Good afternoon everyone. Thank you for attending this session where we are going to talk about two very exciting topics together. And if some of you attended the previous two sessions in the same room, they also talked about it from a very technical perspective. I got a great group of panelists here to talk about it more on what we can do now and in the future. So my name is Larry Corvallo. I'm the moderator for this session. I'll just go over the agenda before I let the panelists introduce themselves. So first thing, after the introductions, talk about the overview of generative AI. I'm assuming everybody in this room knows WOSM, but let's have Aparna talk about Gen AI. And our other two CTOs will talk about how WOSM fits into Gen AI. We'll talk about some use cases, real use cases. And then I would really want the audience to put your thinking caps and say, what are the other use cases you would like to see? And think about where you can start doing some radical innovation with these two technologies. And that's that part of the audience use case discussion. Leave some time for Q&A. And with that, just briefly, I am an independent analyst. And my success story is to bring these kinds of panelists and just watch them do the magic. So start with Tyler, quick introduction. Hi, I'm Tyler McMullen. I'm the CTO and co-founder of Fastly. And I guess like my quote unquote bonafides regarding AI are that I worked on a lot of recommendation systems and vector systems and dimensionality reduction problems and all sorts of things like that. But that was all at least 12 years ago. So I'm like 12 years out of date and trying to get updated on AI now. But it is starting to come together. And I got some opinions. Next, Radu. Hey, I'm Radu. I'm the CTO and co-founder of Fermion. Before we started Fermion, I worked at Microsoft on a few projects that were the intersection of the distributed systems and inferencing on the edge and building this on this thing called WASI-NN. And recently working on AI as well at Fermion and building an AI inferencing service as well. And Aparna. Hi, I'm Aparna Sena. I'm currently a partner at Pear VC, which is a small seed stage VC firm, one of the top in the country with $432 million to invest. I'm leading the AI and enterprise investments. Previously, I was at Google for the last 10 years, most recently in Google Cloud, running the developer group. And before that, an early person on the Kubernetes project. And my background is in electrical engineering. I did my undergrad master's PhD at Stanford in doubly. And I've also worked in consulting at McKinsey. So really very much into the enterprise use cases of AI here. And what I learned from Aparna, you got about 10, 12 startups that you are coaching. That's right, yeah, I'll talk about that later. But yeah, yeah, we've got. I've started a program for generative AI startups as a like a YC kind of program at Pear VC, much smaller than YC, focused on Gen AI. We've got eight startups in incubation since July, doing everything from synthetic data generation to healthcare AI to, yeah, the tooling for LLM evaluation, et cetera. So it's a very exciting time. So what we're going to do for the next few minutes is talk about Gen AI and really bring it together in the same few next slides which Aparna is going to lead. And you know, Radu and Tyler are going to contribute to how Wassum fits into that, okay? So with that, Aparna, just let me know when you want the slides to be moving forward. Thank you. So I'm kind of an accidental venture capitalist. As I mentioned, I was an operator at Google. When I started at Pear, which was by accident as an entrepreneur in residence, AI was generative, AI was just starting to take off. One of the things I'd been working on at Google was using AI models inside Google and using them to generate code to make developers more productive in Google Cloud. This is something GCP just debuted in the form of one part of the duet offering from Google Cloud last week. And so at Pear, they asked me to put together our thesis on generative AI. We were starting to see upwards of 10 startups a week that were all focused on generative AI and we needed to figure out what to invest in and what not to invest in. So I'm gonna present some of that thesis. Pear, as I mentioned, is very early stage. So the startups we see are going from idea to product market fits zero to one. Yeah, we can click into this slide because there's a build, right? So we believe that what we're seeing is a shift just like what we saw with web technologies in the 1999, 2000 era and then later with mobile. That's what we're seeing with generative AI today. And that means also that it's very early days. Many things still need to happen just like with web and mobile, a new interface needed to be developed. One of the questions we are evaluating is what will be the new interface? Chatbots are kind of like very 2020. And now here we are in late 2023. Are there going to be other types of interfaces that are more interactive, lower latency, richer, browser based? And I think there's an opportunity for wasn't there. Secondly, the application architecture that incorporates generative AI that has been evolving just in the last 12 months even where there's a lot more use of retrieval augmentation because obviously generative AI models hallucinate. So how do you make it usable say in a hospital context or a legal context or an enterprise context? So retrieval augmentation is something that is emerging. It's certainly not a solved problem. And also the use of plugins. Again, that might be an area where wasn't has a role to play. And then lastly, hardware evolution. We're starting to see models that can run on multiple different kinds of hardware. That's always been the case, but now the models are much more capable and much more easily accessible. And