177 Huntington: Yep, yeah.
177 Huntington: And
177 Huntington: yeah. Sami, you comfortable presenting from back there? Or do you want to present up here? No, this is perfect. Okay, a room full of intellectual minds trying to listen to what a Pm. Has to say, and engineers, to course, correct me because my work, my view of the world is 1,000 foot above. And actually, what's happening. But I have a good insight 10 months into this project. So I'm happy to take over. Okay, that's great. So let me let me introduce you. So so the Ndif project is called the National Deep Infant Fabric, and and it's an Nsf funded
177 Huntington: project where we have funding for 4 years to create research infrastructure
177 Huntington: for cracking open large scale AI models and giving researchers economical access to be able to do research inside them.
177 Huntington: And so it's been about creating the technical libraries, the interfaces to do this creating the back end infrastructure the server that you know the live servers and the accounts and everything to make it all work. And it's it's like a little mini prime development effort.
177 Huntington: And so we recruited product development expert sumit. What is your background? Used to work at Google and Tripadvisor as as a Pm's project project manager at Akamai
177 Huntington: and and so he's been he's been sort of managing our little pirate ship of a pirate band of academic style researchers here to try to build up, you know, really a a small scale professional
177 Huntington: engineering effort to build Ndif. So so the the Nsf requires us to give them a report
177 Huntington: every year. It's actually kind of a grilling where they come and tell us everything that we're doing wrong.
177 Huntington: And you know they have all these out outside auditors, and so on. And so that that review process will happen sometime this summer.
177 Huntington: And and so we're beginning the process of practicing. You know, reviewing what it is that we've done, and you know, presenting it, and so on. So this is our 1st call around. So so sumit, go ahead, take it away. What have we been doing in the last year? Yep, this is where we go and talk about our bravado stories things. But, more importantly, I feel like a couple of things I would love to take away from this. And I'd love to make this interactive, especially for users that are hearing about end of.
177 Huntington: And the 1st time the the best thing that can happen while you're building an Mvp. Is get feedback from real users. We have a lot of theories. But this is a good way, a good podium, a good segue to validate some of the things we build and some things that we are planning to build for future. And, like David said, I mean.
177 Huntington: we are in this business of crack. Opening the models, David's put in substantial effort. It took us 18 months to get money from Nsf. And now that we've hit the ground running, we've bootstrapped this, and we are 10 months into this. You know, talk through about some of the key things we've done. But, more importantly, we wouldn't be able to do this
177 Huntington: without the help of our team and we are. This is, is this, is this yours? Are you in control, or is this we we wouldn't have reached in so far, so fast, and you know we have a long journey but this team comprises of Adam to my right, and Michael with the headphones. He's a Canadian friend.
177 Huntington: and Jaden, who is on his yara trip. Emma is also on a vacation. And David, Bob, it's it's very targeted and focused effort. I mean, what? Honestly, my role is all about making sure I'm I'm I see myself as the orchestrator in this team.
177 Huntington: We have a lot of internal stakeholders. But, more importantly, the external stakeholders that you know in the what I've learned in the academic world we have a lot of copis. David's not the one man army. There are a few other professors that assist us in what we want to be doing, how we want to be doing. The team gets into a deadlock. It's an intellectual conversation.
177 Huntington: We have. Nsf, we have the Esap. That's the board that advises us. And my role is how to be efficient, how to be productive? Are we doing the right things? Challenge some assumptions make sure we have the right gpus work with Delta AI. They are the partners. We don't get our own Gpus. We are funded by the Gpus that you guys run experiments on, and these are the things that have really been my focus area in the last one year since I've started going about these things.
177 Huntington: Yes. So what business is end of it right? I mean, this is the billboard that every time I introduce myself my wife takes me to any house party I'm like, we're building a world class interest engine for researchers. And that's you guys, we're not world class yet. But that's our goal.
177 Huntington: And we want to be world class. And how do we get to world class is we want to increase the existing usage of the user base that we have. We want to be getting more users so hopefully, after this presentation, you guys can go and bring another friend. And that brings another friend. We want to do.
177 Huntington: What the social media world has said, and we want to be getting Nobel Prize. For the research that you guys are doing right. So such an easy business we are in. But no expectations. Right? I just wanna make sure you guys are just aware of what end of does, and none of those things can can hover around your research areas.
