Why didn't the narcissists stick it out with you? Why didn't they try to continue to the end of the difficult or unpleasant situation with you? Why did they just leave you like you never existed? Narcissists are not team players They have an it's all about me mentality They will do whatever they think is in their best interest Regardless of how it may affect you Regardless of how many years you invested into being with them They lack empathy They cannot put themselves in your shoes Which is why it was so easy for them to regard your thoughts feelings or desires They are constantly in survival mode Doing whatever they can to survive They lack the capacity to consider anyone else but themselves and they can also be very stubborn They may think that it is in their best interest to leave you They are resistant, difficult and persistent in opinion But they can be easily persuaded by someone they don't know Which really is a recipe for disaster They were predestined to fail They were predestined to experience unpleasant consequences Because they lack empathy and what's good for someone else can sometimes be good for you too But they lack the ability to be able to see things that way They have a black and white mentality Where something is either all good or all bad Where someone is either the winner or the loser and they are doing everything they can to not lose The Narcissists didn't stick it out with you Because they're very arrogant and audacious They have an exaggerated sense of their own abilities and importance They think that they are superior to everyone They think that they are more intelligent and that they make better decisions Even if they lack the required knowledge or experience They have to think that they always know the correct path to take Because they're very insecure They can't admit when they are wrong or when they don't know something They can't be vulnerable with you But they would rather hold on to their own opinions and beliefs Because they have difficulty trusting people They think everyone is out to get them Which is why they always seek to gain control over you or the situation At any time that they feel like they're losing control That is when they are going to leave If they can no longer influence and direct your behavior over the course of events They're not going to stick around Narcissists are paranoid and hyper-vigilant They feel like they're always at risk of danger or harm So they have to be in control of everything They have to know how the situation is going to pan out And if they don't They will make some rash decisions They will act without careful consideration of the possible consequences Because they feel like they're lacking security and stability When those are two of the main qualities a narcissist looks for Or they target a source of supply The narcissist didn't stick out with you Because deep down they knew that they wouldn't be able to keep you under their control They saw you as someone who was too strong Someone who is not going to be their doormat Someone who is not going to be their emotional tampon They knew that you were not going to put up with their BS They knew that at some point you would probably get fed up and discard them And they want everything to pan out on their terms They have to be in control of everything They also knew that they didn't belong with you They cheated their way into your life There was no authenticity They weren't being real with you They mirrored you They reflected back to you what they thought you want to see While knowing that it wasn't what they were really about They know there's no future in something that started out as a game Because it was built on lies and manipulation It lacked substance It wasn't built on anything that really mattered It lacked any intrinsic value Because there wasn't any purpose to it They were just there to use you Once they've got what they wanted There's no reason for them to stick around And they know that once you figure out what they're really about You're not going to stick around Because most people would not want to be around someone who lacks empathy It's a miserable experience And anyone who values themselves Would want someone who cares about them They would want someone who is able to understand and share their experience Narcissists want convenience They want to be able to proceed with something without difficulty They want an easy and effortless way of life But they need someone who is susceptible to their manipulation to accomplish this They need someone to be their fool They need someone to be their doormat They need someone who is going to submit to them And if you are willing to be that fool They will stick around But when you start questioning and confronting them It makes life difficult for them They can't get away with things anymore So they no longer feel like they have a constant source of supply They feel like it could be liable to change at any moment They're also paranoid and hyper-vigilant They have difficulty trusting people They feel like even if they did stick it out with you You would eventually leave them You would eventually find someone else They believe that you would eventually leave them Once you realize they're not the character they portray themselves to be Because they know that they've manipulated you into seeing them in a certain way And they fear that one day you're not going to be under their spell One day you're going to wake up They're not going to remain in a situation But they feel like they might be rejected Or whether they're not being admired or validated If the narcissist doesn't feel desirable or powerful when they're around you They're not going to stick it out with you They're going to leave They don't care about you All they care about is supply They will find someone who is more susceptible to their manipulation Someone who is willing to be their doormat Someone else to be their fool They cannot remain around someone who sees through them They need someone to reflect back to them how they want to be seen Even though they know it's not real As long as it feels real That's good enough for them But if you're not willing to pretend If you're not willing to play make-believe The narcissist is not going to stick around They want the fantasy They don't want to deal with reality But that's what you are to them And once you become that They're not going to want anything to do with you They need someone to validate their false self And the illusion that they are trying to portray And as soon as you stop supporting that They will disappear They won't want to be around you anymore Because it's like you're constantly holding a mirror in front of them You're constantly showing them who they really are And nothing will make a narcissist run away faster Than them seeing a reflection of themselves Thank you for watching I hope this video wears in it with you Please like, comment, share and subscribe If you would like to donate My PayPal link is in the video description Coaching inquiries You can email me at coachingatnarksurvivor.co.uk Thank you for watching and I'll talk to you soon


Hey SJSU iSchool students. Do you feel like you're already falling behind? It's because there's so much happening at SJSU iSchool. So let's catch up with an August recap featuring the news, career blog, Siri blog, curriculum center blog, iSudent blog, Mara blog, and Fakara blog. In the news, August saw some thrilling developments. So check it out. At the ALA conference, the iSchool community poster session in June 2019 featured multiple students in the news. Tamika Beckett, a 2018 MLS grad, presented on intersecting data analytics and student engagement through active and service learning. And a current student, Rosa Conrad, presented her poster on your invited, how interactive displays can transform library spaces for student engagement. And Terry Chavont, current MLS student, presented on the planning, development, and digitization of Judy Art Studio Collection, the Penn State Library. And current student, Susan Crow, presented on technology and the law, closing the justice gap. Congrats to Tamika, Rosa, Susan, and Terry. We also have some exciting faculty news. Dr. Anthony Marine was awarded a first-generation student research grant and is part of the ALA history roundtable. Dr. Deborah Hicks was awarded a $5,000 grant for her research in debating diversity, how Twitter facilitates professional discussions. And Dr. Michael Stevens continues to excite with word that he is part of the advisory board for MLS on civic engagement. And the career blog is currently written by iSchool MLS student, Greta Snyder. That's me. Hello. And some top career reads include a June and July recap. Unexpected benefit of the interview is confidence. You have to read it to believe it. And then an excellent LAS career books roundup featuring iSchool career consultant, Kim Doherty. And always a good question to ask yourself, is your workplace toxic? Read more to find out. Siri blog is SJSU Center for Information Research and Innovation. And it's LAS research for a global audience. The blog is run by Siri coordinator, Dr. Lily Litt. A must read from August is Dr. Lisa Dalby providing an invaluable primer for information professionals on the EU GDRP, which took effect in 2018 and is designed to protect the privacy of individuals through the right to be forgotten. A very compelling topic. On the curriculum blog, it's managed by Dr. Linda Main and can be found at iSchoolBlogs.SJSU.edu slash blogs slash ICC. The link is from the main website. Don't worry. Spring 2020 courses have been announced, believe it or not. So that's a must read for current students. Also a must read for current students is the iSchool blog. It's written by iSchool MLS student, Pablo Steinman. It's a great way to stay connected with other students. Check out A Day in the Life with Keele, Magrino, written by Marta student, Kenna Walker. This is an excellent look at A Day in the Life of a student. And you also find out that Keele loves Nutella and much more. So you must read it. Miracosta Community College Library is thriving. Find out how. And the Mara blog. As I just mentioned, Kenna Walker is a Mara student and she is the current writer of the Mara blog. There were so many excellent reads in August. Some top highlights include tips for online classes, how to make it all work, Kenna outlines time management, holding yourself accountable, being realistic and planning ahead. And then also, are you interested in how Ebony and Jet and other publications might have shaped history? Read more in this very interesting article that Kenna posted, which also talks about the future process of archives. And JSTAR is now making interviews available, which is very cool. Vakara is the Virtual Center for Archives and Records Administration. It's currently managed by Mara coordinator Pat Franks and can be found at sjsuvstra.weebly.com. And the 10th annual Vakara of Conference Recap is posted in the blog this month. And the 22nd conference will be in April. So check it out if you're interested. And finally, thank you. Thank you again for watching. Look forward to the September Recap and be sure to check out all the resources available through sjsuischool.ischool.sjsu.edu in the news and events section.


