Decentralized Networks Summit at BUIDLWeek

Church ES I can't find pain there we go so welcome everybody my name is Tom Trowbridge I am a co-founder of fluence labs and we are pleased to be co-hosting this decentralized network Summits um we're just getting it kicked off right now and very happy for all of you guys to come we have a great cross-section of kind of critical infrastructure providers for a decentralized network and a decentralized world and you're going to hear from all the people here fluence obviously sphere and Jensen Phil mine streamer and lit and you know follow kind of a normal element we will start last is first so lit is on the far left but we're moving them first David Snyder um founder of lit is going to introduce it it's a decentralized key management protocol which has you know huge implications for keeping data safe and managing data securely and in a decentralized manner so with that I'm going to turn over to my friend David thanks Tom I really appreciate the fluence team for inviting me here and thank you all for attending um as mentioned my name is David and for the next several minutes we're going to talk about Key Management specifically decentralized Key Management so to start by a show of hands does anybody recognize who this is yes excellent and so another way to think about who this is from the perspective of Key Management this is a centralized trusted custodian if you are familiar with this uh film and if you know how the story goes it is that centralized custodian this is from The Matrix that gets compromised and lets Neo into the source and just using this as a kind of a fun example to illustrate some of the issues with centralized custodians it's not just in web 3 but also in the world of film and so it really comes down to the question where where are the keys stored so in The Matrix example and in many examples across the internet keys are stored centrally in Key Management systems or of course self-custody by individuals who want to interact with distributed systems or privacy systems like signal chat for example and then for us in the context of building a distributed Key Management Network where are the keys stored another way to ask that is can the internet keep a secret which is a big question that has been floating around this space for a long time and I'll tell you about our attempt to let the internet keep a secret as well as what that can be used for so first a little bit on the network itself this is as mentioned a distributed Key Management Network there are three core components that I'll quickly go through that make this possible that are listed here it's a dkd which is distributed key generation that is a cryptographic process where a bunch of nodes can come together they exchange polynomials and as a result each of those nodes now custody something called a key share that key share does not represent the entire cryptographic secret but a threshold is set whereby a 20 out of 30 nodes for example or in the example of like coinbases uh coinbase's dot browser they do a two of two scheme but the scheme is tunable where you can say you need X of n of these key shares in order to use this underlying distributed key so that's one of the core cryptographic Primitives at the heart of this network the other one is something called PSS which stands for proactive secret sharing and sometimes you hear this referred to as a key refresh and so the problem that this solves is okay you have a hundred nodes they have this shared distributed key 70 of them need to sign off or authorize any action with this underlying distributed key but how does that Network persist over time how can you ensure that nodes can't join and leave the network over time and that is where proactive secret sharing comes in which is a method for assigning out new shares to the next Epoch of node operators where you can maintain the same underlying secret that same underlying elliptic curve in the example of web 3 but have a different set of parties holding that secret in distributed custody and then the final element that makes this network complete is the use of secure encrypted virtualization it's kind of like an enclave it is a is that a black box and so all of the nodes in this network perform all of their operations inside of Sev and the role of that is to prevent the node operator from ever having access to any of that private key material which in this case as we talked about earlier is that private key share and the top two things dkg and and PSS uh typically like these are these are sub components typically you hear this referred to as a threshold cryptography more generally and then the overarching category that these processes are part of is something called MPC or multi-party computation so another way to think about lit is as uh MPC key as a network or a distributed Key Management Network as we've been talking about so now let me tell you about how it specifically works just like with public key cryptography so independent of anything distributed just with an asymmetric key pair a key pair basically gives you two core features the ability to encrypt stuff like how signal works and the ability to generate digital signatures like how metamasks works when you want to sign a transaction and push it to a blockchain and so I'll talk about both of those features in the context of distributed Key Management first starting with encryption and decryption and so what we're looking at here is an oversimplified diagram but basically the way that the process works is Alice can encrypt some data client-side so let's say it's like a video Alice can encrypt some data and go and store that on ipfs on a blockchain on our weave wherever and with that symmetric key that's generated client side from Alice Alice can encrypt that to LIT which again is a key pair in its own right and set a rule cool and so some of the rules that we've supported thus far are based on blockchain state data so developers can say hey let somebody get this key when they hold certain Assets in their wallet and so this is like an oversimplified way to describe this is like token gating so you could say somebody needs to hold this nft in order to decrypt this content and so the cool thing about it is the decryption so Alice encrypts that data she encrypts that video she sets a rule that says only somebody who's a member of my Dow for example can decrypt this video and view it Bob shows up later on Bob is a member of a Dao he signs a message which is his proof and he broadcasts that to the nodes the to the lit nodes the lit nodes then as we can see here validate that data against the blockchain is Bob a member of the Dao and then if yes each of these nodes generates something called a decryption share you can think about it as like an authorization and sends that down to Bob client side and this is what's really really cool about MPC is it's almost like a client-side consensus there's no interaction between the nodes this is It's like a nice mental model is similar to the way torrenting works but rather than Bob collecting little bits of a movie what Bob is collecting is little bits of the ability to decrypt that symmetric key so Bob collects all those decryption shares client-side and now he can decrypt that symmetric key that Alice initially generated and then use that to decrypt the video and yeah he's in business um and so now we'll walk through a couple specific oh yes one more thing I want to mention before we get to the use cases is it's not an exact definitional match but this is kind of like what they would call identity based encryption and I pulled this page from Wikipedia and as you can see like identity based encryption to date does require a central authority to do that key provisioning and that's the invention that we've have here is being able to do the key provisioning but without a central Authority in this threshold-oriented way by dividing the key up as we discussed earlier and now let's talk about some of the use cases so if you're familiar with lens protocol it's an nft based social protocol inside of the lens SDK they have the notion of gated Publications this is a screenshot from the lenster app where when somebody makes a post they can say only collectors can view this or only people who follow me can view this it's the same process that we talked about before where that poster or creator that content is encrypted in the lens case it's stored on ipfs and then lit is being used to validate does somebody hold the follower nft or does somebody hold the collection nft and if so they're able to decrypt that content this is also being used by orbis club which has built out a social graph and chat interface using ceramic as the storage mechanism so similar kind of mechanic this is a screenshot from the orbis SDK and so lit and the key provisioning that we're talking out here is being used in the context of token gated messaging and chats we'll see if this is going to play but I'll be able to talk you through it so what we're looking at here is quite cool this is an example from a team called krebit and what they're doing is using a third-party vendor to oh yes full screen good idea what we're seeing here is they're using a third-party vendor to validate some kyc information and then that kyc information that proof of somebody's age for example is going to be encrypted client-side and also signed by that vendor and then in this case stored on ceramic and then that user gets selective disclosure around who can decrypt that information and so like selective disclosure I just want to Define that for a second a kind of a a web 2 analogy of what selective disclosure is if you've ever signed in with Google and you see that screen that says hey we would like access to your contacts or your calendar or whatever these Scopes might be that's obviously if there's three parties in that system there's the user there's Google which is custodying the data and then there's the third party app that wants that data in this example that zero party data that data belongs to the user and then they can have a direct relationship through provisioning a decryption key who in this case can see who can decrypt this kyc information just in the interest of time I'm going to continue to move along here and then I know the team from streamer is talking later they've been up to some really cool stuff around using this key provisioning mechanism conditional decryption is another way to think about it access control is another way to think about it for having a distributed data Marketplace so that is some use cases and how the encryption works um oh yes and then there's one more feature that this actually just I just put this in last minute this got uh released two days ago so for about the past year and a half uh the SDK was set up to only be able to provision decryption keys or perform the access control based on any state data uh it is now possible to have this network provision decryption Keys based on any HTTP endpoint any API so really any data on the web can be essentially used as the input for an ACL or Access Control list and this feature is called lit actions conditions and I'll talk more about lit actions in just a moment when we get into some of the signing stuff and so the product uh around programmable signing and again just going back to the framework that I talked about earlier with public key cryptography you have encryption and you have signing and so this is kind of the other core feature of public key cryptography we have this product called a programmable key pair and what it is is exactly what it sounds like it is a distributed custody wallet AKA key that can sign based on rules and um let's take actually a look here I want to show you what these rules look like so these rules are encoded in JavaScript and what we can see here is a JavaScript function where it says sign ecdsa I don't know if you can see that here but basically it is a mechanism in a single function to say I want this distributed wallet to sign on these conditions for example well actually we'll go ahead and look at some examples for example in the context of um of creating wallets for users so user onboarding and or what you might call retail custody so what we're looking at here is a few wallets being generated via oauth credentials so the user in this example is not custodying any seed phrase but also not reliant on a centralized custodian to to hold onto the key and that one is really really cool really excited for that as it relates to mainstream onboarding of web 3 using this distributed key essentially as the user's wallet and there's a lot of really interesting things around like where this touches account abstraction as as well another really interesting use case is around D5 automation we'll see how much of this you're able to see but this is a project that a community team built for using one of these distributed Cloud wallets to basically function as an immutable and automated escrow agent for cross-chain swaps so Alice sends some tokens from polygon into this wallet Bob sends some tokens from uh arbitrum into this wallet and then when they both approve the wallet because it can sign programmatically sends the requisite tokens to the appropriate counterparty and so if you think about like OTC deals over the over-the-counter trading of tokens this would be a mechanism for not having to have uh like a trusted escrow agent but basically having code as the escrow agent between blockchains and what's being shown in this video is just the process for sending the tokens to this programmable key pair and then approving it and then and then receiving them awesome and then the swap is about to happen and all this stuff is on our Twitter if you want to look at these videos we put out a lot of content as well and then this last one is from krebit as well this is really cool if you're familiar with what verifiable credentials are it's basically some signs Json links data uh in this case credit as we talked about stores this data on ceramic but it's like who is the signer of that verifiable credential what is the key that you trust and so what we're looking at here is a user who is connecting their Discord and then it is this lit this MPC Network that is signing that and so this user can basically add in a privacy preserving way their Twitter name their Discord name Etc to their data backpack as assigned verifiable credential and the signer of Last Resort isn't a user because how can you necessarily trust it's one user versus another but it is again this distributed signing mechanism and then so I'll kind of end this to say where are the keys stored coming back to this key question um and it's really interesting so we're right now in the midst of rolling out the the network there's a handful of node operators and I just want to kind of make a mention that if you want to operate a node in this network there's a contact us form right on lipprotocol.com we are like right in the midst of rolling this out so if that's something that you're interested in please do get in touch and uh maybe I'll have a time check here but if there's maybe a few minutes for questions I would be glad to take them and thank you so much for your time and attention [Applause] if anyone have questions let me know there's a question but I think I must yeah oh there it goes hey uh so the question is how does this differ from new Cipher and