Advent of GenAI Hackathon recap

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Welcome to a very special Fireside Chat which corresponds with the ongoing advent of Gen AI Hackathon that will also be reposted on the Practical AI Podcast. I'm very pleased to have been participating in this hackathon as one of the organizers, but I'm also joined here in the Fireside Chat by an amazing team from Intel's Liftoff program for startups who helped organize this hackathon that we'll be talking about throughout the day. So I'd like to kick it over maybe to Rahul to describe a little bit about what Intel Liftoff is.

Hey hey all. Thank you Dan. This has been an incredible experience. Let me before that even talk about the advent of Genai2 sentences. This was probably the biggest generative AI hackathon that our team has organized and the submissions, all the different chats and things that we have seen, this has been really awesome. We got a lot of positive feedback and a lot of things that we need to improve for the next time. So thank you for participating for the hackathon and we would be announcing the winners of the final product development in couple of hours. Before that, let me talk about Liftoff. Right, so Liftoff is an accelerator program, specifically a technical accelerator program for early stage Startups. So if you have an idea, you're a seed startup or till series P. You want to scale, you want to build some cool things in AI or machine learning, please join the program. It's free. I would categorize the benefits basically into three different pillars. World class technical support and technical expertise. That's me and Ryan here. We lead the engineering side of things and we have an incredible engineering scale team. Some of the folks are here like Jyoti and Virat. Then you get access to technology, both intel software and Intel Developer Cloud. Intel Developer Cloud is a production ready cloud specifically designed for AI workloads. Prediction Guard is one of the startups that came to our program earlier this year and they are running on Intel Developer Cloud now using Goud E2 accelerator. And I'm sure that many of you folks who have participated in the hack app would have used Prediction Guard LLM APIs. The third one is CO marketing and bringing your. Once you built that product and deployed it, the next thing is to make some money, right? So we co market your startup, your idea through all of intel channels and also we have a network of accelerators and network of folks that's beyond intel that we take the product you have built, the company I built and basically market it all over the world. We also connect you with our sales teams to see if there's a potential for selling the things that we're built through intel channels. It could be a service on IDC or it could be separate that one of our customers of intel is looking for. I would urge anyone who is looking to really bootstrap and accelerate your startup journey to join Intel. Liftoff.

Yeah, that's great. That's great. Rahul, could you describe a little bit? So I remember initial discussions between a few of us, you had this idea for the advent of gen AI hackathon. Now a lot of people in the audience out there might be familiar with like advent of code. So where, like how did you start thinking about this advent of gen AI hackathon and like what was your initial vision for it?

A lot of the geeks in the call, which most of us are, would know Advent of code. It's a set of programming challenges. You can take any sort of programming language you want to learn or to attempt to solve these algorithmic questions. I've been doing that for many years and I thought, you know, Genai is something that's new and something a lot of people are talking about. But there are no fun set of exercises or that you can use to learn and Also build cool things with the technology that's existing today. So I was, I was thinking why not just create something set of challenges that's tailored from a person who might not know how to code, but have seen prompt engineering and creating cool prompts to create images all the way to people building with LLM APIs. So we designed a set of challenges to bring in as much as audience as possible to introduce them to Genai and also learn through that process. Many folks I've seen in the chats where they came with just prompt engineering knowledge and I built really cool things, graduating from one challenge to another and there has been a really good community help. Also like people talking to each other and trying to help out how to run, how to build these things. This has been a really, really good exercise. Even some of the challenges when I was building it, I was like, oh, whoa, I would like to do that because we always wanted to add a fun element to it. So no challenges drive just an algorithmic question. But there is something fun element to that. That's where the whole advent of Gen AI came about. And we want to do this yearly. So we next year it might be a new technology, it could be a multimodality hackathon, some other things. We already saw some cool engineering stuff. Folks are building this challenge, but it would be something but I'd like to do in December Advent or something else that liftoff does for the community.

Yeah, that's awesome. So if you're listening to this after the holiday season or as this will be coming out on the podcast later, you didn't miss out on particip. Well, you missed out on participating in 2023. But there's going to be more opportunities to participate in things like this that intel is going to put on later on. So I definitely recommend people keep an eye out on the liftoff program and social media to hear about things. Could you speak a little bit to the response to this first advent of Genai and like the participation that happened, I think we were all a bit surprised at how many people joined in to this. So could you speak to that a little bit and anyone else from the liftoff program kind of any of your observations for the type of people that joined in and the range of experiences.

