Powering the AI-Driven Science Revolution with Lilypad

Transcript

GM GM welcome to what's going to be another great episode of Devon so if you didn't know Devon is a 30-minute podcast held every week to allow a uh Founders hackers and anyone in between to Showcase what they've built and today I'm excited to welcome back uh the lily pad team uh this time comprised of uh CEO Alison hair and head of research uh Stanley Bishop so if you didn't know lilypad is a decentralized computer Network powering use cases from dii AI agents and everything in between so if you stick around for today's episode you'll see Allison and Stanley share their story behind the lead and how it's powering the aid driven science Revolution all right let's get into [Music]

it gmgm welcome to the show Gang pleasure to have you both on gmgm thanks snob it's uh great to be back again uh talking with another OG of the developer Dow Community indeed indeed and uh Stanley uh welcome uh on the show I know this is your first time but you too uh are a developer D member uh as we both Ally and I have learned in the last five minutes one of the many treats from the pre-show is that there you go there you go indeed uh yeah uh so uh like I mentioned um exca to have you both on um really excited to to hear how uh well I

know how it's evolved but uh to hear you both share how lily Pat has evolved uh since the last time uh Ally you were on the show uh but for just in case folks um weren't tuning in to that episode and and missed you there uh would you both like to give a brief introduction about yourself and then we can get into the meet of the content yeah absolutely um so hey everyone uh I'm um founder of uh lilypads uh compute um I I where where do I start so I was working with protocol labs when we first founded lilypad uh so I was uh part of the f um the launch Team on the filecoin virtual machine there and also then

moved over to a compute project called bako at the time uh which is actually how I met Stanley uh and then uh we founded lilypad out of kind of a combination of both of those experiences for me um I previously worked at IBM but you know also I just been uh riffing in this space for a few years now and and even met NAB a couple of years ago at at what event was that at was that e Denver I can't remember that uh it was um a graft it was at graft day um but it was actually a developer Dow um a side event that we we started to chat really but it was in it was in San Francisco wasn't it it was yeah yeah yeah that's right yeah

yeah awesome yeah amazing hey Stan hey I'm uh I'm Stanley um I'm a data scientist I work in uh machine learning and high performance Computing and I uh particularly do a lot of work supporting um the compute needs of different um uh scientists and researchers mostly in uh bio and Medicine um I I like to say um I am not James Bond um like all these incredible Physicians and researchers but I'm I'm Q who gives them the gadgets they used to blow up cancer and other like

that yeah uh and I think you're being humble there I think you do uh much more in the space and and we'll get into that um so um just interested uh to kind of hear um both your stories on like how you started in both your respective Fields so um Ali I know you're you're big in Tech and you mentioned starting in IBM and whatnot uh like what what was your inspiration for um going into the tech field like yeah um definitely it's a bit of a story actually because I ran a a cafe actually in my 20s so I owned and operated a a cafe in Australia um in my 20s uh which was kind of accidental that's a different story though uh but

ran that for about eight years and when I uh sold it I I you know didn't want to be in Hospitality forever so I moved um I went to back to UNI to do or back to college depending on where you're from uh and did uh started in mechatronics engineering so robotics engineering um I'd always been interested in science and maths uh you know throughout my uh know high school and schooling years uh so I went back to to do this kind of engineering degree I thought this would be like kind of a practical application of of those interests um and part of that was uh a coding course so I did a coding course as part of engineering uh and that's really where my uh kind of love of Technology uh and this like you know software space came from so I I did

this uh coding course and I just like wrote this my first program at like 30 years old or something and like oh my God I'm in charge of the internet wasn't quite like that I'm sure everyone kind of has this moment uh where they remember like writing something like that in their first hello world and and you know uh feeling like a pretty powerful um but yeah so I decided to do computer science as a double double major or double degree um and yeah I haven't looked back I was part of a lot of hackathons I even asked ran a startup when I was still at University um so I was doing my thesis I uh founded a a startup uh with a friend of mine uh we we won an accelerator program so we

went in hackathon we won it and then we went through through an accelerator and we were going to uh um you know go further but we had a bit of a difference of an opinion on taking taking external funding so we decided to uh to throw it in uh but it was a great experience to have uh really early on um one of the reasons though I think um and this has evolved over time um one of the reasons that I I love technology so much and it's not just because I feel powerful with it but um I feel like it it is a tool for helping us um so you know we especially um I did this presentation at IBM I looked at over the last 100 years of of technology and how much it's changed our society um and you know that's one of the things that you know I

