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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. 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, Ryan. This has been an incredible experience. Let me before that even talk about the advent of GenAI two 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've 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 will be announcing the winners of the final product development in a 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 build series B, 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 Chiotius and Varad. 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 Cloud E2 Accelerator. And I'm sure that many of you folks who have participated in the Hack Hub would have used Prediction Guard's LLM APIs.
The third one is co-marketing and bringing your, once you've 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's channels and also we have a network of accelerators, a network of folks that's beyond Intel that we take the product we 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've built through Intel's channels. It could be a service on IDC or it could be a separate that one of our customers of Intel is looking for. I would urge anyone who's 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 advent of code. So how did you start thinking about this advent of Gen AI Hackathon? And 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, Gen AI is something that's new and something a lot of people are talking about. But there are no fun set of exercises that you can use to learn and also build cool things with the technology that's existing today. So I was thinking, why not just create something, you know, 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 Gen.AI and also learn through that process. Many folks I've seen in the chats where they came with just prompt engineering knowledge and have 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, 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 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 doing this challenge. But it would be something that I'd like to do in December advent or something else that lift up to us 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 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 Gen AI and 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, 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 the experts in LLMs and Gen AIs. I worked on it. And some of the products that they've built, some of the challenge solutions.
It's like an MVP startup will build. We have many startups who are building a similar solution. We're 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, or the Python code explainer, things like that are difficult. And they have even gone further. When we asked about how to 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 participator. That's also a very positive thing where we see you can, in a 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 at a level of participation. I didn't expect this many people to participate. And we had to stop registrations after 2,000 people registering for the event. And this has been just great. Ryan, do you want to add something? Yeah, I just remember this was Rahul's idea when he first called me. It was like cleaning up after Thanksgiving in my basement or something. He's like, you know what I'm going to code?
Yeah, of course, of course. I want to do ad in a gen AI. Like, all right, what do you want to do? We worked it out and it was like, set the goals. Like, let's get like, it's like, yeah, we can get big. Like, you know, a couple hundred people at least would be a success, but like stretch it. Like, Hey, I mean, we can get maybe a thousand even and do the biggest event of the year. Well, we ended 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 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 Gen.ai 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, ability to code, I would say. It's not difficulty, exactly, and creativity also, because a lot of Gen AI, what we see, at least in Liftoff, is that AI has become truly commoditized, and you don't really need to go through a master's or a couple of courses on neural networks to build an application right now. You have the amazing transform ecosystem. It's really easy to integrate some of these things, the AI applications to, the 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 up on, and just play with it. But the thing is, creating a transition from an image to another and creating a whole story with set up by images, it's not easy. It's really difficult. And we even saw some folks creating a comic book generator.
using this challenge. That's 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, build it. You take the final challenge, right? That was Python code explainer, where you're giving a Python code, you use LL model to understand the code and give an explanation of it. We had 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 cards, LLM APIs to get the right model. And you have to design a UI for it, all in the constraints of a Jupyter notebook. So I'd say there was a progression of difficulty. Difficulty is very relative, 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 GenAI, we have released all the resources.
that we have built for this. You can take this to basically get an idea of what Gen AI can actually help you to build and infuse your applications. And you can just go through the different challenges. Or if you're an expert in LLM write-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. It used to be very challenging. Now, 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. You cover image to image.
So image editing with AI, then the third one, and then rag-based applications with LMAPIs. And finally, we thought, you know, the fifth was the most advanced, like the code explaining 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 like many billions of dollars of value. So all of 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 students.
like whatever, anybody who's interested in learning AI skills, if I go 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 the notebooks and 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's set of data. In the case of the challenges that you all put together, maybe that's external Python documents.
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 were 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 um 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 computer-heavy workloads can run really well on IDC. So for this particular hack, we provided anyone logging into Intel Developer Cloud registering on IDC as a standard or free-tier user, you get a shared JupyterHub instance where you get access to Intel's data center GPUs, Intel Xeon processors.
I would say this system, for a free tier user, I don't think any other service provide. I mean, there are many services with the JupyterHub frontend, but the amount of compute and the amount of memory and RAM and even file storage that you get in the systems, I'm seeing a single cloud service provider providing that. And we've 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, LLMA 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 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 Gen AI and AI workloads. We have general-purpose GPUs, the data center max-inist GPUs, both with 48 gigs and 128 gig versions. So the folks in the hackathon, they actually used both of our fourth-generated 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 the CPU.
to sometimes even take your workload to 2x, the performance you got in an early generation. It's all about making the CPUs. Obviously, 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. But then, like I said, the data center maxed these GPUs. 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 cluster 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 surveys, 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 performance accelerator cloud like IDC out there. I'm sure that there are different hyperscalers. But this is uniquely for startups. From my personal experience, this is a really, really awesome solution. I'd like to know more from you, Dan. You are one of the first customers of IDC, right? What are the things that you thought that really...
made you decide to choose ID6 for the performance and also the team side also, right? I 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 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 Havana. 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 Datacenter 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 uh causal lm or whatever it is with optimum a lot of the times either you can just do like a one a couple line replacement and just replace that with the optimum version of those classes or do some uh wrapping of the various models um with optimizers and this allows you to run run your your model very fast on a wide range of architectures so i think to your point uh rahul i think one of the things that we found really useful is the actual ease of use in coming in and saying, OK, 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 like 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, you know, Intel is working directly with Hugging Face on this tooling.