And all of that, this has been fantastic. We had grad students, we had even students who are in school who have taken prompt engineering courses who just wanted to have fun working on some of the earlier challenges. There has been experts also some of experts in LLMs and Gen AI worked on it. And some of the products that they built or some of the challenge solution is it's like an MVP startup build. We have many startups who are building a similar solution who are taking like six months to a year to build a full solid solution. But some of the challenge answers, especially using the rag example Dan and or the Python code explainer, things like that are difficult. And they have even gone further. When we asked about how to can you create a storyteller chatbot? People have created a story plus image chatbot, a multimodality chatbot. So these are many levels of experience and that's even some of the folks from intel participated. That's also a very positive thing where we see you can, in the level playing field, work with folks from intel and solve the challenges together. We had folks from Berkeley, folks from many different enterprises participating. So this has been a mix. And I was amazed the level of participation. I didn't expect this many people would participate. And we had to stop registrations after 2000 people registering for the event. And this has been just great. Ryan, do you want to add something?

Yeah, I just remember his idea when he first called me. It's like cleaning up after Thanksgiving in my basement or something. He's like, you know, I have an A code. Yeah, of course, of course. I want to do advent of gen AI. Like, all right, what do you want to do? We worked it out and it was like, set the goal of like, let's get like. It's like, yeah, we could get big. Like, you know, a couple hundred people at least would be a success, but like, stretch it. Like, hey, I mean, we could get maybe a thousand even and do the biggest event of the year. Well, we end up getting up, cutting it off at twice that, twice the stretch goal. So then of course, the entire time we're like, oh, man, did we do a good job. Are people going to like this? Are we going to get submissions every time? Knock it out of the park. Way more submissions than we were hoping for. And the quality was excellent. And the amount of people we saw in the chat helping each other, it's like somebody, you know, we're all trying. It's 24 hours a day. So we're, you know, our team is trying to stand and answer questions as much as possible. We'd set that as a goal. But what we saw, like from Go, like from when it started was when people would ask questions, other people would jump in and like, link them to the explanation or the documentation or whatever. These were all the dreams for the event. So it's been incredible and I want to thank every single person who was involved.

Yeah. And maybe it would be good for those. Maybe some people jumped into certain challenges and not other challenges. Or they might be hopping in at one point or the other, or they're learning about advent of Genai as they're listening to this. So what were some of the challenges that were presented to the participants and how would you consider them in terms of like relative challenge level or skill required to complete them?

We designed this challenge in a progressing level of I wouldn't say difficulty, but I mean the ability to code. I would say it's not difficult. Exactly. And creativity also because a lot of gen AI, it's what we see, at least in Liftoff, is that AI has become truly commoditized and you don't really need to know, go through masters or a couple of courses on neural networks to build an application. Right now we have the amazing transformer ecosystem. It's really easy to integrate some of these things, the AI applications to AI superpowers to the applications you build. I would take the first challenge, for example. The first challenge was to create a narrative based set of images. And if you look at the challenge the first time, it looks like it's very easy. You just create a couple of images using stable diffusion. It's all about prompt engineering. You don't need to know a single line of code. All the notebooks and the models, everything is available on intel developer cloud. You just create a standard account log in there, get jupyterhub open and just play with it. But the thing is creating an transition from an image to another and creating a whole story with set of PI images. It's not easy, it's really difficult. And we even saw some folks creating a comic book generator using this challenge. That sort of imagination, Right. That's what I really wanted people to do, but I didn't want to say that. Okay, please create a comic book generator as a challenge because it's really difficult. Some of the folks, even without knowing that built it. You take the final challenge, right? That was Python code explainer where you give in a python code. You use the LLM model to understand the code and give an explanation of it. We had additional challenge, additional sub challenge there, show the source of documentation or stack overflow questions where I can go into and learn about it more. These kind of additions makes it very interesting and a little bit more complicated where you have to use a vector database, you have to use prediction guards, LLM APIs to get the right model and you had to design a UI for it all in the constraints of a jupyter notebook. So I would say that there was a progression of difficult. Difficulty is very relative word. But yeah, there's a progression of difficulty if you're just coming to Genai. And the whole idea is that it's a single package. So even if you're not participating in advent of Genai, we have released all the resources that we have built for this. You can take this to basically get an idea of what Genai can actually help you to build and infuse to your applications and you can just go through the different challenges or if you're an expert in LLM rag based application, go to that particular challenge and take a look at it. So it's now become a learning resource also not just a hackathon.

