Hello everyone, I've got one science here this morning at Moodle Moot Global. One is the CEO of Edulabs, a premium certified partner in Colombia, Mexico and Ecuador. And I wanted to talk to one because I'm so interested in the presentation that you have this afternoon, which is called the LMS of the future, what students and teachers can expect in the age of industry four. Such an interesting topic one. Can you give us a bit of insight into what you'll be talking about? Of course, sorry. Thank you so much. I'm so glad to be here at the Global Moodle Mod after three years. And today we'll be talking about the LMS of the future. What are customers, what people are expecting from an LMS? So we will be talking about, mainly about sci-fi, educational sci-fi, things about artificial intelligence, VR, AR, blockchain, the kind of things we expect to see within the next eight years in an LMS. That resonates with what Martin Duggy-Armas was talking about in his keynote this morning. He spent quite a bit of time talking about AI and VR. How can you give an example of how AI would be used in an LMS in Moodle perhaps in the future? Yeah, of course. We can learn a lot of things through an LMS. We can learn a lot of things behind the screen. But there are certain things you cannot learn just by watching a video or by reading something on the screen. You need to do these kinds of things with your hands, with your body, you know? So with the VR and the AR or even inside the metaverse, you can learn those kinds of things. You can learn how to manipulate a machine, a vehicle, how to perform some kind of surgeries. You can learn a lot of things with your hands in a virtual environment. You cannot learn those kinds of things behind the screen. I can imagine that will be incredibly useful in workplaces where people are learning new skills, whether it's with machinery, as you say, or in a surgery environment for healthcare. Can you see an application in education as well, more traditional education? Yeah, of course, because finally all the people need to learn those kinds of skills and knowledge in an university or a technical school. So in the first place they need to learn how to perform those kinds of things. That makes a lot of sense to me. And I want to ask you about blockchain, because for those of us who aren't so technically oriented, you know, we might not understand what you mean by that. So can you unpack that for us a little bit? Well, yeah. In the last few years, a lot of people were talking about blockchain, and mainly there are people, they are talking about cryptocurrencies like Bitcoin, Ethereum, and those kind of things, trading stuff. But blockchain is more than that. Blockchain is the foundation of the cryptocurrencies, but blockchain can be used in every field. Every software application backed up by a database could be replicated using the blockchain technology. So in education, talking about build-up education, talking about technology for education, we can imagine a system that is backed up by a blockchain database where you can, for example, share your badges, share your certifications across multiple organizations, across multiple systems, and it's a very reliable way to verify your badges, your certifications. It's just one example of blockchain. A learner to facilitate learners, yeah, sort of, I understand what you're saying. Well, I look forward to hearing more about it at your presentation this afternoon. But tell us quickly a little bit about edulabs and your work. Are you focused in education or workplace learning, or perhaps both? Well, we are a Colombian company. We have been in the e-learning business for 11 years. We are premium partners, model premium partners in Colombia, Ecuador, and Mexico. We have customers all over the region, all over the Caribbean, all over the Latin American region. We are a company that keeps growing year after year, doing our work with passion, with love. And we are proud sponsors of the Model Global in Barcelona. We are so glad to be here. Excellent. Thank you so much. Have a great week. Thank you so much, Sherry.


I've been in Ethereum since 2014, worked in C++ Client in the Solidity compiler, did DAO stuff, White Hat and the ATC and DAO wars, but then I worked on L2 payments for many years and found it road key. Today I will be talking about user transactions and how we can start to understand them, like what are they and how can we decode them in a human readable format. Everybody has used Ethereum in here and we have all faced these two big problems, like you do not know when you have a transaction, you do not know how to get it, like there is no built-in way to get transactions for an address, you need to utilize some kind of third party service and that will never be decentralized. Also once you have these transactions you really don't know what I mean, there is a complete lack of a universal tool that will decode a transaction to human readable format, there is some third party services but again they are centralized and they tend to be protocol specific. What does a transaction look like, everybody has opened Etherscan and sees this, you know, hex block, there is no metadata, there is no human readable info, that's what every new user to Ethereum is greeted with, there is no way to understand what your transaction like three, four years ago has done. There are ways, we are no longer in 2014, there are ways to gain insight so you can see stuff in Etherscan with the graph and other centralized APIs such as covalent, moralis, et cetera. So we have all used Etherscan, what are the ways to decode the transaction with Etherscan is that it looks like this, it's a complicated transaction that swaps over multiple protocols, it's easy to use, you just type in Etherscan, you know, transaction slash the has and it gives you useful insight and it's totally free. Of course there are cons with Etherscan that's centralized, it's proprietary and close so that means that there is no way for me as a developer to see how they do what they do or extend it in any way. They know everything about you. I know the guys from Etherscan, they are good guys but you never know who in there could be malicious, they can match IPs to your addresses and know who owns what address and where they are located. And actually it does not decode everything. We have all seen that there are transactions that Etherscan doesn't have insights for and there are other tools that actually can do this. The graph is a way, it's kind of an index per protocol. They have subgraphs and they index the chain for a particular protocol and they query this index and get any insight you want. The pros is that it's very good for single protocol data. So for example here we have the other Ethereum by Messari, this is a subgraph that say the other guys can run in their interface and query any information they want for their protocol. But it has many, many disadvantages. It needs payment per query. This is their vision that everybody should be paying for every query. It's built for single protocol data, so the subgraphs, so there is no generic solution. If you have a portfolio tracker or a wallet or something that wants to decode every single transaction no matter what protocol it is in, subgraphs will not work. You will have to basically query every single subgraph and create a subgraph for everything that doesn't, is unsupported yet. And it does not work with local apps, what I like to call true dubs. So basically when the company that makes the application hosts the code they can have an API key and pay for the queries. But when it's a local application there is no way for this to happen. And there are decentralized APIs like Covalent, Morales and Alchemy. They're easy to use. They have pretty cool APIs which can decode transactions, give you all the transactions, but the same concepts can apply. Like it's centralized, it knows everything about you, it's proprietary so you cannot extend it. It's not modular at all. So having seen the ways that we can get insights about data, let's see how we can actually get accurate historical data. And let's go to the original scene of Ethereum, like this is absolutely bonkers. If any of you has tried to get the history of transactions for your address you will find out very easily that there is absolutely no built-in way to do this for Ethereum. There is no RPC method. This is all due to the way that EVM works and how the clients are built. But it is really like it's absolutely crazy that there is no way for you as a user to get all of the transactions for your address. Someone that comes as a developer outside from Web3 and comes to Ethereum and sees this, they think that we are just crazy, that this is broken. It's not all gloom and doom though. There are ways to do this. Etherscan again comes to the rescue. They have many APIs and if you combine three I think. So this one for transactions, then there is one for ERC-20 transfer and one for NFTs, if you combine all of them you get a pretty accurate picture of what transactions your address has done. Of course it has Dropbox, right? It doesn't detect all other appearances, it's rate limited but you can pay for bigger limits. But rate limiting means that it takes time for your query to actually work. It is centralized. So it can go down, they can cut access to the API or they can do what I said before that they