threshold Network so what they're doing is something called proxy re-encryption where the input to that function is a key so I could say this key used to be encrypted to person a but I want it now to be encrypted to person B and so the input is a key and so that person who wants that key to be re-encrypted to another party needs to be online it's very very interesting technology but for the specific use case that we were trying to solve around conditional decryption we actually looked at using them a few years ago it didn't quite meet that which is why we went through this MPC key provisioning mechanism but yeah it's really cool but basically the the the difference is that in proxy re-encryption you start with a key and it gets encrypted to another party wherein what we're talking about here around conditional decryption the key is encrypted to the network and the input is a rule and then the output is that same symmetric key so I have a question that uh how is it different from the Rand btc's MPC how is it different from the ren btc's MPC Ren BTC NPC yeah so like when BTC is also doing like that so how is it different from that one and also can you elaborate on the part that what account abstraction and multi seek put to to these protocols yeah absolutely so I'm not that familiar with this protocol so I can't talk to that in detail but I began to chat with you right after this and uh and learn more um as it relates to account abstraction to me probably the most interesting use case that I've seen around combining MPC keys with account abstraction or smart contract wallets has been around user onboarding uh there's a there's a team called patch wallet where basically they have set up an AA or account abstraction wallet where a user who's new to web3 can sign in with Twitter and the MPC key acts as the signer on the AAA uh on the AAA wallet and then it lets the user move to self-custody over time so once they aggregate some assets and some reputation they go you know what I've had enough of this MPC key I want to move to Hardware wallet they can call a function to boot off the MPC signer and and move to self-custody and it's a pretty interesting on-ramp for individuals that are entering the space we'll talk more about Ren too any other questions yep can you speak a little more to the like the type of conditions you can have like I you said something about HTTP endpoints but can you query a contract state for instance yeah so so um let's take a look at some of these conditions so the conditions like on evm chains for example can be any like here's an example of somebody needs to hold an nft within a given collection but also any kind of Custom Contract can be used as the input for Designing this condition so here in the moloch Dao contract they create the construct of a share and here you say if somebody has more more one or more shares they can do the decryption and then the new piece that I was just talking about in terms of using even off-chain data it's writing that function in those lit actions that we were talking about earlier that JavaScript and that can be connected to any data on-chain or off cane and then return a true or false and that's just encoded in JavaScript and you can connect to any point there and then you can basically have the lit node say you know if this returns true based on this JavaScript execution environment turning complete connected to anything provision the key to that individual yeah any other questions yeah okay hi I just have one very simple question you mentioned earlier there is a user case that uh so the the content creators can use semantic ways to encrypt the content information correct and Bob which is a Tau member of Addis can use some way to get this semantic keys from your distribution node so there is a question is that is it possible that the bulb could leak this semantic key because we all know that once you create create a symmetrical key and it's leaked all the information will leaked online is there a way you can create some kind of a time-based semantic key which gonna change over time yeah um so certainly you could Implement something around key rotation but like you're it's a very interesting question and the question is once somebody has that symmetric key or their content what's keeping that from putting it on YouTube or doing anything with that at and frankly there's no kind of obvious solution there so if you think about like patreon for example somebody subscribes to a patreon if I'm a patron of Mr Beast I can screenshot or screen record that video so they have access to the key and they have access to the content the reality is is that somebody's own laptop their own clients is their environment and they can take the content at that point one more question maybe yep um so we haven't talked a lot about the nodes uh but at the beginning you've hinted at at them using secure enclaves in order to not leak this information to uh the node Runners right um so what type of Enclave are you using because the sgx has been pawned several times and it doesn't seem to be better standard for anything yeah so what how have you solved we're using AMD SCV SNP it's the same processor that's used by a Google confidential compute and hasher Corp Banks Banks use it it's pretty new technology if you're interested in this type of stuff definitely worth taking a look at secure encrypted virtualization um it's technically different than an enclave and the specific use of that it's basically it's not the primary method of security the primary method of security is Distributing the key but it's functionally a backup method and my personal take is that anybody that's working on distributed MPC absolutely needs to be using some kind of encrypted virtualization or Enclave because it starts to get really scary uh if like if the node operator can be doing something with the private key material AMD SCV SNP great amazing thank you so much you're awesome another hand thank you fantastic um up next will be uh evgeny who is a co-founder with me of fluence labs fluence is a distributed serverless compute Network and I think all the all the companies that you're going to see all the protocols you're seeing today are critical and all function together fluence is unique in being just a compute piece of it and Afghani is going to go into a lot more detail about what that means but one thing I will tell you it's not necessarily it's not just compute at the edge and it's also just not um a compute that happens where the data is what's critical when you have serverless compute is that compute has um redundancy and is built in failover resilience and that is what I think has been missing in a lot of the distributed compute protocols that um have kind of been have have been out there so far and that's that's the pro that's the problem that fluence is working hard to solve so I just stole a little bit of um of evgeny's Thunder here but um I think he's got plenty more to talk about besides besides that um I think he's ready to go so with that again you want to take this mic and with that evgeny palmarov hello um so yeah I'm going to talk about fluence um so like basic introduction it's kind of obvious things you know we're moving into world of decentralized platform decentralized compute in particular um and you know if you think of how compute projects in webg space were designed you know like the first most Innovative for blockchains blockchain the new type of compute where you have consensus but because of consensus it's kind of restricted and slow and and uh kind of useful for specific use cases like uh digital value and then there was a like the generation of projects building um using blockchain to create uh compute marketplaces and basically it was about Hardware marketplaces to aggregate a lot of compute resources and solve all the um you know problems of humanity like big big scientific problems or like applied to some computational heavy tasks like rendering video for example um but but we um we've been looking for uh some solution that would allow us to have some general purpose um compute infrastructure to put our like application backends on or like you know basically like whatever we have right now in in a web 2 space um in Amazon and Google in like in the clouds to put it somewhere like to to run the actual code um somewhere um securely and and just decentralized fashion um and um you know uh obvious thing um platform risk in web to space if you're building on the cloud they can shut you down they can send the real account um if you using um some big social media or like a big platform API like chat GPT for example or like more older Facebook apis they can shut you down they can censor you they can you can lose business if your business depends on particular API provider um but we see uh similar things happening in web tree as we see um you know stories about tornado cache uh with all the um their accounts in in GitHub and everywhere shut down um we see how how metamask and inferior sensor IP addresses um and kind of some some people basically lose access to to the infrastructure and that's um kind of not cool we want to have something more um resilient and and more Universal more permissionless and open for everyone um and similarly to um web 2 API dependency and platform risks we have similar things in webg like today um many many companies in webtree um when they build their products on top of on-chain data usually they're products like if they ship their products as apis for the well for developers this API has been hosted on particular servers in particular Cloud uh usually managed by the team who runs the project and it means that whenever any issue happens any government pressure or you know bad day in in some of their team members they can you down they can you can lose access if you're using their API you can lose access um and and but the is that it's not because you know all these projects are bad it's because there is no just proper infrastructure that you can use to create your API and ensure it for other developers the way that you cannot really um get cut access from them and um yeah so so we see uh decentralized Computing like a kind of solution to um solve this problem and uh um it sits somewhere in the middle between like in terms of like capabilities uh between on-chain and and centralized Computing like for example uh it doesn't doesn't have any middleman doesn't have any uh particular company that that operates uh the network so you should be um in like you should feel good as you are not dependent on particular provider um at the same time it's verifiable um but it's not constrained like like a blockchain um and um you know if we have such infrastructure then it's useful for many many things we could do uh whatever uh you know we could do with our applications we want to manage by Dao because like if they are not in proprietary Cloud then the dial can make a decision about like application upgrades or like some changes and stuff like that so it could be like immediately enforceable you could build all kind of trading Bots uh you could you could think of you know distributed validators NPC RPC layers indexers oracles all kind of things and also you can think in terms of like a standard ideas about hey let's do better messaging social media and stuff like that because like you need some infrastructure to to build this and basically this is what values enables you influence is two big parts so it's a developable platform giving you tools and it's computer Marketplace organizing Computer Resources from different providers into a network so you can deploy on these resources and um yeah so again fluence is cool because it's permissionless potentially lower cost than clouds sensor resistance less dependency on vendor looking and secure um a couple words about developer platform so we're trying to replicate kind of um experience that everyone used to and best experience in the cloud is serverless so what we have is basically uh several tools that you can use like for example Marine is about us on the runtime you can think of marine as an analogy to to Lambda it's a little bit not the same thing but you know it's useful analogy we have Aqua which is um our distributed um execution protocol and the language but it's kind of similar to Amazon step functions so Google workflows which you use to express your business Logic on top of Lambda functions and then there are like some some other tools that also kind of similar to traditional Cloud stack um so a couple of things about how it all works um under the hood like usually in the cloud when you build an app uh you usually have some several micro services that gated by some API Gateway and your like users client devices they you they talk to backend through a single API Gateway that runs in the cloud and fluence implements the coordination free distribute execution protocol that allows you to like whenever business logic you program it allows to execute it over the over the network of providers without any Central centralized gateways so literally your app is being executed um without you know getting back and forth to single single place and um Aqua exists as a you know scorpion language that allows you to express um algorithms in terms of distributed Network so you can say for example if you have some tasks where you need to calculate some average of let's say um bitcoin price you can have the function that executes that that calculates the price on one machine and you can have several such nodes that only Host this function and only provide this function and then you have other machines other nodes that provide function that does average calculates average from the prices and then you have the script saying I want the input data to go to first to nodes that calculate the price and then force them after they calculated the price to send this data to nodes who calculate average and then to the calculated average to send to the um some some another user or like some output database or somewhere else and this will be executed by by the network by this participating peers without any Central coordination there will not be sending you know data back to initial client they will not be sending data to some blockchain or somewhere else it would be just like coordinated over the network um and aqua is super useful to build um all kind of distributed um systems from the ground up so from simple things like a pure Discovery resource Discovery um to um things that we used to have in the cloud like fault tolerance load balancing failovers Auto scaling and stuff like that um so basically what we have right now like some of the things exist right now um and and some of the things end up to to um like we commoditize some of the things into um more general concepts like for example we introduced concept of subnets and subnet is basically um the on-demand sub network of nodes that are providing some piece of application for example subnet could be like replicated having consensus provider of some data for example you can have like a ipfs subnet if you want a full tolerant provide provider on of ipfs data influence Network you're just rolling out ipfs subnet and then every every node participating in this ipfs subnet will be connected to ipfs network and they can optionally have consensus on top of data they may not have consensus on top of data you can basically program and customize