One time, when I was in middle school, I was offered a spot in an exclusive club of cool kids, but I turned them down. I know better than to mess with clickbait like that. If you've ever been to this... youtube.com? You may have seen this handsome devil smiling at you on the front page. This is Derek Muller, and his PopSci Education channel Veritasium is a phenomenal success. Over a billion views. 11 million subscribers. One and a half million dollars per year just from youtube before the paid sponsors. In one of his recent videos, titled Clickbait is Unreasonably Effective, Derek shows how A.B. testing his videos, titles, and thumbnails really boosts his view counts. He also argues at length that, despite what some critics might think, this sort of deliberate optimization for views is a good thing. So long as there's nothing untruthful about the title, nothing that misleads someone into expecting one sort of content and getting something else, how can we possibly hope to separate clickbait from good, engaging writing? Isn't the whole category of clickbait just a fuzzily defined matter of personal taste? Derek's speculation about the nature and propriety of clickbait is part of a long philosophical dialogue on journalistic ethics. People have struggled for millennia to figure out what principles underpin the moral character of media like Veritasium. Artistic works intended to inform or educate the viewer. It's an important subject to wrestle with. Understanding what sorts of things separate diligent reporting from misleading gossip can help us to feel out how much stock we can put in a given piece of media and how to contextualize the goals of its author. If you pick up a copy of The Sun at your local supermarket and expect to be educated or enlightened, you're going to have about as bad a time as you'd have opening up a Wikipedia page on bounded operators expecting some light entertainment. The journalistic ethics of clickbait run up against issues those philosophers have raised about sensationalism, what Maria Grave et al. define as a form of media where production style overpowers substantive information. The term sensationalist only acquired the sort of negative vibe it currently holds around the time of yellow journalism where newspaper giants Joseph Pulitzer and William Hertz battled for dominion over the New York City news industry by using every dirty trick they could think of to sell more papers than the other guy and, hopefully, put them out of business. Big colorful pictures, hyperbolic language, overreporting on crime and scandal, overblown scare headlines and huge font. The dueling newspapers descended rapidly into a full-out assault on the senses, sensationalist. A large part of why sensationalism is seen as distasteful is because it undermines the value of a medium by trading away its unique substance for homogenous attention-grabbing style. We buy newspapers because of their distinctive character. We want to know with some amount of accuracy and emotional distance what's actually going on in the world. We don't buy newspapers because we want to hear wild speculation or thrilling storytelling that gets our blood racing the way that we might with other sensational media like action movies or spy novels. Of course, people will still consume sensationalist news. It's not like yellow journalism was bad for business, but it did erode what the public expected from newspapers, diligently training them that big scary attention-grabbing headlines might be something they'll care about, or maybe not. As the New York world and the New York Journal went to greater and greater lengths to outdo each other in that arena, they lost their distinctive desirable news-like character and became just like any other media that exists solely to appeal to the most reliable sources of human attention- bright colors, fear, shock, and awe. The conditions that led to yellow journalism are more or less the same conditions that produce clickbait, brutal competition for a limited reserve of audience attention. YouTube's mysterious algorithm is opaque. Nobody knows exactly how it works, but we can be sure it's ruthlessly optimizing to capture as much of your moment-to-moment awareness as it possibly can in order to show you the greatest number of ads. In that environment, every YouTuber is pitted against every other YouTuber in a massive Pulitzer-Hertz cage match, where failing to appease that algorithm can mean disaster. If your description and thumbnail are just slightly less eye-catching than the ones on either side of them, that could be the difference between a six-figure income and being suddenly relegated to the darkest corner of the YouTube let, forgotten forever. As with yellow journalism, there's a long-term cost to this single-minded pursuit of attention. As the nonsensational creators are eliminated by the algorithm and replaced by shocked face, red arrow, one weird trick attention hogs, the landscape loses its varied and distinctive character and becomes saturated with pure spectacle, and it gets harder and harder to find a reliable signal that any given video is interesting or important in the noise. Take this video, for example. What's it about, do you think? It's actually a history of analog computation devices, a fascinating subject that I'm really interested in. Unfortunately, there's no real way to determine that's what it's about from either the title or the thumbnail. I might be able to recognize this particular mechanism or guess that it might be an analog computer, but other than that, I'm sort of left in the dark. The most powerful computers you've never heard of could be referring to just about anything. Is it talking about, like, esoteric supercomputers, optical computers? Who can say? I'm sure if the video was titled something boring, like a brief history of analog computation, it wouldn't have 5.6 million views, but it would convey what the video is actually about, and I, a person interested in analog computation, might have watched it, but it's not about accurately representing the content. It's about commanding attention. This might sound like a critique of creators who decide to lean into clickbaity titles. We tend to focus a lot on individual choices, because they work on a scale that's very easy to understand. You make choices every day. But Yellow Journalism newspapers didn't spiral into sensationalism, because all the writers and editors and artists and everyone involved was actively deciding to erode the unique character of news in favor of homogenous spectacle. It happened because of the persistent, systemic pressure of competition for attention. The YouTube algorithm wouldn't care one bit if a clickbaity creator suddenly had a change of heart and started tiling videos to accurately convey their content, rather than optimizing to get them on the front page. They would simply be replaced with another, more attention-friendly creator, and the process would continue with them instead. A creator's decision, one way or the other, wouldn't affect the direction of that ratchet. It only changes which individuals benefit. Printed news was rescued from the Yellow Journalism race to the bottom by a number of factors. A shift in the legal conception of privacy and libel. A well-funded competitor who could offer something more credible and less sensational for those who wanted to escape the battle of spectacle. The united backlash from journalists against the devaluation of their profession. All these elements changed the slope of the landscape away from the pure attention game and allowed newspapers to flourish based on other criteria. Instead of doubling down on sensationalism as a desirable direction for YouTube, maybe we should look for similar ways to change the incentives that force us to make concessions in substantive information in favor of style. Do sensationalism and clickbait erode the overall quality of the medium of YouTube? Please leave a comment below and let me know what you think. Thank you very much for watching. Don't forget to blah, blah, subscribe, blah, share, and don't stop talking.