can monitor you and map IP to your address. The truly decentralized way to go around this is something by my friend Thomas Deras, it's called Trueblocks. It is really the best and most complete way to get transaction data. Trueblocks detects all appearances of an address. Really I have seen demos where TJ basically shows Etherscan and then compares it with Trueblocks and you can see that for some addresses, Trueblocks does indeed detect more appearances than Etherscan. It is decentralized. So it runs on top of your local node so you do not need to do any other network queries. It's super fast. Like it's really like milliseconds or seconds depending on the amount of addresses that you query for. And it's built to share this index with others. Of course, like with everything in software there is Dropbox. It is hard to set up. So TJ was a long wolf since I met him in Shanghai in 2016. Now he has a bit bigger team but it takes time to build something that's easy to use by others. It does require a local node so you need to be running an Ergon node, I think. I'm not sure if it works with others. And of course you require Trueblocks itself to create the index. So building on this, I would like to present what I try to call the stack of truly decentralization which is something that we should be striving for in crypto. So everybody should try to run their own node. So something like a DAB node or Raspberry Pi with whatever setup you guys want to have. You should run your own client, right? For whatever chain you have, run a client for that chain that you want to use. And Trueblocks actually works for all chains, all EVM chains. So you can have an indexer like Trueblocks on top that will index the chain and provide you an answer to the question, how the heck... So on top of it all, to come and bind it all, you have the aggregating and decoding level that something like ROTC, so ROTC right now is an application but imagine a platform where you can have a genetic way to go from transactions to a common readable format for what they do. And it's actually consumable by humans. Going from how we get data to what actually would go into this decoding platform that my talk is about. So once you have all the data, like either from Etherscan or from Trueblocks, what kind of data is this? If you have tried to play with understanding transaction history of Ethereum, you know that there is two ways to get data. It's either a trace, a transaction trace, or transaction receipts. There are two kinds of traces, one is the gith style trace and the other is the parity trace. I'm going to go through them a bit fast for those of you who do not know what they are. So gith style trace is the tracing that comes with the gith client. When a transaction happens, it touches multiple contracts, right? So you make a transaction to a contract and this one may make a call to another contract and so on and so forth. And as they do this, they touch the state of these contracts and they make some changes. So this is what the trace of a transaction is and the gith style trace is the most complete. It's like super detailed. It has every single step of execution with the opcode, the program counter, the storage diff, et cetera. It's super detailed. It's very hard to use because for complicated transactions you have like a huge thing that you don't really know what it does and it can grow extremely huge, like in the gigabytes for really complicated transactions. And then we have the parity style trace, which comes from the now defunct parity client. But it is used, I think, Ergon for sure and NetherMind and maybe Beshu, I'm not sure. They have three commands. One is a trace that's pretty cool and useful.  And this actually does not require an archive node. The screenshot is from a very nice article by Bantech on traces that came out like two months ago, I think. So Google like Bantech and transaction traces and you can read about it in more detail. And the other thing that you can use to understand how a transaction, what has it done is the parity style trace diff. It gives you a state diff. So for each account that you touch, it gives you a difference in balance, code, nones and storage. The cool thing is here, if you have the ABI, you can play with it a bit and you can have readable names for the storage slots and how did they change. So this is a very useful insight on what did the transaction do. And of course, then transaction receipts. We've all seen like how a source code of a contract looks like. They have events. These events are actually contained in something that's called the transaction receipt. So let's say for a token transfer, it's like transfer, source, estimation and value or something. Almost everything generates them. It looks like this. It's all hex, but if you have the ABI of the contract, you can decode this into a human readable format. This is how you gather the data, but gathering this data is actually expensive. It takes time. And exactly because this is expensive in resources, persistence is key. So any kind of platform that you create and the thing that we have created, the draw key, needs to have data persistence. You can choose various ways. We've gone with a simple SQL database for now. But this way, when you have gathered all of the data and you know that they are true and will not change, then you can just take it out of the database and reuse it instead of having to re-query again true blocks or ether scan or make a trace again. We talked about where you can get data, how to get it, and then I'm going to go to the meat of the presentation, which is the decoders themselves. So we want to get human readable format of the transaction data. So we have gotten either receipts or traces, and with this amazing Inkscape graphic that I made, guys, I'm really proud of myself for this, we can see that you can check for each log the address and then send it depending on the address to either the generic ERC20 transfer decoder, if it's a Uniswap to the Uniswap decoder and so on and so forth, like AVE, et cetera. And all of this will at the end emit a common event format. What's more, some decoders feed data to other decoders. So the ERC20 transfers create the ERC20 transfer event and this gets fed to Uniswap, which translates it into swaps.  This is a ROTKIS repo, and this is where we have all the decoders, and it's like a huge list, it doesn't have all of them because they don't fit on the screenshot. But the idea is that it's easy to write, easy to use, and drag and drop. That you just drag, so you drop it in there and it's caught by the system. And then a new decoder is taking into account whenever we decode a transaction. That's the idea. We're not there yet. We build binaries and this is not as modular as it should be, but the idea is that it should just big drag and drop. This is how the source code looks like. This is, I think, a hop protocol to bridge to another chain. So it's hard to read probably, but the idea is that you get from the ERC20 transfer decoder you get the ERC20 transfer, and then you see, oh, it's a spend. The counterparty is the F-bridge of a hop and the asset of F matches and the amount matches. So then we transform it into the common event format for a hop and give it a nice readable explanation which like bridge the amount of ETH to either your own address or to some other address in the chain. So the name of the chain via hop protocol. I talk a lot about the common event format. It's kind of a POC because we are only consumer right now. It's changing. So this is how it looks in the code. It has like a sequence index inside the transaction. So where did it happen in the transaction? Time stamp, location. Location is mostly something that we use in Rotki because we subtract everything into this format. Not only Ethereum transactions, but your Kraken trades, your Ethereum staking, everything gets subtracted into this list common denominator format. We have the history type, history event type and history event subtype. So this is what defines the meat of how you define an event, the asset and the balance chains. And then some extra stuff like the location label is along with the counterparty is if I sent it from me to someone or if I got from others. And some extra data like if it's a CDP for maker, we have the CDP ID here, et cetera. As I said, everything is broken down into this thing. Like a swap is three of this event. So it's amount out, amount in and fee out. Or it can be two if there is no fee. We are working on this. It's not final, but this is like the idea. This is how a front-end can consume and show this code. This should be read, unfortunately, because I didn't take a nice case from the bottom to the top. So I claim my buzzer airdrop, so it has two events. The gas fee that's burned and the airdrop claiming, then approval to one inch V2 and the gas fee for this. And then the swap for in one inch for basically immediately dumping the tokens. This is the same thing that you saw in the previous screenshot, basically, these events. Each one of them is one line here. So this is how they are consumed in Rotkey itself, which is a portfolio tracking application. I would like to actually also talk a little bit about the abstraction layer of the vision of Rotkey, which is if you take this a step further and not just focus on just EVM code, but anything, like any event you can get into