177 Huntington: So yeah, I mean, if we can get some feedback around what we've been able to do so far, and what we plan to do so in 10 months? How much can you do with the small team? But you know, when the team is working what? 80 h? Not sleeping and just writing good code. But is it worthy enough? We've been able to do quite a few things that I'm really proud of? And again, the team should also be really proud, which is.
177 Huntington: the board is functional. This board tells us what we're doing, not only just for AI ml, and interpretability, but also the goal is to expand the footprint of Ndiv into interdisciplinary research. So someone was
177 Huntington: doing stuff between psychology and AI. And that's the idea. We've been trying to go more cross functional as we get into Year 2,
177 Huntington: we have a website that's functional. That's a shop where we try to target audience. Whether can they search us? How many people are coming? We have a published preprint.
177 Huntington: Jayden, you guys must know this engineer. 2025 accepted. Wow, yes, Major AI conference
177 Huntington: increase the font size
177 Huntington: for accepting. Yeah, I see it. Yeah, it's okay. We'll we'll put. We'll put the thing the text in for you later.
177 Huntington: but it's Icln.
177 Huntington: Alright, I will do this later a bit better. So
177 Huntington: it's accepted. That's great news. David was talking about the library. We have the N. Inside Api library that Jaden orchestrated and bootstrapped from ground up. It's gone through 3 releases. But a lot of bug fixes. So we have iterative releases.
177 Huntington: is it 4 releases now and inside 0 point 4? Yes, sorry. It's 0 point 4. That's why you know, I go go for such things, and without engineers I go and oversell the product, and only to come back that we have a lot of old. I'm glad, Michael corrected me.
177 Huntington: We we weren't as stable as we used to be. There were early days. We were down a lot we had this benchmark
177 Huntington: to at least at a minimum. We wanted to be up 3 days on an average in a given week, and the team's done a lot of good work Michael leads the sre aspect of it. And we've superseded that aspect so hopefully. So down a lot less than it used to be.
177 Huntington: We've expanded a user base to 30 plus researchers. It's it's been slow and steady in
177 Huntington: the other challenge that we had this year was hosting 4 or 5 v. Which is requires 2 dB. Of RAM. So we had 4 nodes on Delta AI and hosting. That was another challenge that the team overcame, and just in general, and they've started prototyping on northeastern machines Coney and Nagoya, and now we are in Delta AI. These are our partners who provide us with Gpu. So these have been
177 Huntington: some key marquee items that we thought we wanted to get there. And we've achieved that in year one and again, it's still a month and a half to go
177 Huntington: any questions.
177 Huntington: Yeah, how many users are there in the open source world? I I saw you have, like the pilot program listed there. But
177 Huntington: I feel like looking on the discord. There's so many more users.
177 Huntington: I I feel most of those users.
177 Huntington: So specifically, we've just started data analytics, the number of users that started using end if and we measure in terms of the Gp users. So the people who are using the back end intra is we, we have 30 unique 32 unique
177 Huntington: Api keys but that translates to so ball Lab user could have many more users beneath that. So we have about 80 or 90 Ips unique. Ips, right, Michael.
177 Huntington: I don't remember something like that in that order of magnitude, but I think the discord is also flowed by a lot of an inside users.
177 Huntington: Therefore that's a superset. You can probably also track the number of pip downloads. Yes, yes, the pip downloads, and there's a slide that says about the Github stars. Historically, it looks like we had 160 keys, but that includes a lot of the old generated ones and lots of duplicates and stuff. Yeah, so we've started from ground up. And Michael did all of this cleaning the house, which is the most office thing. So we can track and target audience and get some answers
177 Huntington: so from the outreach aspect and community engagement. We've got Emma. She's done all the heavy lifting. Believe it or not, she is a Phd. In biomedical. And now she talks
177 Huntington: every time she talks. So she's like, yeah expert. I'm like so amazed at how she's on boarded, and now she spreads the word
177 Huntington: she's been able to help us put the word out there more specifically. What we've done is a few tutorials. In. We have 14 research articles which have leverage, and if and 2 of them were accepted.
177 Huntington: and we've contributed to 8 major conferences, and the preprint is not a preprint like we heard. It's it's already accepted.
177 Huntington: But it's it's the brand and the community. And I feel like we're still in the nation stage. We've had partnerships with. Outside of
177 Huntington: the research lab. It's in the industry. Goodfire AI. We work very closely with them. Adam also works, and engineering team also has a few sessions with these people on how to tweak
177 Huntington: existing library, and, you know, have some features that help them. So there is some traction from the industry. Also, it's not just research labs. We've had workshops. We've had feedback sessions from these pilot users. We've had the thorough program on who's interested and then, based on the experiments they want to run. We've given them slots.