Yeah Pick up ready Gabby so you see that she's guarding right and they say See how hard she's biting right now


Hey everybody, Forest here ripping 2022 tops definitive baseball hobby three box case number 43 One second before I start these hoffs. Sorry Pick your team case 43 definitive here Aaron F red Sox Dodgers Daniel W pirates Danny C angels David a Rockies Debbie Reds Harper Braves Jordan W Mets Kyle H Phillies Matthew W twins Michael D A's Rick E Cardinals Russell M Marlins Ryan M has the Ryan M. Blue Jays Nationals Steven C Rangers Thomas B. Guardians Timothy H white Sox Timothy M giants Tobias with the D backs Cubs Tigers Brewers Trey H Just want to make sure he has all three here. Yeah, Orioles Padres and Astros Tyler C with the Royals Whit H Rays Mariners in the Yankees Yep, cool Good luck everybody. Thank you for joining Last baseball break for daytime here, and then we've got some finest champions league. There's just A couple spots left for case number three, which is the last daytime break More group breaks and personal tonight guys. Personals are full for tonight We're still a little backed up due to the internet outage that took place Monday and did not allow us to break So thank you for your continued patience and we do apologize for doing our best To catch up on those And it also caused a time crunch yesterday having to push back an entire night of breaks onto an already Full nighttime schedule yesterday Thanks guys appreciate it Dude a big grease not to be sick I'm excited for some finest champions. I'm excited for all finest So we'll frame on the bottom here interest Philly's jersey card Andrew McCutcheon 10 out of 35 patch card Wow nice Padres patch red Sox legend Eric Osmer 16 of 15 Got Sam with that one They like don't like him there and I was like oh his stats was too terrible. He's about like 270 this year I think it was I think it was more so like the previous years. Yeah Jersey auto braves chipper jones 13 of 25 It has just been nice to see an actual first base on the red Sox. 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That's pretty insane huge piazza there I love these things man, but yeah, that's what I say this every time, but that's what the ultra is It's hand numbered because they're all unique Like logos or whatever That's it Freddie freeman defining images auto braves braves doing a Doing well chipper and freddy Wow, what a first box gold frame Mike trout auto three out of 10 True you hear that echo That's pretty lame, huh? Nice hit for the angels there dany c but also Congrats to the Mets jordan w man. Nice hits great first box Fantastic first box What's that magic? It's insane. That's so sick Check for a junior son with the blue jays Did he? Oh, that's right. Yeah, yeah Long saying that yesterday son I got a ribbon blue jays kind of Jackie's not the best hitter, but love jack Oh, yeah Padres jersey card fernando tatsy's jr. 15 of 50 tatsy One-of-one jumbo nameplate patch cody bellinger You guys think that's the first e or the second e in bellinger. 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Oh, yeah, uh relics tatis osmer like that patch Um, it cuts in as a filly shane boss on the auto here Rest of the autos yellich Mattingly a winfield dual auto Yeah, of course, even thanks for joining me. Eloy Jimenez and jim tome. There's white sock sluggers there Yeah, carnell's paul gulch mitts. 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The presidential candidate of the opposition, all people's Congress APC, Samora Kamara, has raised an alarm over police's threat as votes from the just concluded elections are being tallied. On his Twitter page, Kamara alerted that live bullets were fired at his office inside the party's headquarters in the capital, Freetown. In a statement, the Sierra Leone police said the members of the APC had paraded through Freetown announcing to the public that they had won the election. They attracted a huge crowd of supporters outside the headquarters who started harassing passers-by according to the force. We triggered the release of tear gas canisters to disperse the crowd. So you get notified about fresh news updates.


Hello, everyone. Today, Xiangqian and I will give an introduction and deep dive for the Kubernetes data protection working group. My name is Xin Yang. I work at VMware in the cloud storage team. I'm a co-chair of the CNCF tech storage, a co-chair of the Kubernetes 6 storage. I also co-lead the data protection working group with Xiangqian. Welcome, everyone. My name is Xiangqian. I work in Google storage department. I lead the data protection working group with Xin. Next slide, please. Today, Xin and I will go through all these items in this agenda. We talk about why we are doing this working group and who are the organizations involved in this process and how building the data protection concept in Kubernetes. And then we'll talk about what exactly we want to achieve for data protection in Kubernetes and what are the existing building blocks that allows backup vendors or application owners to protect the valuable assets. And, Xin, we'll talk about missing building blocks in the community. We're looking to define or provide to further bring the availability to backup vendors or application owners to protect the workload. And, last lesson, we'll get into how can you get involved. Next slide, please. The day one operations for state workload as of today in Kubernetes is kind of well-defined. First of all, there's a very matured persistent volume operations supported either via CSI or in-tree plugins that allows state for workloads to use a persistent volume layer. On top of that, there are workload APIs which uses the persistent volume to define workloads. Some of them are deployments, some of them are state processes, etc. With a strong desire of moving to a container orchestrated architecture for state for workload, there is a deep requirement in terms of how can I protect my data should disaster happens. And unfortunately at this moment, the day two operations for protecting those workloads in Kubernetes is still insufficient. Some of the organizations or backup vendors utilizes GitOps, but GitOps can only protect the application definitions, basically the YAML files, but not necessarily the persistent volume data that is typically managed by the underlying storage system. Given all that, we build the data protection working group, try to define a solution in this scope. Next slide, please. So who are involved? There are many, many organizations involved actively in this data protection working group, and here are all the companies. If you feel I miss some of you, please feel free to reach out and we will add. Next slide, please. So what exactly is data protection in Kubernetes? First of all, let me just explain from a really high level what's the main goal. The main goal is to enable an application owner or cluster administrator or a backup, typically in a big organization, there's a backup administrator such that any valuable assets running in the Kubernetes environment can be restored to a previously preserved state. Should any disaster happen? It can be loss of cluster, loss of the storage system, etc, etc. And to define that in the Kubernetes context, it mainly involves two pieces. One is the API resources that constitute an application or compose internal application. The other piece is the persistent volume data that application relies on. This is a complicated and later problem that basically means it includes backup and restore at persistent volume level, application level, and further down to namespace and cluster level. Next slide, please. So part of our working group's charter is to define what other components needed to support all this backup and restore operations. So of course at different levels, persistent volume level, like volume snapshot and how to backup a volume, and how do you restore a volume, and application level, what are the resources needed by an application? And in some cases, application wants to be application consistent when a backup is created and mechanic them to inquires and inquires an application such that normal rights is accepted during that process also needs to be supported. And further to a bigger scope, how we do this in a namespace and cluster level. Next slide, please. Here are some common use cases we gather in that protection working group. A typical one can be I as an application owner, I once protect my application to cover common failures. For example, I rolled out a bad software, I want to roll it back with exactly the same configs I used to have as well as the data I used to have. Another example for that personnel, it's basically I want to migrate in between namespaces, in between clusters, and even across different availability zones. Another one is standing from the Eury cluster administrators point of view. They want to protect the namespaces running in their cluster as single units. So they want to provide data protection over a namespace level. And finally, another big case is I as a data protection administrator in an organization, I don't really quite know the details, what kind of workloads is running in each Kubernetes cluster, but I also want to enforce organization policies around those workloads or the aura around those clusters. So how do we provide the utilities so that backup vendors can kick in and build those solutions for them? It's also critical. Next slide, please. So we're talking about the definition. Let's try to see what a typical an application will backup. This is really a very high-level view. So we talk about two pieces, Kubernetes resources, as well as the persistent volume data. The user kicks off a backup, and the first, that the two things will happen. First thing is to gather all the resources that compose a specific