like an open source middleware that offers an abstraction layer for everything, accounts, balances, PNL, over multiple protocols and jurisdictions. So this is kind of the vision that we would like at some point to go with Rotkey. So we went from a portfolio tracking app to a common EVM decoder and now more towards a middleware that would offer an abstraction for everything in accounting for crypto. Why? Like people would ask, what the heck, why would you need this? Because everybody is reinventing the wheel. Again, as I said, different protocols, different exchanges, chains, jurisdictions, it's impossible to keep up. I have spoken with people in both small startups and big names in the field for portfolio tracking and crypto accounting. Everybody is saying that this is just too much to keep up. Maintaining just one module is a full-time job. So I believe that there is a solution to this problem. So the problem of everybody reinventing the wheel has a solution that we can have an open source platform, middleware, if you want, maintained by a core team but with contributors from the entire industry and used by multiple projects. And for the problem of different protocols and jurisdictions and being it impossible for a single organization to keep up, I would like to propose a solution that we can have people incentivized from each chain and protocol with appropriate know-how to come and implement modules in this platform. Again, my amazing inkscape skills, imagine a middleware where you have someone who wants to do portfolio tracking and accounting, the core uses Bitcoin and Ethereum, plugs in the Yen module, the AVE module. He also wants to do accounting, plugs in the accounting module, and because he's in Germany, he plugs in the German accounting with FIFO, multiple DPO, and LIFO accounting methods. And imagine this middleware basically being used by many people in the field and Indian, just everything plugging into this. Because it's better to use a common open source middleware rather than every single application reinventing the wheel. Any such platform would have some super basic requirements. It needs to be open source. Everybody tries to reinvent the wheel in a proprietary, close source way. This is absolutely idiotic. It needs to have a modular architecture so that, as we saw before, be pluggable, have pluggable modules.  You don't use Ethereum, you use Kusama. You do the substrate module, and you can do all the polka.kusama, et cetera. It needs to have, this is a hard requirement to achieve, but it needs to be multilingual. It needs to have multilingual bindings. Because we at TrotQ are a Python house. We know how to use other languages, but most of our code is in Python. Such a middleware should not limit the user. We cannot ask the entire industry to switch to Python if they are to use such a thing. The platform should be built in a multilingual way. And as for incentivization, the creators and maintenance of modules should be incentivized to actually contribute to this platform. If it becomes an open source standard, then everybody should be like, oh, wait. We made this new platform. We need to write a TrotQ module for it also because otherwise it just, like, nobody will use it. And the core team that builds and maintains it also needs to be incentivized in some way in order to be able to keep building. The ways that this can be is through support to the various teams or through SLAs for software level agreements for companies that may not want to have open source code. So you can have dual licensing. So it's a bit funny. I wanted to show this. So the timeline thing, and I was in the template that they gave us and I thought, hey, why not put the timeline? So how the heck did we get here? 2017, I just need to do my taxes in crypto. And I was like, okay, I'm not going to, what is the way to do this? There was Bitcoin.tax. There was nothing else back then. I'm not going to use the centralized service. I just don't trust them. So I just made some Python CLI scripts. It worked. I've not been sued by the German government yet, so it worked. I don't know. In 2020, we made it into a company. We were a team of two people and maybe we had 200 users, 300 and maybe 10 paid. So last year, the app had grown. We hired one more developer and we were 2,000 users and 200 paying users. In the beginning of this year, there is many people who use ROTK right now. Some like it. Some complain. They always want more and more. But it is at a level that many people can use it. We are a team of 7 now. Around 6,000 users. It's hard to know because it's an open source app and we don't have analytics. And 550 paying users. We came all this way without anything. Like it was completely bootstrapped and from basically your donations through Gitcoin and from integrations with other companies like Optimism gave us a grant lately before the Kusama and so on and so forth. So for getting this POC that I described here to the full ROTK vision, we would need to go further from here and try to grow and potentially get some funding because with the current team that we have, it's impossible to actually build this vision. The POC cannot grow to a level of something that can be used by the entire industry with just 6 developers. It's just impossible. So with this, I'm coming to the closing notes. So if you like the stuff that you had like open source, local first, the modular thing that can be used by everybody in the industry, then please talk to me or check out ROTK.com jobs. We have some open positions. We came here thanks to you. Like seriously, it's a bootstrap project and we would not be here without Gitcoin donations and without our premium users. So keep supporting us. You can donate in Gitcoin grants or buy premium subscription and unlock all of the features of ROTK and you can join our community in Twitter or Discord, like that's where all the support is. It would be pretty cool for you if you can join the chat and join our community. If you're interested in helping us grow in realizing this vision that I tried to present here, then talk to me either like any day in the conference or write an email to lefteris at ROTK.com. Yes, with that, that's all. Thank you very much. Okay. Like you mentioned the graph and true blogs. One thing that I don't understand about these two tools is to query historical data is that they use a client node and this node is basically in charge of storing these data in an SQL database. Isn't this central as way of saving data? Is this when IPFS comes into play and if that's the case, can you explain how IPFS and SQL database work together to solve this? IPFS doesn't come anywhere in there. The graph is and true books complete different things. So the graph is it creates an index on top of your already existing node data and true books does the same. But true books does it in a generic way for all of your transactions. While the graph has specialized subgraphs written by developers of particular protocol that basically write an index just for this protocol and this leaves on top of your node data. It is decentralized. Like the graph by design is also decentralized true books is itself also decentralized. It creates this index and this index is shared. I think it's pinned in IPFS and shared with others who use true books. I'm not totally sure on the details of sharing of true books because I'm not a developer. But I think that this is how they do it. As for the graph, they have a decentralized network. If I'm understanding this right, the idea is if we build this out and get it out there, then like we could get around using services like tenderly and just basically run tenderly at home for transaction tracing and simulations and all that. Yeah. I view it more from a historical perspective and tenderly is a current emulator. But yeah, I suppose that you could also do the same. Correct me if I'm wrong, tenderly is proprietary, right? Yeah. Yes. Yeah. I think that they work with traces. They're pretty good, but yeah, proprietary. With such a design, yes, I believe that we could use this as an alternative. Hola. Awesome presentation. Great work. From the developer perspective on a solid developer, I think it will be very useful for, for example, I write a smart contract and I write the decoder. Let's say I write the decoder and I host it on IPFS. Have you thought about that, like how we can have like a standardized, I know, let's say parable where we define the URI or hash where we set our decoder and you guys can use it because you have a list of decoders on GitHub, right? Yeah. Yeah. I mean, this is just a different medium of delivering the decoder. But yes, the idea is exactly what you described. So I'm not going to write every decoder. My team is not going to write every decoder. That's impossible. But the idea is exactly that you, when you write your smart contract for a protocol, then you say, okay, I'm also going to write a decoder for this. And then somehow it should be delivered to this mid-ware. Yes, it can be through a link. This is just a POC of what we have right now, the drag and drop in the folder in GitHub. I think it does a lot of value if you can also validate the decoder. Because if I do it, I can write a malicious decoder, but we want it like a validation system and this is a place where I think you can add a lot of value. Okay. That's good feedback. Thank you, guys.