the the coordination mechanism between these nodes in the subnets and by default fluence right now when you deploy your application your your function on fluence it will be deployed to on-demand subnet so the nodes will will opt in into providing your function they will um coordinate with each other and you can customize the way they coordinate you can implement it with basically um writing Aqua algorithm um so like fluence network is heterogeneous so every every beer is different every note is different it could host like every node decides basically whatever applications or functions it hosts and every every um node can announce and advertise that okay I'm I'm I can provide access to this this and these external things like data or like a blockchains or external apis and stuff like that so every node can be connected um to um external data and then provide it into the into the network uh for for the functions that run in the network um so the next thing is um computer the marketplace which is you know if you're familiar with things like filecoin filecoin is the marketplace of storage provider we're building Marketplace of compute providers and basically like if I have some compute capacity some servers I can join the network I can say okay like my um you know I I'm gonna charge this amount of money uh for my um resources and also I'm gonna provide these types of data for applications on the network and then I compete with other providers and basically whenever I deploy my application I can deploy it to many many providers at the same time by rolling out subnetwork um and the other thing is we um like basically when we think of decentralized um Computing infrastructure uh we want to like we think a lot about security like how do I like if anyone can join the network anyone can be provider how do I trust whatever they execute right so um and um in many solutions that we see on the market right now is basically I have to trust the provider I have to trust that they're gonna do the work correctly um and what we are trying to do is uh we're trying to bring cryptographic proofs to any types of execution and um because of the design how how the influence Works under the hood because of this aqua and Marine components we have several types of proofs we have proofs on Aqua execution which is like a proofs proof of um this distributed that this distributed execution were executed correspondingly to the script and all the nodes that executed every piece of it um you know signed the data correctly and so so the flow of the computation is correct then the other part is that the actually the function that every peer was executing is correct that's the proof of execution and then the other thing we want to bring is capacity incentives which basically when providers will prove that they um they own some you know amount of compute capacity they can prove that they they own it at some moment of time and then based on this we can like incentivize them with tokens to to be on the network to grow the network faster um so um how like where we are right now um um basically all these proofs are uh so proof of process improve Aqua is coming soon like maybe in a couple of months uh all other proofs are down the road um in terms of like developer experience and developer platform we are much more advanced so um this week we are hosting several workshops where we'll be showing the developer experiences influence and like it it's kind of like it the what we are releasing this week is basically the smooth experience so you can uh create projects and deploy it easily with like a couple commands um and under the hood it will it would do all the complex work it will uh create the subnet from from the notes on the network they will actually participate in the compute Marketplace that would be like hidden from basically like abstracted out from developer tooling um so they would under the hood would put some like token Stakes that would um um you know coordinate into the compute deals and stuff like that so like basically the state of the of fluence right now is developer tooling is Advanced so you can build applications you can deploy on a network the compute Marketplace is on a test net so like all the deals and stakes and stuff like that it it like it works but it's not live on the mainnet and then like we we begin to work more on implementation of proof security and and moving the compute Marketplace on mainnet after proofs are ready um yeah so some some of links um obviously and and a couple of things that also I wanted to cover um we're gonna host the several Workshop this week where we'll be showing the um one of the example applications like the simplest application that you can create on fluence it's a distributed um RPC resolver basically it's sort of decentralized infuria uh built on fluence it's a app that um when you use it you can deploy your own instance of RPC resolver that you're gonna that you can use with your website app and under the hood it would be a load balancing requests to uh different RPC providers that you can specify and basically this is like this RPC this nodes that providing access to to RPC providers would exist as a subnet on Plus network and all the RPC requests from your dap would be routed through fluence Network to one of these RPC providers based on the aqua algorithm that is written into this example application and that you can customize so we have bounties uh feel free to check out uh yeah and other events workshops join us this week yeah thank you any questions to evgeny yeah one minute can we have mics a little bit louder because we here in the second stage more hi uh yeah it's very cool I love Chevrolet is very cool but I have two questions for you first is that in today's Computing Services in cloud services serverless is not entirely replace all the computer computation like in AWS still I think more than 17 percentage of the Computing resource comes from ec2 not from serverless so the cross first question is how do you think the future of the web story of the Computing do you expected that the developers will use all their computational power and their resource in serverless which I don't think is possible there is still some kind of computational task which serverless cannot take so do you require the developers in the future gonna do a combination of the web 2 is A2 plus the decentralized the serverless to do the sense yeah well basically we we're looking at um Docker as well right like so so the thing is like you can um create secure system if you um have like but basically like if you if you build on Docker it's super hard to make it secure it's super hard to uh build proofs uh for the execution of Docker and you know you have a lot of other um issues with you know um slow start of Docker containers and the amount of memory they take amount of resources they take so but I I agree with you like you cannot put everything in serverless yes so like mainly right now we are focusing on wasm and serverless experience but like at some point Docker will be there it's just like will be a little different um security model for running Docker influence okay my second question is about the security because when we're running something no matter it's in ec2 or serverless sometimes we have to put some like security stuff inside like a connection stream to the database or some kind of a key to the API call so in your serverless Computing stuff if how could we trust that we can safely put Rose connection stream or the security key inside this random node and also beyond that you mentioned about you will do some proof of something to guarantee it's going to be execution by the way of the script but how could you kind of guarantee the nodes won't link the information or data during the runtime outs yeah yeah that's a good question so um with security Keys um we are like it's coming also like it's not there yet so right now basically like you can pass the key to the node to your function and deploy this function to the node but then the node will be able to like you know scan memory or something and and just see the key um so the solution is uh basically like end-to-end encryption so you protect it from at least Network nodes not senior key and then um using like securing place on the notes so like the note doesn't you know cannot cannot access your key and then um it still lives some sort of um trust to to the node provider because like um you still trust the provider that they run proper secure enclave and stuff like that and you know the only other option basically that exists is you always use NPC for signing whatever for accessing whatever external apis um and the nodes influence Network should only um oops should only get the shares of the key and that's that's the solution okay thank you yeah thank you yeah love your presentation so I'm really curious like if you could elaborate a bit more about how the execution flow or how actually the deployment flow Works say I want to deploy something like bft related right and then uh how do I contact with your P2P Network and in the process right say uh the nodes are executing some bft logic and then like some notes goes offline and how do you like uh do the fill over uh and in the process like do the notes like they just talk to each other and know each other's uh like performance ranking like how does that process work so basically um your bft algorithm is part of your application so like you you write the like you you have to implement this um algorithm of how the coordination between nodes will work and fluence protocol gives you discoverability it means like you when you write this script you can say Okay I want to like if I see this you know beer not available I I should you know replicate this to another peer or I can call this here and there um and then you deploy it to like to network when you deploy it to a network you deploy it to some nodes so it means that some nodes will have the basically like the the instance of your application and after they have it they can run it right so so so uh it's a little more complicated but like uh you can um basically like you can create current jobs right so uh you can run this like you can trigger this script by time this script that you written will do whatever you written like we'll go to this node and send this piece of data in and we'll wait for this response and then they will you you can write this coordination of course so uh say in the case of uh you know me running some bft logic right and then you know some notes goes offline and then the the notes that's currently executing will be finding some peer to uh basically fill up the spot right and in the process like do they know like which peer is better or worse you know how does that part work so this is right now is not part of you know default protocol so like the the protocol doesn't um give you automatically like you know substitutions of notes that that went offline or something like that um it has to be part of your application but we working into like you know for you to have it as a kind of libraries sort of uh when you build it up yeah thank you I saw some other questions here um yeah you go first uh in your example for aqua it looked uh it looked like a trustless mapreduce uh was uh was that just an example or is is uh is it more than that like can you do more than that with aqua you can do like any distributed algorithms yeah like okay this was just some operatives for example okay okay got it kind of related to the previous question in terms of how the notes are selected or who does The Selection so if I want my app to be run by like five nodes and then fulfilling certain criteria right how is the process of selection done or who are the person to select yeah so so if you deploy into compute Marketplace which is like a basically a public network of providers you have to make a deal with them right you have to negotiate the price and you have to negotiate whatever you're gonna run on them so this is a part of um deploy deploy protocol that like fluence just gives you so this kind of negotiation thing currently at the most basic setup uh the because the compute marketplace right now is not available for everyone I mean like for every any provider it's uh mostly nodes that runs run by influence Labs currently you deploy like when you deploy your application uh fluency lab nodes see that you want to deploy it and like basically a random subset of them say okay I'm gonna host it I like this application I'm gonna make a deal with it but this is the part of like making deal foreign thank you yeah we'd love to hear more questions and we have time for it any others yeah maybe oh my gym hi uh is it possible to run tendermint of Cosmos consensus on top of aqua well if you implement it in aqua yes but is it technically feasible to implement it should be um here's Bernard who is our chief scientist you can ask these questions and is it possible to run both modules deployed to fluence network on local device so if you have I want to just download the package and run it as I wasn't at any platform sorry I didn't get it so I want to download some fluence package to my iPhone and run this application like maybe this is a bot or conversion library of what as applications will run on top of lens well you you need to have fluence node to run the the application I mean like you need to have Aqua virtual machine and you need to have a marine execution like wasm execution thank you great I think we're good great thank you evgeny let's give it and give me a hand please thank you and you know one one question that came up earlier I want to address is is everything moving to serverless the way evgeny had a slide up there earlier you know the cloud has a dominant share of compute now but there's still people running compute on servers in companies in businesses right so it's been a long transition over decades to the cloud and we expect there to be a transition to serverless decentralized over time as well it doesn't happen all at once and there certainly would be applications for which that's not relevant but over time serverless will probably take a material share um happy to introduce Harry who's going to be talking about a super exciting topic which is the training of artificial intelligence models using decentralized compute and I think there's probably no hotter topic right now than the chat GPT you know llms and that is has a terrific overlap with decentralization and I think the further these models get in use the more important is decent realized training is going to be and more more we're going to recognize the need for it fluence actually fits into this not from a running of it but um or training of it but from an actual running of the models and so when you think about llms there's the training which is massively compute intensive and I think that's what you're going to hear a lot about from Jensen and their advantages in doing that and that has um really open source kind of um implications as well but then once they're trained they also have to be run in a decentralized way because there's lots of censorship potential in the actual running of the models I'm actually giving a talk on on Thursday the Denver on on exactly that topic and influences potential role in it but that's separate from the actual training of the models which is a more intensive computation more cost intensive process so I think