, with the data. So the first is how Chinese expresses convocation, like if we use the first-order logic, then we know that there are ways to express universal convocation and its existential convocation. But what we care about here is mainly the universal convocation, which is on page 1, the last line. So if you want to say something like, every boy sleeps, right, we use the logical formula as OX, it was a missing X here, OX, boy X, and sleep X, something like this. And then for English, then there are several ways of saying that every boy sleeps or all boys sleep. And on page 2, there is the existential variation that doesn't have to concern us here. But page example 3 gives us an example in Chinese, which is special. It is different from English in the sense that Chinese has a rich, classified system, right? So the expression of universal quantification, like every, can be reached just by the repetition of classifiers. And that, in fact, was the older, the more colloquial, more oral way of expressing the idea. And that was also recorded in earlier works of Yuan Ren Chao in his paper notes on Chinese language and logic. So it can be something like, nǎng hǎo ge ge du sui le jiao, right? So we have the gloss here, boy and ge ge, classified. The fact that I repeat the same classified means universal quantification for Chinese. But that is one possibility. And note here that in example 3, we already have this use of do, d, o, w, with the first tone. So it's do here. So it seems that the mere repetition of classifiers is a sufficient condition. But then, at the same time, we also need do to be present. So it sounds like boy, boy, or sleep, right? Boy, boy, or sleep. So it looks a little bit redundant, right? Like you have boy, boy to talk about universal quantification but why you have all at the same time. And that is a question that people talk about later in their studies. But then there is also the more common expression of universal quantification in example 4. Mei ge nǎng hǎo do sui le jiao. That's every classifier and boy and the all sleep. And this, I believe, is a later kind of expression, partially Westernized expression. But then in modern Chinese, this becomes more commonly seen, especially in written language. And it is this kind of sentence that commands our attention when we deal with do. So do is strange in the sense that first, it can be translated as all, right? And it expresses universal quantification. But it seems that on the other hand, we also have this each classifier and plus now construction, right? So it seems that universal quantification is achieved by these two means at the same time, right? If we take away this do, then the sentence is not considered to be well-formed. So if we have every boy sleep, it's not enough. You need this do support. So you have to say in Chinese, every boy all sleep otherwise and people won't accept it. There are some exceptions, but we don't have time to talk about it, right? But this is the canonical form. And so compositionally, this presents a puzzle, right? Why do we have these two bits and pieces? What is the compositional contribution of each? I think that people working on the problems do don't come up with a satisfactory answer. So here, many questions are probably still open, right? And because people don't have ready answers, then a lot of people have to come up with some ad hoc stipulations that I think is the state of the art. We try to avoid these arbitrary stipulations, but then people don't really have commonly agreed solution that is satisfactory. So what is the contribution of this each, right? If it were a universally quantified structure, then why do we have dough, right? But do we have both of them contributing to quantification? In that sense, then it will be at odds with the stipulation of logic. Because in logic, then you tend to think that only one unit will serve the function of universal quantification. At least, we don't have a compositionally sufficient analysis for each of them. And then there is the 2.2 is a question that I started discussing about 20 years ago first, is that we want to know what is the quantificational target of this dough or all, right? So if we take this dough as something like a floating quantifier, then what does it quantify? So what is this? We call dough traditionally as a quantifier of scope or adverb of scope. So what is in its scope? What does it work on? And we opened up this discussion topic about 20 years ago, arguing that its scope, its quantificational scope, is always to its left. And that gave rise to a lot of follow-up discussions. But why only to the left? Well, it's because when we studied the data, it seems that Chinese simply works like this. And it's not too surprising because if we look at the NP structure in Chinese, then we know that the NP structure is strictly head final. And it's just typologically works like this. So we argue that the target of dough is always or only to the left. So we look at some examples. For example, in 6, Tzu Shi Su, this book, he and dough read, and then the aspect optional suffix. So it means that he has read all these books. So translation doesn't reflect the use of this dough here. So you have to say he or read these books. And this all is related to these books in the topic position. So it has to be plural, generally, for dough to be related to the noun phrase. So it's to the left. And 7 is an example where you have a NP between the subject and the predicate. This is now taken to be grammatically the grammatical focus construction, what appears between the subject and the predicate.  So something like Zhang, these two courses has taken. But in the universally quantified construction, you have to say Zhang, these two courses all has taken or all has taken. There is no special word for both. And people may also wonder whether this dough here works as a universal quantifier or not, because it is simply a matter of plural NP. If it's not universal, at least, it works like an exhaustive quantifier. So again, the bolded unit is to the left of dough. And number eight is the typical Ba construction. So Ba used to be a content verb, that is a verb with a concept in classical Chinese meaning But then the meaning of Ba was cementically bleached over the change of time and the change of language. So now it is used to add a noun phrase before the predicate. Some people would call it a causative construction. But the causative construction, if taken in this way, has a lot of non-causative meaning. So some people would also call it the dispensative or dispensary construction. But from the recent talk we organized last week, we also learned that some other people would argue that this Ba construction simply functions as promoting one post-verbal NP to the front, because there seems to be a very general vague and again, inexplicable phenomenon for Chinese is that the language hardly tolerates two linguistic elements after the verb, two visible linguistic elements, whether they are arguments or adjuncts. It simply doesn't like it. There are some exceptions. But then the further north you go in China, the fewer cases you will find for sentences containing two linguistic units after the verb. And this is considered by many people as a strange case. But this is also the special typology for Chinese. And some people even put it in the form of an X-bar convention. So if you are talking about this X-bar convention we have to be careful, because other people would mistake you to be talking about X-bar syntax in general. But it's two very different things. This is descriptive case. So number eight is a sentence in which we have this special Ba construction. So it's like Zhang and this book but headed by this Ba construction. It's like proposing or attracting an MP or some other element away from the post-verbal slot. So it's like this Zhang, Ba, every book. And this Dou read once. Dou read once. So it's like he gave a reading to every book. So again we see that Dou quantifies to its left not to its right. And then nine and 10 are like this as well. But in nine we have the adverbial every day. Time adverbial every day. So it's not in an argument position. But it's also for Dou to quantify over. And then 10. So he at the beach and Dou take photos. So that means that he took photos at every point in the beach. Then the controversy was on some apparent counter examples. And that was what many papers discussed on, especially works in the 1980s. So I have included a very detailed bibliography, thematic bibliography containing about one-third of the papers and works on this Dou. But there are many others. But