177 Huntington: We've had a lot of sessions with the Hpc cluster. That's Delta AI, based in our
177 Huntington: Urbana champagne Chicago area on how we want to be using their Slurm Slurm is just an equivalent to Kubernetes, if you guys are aware of that, and we've had this workshop last year.
177 Huntington: But our social media presence is growing. We track that on a regular basis. And if you guys haven't done that yet, please, there'll be Link, and later in the slide. Go, follow us on each of these social media channels.
177 Huntington: So what's the artifacts we've created? I mean, 517 Github stars. I feel that's a good takeaway for the library that's being used.
177 Huntington: Discord, like, you guys are aware, we're trying to move to discourse, which is an easy way to help us find all the wiki stuff and questions that have been answered like reddit style.
177 Huntington: We have a bug flow process where user wants to tell us what's wrong, and they can submit their pull request and our team will test and fix them together.
177 Huntington: We will continue to optimize our website. We need more analytics so that we are targeting researchers and converting more. We also want to be doing some churn analysis on why a user abandoned us, if at all
177 Huntington: in. We continue to have a lot of updates on the documentation in bug fixes that happens iteratively after all these features that inside is already published in all the work that we do is open source. So there is no stealth mode about it. You guys can go and play with it and bring your own Gpu and try it out.
177 Huntington: Do you think that doing all of our work in open source is like
177 Huntington: causing you guys to write higher quality code
177 Huntington: cause. It's all out there in public, I mean.
177 Huntington: personally speaking, a lot of my code is not publicly based. Oh, it's not all my deployment. Do you think it should be public? Absolutely okay. It is a bit specialized, though, for our use case. But we don't. We don't have like secret keys in there, or anything like that, it could be. No, no, I've been careful that we could just flip it to public
177 Huntington: good. Yeah, maybe improve.
177 Huntington: Okay. Sorry to interrupt. No, no, this is good. We'll make it public soon.
177 Huntington: So what you know, believe it or not, the biggest it's it's it's we talk about like the airline industry. And people forget. We talk about pricing, we talk about features. The biggest thing is the plane itself. And I believe the small engineering team is the real code of what we've been able to do. And now, believe it or not, we have an on call, sre, if you guys are in trouble we don't have an 1 8 0 0 number, you know it's discord.
177 Huntington: and we will go and fix it, and we will reboot the whole system. So you guys can run experiments. It used to take longer. And now it takes much shorter to go ahead and restart and put
177 Huntington: there have been multiple aspects right? About this engineering that we've done. And it's never easy to call the shots when we don't have users. We've had this theory on what is actually going to get customers and researchers.
177 Huntington: And over time we've we've been able to collect some feedback. Some is intuitive. Some is about the experience. The team gets itself from David and Co-pis and myself. And it's it's it's not been hunky, Dory, to say the least, and the teams put in a lot of effort. So from a performance standpoint, yes, we are up 3 days, but we were down a lot. We are now investing in a lot of real time, monitoring, load, balancing, and alerting.
177 Huntington: We didn't know who the users were, and now we know we didn't know where the system, how it was behaving under different workloads at different times.
177 Huntington: We had a significant degradation. Once we migrated to the Delta AI infrastructure. We were 9 token per second, and that was not what we set out to do at a minimum. We wanted the same performance when we were in the northeastern machines in December. Oh, we were. We were 9 token seconds opposite. Yeah, sorry we were 9 times slower than one token per second. Yeah, we were 9 seconds
177 Huntington: to get one token. Yeah. When we moved to Delta AI. It was a disaster. It made the whole system
177 Huntington: unusable, right?
177 Huntington: But we didn't have funding to be, you know, to buy our own computers. We had to use the Delta AI computers. And so when they're like
177 Huntington: 10 times slower than we wanted to be.
177 Huntington: And we had this major engineering
177 Huntington: crisis right? That was sort of the focus of the team for a while.
177 Huntington: and we had a lot of back and forth, but it was what was it, Michael Nicole, the optimization that gave us the different version of Pytorch and change particular nuance detail about how the job scheduled.