Hi everyone! I am back with a new video and this is a tutorial on how to draw hair. This video is also sponsored by Skillshare, an online learning community for creative people who are always looking to learn something, whether that's a new skill or to brush up on a skill you already have. I feel like this is a really great platform for that. They have so many online classes ranging from so many different topics and just some of my favorites include illustration, graphic design, and something that I'm currently into animation. I am so excited to share with you some classes that I recently watched and loved, which I will talk about at the end of the video. I will also recommend some classes that you can watch along with this tutorial to help you draw hair, so stay tuned for that. And because this video is sponsored by Skillshare, I have a special link in the description box below that will give the first 1,000 people to sign up using that link two months free of their premium membership. So a premium membership gives you unlimited access to all of their classes in the platform, which is normally valued at less than 10 US dollars a month if you pay annually. So two months of free unlimited content is a really good deal. So I highly recommend that you check them out and you join using that link if you haven't already. In order to draw hair, we first need to have a head, which I've already drawn. And if you want to learn how to draw the head and its facial features, I have a video for that. I also added the neck and shoulders for this demonstration. The first thing to determine is the hairline, which is located on the forehead and it indicates the roots of the hair closest to the face. If you draw seven lines equally spaced down the head, the second line on top is our guideline for the hairline. There are many different lines you can draw for the hairline depending on the face shape and you can see them all on screen. But for the demonstration, let's keep it simple with an oval face shape, therefore, an arched hairline. Next is to determine if there is a partition on the hair. As we know, hair can be parted on the left, center, or right side of the head. Therefore, the guideline we will use is a line located on the top of the head and we can change the position depending on where the partition is located. The partition line that we draw also indicates the roots of the hair and we can also change the length of this guideline to show different types of partitions. For the demonstration, we will have a center part on the hair. Therefore, the guideline is located on the center of the top of the head.  Next is to determine if there is a hair accessory or anything that's gathering or pulling hair strands together. For example, if hair is tied in a ponytail, this means that hair strands gather to a point where the hair tie is located. So we add a guideline, which I will call the gather point. This is located anywhere where there is hair. And we can change the position and length of this guideline depending on the hair accessory. For the demonstration, I'm going to tuck some hair behind her ear, which isn't a hair accessory, but it acts like one as it gathers hair to that one spot. And because of this, the guideline I used is not a point, but instead a line that follows the top of the ear. And once again, if you decide that a hairstyle doesn't have any hair accessories, you don't have to draw this guideline. Now that we have the guidelines in place, let's draw the strands of the hair. And there are two curves that I like to use. The first one is the S curve, which I use to draw long hair strands. So that's what I'm predominantly using for the demonstration. Keeping in mind that hair grows from the hairline and the partition line, this is where we start to draw our S curves. It follows the shape of the top of the head and it falls down to the shoulders. And depending on the type of hair, we can change the degree of the S curve and you can also make it irregular. So a subtle S can be used for a natural straight hair and a strong S can be used for wavy hair. You can also repeat the number of S curves in that strand to create curly hair or kinky hair. So if you draw these curves repeatedly side by side, you get something that vaguely resembles hair. That's pretty much the idea of hair strands. However, I personally don't like to draw every single strand of hair. Rather, I like to think of hair in bunches or in bulk. In order to draw that, let's take our single strand of hair and draw a similar looking strand next to it, but draw it on an angle. This way, the lines meet at one point and we get a pointed end. Another way is to draw the letter S starting from the top tip of the strand and making sure they meet again at the bottom tip. Now, let's draw a letter S again starting from the top tip, but this time it doesn't meet at the bottom tip and if we draw a horizontal line at the bottom, we get a flat end. Basically, we've just done four different ways to draw bunched up strands of hair and that's what I'm using for the demonstration. To give it a layered look, some hair strands are shorter than others. I also make sure to vary the S curves that I draw so it looks a bit natural and sometimes I change the direction of the hair to create some flyaways and it just adds movement to the hair. I also like to vary the line thickness where a thicker line is used to indicate a bulk of hair and thinner lines are used for detail. This is where the second curve comes in handy. This is the C curve, which I used to draw short hair strands as well as details for the hair. Similar to the S curve, we can once again change the degree of the curve so it almost looks like a straight line and you can make it as irregular as you want depending on which part of the hair you're drawing. For the details of the hair, because the hair isn't as wavy, the curve I'm using is a very subtle curve. I will also be using this for the part of the hair coming down from the partition line to the ear where I've drawn a gather line. As you can see, the hair starts from the partition line and every single strand meets at the gather line. The idea behind this is that the hair is being pulled from the root to behind the ear and the hair falls back down to the neck and the shoulders. I then continue to draw the rest of the hair as well as add some details that I feel are necessary, but that pretty much explains the foundations of hair. You can use these guidelines and curves to create a variety of hair types and styles, allowing you to experiment and use this as a starting point to stylize and create different characters. As you can see on screen, the guidelines can move around and change to your discretion. You can create short hair, long hair, hair that's tied up or hair that's kept down, braids and updoos. The basics are there, and now it's up to you on what you want to create with it. And as I mentioned before, I've included some resources from Skillshare down below, which I believe are really helpful when drawing hair. I really liked Hairstyles 101, Draw and Color Hair for Character Design by Maria Lea Melandrino. I think that would be a great follow-up video to this as she talks about how to draw hair for different characters and personalities. This one isn't hair related by absolutely loved the animation classes by Libby Van Der Plug and Mann and Luart. I'm not sure if I'm saying their names correctly, but they both inspired me to improve my animation skills, which I've applied onto this video and I hope that you guys appreciate the little animations I did to help aid my tutorial. I will link all of the classes down below, so I highly recommend that you check them out. A few final tips are to, of course, practice and don't be afraid to use reference photos. I think it'll be very helpful to see a reference image and identify where the hairline is, the partition line is, and if there are any gather points or gather lines on the hairstyle. I think this will help a great deal when it comes to tackling hair, which can be as simple or as complicated as you want it to be. And that is it for this video. I hope that you found it helpful. If you did, please give it a thumbs up, share, and let me know your thoughts on this tutorial. I would greatly appreciate your feedback and take it on board for my future tutorials. If you follow this tutorial, tag me on social media as well. I would love to see it. And don't forget to subscribe for more videos and push that modification button to know when I upload. Thank you so much for watching and I hope to see you in my next one.