these are the ones that I think have attracted more attention. So mainly in the bibliography, the works that I cited in the 1980s, they talked a lot about these descriptive counter examples. And 11 has now become a very famous example almost every paper tried to deal with. So it's like Xiaoli can probably be translated into English as something like Johnny. Johnny and all or Dou buy a tweet clothes. So for this construction, then it seems that Dou's quantifiable target will have to be to its right. Because that's what the linear order tells us. But the problem is that when we do a meaning analysis, we find that the sentence doesn't mean that he bought all the tweet clothes. What is meant is only that of all the things he bought, they are tweet clothes. So there is this difference between the two meanings. So it seems that the target of Dou for this sentence is still not to its right but to its left. Or at least, it's not to its right. It is something in the presupposition of the sentence. It's like of all the things he bought, they are all tweet clothes. But the sentence in the context does not contain all the information. And that is typical of Chinese. And that is the special characteristics of doing Chinese syntax is that all the time you want to talk about the syntax-paramethics interactions, because there is no morphology. So morphologically, you can't find evidence. So you change the context, and you change the distribution and see what evidence you can get. And then for, so this is what we propose to reanalyze example 11 into something that will argue that the quantificational target of Dou is not to its right, but it's something that you don't see in the presupposition. And for example 12, that is another kind of sentence. So if we look at the explicit sentence content, it seems that those quantificational target can only be to its right.  In my talk, there are a lot of examples concerning steamed bread. I hope you're not unfamiliar with it, or I'll get some chance to make some for you in the future. But it's all about steamed bread. So it's like, well, he doesn't eat anything else. Dou eats steamed bread. So it seems that Dou has to quantify over steamed bread. But then logically speaking, it doesn't make the representation well formed if we were to translate it in this way, because you can't really say of all the steamed bread that he had it all. Because again, it carries a presupposition that he has eaten something. And then of these something, all of them are steamed bread. So again, we argued for reanalysis and through which Dou quantifies something invisible, not to its right, but to something probably hidden in the presupposition. But we can't really say it quantifies to the left either, because what is presupposed is not explicitly stated. We don't know where it is. And maybe we accommodate it at the flesh of the thought when we process this, didn't eat anything else. So with that, we accommodate the presupposition. And 13, he all wrote novels. So again, it doesn't mean that he's the only person that created all the novels in the specific domain. What it means is that of all the things he wrote, there are all novels, not anything else like poetry. So again, we argue that it is something presupposed that is quantified over by Dou, but not the rather generic noun, noun NP to the right. But this is only the type 1 cases that were raised by the more senior generation of descriptive grammarians. And there are also the type 2 cases. The type 2 cases are also tricky in the sense that we have the nouns in plural form appearing to the right of Dou, but then still it seems that we don't want to take them to be the target of the quantification force of Dou. Because take 14, for example, I or inform them. So logically speaking, it seems that it is quite OK to say that I have informed them all in Chinese. But then the tricky point is that we have to understand them as anaphoric. So it's not OK if we replace the anaphora with the real names, like in 15. 我都通知小王,小李,小趙了. You say, I all notify John, Peter, Mary. That doesn't work. Because although what follows Dou contains three nouns, we don't usually put it in this way. So I try to reanalyze the type 2 cases as involving anaphoric cases. So what Dou quantifies over is not really the anaphora, but the antecedent. And the third cases involve WH expressions I can omit because one puzzle that many people reached is that this all doesn't seem to relate to a WH word. I think it also is the case in English, because there is the awkwardness of saying that what did you all buy, meaning that this what is plural. It seems that you're just about to ask the content of the what. And so it seems to give us a processing conflict when you say, what did you all buy. But this all doesn't modify you, but is used to modify what. So I omit the detailed description of that. But if we look at example 19 on page 4, I want to know who bought what. Who bought what. That will give us usually a pairing list reading, like in A. But then it is also likely that for each person, he or she bought a list of other things. So it makes the option open if we use 19. I want to know who bought what. So here no Dou is involved. So I think that for 19, we can have both 20A and 20B as representations. But for 20, if you say, I want to know who Dou bought what. The difference, I think, is that with 20, you presuppose that everyone bought a multiple list of things. So for 20, then only B is available, but not A. But the reason we want to make this minimal contrast is that we want to show that Dou is related, again, to something presupposed, but not to this WH word to the right. And for later discussions, I didn't follow the topic with too many writings. But then if you take a look at the bibliography, then it's number 57, 58, 59. The person who works, Professor Yuan, working at Beijing University, he has written several papers in support of this argument, saying that Dou never quantifies a WH word, even if you want to take the WH word as plural. And then I summarized the point again in this number 27 paper. So these are the three cases involving the apparent rightward quantification of Dou that if we can dispense with these cases, then we can argue that Dou never quantifies to the right. So this creates a special theoretical problem or research issue within the study of Dou is that quite some papers explicitly address this on the rightward quantificational possibility of Dou or on the impossibility of Dou in doing rightward quantification. Some people would argue that because minimalist syntax never rule out this possibility, therefore this is not the case. And they went on doing minimalist syntax on it. But I think that, well, at least we have to look at the data first. And this is still a heated point, because in the end, I will cite a recent paper that talks about this topic again. But then the study of Dou went on because Dou is related to the plural. It relates to universal quantification. So some papers started to dig out a lot of properties in plural semantics. And their description of Dou proliferated because they consider many plural features to be the unique features of Dou quantification. Or again, it's because plural semantics is relatively complicated. So no thorough treatment has been given from the point of view of plural semantics. But if we link this kind of study with the study and works of Goddard Link, who wrote a series of papers and put some plural semantics, then we know that many things match each other quite nicely. For example, for 21, which is again, which is another example involving steamed bread. So he and this dispensary construction, he dispensing one steamed bread or eat. Superficially, it creates a conflict because we want to say that Dou quantifies on something plural. Then how can one steamed bread be quantified over by Dou? But then we know that it relates to the possibility of a steamed bread being quantizable because if you have water and you have part of water, it is still water. But if you have a man and part of man, it's not a man anymore. But that is the case with steamed bread. If you have part of the steamed bread, that is still steamed bread. And when you talk about the predicate of eating, then you are really using the action of eating to quantize over the steamed bread. It depends on how you segment it. But then the action of eating will make the bread pluralizable in this way. So this is an interesting example showing that plurality requirement can be compromised or coerced when we look at the actual meaning. And here we want to say that context also provides the