177 Huntington: Yep, and all of that, too.
177 Huntington: And and is that so? Was that because Delta is on the graceshopper architecture that it requires this special stuff. So, yeah, yeah, so, Delta is this unusual
177 Huntington: you know, back end it. It's a non intel based.
177 Huntington: Nvidia. Only Grace Hopper, things that are all arm based
177 Huntington: has some weird, shared memory architecture. It's like custom, networking setup and everything. So if you standard pytorch phones really badly on it.
177 Huntington: Yes.
177 Huntington: cool, and that was not the only thing right, I mean, the system needs to be stable and performance. So we've had Gpu memory management and caching strategies. I mean, there's a best way to optimize
177 Huntington: the memory within a machine. And then the machine to machine communication.
177 Huntington: We've tapped into some of those aspects the observability tools. Now tell us
177 Huntington: how we have actually users job getting submitted to actually getting running experiments, caching and object store and then getting the results back. And Emma is now starting to. In our believe it or not, the month of April is all about the user information and the email addresses and actually targeting to ask them a lot of loaded questions which helps us refine our roadmap. I don't think we've done any Gpu memory management or caching strategies.
177 Huntington: I think what you're getting at is more the tricks we have to do to work with the Grace Hopper to deal with the mem, the unified memory issue. Yeah.
177 Huntington: Unified memory issue. No change that slide
177 Huntington: cool and then deployment and infrastructure.
177 Huntington: the most thing of llama 4 or 5 B and nickels a user. He can tell us about the user experience
177 Huntington: for 4 0, 5 B. After these slides, I guess. He's been an avid supporter of end if
177 Huntington: and optimize support for a lot of smaller models. And you know, we there was a time we used to only think about optimizing 4 to 5 B. But in general our our team has now been optimized end, if in general not just a very, very large models, but end of as a service should be optimized for
177 Huntington: faster performance and reliability.
177 Huntington: We chose Ray that was the orchestration infrastructure. Just how Redis and Job queuing and things go back to the worker node and come back. Michael's worked all on the deployment side as a deployment repo expert and he can tell you about how we are going
177 Huntington: about the automated deployment. Once end, if is done, we just not. We don't need we right now need an on call, sre. But now it's gonna be some scripts that's gonna automatically Bootstrap and get end up.
177 Huntington: We're addressing the job, scheduling inefficiencies in delta getting towards one month deployment. And now we also have the ability, thanks to Michael, a non prod environment so that we don't have to take the prod environment now.
177 Huntington: And the other aspects that engineering has been able to produce is the end side. The Api has just grown from strength to strength. We've just had key features that we've solicited feedback from. And the versions have incrementally helped hopefully the research community. And there is an iterative process of which bugs to fake. How? How people file those bugs? And do we want to pair program with them? Or can do. We have to do the heavy lifting.
177 Huntington: It's been a lot of effort also gone, and teaching what those features can help with and how to best run those features on the client side, and how it actually goes through our system and then measure the performance
177 Huntington: the other aspect that we really want to be focusing on until the summer. And the next user base base grows
177 Huntington: is
177 Huntington: the user experience piece. Michael was saying, we we had a lot of Api keys, and it was all scattered over. We want to be giving an Api key and then be able to have this accounting system
177 Huntington: ensure. Bad actors don't have access. And then we also know who these people are and the faces slash the email addresses behind which these people are. So we can answer a lot of their questions.
177 Huntington: The 30 plus researchers has been
177 Huntington: a a good target. But we were happy to beat that target, and we still have a few more things to do. In order to increase the user base. In in.
177 Huntington: we've we've had to create a lot of structured onboarding materials and documentation to guide these users. Love, because it's it's not easy, since we are targeting a lot of technical users. And Emma has been instrumental in helping us with that. The activation steering guide, Tutorial library, all the stuff you see on the end inside website, and just the whole brand and community and hosting these open hours
177 Huntington: the office hours and then working offline and piecemealing some of these questions to make sure that the user. Assist the other users. Then you can see all of the traction we receive, both on discord, and Github is been super helpful.
177 Huntington: Of course, the challenges nothing was possible from day one
177 Huntington: we've had allocation delays. So we figured out a process and how to make sure that we don't run out of Gpu. So there's no denial of service.