love about it so things like um appliances in the home really changed the game of for women especially um you know they could go on work then uh they could get out of the house things like that and then there's obviously the printing press and the internet which gave education to the entire world uh and now we're looking at um you know a revolution with AI in my opinion uh that is going to fundamentally change how we collaborate and communicate in the world as well and some of the breakthroughs we can have in things like uh rare disease and and Science and I'm sure I'll hand over to Stanley to kind of uh tell you how he got into it as well but uh yeah that's that's kind of my fundamental drive I'm why love technology so much it

changes how we how we do things as a society it can solve problems for us 100% And yeah I couldn't agree with you more and also found out today that you were uh part of the restaurant scene uh before texting that that's a cool story to hear supposed to retire to the cafe after the startup right that's right that's right that's right that's right mix that up all right to be unconventional like we like that we like that on the show um organize a busy coffee shop I think's up there with an aircraft carrier you know maybe but but less consequences if people well maybe people don't the coffee I don't know Ally have you seen me when I don't get my

coffee yeah me too yeah all three of us are quite Avid coffee drinkers I will also take a sip to join you uh but yes uh Jokes Aside um Stanley uh I know that uh you are regarded as one of the ogs in the d space um I'd love to hear kind of your origin story of um how you got or what your motivation was to to going into that field and how you mentioned before where you're the provider of gadgets um for uh folks looking into cure cancer and other rare diseases um would you like to get into that a little bit yeah I'm I'm happy to and I I wish I could say I did it all on purpose um but

my original intention was to be a Quantum field theorist and I was actually in uh grad going through grad school uh working towards a PhD um when when I actually I actually got very sick about a year and a half into grad school and this was before um we we had the Affordable Care Act in this country so I uh did not qualify for care because I had a pre-existing condition um because it was diagnosed right between undergrad and grad school wonderful system really wonderful system um ended up um needing to take a role um and I I got this really cool role I got kind of lucky I

was a a principal machine learning scientist uh doing language localization for Google's code base and a number of other uh projects at Google um through a a system called Falcon and Falcon was a system that used um some of the very early what we call multi- inter node uh GPU compute topologies so um these are compute topologies that have multiple GPU nodes and then the the speed and the the data Logistics across all the nodes is sort of the most non-trivial part of the um engineering and and yeah we were building these systems to actually support the work of about a 100,000

language translators all over the world um so you might describe the platform as Uber for for translation or localization um so yeah just really fun uh juicy project uh made me very passionate about um these kind of systems and how to build them um but more so it it kind of gave me my big the big passion for my career um which is building uh robust systems containing both human and machine intelligences and then that that's definitely the threat I've been following and then particularly when it comes to scientists whose work I find very fascinating I like to build you know systems that uh support and accelerate uh scientific

work without uh being frustrating for the scientist amazing amazing yeah and the work you do in uh in your field is uh amazing and and and we all applaud you for it um but uh this is actually a great little segue here uh into how both of you kind of came together um at Lily Pad because your your stories are have the the Synergy together where you're both really into tech and and looking to use it for for for good um so um I guess either of you can take this but uh how did you both come together um to found lilipad yeah I'll talk a little bit

about the origins uh so I think I I kind of mentioned it briefly in my intro but I was working with protocol Labs I just um one of the projects I was doing was helping to launch the filecoin virtual machine and that included um you know uh you know getting it to Market getting it across developers trying to make it um accessible to developers and building out the the programs uh to onboard developers there uh and then I um kind of hired a team and finalized that and moved over to a project really interesting project called the bako project um and that was run by um one of our advisors still uh called David aronik who was one of the leads on kubernetes in the time uh under the hood

lilipad still actually uses the blear project but I might be putting the cup for horse there so I'll just I'll park that one uh but I was working for the backa project and this uh it was all about compute Pau is a peer-to-peer uh compute uh distributed compute Network um fully open- Source uh compute Network check it out if you haven't heard of it already uh super powerful um but it didn't have any incentives uh and so I just come over from this you know really incentive driven like you know chain uh into this peer-to-peer compute project and I'm like these two should be together we should be able to you know run uh this kind of thing with incentives based on blockchain uh with kind of uh these