If I could just add on to that real quick. It did on purpose go up in difficulty. Like the level of each challenge was supposed to get harder and more advanced, let's say in terms of coding ability as we went on. But the real focus was about skills and understanding the tools that are being used within the industry. There's been a huge focus on creating new levels of abstraction to make neural networks easier to use and build with. Used to be very challenging. Now not, I mean not nearly as much.

You don't even need to stand up.

Your own neural network anymore, right? You can grab an API. So if you look at the challenges, each one is kind of focused on a different skill. And if you go through all five, you cover prompting specifically for images, text, image. You cover using an LLM API and the different things you can do with it. Cover image to image. So image editing with AI and the third one and then rag based applications with LLM APIs and finally we thought, you know, the fifth was the most advanced like the code explainer one that's, you know, there are companies that are basically that are big companies that are working on that exact problem that are betting that, you know, it's going to a lot that a good solution for code explanation improvement and generation is going to lack many billions of dollars of value. So all these things are focused on these different skills. And our hope was that for people who are maybe software engineers looking to move over to AI or to students, like whatever, anybody who is interested in learning AI skills that by going through the ones they chose to, or all of them, by the end of them they'd have kind of a portfolio of knowledge in their head about the different Skills, both on understanding how to use them and how to do a good job, like with prompting, but also by going through the code and understanding all of the code and all of the notebooks, an idea of what else they could build outside of the narrow set of applications that we asked for over the course of five days.

I think one thing that impressed me about the set of challenges that you all came up with was that it focused really around image generation, coherent image generation, and on the LLM side, sort of retrieval based methods along with chat. So I think all of these things are the things that people are finding most utility out of when they're first implementing AI solutions within their actual enterprise or industry or startup or whatever environment they're working in. In particular, retrieval based methods and RAG systems, at least for our clients, we're seeing that's like the first thing that everybody is building, right? You have your own company set of data. In the case of the challenges that you all put together. Maybe that' external Python documentation for the code explainer, or maybe that's just some external PDFs or YouTube videos or whatever it is for the RAG based solution. But lots of companies have this data that has this sort of unlocked potential and is unlocked via these retrieval based methods, which is a lot of times what people are building first when they adopt this technology. So I think it was great that you all tied that together with the participants to give them practical skills in that area and kind of help them learn, you know, what is a vector database, what is a RAG system? How do you implement this with custom data rather than kind of immediately hopping to fine tune a model, which I know of course you can do more easily than ever as well, but there's a lot you can do even just by integrating your own data with retrieval or other sorts of methods. I do want to ask here in a second some of the solutions that you saw and what stood out to you, just to highlight some of those really cool things that we saw before we do that. So you can have that in the back of your mind and think through some things you'd want to highlight. I'm wondering if you all could speak to the intel developer cloud specifically, which is something of course, I've found utility out of, but it was something that was kind of unique about this hackathon and it might be something that the participants here are a little bit like this was their first time using it. But also there's a whole lot available there in terms of different ways to run AI models that are maybe, maybe some people are less familiar with. So could you describe a little bit the Intel Developer Cloud and maybe also highlight some of those like different unique ways that people are running AI models outside of just like throwing it on a gpu? There are actually some interesting kind of other either tooling or hardware software available for people. Could you highlight a little bit of that and the unique ways that people were specifically running AI models throughout the hackathon?