background. But then for 22, that is also an allowable example. So he and dispensing one bottle of water and all spilled. So how do you spill a bottle of water? Well, we can imagine you spill it bit by bit. So you can use some dough or ore to quantify over it. But let's suppose that if we produce a sentence like 28, no, 23, I'm sorry, 23, then he and bar a glass vase or throw away. Then it will be harder to process if we want to think about it from the point of view of universal quantification. The sentence makes sense from another point of view, that is the even interpretation that I'll talk about later. So it's like we can't really coerce a reading for it in which we segment part of the vase bit by bit because it's made of glass unless you smash it. But it doesn't make very natural sense to use dough to quantify over it just because practically it is not possible to segment it. So all these are related to the first foremost use of dough that is related to this universal quantification, which is equivalent to the English every construction. But then there is the second use of dough, which is even. And the more complete construction is a lian dough. That means some link plus an MP and then plus a dough and plus predicate. So if we say even Zhang came to the meeting, then in Chinese it would be link Zhang even or link Zhang dough come to the meeting. So it's like Zhang is the least possible one to come. So we say link or plus Zhang, like I'm adding the least likely item and followed by dough. And this is the second use of dough. And sometimes it is descriptively marked as dough two. The third one is the meaning equivalent to already. So you say dough 12 o'clock. Why don't we dismiss the meeting? So that meaning is like it is already 12 o'clock. So many textbooks and grammar books would at least give us these three uses of dough. Sometimes it's called dough one, dough two, and dough three. Descriptively, some textbooks would even come up with even more such cases. But then from a point of view of theoretical studies, we recall the famous crisis modified Occam's razor. And that typically studies these cases and come up with a unitary treatment. That is, we give a general and abstract enough definition of dough so that we have it as the basic meaning. And then all the other specific meaning is obtained through pragmatic inference or is obtained through the coercion of context. So I proposed a unitary treatment of dough. That is gradually turning the study of dough from a descriptive point of view to a theoretical treatment. So we propose that, in fact, there is only one dough or which is like universal quantification. It's only that the quantified scope contains nouns or a set that contains members that can either be unordered or they can be ordered in a way that if we think about it from the point of view of scalar model, then we know that some members in some sets can be quantified over, but then they have their own internal scales. The earliest studies on scales is probably Gilles Le Fauconnier's pragmatic scales. But then later we have a few more Paul K. and Goldberg giving us the notion of scalar model in construction grammar. So I was borrowing these ideas to propose that we can collapse three doughs into one. It's only that the target of quantification itself can involve a set which is gradable or a set which is unordered or a set which contains temporal points or intervals. So when we have temporal points or intervals, then we use dough to express this even lateness. So that will give us the already meaning. But if we have a scalar treatment of the target of dough as a set involving graded or scalar model, then we can treat dough as an even, carrying an even sense. But otherwise, then we have the unordered set which will give us universal quantification. So at that time, it created some differences to the study of dough because before that, there were people only talking about dough one, dough two, and dough three. And that is the typical traditional Chinese grammatical treatment is that they divide into different categories. But if they still find some discrepancies, then they divide them into further smaller categories. But our proposal was to treat it as a unitary one. There is one dough proposal. And there were a lot of follow-up studies. Some people talked about only two doughs were possible. Some other people still insisted that there should be three doughs. And some agreed that there could be one dough. So over the years, people worked a lot on it and hence the titles, much about the topic. So this briefly summarizes the work in these areas. But I would like to make use of some final minutes to talk about some recent works. So if we take a look at the bibliography, because most of them are Chinese, but that is what the state is. Page eight, I want to bring your attention specifically to some works. One is number 40. That's by St. George. Those of you who know him is that he was the head of the Linguistic Institute of Chinese Academy of Social Sciences. In China, position does matter. Well, so that's why I mean people attach great importance to his work. And his work comes with a rather surprising title, isn't it, for those who know. If you talk about this, the working out of the misleading study of dough, it seems to have some implications against some others. And indeed, if you read the paper, you'll find that he accused some others of committing the fallacy of dogmatism. And that, again, is a familiar notion in China because he was politically related to Marxist practices in the past. So I was feeling a bit surprised that he wanted to use these terms. So the whole paper sounds a bit authoritarian if you want to read it. But that is still not the rudest one. The rudest one is somewhere in the bibliography by a person called Li Wen-sun, year number 31. If you are interested, you can check out our Sewers Library database and dig out the paper and give it a reading. That's also an interesting paper. But then the other one is Professor Shulie Jing's paper that's number 52. He gave a lot of interesting cases to argue that, well, the Chinese dough is different from universal convocation. Because when you think about exhaustivity and when you think about the universal convocation, when you think about plurality, when you think of subjectivity, then it seems that dough always contains some exceptions. But I want to talk about St. John's paper a little bit more because he addresses the past literature and he comes up with a different conclusion. He wanted to say that the former studies suffer from the commitment to dogma, according to him, is that the study has been made unnecessarily complex. He argued that when you talk about universal convocation, when you talk about treating this dough as a maximal cover, that is, for some of you who know, that follows Swartzschild's book called Pluralities. Or for some people who treated dough as an introduction of a scholar-mised constant or something, that would infuriate the logicians quite a lot. But if you're interested, you can take a look at this so-called scholar-misation and the dough, which is saying in the bibliography page 7, number 9, universal convocation with scholar-misation as evidence from Chinese language, which is in fact a book I forgot to put on the publisher. I think it's Matthew, but I have the book if you want to take a look. So he was talking about these unnecessary, complicated cases, and he was arguing that the target of dough should be unanimously considered as rightward quantification. So that goes squarely against the early claims made by me and others. But when you look at the paper, then you'll find that he was not really talking about the counter examples, because what he adopted was a focused, theoretic, tripartite reanalysis of sentences involving dough. So I have included some excerpts from his paper towards the end on page 5 onward. So if you reanalyze a sentence into the focus construction that will start with the operator and then with the restriction part and then with the nucleus part. But if you do it in this way, then you distort the order of the sentence completely. Of course, if he wants to talk about rightward quantification, that's fine. But that is because he has carefully maneuvered the structure to suit his focused, theoretic reanalysis. So when he's talking about rightward and not rightward, it's no longer in the same sense as when we talked about it. And so that's the major thrust of his paper. And there have been people who are preparing papers against him, but then I have been reviewing some papers because they haven't been out. I'll have to