then the hardware itself is evolving to become more efficient for these models. So these are some of the things that are yet to happen in AI. And 2022 was just the beginning. We've got a road ahead I think for the next four or five years as this evolves. And I'd like to invite my fellow panelists to comment on some of this if you'd like. I'll say things as soon as I disagree with something you say, but you're just dead on. What is there to add? Well, anything, we have more areas as well as we go on for you to disagree with me. So let's go to the next slide. Yeah, I do think the hardware evolution is where you're going to see some role played because what Wassum brings to the table is run anywhere, run efficiently. And what my view of this is as hardware evolves to be better, obviously, latency and efficiencies, especially energy efficiencies, that you're going to be able to see some things run on the edge even in a disconnected way, much better and faster. Yeah, I'll say a couple more things here. So I think if you look at the timeline there, you can see the web in 1999 was a lot of, again, as it mentions there, static websites. And the upside of that was that static websites were fast. Your internet connection might not have been fast, but the websites themselves were pretty fast. And as we started to evolve those sites and evolve those interfaces, they started to get slow. And then a lot of applications, a lot of companies out there, realized, oh, wow, there's a really strong direct connection between the latency that end users experience and the amount that they use our product. And so as we moved into the mobile phase there, that was even more important because mobile devices were less powerful, because mobile connections were less consistent. And so there was a lot of emphasis on the user latency. And so I think one of, maybe this is just from the Fastly perspective, but I think it's also from the Generally Web Assembly perspective, because I think we have a lot of focus on latency and speed inside of Web Assembly, making sure that we find ourselves in a place where we don't lose all the effort that we have put into reducing latency for users as we move to this new AI generation. Yeah, there's a lot of opportunity here. I've started to see startups that are running the application entirely in the browser and then making calls to an LLM. You're starting to see applications even on the phone that are chatbots that are speaking to you. And there is a latency associated with the speech and with the recognition. There's a lot of opportunity to kind of make that experience better. Yeah, and from my perspective, I think the most exciting intersection between AI workloads and Web Assembly is through that portability, right? Being able to build an application and not know ahead of time where you are going to execute that application, whether it's going to be on device or whether it's going to be on an edge or somewhere in a cloud and being able to dynamically move that. Web Assembly is a technology that can fundamentally make that possible. And that's one of the most exciting use cases that I'm seeing for that. You stole my point. That's such a good one. So I always like to show a little bit of the history of where this technology came from. It's been many decades of work that have led to the breakthroughs that have led to Generative AI becoming something that is consumer grade and is useful for you and I, as well as useful in the enterprise. But the most recent three breakthroughs that I think are notable. Number one is the invention of the transformer and this, you know, attention is all you need paper. That came around 2017 from Google. And this transformer model is something that has been used extensively across many different types of Generative AI capabilities, whether it's LLMs or it's diffusion models or other kinds of models. And specifically I think the discovery that, you know, as you make these models larger beyond six billion parameters, there's emergent behavior. That has been somewhat of a surprise and a big breakthrough in machine learning. You know, breakthrough that I think people have dreamed about for decades. And combined with that, you know, training these models, these large language models on enormous general purpose data sets, like this is an example of the pile data set and you can see there's many different types of data here, including code and text and what have you. That, you know, not labeled data set, really a unsupervised or self supervised learning over these large data sets have led to models that are very general purpose. So you can ask them any kind of question. And then lastly, the third piece is really refinement of the strategy of transfer learning, taking that large general purpose model and then training through fine tuning a smaller model that's task specific, whether that's task is question answering or it's fine tuned on a specific data set, say for a hospital use case. That can be a much smaller model and it can still have very good performance in terms of question answering for that domain. It can have very good domain specific performance and yet be a much smaller model, which means it's inference is at lower cost potentially on different types of devices.  