Sure. So Intel Developer Cloud is Intel's production ready cloud specifically for AI and machine learning workloads. And of course when we say AI and machine learning, it's matrix math. So many other compute heavy workloads can run really well on idc. So for this particular hack we provided anyone logging in to Intel Developer Cloud registering on IDC as a standard or free tier user. You get a shared Jupyter hub instance where you get access to Intel's data center GPUs, Intel Xeon processors, and I would say this system for a free tier user. I don't think any other service provider, I mean there are many services with the Jupyter hub frontend, but the amount of compute and amount of memory and RAM and even file storage that you get in the systems, I haven't seen a single cloud service provider providing that. And we have seen a lot of people really using it and giving us feedback on how we could even improve it. Today in IDC we have a lot of models or LLMs already. There are tens of, even hundreds of local models that we are planning to add further to boost this, there are stable diffusion models, LLAMA models and things like that beyond that for productionizing the workload. Right Dan, in your case you are using the Gaudi 2 accelerators. Those are specifically designed for workloads that request high bandwidth like LLM and Genai workloads. And I like this, I lost my train of thought. But yeah, we have Gaudi 2 accelerators which we are seeing incredibly competitive and sometimes outclassing the best out there for your particular workloads. Along with Gaudi accelerators, those are specifically designed for Genai and AI workloads. We have general purpose GPUs, the Data Center Max series GPUs, both with 48 gigs and 128 gig versions. So the folks in the hackathon, they actually used both our fourth generation Xeon, that is the latest Xeon that we have, which what it particularly does is that it accelerates your machine learning workloads. We have dedicated instructions in this CPU to sometimes even take your workload to 2x the performance that you got in an earlier generation, it's all about making the CPUs how we see it is that making it as efficient as possible and making it as fast as possible, just still maintaining the general purpose utility of a cpu. Then like I said, the data center Max series GPU is a little bit more generic solution where you can run your AI workload, HPC workloads. Each of these machines, when you're productionizing, you get a VM, you get an 8 node 8 card system. There are also clustered systems available. Then comes the Gaudi accelerators. Both there are single node machines and also clustered machines. If you want to do pre training or big fine tuning, all those cool things and soon we'll have Kubernetes, service, object store, file store, all those things coming up. So it's going to be great. What I see is that if you're building a startup, it would be very difficult to find a performant and accelerator cloud like IDC out there. I'm sure that there are different hyperscalers, but this uniquely for startups. From my personal experience, this is a really, really awesome solution. I'd like to know more from you, Dan. Like you are one of the first customers of idc, right? What are the things that you thought that really made you decide to choose IDC from the performance and also the team side also. Right?

Appreciate that and appreciate the support that you all have given. I think it's interesting maybe for people out there that are less familiar with the various options for model deployment to understand that there is really good tooling like you say, whether it's optimizing a model and deploying it on a CPU or an edge environment or just a cheaper inference solution or. Or it's like all the way to these Gaudi 2 processors that we've been experimenting with. I think there's a lot of interesting and approachable tooling for that. So I first came across some of the tooling around Gaudi 2 by actually seeing blogs on the Hugging face blog about Gaudi 2 and I think at the time like the Bloom model, which is a very large model and running it on either a single accelerator or spread across eight accelerators with really high throughput on the inference side and doing that with tools like Optimum Habana. So for those of you that are out there and wanting to explore things, actually if you look up the Hugging face Optimum library, there's a lot of great tooling that you can play around with there. Even not for Gaudi, but for other processors too. So whether that be CPUs, GPUs, the Gaudi 2 HPUs, the Data Center Max GPUs Optimum kind of provides you a way if some of you can visualize maybe you're writing in your code and you're importing a model from hugging face, it's just like Auto Tokenizer or Auto Causal LM or whatever it is with Optimum a lot of times either you can just do like a couple line replacement and just replace that with the optimum version of those classes or do some wrapping of the various models with optimizers and this allows you to run your model very fast on a wide range of architectures. So I think to your point Rahul, I think one of the things that we found really useful is the actual ease of use and coming in and saying okay, well we have this stuff running on a gpu, let's try it on this various other architectures. I remember even like maybe two or three years ago trying to do some of this model optimization things for edge deployments is very, very challenging. So like a lot of times I would try to optimize a model at the time working on speech models and other things and it just wouldn't work because operations wouldn't be supported or something like that. But this tooling, which is cool because Intel's working directly with Hugging face on this tooling and of course the ease of use has just been ramped up drastically and we've been applying that with really good results particularly for inference for LLMs. So that's been a key feature to that change happen.