leave it to another occasion to talk about them. So basically, I think the study of dough will still go on because people treat it in different ways. One recent PhD viva that I attended was recorded on page 9. It's number 68, as by now, she has already acquired her degree. So it's Dr. Zhang Ying, who wrote a dissertation on a typological approach to multifunctional adverbs in Chinese, which includes dough. But it's a dissertation done in Hong Kong University of Science and Technology using the rather new theory called semantic map theory that has been practiced by some typologists. So if you're interested, you can also ask me for that dissertation, which is extremely long because she wanted to do everything. Well, that's it then. Thank you very much. For his new analysis on this very mysterious field. So if you have any questions, it's a good time to ask. Or any comments, they're welcome. Oh, yes, Hannah. Yes, thank you very much for a very rich talk. In terms of data, it's outside of my area of familiarity. So I have lots of questions, actually. You mentioned early on a few times about the tension between the more oral and colloquial and then the more written, maybe formal style. And I wonder if you could say something about that and how it relates particularly to this. Are you seeing patterns with one tendency and written forms and other oral forms? Oh, yes. I think that the Mandarin Chinese has the way it is portrayed in grammar books appears as if it is homogeneous. And it is unanimously agreed by native speakers. But in fact, native speakers vary so much. For example, there is always the tension between people coming from Taiwan but working on syntax and people working in mainland China because they have been very sharp, harsh criticism on the data. And in the end, then those people from Taiwan will have to say that I have been working on a particular variant, which is called Taiwanese Mandarin or something. But then even in China, then there is also the tension between the more southern Mandarin, which is very much influenced by Cantonese, Wu dialects. Well, of course, if it's influenced by Cantonese, then it's not really considered to be authentic Mandarin. But if someone like me who is very much influenced by Shanghainese and others, then people can't really tell if I don't tell them that. But then the further north you go, the more special the structures would become. So I think that there can be a lot of disagreements on what you can say with those. Some people will put it in this way and other people won't agree. So a lot of papers listed here, they argue a lot about the data. Yes, please. I always felt that the Chinese was grammatically right, isn't it? Mm-hmm. Yes. Yes. Everything else was different, but the question was particularly easy. Yes. And so is this sort of Western influence the way or this analyzing it so meticulously? Yeah. Or are it the Chinese, or if you were in China, also analyze Mandarin so thoroughly historically? Well, yes. I think the, well, we say that modern linguistics came from the West. So before the first half of last century, then in China there was probably only, very safe to say, there was only something like philology. And the philology was studied to serve the need of reading classics. So, but then so was modern science, right? And Chinese, in the past, in China, there were only technicians and not real engineers and scientists. And technicians were not given very high prestigious places. But of course, one possibility is that the first emperor beheaded all those people who sought metaphorically, not metaphysically, right? And he killed about 400, 300 people. But then Chinese grammar was also uninteresting in one sense, is that it doesn't have rich morphology. So what I think a lot of conferences or classes when they look at romance languages, it's a lot of, well, for example, Irina's projects here, right? It concerns a lot of morphological variations that I think that they simply don't exist in Chinese. So that shows the variety of linguistics. But on the other hand, in China, people are many. For all the tea in China and for all the people in China. So whatever we study here, for each rather minor topic, then there are about half a hundred people working on it. So always there are quite a lot of people. And now, because there are so many universities, so many PhD programs, so these topics are no longer considered to be, well, too restricted for these people. And so what I'm talking about here, most of these topics are very familiar with by people over there in China. And with the added fact, there's many people studied here, and then they're now working there. So they're teaching about something from South West or something. And they said, usually for those people, they left from China by all quiet and meek. And after they returned from source, for example, studying government phonology, they became very aggressive. Yes, yes. Thanks. I think around, for example, 11, you have a kind of paraphrase of it, like all the things you bought. There is three clothes. And then you also talked about this, though, like two, which was the least likely. Yes, the even reading. Even reading. And I was thinking that both of those really draw heavily on real world knowledge and expectations. So you need to know what is expected in order to be at least likely. So yeah, it's interesting that kind of, yeah. So it's like you can always use, though, for two senses. One is the even reading. One is the universally quantified reading. You can say, I, though, ate crabs or something. So it's like, even I ate crabs. But the other possibility is that crabs, I don't eat. That means I ate all the crabs. So in some rather neutral contexts, you have to show through intonation that one reading is preferred. And again, this is especially bad when we write it down as a sentence on paper. So you may disagree with one reading, but the other reading is also still available. But even I reading, does that mean that there were a group of people and they ate crabs and even I ate crabs as well? Yes, that means that I am probably the most likely one to be a vegetarian, like Ruth. But even I eat crabs. So again, this is kind of less likely unexpected, almost counter-exploitation. Yes, yes. That's why we build up a pragmatic scale on top of that. But that's the way of how you explain it. As far as I know, first there was this paper by Foucault Nie in 1975 on pragmatic scales. So he was talking about when you talk about even, you can say I even bought an Apple notebook. But then the hero can quickly build up a pragmatic scale. So what you're talking about is that an Apple Mac is probably the least kind of thing you expect to buy. Maybe you arrange it on the scale of pricing, which is more least likely is what I can least afford. But on other occasions, you can build up a pragmatic scale on other things. You can say, oh, he even ate crabs. So that is built up not probably on the basis of pricing, but on the basis of personal preference. So it is a kind of pragmatic scale because there is no necessary logical scale to back it. And everyone's preference can be different. But the reason you say even, then it gives the hero the reason to build it up, to accommodate the pragmatic scale, even if you don't mention it explicitly. And then the later kind of paper that I knew of was by Paul K. because he wrote a paper called Even. And that was just one of the series of works and dissertations. One recent one, dissertation is simply called Getting Even, as in English. And so in Paul K's paper, he introduced the notion of a scalar model in which he was considering all the different cases. So for Fogonier's case of pragmatic scale, it's two-dimensional. But Paul K. was thinking of cases which can be three-dimensional or four-dimensional. Although I find it hard to understand the four-dimensional scalar model. But the three-dimensional one, he gave very good illustrations of them. And then it quickly lead to pragmatic studies on scalar meaning. And that, generally speaking, you can still say it is related to scalar model. But sometimes I do find some papers or dissertations when they talk about scalar implicature. In fact, they're talking about scalar model. We're not really talking about scalar implicature because that can be, should be more rigorously specified with what they call home scales or others. Thanks. And then we will, if you have any funny issues or you wish to catch up with your friends or colleagues, please join us with I.O.E. Bar, you know. Thank you. So thank you all for your participation. Thank you for coming. Thank you. Thanks.