application, one namespace or cluster, and ship it externally, typically, to a secondary object storage system or a secondary storage system. And the other piece is to backup the persistent volume data. Because there are many, many applications and they have different flavors of doing things. So in this process, it can well be an application-specific native data dump, for example, mySQL dump or Postgres dump. Or it can well rely on existing Kubernetes components, like control-coordinated volume snapshot process or volume backup process with the choirs and unquires hooks so that it can achieve application consistent. And after this is done, the volume data, a volume snapshot, will be copied as well as to an externally managed backup repository. In this case, the application can safely claim that their data and the config has been stored in an externally managed system that has not tied to the cluster and the application is running on. Next slide, please. Let's go to how the restore will happen. The user starts a restoration process, if it's needed, which imports a backup, including data and a config from an externally managed storage system. And this process will also take place in two pieces. One is to restore the Kubernetes resources, which consists of the application. And the other piece is to restore the PVPVC data. Of course, this will need a specific PVPVC data restoration process, and it also depends on the nature of the application. For example, if it's a native one, there are tools that the native application will provide to restore the data into the format they want it, or a Kubernetes controller managed process which just backed by restoring a PVPVC from a snapshot or a volume backup. Next slide, please. So in order to achieve all this, we need a lot of building blocks. And there are already some existing ones in the community. But for application, to figure out the application, what resources are needed to backup, there are workload APIs like Step4Set, deployment, etc., and there are also application CRDs. To restore and backup a volume, the components like warm snapshot features, which is already G8, can be used as well. The thing over here is we are still having a lot of missing building blocks. We'll cover this later by soon. Next slide, please. Now let's see how this will be used. Recall that we need to define basically a group of resources that consists of an application. And that's where the workload APIs or SIG apps application CRD can help. Typically, this is achieved by a label query or within the namespace to get all the resources that consist of the application. Next slide, please. For the volume backup piece, the warm snapshots feature plays a good role here. Because warm snapshots typically is really, really fast. And for many storage vendors, the implementation is just the internal marker of the current state. And that allows the application to freeze and unfreeze themselves in a very short period of time, like in the millisecond level. A warm snapshot feature can be used to plug it into the warm backup process. Next slide, please. But in the restoration process, very similar way, the warm snapshot can be used to rehydrate a PVC such that the PV will contain the data that when the warm snapshot was taken. Next slide, please. By saying that, we're still missing a lot. For example, warm backup. How do we do a warm backup? Because warm snapshot in many implementations is a local, still stored locally on the local storage system. How do we expose, explode the warm snapshot out? We really need a warm backup module. Like, where do we store the backups? Where do we store the config, the application API resources, and the volume data? How do we do a choirs? How do we trigger choirs against a specific application? And how do we coordinate the application snapshot or application backup process, which includes backing up the config, backing up the volume, before backing up the volume, maybe choirs and un-choirs as well. Next slide, please. So, putting things together, this is, again, a really high level view. The blue pieces are the workflow. And the green pieces are what is already there. And the yellow pieces are in progress. And orange pieces are still to be designed or discussed. So, given this whole picture, we need something that can coordinate an application's resource backup, as well as data backup. We need a place where the backup will be restored. It's called a backup repository. I put the name over there. And we need something to really backup the volumes, which is a volume backup component. The choirs and un-choirs hooks, there's an ongoing cap. She will get more details into that. It's got container notify. In the restore workflow, next slide, please. Up. Similar thing. So, the backup repository needs to play when a backup is imported. And some application restore API to coordinate or orchestrate the whole restoration process. And then, finally, the volume backup module, which can import the backup volume data into the Kubernetes cluster. With that, I will hand it over to Xin to discuss all the missing components. Thanks, Xiangqian. The first missing building block we identified is volume backup. We need this because we need to extract data to your secondary storage. We've already got a volume snapshot API, but there's no explicit definition made in the design to have snapshots stored on our different backup device separate from the primary storage. For some cloud providers, a snapshot is actually a backup that is uploaded to an object store in the cloud. However, for most other storage vendors, a snapshot is locally stored alongside the volume on the primary storage. Without our volume backup API, the alternative is for backup vendors to have two solutions. For storage systems that upload snapshots to object store automatically, a snapshot is a backup. For storage systems that only take local snapshots, use volume snapshot API to take snapshots, and they have a data mover to upload snapshots to our backup device. We are at a very early stage of discussions for this one. Let's take a look of the diagram. Volume backup is next to volume snapshot here. We put it in an orange box to indicate that it is a missing Kubernetes component. We have started discussions, but there's no concrete design yet. The next missing building block is CBT, change block tracking, and the changed file list. Without CBT and change file list, backup vendors have to do full backups all the time. This is not space efficient. It takes longer time to complete and needs more bandwidth. Another use case is snapshot-based replication where you take snapshots periodically and replicate to another site for disaster recovery purpose. So what are the alternatives? Without CBT, we can either do full backups or call each storage vendor's API individually to retrieve CBT, which is highly inefficient. We are currently working on our design for the feature. Let's take a look of this diagram. The CBT box is next to volume backup and volume snapshot, as it is used to make backups more efficiently. It is in a yellow box indicating this is a work in progress component. The third missing building block is the backup repository. Backup repository is a location or repo to store data. This can be an object store in the cloud, on-prem object location, or it could be an FS-based solution. There are two types of data to be backed up that we need at restore time. The first one is Kubernetes cluster metadata. The second is local snapshot data. We need to back them up and store them in a backup repository. Currently, there is a proposal for object store backup repository. That's the proposal for object bucket provisioning or COSI. It proposes object storage Kubernetes APIs to support orchestration of object store operations for Kubernetes workloads. Therefore, bring in object storage as the first class citizen in Kubernetes, just like file and block storage. It also introduces container object storage interface, or COSI, as a set of gRPC interfaces for object storage providers to red drivers to provision or provide access to object stores. Kubernetes COSI is already a sub-project in six storage. It has weekly design meetings. It's targeting alpha in under 23 release. Let's take a look of this diagram. COSI is in a yellow box, indicating that this is a work in progress Kubernetes component. This is a object store backup repository. It can be used to export backup and store the data. Now let's take a look of restore. COSI is used to import backup data at the restore time. The next missing building block is voting populator. Without voting populator, we can only create a pvc from another pvc or from a voting snapshot. But what if the backup data is stored in a backup repository, such as an object store? The voting populator feature allows us to provision a pvc from an external data source, such as a backup repository. In addition, it allows us to dynamically provision a pvc. Having data populated from that backup repository and honor the wait for first consumer voting binding mode, during restore to ensure that voting is placed at the right node where a pod is scheduled. There is in any voting data source of a feature gate, which was introduced in Wandao 18 and we had a redesign in Wandao 22 release. There were repos created for a shared library for voting populators and another repo for a controller that is responsible for validating pvc data source. We will have our very first release from those repos very soon and this feature is targeting beta in Wandao 23 release. Let's take a look of this diagram. We can see that voting populator is needed at the restore time. Voting populator is in yellow box indicating it is a work in progress Kubernetes component. It is used to rehydrate pvc from a backup repository during restore. The next one is