That's really awesome to hear Dan. And particularly also the thing you mentioned. Right. So think of intel in two ways. Probably the biggest semiconductor manufacturer, the coolest chips. The other is intel is an open source software company also we are contributing to almost all big open source projects, Linux kernel, most all things. If you see we would be anywhere in the top three PyTorch, TensorFlow hugging face. Any sort of open source solutions out there, we work really hard across to make sure that your adoption of a technology is as easy as possible and try to upstream as much as possible to the core Pytorch library or tensorflow library and things like that. In cases where we feel that there are further optimizations that could be done and these things cannot be upstreamed in couple of months to the mainline repositories. We released extensions also. So for example, if you take the out of box Pytorch and run it on a cpu, you already get a lot of performance because Intel's neural network accelerator library, one dnn, that's powering a lot of these operations when you're running on a machine like an Intel Xeon for example. But if you want to go a little bit further, right, we have things like intel extensions for Pytorch that with one line of code, essentially it's intel extensive Pytorch as ipex and ipex optimize and pass in the model, we add further optimizations to run it as fast as possible. We are also working on even upstreaming whatever possible to Pytorch's mainline. So that thing you mentioned, it's very important to work with the community and enable the software that the community uses rather than having a completely different architecture and something that sometimes it's closed source and working on it. That's not the way intel thinks. Even the whole concept of one API, heterogeneous programming, everything open about it, where other vendors can come in and add their accelerators to the OneAPI and use the OneAPI standard where if you're writing code for a CPU there should be minimal to no changes that's required to run that on another accelerator. That's the philosophy that we are working with overall, the OneAP architecture that works underneath all these acceleration libraries. Optimo Habana yeah, we've been working very closely with HuggingPeace team. Almost all LLM models work out of the box. There are models that we have tested and benchmarked that's available on GitHub and things like VLLM, right? Or info support. All those things are enabled through intel libraries, for example, Big DL and things like that, giving a higher level abstraction beyond Pytorch. Because when we talk to startups these days, we feel that Pytorch is considered as a low level library right now. And that's a little bit funny for folks who have worked in Theano or even beyond that in 2016 and 17 and coming from the early days of TensorFlow to see PyTorch going low level and there's this higher abstraction libraries to work on top of that. It's really an exciting time to be in and work with you all.

Yeah, for sure. And I also want to highlight in addition to open source code, it's been cool to see intel recently release NeuralChat which was a fine tune on the Mistral model, which is openly accessible on Hugging Face and permissively licensed. So we've been experimenting with that and we saw usage of that in the hackathon, so it was cool. To see people like a couple of these models. Neuralchat, which is a fine tune of Mistral, came out like I don't know, maybe a week before the hack and notice, which is another fine tune on Mistral came out like a few days before the hack and both of those were being used in the hackathon, which I think demonstrates the ability to kind of rapidly adopt this new stuff that's coming out.

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Well, I do want to make sure that we have time to highlight a couple of cool things. So what were a couple of the highlights for you all in terms of solutions that you saw or methodologies that you saw or just like cool things you didn't expect. What stands out in your mind?

I'll start quickly and I'll let Ryan talk about this. So we both have been spending, I mean Dan, you were also there where like every day going through these submissions and like it was very difficult to figure out who is the best submission because each time we we think that this is the best and look at the other one we're like oh my God, like this is incredible. Even the first submission, right? The quality of image creation that was. I was surprised that how can you even create these kind of images with the models that was there? The time that was spent in prompt engineering and even using custom models to combine these sort of images and create these solutions. There are a few really interesting rag examples. Taking YouTube videos, parsing the audio, figuring out a YouTube search. That was something that stood out to me. And for the Python code explainer, there was a submission that came maybe I think in three hours. The first iteration of the submission by this person were that solution can do Python explanation but also give references to where exactly that the model got this information from. Really, really good use of rag and LLMs. Ryan, what are the things that stood out for you on submissions?

What always stands out is when somebody. The jupyter notebooks which Rahul had put together, I think are really well designed as learning activities and to get something done that's cool just by going through them. And so people use those to do a lot of amazing work. And I was stunned by the quality. But what always stands out to me is when somebody like takes the concept, takes what's in there and then runs with it. And like we saw people setting up AI agents for some of these challenges, like the comic book generator, you know, in the code explanator, in the fifth challenge of where code explanation comes in, it's like, oh, explainability. Like, listen, we're going to do things like we're going to do the explanation. The model will then cite the sources it's using.

Right.