Now over to our last deep dive on IPFS operator, Corey. Hello, my name is Corey. I'm with the Filecoin Infrastructure team and this is about the IPFS operator. The IPFS operator is a Kubernetes operator that is designed to help people run an IPFS cluster or a set of IPFS nodes in the Kubernetes environment. The key feature of it is that it is a turnkey IPFS cluster you can see the GIF working over there on the right. That GIF is actually going through the entire process that is required to set up in a full-fledged IPFS cluster. You could see that it's quite simple, boils down to basically one command. The goal of this project is to spread the adoption of Web3 and enable it to be more easily run in high production environments, particularly those that are found users who might run Kubernetes. What we would like to do is we would like to, for Kubernetes operators, if they have a storage need, if they are searching for what storage projects or what storage product that they want to use, IPFS should be right there next to, you know, Ceph and the rest, when they go through their catalog and they wonder which product to make or which product to select, IPFS should be there right in their face and it should work for them and they should have a good time with it. This project is being developed in partnership with Red Hat, so I've included our GitHub link right there. We have moved recently, the code is now in the IPFS cluster or it is the same code and the same project. This is just a zoomed in version, just to really hammer home how easy it is to set up. You can see what we've got here. It's a very simple configuration file that you can set up. You can just use your standard Kubernetes utilities that operators of this type will be very comfortable with and apply it to your cluster. A brief overview of how this actually works. This is actually two Kubernetes controllers built into one binary. One of them is the IPFS cluster operator itself and the other one is a controller for LibP2P. This is an optional component that you can add in in case you would like to use whole punching services that are not simply the public whole punching services. But what I want to stress is that this offers a complete package for everything that you might need to run your IPFS cluster in Kubernetes, including all the things listed here, configuration, cluster following, all this comes straight out of the box. How do I get this thing?  It will soon, although not yet, it will be featured on the operator hub, which is one of the things that you, particularly users of OpenShift will be familiar. This is a catalog of other Kubernetes clusters or excuse me, other Kubernetes operators. It will sit in this catalog right alongside other things like SAP or Rokr, something like that. Additionally, this is for internal use cases mostly at the moment. If you happen to be running on our weave clusters, IPFS is available as an option. You can get a cluster built by us to that will have the operator pre-installed. These are some features that we are soon to land. These are not entirely functioning if you were to run the code right now, but there are PRs available for it. What we would like to do is have better support for external facing services. Wouldn't it be great if you could take advantage of this one-click installation facility and then use this to operate your own IPFS gateway? I think so, and we can make that happen. We have some changes that are coming down the pike that will be landing before we get to the IPFS camp. Where can I learn more? I've put some screenshots here. Documentation can be seen in a number of places now. I've listed these in the comments of this slide if anybody is wondering, but you can always reach us at the IPFS operator GitHub page. Also, there are documents, documentations that can be seen on Read the Docs. We are featured on Red Hat Next project, which I have a screenshot there. There have been a couple of talks that have discussed this project. One of them was by myself at the IPFS thing that happened in Iceland. And also my colleague Oleg from Red Hat has done a talk at DevConf 2022. Adding to this list, Oleg and I will be at the IPFS camp in Lisbon. So we will be doing a talk there as well. So we will see you there.


Rafael says needs help I have a personal and commercial account in teams in my personal account I can see calendar and also create meetings, but in my commercial account. I can't see calendar and not create meetings What do I have to do? What what do I do have? awkward What do I have to do my commercial teams account? I think there's a conflict between my personal and commercial account Okay, this this goes back to you know, how did you create it? So there's two scenarios that I see here the one I've experienced personally where I created a personal account using a business email at the time and then later on the company subscribed to Office 365 and then now I you there's two different Basically servers that manage those so you've got the Microsoft personal accounts and then you've got the M365 accounts and two separate things you can have the same email and either one and They they don't conflict with each other So if he's if that's the case and he's logging in and it's looking at the personal account in his commercial Tenant then it's it's gonna say he doesn't have the rights He doesn't have the license in that tenant to be able to to have the calendar because we Experience that now as guests and other people's tenants like I don't have the calendar option of teams and a lot of the places Where I log in and community events and things because I don't have a license in their tenant I'm a guest so there's no effort to store those. I'm wondering A are the accounts, you know, watch how you're logging in because I think if you're logging you with the personal account And the commercial tenant you're not going to say the calendar Yeah I mean the changes going on between teams personal with the that side dropping off and it all kind of merging into You know the sort of the new version of teams and it's all coming together you might now and see Some of that dropping off in terms of tenant switching, you know, it might actually help There's there were some glitches back when That might now be This issue Like when I when I go to log in and I use my collab talk email I get to two boxes pop up one is is this my work related ID by Vince one or is it the Personal and so both the same email to your point sure Yes, I've got that to add logins on both of them with the same email But now you know into two separate areas so always make sure for the work one and jump in that way now if Use different passwords for him to use the same password then you get yourself confused as to which one you're logging in Yeah, yeah, try signing out of one You know the personal one tries to do a full sign out and just say signed in on the personal See if that ones then do a full sign out of the personal and like make sure you can let you signed out signed in with the Commercial one if you're signing in with the commercial one and you're still not seeing in the calendar Then there's definitely you've got some confusion cross over there