um with that we're about to learn a lot more about that exact process and the costs and um and Security benefits of running it um with Jensen okay in 20 seconds personal things we're good so that Harry from Jensen give him a hand thank you perfect cool uh great to be here everyone and um hands up if anyone got a t-shirt or a hoodie from the back uh the back stand a few a couple people okay there's lots back there and we have to get rid of them um so um I'm gonna do this presentation minimize because I can't uh connect to the Wi-Fi here but if it changes uh we'll we'll go ahead and do that so just to kind of kick off um we're going to talk about uh kind of three key things today firstly deep learning as a topic area deep learning is something which is in the public eye uh at to a huge extent currently lots of people are talking about it lots of people here are talking about it we've been doing these conferences for a while and you know it's good to hear it kind of coming back into the back into the kind of uh the Limelight uh so we're going to Define that quickly we're going to talk about the compute problem which is inherent in deep learning today and then finally we're going to talk about what Jensen's doing in terms of building a permissionless supercluster for deep learning computes so to kick off we'll talk about what deep learning is we'll give some examples and then we'll talk about the kind of wider Market the the easiest way and I think I described this to some of you over the stand at the back to think about deep learning is to think of it as a subset of machine learning and inside of that machine learning a subset of artificial intelligence if any of you have ever done any algorithmic trading or if you've ever kind of done any sort of logical programming ever uh you could make an argument that artificial intelligence starts when you tell the machine to do something and it's able to operate on its own and execute that task simply here you know buying Bitcoin under thirty thousand dollars which maybe wouldn't have been a good trade um machine learning takes that idea of asking a machine to do something but it lets the machine draw the conclusion about when to do it so specifically you give a machine lots of data about the price of Bitcoin versus dollar and then the machine determines say using a line of best fit when the most optimal point to make a Traders so like above the line sell below the line by Deep learning is a subset of machine learning with two kind of key caveats the first of which is it uses vastly more data orders of magnitude more data so if you think about some examples like a driverless car um image recognition you have to have hundreds and hundreds thousands of hours of Drive of a car driving around with the inputs to the steering wheel to the brake to the gas Etc in order to to build a network there whereas statistical machine learning classical machine learning typically uses smaller data sets and secondly they use neural networks which are models that roughly approximate the biology of the human brain But ultimately just allow for something called hierarchical feature representation which is essentially the ability for each consecutive layer of a network to learn something different to the one before it to kind of paint a picture of this uh free blue on Brown did a fantastic overview of how an image recognition model works for a handwriting data set this is the amnest data set if anyone's familiar with it essentially you take an image you make the image into a vector and then you push it through lots of layers and you know you know a four handwritten four equals a four you can tell the network that and over time a network learns to slowly calibrate the various parameters it has in order to produce what it sees a four it's never seen before the number four uh in terms of an output the idea here is you expose the model to lots and lots and lots of data and over time it slowly nudges down to understand exactly you know what certain images mean in terms of the outputs um the a kind of more mathematical way of thinking about it is as a kind of lost space so if you have say the image problem the more you expose it to kind of different styles of handwriting maybe maybe slightly blurrier um blurrier images of handwriting Etc you're able to over time find the kind of global minimum in a search space where the model is appropriately generalized to the problem and you could get a totally new handwritten number show it to somebody show it to the model rather and they'll be able to classify it correctly the trick is though as we've said lots of data and also that data has to be exposed to the model a lot and that typically means enormous computational capacities required the kind of flip side of this is the more compute you have the crazier things you can do so these are kind of a Jensen are kind of four favorite examples one of them is the neural link monkey pong studies wherever you use neural networks to process monkey brain signals in order to make a brain computer interface allow it to play Pong remotely secondly if you think about whenever you're on a video call many video calls or radio platforms or whatever they use a form of compression which uses machine learning to essentially upscale the image so you can have a kind of identical bandwidth to a normal codec but using this kind of machine learning codec you're able to kind of increase the resolution effectively thirdly one that everybody knows recently is chat jbt this is the kind of output of an enormous neural network which is trained on the majority of the data on the internet uh so the kind of scale of this is kind of quite quite difficult to kind of frame basically internet scale Text data sets put for the model you know more than once in many instances although we don't know yet because the research hasn't been released and then finally one of the classic examples of driverless car image recognition if you want to look at this kind of more from a market perspective every three months the average state-of-the-art model size doubles so essentially like for like doubling the compute requirement of anyone using uh using those models and this corresponds to roughly Two showing dollar market by 2030 uh for Hardware so it's hard to understate a the speed of advancement in this space but also the kind of enormous economic uh economic kind of consequence of that advancement so to quickly summarize some deep learning it uses artificial neural networks to learn patterns it requires lots of data and crucially for the rest of his talk lots of computational power it's a lot of compute Cycles the Market's enormous and as we've seen recently of chat GPT and we'll continue to see as the world gets weirder and it's going to completely change the modern Human Experience so at Jensen we're solving the compute problem within this um within this kind of scenario let's talk about three things so principally the key issue which is a price versus scale trade-off we'll talk about the supply side issues and we'll talk about the concept of freedom of compute which is something that fluence mentioned so everyone's familiar with the kind of the kind of bad guy in web 2. um many people kind of they don't usually put numbers to it 61 to gross margin on ec2 it goes up to sort of 70 on Azure uh it could be higher for more kind of bespoke compute providers essentially all if you're trying to train something of the order of chat jbt over half the cost for training which is the main cost is going into someone else's um kind of bottom line profit which isn't very helpful if you're trying to advance the frontiers of deep learning typically when you want to train a large neural network you have kind of a few options you can either buy all the gpus yourself which is really only an option if you're a large player um or otherwise you basically have to work with AWS so for a Nvidia a100 which is a kind of standard deep learning uh GPU on AWS that's going to cost you roughly four dollars if you use a specialized deep learning compute cloud provider like core weave it's going to come down to roughly two dollars but you won't be able to get as much as you could have got in AWS and if you go even lower you could buy the physical a100 yourself the cost is very low but very very hard to scale this creates an issue whereby if you want to compete with Google or you know Microsoft you have to essentially pay an absolute Fortune GPT for example is owned by Microsoft it's trained on Azure compute Farms the original deal they did with child with open AI was for a billion dollars of azure credits uh that's very difficult as an outsider to compete with and most people are Outsiders thank you secondly even if you did have access to that compute it can be turned off if this is something which is ever present in these conferences it's something we think about a lot as neural networks begin to replace not just human decision making but also speech I mean chat GPT is a speech bot it is a language model we've kind of made the sort of we've run the kind of third chapter of technological advancement here we had originally kind of constitutional biological rights you know you can say certain things have been shifted over into kind of transistors around the middle of the 20th century and now we're in a position whereby people can be programmatically censored models are you know being requested by core dictates I have kill switches put them in the European Union um and there's now of course a scramble among all the states in America to enact laws to prevent large language models and their consequences but more broadly it may be more relevant for this audience there is simply a huge amount of computational resource which isn't being used in the world today if you were ever like an ethereum Miner you'll know that the category Days Are Over they're if you're holding on to any of that Hardware if you want to try and attach that to something which is going to give you a yield your options are very very slim and the yield you're going to make is a fraction of like what you previously received it's caused many miners to go out business but moreover it also doesn't provide a kind of incentive for underused compute to to find out find a home serving useful work so to summarize oligopolists are extracting fat margins even if you pay the margin it's hard to get the scale oligopolists governments like the censor people and we've seen that kind of you know Ad nauseam for the past kind of decades and finally there's extra flops of compute which just aren't being used in the world today which could be you could be kind of put towards useful work so at Jensen we saw this by building a deep learning supercluster we're a trustless layer one protocol which is solely focused on training users submitted neural networks crucially when we say training we don't mean inference so when you use chat jpet you're using the model in a kind of inference setting it's producing a result for you we're fully focused on everything up until that point so all the dollars spent to build chargpt and it's kind of um kind of its analogs we reckon that the price can be up to or more than 90 cheaper and your standard AWS GPU compute using an a100 is a benchmark we're able to maintain security at scale given a novel verification system I'll speak about in a moment and we're seated with over 50 000 gpus in our community many of those coming from disgruntled ex ethereum Juan miners and were backed by a number of uh of uh leading investors so just to touch on our verification problem here uh when you think about a model learning essentially uh kind of a trivial way to think about is imagine the models as taking a test you know how many mistakes is it making that's the model loss and over time models typically make less mistakes because they're exposed to more data and they've also made the mistake before so they're able to kind of correct for it probably solve with the kind of handwriting example the issue of training neural networks in the context of verification is freefold the first is neural networks are very computationally expensive people in our community spend weeks training individual models so that's an AWS ec2 instance in the cloud running for multiple weeks on end for one model secondly models are typically probabilistic they're not very deterministic so lots of the GPU operations which have made Modern neural network and deep learning breakthroughs possible are you know the the speed advances comedy expense of essentially determinism so if you want to do any hashing that goes out the window because it's difficult to kind of maintain identical results across different devices apparently and finally these are just very big models if you want to kind of paralyze them if you want to send them around typically uh typically the network overhead is is often the bottleneck all their ways to get around that for us we solve this problem by using a form of polynomial or a contraptive proof and targeted replication based on that proof our thesis is that basically as the model trains you can monitor the loss of the model that the loss begins to increase on an unexpected way you essentially do targeted replication you pinpoint that part of the process and because you're using a proof there's much less overhead required in terms of sending around model parameters and data secondly we also apply contiguous replication over the top of the model training process in order to prevent what we'd call a laziness attack essentially there's kind of two types of attack we're trying to prevent here the first one is laziness so basically someone wants to get the yield for training a model but you don't want to train the model that's number one and the second is vandalism that's the idea that maybe a malicious actor is on the network they've seen that you've given your model to the network and then as a consequence of that they've decided that they want to you know cause a problem uh our system catches and solves both of those problems and the kind of consequences when you think about that kind of previous trade-off between scale and efficiency we're able to basically take that very very cheap unit cost of compute that sort of two cents on an hour compute that you kind of see in the ethereum classic mining pills to an a100s and we're able to take those gpus and apply them to this much more valuable task at a global scale a scale which isn't kind of you know limited by the size of a network to kind of like paint a picture of this um you can think of the community being four types of uh well two kind of four types of user in the network two in the demand slides and then two in the supply side on the supply side you have your kind of classic ethereum Miner you might be mining Raven protocol right now they might be mining some basically any number of proof of work clients which are materialized proposed the ethereum merge uh if you've got a100 standard deep learning GPU you're typically looking at two cents an hour um if you're lucky uh for for you know maxing out that GPU equally if you're a cloud provider so let's just say your main business isn't inferior mining it's not even web free but you've just got kind of spare compute Cycles uh you want Revenue predictability we speak one of the largest data providers or our rubber Cloud providers in in Europe who for three or four years actually was a