Which made me think we used to. In years gone by, people were very concerned with explainable AI. And what they always meant is like, well, if the model is making a recommendation or classifying something in such a way, we should be able to figure out exactly why. And so there are all these discussions of how best to do that, like, oh, you can use shap values or whatever. And I think what it turns out is like, well, now that we have Gen AI, you know, and we have retrieval based methods, it's like, just ask, what are you. So, okay, this is your explanation, like, where did that come from? And you know, we see like the setting of sources, so that creativity not just in application, which was astounding, but then also in people bringing in methods, cutting edge methods from outside of like what we even included in the notebooks. That always blew me away. And there were some people that just always ended up in the top five, like Tomas Barshi, I don't know if I'm pronouncing that correctly, who I actually reached out to because it was like, what do you do for a living? Like, this is. Your work is incredible because there are so many. Simon's team Pranav. Pranav. Yeah. I haven't sat down to compile a list. You know who you are because you can go back to each winner's post that Rahul made and find those names and that's something that we'll follow up probably with a blog about and you know, maybe reaching out to somebody to be on a podcast or to talk to us or whatever. I would also like to highlight that our youngest participant I think might be on the. Yeah, I see his name Arjan, who is a middle school student who owned us every day like at around the time when we were supposed to be posting a video or whatever with the same like skeleton, like waiting, tapping his fingers, you know, like patiently waiting for this video that was supposed to be here five minutes ago. That was a wonderful part of that for me.

Yeah, even the thing, right. Like you were mentioning the Python explaining. I mean there were sufficient for. Okay, now you have explained the code. Now click this button to optimize the code. I'll give you an optimized version of the solution. Taking the challenge in spirit and not just in words and going beyond that like incredible work. It's truly I really feel generative AI and the commodity session have really, really helped a lot more folks who might not have been here to do this AI kind of work. Really democratizing the solution, all the toolings, the API based approach for example from Prediction guard the hugging face ecosystem, making it as easy to use it. And one thing and when Dan was mentioning that people were using neural chat for LLM APIs, that was because of dancing incredible team adding these models and scaling it in matter of hours. So it's still a challenge to deploy and scale this. You have an incredible team Dan there. Who else also participating in the conversation?

Thanks. Well, I definitely think so. Appreciate that. And speaking of where people can find out more about some of the specific submissions, even seeing some screenshots, some code that people generated. Eugenie, do you want to comment? You created some amazing blog posts already and I think there's more in the works. So do you want to just describe to those listening where they can find out more about some of the solutions and maybe also where they can keep tabs on future events and things coming through the Liftoff team.

Thank you Dave. So we posted already three blog articles at our landing page developer.into and I just want to also give you insights as I reviewed always the top submissions and other honorable mentions. I had a look at the profiles of developers and it was really exciting. Mix across regions and as already said we have students, we have individual developers, we have founders here, we have here so far software engineers from big companies. But also I saw very active software developers from intel this is very interesting that I mean indeed like Intel Liftoff is more targeting startups but it was very diverse portfolio of developers and from across regions. So it's really like amazing because in our Slack channel for hackathon we see really always from 24 hours messages there with submissions with questions because of this diversity. So it's really like a global hackathon. At the end of the year we are very proud about it. We will post really articles about each challenges and also about the last challenge with two days development sprint. Now you can read three articles not only like it is not only announcement of winners but also their own comments and results of works what you can find in this blog articles.

Awesome. Thank you so much for your work on those. It was cool to see like the traffic coming in basically all day and night which was awesome and like it's hard to sleep while this cool stuff is going on. So as we draw close to an end here I want to kick it over to Ralph who leads up the liftoff program and just get any sort of final thoughts like what did you think of this whole process? What were you encouraged to see and what are you looking forward to in the new year in terms of of things related to Generative AI and liftoff?

Hello everyone, it's me, Rav. I'm sorry for the noise here. That's why I was on mute for the all the time because there is some at the office, there is some kind of year end party going on. So yeah I was completely amazed by what happened during this hackathon and I'm very grateful to the team starting with Rahul the Rockstar developer of this hackathon. And also thank you very much to you Dan for supporting this, for really running this with us and then Ryan who is the second Rockstar developer here and of course Eugenie who made it all happen. And so I really look forward to the impact we can make in the developer ecosystem in the AI developer ecosystem. I really look forward what's going to happen next year and we want to have a share of what the future might bring to us. And I can tell you the Intel Liftoff team is ready for whatever comes in the startup world and yeah so see you next time and great to have you all here.