These are I think the three breakthroughs that have led to the kind of modern generative AI applications that we're seeing. There's still a lot of evolution happening on the system side into finding alternative mechanisms for attention, increasing the context length, potentially finding alternative mechanisms to the transformer, which is still a very energy hungry architecture on GPUs. And then also on the data side, there's entire legal issues about copyright and how to make the data sets more explainable and put regulations around them and then finally lots of work on RLHF. I think, yes. I'll just say, I think one of the things I'm most looking, if you can go back one real quick. Yeah, one of the things I'm most looking forward to with WebAssembly and the AI spaces, over on the left side there, the explanation of the transformer, that's just one version of the transformer. It's like the original version of the transformer, right? And all these things, all the different versions of the transformer algorithm are potentially applicable across many different types of models. And so when we think about, for those of you who are paying close attention to the component model, you can imagine a world in which we have all of these different versions of the transformer that are actually made available as a component that you can just take WASDNN and plug a new model into. Being able to plug and play different things like this. While also being able to trust the security of the system that's running it, that seems like it'll also potentially be a game changer because let me tell you, I've been spending a lot of time looking at the security of the various AI frameworks and it is a nightmare. Yeah, and on the right side, I think the potential of being able to take a few hundred megabytes of fine tunings and being able to ship your WebAssembly component with a fine tuned small fine tuned models to a platform that already has a base model, a foundation model and being able to run that dynamically and change the behavior of your model. That's one of the most exciting things that I'm thinking about. Yeah, and that is actually precisely our thesis, our investment thesis, and we can go to the next slide. In pair VC is the ability to take these larger models, foundation models. This chart is showing in white the open source foundation models and you are seeing across the vertical axis that there's many different types of models. Obviously there's natural language processing models. There's image and video models, lots of evolution there with segment anything and stable diffusion's gotten much, much better speech and then finally these models are also being applied to protein synthesis. So we see many, many applications of generative AI that are beyond just natural language and we're starting to see this not just available in proprietary form as an API say from OpenAI but also much more capable models available in the open source to UNI and every developer in a variety of different sizes. And this is just accelerating, every time you see the Mosaic MPT model that's very capable in multiple different sizes and then Lama2 comes along and then CodeLama most recently in multiple different sizes fine tuned for different tasks as well and it just opens up a new realm of possibility especially for startups. So our thesis is on the next page and this is the kind of startups we've invested in is startups that are using open source, large language models and fine tuning them. So taking a large model, fine tuning it on proprietary data for let's say a hospital or a law firm or an accounting firm fine tuning it on that proprietary data and building something that's task specific potentially multiple models that are chained together to achieve a particular enterprise workflow augmenting that with sources of truth databases through retrieval augmentation. I will show you an architecture on the next slide but that is a architecture that provides not only a cost effective mechanism to build an application for a startup but also provides somewhat of a moat because you have that proprietary data you've got your own models that you're running. So that is what we've been funding and we believe that that's an opportunity for a startup to become a big company and then of course we also fund and are looking for tooling that allows for the development and the CICD and testing of such applications, composite applications and lastly of course systems, innovations. Yeah, we'll go to the next slide. So why train your own models? Why, and I don't mean pre-training why fine tune your own models? It gives you the data mode, it allows for personalization it provides much more control you're not dependent on open AI or anthropic or anyone else whose model quality changes over time and you have many deployment options which is you can deploy on the edge you can deploy on devices you can deploy in the customers cloud and it's much more cost efficient. So this is I think a good slide perhaps for the WASM CTOs to weigh in because I think this dovetails really nicely with the properties of WASM as well. To that point, I think the being able to run this sort of inferencing and I think taking a step back from at least our perspective we see in the world of AI we see inferencing as being the operation that most people are interested in executing as in training and data processing are beasts of their own but what we see most often users and customers are asking for is I wanna run inferencing, I have this model and I wanna run inferencing on it and being able to do that in a way that's cost effective on one hand and doesn't suffer from minutes long cold starts is something that we've been an operation that we've been tackling for web services and serverless applications and it's something that we've been thinking about for the last few months for inferencing as well and being able to execute inferencing on things like Lama 2 and Code Lama is something that we've just announced on on Fremian Cloud this week together with things like generating sentence embeddings and storing them in a vector database and retrieving and augmenting prompts that you then send to the language model so it's very much in line with what we've been seeing and what we've been building for the last few months. Yeah, if we go to the next slide I'll just introduce this. This is the architecture that we are seeing emerging for applications that are AI