choirs and unchoirs hooks. We needed these hooks to choirs application before taking a snapshot and unchoirs afterwards to ensure application consistency. We investigated how choirs-unchoirs works in different types of workloads. They have different semantics. We wanted to design a generic mechanism to run commands in containers, but we want to mention that application-specific semantics is out of scope. We currently have a proposal called container notifier. Cap is submitted and being reviewed. We are targeting AFAR in Wandao 23 release. So here are some details of the container notifier cap. We are doing this in phases. In phase one, we propose to introduce several API changes. We are going to add an optional field called notifiers, which is a list of container notifiers into the container. Adding an inline type container notifier handler, which defines the command. Adding a core API type pod notification, which defines request type to trigger execution of container notifier single pod. Introducing a new gate container notifier to talk about this feature. And a single trusted controller, pod notification controller, will be implemented to watch pod notification resources, execute the command, and update their statuses accordingly. In phase two, we propose to add a core API notification type and a controller, which processes notification resources. And add an inline pod definition for signals and allows the API object to send a request to trigger the delivery of those signals. More logic in a pod notification controller in Cubelet. Cubelet, which is pod notification objects, runs the command and updates statuses of pod notification objects accordingly. In phase three, a probe may be added if needed as an inline pod definition to verify the signal is delivered or whether the command is run and resulting the desired outcome. As shown in the diagram, container notifier is mainly used at a backup time to do choirs before taking the snapshot and inquires afterwards. The next one is consistent group snapshot. So we talked about the container notifier proposal, which tries to ensure application consistency. What if we can't acquire the application or if the application choirs is too expensive, so you want to do it less frequently, but still want to be able to take crash consistent snapshot more frequently. Also, an application may require the snapshots from multiple volumes to taken at the same point in time. That's when consistent group snapshot comes into the picture. There's a cap on voting group and group snapshot. It proposes to introduce a new voting group CRD that groups multiple volumes together and a new group snapshot CRD that supports taking a snapshot of all volumes in a group to ensure right order consistency. The cap is being reviewed. Let's take a look of this diagram. We don't have a container notifier to do choirs here, but we have a consistent group snapshot that facilitates the creation of a snapshot of multiple volumes in the same group to ensure right order consistency. We have snapshot APIs for individual volumes, but what about protecting a stateful application? There is a cap submitted that proposes a Kubernetes API that defines the notion of stateful applications and defines how to run operations on those stateful applications such as snapshot, backup, and restore. This is still in very early design stage. As shown in this diagram, application backup handles the backup of a stateful application. It can leverage container notifier to do choirs and use cozy as backup repository. Similarly, here we can have application restore that handles the restore of a stateful application. So these are all the missing building blocks that we have identified and are working on. Next, I'm going to talk about how to get involved. As discussed in previous slides, the data protection OIN group is working on identifying missing functionalities in supporting data protection in Kubernetes and trying to figure out how to fill those gaps. We are also working on a white paper on data protection workflow. We have a weekly meetings on Wednesdays at 9 a.m. Pacific time. If you are interested in joining the discussions, you are welcome to join our meetings. We also have a mailing list and a Slack channel as shown here. This is the end of the presentation. Thank you all for attending the session. If you have any questions, please don't hesitate to reach out to us. Thank you. Bye.


Which was supposed to start at 1.40 but since it is clashing with too many talks We thought it'll be better to move it to 3.25 So the birds of feather for a product and AI will happen in first floor at 3.25 p.m. Thank you. The general notices apply as well. Just please don't bring in refreshments cups anything into the hole Make sure the Wi-Fi hotspots are all off and please put your phones on silent So that's all the announcements for now over to Anuj Gupta So I am Anuj. I'll be today talking about sarcasm detection In context with sentiment when it comes to sentiment. It's an important subset of it and How can one build a system that can go about understanding what is sarcasm? The agenda for the day I'll be I'll I'll I'll be introducing the problem I'll be introducing the motivation why it makes sense to build such a system in a in in organization Why is it a? difficult problem Then we will look at certain approaches Result results and the drawbacks of the current literature So what is sarcasm? It is essentially a way of communicating Something which is caustic which is is a weak Way to to communicate a negative connotation to it So if you look at this Warren Buffett talking about livable wage, it is a it is a sarcasm because Warren Buffett is is almost a symbol of capitalism and he he he talking about a Livable wage is an example of it To add to another example is Been I have been given a slot right right after lunch to have a technical talk And this is what every every every speak speaker wants so to to define sarcasm come from from the Greek word Sarkasin which basically means to to to eat into something to tear into something It's a nuanced form of language Where it is it is a deliberate attempt by the speaker to state opposite of what is actually implied So this looks like a great fun is a sarcastic way of communicating that I'm not liking What is happening right now? From a business point of view why is sarcasm an interesting problem, so Organizations continuously tap into social media trying to understand opinions of customers around around product services and and Giving real-time customer support and there are a lot of CRM's which are available in the market to To support organizations in this endeavor and you take any CRM That is available a key offering that you will come across is sentiment analysis now Most sentiment analysis systems are extremely bad at understanding sarcasm. So Here I have a sentence. I love the pain of breakup and What I'm showing you is the if the demo from Stan Stanford and Alan Which are almost like the two two of the best that are available out out here and both of them believe that this is a positive statement and both are getting confused by the word I love the moment they say love They believe that it's a positive way of saying things now Because most sentiment analysis systems go down when it comes to dealing with sarcasm and We are talking about Twitter as the domain This often leads to scenarios where the teams are not able to understand and then respond to it You know in appropriate manner, which often leads to PR disasters and This is something no organ no organization would love from a research perspective If you look at the various problems that are there in NLP Some some problems are easy some some problems are hard it is widely believed that things like question answer summarization question machine trans translation belong to the category of hard hard problems and sarcasm detection is one more addition to this and Any progress that we as a community make in order to to build something that can deal with this would actually be a progress also for the NLP in general and It's only recently that people have actually Solved sentiment to a very good degree So now people have started to look at more nuanced aspects of sentiment like us like aspect based sentiment analysis and sarcasm detection So be it be from a business side or be it be from a research side It is a problem that is worth spending time and energy into and This is what we'll be looking at So what are some of the key characteristics of sarcasm? so It's a sharp bitter cutting expression. It is a surrogate in nature and By definition the sentiment of sarcasm is always negative You would have never heard anything saying that I I gave a compliment Sarcastically, I mean it is never a positive connotation What makes it difficult? It is a deliberate play on the part of the part of the person so the person plays with the language and the nuances of the language to To be able to communicate something so sarcastically So it is subtle in nature It is not that you will have very clear indicators in texting. Hey, this is sarcasm It will be just a play of language where maybe a word is here or there or a Punctuation or a phrase Even as humans To understand sarcasm is not easy as a matter of fact, there are studies which Compare human cognition with sarcasm understanding to believe that people who are good at understanding and creating sarcasm are cognitively better than others When it comes to Twitter There are additional challenges. We all under friend. It is a short form of language at most 280 characters The spelling mistakes the slangs there are acronyms ever-evolving vocabulary so Sarcasm when it comes to Twitter as a source it actually ups only from there So what is the best base? What is the business problem treatment that you typically