177 Huntington: These are. We've we've taken a lot of effort to learn and understand Sloan in this manual reservation process and job timeout issues
177 Huntington: we meet the team every bi-weekly to understand and tell them our issues, because the Delta AI cluster that we host our infrastructure on. They are not well suited for the web service kind of that we have right? And then the slowness of the system we spoke about. It's it's it's been some effort to get there in the last couple of months to make sure that we at least have one token
177 Huntington: the model starting right. It used to take about 2 h. Now. It takes about 20 min to 50 min, depending upon the model. When we restart end. If so, 4 or 5 B, because it takes 2 dB. Of ramp. It takes about 50 min.
177 Huntington: The network congestion and the Gpu bottleneck. That's something we consistently try to make sure. We are solving. For as we scale the usage and the number of users and the more jobs that gets submitted. So the Intra machine and the inter machine and the front end to back end communication with different broke
177 Huntington: right? So this is mostly talks about what was the past. But definitely, the plane needs to land somewhere, and we are headed.
177 Huntington: So what are the top things or themes that comes out of our discussion board. Right? We are actively working to have local tools to cater to
177 Huntington: researchers beyond the Aiml community.
177 Huntington: We wanna get 200 plus users, which is 3 XA user base.
177 Huntington: The platform needs more capabilities for more users. And just the type of interactions and the experiments both for large models and smaller models. We are strengthening our external partnership. Adam is also helping customize a lot of Google collapse. So we can spread the word and people just make one click and they can start running experiments.
177 Huntington: User experience still has a way to go in terms of how we can enhance it and engage them and make sure that they're not abandoning and different. They start using it
177 Huntington: ideally. We'd like end of to be used in a lot of classrooms and have a concurrent users. We also have to test the bench strength of all the stuff that engineering is doing so far.
177 Huntington: optimizing model load time. We have a lot of way to go in that aspect and then better job scheduling users sometimes.
177 Huntington: Blindsided. Once they run their jobs. They don't know how long they have to wait when their number is gonna come and how long the experiments are going to take. So we're trying to build some capability if transparency, and how we want to be telling the user rather than just hope as a plan.
177 Huntington: Yeah, it has to be a takeaway. Right? What have we done so far end of is clearly growing. But
177 Huntington: we are evolving, let's say about that right as a key research tool.
177 Huntington: We are enabling broader AI experimentation in advancing scientific transparency.
177 Huntington: And how do we get there? As the next step is.
177 Huntington: we we have a few roadmap items. I'll show after this, which is
177 Huntington: all around world class inference, engine and engineering and catering to user and analytics.
177 Huntington: The outreach effort is now very going to be targeted, data driven. We want to go broad and 0 down on the existing users. And what can get them to use more. Am I taking a lot of AI transparency initiatives
177 Huntington: in just the performance aspect, the reliability and observability
177 Huntington: has got us to the way we are, and the teams put a gigantic effort for all the technical improvements from where we were.
177 Huntington: If you guys are not aware, then please
177 Huntington: follow us. Use some of these links email us with any questions.
177 Huntington: how we can cater to some in all of your needs and make you an end of researcher for life.
177 Huntington: We'll be happy to talk to you guys and come up with special needs.
177 Huntington: And that's pretty much it. These are some of the minute items that is going to be implemented with the help of a few summer fellows and the existing team
177 Huntington: for what we want to be doing. In some of the aspects, or say, mostly summer, and some will spill into fall. We want to be leveraging Fsdp, that's a better way to utilize the existing machines.
177 Huntington: To run these big models we want to be. We have some node failures with delta air where we host. So how do we make sure our systems available and fall tolerant.
177 Huntington: We want to be minimizing the downtime. So we ideally, we go from 3 day of time to 4, 4 day of time.
177 Huntington: We we don't want to be burning. Gpu Nsf is very, very stringent about. Don't waste expert dollars. So if the models are not being used, and the system is idle. We just don't want to be spinning up
177 Huntington: models when they're not going to be utilized at all.
177 Huntington: And generally we have. We've thought about a better way to just get storage and wait retrieval from the hugging face website to start reduce the startup time, right? So we can cache it.
177 Huntington: Adam, here is building test harness, and he can tell us all about the specifics. But the key stuff is.
177 Huntington: the system needs to tell us a lot more things proactively rather than the user telling us things are not working, and we want to be having alerting. And we want to be monitoring some of those aspects.