In this presentation, we will discuss the discussion question of what are equivalent units and how are they used? If we see a discussion question or an essay question like this We may want to start off with the definition of what equivalent units are or our best guess of it our description of the definition But we probably want to expand on that as well if it's a discussion question We want to expand on the discussion and if it's going to be an essay question We want to pick up as many points as we can by expanding on that now here The first thing we can think about is if we think about equivalent units We're typically thinking about a manufacturing company and not only that we're thinking about one that doesn't have a job cost system But typically a process cost system So that might be where we want to start depending on where we are at if we're working in a section where we're working Just on the process cost system then that might


Thanks everyone. I'm Sherard Griffin. I am a senior manager at Red Hat and I work in the AI center of excellence Where we work on open data hub and I'll explain a little bit more about what open data hub is in the second here Yeah, all right. We're good to go Before I dive into this Dan did an excellent job Talking about what it means from an ethical standpoint to do AI what it means in terms of what we have to think about How we make it into a practical application I'm actually going to take it one step farther and dive a little bit more into the technical part of this How can you get started? How can you your customers get started with your AI initiatives and we're going to round that out with something called open data hub? I'm actually going to start with this with a little bit of background of how we got the name data hub we started this project internally at Red Hat and the focus was more specifically around Aggregating data that was the primary reason I came to Red Hat is because of the experience Dealing with big data and we knew we wanted to do that on OpenShift and show the capabilities of OpenShift as a platform for data Engineering and ingestion we had the data lake all of a sudden one of my colleagues Marcel who heads up the AI ops team said Hey, Sherard, we have all this data. I want to do some data science work on it What can't where can I point my data scientists and say, huh?  No, no, no I want to do it more of an AI type of thing So what we decided to do was figure out how to bake AI into what we were doing on top of OpenShift And that's how we got the open data hub and really the problem that we tried to solve Was how can a data scientist instead of bugging everyone on my team every time they want to do something? How can we enable them to have more of a self-service type of infrastructure? How can they just go into an environment request the resources and the technologies that they want have that working in a Collective ecosystem and then get on get on with their initiatives and be able to get some results out of it So if you look at that whole self-service model, that's one of the big drivers of open data hub Is how do we enable the data scientists to do what they need to do in a flexible manner? It's what we found internally was needed So again, I don't get a bunch of service now tickets and requests for For onboarding, but then we also talked to a lot of customers We've had a little bit of a roadshow over the past year where we've talked to many many different customers And it turns out that they're interested in the same thing Some of the current challenges that the data scientists both internally at Red Hat and at our customers were facing One of the big things is that they were all working in their own isolated one-off environments Whether that's a laptop or a server housed away somewhere tucked underneath one of their desks It's very challenging for them to number one have a way to be able to share their work together Right be able to take some kind of model that they built and say hey, this is really cool It does something tangible. Why don't you go check it out? To the one of the biggest things is just limited resources if you imagine the environment where let's say you do have that machine Tucked under your desk. What happens if you need more hardware? You have to bubble that up the chain even if you have traditional it infrastructure What happens if you need more hardware? You have to you have to send in a request to it It then has to order the hardware the hardware takes a few weeks to come in next thing You know you spun it up and you know you're off to the races, but oh by the way It took you three months So what we're trying to do is figure out ways to address those challenges to not only lift the burden for IT But also make it a little bit more flexible for data scientists What we gravitated towards was open shift helping to solve that problem and when you look at what we're trying to do It's not that different than what you would see in an application Lifecycle you want to be able to have some kind of self-service environment and in this case We're replacing the term developers with data scientists And you want to be able to have them do all the work that they need to do and push something out into production So when we look at why open shift was so key and so relevant for us to build this platform on top of the one thing the number one thing that stood out is the fact that it allows us to Have such an easy mechanism to be able to deploy something into production It's very similar for what an application developer would do you have these iterations of testing something you want to push that out The other thing it allows us to do is be able to load balance these services and be able to kind of You know you you span out horizontally, but you can also span out vertically very easily, especially if you integrate open Open shift along with something like open stack So what that allowed us to do is every time a data scientist deploys a model that models a microservice And then we scale it out depending on the demand and i'll show you an example of that shortly We also have the ability to orchestrate these microservices these these you know machine learning microservices and be able to You know schedule them for training deploy the model as a service do whatever we need to do there And not only deployed in one environment, but we actually real world problem we have today in red hat we have Some of the teams who have infrastructure in amazon and then we have some of the teams who have infrastructure on prem How do we do that same level of workload and just and shift all of that work to whatever resources we want to pretty freely Without we're using the same infrastructure and that's you know the whole hybrid cloud solution that open shift gives us And we just show the capabilities of doing that from machine learning perspective Where is ai strong right now if you look at a lot of the customers that we're working with They're they're starting to do their ai initiative. This is not a proof of concept This is not something that's way out in the ephemeral cloud and everyone's talking about it But no one's doing it. We actually have real tangible results that are being generated using open shift and using A lot of red hat products and open source products in order to in order to do those things So here are a couple that are interesting right here. We have xon mobile. They'll they'll talk about some of their use cases today And it's it's growing. It's a growing list and not only that we're using it internally at red hat as well we generate about you know about 300 gigabytes of data per day that flows through open shift that's available To our data scientists. That's just from our build systems We also have a massive amount of telemetry data that's being generated and we're doing ai work on On a daily basis. So the data volumes are growing and the data scientists are being are having more and more capabilities in order to do their workloads That all leads me to the open data hub project I mentioned a lot about the experiences that data scientists are looking for the experiences that it's looking for How does that all get rounded out? What we decided to do is take all of those lessons learned from running an internal ai as a service platform at red hat And surface that up as an open source project where we have the open data hub It takes all of those little things that we worried about not just the machine learning aspect of it But what comes before that and what comes after so you'll see a lot of focus in the open data hub I mentioned this at the beginning about data ingestion and collecting data and how you build a data lake whether that's a virtual or An actual data lake across many different clouds Then we focus on how do you prepare and massage that data you do things like cleaning it You know if anyone ever sells you hey the very first time I ingested data and it was perfectly Ready for a machine learning exercise. You should probably not get that person on the project again There's always some work that has to be done from a preparation perspective And then you're all familiar with the machine learning building a model training it Pushing it out to production, but then once you push it out to production your data scientists can't say hey Oh cool. I'm done. I can go home now. No There's a lot of work. There's a lot of work that happens after it goes into production How do you model how do you monitor it for drift? How do you make sure that that model is continuing to be accurate over time? Just as if you were doing something with application development and you push some code out You want to make sure that it's actually functioning over time And and it's a little bit of a joint operation between the data scientists and the sre's and we'll show a little bit of that as well If you're interested in the open data hub project t-shirts out front, I think they're pretty cool The funny story about the t-shirts I realized that we've been handing these t-shirts out for about a year and a half now And like no one on my team has ever gotten the t-shirt So I got to stuff my suitcase full of t-shirts on the way back Just to make sure everyone has some It is a blueprint architectures if you look online You will be able to see a little bit more detail in terms of how to get started But then also, uh, you know what the whole vision of the open data hub is and then how you can get involved When we talk about open data hub, I'll show you a little bit about the components that are in there But I want to set the level, uh, you know level set in terms of What is actually the vision of open data hub and where we're pulling from? It's not just a collection of Technologies that are kind of one off. We're actually trying to tie ourselves