look at? Build a sentiment analysis system, which is capable of handling Circassum now in this diagram this this is a typical way that you handle you have a module up front which understand whether the in the inbound text is sarcasm or not if it is we know by the definition It's a negative sentiment if it is not then we then we go ahead and do sentiment analysis on it so for the rest of this problem or For the rest of the star. I'm sorry. I will focus only on So I'll only focus on the part of given an inbound text How how do you detect that it is sarcasm or not? The scope is an important problem He is an important aspect of when dealing with this. So if you look at this sentence This is very difficult to decipher that this is sarcasm You need to understand that she refers to to Hillary Clinton You need to understand that there are facts hidden in this statement That she is running for the presidential candidate. If if she wins, she'll be able to enter Oval office There is a history to it and that history does not bring positive Memories to her This requires lot of Complex work and it is beyond the scope of this talk to be able to a to address Sentences which are of this this complexity so we will deal only with those sentences where all the information that is needed to Detect whether it is sarcasm or not lies within the sentence and it is for this reason We chose Twitter as the source of data. How did we go about building the data set? It is primarily based on the source. So we looked at certain hashtags that are used extensively We looked at certain handles that are known to tweet out lot of Text which is sarcastic in nature So collecting the positive class what belongs to sarcasm was easy What doesn't what is not sarcasm is the harder part that proved to be much harder because everything else is not sarcastic and That domain from which is not everything is actually very very broad So this proved to be much difficult task So what we did was to we also took certain public data sets Filtered out data from there to be added to the class of what is not sarcasm over and above what whatever data that we collected post cleaning we got close to 100k data point 50k per class and We built a test test set which was close to size 20k, but from a different time timeline We actually wanted to understand that how how the vocabulary and how the underlying Distribution plays on Twitter. How does that affect the whole thing? So the training and the test data were collected from a different time timelines What is available in literature on this? This problem has been discussed as a matter of fact Professor pushback spent a good time in the morning talking about sarcasm detection, but So a lot of the work that is there in the literature actually Talks about hand coded features. So it talks about things like Unigram by grams emoticons capitalization positive sentiment in proximity of a negative situation It talks about features based on seek So people have argued that a sarcastic sentence would have certain words that will come on come very often and some words Which will come very rarely so so people use things like count of the gap between very frequent and less frequent In another important aspect is what is called in con con con gritty in con gritty in English means Something which is in stock contracts stock contrast of its background so so a person with the plum face and Very thin body is an example of what is in Congress The so people look at features like number of words which have one sentiment Immediately followed by another word which has to come completely the opposite sentiment While these are great features they work beautifully From a product point of view you want to build a more generic system Something that can sustain a more wider Inbound traffic. I think I need to be closer to this Yeah Okay, I need to be on this side. Sure. Thank you so So as I said most of the feature features that I spoke are spoken about Tend to be specific to the data set that you are working with we wanted to avoid this this thing and What we started to look at was can deep learning help in some way and the reason being that you do away with the Feature engineering. I mean we all understand that you you you give a data of Certain input space and your model is able to transform the feature space Whereby the separation is much much more easy. So for this we first set up a base baseline Being dealing with language. The first thing we looked at was certain language language models like like LSTM and and But we never had enough data to be able to train them well I mean at the end of the day, we only had 100k data points which whereby the RNNs were overfitting very very quickly and All we could do was close to 68 percent Instead because we wanted to reduce the number of parameters We went ahead and looked at CNNs for sentence classification And we actually got a far far better number from there Once we had the baseline in place The idea was how do we push from there? So if you look at literature People have strongly talked about three clues one is sentiment One is emotion and one is personality So what do I mean by sentiment so? Most Circustic sentences will show a shift in sentiment. So I love the plane I love the pain present in breakups, right? So it starts with something like a very strong positive Connotation I love something but it very quickly goes into a negative side and This contradiction between love and pain of break up. I mean this contradiction is a hallmark of sarcasm So then the natural Thing that will come to your mind is it makes sense to pick sentiment clues and use that as input to the sarcasm model So people have already done this kind of approaches This actually but they typically use things like sentiment lexicon, which will be essentially nothing but a But a dictionary of words and send and phrases Where for each of them you have the sentiment and then you would give certain numbers that will go in as features We instead Went about doing it slightly differently. We actually used a deep deep network CNN to extract out sentiment features And I'll talk like how we did that The second part is emotion So emotion is all about feelings happiness anger jealousy grief And one can have simultaneously many emotions Send sentiment on the other hand is a more objective outcome of emotions, right? It is either negative neutral or positive sarcastic sentences are rich in emotions My stellar programming life job offer control C control V resignation repeat I mean we all relate to it in some sense, but It has a lot of emotions to it. There is pain. There is sadness. There is anger. There is disgust the idea was can we use these clues and To help out in understanding what is sarcasm So similar to sentiment we trained another network which will try and understand Only the emotion part of it and give input from there the third pieces was personality The central idea is that People have done studies whereby they have been able to show that certain people are far better at understanding and expressing sarcasm as compared to another so who you are Will greatly influence the likelihood of you being sarcastic and Now how do you model this behavioral aspect of it? people have shown that if you Look at the history of the communication from the person Then you can actually pick this kind of a signal and the signal helps very strongly in understanding sarcasm Now how do you do it for a Twitter? I mean at runtime you have a sentence right and you have the user you From whom this tweet has come Now either you go at that point of time pull in the history of the of the user and then pick the behavioral clues or You have a database up front saying that this is the entire history of the of the user and then pick the clue from there Either of them will have huge implications on the throughput and storage and Most engineering team would hate to do anything like this because this would mean a lot of overhead Both at runtime as well as at compile time. So we never went down this thread I mean we knew up front that this is gonna be a be a engineering challenge So so we took a con conscious call of not going this way So we actually dealt only with sentiment and emotion So what is the largest solution that we had you have a text that comes in You have a model for sentiment. This is what I have shown in the green box You have a model for only the emotion which is the blue box and we have our baseline model Which is what we we trained on the Circassum data now the sentiment model was trained on a sentiment data set and not the sarcasm data set So we built another data set this this was primarily from public domains Which was all about sentiment dealing with negative positive and neutral We had another model which was trained on a data set for emotion So we talk about six emotions and we have the baseline model And what we do is we actually take the features from each of this so all but the last layer Whatever is the output of that that we take it as a feature and we combine the three So we did experiments with both taking the baseline and not So that is why it is in a dotted box And then that becomes the feature vector with which we have worked and this then goes into and another classifier Come to the details now So sentiment model so the larger picture is clear right So we have a sentiment model emotion model and a piece and a baseline So the sentiment model is what I'm going to describe now in detail So we built a CNN for this. It's a pretty simple CNN. It's a tool to layer architecture We have the data which we took Clempf to organize this was the words were mapped to embeddings I hope everybody is aware of what is an embedding embedding is basically a high high dimension vector Which pretty much represents the meaning of the word, right? And so this the