Yeah, what I was going to suggest to that is yeah, I mean one exactly that sign out completely You can also try logging in via the browser incognito And log in that way and log in the separately see if it's still that behaviors there because There are other registry issues like I'm having with with one account Where it works? Fine on my laptop when I log in remotely through another PC for my other workstation but on my primary it has a difficulty there because of Registry cleanup that needs to happen where it just won't recognize those and then the third thing is I think Sherry you also alluded to that is you know for that profile for the commercial license. Is that yours or through your company? Is to make sure that the permissions are set correctly because there are this has also happened where an admin and went in and sometimes erroneously and Change the licensing for the user and took away some rights So I was not using the product or the skew that I thought I was using and had to have the license reinstated to correct that so I could get access to things so Again since we can't you know Raphael since we can't ask other follow-up questions to should try and root out the problem That's where I would start log out. This is the Effectively the have you tried turning it off and on again solution? Which is such a real thing in the Microsoft world? To log in through a separate browser or three go and check with permissions I mean the fourth thing I would say Unfortunately would be if you're still experiencing the issues Contact support have them go in and look and see what's going on with your profile because it could be again something that is Ultimately a registry issue that needs to be resolved through help desk And one of the things that I found One additional place they can check is the credential manager that's on the machine that they're on because when you cash You're you and they said you want to save your logging credentials There's web credentials, and there's Windows credentials. I put a link in the spreadsheet for that So you can see what those look like, but that was I had to go clear all of those out and then Log back in again, and then that seemed to fix my issue at 1.2 So when you save your credentials on your machine Each machine is different. You may have to go in and do that little cleanup, too


Thin walled beam profiles are very common and important in aerospace engineering, thus it's important to be able to evaluate and understand the shear stress distribution in a thin walled beam section. In order to examine this, however, we're going to look at it by looking at a limitation of our shear formula we already developed. And we're going to do that by looking at sort of a common thin walled profile, an eye beam, however we're going to make the flanges very thick. So the shear formula would definitely apply in this thick flange as well as in this web. If I section away a portion of the flange and a portion of the web and applied our shear formula, we would get that there has to be a uniform shear stress acting in the flange and a uniform shear stress over the small area of the web. Now the problem occurs is when these two faces get close together as we approach this sudden transition. At that sudden transition this uniform stress distribution here has to change into this more compact higher magnitude acting over a smaller area. And we know that's not going to happen in some sort of sudden jump. It's not going to just suddenly change from one to the other. Really stresses tend to flow through a structure and that boundary condition will influence the stress distribution and the stresses will begin to flow into that web. This kind of disruption of the stress field that we predict with our equations that assume sort of a well-distributed type of field. We examine this when we introduce the concept of Saint-Finance principle where certain boundary conditions and things can interfere with that sort of well-distributed stress. Well that sudden transition is precisely one of those things. So strictly speaking we should be comfortable with the fact that the shear formula and the stress that it predicts here and here will not be very representative of exactly what happens at that transition. So what if this profile was thin walled? We actually were looking at a similar profile but it had a very thick flange on it. Well if you recall what a thin wall means effectively if we call something thin walled we say that the distribution through the thickness is negligible. We looked at this in torsion where we took the shear stress distribution and made it uniform rather than a linear distribution because we said you could neglect any variation over that very small distance. So that's it precisely what we would do for a shear and a beam however something interesting comes up. If we look at the top surface of our flange it is a free surface and the shear stress has to be zero. If we look at the bottom surface of our flange except in the region where it's connected to the web but just over here then our shear stress is also zero in the vertical direction. That means using this thin walled approximation that the overall shear stress distribution through the thickness of the flange is zero. Now that seems a little bit counterintuitive but it's a result of that assumption that we're making. So all that is actually remaining is that horizontal component that we showed in the previous slide. Shear has to redistribute in the horizontal direction. So what we get is a shear flow that increases from the outer edges, moves its way into the web, flows down through the web and then out again through the bottom flange. And this is the shear flow which we call Q which is just simply thickness times our shear stress. Now if you recall from last video we actually developed an equation for shear and we even rearranged it in terms of shear flow and said that the shear flow is VQ over I. So is it really that simple that we can apply the same formula from the previous videos? Well let's take a closer look at our beam and we'll cut away a section of that flange and we can recall that there will be a bending stress distribution and it'll actually be sigma here. It's a result of M and sigma plus d sigma which is a resultant of the internal moment plus differential moment. Now as a result of the higher magnitude of the stress here we will see that an internal shear flow Q has to develop along this face and that will balance the equilibrium of these two resultants. And because of the principle of complementary shear we will have a complementary shear flow acting in the cross section of our beam as shown here. So yes it really is that simple. We can calculate the shear flow Q with the same formula VQ over I where Q is still equal to Y bar prime times A prime where in this case our neutral axis would be here. This would be A prime and the centroid of this would define Y bar prime. So you have to look at it from a slightly different perspective because now we're doing sections in the vertical direction of the flange here and then when we get here we start doing sections horizontally but the same formula the same mechanics the same principles do apply.