guarantees that blockchain can provide us um and that's kind of where the lilypad project was born what's interesting was uh Stan was in this uh backa space as well so we had been working on um you know building our projects together in in the backy our uh world as well so we were we're talking a bit and then when I when I did go and found um uh uh lilipad like we we started out at at protocol Labs but then when I I did we did go and found it I was like I couldn't think of a better person for chief scientist than Stanley and I let him tell his version of the story of how we met at bako as well but uh basically this is how it happened oh I have to say too it's so um

funny the way um you know our stories uh play out and and always kind of did remember the first uh project with Ally as uh very very special and and of course so excited to have been able to you know grow that energy into such a special um and important project and um 100% like um I am uh very very passionate about decentralized Computing and citizen science um grew up a big uh uh folder at home um had a big nasty cluster that took up almost my whole wall and my my childhood bedroom um and and yeah um being in the decentralized science space and then also having a

background in HPC um I get brought a lot of uh interesting projects and a lot of interesting ideas uh come across my desk and um a lot of them are things that haven't happened for a reason um that kind of sucks like not not having enough compute or you know sometimes it's just the people who want to use the compute not knowing how to access it you know there's sometimes little uh twists and turns that can prevent a scientific uh Computing project from happening and uh nowhere I I think is that problem more critical than in Marine Science and that might sound like a little odd um but if you're kind of familiar with what we get from the ocean and how critical the

ocean is to the whole planet um you you might really sincerely consider the lungs uh the ocean to be the lungs of the planet um so the things that are happening in the ocean are are quite negative related to climate change right now and we we don't even know a lot of what's going on because and and here's the wonderful tagline um there is nothing more decentralized than the ocean right it it's on every every it laps up on every Shore um so the problem of uh generating data and biomonitoring in the ocean incredibly challenging and I had um been the uh technical leader for a project called new Atlantis that was actually uh building what we call metagenomic technology um for for bio

surveillance and Nar I think you'll love this metagenomics is so crazy um you you literally take a cup of water um that that's not what this is I'm not drinking salt water right now um but but you actually sequence every fragment of DNA present in the water and and you can just imagine how many different animals uh have DNA present just floating around from Plankton to DNA fragments from fish and other larger animals and it turns out that actually just with this little water sample you can get almost a fingerprint of the local ecology that can can tell you way more than than you would ever expect because the life in

the ocean is so interwoven but it's rather a lot of compute I I think cost to process one sample right now is about $5 and to monitor just something like the the Gulf of Mexico for a year you could be talking a million samples um so the the problem of finding the compute um you know not just in an affordable way but in a way where it's close to the data uh very challenging and and just when we started working on this problem we realized you know bakala and the decentralized data Paradigm was like a perfect fit that's amazing yeah um yeah I mean five five million five million for for

like one sample that that's that's crazy or a million samples that's that's sorry to just jump in but tell you one other just kind of fun thing because we are talking about the the patterns of Technology a little bit in in this this convo um the cost of Performing that kind of analysis actually falling faster than mors law if you can believe yeah it's Ali it's actually a double exponential it's it's double morsa yeah it's amazing isn't it it is like when uh storage um came out and that was super expensive but over time it's become like uh uh much cheaper to do but it's great that uh we're bringing

access because those kind of walls prevent kind of science from being able to to happen really you they prevent science from being able to be Innovative they you know put up walls so you only have proprietary information or proprietary um models happening uh which is not what we want with science we need need the world to have access to it was the dream of the internet right uh sharing kind of uh data and and information around the world to be able to collaborate and make that better that's the dream of Open Source so yeah it's it's frustrating when I when I definitely I'm frustrated when I see uh blockers like that happening for projects like this uh incredibly important projects in the world um and you know that's that's you know I'm back

on my saap box but this is like you know another reason we need to give tools to to people right we need to give Open Access uh not just to the compute but uh to other resources that they need to the tooling they need to be able to you know create um these things and reduce the walls and pain points for doing so while still you know enabling them to kind of make a living as well because you know scientists are kind of The Starving Artists of the world I think sometimes which is horrible so um this is actually a great Point um so both of you have kind of hinted at it um but I like to now get into um what Lily pet exactly is and it it is definitely solving this problem but like how does it solve this problem

uh yeah we love to get into that yeah cool um so I mean how I like to think about it almost from a a Layman or how I explain it to my mom point of view is I I kind of call us the Shopify of AI to a certain degree so we have a three-sided marketplace where one of them is our Hardware Network so this is the compute providers uh that provide their gpus to our network uh and they obviously ly rewarded uh by being paid for running jobs on our network uh we have users who are looking to um say use one of those protein folding models or use a specific model that's been created by a scientist and then the third part of it which I think is like the Shopify shop front is