based so there's a whole pre-processing step where you maybe take the data from an enterprise typically you have to chunk it into many different files and then create embeddings from that data. You have to chunk it because the model has a certain context length even the largest context length is like 100,000 tokens. Most documents like let's say patient records or legal documents or anything that you might use is longer than that so you have to chunk it, convert it into embeddings which is essentially a much more efficient vector space and then you store that in a database, you create an index that is potentially multiple different types of indexes that are optimized for the type of searches that you might do whether you're doing a keyword search or you're doing a semantic search or similarity search depending on what the query is and in this process you may call various plugins because again LLMs only have a certain time stamp up till which they're trained, you may call a plugin to search the web, you may call a plugin to get more recent information, you may call a plugin for actions, there's many different reasons to call a plugin so the anatomy of a full application has all of this, it has this retrieval augmentation piece that's dependent on this database and index and it has this ability to call plugins and then the runtime, this is where really the input or the query from the user comes in, let's say this is a chat bot, that's where you're talking to the chat bot, your input gets converted into an embedding as well and then the retrieval orchestration matches the query with the retrieved data and then ranks and curates the output appropriately, combines that with the query and any prompt tuning and prompt instructions, prompt templates that you may have and then ultimately puts that out to an LLM orchestrator which will choose and potentially use multiple different LLMs that are again fine tuned for specific tasks that that query may need to go through and in between all of this is of course, logging, validation, rate limiting, all of the things that need to happen from a policy perspective to make sure that you're not putting in information into the LLM that the enterprise doesn't want or you're not outputting information from the LLM that's biased or toxic or what have you. So this is kind of the emerging architecture it has that RAG component, it has the prompt tuning and then it has like multiple specialized LLMs and we're seeing this in healthcare, we're seeing this across the board within our prizes. By no means is this the final, there's lots of pain points here but this is emerging as at least a sufficiently good solution that overcomes some of the problems of explainability and hallucination and lack of consistency and output. Yeah, so I'm sure I'm not the only person in the room who has had the like WebAssembly bugs so far embedded into their head at this point that they look at any basically anything like this and go like I could put WebAssembly there and there and there and there. Right, so like I think looking at this thing it's just super clear that like there are opportunities for WebAssembly across this entire application stack, right? And I think especially when you start thinking about distributed WebAssembly that this gets like particularly interesting, right? If I, I'll give Rod a chance to talk and then I'll come back. Yeah, I was just gonna pick one, probably under-talked box from that architecture which is plugins and point that out as being really, really suitable for using WebAssembly for that which is essentially I want to run some untrusted plugin that someone built in my architecture and I wanna make sure that it doesn't have access to anything that it shouldn't and putting WebAssembly as a runtime for things like langchain actions and things like that would be a phenomenal fit for choosing one box to put WebAssembly in. Just for your information all these slides have been uploaded to our session so if you need those slides I know this is a little busy slide you can even put it down from there so you ready? Yeah, so we wanna talk a little bit about use cases. There are, you know, Generative AI is a horizontal technology we believe it's going to transform many different businesses, you know many different verticals I've listed some of them here, legal, fintech, health, biotech, media, retail manufacturing anything that requires document understanding or process automation also, you know, generative design drug discovery, robotic simulation and I listed these because we see a lot of progress lots of startups every week in these areas using generative AI we also think there's a lot of potential in infrastructure and tooling I mentioned some of that already earlier what we're gonna focus on in terms of use cases here is the first category which is assistance and we are seeing assistance across the board I think the one that is a ripe is of course engineering and all engineering processes data analytics but also personal assistance so let's start with that you know, we're gonna look at this specific use case and how wasm could help this use case so imagine that, you know, you have an assistant for your travel needs or your shopping needs or maybe you know, these days you can find AI assistance that help you with your fashion and beauty and you know, experts on any kind of thing that you can imagine and they're actually getting very good and the reason they're getting very good is because these are not general purpose you know, anthropic or open AI type models these are fine tuned models that are specifically geared towards gardening or beauty or whatever it is like it's like a human expert in that field giving you advice so today it's like a chat bot you know, like you type something and it types back but you know, what if it was also, you know, video what