substantial theory of minor they made about 30 million euros per year uh in Revenue Topline revenue from utilizing your gpus when you weren't uh otherwise being used by their customers um these people either want more revenue or Revenue predictability on the other side you have people who are using ec2 and are paying through the nose for that same a100 these are real numbers you know they almost don't feel real like four dollars on one side and two cents on the other side but this if you you can look at it right now in AWS or the classic pills this is this is as of this morning um there are people who are paying for the nose for these a100s it basically means they can train the models for less time or we just can't train them at all and then there's some people who use more specialized providers but even still we reckon that the price that we can get it to including the verification including the network overhead results in an 87 and 74 saving respectively and of course the benefit to all this is you can train bigger models for longer and bigger models being trained for longer results in much more advanced neural networks which allows many more breakthroughs in the field if you're interested in either a supply and compute so let's say you were a miner or you just actually have a gaming PC or maybe for some reason you have a number of a100 server kind of contemporary devices we'd love to hear from you scan the link the QR code secondly if you're more broadly uh just a deep learning engineer or you're interested in trading large language models or Vision models then as well we'd love to speak to you because we have a solution which will be significantly less expensive than the uh the kind of web 2 equivalents that's it thank you thank you so much Ben and thanks for the swag week we're having food hoodies and t-shirts at the table so yeah feel free to take it any questions guys yeah um I think yeah thank you for a great presentation so my question is uh when you when you when your team got started um what what's your strategy to bootstrap and scale the network of suppliers uh sorry one more time so uh when your team got started uh what was your going to Market strategy trying to bootstrap and scale the network of suppliers of computation power a great question um so we benefited from the ethereum merge basically uh the ethereum merge is a kind of uh a blessing for us because the typically in these in these in our world and I'd probably include some of the other um companies and protocols here today the supply side it tends to be relatively I describe it as like coin operated so if you're a miner and we go to the kind of Bitcoin mining conferences and we're in the various mining circles um these people just want money you know and if there's a way to get it by buying an a100 and standing up in a data center or even a shipping container in Serbia they'll do it um and to post the kind of a fury merge the um essentially the kind of opportunity cost of uh of mining became became a lot lower because they didn't have the ethereum one network to mine on so that was the kind of first kind of I guess Tailwind that we had secondly with the kind of huge interest in uh in deep learning which we're seeing just now there's demand from kind of hobbyists from people who don't have access to these large clusters from small startups as well who typically get to the end of their kind of AWS compute credit package of like a hundred thousand dollars and they're like wow this stuff costs so much money you know I'm bleeding cash whenever I try and train a model who are looking for cheaper alternatives and it was basically going after those people on the demand side and uh the kind of disgrunted miners on the supply side which allowed us to get the initial Community we have today which is about 2 000 people the the kinds of things thank you one more question uh-huh yeah yeah I see hi it's very impressive I have one question because I think many people had the same problem before I used to work for the recommendation system for tick tock as you know we have very large models and we will use very stable cloud services Channel model it Trends one way like you said weeks but there is one thing which suffers a lot is that some note will die in the training because of the memory issue because of the desk is sure because of any possible issues so if some know the fail we have to retrim the whole model again and so in your uh Solutions it's decentralized to use the resource I I personally think that the note the stability of the node will be less stable compared with the cloud Solutions so how we're going to overcome with that what do you think about the ml Ops yeah great question um also uh could you guys turn the turn the speakers up possibly just for the for the audience of us okay thanks um the um so yeah basically machine learning operations how do we think about the kind of fault tolerance of our system versus the uh the kind of incumbents in web too so there's kind of that you have to kind of um bisect that question there's the data and kind of side of it and then there's the compute side of it we've sold the data side of it with a proof of availability subsystem in our Network which essentially Erasure encodes and chunks data so it kind of creates a fault tolerant um data availability layer in terms of the compute side that's a good question um and it really depends on the model architecture you're using so the jobs on the Jensen Network are quite small relative to the overall model and essentially um we try you so if you've got something at the order of maybe gpt2 so a model with over a billion parameters which takes a long time to train nowhere near as much as chat GPT that model will be chunked into thousands of little jobs what we do is incentivize each individual no provider to number one not go offline so you just can't go offline if you go offline you're going to be severely punished for it you'll lose your stake but if you do go offline um at the kind of model level or the abstraction level you're able to train models in ways which don't require every single individual um job to be completed number one and number two you can also see that the job hasn't been completed and then you're able to kind of as a consequence retrain that job the cost of retraining is covered by the uh the node which fell off so as a user it's more fault tolerant in the sense that decentralized networks tend to be more fault tolerant however at the cost of it maybe being of slower so there's a little bit of a speed cost there is an issue that we don't see being an issue in the long run as more kind of institutional players join the network similar to the way that Bitcoin miners have uh somewhat become institutionalized over the last decade I have two questions the first one is do you have some metrics showing um for Bitcoin mining for example we have a farm right at which point it would make sense to get rid of the bit of Bitcoin and do this work number one and the second question is he sure it means um do you absolutely I mean using CK proofs um makes your project viable and without CK proves it would not work cool two good questions on the uh on the first question the way we think about this is how uh Bitcoin uh miners think about it which is time it's basically the time to return the underlying Hardware so if you're buying a kind of ant Miner um to mine Bitcoin and you're putting it maybe in someone else's data center or someone else's mind you think you know what's the time to return really on the Blended cost of the Bitcoin over that period usually the number that you go for is between 12 and 18 months you want to return the cost of the hardware in 12 to 18 months hour 50 free cents an hour estimate bakes in the idea of some an individual going out buying a Nvidia a100 on the market brand new hosting that in a Data Center and then the break even costs we're doing so including the kind of increased verification time required on the network kind of shaking out to 53 cents an hour so at 53 cents an hour if that's the kind of steady state price as a Bitcoin miner provided there's enough demand in the network you should be in different about whether or not you want to mine Bitcoin or you're one of mine mine Jensen lower than that it gets a bit different but you have to satisfy both sides of the market so that's 1.1 uh your second question was in ZK and whether or not it makes the kind of network um I guess you know work um it makes it work better uh it does it's not essential and as much as we could have a dumber more expensive version of a network which uh maybe catches less of the second class of attack I talked about which was the kind of vandalism attack uh however using the the proofs that we use which technically aren't zero knowledge they're just proofs um by the take some zero knowledge research in polynomial interactive Oracle person take out the zero knowledge parts to get a speed increase um they make them all they make the entire uh verification system much uh much more uh a efficient and B uh robust too many questions like yeah let's keep it a little bit shorter and we can go with two thank you I really enjoyed the talk my question is um besides neural networks do you think in the future you could uh use uh your application to train other sorts of models that require a lot of data and Computing powder yeah we think about this a lot and it's kind of similar to the kind of question of inference as well people say you know inference is easier to the extent and there's less computationally expensive maybe it's a better go to market we think training neural networks is the market to go for for two reasons uh the first reason is neural networks on your network training represents a market which is unique in the world in terms of computational expense it's going to be bigger than rendering and we already is you know it's going to continue to grow much larger um it's something which is foundational to our economy going forward many models once they're trained have to be retrained and for example um unfairly if we Branch out to kind of more statistical machine learning models so for example xgboost or like random Forest um while those are all used by people number one they typically don't have the same kind of extreme computational requirements as um as as neural networks which means that the kind of the kind of the kind of calculus or someone switching off of AWS onto ours is a little bit less clear number one and then number two those models as well we think for many of the use cases which are coming you know we're talking like kind of artificially General intelligent use cases where you have um your autonomous autonomous machines which kind of look similar to kind of Androids or where you have Oracle levels supercomputers um it's going to be Eclipse by neural network training there's another argument whether or not we do compute like rendering um we think we're better being focused on one very tight vertical which is neural network training thank you uh do you expect that uh these nodes do you expect them to hold the entire model or are you also compatible with Federated learning it's a really good question um it depends on the size of the model the short answer is no for some models so actually further the image the handwriting model the answer is yes but for the um for any kind of modern Transformer model or language model um over sort of 500 000 parameters the answer is no um the model would be chunked higher up before it hits the node into a smaller job so they wouldn't have to hold all the of the model and secondly they wouldn't have to hold all the data either because the data is eraser encoded and changed them distributed around the nodes of the network um and so when you're doing your verification are you passing through one layer so like this comes back to the third grade learning part so that means that like if you're are you just are you verifying what one node does through its layers are you verifying one layer like how is that working it's a really good question uh the short answer is our thesis is it won't be Federated so it won't be doing Federated averaging uh purely because Federated averaging isn't the way that large transform models are trained it would look more like a distributed machine learning workflow like pipeline where if you had an Envy linked um cluster uh it would look out like that uh with verification checkpoints but the um the short answer is you could do a Federated model on top of that as well if you wanted um yeah I can talk to you more about that after that's a more technical question great thanks so much appreciate it Ben so let's give them a round of applause please and coming up next I'm introducing Prashant from spheron we've already had um very we've been very infrastructure focused and I think everyone who's spoken so far are building critical building blocks the infrastructure of web 3 but that doesn't help if we don't have users and so what spheron is building is actually on is an onboard they're basically an onboarding um effort to bring applications and users to web3 and so from Prashant we're going to hear their initiatives to have applications native to web3 and bring um the users that we all want to see into the web3 ecosystem so with that round of applause for for Prashant and I think last time I saw him he was in um we were in Singapore it looks like he's come from that climate because he's wearing his jacket inside so he may be used to a different climate in here yeah I'm coming from Dubai so it's a really hot weather there but thank you so much uh Tom and the entire fluency for having me here um so I'm going to make this entire discussion a little bit more interesting rather than like more focus on the tech side because a lot of tech people have already spoken about a lot and as Tom was sitting about like it's good to build infrastructure today but it's also uh is where we start thinking about how we are going to drive the adoption for it and that is where is Faron kinds of kicks into the picture we are more focused on uh building the middle layer on top of all of these infrastructure which you are you guys are seeing today uh today at sphere on we are proud enough to have aggregated more than around five plus infrastructure out there and we are also proud enough like one of the best tools out there to host your launch your computers and lot more things I'll try to dive into much more deeper into it and if you guys will have any questions feel free to shoot me up after the after the talk um so yeah I am currently working as co-founder and CEO of a company uh taking care of making sure like swear on goes to the global stage and also making sure esperan also getting into the developer's hand now coming to the talk which I'm going to be specifically talking about is going to be around improving the whip 3 infrastructure accessibility um and uh that is what I'll be talking about and these are the feature sets which you can go ahead and start using today uh Inspiron like decentralized hosting compute web3 storage and dedicated Gateway and believe me once you go to the sphere on you will find it very easy to use so let's get into the state of the bit 3 currently you guys have already heard about a lot about the tech infra where all of these people are building uh very core label module module level blockchain uh Sovereign level blockchain which