uh our module Marketplace so people that have created these AI models or specific models for you know potentially for you know these Marine analysis or for rare dis disease analysis um they kind of have this option to monetize that model while still keeping it available to the public so I think that's kind of a special thing that we do here um and that's basically it right so three-sided Marketplace you uh as a user you can um you know go and choose a model uh run whatever you like get the results backck um as a hardware provider you're paid to provide Hardware to the network and as a um you know AI data scientist or someone that likes to create modules or even if you just want to wrap up hugging face

module for lilipad uh you have an opportunity to kind of monetize that model um and you know have a have a kind of living out of it and have this third option that's outside of being paid by someone proprietary and having to like have the rights go to them or you know open sourcing your model and and getting nothing for it so I think that's what's really powerful and did I miss anything there Stan I think that's kind of I think that that three-sided Marketplace currently the triangle is not fully one of the sides just broken people buing moduls or just doing it as a labor of love love and it it ain't necessary yeah but I do have a fun thing I I would mention and and Nar just for the

audience's context uh we are uh talking on a very special day Ally is actually here in LA with me and and we're about to meet up and hang and Ally I believe Amal is joining us so I am so excited to introduce you to amaly um narb is I think considered one of the most influential and highlevel um open-source developers on hugging face uh working in biological machine learning and she is to me such an important example of what lilypad is going to do um this this is a person who is in love with molecules um when when she talks about

the molecules that she's designing you can just hear the passion and the fascination come through in her voice and you can kind of tell within a couple minutes of talking to her she she probably thinks way more about molecules than how she's uh handling her bills or like where her parking space is right um and and so I became aware of the fact that um probably literally 10 well-funded startup companies had launched in the past six months um based on the work that this open source developer was publishing and she didn't have any support um she was just kind of piecing together uh GPU access in her local network to do you know this work

that was guiding a revolution and and then here's the idea let's pay her right let's like make her comfortable let her you know do that work that's in demand and and yeah we have this enormous resource of untapped compute and and by putting that to work you know we can kind of create a an economy that sort of supports those kind of really important developers yeah that's amazing and quite the the gamechanging um Tech um especially since it sounds like uh this this side of the world really needs that compute power and have been pouring their blood s and tears into the work they've been doing but can't get to that next level uh because the the hardware

may be too expensive for them or um it just doesn't make economical sense um so from that standpoint um um the hardware um like who who can be the provider of this Hardware um what does it take to join uh the lily pad Network yeah currently we're we're fully decentralized so if you have a GPU you can join our network uh and provide compute power to run jobs um and and I think that's important thing that maybe I I wasn't clear about as well or a job-based network so rather than renting out your GPU for a certain amount of time time um you know like most other kind of vast AI or or similar platforms like that we do job-based uh networking

so we're coordinating jobs on the network rather than coordinating time-based uh rentals um and that's kind of powerful because if you that means we can we can kind of collaborate with other deepin networks out there that maybe aren't being fully utilized for example so you know we could potentially collaborate with the cach and have them run Lily Pad nodes while they're waiting for a time-based uh you know uh rental for example um so I think that's kind of a powerful thing but yeah just uh jump on the docs and you you'll find out how you can contribute compute to the network um and yeah grow the network with us it's um um such a uh important part of the model too because it creates an incentive for developers to build

modules that um don't just perform well but are are reliable um I think every machine learning engineer has that experience of uh wincing when you log into a hugging face and seeing uh that it hasn't been updated in uh 3 or four months and you you know another part of the lily pad project that's been so interesting is is this continual feeling of trying to um aim past where the target's moving because I I think we've all seen the space changing so quickly um in terms of what Hardware is in demand in terms of you know what kind of workflows we're using um so yeah kind of like staying on on top of that making sure uh Hardware runs reliably a very

non-trivial job so almost becomes critical that we find a way to support the developers doing that work yeah yeah 100% 100% Ellie were you gonna chime in with something else for uh I don't think so maybe but for all good uh I wanted to uh double click into the concept of a module a bit um so for people who aren't familiar with nomenclature of lilipad could you give a high level explanation of what a module is yeah uh so basically a module is is just a uh dockerized it's just a container with a lily pads back over the