to upstream communities And the relevance for that is as the communities grow then we enable these ai workloads on open shift and those capabilities will grow As well, so we do a lot of partnership with nvidia We we have several components that are part of coup flow and we're having stronger integration with coup flow in open data hub Uh, we have some things with seldom, you know pie torch spark a lot of open source technologies We're kind of pulling from those communities and wrapping it all into a nice package that can be delivered on open shift Now let's get into the the meats the the meat and potatoes. I'm a meat and potatoes kind of guy So with this what I'm going to show here is just a little bit of what's deployed when you actually go into open data hub We have a number of different things that are relevant for both the The data engineering side of it and the data science and machine learning side We have for data science work. You have jupiter notebooks. You have seph for your data lake For ingesting data. You have kafka in this case. We use trimsie, which is an operator We also have argo, which is great for your pipelines And then we also have from a monitoring perspective for meaty from ethios and grafana We have seldom for model serving and we have spark. This is just what's available today in open data hub Internally, we run a lot A much broader stack. Don't worry about the details of this diagram You can go to the website and get more information But this gives you a little bit more insight in terms of what's running internally at red hat and what's being poc'd out to move back up to the open data hub One of the things that we do is we do a lot of processing data as it flows through kafka So we do things with kafka connect. We do things with ksql And kafka consumers and producers. We also have log stash fluent d rss log for for data ingestion We all we build a data lake. We just happen to do our data lake in seph But there's also s3, you know any other technologies you want to do for your data lake and then from a You know from if you look all the way at the top From an analytics perspective We do a lot of analysis with hue which is cladera hue and then we have cabana as well And then for the model lifecycle, we have kube flow ml flow selden We also have something called the ai library ai library is a predefined set of of machine learning models that you can get up and running really quickly and those were been built with Kind of community efforts. So you have things like sentiment analysis Cluster detection, you know all kinds of interesting things that you can have right out of the gate And then for our business intelligence perspective, we're rolling out superset pretty soon Now going really quickly right before I get to the demo here I just want to you know kind of go back to what we said before We're moving from a world where data scientists are on their own machines Or they have some some isolated environment that they're doing their work And what open shifts allow allows us to do is to move them to more of A centralized place to do all that work not only can they share the resources they can share their models their notebooks But then also it just makes it a nice place where you can actually push that model out into production as a service And then that service itself can be managed and monitored just as if it's any other application in open in open shift So with that said I'm going to roll this demo This is going to be interesting because all of my links to the demo is on the other machine So I'm going to try and do this from this machine and we'll see how this goes All right diane. I'm going to pull this up here No, we don't need the vpn All right So I'm going to log in here And one of the first things I'm going to show you if you want to get started with open data hub Very again very easy to do what you would do is we're going to go into this Thing called operator hub and just show of hands how many people have actually played around with open shift 4 Okay, so a good amount of folks. I'll take a little bit of step back and explain operator hub We're moving into the world of operators. You'll hear this a lot diane mentioned it earlier Operator hub allows us to build out these operators that are really intelligent You know ways of managing infrastructure and managing your applications And we've released open data hub as an operator It's basically you can think of it as a meta operator where it's responsible for other operators like spark operator Strimsey kafka Selden so In this case if you want to get started instead of looking for all those individual applications that you want to install You can just go in here and go to And and type in open data hub And then you'll see this open data hub operator if I click on this open data hub operator I then have the ability to Install it and if i'm installing it then That'll allow Anyone who has a project in an open shift to be able to deploy their own version of open data hub now For the sake of time i've already done that and i'm going to zoom this in a little bit in case that's a little hard to read back there And I actually have a project already ready to go And if we look really quickly, I just want to show exactly what gets deployed in open data hub And again, this is just kind of the options that i've selected you can choose different things to to deploy Whether you want all of it or just a couple of things you can it's at your will But I mentioned this before we have grafana. We have spark operator.  I want to show you one more thing here One of the capabilities that we've added into jupiter hub So i'm going to log in as a environment here And i'll show you Some of the interesting things you can do with jupiter hub here When you first when your data scientists first goes into jupiter hub They have the ability to select these notebook images in this case We have several different images and the image it allows us to kind of prepackage up Some resources that we want available to the data scientists right out the gate in this case What you'll see is a spark along with side pie, but you can do anything like tensor flow Whatever other types of technologies you want to add there They have that ability to just kind of select and choose this is We have some predefined ones that you have out of the box But of course it's a community if there are others that you want to add you can always contribute it Or if you have something private that you want to roll internally you can do that as well They can also select the size of the cluster that they want here So anything small medium large and in this case, this is specifically for the container running the jupiter notebooks The other thing that happens here I'm not going to show an example here, but you can play around with it on open data hub Is when I select that I want a spark cluster Behind the scenes when I start my notebook server You'll actually see a spark cluster spin up specifically for that data scientist And that can be whatever size that that you want You know anything from just a couple of workers to 10 15 workers Whatever you need for your for your work And then the cool thing is once you terminate that notebook and say hey, I'm done with today's work The spark cluster cleans up automatically for you and it goes back into the open shift ethereal of hardware available Also, what you can do here is decide how many GPUs you want to use And the workloads will actually be run on that GPU So if you have GPUs enabled in open shift It's as simple as changing this number to whatever you want and you have the ability to do that Some of the other options here I won't really explain, but if you're interested in about about them, you can always take a look online So I've already started a notebook server for you For for this demo and what I'm going to do is walk through a couple of things here The first thing I'm going to show you is we have this feature engineering workbook It's feature engineering workbook. It's really about Preparing the data and making sure that we have training data ready to go for the model in this case you know what I'm what you'll see here is it's All this fancies data.  And so we're good to go We know that we have a good training data set That we can use and now let's move on to the training aspect of it. So I'm going to go back and select The notebook to train my model Now once I have this notebook for training my model You'll see here that I'm going to just quickly and let me zoom in a little bit here for you guys What I'll show you here is all we're really doing is Deciding what is a legitimate message and what's a what's a uh a spam message and in this case Dark blue is great. You know this lighter color is great as well That means again the messages have diverged and we can clearly tell what a legitimate message is from a spam message Awesome, we're good to go And then what I want to do here is let's let's play around with this a little bit Let's actually See if we can Get a just a little bit more information about what's going on So in this case, I can see that I've got I predicted that this these messages were legitimate and the actual result was legitimate I have a pretty good accuracy of 94 percent. I have some that were predicted legitimate But we're actually spam. That's only you know, that's only about five percent I have some that was spam that we were detected that uh was actually Legitimate, but they were we thought they were spam That's about two percent and then the ones that were spam that actually were spams 97 percent So i'm good, right? I'm happy with those results. Again, it's not perfect like dan said, but it's good enough to start with And then i'm gonna just run this next one here and show again the accuracies, you know, everything looks pretty good So now now that I have that what I want to do is Start to deploy this as a service And as I deploy it as a service what i'm going to do is i'm just going to run through all of these cells really quickly It doesn't take long And we'll see some results start to come out here so And one of the things I actually want to show you is how I deployed it as a service To take a step back here and go into my build configs And have something called pipeline and in pipeline what i've actually done is i've taken the model itself So this model notebook and you'll see you'll see this is the actual model notebook that we have here We have something called source to image But we built source to image to work on notebooks If your data scientist has a notebook and they want to deploy it as a model As a service then they can quickly run source to image on this just as if it were an application and deploy it Into open shift as a running notebook and you'll see here, you know, we have that actually running here If I can show you which pod The spam filter pod and we can again scale scale that guy up and down however we want right, you know We can scale it up to 10 different ones So with that said We'll look back at the Service notebook and then we just ran a quick test now that it's up and running All I all I want to do is send it a note of a message and i'm sending it a message to arrest I'm saying hey I'm going to send you something dog food dog food is detected as spam And then i'm going to send you the second message and that's the second message was sent as legitimate And great everything looked good Then I can keep going here and i'm actually going to Now predict a few more few more messages here And what you'll see is again all i'm doing is i'm sending arrest message to the service and i'm getting some results back You can see these were pretty good, right? It's pretty accurate again So now that I have that A couple of things that I can do here Open data hub also deploys with prometheus as a data scientist. I want to i've tested this out. That's awesome Now I want to actually see how does it performing over time? And that's always an interesting one because we always think about the Deployment of it is hey it worked that first time, but we don't ever Think about how do I test this and validate this over time? So what I want to do here is i'm actually going to go back to back to the Prometheus And I'll show here Exactly what's happening over time and let's see here If I can remember the Med trick hold on one second here.  