Hi everyone, Joe for jaspyscasebrakes.com. That's me coming at you with that 2019 Panini Mosaic basketball team all edition That's the Asia edition. It's a big e-commerce site in Asia there and a three box three boxes Random team break number two. I gotta get an extra box. I only got two An all-card ship So let's triple your names up one two and three And all the names are right here. Let's roll it. Let's randomize it Five and a six eleven times for names and teams one two three four Five and eleven the final time. We got Gretchen down to Roy Are the Suns playing the magic tonight? We were just talking about what a Aaron Gordon for the magic of that has asked out And I was talking to Joe P. You know, maybe Cameron Johnson Aaron Gordon could be an interesting little upgrade there for the Suns We're playing well five and a six eleven times for the teams one two three Four and eleven then final time We've got Sacramento Kings down to the Celtics It could be an interesting move Gretchen with the Kings Roy with the pistons Nick with the heat and the magic Gretchen with the Bucks Jonathan with the Gris and the Wizards Tana with the T wolves Gretchen with the Hornets Matthew with the Suns and the Hawks Allen with the jazz Tana with the cows in the thunder Allen with the nuggets Gretchen He got my Lakers enough with the Nets Nick with the Rockets Warriors and Nick some Matthew with the Mavs Raptors for Jonathan Roy with the trailblazers Enoch with the Sixers Gretchen with the Bulls Allen with the Spurs Nick with the Pacers enough with the Clippers Gretchen with the Pelicans Roy with the Celtics And Jonathan had the Gris up there as well. So let's put all this on one page and let's sort by column B Let's box these up I'm gonna pause the video. Yes trade deadline is tomorrow. I think 12 p.m. LA time 3 Eastern something like that early But yeah, it's tomorrow kind of crazy We'll have a trade window right now for these teams on that screen right there when we come back We'll see if there's any trades and then the break stick around All right. Welcome back folks. There were no deals done That's all good here in three box random team break number two another three boxes posted in the store So go check that out big. Thanks everyone for getting into the action Remember one spot gets you three teams. That's not a bad deal This is the what we're looking for in the team all additions the red wave and the gold waves especially the gold That's the more shorter. Well, these are pretty common. That's the short printed one Otherwise checklist is pretty much the same as mosaic basketball Think fewer fewer autographs Inside the team all additions. All right. Good luck We have yeah all card all card shim this just want to confirm that all card shit is absolutely incredible Right Roy Mack where I put one in the pool. I do see some gold in there Maybe one or two gold cards and all cards ship We want to see rookie red waves rookie gold waves There's our first gold. It's gonna be Chris Paul Thunder edition here going to Tana Joe, thank you guys for the fast shipping. Yeah, no worries our sorting and shipping team Work around the clock Making sure your hits get to you as soon as possible Thanks, Jens. It's Karis Leverg gold for the Nets That'll be for Enoch. There's Andre Drummond. He might be in the buyout market There's LeBron James get well soon And there's Yanis MVP's red wave for Gretchen and the bucks I think she also has the Laker. Yeah, there you go Gretchen nice Norman Powell RJ Barrett having a good season Nick's Nick's might be in the playoffs ladies and gentlemen. That would be hashtag good for the hobby That's Kai Bowen rookie red wave Jonathan Isaac and Tyler hero for Miami Nick with the Tyler hero Kai Bowman for Nick K as well and The Golden State Warriors. No, yeah, no, uh, no liberal. We saw LeBron. No Zion and Moran Maybe this next box here. Oh, we've certainly seen better boxes And that first one They're not all gonna be filled with gold wave zions Unfortunately box to PJ Tucker Jared Culver another LeBron James nice and a nice PJ Washington NBA debut red wave That's for Charlotte That's gonna go to Gretchen and the LeBron another one of those goes to Gretchen and so does THT without LeBron No ad You see THT prices go through the roof Now Nicola Melly rookie red wave NBA debut RJ Barrett Kristaps Porzingis Deandre Aiden red wave. He's having a good year Kevin Durant There's Aaron Gordon. We're talking about him earlier possible trade candidate means asking out of Orlando and Arrajan Rondo gold wave for the Lakers Gretchen Well, then they'll go right back down when LeBron James and AD come back right down where? Seiku Dumboya gold wave Nice Roy with the pistons Go right down to the to the one seed. There's Darius Baseline another rookie for the Thunder Tana and Mufundu Kevin Gehle and the third and final team all box Good luck. Oh, oh, you're talking about THT prices. Yeah THT prices will go up and then they'll go right back down. Yeah when LeBron James and Anthony Davis come back But for now those THTs Could be hot He could he could get like a 12-point triple double or something like that Then and then get ready to sell that card right away Yeah, I think he's good too. I think he could be He's he could be a great six seventh man right now still raw But the next few weeks are gonna be kind of interesting because he's gonna get a lot of the Lakers players Are gonna get a lot of minutes I mean, maybe we'll even see more more costus onto the compo. Whoo Baby honest on the Lakers So it'll be it'll be like watching a summer league team No, Dennis Schroeder is pretty good Schroeder is gonna look pretty good See if I go to the Lakers depth chart really quick Yeah, a lot of minutes for Dennis Schroeder a lot of minutes for Alex Caruso A lot of minutes for Talon Horton Tucker Kuzma Montrez Marquise Morris All right, but I want to see more costus onto the compo out there. Yeah, little baby. Honest that could that could be great for the Lakers What if you turn to him? There's Kyle Guy rookie red wave and a Ben Simmons gold wave nice That'll be for the Sixers Enoch with the Sixers Kyle Guy going to the Kings. That'll be for Gretchen this guy Terrence Mann rookie red wave for the Clippers that'll be for Enoch That's white Howard Red wave Kevin Durant and we got a Luca Samanich gold wave for the Spurs That's gonna go to Alan. You're trying to move Lamarcus Aldridge Does that mean more playing time for youngsters like Lucas Samanich? I'd be hashtag good for the hobby. All right, the last little group here There's a West Matthews red wave Isaiah Roby Derrick White red wave Andrew Wiggins and Grant Williams. All right, there you go ladies and gentlemen That's a three box Ram team break of 2019 2020 Panini mosaic basketball team all addition now listen kind of sucks But we didn't see any drama rants or any Zion's So maybe the next three boxes will yield some more of those top tier rookies I'm Joe for jaskiescasewrakes.com. We'll see you next time for the next one


What's going on hip and a fan welcome back to another video if you guys are new to the channel Make sure you guys hit that subscribe button turn on your post notification about leave a positive comment down below for a chance to get a Whack for a chance to get a shout out in the next video. That's right guys So as you guys can tell by title and thumbnail video today We won't be playing a little game called two truths one lot You want to break it down what that's about basically? I am going to be telling him two things that are true that I've went through and then one live something that I Didn't go through or didn't do or whatever. Alright, so how many scenarios you got in total? I have five total Okay, so now let's make this a little interesting.  Okay, and everyone you get wrong You have to get me a makeup product But an expensive one because it has to equal the price of the car part Oh, I don't think there's gonna be none that he was the price of the car part. Okay. Well, then you have to wait We'll do it. No, no, no. They had to pay for a vacation of my choice for wait. Wait because if I get too wrong I gotta pay for two vacations. Yeah, like a little weekend getaways.   