177 Huntington: We're also creating a single docker image
177 Huntington: this is for folks who want to run end of on their own Gpus, and we want to build a light version of end of right just catering to the different use cases. Again, this is helping us go broad and get us the user base that we need and ideally want to validate a lot of these things that we build fast forward
177 Huntington: and the experience side of things.
177 Huntington: We want to be hosting multi models. Very soon, and therefore we've requested more. Gpus. We want to be telling users how much their wait. Time is going to be in the queue. We want to be telling them what model schedule is automatically so 4 0. 5 B. And 4 to 5 B instruct. We have to swap them on a regular basis just because they take a lot of notes.
177 Huntington: we want to be serving these models on demand. So we want to have a mechanism to pull the users, and then a docker container spins up to serve those specific models.
177 Huntington: Of course, we can't serve all the models, but we want to be efficient about validating which users like the offer. For for example.
177 Huntington: how do we? We also want a feature. We've heard from users that they want to be saving an edited model where they don't have to keep
177 Huntington: probing around where, how they modified it. And you know what the changes they want to be. They've done in the past. They want to be reusing it. So we want to optimize the workflow.
177 Huntington: And we wanna give them faster access to the results. Reducing the computation time and cost it takes for them to wait.
177 Huntington: Yeah, this is pretty much the end of my slides.
177 Huntington: and these has been 10 months, 10 and a half months with a long journey. This is just a quick stop. And I feel like end of year. Love to hear from you guys
177 Huntington: on what it takes to convert you guys for an end of user.
177 Huntington: Thanks. Thanks, mate, all right. So pretend that you're the Nsf.
177 Huntington: What do you want to know from Smith that he hasn't explained to you what's an inference engine.
177 Huntington: What's an inference? Engine? Inference engine.
177 Huntington: how we use any open source models.
177 Huntington: And then how do you run queries on it? So same the same way we use chat gpt, for example.
177 Huntington: We're running inference in it by asking questions, and the model is getting smarter over time and running. Inference is, my understanding is that's how we run experiments and the behemoth which supports
177 Huntington: all of the backend stuff.
177 Huntington: The models that we host people can run experiments. That's the way of running, influencing.
177 Huntington: What would your answer be for Western inference engine? I think that's the wrong explanation for it, because it emphasizes more inference and efficiency and trying to get inputs and outputs out quickly. I may not have the perfect term for it, but I would emphasize more that it's accessing parts of the model that are previously inaccessible, using services like chat Gpt. So more emphasizing the interpretability part of it
177 Huntington: which will highlight more of the use case, which I think is kind of implicitly assumed, like everyone here is kind of probably assuming, just based off of the context of the lab and whatnot. But so let's do this. So let's so I agree. And like we should like with like that, I love your slide.
177 Huntington: That says, you know, this is, we're building a world class inference engine.
177 Huntington: And maybe so we have. We've made. We've made all these technical diagrams of like what the big idea of endip is, but they're all for a technical audience.
177 Huntington: And like, maybe what would be helpful is to make a diagram.
177 Huntington: Maybe you could help us with this, they can make a diagram for more of the Nsf manager. Audience of like what it is. What is a world class research.
177 Huntington: different extension? Right? What does that mean?
177 Huntington: If you know what are the inputs and the outputs? And what does it give you that
177 Huntington: as a standard interest engine doesn't do it right?
177 Huntington: Like, can we make that like a really simple cartoon? Yep, yeah.
177 Huntington: How specialized is the audience for this? Like the Nsf managers, are they going to be focused on computer science stuff are they going to be? It's it's hard to know. So
177 Huntington: I'll tell you the the
177 Huntington: we have 2 program officers.
177 Huntington: In the Nsf, one of them is a machine learning person. Right? So it's Nlp, you know, researcher.
177 Huntington: And then the other person is a systems expert.
177 Huntington: So like an expert in computer networking. So one of them knows
177 Huntington: nothing about computer networking and stuff like that. And the other one pretty much, knows nothing about machine learning
177 Huntington: and then and so if that if that's an indication like, if we have reviewers.
177 Huntington: then we'll have reviewers that similarly have, you know, narrow
177 Huntington: specialties. They might understand some aspects of what we're doing in that other aspects.
177 Huntington: Right? Sure.
177 Huntington: Oh, where'd you meet? Go watching the questions.
177 Huntington: yeah, we're pausing the question, what are all these? Well, there's green tea. There is also Boba tea, which is the white one, and then some sesame. I would like the sesame one, sesame one. Do you have the box for it?
177 Huntington: I do have it. Yeah. Do you want to see it?
177 Huntington: Let them grab it. Thank you.
177 Huntington: That's probably nice. Yeah, please.