if it was also kind of much more animated what if it had access to plugins and had access to your on-device data so that it could give you much more personalized responses in the case of let's say a travel assistant what if it could actually look up your calendar and tell you when you have vacation could look up your kid's schedule and say when you know, is the family aligned to go somewhere and then had access to plugins let's say Expedia or whatever, travelosity, et cetera and could do not only the searches for you but could actually also take actions and reserve and make bookings for you it doesn't seem that far away from where we are you can also imagine like it giving you a feel for like what the experience might be like to go to a particular place or to try on particular clothing or you know, a specific beauty regimen or let's say, you know, home improvement these are all things that are kind of just within grasp but they're not because they're actually quite expensive and they require low latency they require privacy and secure access you know, they require, you know real efficiency on the machine learning side and so that's where, you know I wonder if Wasm could help us in some of those Yeah, I mean this is the exact kind of case that I was talking about earlier when I was thinking about like how we as an industry have fought so hard to get back every individual millisecond improving like the user experience, right and so especially when it comes to like specialized assistance for travel and shopping and things like that in particular like you really don't wanna lose the like the advantage that you have like gained by like improving that like latency of the user experience I mean, I think the other thing that comes to mind for me with this is that like especially in the internet as it exists today which is to say like one where there are like an increasing number of like data sovereignty laws and things like that across the EU there's also examples of this in Australia California itself for instance and when we think about like how user data is going to be used as part of these and where that user data needs to say live, right this is the kind of case where I think that WebAssembly becomes very helpful because you may need to run this computation across many different platforms many different platforms in many different locations around the world so to me it's just like the fact that WebAssembly is so natively portable the fact that it is built to be able to run from tiny devices to massive supercomputers like it gives it a significant advantage in this case and then to add on to that if we look into this future and we'll see each individual person having 12 assistants on their device at any given point specialized on different different use cases and different tasks you can imagine having to install on having to configure and having to give permissions to each individual assistant unrestricted access to your devices really scary so having the ability to run for example plugins or extensibility mechanisms or even the core action of a such an assistant having the ability to run it inside a secure sandbox that you can control access to and it can control what data it can access and potentially move that across devices and across edge networks is something that I think will have to be a requirement eventually and WebAssembly is really well positioned to tackle at least some of that aspect so when I look at this just from my perspective you will see improved customer experience for the people who are going to be using this and that's going to be the differentiator in this measure improved customer experience as well as improved security to me that's the improved security and privacy are the two big things that I'm like that is going to be killer and in a few short months when one of these frameworks gets popped across the world. Right, so. It's going to happen, I'm very confident in this. Yeah. Go to the next use case. Second use case, yeah this is a real use case again I mentioned healthcare you know this is obviously a regulated industry obviously accuracy is extremely important privacy is extremely important but one of the use cases many companies are working on this including OpenAI and Epic as well is summarizing patient records this particular example is for the emergency room a doctor comes in and there's not a lot of time to understand what it is that the history of the patient is and what's actually salient in that history AI can summarize across very large patient records again you know since the context window is small this is where you have to do the chunking but you know these applications have that built in you chunk the record you use multiple different models to some to analyze the x-rays and images some to analyze the text and other notes you create a summary it's like a co-pilot today because again you can't trust these summaries 100% despite the fact that there's a lot of research that's gone into how to suppress hallucinations how to avoid hallucinations and most startups are very very careful about that and certainly hospitals are not gonna use things that aren't accurate but right now it's all the pilots are as a co-pilot where this information is given to the physician the physician decides whether they're gonna use it or not and then there's a lot of sort of paperwork and filing that happens post that post that you know that patient interaction such as to file insurance claims a lot of that is error prone and can be done automatically using generative AI and you know it saves time it makes it more accurate you know attaching the right evidence et cetera so this is a real use case real companies working on this real hospitals trying this out and again I think there are ways that Wazem could help here I mean the exciting thing to me about this particular