word vectors were used to initialize the the embedding layer Okay, so we took three classes negative positive neutral We had a public data set and we added some custom data To be able to match to our needs The CNN that was trained was all about 1d convolutions How many of you know about CNNs for text classification Kind of 5050 so maybe I'll just spend a minute on that. So the idea is pretty simple So CNNs on image is basically a 2d convolution where you move across breadth and the height of it In this case you actually move only across the height of the Input what is the input you have a sentence You tokenize it you map each token to the embedding So here I have I like this movie very much and I have taken a five-dimensional embedding space So I have seven words and five. So this is a seven seven cross five any Any kernel that is going to operate on it will always have the width equal to the dimensionality of the embedding So a so here I have two filters, which are of size four into five Two which are three into five and two which are two into five You do the same convolution of operation as you do in images You take the vector from there you apply the max pooling. So in the last from each we are We have the maximum value of the of the vector We combine all the max and that becomes our feature vector. This is the central idea of what is CNNs for text and Then you you have that for your classification one versus zero So all we did was we took sentiment data built a CNN in this fashion and trained to predict negative positive on your daughter Likewise, we built the emotional model. So here it's about We will use all but the last layer of it to extract the features It's exactly the same flow for this work. We used glove embedding from Twitter. So Glove has has multiple Embeddings that are available. There is one which you've trained on close to two or three three three billion tweets and we actually use them directly to Initialize the embedding layer and then it's all about predicting for the six classes anger discuss our price Sadness, joy and fear. This is There are a bunch of public data sets that are available for this We augmented this deep data sets with some of our own data and then again very similar to how we built the sentiment model the network was pretty simple if it's not the kind of CNNs that you see in Computer vision. We never had the data at that scale So it's just two layers of Convolution max pulling then fully connected and then the soft soft max these are the various kernel sizes that we used typically three four five six and Then depending on so for the first one, which was the baseline trained on sentiment model So it was only two outputs the On the sarcasm model, sorry the the sentiment was three And the emotion was six. So accordingly is the soft soft max So out this work, we never fine-tuned the embedding layer. The embedding layer was frozen I mean this this was our first attempt at the problem So so so we tried to keep things as simple as possible and see where it takes us So Again just re-treating the key takes so the green box is a CNN model for sentiment Blue box is a CNN model for emotion There's a baseline and we take the features and from that the feature of vector was fed to a bunch of class classifiers In one we tried end-to-end where it was a CNN the other of logistic regression and the third was an S PM and the baseline gave a Okay, result but clearly when you augment this with sentiment and emotion features we actually saw substantial increase So the best that we got was close to 88 Which was fairly decent now from the part of Future work you can actually go ahead and try a lot of ideas Train your own embedding because it's Twitter the better you are likely to do better But you have to deal with the problem of out of vocabulary words very often So it's better to go towards and to towards n gram character embedding Try and collect more data that actually proved to be one of the more challenging problems in this project and then try and get back to RNNs and see how well they work I mean in principle, we never had the enough data to be able to say with very surety that this is gonna work or this is RNNs are not not gonna work Electing negative data is still a very tricky part of it because it's a huge Domain and you actually would like to sample your data with the pretty uniform consistency across this but that is a difficult thing What we saw was Adding public public datasets of sentiment So there are a lot of datasets which are tweets and these are labeled as negative positive and you're pure neutral When we added those to this the performance increased dramatically Because it's not just about sentiment clues, right? It is also about how is that related to sarcasm? So we needed a lot of data Which is strong in sentiment, but it is not sarcasm, right? Otherwise the model was doing pretty bad How do you bring in user behavior? I think that's more of an experiment that we would only do sometime much later in future Because as I said it has engineering challenges which which we could Which has serious implications from a product point of view. So actually we never went that way What are the key learnings from this? So one it's an important problem from a product point of view as as we are we mature from a data science part of Perspective having just a sentiment module is not gonna be enough Especially if you're dealing with what public domain is like Twitter Facebook and other at some point of time you will have to go and address this Lot of data that is gonna come in you are likely to do very bad because there are long-term dependencies There are subtle variations There are references to historic context As of today, we do not have the techniques which can actually address them in a Reasonable fashion as well. I mean forget about being great Most sentiment analysis systems are extremely bad at Understanding sarcasm. It's a very simple experiment go build a data set on sarcasm And then you have to try and run through a couple of sentiment analysis tools and you'll see what I'm talking What we worked with was it's it's a deep learning So we tried to automate it as much as possible in terms of feature engineering But still there is a domain understanding of that any sarcasm will have sentiment and emotion component through it And that actually helped us a lot Most important part was Both the features that we were taking were from pre-trained convolution So there is there are I mean computer vision is is full of such examples, but there are very limited examples of how Pre-pre-pre-tained CNNs can be used in language understanding here We actually found that this that actually helped as a big time Yeah, how you collect your data is a very very important aspect of This particular one because what is not sarcasm? You need to have a very calm comprehensive set With that I'll end my talk happy to take up any questions So hi, this is Deepak So you said about convolution neural networks for text Just wanted to know you said you only have a Window which is vertical in nature right like a filter So why only vertical and why not a filter like like in images? Okay, so in images if you look at what is the image the data itself is rich is Organized in space if you look at a embedding vector I mean if you are looking at say a 300 dimension then you can't say where is a more critical part of the world The vector as a whole represents the word and its meaning Right, so which is why the convolutions and if you look at language the central idea is the The units of language which are words or characters they compose to give meaning So it is better to look at vectors which are adjacent to each other in their full full view That is why you have what are called one deconvolution for text Okay So I just want to understand like all the corners are you have to be a little out. Sorry. Can you hear me now? Yeah, yeah, so just wanted to understand are all the kernels 2d or like after first convolution layer you will have So so everything is a one deconvolution here As I said the idea essentially is still that you the meaning comes from words that are in prox proximity of each other So which is why all everything is one day Hi Just coming back to the the comments you made on Negative data's data points So just wondering if you could elaborate a little more on how you went about collecting that data because as I understand it's important to have negative sentiment Tweets but not sarcasm So one as I said was a strategy where we took public data sets for which we already had sentiment and That that became a part of directly of what is not sarcasm So we actually went through the these sets to understand that none of them is a sarcasm and we put that The final data set actually came through a lot of iterations Whereby we would train something evaluate how it is doing look at what it was failing and then from there make Derivations of what are the kind of things it is getting confused with and Then try and pull data off more of that type and add to it But if you think carefully the eye the ideal way of solving this is not a binary classification We're saying sarcasm versus not sarcasm. It should actually be a one-class classification I know what is sarcasm and the model is only about about that So when something comes in it only tells whether it belongs to the class sarcasm or not Right, but one one class classification techniques as of today. No, they actually never took us anywhere I think we have one more So I have one question like did you drive POS tagging like did you append it the Append the input with POS tagging I'm sorry. I'm not able to hear. I'm saying did you I tried Try appending the input with part of speech tagging that could have So did we try putting part of speech tagging? We did try initially, but we did not Experiment it with thoroughly to be able to see its effects You're tagging also for small text like tweet with all kind of language Throw thrown in it itself was not working. Well, and then using a signal from there You're not we actually did not see games from there another thing is you mentioned that In normal sarcasm detection the sentence has a There's a change of nature and sentiment like from positive or negative, right? so I think just an intuition like bi-directional RNN or like In the phenomenal paper the sequence to sequence translation. They tried inputting the sentence in reverse If you try here inputting Reverses and provided as a second input, right? But for those kind of architecture we would need much more data than what we had currently Lot of our time went into to building this data set itself Which is why we did not go that way when we experimented with RNNs They they will tend they were overfitting very very quickly And we understood that we'll have to spend a lot of time in data collection Rather than just being able to build a solution at least to begin with but yeah I completely agree that a bi-directional LSTM will should do at least in tea and tea intuitively Good good job at this. Thank you. Any more questions? I think I'll have to cut you there. Thank you so much Thank you so much, Anuj So next on stage