It's on my machine But i'm not gonna have time what I would actually show you is uh, it's a really cool graph. I wish I could show you here Let's see here No, I won't be able to pull that up unfortunately. It's on my machine the the command for that. Um, but what uh, well What's cool is you'll actually see a graph of activity here But one of the things I can't show you is that if we look at the metrics itself here There are tons and tons of metrics that come That come from What I actually have here and what you'll see is these metrics like pipeline predictions created total pipelines the You know that how many basically just you know, what's going through the system? So if I if I click on one of these let's say this guy um You'll actually start to see some activity and here it's not as cool of a chart as I wanted to show you But this is how you can see right out of the gate. I've deployed something It's a nice microservice and it's actually giving some results and these are actually changing over time This is all coming through live And the data is flowing through one more thing really quickly that I want to show is that we also have Grafana that's hooked into this and so when we go through grafana We can check out a number of other Metrics that come across and in this case What we have here is some kafka metrics and again, you'll see everything is flowing through this is when I started the spam Detector this morning and you see that some data is actually flowing through the system So again, all of this with open data hub you have prometheus grafana seldom all of these different technologies rolled into a nice deployable package for you and we're going to continue to release more and more technologies to Round out the the whole ecosystem of the end-to-end ai pipeline for you. Um, and that's that's really it You know, it's the whole project and we're very excited about it We're great to to have it as a nice foundational piece of how you can do ai and ml on top of open shift The the landing page so people know how to find. Oh, yes. Yes. Yes never remember to do that. Yes So of course always go to open data hub dot i o That's where and then there's a link to community if you want to know how to reach out to us And then also docs if you want to know how to get started. Thank you dian. Cool. Yeah, there you go. Great. Thank you


Good morning. My name is Hulman Haddad and I'm here to tell you how the United Nations World Food Program is using Ethereum to distribute aid to refugees. DWFB's mission is to end hunger and traditionally this has been simple where people are hungry, we bring food in kind like corn, rice and so on and so forth and we give it to them so they can eat. For a few years now we've also been doing something what's called cash based interventions which means that instead of giving people food in kind we give them the means to purchase their own food and this can take the form of bank cards, mobile money, e-routures and so on and so forth and the idea behind it is that the beneficiary is in the best position to make their own purchasing decisions so that essentially instead of saying okay here some food eat this they can make their own choice and there's an element of dignity in there and also that it can have a multiplier effect in the local economy. In order to enable this sort of modality we normally have to rely on financial intermediaries such as banks, mobile money companies and cash agents. So the services that these financial intermediaries provide falls under two broad categories. One is practically accounting which is creating accounts for beneficiaries, providing a distribution mechanism such as SIM cards or bank cards and so on and so forth, authorizing transactions when beneficiaries spend their entitlements and then eventually reporting to us so this let's call it accounting overall and separately it's actually paying the supermarkets or retailers where beneficiaries have made the expenditures. So a typical setup could be that at the beginning of the month the WFP gives a list of beneficiaries to the bank saying here's our beneficiaries, here's how much each one is getting and they will create the accounts and load them and we also have to advance money for usually a month to the bank to enable this essentially transfers to happen. The bank then contacts the beneficiaries saying here's you know you got some entitlements the beneficiary goes to a store initiates a transaction, the store pings the bank for authorization, the bank checks the account if they have enough entitlements they confirm back and then eventually post-factum on a periodic basis the bank will pay the store for the actual transactions that have taken place and let's say at the month end we get a report from the bank saying okay this is what actually happened. So a number of issues that exist with this model one is cost of course the bank will charge a fee for this and at our volumes that cost can be quite high there's the element of risk because we're advancing money to the bank and especially in the kind of places that we work that risk can be quite elevated if the bank goes bankrupt or comments fraud or whatever. We have to share beneficiary information with the bank they need to know essentially who they're opening account for and you know privacy could be compromised. There's the issue of reconciliation so when we get the report back from the bank we make all sorts of efforts to verify the report to make sure that the transactions that they say took place actually did take place and this is very laborious and time-consuming and we can never really be a hundred percent sure. There's element of control so normally for everything we want to do to add a new beneficiary increase entitlements whatever we have to formally advise the bank and they would do it on our behalf and in addition to the administrative burden there could be a time delay which could mean somebody goes without food for a while. We make a lot of effort to reduce our financial risk and also to protect beneficiary privacy but this also means the contracting can take quite a long time which again would delay operations and then there's the issue of flexibility so for example if we wanted to do e-vouchers and mobile money and ATM and these companies don't talk to each other and we don't know in advance where beneficiaries are going to redeem their entitlements we can't really enable this. I mean the best we can do is you know divided a third third third or half and half but it's not ideal so it's not quite flexible. So what we've done essentially is replace the financial intermediary with blockchain and so what we do is now we load the you know create blockchain accounts for beneficiaries load them with the entitlements we inform the beneficiary they have entitlements that they can use they go to a store they initiate a transaction the store now pings our blockchain instead of the bank for authorization we check the entitlements provide confirmation and then at the same frequency that the stores got paid before we know essentially which store to pay how much by when and we make the transfer to this door directly ourselves. Just a clarification here we have bank accounts and this transfer currently is happening through regular banking channels in the future it could be crypto for example or a digital national currency but the difference is that we have 350 global bank accounts that we use for you know making staff payments buying corn and so on and so forth and the cost of payments directly for us is really cheap the main expense to us with the previous model was this accounting issuing cards POS terminals that was essentially the main cost so yeah just to clarify that you know we have our corporate bank account where our bank accounts are and then usually we have to have a separate bank to provide those distributions and that's what we've replaced. So we did a quick proof of concept in the Omarkot village in the Sin province of Pakistan in January and based on that learnings essentially we've launched the pilot in the Azraq refugee camp in Jordan since May this year in the past five months we've been assisting 10,500 beneficiaries we've distributed $1.6 million $200,000 transactions without a glitch and we've reduced the fees essentially by 98% so that is what that accounting portion costs us and the 2% is what's costing us to make these direct payments in the future with crypto or something else that could even go maybe near zero we're not sharing any beneficial information with anyone so again in a refugee context that's very important we're not advancing any funds to anyone so our financial risk is greatly reduced we have full control so if we want to change entitlements at someone new we can practically do it in real time so the assistance is not delayed and very important for us we've authorized every single transaction so we have 100% certainty that you know what we think happened actually happened and that significantly improves our reconciliation process and the associated payments so it actually reduces the effort on our side then because we don't have to worry as much about financial risk and privacy protection then we can actually contract people faster which