one is like as mentioned up there like the cross-platform developer experience so like in this particular case you know if we're talking about you know a particular platform that is actually in the hospital like right like it's actually a physical server that you know it's a crazy idea but like a physical server that is like placed in a building right being able to write code that runs there and also runs say on Fastly and also maybe that server is running spin maybe it's running some version of Fastly maybe it's running some version of like Microsoft's offerings in this case being able to take that code and apply it there and yet apply it across the world as well or apply it to your individual cloud outside of there like and have a relatively consistent developer experience across all those that's something we've never been able to have before Yeah it's extremely important in the healthcare context because most hospitals will not share patient information may not put it in the cloud the training and the inference have to happen on the hospital servers and in other cases you know it's patient data that the patient has access to and may or may not want it leaving their device et cetera so it's a great point So I'll just tell you having a daughter in the medicine medical industry or as a physician she says how many doctors take a vacation day only to catch up with writing these reports so anything that can improve productivity you know literally where people's vacation days are just going to catch up is huge in value for everybody the doctor's physician's life as well as the productivity for the hospital so you had a question about this slide or something I'll go right into Q&A because you raised your thing We've got opinions on this but I'm curious all right I'll say my opinions so I mean and I don't think this is a setup to be clear Matthew works it fastly but we did not talk about this in advance My perspective on this is like there's a way this should be done which is that there should be one canonical copy of the information and then ideally if we can get the latency low enough being able to generate these things on the fly would be extremely great I am curious like how it is done at the moment though Yeah at the moment I mean for this particular application which is live in the emergency room this is online meaning it is happening in it's not super low latency but it's happening during the patient visit that the summary is being created it's not hours before but in theory you could certainly the post patient summary could be written later and then you know maybe it's all these summaries are batched for the doctor to go back and mark them later that is traditionally without generative AI like you know when you scribe the patient record and somebody goes through and actually like looks at the summary and approves it that's traditionally how it's done like in the past but with generative AI there's an opportunity to do it in the actual meeting same thing for legal but there's no reason why those things couldn't be done offline it's just that yeah the startups I'm looking at right now doing it in the visit The only problem with doing it ahead of time is if you have billions of entries and you don't know which one you're actually going to need doing it ahead of time for every single entry stops making sense so if it's low latency enough doing it just in time might just be the best fit So I would love for you guys to come up with some use cases which we have if you have some time for that and the only thing probably you're going to hold you back is from that wine there so I think the other thing about summarization there's also chat summarization like if you look at I think we had a CRM use case you know you have a team that's trying to do a sale oftentimes you want to summarize the chat or you want to say like here's what salient about for this customer you know you need to do that in the reasonable time in the chat not like hours later My question is have you seen any applications or startups in the tutoring space for example I would like to send my daughter to a chatbot for her Yeah there's lots I mean edtech is an area that has seen tremendous upheaval I would say you know there's a whole sort of set of VCs that specialize in edtech we don't do as much of that but there's also been companies that previously were not using generative AI that have lost a ton of value you know after generative AI has come up because it is so easy to for example you know do language training you know to do translation to create flashcards to create adaptive tests with generative AI to have specialized personalized tutors and in fact one of the major areas of impact of generative AI is personalized education I think one of the best examples is Conmigo if you haven't tried that this is Conacademy's tutor it's called Conmigo I think it's a non-profit but it's like $20 you have to make a donation in order to use it but it does all of these things it comes up with learning paths you know it provides specialized tutoring in many different subjects I think the danger here is in people assuming that they can just use generative AI to generate the actual content that would get into this which really scary and not something that I think people should be comfortable doing at least not right now but for something like Conacademy an academy that has like years and years of data and actual lessons and sort of phenomenal use case for that yeah living example of the fine tuning point that Prano was making earlier yeah I mean the general purpose models like GPT-4 which is what Conmigo uses already has it's trained on so much data public data that is you know that can be used for let's say historical you know let's say you want to learn about the history of a particular play as it already can generate content but the