is more focused on uh solving the lower level use cases but if you see all of those as a use case these are the current state of the way three infra is like it requires a high learning curve to go and start using any of those protocols lack of Automation and tourings so if I ask you today it is like to automate the entire server operation of yours or devops process I can challenge most of you will not be able to do it if you can then I would love to give you a coffee right now after this this uh particular talk of mine highly broken out which basically means like it is it is like if you have to launch a d app you have to literally go and talk to four to five different protocols to make that happen so it's like highly broken out token is something which is playing a very vital role personally speaking um if I'm a I'm a solution architect for your everyone information as well if I have to integrate with three take into my solution I'll not go directly with the token model the reason being because if I go directly to the Token model I don't even have opportunity to go and explore any such kind of Technology before even I start using it so that is where the token Gap kinds of start playing the very vital role fragmented in various ecosystem again it's like you have to sort of uh if you ask me today I see crypto in two verticals one is called transactional chain one is called infrastructure chains uh transactional chain people are the defy users and nfts endows and all of those things but infrastructure users are more around like storing your data assets storing your nft assets and all of those things imagine if you are making a money in infra transition chain but you have to pay for the infrastructure chain and then you have to go for a lot of token swap here and there and go to finance go to different exchange and get rich yourself and then uh once you are done with recurring yourself then go ahead and stop using the infrastructure completely so these are the fragmentation which you can see today uh lack of Grant dispersal model and this is one of the best use cases uh which at Spirit we have kind of built over the time if you see the infra as such as a whole it cannot scale if you give out token to normal like all the developers like that because they will have opportunity to go and dump into the open market and the moment they dump into the open market you know what is going to happen yours your protocol is not going to scale anymore you are going to be basically giving out free usdc to people to start partying into the parties out there so that is where the grant dispersal model is kind of a very vital role um and at sphere on as I personally believe and my entire team also is in one thing web3 education is very important and when I say web 3 education it is basically around infrastructure because lot of people talking about D5 and fds and whatnot but really you will find people talking about web3 Native infrastructure and that is where we want to focus on um and that is where our educational materials and things are kind of lying around um coming to Esperon and how we are kind of changing the state of the way three infra um these are the a couple of things which you do we are the aggregators we are building one of the best aggregation out there what we are trying to do is let's say if blue fluency is building something fluence has a serverless model and everything uh what we can do at Inspiron is we can build a wrapper on top of it so that fluence can go ahead and start Distributing those wrappers to the developers out there within few clicks so they don't even have to worry about fluence project as such but they should be worried about solving their use case and that is what we that aggregation kind of means here brazing token gaps today at its way around once you log into a system you don't even require any token to start using any protocols out there it's very straightforward go as a premium model once you start loving the system then go and start working on the token or even thinking about the token and that is where we kind of play a very vital role apart from that uh spheron currently exists on all the evm chains and on Solana as well at this point of a time where scaling ourselves heavily into transactional change so that we can bridge the gap and bring the money to the infrastructure chain so that infrastructure Innovation can be kicked in and a lot of people can start building into that space no code Dev tools and automation which is already built in so you have to if you have a GitHub if you have a gitlab bitbucket or whatever you use as your repository go and configure entire thing as Automation and it is just a few clicks away you don't even have to like learn anything new it's just State web 2 but underlying infrastructure is going to be utilized as a web 3. built-in Grant dispersal so as I spoken about earlier we all have we have these things I would love to um after I'll not have enough time to basically show that here but would love to if anyone wants to like discuss deeper into that uh we'll love to but just to give you the understanding how exactly the sphere on Grand espresso model work so for example today um let's imagine a scenario today Florence wants to go ahead and give out their tokens to people who are building over who are fluent but they want them not to dump over a market right directly so instead of dumping into the market they should be able to use that token to excellent or use their infrastructure so what we have done is like we have built an alliance based model into the system uh where we can create a liquidity pool for ourselves internally we give out Grant to these developers in a in a virtual system rather than in the real coins it will be a virtual coin which is which is a wrapper around it and they can just use it in our in the system itself they cannot use it outside of their system which which basically means you you cannot withdraw it out and also by this introduction of mechanism you also Safeguard yourself from a lot of legal troubles as well so that is where this dispersal model kind of plays a very vital role and then you can do a lot of incentive mechanism so for example we partner with Akash Network and when we partner with them we built one of the coolest feature on top of them was the incentive model so today if you want to go to Akash go to Akash ask them like if you can instant advise us they will Instant advise you via their Akash token over by this Veyron where you can get those token and start building on top of them very easily education and best support channel so yeah out of uh if you are if you are new uh to the web 3 ecosystem if you are new uh if you are moving from web to the web 3 we have a dedicated support channel in Discord uh go ahead and open a ticket for yourself one of our team members will be jumping into there and will be helping and making sure like you guys are able to shift to the web 3 and we'll make sure you have a nice and great experience there um so let me play this video this basically represents how exactly this Varan works and how we basically make sure that it happens so here on the left hand side there are users in the center there's a sphere on and this particular engine which you are seeing which is rotating now it can be scaled horizontally in multiple uh nodes what basically means like we just don't believe in having a decentralized infrastructure aggregating but we also believe in making sure like our infrastructure you're also getting decentralized over the time and that is how the infrastructure kind of looks like our model is very simple uh just imagine uh you are working in a you are building a service service based company where uh on the other side of the company you have people who have uh let's say in you have a notes which has working uh capability you pick those nodes what those nodes do is like they just upload the data to the specific providers wherever they want to and it's where on what it does in the middle layer is like it make sure it facilitates that particular process so that our users has a very great experience there and and in while doing any of all of these things we are building a nice incentive model for all of the people who can really basically participate and start scaling themselves and as you can see like on the user side user can be on polygon optimism or wherever we are and just start accessing any decentralized infrastructure out there um again uh just to uh pointing out like uh it's again a chilling point where I just want to chill as well more swear on as well uh wise we're on today um is because we are saying agnostic uh we are we have all of these automation Works uh if you have a d app which you want to accelerate using and you want to make sure like with three infra kind of getting used for your D app um go ahead and start using sphere on you don't even have to have off-ramp your crypto to make the payment for your infrastructure costs so go ahead and start using it and most importantly we have been building this particular space for the past three years so we have learned a lot of problems which infra companies might face in coming days if they are building on top of it so we are well positioned to make sure uh we we take the infra to the mass adopted on the on the developer experiences side coming to our achievements which I just wanted to focus on as well uh what we have done so far uh we have onboarded 3300 plus developers we are very heavy in India uh if you go to India and ask about us most likely we'll get to know about us so most of the developers we are targeting we were targeting into Indian space now we are expanding globally as well we are getting into Vietnam we are giving getting into another Asian market and we'll make sure in the next one year we will I'll show you the number what we are kind of looking for but that is something which we are kind of targeting for uh we have more than 17 000 plus uh unique web application deployed so far uh served more than 155 gigabit within last three months we started calculating this data from January um 100 plus unique domain which means like 100 plus new businesses are getting served via on gateways 50 plus active compute and this is what I wanted to mention it here Akash as well so that uh the moment we integrated them we had around 50 plus compute which has been currently active um via spiron itself we have around 2.

5 k plus a KT been distributed so far we have into the liquid pool as well where we are planning to distribute as well ysphere on this one our our grant dispersal model uh coming to the next what we are looking for achieve to achieve and these are the couple of things which we are on our on our track list uh product is solid everything is is on the line the next thing is we are looking forward to onboard next 20K plus developers around across the space when we say on board that basically means like there should be by any chance they should have one GitHub unique accounts they should have one gitlab or whatever unique accounts that is what we when we call them as a developers or else we don't if they have certain commits also in the system then only we call them as a as a developer so that is what those numbers kind of is to represent like um and that is where we'll be working we have all the plans in line to make sure like we are able to reach that number and we'll make sure by the end of this year you will once most likely next year when I'm back on the stage we'll have those numbers in-house um and coming to other part as I said education is something which we are most focused on uh we are going to be launching sphere on University soon and the focus of the university is not to talk about defy not to talk about anything else it is focus is only going to be around talking about infra providers and how exactly the web 3 infra can solve the bigger problems and issues which most of people before me were talking about like censorship and all of those things since uh if you have seen that architecture earlier uh there was an arc there was a node which was sitting around and that was uh the Dao part where Dao wanted to introduce and make sure like these Dao's are able to make sure and run these dedicated gateways and as not as a Founders we are running those gateways uh more infra Partners we are currently working to onboard more infra Partners so that we can provide various other features on top of it so for example uh today we don't have a serverless execution rate so that is something which we are kind of working on most likely that will also be done um the most important part is the infra Marketplace um so today if you have to launch I'll be very honest so if you go to the market a lot of people have a WordPress application right and uh one of them another best places to capitalize the market is also to provide support of the WordPress applications so infra Marketplace is is a place where all these developers who are building over a web 3 infra can go and submit their um in their templates their uh like they can go ahead and sell their templates as well for example if they have built their Docker which can run on a cache Network or they have built a user interface which can be deployed on filecoin or ipfs or RB or wherever they can go and monetize that by putting that over infra Marketplace and other side of the user they can go ahead and buy it and immediately start running it without any hiccups so yeah that is it thank you so much I believe I have not bored you a lot so again if you if you love what we are building we'd love to connect with you if you're on HQ is our headline we have a foundation which is which runs at sphere on fdn and yeah so let's bring million users and million developers on the web 3 infra in coming days thank you so much but yeah hi thank you uh it's very good the presentation I just have one very simple question so uh I I didn't really get clear about your decentralized network the details of the technological side do you have your own decentralized network or in other words could a normal manner join your network to provide your services to get some coin incentive or your decentralized network your infant resource is built on top of other network yeah so generally let me just go back here again this is where I was kind of showing that if you see this uh in the network that that part this part which is uh their nodes are there those notes are nothing but alkaline we call them as a worker nodes and their job is to basically just imagine this way in this room we have 100 people sitting out of that 100 people 10 people owns a kitty token 10 people owns fluence token 10 people own RV token or whatever token they own what we tell them is if you have these tokens which are just lying around with you and you are predicting that and speculating in the market to basically pump that token up so that you can dump it over the market instead what you can do is like you can use that token and generate yield for yourself by participating in the network and lending your token there so that these workers can what they can do is like they can use your token to upload these assets to these better digitalized infrastructure and once they do that they will get instant device and as of now we have not yet built the incentivization layer but our aim is to build some incentivization layer where all of these developers who are all the nodes providers