top of it so um I mean you could tell everyone da you are one of our main protocol Ed is here now but uh yeah so a module is basically just a a a Docker job uh with a lily pad spec and the lily pad spec enables us to match you up with the right kind of compute uh and the right kind of client uh for for running that job uh basically y yeah I of course I could have told the story but i' rather hear from both of you oh I hope you will mind if I I give a slightly um not not even a different answer but an answer that's really res or a thought that's really resonated um with with me on this question and

actually like hearkens back to those early days of multi inter node GPU compute engineering because narb let me tell you it was not easy this was uh the pre- doer days um so we were literally for each node type uh having to write scripts and test scripts and you know do pretty messy deployments and you know typically like if if we got on a thousand node deployment like 70 80% of nodes uh turning green on deploy uh that was a good day right and then we would spend a week or two sshing into 200 machines and trying to figure out what was going on and I I felt at that time a a pain a psychic pain um and and I feel

like that pain that I and many other developers were feeling kind of led to Docker and and the container Paradigm and I I feel a similar pain um I I think decentralized a is challenging because the hardware is complex um because the models are complex they require uh complex objects that need to be available on runtime uh with performance constraints on interaction between the the cores hosting the model and and the data artifacts and that's before you even get into these things needing to talk each other to each other and and the fact that agent-driven systems have very specific and challenging sort of data and uh inter operability concerns

and you know then you also build in these kind of Social and recommendation elements where like you know a module isn't just its own software runtime it is the the marketplace and the data about that model's usage that's going to um Define the usage of a a future user um so what I'm I'm really getting at is I believe and I I hope it will be the lilypad module or a standard that we are able to contribute to but in a year or two we will have new Docker container um and it will be the kind of standard atomic unit of decentralized AI compute um and I can't wait because like I said narb I'm I'm feeling the pain and yeah that that would be quite

uh quite the feat um that would be amazing if we can do the same thing um that Docker did for the software world uh but for the U AI asan World here um and I guess uh kind of around the same point um we see a lot of other uh decentralized compute networks in the field now uh mean it's to be expected with the rise of um AI agents and whatnot um but like how would you both kind of describe um how you how lily pad differentiates itself from um those competitors uh yeah I I'll take that one a little bit I I think um I kind of suggested that the differentiation is

what we've just been talking about uh one uh this standard of toic unit I love that they for four modules to a certain extent uh creating kind of this standard for running them uh also we're a job-based system so I would say that a lot of Who You may initially think of as a competitor so some of the deepin networks are really a collaborator for us uh I think we can can work with most of those networks uh as long as there's some sort of standard uh to you know provide them with more Revenue as well uh and you know vice versa they're running jobs on us and and helping us build out our deepin Network so that's one way uh I think we differentiate and yeah I mean the standard for kind of modules is is a second was or a

job-based ecosystem I heard this word the other day on a podcast that I quite like which is um instead of being competitors uh instead of being competition it's coopertition and I I think uh I really like that word now I think a lot of that is is what's happening across the decentralized AI I space in web 3 as well um it's not necessarily competition but it's each coming at different parts of the problem in a different way and I think uh we'll see a lot of that coming together over the next year uh there's lots of lots of bright Minds working on different parts of this problem and and we need uh lots of different Minds to be able to essentially make a decentralized cloud that's what we're doing here I mean it

took Amazon 20 years uh and making a decentralized cloud uh needs needs a lot of bright Minds working on a lot of different parts of the the project which we're seeing in in in web 3 at the moment as well so I think it's really powerful and I'm looking forward to the next few years uh to see what what happens I I really honestly believe and I've been in web 3 for a while now uh but I honestly believe this is kind of the Breakthrough use case these agents um and and AI this is this is what um blockchain's kind of been waiting for something like this and we're mature enough to do it as well we wouldn't have been a year or two ago so and it's quite interesting to see um this narrative playing out now where you see Ai and web 3 much more intertwined

whereas uh a year or maybe not even a year but a couple years ago people were like oh no it's like there's nothing to do with each other um I know there was a few projects that came on the show um and I started to probe into like what that potential could look like and it's it's just amazing for me to see like how um expion explosion exactly exactly exactly yeah I think well I think you know this is an inflection point in history too it it was a combination of things coming together before we wouldn't have been able to do distributed compute very well uh on one side and then on the other side AI wasn't really uh kind of um mature enough uh or mainstream enough now it definitely captured the