Hello everybody, right now. It's 111 in the morning and we're gonna see what's going on with these guys so we've given them time to basically Put together a plan and get an idea about what they're gonna do. Let's see how far they progressed so, hey Hey Hey, so come tell us what you guys have done. You guys are all working together, right? Huh, seriously? Oh, okay, okay, so it's gonna be more interesting. All right, fine. Let's start off one by one then So, who are you? What's your what's your name and what are you doing? My name is Selma Maury What do you mean by what I'm doing right now? As in what are you doing? Considering you're at a hack night. What hack are you working? No, that's all of us together, right? So I thought you guys just broke up No, no, no So it's a 3-2 split, huh? Okay, I'm really explaining this picture from In the picture, which picture is that? We are working on the UI of the we are creating a game. Okay. All right So we are working on the I'm working on the UI part of it. Like, uh, it's a HTML5 powered 2D output game, okay, so Oh, basically he's working on the ports And yeah, I can see that I'm working on the UI part of it This is it looks like Mario. I mean the pipe from Mario Yeah, somewhat like a pipe kind of an interface that I figured out But first I had a plan of like some mass invasion or something Like digging out for Mars or this game is like more complicated towards the Like an escape kind of thing. All right.  You're making pipes for an escape. Yeah. Okay. Cool. How about you? Yeah, I'm just starting to walk on Creating the game. Are we we just finalize what we'll do. Okay. Cool. Um, so fine So you're working on the inner code of it, right? Yeah, and you as well. Are you working on it? Okay, so both of you guys are working on the coding part of it So can you give just give me a broad idea of like what you're trying to do here in the code? Yeah Okay So Nice All right. Hey guys This is group two. I guess the split off group. So anyway, so who you guys are and what you guys are doing But also if you guys could just sit together, it'd be so much easier to just Do this. Okay. Good. Good. Good. Okay. Yeah. So who are you guys? But first, who are you guys? So I'm Ashok You forgot your name. No, I was wondering you forgot your name. I am All right. Sorry. Yeah, go ahead All right, yes So, I mean it's telling me what you guys are doing now Uh We are we are building a youtube mashup video. So it's kind of like stumble upon per video But it's a seamless experience where you can it just plays you don't have to stumble stumbles You don't have to press stumble it like it's it's a mashup of map. It plays random videos random seconds. Okay The result is going to be a Mash it's going to be a It's going to be awesome So how far are you guys into that? I think you'll finish youtube is screwing with us. So you'll picture Okay, perfect. So you guys would be done by morning. Yeah, we'll call Larry if we have Sure, excellent. All right, just check what you check back with you guys later in the morning Okay, people. Hello. Hello. Hello. So who you guys are and what you're doing? That's our That's our bishop And what are you guys doing? We just started working on Hold on if you guys could speak a little louder. I'll see Okay, uh, yeah, yeah, sorry. Could you just speak a little louder? So you have to We just started working on We just started we'll be getting a Simple single page after That's what we have done so far. Okay, and uh, what will it be by morning time? What will it be? Yes, what is the future? I mean after the cocoon, what is uh, followed by it'll come out It will be an awesome map where you can It seems to be the word of the day. It is an awesome. It will be an awesome map. Yeah Where you can upload your photo and then you can see them Okay That's our first night js app. So we are trying to Uh Okay, cool. Excellent. Thank you guys and um, do you guys think you'll be done by morning? Okay, cool. So you have Oh great Nigel Nigel Nigel Nigel Nigel you actually will end up on video dude. Sure Okay, I took busy at work fine js I'm watching what he's doing. Seriously. What is he doing? Okay, so we're gonna line it up at the same time. You can't do that. This is what matters Let's go. So what are you guys doing? We're trying to get curves to align. Are you not kidding here? Actually spending time on the phone. Of course Okay, so this you think you might get the curves in the phone by morning Yes, for every letter or just for the one As you can see those guys are really font obsessed and everybody else is out to make an awesome app So let's see what happens in the morning. This is Billy tuning out for now. Bye


Coffee is highly vulnerable to climate change. We expect a 50% reduction in suitable coffee production area globally. Small, local coffee farmers stand to lose the most, their livelihoods. Traders, roasters, retailers and consumers also lose when climate change causes coffee production to dwindle. Climate risk cascades across sectors and borders. But who should govern this risk? Private companies that develop certification schemes in cooperation with governments. Governments in developed countries through development assistance. International institutions through agreements. Global markets themselves. Or national governments by adopting domestic laws, strategies and plans. Read about the pros and cons of each of the pathways of governance that this SCI Brief explores.


is a space that brings together all these actors who are related to one another and they can discuss issues which are important to daily development in Tanga, to improve milk production, as well as ensuring that farmers gain more income and employment through the development. From the start of the dairy platform in Tanga, the farmers themselves, the primary society, that means the board members of those primary societies, the TDSU Board and the processors, which by then were the key drivers of the dairy sector in Tanga. So we were all involved from the start. Farmers, through their farm organization, through the primary cooperatives, and of course the processor Tanga Fresh is also a beneficiary. So the government itself is a beneficiary because this is how the platform is contributing to the implementation of the policy. Not all complaints of the farmers can be settled by Tanga Fresh. Some of them are channeled to the service providers like the drug dealers, the veterinary drugs, the cattle feed, the government, the extension officer of course. Then we see that is a positive idea. We want the farmer to know that there is also a cost of operating a processing factory and it's really costly. We don't provide monetary support. SNV is a capacity building organization. We provide advisory and technical support. If we need to engage the local capacity builders to gather more information, do more research on how can this be done even in the area of price setting or coming up with the price policy, we still can engage our expertise, our technical skills to help the platform get a better understanding on how to do this and then bring them together to agree on how to implement that. It's seeing different stakeholders in their value chain coming together voluntarily without expecting any payment from any of the stakeholders. That farmer will travel more than 300 kilometers to come to represent his co-operative or himself. Someone has to really be committed and be motivated to attend. For us it's something which is special. We use some of the experts from the government like the livestock officers who provide extension services to the farmers. The regional government again is even providing some of its resources to be used by the platform. So the support is there and of course we are all contributing to the Tanzania livestock policy. So it's only the platform which gives us a chance to ask questions and make anybody accountable beyond the normal way of working. In the platform the RAS is our boss here in the region but if we see in the RAS office there is something we think that we need to improve. In the platform I am also free to say that I think in the RAS office 1, 2, 3 has to be improved and also the RAS office will respond to the members. Yeah, I think it's a very effective model because it has all the elements which are needed to increase production, to ensure benefits to the actors. Scaling can come up if we have partners. It can be beneficial to other parts of the country.