What is the first scenario the first scenario is I found a baby shaped lollipop as a kid And I got in trouble for asking to eat it. That's gross I went to the doctor's once without any underwear on and I walked in on someone doing the nasty Okay So the lie is the lies that you went to the doctor without any underwear on that is the lie. No That's not the last truth. That's so what's the lie? The lie is that I walked in someone on doing the nasty like I've never walked in on someone doing a nasty before Thank God, I thought you walked in on your parents. No, I've never walked in on anybody That would have been traumatizing. I think I walked in on my mom when I was younger. That's disgusting Never ever want to go through that. Thank God. I didn't so you want to tell me why you didn't why you went to the doctor without any Other world because as a kid or like in middle school There was this girl that like never wore underwear and I thought that back in the day Penny lines were just gross and everyone kind of just hated it So I just thought I should do the same thing, but I quickly realized I shouldn't So you went to the doctor without any underwear, you know, we're not gonna get into that conversation next scenario Next one is I love taffy candy I once had a bunny rabbit and I hate romance movies Which one's the lie the lie is wait say it over again.  What's the lie the lie is the what was the first one? I love taffy. That's yeah, that's the lie right there. Yeah, you don't like taffy, right?  All right guys. We got one car park locked in added to cart Yeah, whatever you're gonna get an air freshener All right, so you know when I say car park I mean car park not car accessory. Nope. Anyway next I like black olives, but I never buy them I once froze up for an audition or I've danced on top of the table in a restaurant before That's the lie right there. You never dance on top of the table in a restaurant. Yeah, that's the lie You would never do that. I wouldn't I could pay you and you still wouldn't do that I wouldn't I would never do that But it's something in my head that I think would be really cool to do what do you think you'll do it if you're like lit Probably I probably do it if I really it's not a restaurant though. It has been like a lounge or something I wouldn't do it at a restaurant unless someone paid me big bucks to that one You give me lit enough. I'll do it at a restaurant I was like off my shirt and everything to the next one is I kiss my middle school crush at a party Excuse me. I've gotten rejected by boys. I like because I had curly hair and I had my first alcoholic drink at 14 Okay, so the lie is what was the first one again? I kissed my middle school crush at a party. That is the lie That is a lie, right?  No, I didn't 8th grade 8th grade That's middle school for me. Whatever.  No, not at all because I remember you said you had your first kiss Like way late. I had my first kiss. What was my first kiss? You were like five. When was my first kiss? I don't know. It's not about you preschool. Oh He thinks he's so cool I remember the girl's name to her name was Marilyn and she gave me a kiss because I let her be line leader And then when she did that now that I'm thinking at this age I'm like, yo, that girl's gonna be a fast girl and she grew up little daddy She already kissing boys at the age of five But yeah, I did have my first alcohol to drink at 14 and that was only because I didn't know that there was alcohol That's my cousin was like here drinking and I'm drinking it I'm like, why does it taste like that? And it had a grunit.  All right, and this is the last Scenario I almost got in trouble with the law for being a good friend I've always wanted to be on Broadway and I'm my happiest when I'm in the shower Which one's the line those are pretty random Okay, hold on you do me Say it again Okay, I almost got in trouble with the law for being a good friend. That was the lie right there I've always wanted to be on Broadway. That's the lie right there. First one was a lie The first one's a lie. The first one was a lie You got in trouble with the law for trying to be a good friend. That is a lie. Nope Then I wait time out cuz what's the second one? The second one is I've always wanted to be on Broadway That's true. That's not true. I've never wanted to be on Broadway You talk about that all the time. I love Broadway shows. I never want to be on Broadway. I want to be on TV I want to be in film. I never like No, it's not the same thing for you guys who love acting is something that out there Theater and Broadway and all that is completely different from film. You're on a stage acting No, it's not the same. How is it not the same? You're on a stage Because the acting in a TV show is completely different from acting on a Broadway show That's actually my opinion pretty miserable doing on stage theater. It's fun, but I never wanted to be on Broadway That was your last one. That was my last one. Now. Hold on guys.  Oh, this is way too bad I'm gonna need you to come up with one more scenario. I'm fine because it looks like I only got one car park Yeah, so we need to come up with more. I gotta add more stuff to my car one more scenario guys Hold on. We're gonna make one more. All right. Just a bonus one forget the car part. Just another one Fine, we're gonna make a cut right now. Hold on. All right guys, so she says she's ready So you got one more scenario for me, right? I actually have two because this is too short apparently So I just I'm gonna make it a little longer. Are you testing my gangster? Well, you're not getting any car parts from this, but I'm still getting vacations I already got one car part and with these two I'm about to add another two to the car Yeah, keep in mind. I already have like three vacations so far. Yo, you're going to the park You're going to McDonald's that's another vacation Give me the first scenario, but it's about to be a car part. Let's see.  Are you serious serious? You can't twist your hand all the way around I can You can't wait. All right, so hold on. What's the lie? The lie is that I stopped professional dancing in fifth grade when what I thought you Well, you wanted to go to India. Yeah, I've always wanted to go to India I used to think that you never told me this I used to think that the cheetah girls was in India I don't know why I know you were gonna say that I knew the cheetah girls were leading I wanted to go to India so bad because I thought it was like so cool watching I guess we can add the zoo as another vacation. It's not the zoo. We're going we're going to a lot of places We're not going anywhere. What's the next one? The next one is I used to only eat bagel bites growing up. That's true I've changed its higher before with my dad and I used to double dutch in elementary school You never changed the tire with your dad. That's true. That's true. No, that's the lie. That's the lie You never changed the tire. So I got that one right. You got that one right. All right So that one we're gonna add a front lip You said no for car parts. Then for my second car part, I won. What do you mean? You got that one wrong. Maybe a suede steering wheel. What do you guys think? I think that's good. You're not getting it. Shh In total I got two right out of seven pretty much. I think I did pretty good You failed you failed miserably I think I did a good job though because you literally know everything. So now clearly. I don't Now you All right guys so with all that being said that is the end of today's video if you guys want to see a part 2 to this with Me asking her questions. Let us know down below again smash that thumbs up button comment down below team Isaiah We're gonna get them next time. All right. I need my car parts guys and like I said, we're gonna take you to the park We're gonna take you to the zoo. I'm thinking maybe McDonald's for like a little lunch date Where else do you want to go? We can sit in the car one day. I can take you to the lake. No Okay, whatever I'm grateful. Yeah, whatever. Alright guys, so with all that being said it is time for today's post notification shout out Today's post notification shout out goes out to motherhood with aerial shout out to you Thank you for all the love and support if you guys want to post notification shout out All you guys gotta do is like comment share subscribe and take your post notification bells on so you know If I would ever be posting a video and with all that being said we'll catch you all in the next video I was supposed to say that man. I was supposed to say that