Thanks Ralph.

Awesome. I mean we co created this together with Prediction Grump on day one. Like we had meetings on how to do this and what other things that we need to do on this do you want to say for the folks who don't know about Prediction Guard, you know to Introduce Prediction Guard also to. What was your experience working on this hackathon with us and what are the things that we need to do next? I'm sure that we need to do it big. 2000 is now sort of our baseline. So next time we do maybe 4,000 people.

Pretty big.

So let's. Yeah. What's your take on it there?

I think one takeaway is like when you do a hackathon with intel liftoff, you better be ready to scale your servers. So we'll, we'll take that takeaway for next year when it's 10,000 people participating, I'm sure. But yeah, it's been great. One of the things, like I say, is we really appreciated actually interacting with people, creating practical solutions with LLMs. That's what we're about at Prediction Guard and seeing people actually apply some of the latest models like the neuralchat Notice, Zephyr Yi Wizard Coder. Seeing them actually access these things and even combine them together in unique agents, I think that gave us such encouragement to see people actually kind of fulfilling this vision that we have, which is, is providing these open privacy conserving hosted models to people and them combining them in unique ways to create real enterprise value. That's what we're excited to see and do it in a way that is actually trustworthy. Intel of course, has a great history with security and privacy confidential computing. But to be able to sort of be partnered together and see people creating really both trustworthy privacy conserving and scalable solutions with LLMs in this environment is really encouraging, I think for the future of AI because as we've seen even over the past week with Mixtral being released and Stripe Tyena and all of these models, the open models are just getting better and better and providing ways for people to access those in a scalable way and build real solutions. Yeah, it's really exciting to see that happen in the industry. So thank you for hosting this and making it happen. It was a great experience.

Thank you Ryan. And thank you to the entire team, like Scott, Ralph, the team that I talk to daily, Ryan, like we practically talk every hour. Eugenie and the whole of engineering team at Intel. Liftoff. Jyoti, Vasantha, Raj, you guys are incredible. And all the teams in dance side also like being in the Slack channel and answering questions. All of us had reservations but we kept that to ourselves. We didn't know how it's gonna go, but everyone pitched in with really cool ideas and with mindset to help and that really shows even the community all the messages we get, we had messages where folks who are saying now I can take this thing to my boss and tell like, you know, I need to implement these sort of things in our day to day work and this is really, really gratifying to see that. And next time we come in we'll fix all the shortcomings, we'll do an internal review carefully. If there were any shortcomings, I'm sure there are to fix them. Bigger, better, more scalable, more cooler challenges. We want to continue this and grow this community. So any sort of feedback, Eugenie would be, I'm sure would be sending a survey. I know it's very difficult to answer any sort of surveys, it's easier to delete that email. But we would really appreciate, I personally would really appreciate your feedback on what we can improve. What are the things that we could add more and make it more a community driven effort. We don't really like in lift up the top down top approach. We really want your feedback and, and the things that you want to see and build around it. So thank you once again.

Yeah, thank you all closing out here. I just want to encourage you also to not only keep tab for hackathons but all of you who are building amazing startups. And I know many of you are who are part of the hackathon, they maybe are too humble to say it but this Liftoff team is doing amazing things and as a startup that's participating in it, your startup should join Liftoff and reach out to them because you'll find amazing, amazing benefit and scale and access to expertise and hardware. So reach out to the team. They truly are rock stars like Ralph said. So reach out and get involved in Gram and the community. With that we'll close this advent of Gen AI out. We'll give you the last word, Rahul.

All right. Yeah, I forgot to mention one person. Kelly. I don't know, I don't know how I forgot. She has been incredible starting from the website, creating the content, creating the video. I mean she was sick while she was doing it but she had some few hours that she, she had to take off. But she has been incredible in the pace at which she was able to help us. And, and thank you Kelly for doing that. I'm sure that we'd be doing many more of these things again, the entire team. If I mess missed anyone, I'm really sorry but this was truly a team event. Everyone contributed and without a small contribution this would have just been an idea. So thank you all for doing that.

Thanks everybody.

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