who are becoming the active web worker here they are getting instant advice either in the ethereum token or either in the stable coins or either in spiritual who knows how exactly we will design it but that is what our long term um aim is but at this stage we are more focused on uh driving red option in terms of understanding and letting people know about web3 first rather than just introducing the token at the first so okay we can talk that offline yeah if you don't mind any other questions I saw one hand one more hand no you want to ask something else right all right okay yeah um so we have time for questions because book is like um for a couple minutes he's out oh great all right well thank you Prashant yes we're done thank you um give a hand thank you and just um I know Prashant has come from Dubai our next speaker has come from Ecuador but uh we've just lost him for a moment where where is he uh he I just saw him he was sitting right there and he vanished um Luke who's building fill mine which is a protocol for um for decent for basically compute on the filecoin network for both running running um decentralized uh hosting and storage that basically allows for uh contribution of storage capacity and the staking of file coin up and there he is running in um this allows the staking of file coin um on on fill mine to basically enhance your your reward profile um so fresh from the Ecuadorian jungle uh please welcome um please welcome VOC ation I think that's the web link there you go I'm good [Music] yeah I I just finished the slide so bear bear with me and also ignore the Solana they gave me the hoodie for free because they were closing the store in Miami and it's pretty good anyhow uh yeah I thought they would talk about a bit of a different topic usually I would talk about the centralized Computing coordinating all the jobs that we heard about today and figuring out how to take like a job from a network and deploy that into a particular provider today I'm rather gonna talk about like what are the implications of all of this but on a token economics level and how to think generally about these Networks so first what do we actually mean by webtry in this context anyone has an idea uh yeah I mean generally web free was framed by parity I believe in 2018 and it was kind of mixed term it was uh really cool because the free is larger than two so everyone thought this would be actually the next thing but that actually there is a lot of ambiguity on what web3 is and I think that we are coming now to a point where web3 is actually getting like a sense so it's not the L1 Network where you have transactions uh that are happening with some consensus it's rather focusing on something that provides more of a utility so you have networks that provide a particular utility so you could imagine falcoin being a utility Network the utility of that network is the storage so you provide the storage and you have utility providers and the utility providers in that context provide the storage you could have live your network providing the encoding you could have the fluence network providing some generalized compute but those are utility Network you're not talking about Solana we're not talking about ethereum we're talking about networks that can provide a particular utility and in those networks always you'll have like two main roles one is the utility provider and the second one is the token holder that is basically a stakeholder in the entire protocol so if we look at those two roles you have research providers so those are folks that are basically providing the hardware or providing like uh the software that is giving that utility so in the case of chain link chain link is providing the fit that the utility that the chain Network provides in the case of flybear again like the encoding and so on well the talking holders are just holding the tokens of those individual protocols now uh how does a utility provider look like these are utility providers that can do like some kind of encoding some kind of computing and so on those are also known as miners but the minor word is also used for a bunch of other things so I'm going to try to avoid using it but you could imagine a utility provider like this or a utility provider like this these are more frequent in the Falco Network because it's not enough that you have gpos but rather you have some kind of storage but also you need some Computing because you need to do a lot of cryptography that is necessary to board data to the Falco Network and so on while talking holders are basically yeah just chillers and like people that just believe in the network and want to actually be part of that Journey so how does a resource provider interact with the token holder so usually uh you would need some kind of liquidity as a resource provider in order to provide that utility let's get a bit deeper into one particular protocol which is the Falco Network so the Falco network does provide 15 exabytes of capacity today this is committed capacity that is being proved every 24 hours so there is a proof of every sector of 32 gigabits that that particular piece of data is actually stored on the Falco Network you have a zika snark that is done in the background free petabytes of there are being on boarded every day and very soon like it will have evm compatibility but the point is that in order for this 15 exabytes of capacity to be on the network available for everyone you need a resource provider that research provider needs to do a set of activities they need to First provide and commit a particular amount of storage so let's say it's 32 gigabytes which is like the size of a sector the sector is very well known in the Falcon ecosystem but I'm not going to get deeper into it than what the the storage provider needs to do is they need to commit a particular amount of collateral so like you need fill that will be locked as collateral in the case of the storage provider going offline uh and that collateral is not small like you'll need to commit 0.2 fill uh just as a commitment and if you're really starting there you would need to commit to fill to fill is actually 20 bucks and that's for 32 gigabytes of data so the requirement of the liquidity is super high and yeah just to walk you through how the protocol actually works is that once you have committed the collateral then you have like 24 hours you have the storage power the storage power is used in the same way as hashing power on the Bitcoin Network you're getting rewards because you have the storage power and every 24 hours you need to generate another proof so that you'll get like rewards for the next 24 hours and basically in that way you're getting blocked rewards from the inflation that is happening on the network now uh of course there is also risks involved the collateral is not there just because of the sake of the collateral it's actually there to uh have the ability to punish a particular storage provider if they go offline if the data is really not there and they have committed that they are going to store the data for a particular amount of time they will get penalized so what will happen in that particular case is that the storage provider fails to send the proof so they have a particular grace period in which they can not get rewarded but still not get penalized and ultimately they are gonna start getting slashed and if they are offline for a long enough of a time frame they are just completely gonna get slashed so like all the collaro that they have is just gonna be burned because they are not providing the utility which they have committed that they are going to provide to the network okay but this is just one instance of like a web 3 protocol what about the other ones guess what uh most of the other ones have like a similar model and the reason for that is that they need the economy that is self-sustainable because if you don't have a demand for your particular governance token or whatever the utility token you're using it's super hard for you to build like a sustainable economy so in the case of light peer you have the the abstraction of orchestrator which is basically someone that would get jobs from the network and would execute those jobs now the orchestras can run also the workers which are basically doing the the encoding or they could just offload that to someone else but similarly you're basically as an orchestra taking the tokens and because you have a particular amount of tokens ticked you're able to get a particular amount of jobs so you always want to be sticking more so you get more jobs and with that you increase the utility which you can provide and with that you increase the reward that you can generate and of course when you return the results to the network you you're just gonna get rewarded by the network uh okay uh does anyone see a problem potentially here um I hope yes but I hope that everyone is timid uh ultimately the issue here is that liquidity is a linearly dependent sorry the utility is linearly dependent with the liquidity meaning that in order to increase the reward and with that also increase like the utility that you're providing to the network you need more liquidity and now if you roll back and think who the utility provider is in this particular case it's a minor why would a miner care about holding tokens they don't they care just about generating profits and just growing their infrastructure business which is fine like most of the businesses today work in that particular way but all of the networks that we have today are basically designed more from a top-down approach where uh the economy is actually dictating how the protocol should be built rather than interviewing a particular like personas and trying to understand like what a miner would like to do the miner doesn't care and generally like everyone that was designing the webpray protocol couldn't care because they had to think about the economy so yeah generally the reason for that is only the fact that you need to create enough demand for your utility token so that your token is not gonna go to zero because you're just inflating your economy because if you also zoom out like most of the economies in webpray need to have some inflation the inflation is there not to scrub like the existing token holders is there to incentivize the utility uh to be provided on the network and most of the networks are just going to gravitate towards this model I haven't seen anything uh different that is not actually like using the utility token and just pushing a lot of Demand on it but how can we actually solve this without changing the the core protocol because that's most likely not going to happen uh and uh yeah that's something I'm super focused on and that's basically what we're building we are building staking for web free and when I say staking there is no staking in web free because like most of those protocols do require a utility to be provided so when we say staking it's not like ethereum where you just take it directly in the node you actually need to coordinate a bunch of uh jobs that will be executed on those networks and when those jobs are executed or the utilities provided like in the Falcon ecosystem you're gonna get the reward and then you can do something with that reward and that's how you generate the yields uh in the case of the Falco Network basically you have a token holder which in a way just uh sticks in a particular search provider and because you're generating the rewards uh you can generate now of course this is how everyone thinks about staking actually it's a bunch of other things that we have to figure out just to make it possible because ultimately you need to do a bunch of things to provide that utility and the better job you do to provide that utility the better this taking protocol is going to be and with that also the higher yields that you can produce are going to be so how it works the token holder just takes in the protocol uh the tokens get distributed to the utility providers they use the liquidity to provide the utility and then the protocol generator rewards and everyone is happy because everyone makes money so yeah it was super fast uh by the way like I have another set of slides on the Computing front thank you this yeah oh this one [Music] yeah I thought it was me this one or the other one this one okay so talking holders just taking the field mine protocol which is the protocol that we have built the tokens get distributed to the utility providers also known as miners the miners use those tokens to provide more utility because they provide the utility to the network the rewards are generated and because the rewards are generated the cat funnel to the protocol and then they get distributed to the individual parties all right one minute I am afraid of yeah I'm gonna start with this and they have a lot of backup slides that that they have a assembled for understand that they won't work on so in the Falcon protocol uh in order to onboard Dia or commit capacity you actually need to do a lot of cryptography that's called proof of replication it does last like four hours in memory and it does require like a full core and then you need to pass it to the next process and the next process requires gpus then you need to pass it to the next process require some iops then you need to pass it to the next process requires again a GPU so there is a lot of cryptography for this to work because you ultimately had to balance out between having a lot of compute happen in the beginning but proofs that are sent frequently like every 24 hours be lightweight or do a lot of computes like every 24 hours so most of the compute was actually pushed on the onboarding phase now this is just like a picture but actually how it looks like is let me see here it's actually like this oops this was the next slide [Music] yeah actually and even this is like high level like under this there is another layer so basically on the right side you have the storage providers then you have like the compute providers that are helping us on board there then you have the ZK snark producers and like all of these are super different because for the middle process you need a lot of iops and a lot of memory and you need like server grade CPUs the networking needs to be super good because you are transferring a lot of data you can see that you're transferring 32 gigs between like that provider and the the storage provider and then like you can just offload the snarks to someone else and that is basically just a GPU activity which ethereal Miner could do and then that gets it back to this process and then it gets back there guess what we built all of this just to be able to stake tokens so we actually started like a sustaking protocol and I thought I would do cool D5 stuff and actually we we spent a lot of time just building infrastructure to make it possible so I think this is going to happen in Dr protocols as well we are looking at live here we are looking at many other protocols that are doing snarks and this is super interesting but again like it's a lot of deep technical work just to be able to like do a simple activity on on the first layer okay the question was is this still considered decentralized yes absolutely because we are not the ones running these nodes we are outside building software I mean I love computers don't get me wrong we have a lot of servers but again like we are trying to build protocols