means we can operate faster and we're much more flexible now because all the accounts are on blockchain we can connect to as many outlets as we want and they're all coming out of the same wallet so we can provide that flexibility to beneficiaries which again it's great so you might ask why are you doing this on a blockchain you could do this essentially on a traditional system and that's completely true we could but one of the main reasons is that there's currently a lot of fragmentation and duplication in the system and this is only for illustration but essentially imagine if you have Bob and Bob is receiving assistance from four different agencies each agency would have their own identity system for Bob they would have the contract with a separate bank and they would channel assistance through each one of these and of course you know there's a cost and the effort required to maintain all the system the contracts with the bank and they don't talk to each other so that really the things are not optimized or harmonized in a way so what we're hoping to achieve and actually one of the reasons this exists is that the technology in the past has prevented collaboration among the agencies but it's various ownership issues so if we have a system and we have a contract with the bank and someone else has something else we don't want to use theirs because it's not cool and vice versa and what we hope is that blockchain can cut through that as a neutral mutually interoperable equally owned all of that you know open source system then it's not really owned by anyone but it's just a mutually operated system that everyone can use and this way we can have beneficiary let's say identities or wallets on the blockchain for Alice, Bob, Dave and Erin and various UN agencies can link to that and provide whatever service they want to provide and giving our various networks like for us as WFB with food retailers someone else might have health network like hospitals schools whatever else it might be but we can really dramatically increase the convenience to beneficiaries and if we're all using the same system we can really optimize and harmonize the global aid effort into an unprecedented degree with big data artificial intelligence whatever it might be and I think extremely importantly is the element of financial inclusion I mean even now without using crypto we were adding quite a bit of that to the people who may not have a bank account before but with the advent of that investments, loans, microloans insurance, international remittances would be available to many hopefully soon at low or no cost another reason why blockchain is that essentially so we have now let's say an identity for a person on a blockchain and we as WFB are adding financial transaction histories to it and just showing that you can spend money is not very meaningful but if you show that you can save a little bit of it that might actually count towards a credit history and this credit history now is not tied to a specific financial institution let's say in Jordan so if a Syrian refugee were to go back at some point they could transport this history with them and maybe get a small business loan and get back on their own feet if we manage to then bring other actors on board we can maybe add education data, health data, asset registries, work permits and make this profile increasingly more and more useful to the beneficiary wherever they are without the fear of persecution for example because of their origin or whatever and I don't think it's unfeasible to think that one day maybe even government IDs could end up on the blockchain and we've heard here a lot of things about privacy protection, zero knowledge proofs and I know it's at an early stage but essentially this is the path we've just started and we hope as technology advances connectivity becomes widely available the price of smartphone goes down in a few years time this could be a full-fledged essentially tool for beneficiaries to have more convenience and flexibility so our invitation is to other humanitarian actors who don't have a profit model built in is to not to reinvent the wheel so we have note set up we have essentially smart contracts for cash transfers and we're asking if they have resources they join us open source free and dedicate their resources to making the system better, more secure add additional modules like identity, supply chain, health, whatever and also make it available for free so that it can essentially grow to be a system everyone can use. Currently we're using a private chain and mainly that's because the transaction throughput that we require can't be handled on the public chain but one day I mean philosophically we're leaned if we can ensure security and privacy and all of that to move there so the system is self-sustaining and doesn't even require the backing of WFB or the UN or whoever else for it to function. So yeah please join us and we'll see how the world is vulnerable. We really look forward to collaboration. This is a photo from our proof of concept that we did January of this year in Pakistan essentially and for this we used the test net to distribute food and cash to these beneficiaries essentially to illustrate the point that it can be done and using that learning then we've done our Jordan pilot which is now going well it's five months in production and we're soon going to scale up to 100,000 beneficiaries and a few months after that to 500,000 which is all of Jordan and from there other countries could follow that have you know that could potentially benefit from this solution. I have many many people to thank of course WFB itself for providing this safe space for this idea to take root. The Ethereum community at large who has been very supportive for allowing me to be here today and get this message out and also Rich Boto, Rich put your hand up who we met at the Singularity University around this time last year where this was just an idea and we had very little resources and support and we told him about it. He helped us think through it and he pretty much hacked together what we used in this proof of concept in the photo that you see and he did it for free. No matter how much we insisted he didn't accept any funds so in a way he gave us that initial push that we really needed to get this pilot flying essentially. My email address is there if anyone is interested in more information and since I have a few minutes left I would actually like to show you a video of how the system is currently working. So in Jordan they happen to use IRIS verification even before we went so we wanted to seamlessly integrate into that so we don't change the beneficiary experience especially if we're going to fail and the system would have to be rolled back essentially. So what you would see here on one side is the cash register at the supermarket in one of the refugee camps that they ring up the normal bill, the amount comes up the amount is put into the IRIS scanning terminal, the beneficiary scans their IRIS, that IRIS then goes to be checked against an identity the identity comes back and at that point the terminal has both the amount and the identity of person trying to do it, they ping our blockchain can this person do this transaction, we reply and that's essentially the lot of oranges. On the bottom right you'll briefly see the amount, 1280, beneficiary scans their IRIS, transaction goes, the IRIS imprint goes to the database, comes back with the identity now it's come into our blockchain with the amount and identity and that's it. I have four and a half minutes so maybe one or two questions I think there was one already there. In this case you have this IRIS scanning infrastructure but in general in order to have established the blockchain identity especially a long term one you would need a private public key pair and do you have any solutions in place providing people with public private key pairs in such a way that they can control them and they have adequate security with them? Yes so good question one of the things we cannot assume right now is hardware and connectivity for everyone especially where we are right now in fact the internet is cut off again we're building towards the future so as you might know Google, SpaceX one web a whole bunch of people are working to bring affordable satellite internet everywhere in the world and the price of smartphones is just dramatically dropping so I think by the time we're ready the standards for identity have been sorted out zero knowledge proofs whatever we can then transfer beneficiary private keys to them potentially under devices and the device could even have a biometric authentication to trigger the private key but for it to be the data to be stored with each beneficiary and not centrally not sure if I answered the question. I'm working with a group in Bangladesh handling the refugee crisis with Burma or Myanmar it's about 1.5 million people first question how many beneficiaries can this scale to sorry how many beneficiaries? Transactions can it scale to can it do a billion a day because we're looking at these kind of numbers with 1.5 million refugees the other question is can it also not just do payments but learning outcomes and then settle with some incentivized collection of meals or some kind of incentive and what do you think can we connect later because it's a long question yeah definitely and how do we become partners with you? Sure get in touch my email is there and so on the first question as I mentioned we did our proof of concept on the test nets we're doing a private implementation right now and we can handle quite a bit of transactions per second so a million a day won't be a problem in fact we're shooting more for more because WFP itself has 80 million beneficiaries that we serve in 80 countries and we've heard over the past few days the efforts are in place to essentially speed up everything potentially even the main net and so we don't foresee the throughput to be a limitation either now on what we're doing privately or even in the future and then yeah I mean definitely that's I think the beauty of smart contracts that you can program whatever logic you want into it so for example we have school meals which means that generally speaking let's say if a child attends school then the child in the family gets food so it's in a way to ensure children attend school and also that some nutritional outcomes are met and with smart devices and everything yeah we can essentially sort of automate that so that you know the entitlements go into a smart contract in escrow and if certain conditions are met then they're triggered to the end recipient or recipients 56 seconds for a really quick one in case there's one cool thank you


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