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 happened. That's really awesome to hear, Dan, and particularly also the thing you mentioned, right? So you 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 of all things, if you see, we would be anywhere in the top three, PyTorch, TensorFlow, Hug&Pace, 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 can now be upstreamed in a couple of months to the mainline repositories, we release 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 new network accelerator library 1DN and 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 extensions for PyTorch as IPEX and IPEX.optimized and passing 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 that you mentioned, right?
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 is closed source and working on it. That's not the way Intel thinks. Even the whole concept of OneAPI, 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 in the OneAPI architecture that works underneath all these acceleration libraries.
Optimum Habana, we've been working very closely with this 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 BLM, our info support. All those things are enabled through Intel libraries, for example, BigDL, 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 Tiano or even beyond that in 2016 and 2017, 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 Neural Chat, which is a fine-tune on the Mistral model, which is openly accessible on Hugging Face and permissibly licensed. So we've been experimenting with that, and we saw usage of that in the hackathon. So it's cool to see people like a couple of these models, Neural Chat, which is a fine-tune of Mistral, came out, 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. This is a changelog news break. Vana.ai is a Python RAG framework for accurate text to SQL generation. It lets you chat with any relational database by accurately generating SQL queries trained via RAG, which stands for Retrieval Augmented Generation, to use with any LLM that you want. You load up your data definitions, your documentation, and any raw SQL queries you have laying around into Vana, and then you're off to the races. Vana boasts high accuracy on complex datasets.
Excellent security and privacy because your database contents are never sent to the LLM or a VectorDB. It boasts the ability to self-learn by choosing to auto-train on successful queries and a choose-your-own front-end approach with front-ends provided for Jupyter Notebook, Streamlit, Flask, and Slack. You just heard one of our five top stories from Monday's Changelog News. Subscribe to the podcast to get all of the week's top stories and pop your email address in at changelog.com slash news to also receive our free companion email with even more developer news worth your attention. Once again, that's changelog.com slash news.
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 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 think that this is the best and look at the other one, we're like, oh my God, like that's incredible. Even the first submission, right? The quality of image creation. That was, I was surprised that.
How can you even create this kind of images of 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 ragged 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. That solution can do Python Explanation, but also give references to where exactly.
that the model got this information from a 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 for learning activities and to get something done at school 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 takes the concept, takes what's in there.
and then runs with it. And we saw people setting up AI agents for some of these challenges, like the comic book generator, in the fifth challenge where code explanation comes in. It's like, oh, explainability. Listen, we're going to do the explanation. The model will then cite the sources it's using.
Right. Which made me think, you know, 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 like discussions of how best to do that. Like, oh, you can use shop values, you know, 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 your, so, okay, this is your explanation. Like, where did that come from? And, you know, we, 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 Barsi, I don't know if I'm pronouncing that correctly, who I actually reached out to because I 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, yeah. I haven't sat down to a compile 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, Arian, 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...
The thing, right, like you were mentioning the Python explaining, I mean, there were solutions where, okay, now you have to explain 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 commoditization of AI 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 phase ecosystem, making it as easy.
use it. And one thing when Dan was mentioning that people were using neural chat for LLM APIs, that was because of Dan's incredible team adding these models and scaling it in a matter of hours. So it's still a challenge to deploy and scale this. You have an incredible team, Dan, there. Who was also participating in the conversation? Thanks.
Well, I definitely think so. I 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've 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, Jeff, Daniel.
We've posted already three blog articles at our landing page, developer.intel.com. 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 I already said, we have students, we have individual developers, we have founders here, we have here software engineers from big companies. But also I saw very active...
developers from Intel. This is very interesting that, I mean, indeed, Intel Liftoff is more targeting startups, but it was a very diverse portfolio of developers and from across regions. So it's really 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're very proud about it. And we will post really like...
articles about each challenges and also about the last challenge with today's development sprint. Now you can read three articles, not only like it is not on the announcement of winners, but also their own comments and results of works that you can find in this blog articles. Awesome. Thank you so much for your work on those. It was cool to see the traffic coming in basically all day and night, which was awesome. And it's hard to sleep while all of 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. 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 things related to generative AI and Liftoff? Hello, everyone.
It's me, Ralph. I'm sorry for the noise here. That's why I was on mute all the time, because at the office, there was some kind of ear-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 to 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, Ralf.
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, 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 it's 4,000 people. It's 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 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 PredictionGuard and seeing people actually apply some of the latest models like the neural chat, Notice, Zephyr, Yi, WizardCoder, seeing them actually access these things and even like combine them together in unique agents. I think that gave us like such encouragement.
to see people actually kind of fulfilling this vision that we have, which is providing these open privacy conserving hosted models. two 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 Mixtrel being released and Stripe Tyena and all these models, the open models are just getting better and better and providing ways to...
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, Jyothis, Bharat, Basanta, Raj, like you guys are incredible. And all the teams in Dan's side also, like being in the Slack channel.
and um answering questions uh there was also said reservations but we kept that to ourselves uh 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 in the community all the messages we get we had messages where folks were 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 uh 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 and lift up the top-down approach. We really want your feedback 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 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 the 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 how I forgot. She has been incredible, starting from the website, creating the content, editing the video. I mean, she was sick while she was doing it, but she had a few hours that she had to take off. But she has been incredible in the pace at which she was able to help us. And thank you, Kelly, for doing that. I'm sure that we'll be doing many more of these things. Again, the entire team, if I 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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