that can be used by everyone and everyone can participate in that and even then the question is is it really decentralized depends how you build the job queues so if the job queues are decentralized enough where you can actually get the jobs from the queues and execute that commit that you're going to execute and then finish the jobs yes it's absolutely decentralized enough and the question is where you want to put the line on the decentralization and they feel uh yeah that's something we'll figure out as everyone is building in the space by the way there are many similarities on what we're doing and what the other teams are doing so I think what we are going to see is like a more generalized protocol that is going to merge all these protocols I was hoping that's going to be me building it I still hope but like I have a lot of work on the D5 stuff so maybe someone else can do it but yeah super fun let me get back to this slide which was the most important one for the first time we are hiring we need like product people and like protocol engineer we are a team of 10 people and we are funded by some of the best uh funds in the space thank you so much great thank you book great we've got our um very last presentation right now Nico is on his way up from streamer um this is the last piece of infrastructure we're going to be talking about today critical piece of it it is the decentralized data streams and I think we're all accustomed to receiving data streams all of the financial world right now is a custom receiving proprietary centralized data streams having decentralized data is obviously critical because it eliminates a point of failure and a point of censorship so as you we think about building this decentralized world we've got compute we've got key pairs we've got staking streaming is a key piece of this all of these pieces need to be in place to build this decentralized world so with that I'm going to turn it over to Nico questions hey guys how are you doing today um I'm Nico I'm the developer relation engineer of streamer and today we're going to talk about the detritalized real-time Data Network so I'm going to start off with a video I hope it works we'll see it does okay okay there's no sound but just read the subtitles [Music] commodities um and most important things okay this looks a lot cooler with sound I promise okay let's just start um and go to the first slide okay so what is streamer um we are a decentralized P2P Network and how that actually works I will explain in the next slide but essentially we have topic based published a public a topic based published subscribe system and if you haven't heard of this don't worry um I'll explain that in the next slide as well um essentially we stream data to any ethereum accounts in real time so you can get your data from A to B in a decentralized manner that's stream in a nutshell really so streams topics subscribers Publishers what what is that I don't know who who's heard of uh pops up before okay so basically no one well if you have heard of it um streams are essentially topics and if you haven't heard of it think of it as some sort of a list where you have Publishers that write to that list and subscribers that read from that list right so you have uh for example this weather station here um that is publishing data writing to this list to this table to this stream however you want to call it and then you have the subscriber that can then read data from that list so for example you have a Json where you have temperature humidity all these kind of things and then you have this application that then reads this data and consumes it and maybe displays it in a dashboard or I don't know shows you the weather forecast right so this is kind of streams in a nutshell so here we have an example stream from ultrasound money and what this does is it streams The Firm 2.0 stats burn stats every minute so you can see here you've got like 48 then 49 in the timestamp every minute there's data getting published to this stream and then someone can just kind of subscribe to that stream and read from it right now how does this work how is it decentralized how does how do we get the data from A to B we zoom in into our Network we've got this example stream here and essentially it's a topology of 30 nodes at the moment the streamer network is made out of five over five thousand notes at the moment but we are looking at one specific stream topology topology is just another word for uh Network constellation so how the nodes are connected to each other and in this case as you can see most of these nodes have four connections to each other not more than four and they have these neighbor nodes and if you publish a message it gets kind of distributed over this whole topology Network how this works I'll show you in the next slide so we zoom in into this Denver node here you can see these uh his neighbor notes um displayed on the map and if I now want to publish data right let's say the standard node publishes weather data it wants to publish weather data so what it does is it it sends this message this data to all its neighbor nodes including the one here to Lisbon which we're going to look into a bit more so it sends the data to the lispin node and then the lispin node sends it to its neighbor notes and so on and so forth right it's like a chain reaction and it kind of floods the network with this message right here but only within the stream topology not outside with the five over five thousand notes that we have so we don't publish the message over this whole network of 500 5000 nodes right now so if you have let's say this weather app you don't want a random you don't want to consume a random data stream for example fridge data or some other data right you just want your weather data so we have a noiseless network that only streams the only nodes that are interested in that stream which are going to receive the the data so that's the scale um well we did a test of this network with over 90 000 notes and I believe 85 countries and yeah in over 85 countries all over the world and yeah the results were quite interesting so I just told you right we have around 5000 nodes in total like globally and we did this test net with 80 000 notes so it's quite quite impressive and what we've found is that the mean propagation delay which is kind of the delay until the message arrives at the at your node is around 400 milliseconds so it doesn't doesn't really exceed this uh 400 milliseconds so does it scale I would say it does scale because 400 milliseconds is quite nice for decentralized b2p Network and yeah here you can see it's kind of converging um right so streamer Network properties uh fast scalable real time it is permissionless and censorship resistant it's tamper-proof end-to-end encrypted uh talk about that in the next slide and it is free to use so anyone can use it anyone can kind of you know create a stream uh connect their notes to it and then start start streaming your your use case essentially so yeah access control and security uh it's a public network uh you know it's uh how how do we how do we contain the access and how do we how are we secure so I just talked about this P2P Network and decentralization and everything so it's not too um it's not a blockchain I haven't talked about any blockchain right so how do we use um smart contracts and and the blocks chained to utilize this access control is um well with a smart contract on the polygon Network so what we have is a stream registry which is essentially a smart contract that keeps track of all these streams and um and the addresses that can stream data to this Stream So publish data to a stream or subscribe to the stream let's say you have a chat room right you don't want anyone to just publish messages to this chat room and you also uh don't want anyone to subscribe to this data right so you kind of need to keep track of the access so this is this is done in this smart contract it just kind of registers okay this address is allowed to read and this is this address is allowed to write onto the Stream so in this example um let's say the subscriber node gets gets data right and it is encrypted so it's like okay uh I need to read it but I don't have a key so it needs to ask for group key from the publisher node and then the publisher note is like okay well let me first check if you actually have permission to read from the Stream So it asks the smart contract does zero X ABC has permission and then the smart contract says yes it does indeed have a permission and then it sends the group key to the subscriber node and is able to read from it all right so again let's recap what's happening off chain off chain P2P data channels so this is where the data gets sent from A to B and on chain is the access control and who gets to read from the stream who gets to um send data to the Stream and yeah network security so in an ideal scenario uh well everyone's honest no one would um leave the network everyone would stay online but in a real world condition we all know we have some malicious peers we have people that want to harm the network and this is how we kind of counteract so we have two main attack vectors at the moment um which is one the eclipse slash Hub attack if you've never heard of that that's essentially when uh notes or malicious nodes come to the network and connect to you saw the Lisbon node earlier right it has like these connections and if only if it's only connected to malicious nodes and they would be like Oh I'm not going to send data to you or not going to let data through you kind of disconnect the net the node from the network um how do we counter this this Eclipse attack in this Hub attack we say we don't let any node connect to we don't let the nodes choose who they want to connect to so if there's a malicious note joining the network it can't just say I'm gonna just connect to you and then I'm going to take three other notes and just connect it to you so you're not able to connect to the rest of the Network anymore you can't choose right so it's it's done by an algorithm that chooses those connections randomly we also have the DDOS attacks um so you know if the network gets flooded with a lot of messages or data and it it kind of takes takes away the bandwidth and there's no room for actual data to flow through then we have a DDOS attack and we measure this by restraining um this topology to only the streams right so the data doesn't get distributed over the whole network so if the network if one particular stream gets attacked it's not the whole network that gets attacked and then of course security measurements is also the redundancy so we have a lot of peers and there's actually good notes in the network as well um right okay I'm coming to the end of my presentation so if you want to build with us um we have this streamer client so if you want to integrate our technology we have this npm package and here you can just npm installed into your node application and then write some JavaScript down for example here can you see the mouse oh yeah so streamer.publish for example you just kind of publish this object hello world for example you just publish it and here you can then subscribe to messages from that particular stream [Music] and if you want and your Builder I highly recommend to check out our docs at docs.gma.

network there's some quick start guides how you can just get started and check out how it works you can also just talk to me I'm happy to help um yeah and what can you actually build with it mostly real-time data messaging dapps so that could be iot applications I talked about this fridge earlier so smart home is great um maybe you have car data that you want to share um maybe you have a chat app which we build actually um right here which is called the chat dot app and it's actually going to release tomorrow as a better version and yeah essentially What's Happening Here by the way is this chat right here this chat room is one stream and then we keep ax and and then you can log in with your ethereum account and send messages to this stream or to this chat room if you have access to it right so it's like it's almost like WhatsApp but it's peer-to-peer and completely decentralized so that's quite a big deal because everyone and their mom has a has a chat app nowadays but streamer um streamer does this peer-to-peer and decentralized which is kind of cool all right yeah if you have any questions you can either reach out to me via telegram again I do develop a relation so if you have any developer questions reach out and if you have any Partnerships partnership questions it's better to reach out to Matthew who's our head of ecosystem all right awesome that was my presentation thank you guys thank you so much yeah uh any questions from the audience one thank you how do the notes make money or get compensated so how do streamer gets money or the nodes the node oh it doesn't so essentially you just the notes it's themselves are your application or your iot device so you kind of become your own it kind of becomes its own node and this makes up the the network this is kind of peer-to-peer so if uh we both would create a chat room where we want to communicate to each other my phone becomes a note and your phone becomes a node this is how it works and if if we have a chat all over the world and it's not just this you know Wi-Fi network it's uh you know a node would come up in Australia for example so this is kind of how it works yes so I saw that like you launched your tokens Can I know what what is the usage of the token uh the usage of the token at the moment is used for um broker notes and so I didn't put this in a presentation because we were a bit short on time but essentially we have this uh Bounty contract for node Runners so if you want to run your own node to provide bandwidth to the network to particular stream you get paid by token and this is how we use the data token [Music] hi do you use any libraries like lipdp or any protocols like waku or xmtp to build your product oh I don't know that but if you want you can just send that question to me and then I will just ask the network team sounds good thanks feel free to contact Nico directly if you have any other questions all right so I think we've finished yeah me today great well thank you all for coming we are we that concludes the uh today's program for the decentralized summit so thanks everyone for coming and thanks to each of our presenters some of whom are here some are not if you have interest in talking about fluence Bernard um is right there Chief scientists and we'll give a big round of applause to Nadia for organizing all of this um she deserves all the credit for making this happen um and appreciate it hopefully we'll see you guys around we have a variety of other events for fluence coming up um Nani do you want to go through some of those upcoming events think that I will just ask you to come for a workshop but it is mostly for developers so if anyone want to try and build on top of loons so please come on the 1st of March at Veep Hotel and our breakfast is when and breakfast is also March 1st so yeah we should have slides about that um yeah so this is the way would be to join our Twitter or telegram chat there's all the information about upcoming events so yeah we'll be here for the two weeks and see you around probably great thanks very much everybody bye [Applause] [Music]