mainstream Imes but it wasn't mature enough either so we've kind of seen this uh Power Play of both things coming together distri distributed computer also blockchain enabling Primitives like payment methods um I mean an agent can't be banked so how else are you going to give it money um and then you know payment not just payment methods either but like that transparency in Providence guarantees that you can get with blockchain uh these are things that AI kind of desperately needs to solve some of its problems so I think uh these this is why uh we're seeing uh so much collabor or cross collaboration of of the two Arenas and uh I'm super excited about that yeah yeah I'm sure lots of folks are um I I see we're more or less coming to

time uh I just want to finish off with a couple more questions uh firstly um is there anything that uh you can or would want to share about um the lad road map uh for the upcoming year or or Beyond yeah uh so we're really um on a um we're really heads down at the moment are heading towards our product uh launch uh so we're hoping to launch uh early Q2 uh this year and that means tge and uh production ready uh kind of main net um we're not a full chain so um when I say main net uh we it's production ready code on on a main net chain um and we're fully evm compatible so like a lot of

the AI projects and agent projects you're seeing uh we're not necessarily tied to to a single chain here which I think is also amazingly exciting uh but yeah so our whole team is fully focused on getting towards that our engineering team's working hard on on defining the protocol and I know you are as well NAB one of our MVPs even um getting to that main net launch but if you want to participate uh we have plenty of kind of we're running an a Rewards program for Builders uh we're running rewards for building modules on our system so bringing over even hugging face models um to our system and and doing that extra 2% of code that's needed to to transfer it over to lilypad it's a

little non-trivial at the moment but that's web three for you we're working on the experience as we go only got so much time and Engineers but we'd love you to participate and the other thing we're doing is really promoting uh lilipad for agents because one of the uh Big Value props here is you can run uh kind of parallel uh compute jobs uh on Lily Pad so your agent could run a thousand uh llm jobs at once on uncensored llms or unbiased or censorship free I should say um llms uh you know provided there on there uh on lilypad uh and have that return and that's something you can't just do on your local machine um so we're working towards having an API out actually I think uh due this week um that you'll be

able to kind of call lilipad and and run those kind of Agents so we'll have a our next uh phase of our Builder program will include uh poting over agents to liad so we're super excited to see what people might want to build with some of those agents because that's definitely exploding and I I can see why like it's just so practical across so many verticals uh so we'd love to see what people build on on our system with those kind of tools um oh I'm so sorry yeah yeah oh well so I I was just going to say um I I love me some big fancy computers like when I was a kid I I liked working on cars and now it's big clusters but you know the the right computer is the one that the user

needs and the one that's set up for the user right so i' I've always felt that as a a data scientist it's like you really do have to kind of Meet the user uh where they live and that's usually on their their MacBook Pro um and and the new Macbook Pros are really inference beasts and so I've had so much fun showing a lot of my friends in different areas of science how you know they can just run some basic agents on their their local laptop um but there is this common pattern where um you'll have a a gummed up node in your your agent Chain of Thought um one where you're maybe simplifying the problem or restructuring

the problem on mass over your whole data set so you need to kind of Chunk the problem and have a bunch of smaller models process it piece by piece and and that can kind of take what would otherwise be a 20 minute agent runtime and and make it an eight or a nine hour agent runtime so um having what we might describe for these like local agent users um as like a pressure valve so when they get to one of these nodes that's just like so overwhelmed with demand they can just connect that up to Lily Pad and and sort of you know get a system that might be based on their local but has just the right uh amount of uh Cloud resources to call in um yeah

I think this will be just incredibly enabling for scientists looking to be part of the agent Revolution amazing and uh well said by both of you uh and ali uh you answer both of my questions uh as well so uh great stuff great stuff um yeah uh thank you so much again uh to you both uh for taking the time out of your busy days uh to come on the show uh and talk shop uh really really appreciate that uh and all the resources that you need to get started with uh lilipad are provided in the you YouTube description below um and if you watched us live today uh I'm dropping the link uh for you to be able

to grab your attendance badge uh on bit badges uh so you'll have an hour from now to to grab that to prove that you watch the show live um so uh again thank right right yeah exactly there's a leaderboard in everything yeah wow that's really cool I love that shout out to bites yeah go D day yeah exactly um and yeah with that I just want to wish everybody a very happy Friday a happy weekend wherever you may be and we'll catch you back here for another great episode of Devon tell thanks always lovely chatting with you too thanks likewise cheers bye