content that I've seen originally you know is not necessarily verifiable and so it is frightening Conacademy of course is trained that on their special purpose corpus and you can imagine fine tuning and doing that I think definitely is necessary again that goes back to our thesis like you know we don't recommend you know enterprise applications without accuracy we have run out of time but if there is any more challenging questions use cases that you want to throw these three wonderful panelists on the you know thing but otherwise any closing comments from you guys for this whole thing that we started off and yeah quickly I think what we're what we're starting to see is AI making its way into a requirement for full stack applications and a requirement for developers to start injecting capability isn't in their existing applications where the central piece might not be AI it might be a business application that's been around for years but suddenly they actually have to start thinking about how do I inject these these capabilities into my application and this is basically what we're starting to build with the serverless AI offering that we're working on and would be happy to chat and would love to learn about your use cases job by our booth I'll just quickly add that I think that I think if I had one point to make it would be that well in addition to that AI is cool it would be that like I think the current and future of inference with AI is going to be heterogeneous it's going to be heterogeneous across multiple different kinds of hardware it's going to be across the entire world and I think that WebAssembly is basically in is the kind of the only platform that is in the position to be able to handle that kind of requirement from a new platform and so I also tend to think that Fastly is in a particularly good place to handle that as well so yeah I would say that Generative AI is a game changer it's an excellent time to start a company you know because there's going to be you know opportunities across the board in any kind of workflow any kind of industry what I would look for is access to proprietary data access to expertise that's that's like a differentiator a moat builder in this area and I think that with Wasm you know I'm quite bullish on you see the trend of large language models moving from proprietary to open source and from large to fine-tuned small specialized able to run on different types of hardware able to run on different types of form factor I think that that's a real opportunity for innovation and I think Wasm is a real opportunity for AI workloads so I'm keen on the developments in this space and you know I'm bullish about investing in this area and potentially building in this area great with that thank you for attending this session and please give a hand to our panelists for a wonderful session


For 35 years, the Niswa Women's Club has been raising funds for local scholarships by walking down the runway. A Sarah Winkleman has the details of the annual fashion show. Paired with a lightweight, long white cardigan, this completes your look and it's easy to layer. Minnesota is in full bloom as models go down the runway in the latest spring styles. We can get rid of our winter paraphernalia and bring out the summer clothes. This spring, the effort to help the community is in style. All of the money that we raise, whether it's from our grand prize raffle, the bucket raffle, the medallions or lanyards, all goes towards scholarships. The funds raised will help students from Brainerd and Pequot Lakes High Schools receive scholarships to further their education. And it just really was really nice just to see these worthy students receiving the scholarships so that they can go on and make a difference in their life. However, today's lesson was all about giving some fashion advice. A lot of new fashions showing. We have 13 merchants participating this year and oh tons of models with the latest fashions. And keeping it fashionably local. It's nice that they can come to a local place like Niswa to get clothes like this that are very stylish. Comfy cool layers were common for models in today's show. Spring's arrived. We're here celebrating this great community and this great organization that's done so much and donated so many funds for great causes. With a long standing tradition of making the community more fabulous. We're just excited to have everybody here that's you know you get to see your old friends and you know lots of lots of people from the community. A generous community that has given $112,000 over the last five years. We want to support the community, the high school kids and the college kids. It's a big reward for a short walk down the runway. Reporting in Brainerd, Sarah Winkelman, Lakeland News. The scholarship money also helps non-traditional students at Central Lakes College. If you enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland PBS.


firey horse for the speed of light, a cloud of dust and a hearty, high-o silver, the Lone Ranger. Astride his great horse, Silver, the masked rider of the planes fought crime and criminals through the length and breadth of the early western United States. The stories of his strength and daring have come down through the generations. What was he more than any other man who brought law and order to the lawless frontier? Return with us now to those thrilling days of yesteryear when eventually at the end of every trail, the Lone Ranger rides again. Ezra Pike, a wheat farmer near the small western town of Hillsdale, was eating his evening meal with his wife Martha when suddenly she pointed out the window. Ezra, just look outside. Well, I'll be more of them dead-rated cow punters.  Cut my fence, that's what they did. Well, I'll show them. Now, don't start a fight, Ezra. Where'd I put my gun? Now, here it is. Oh, please, Ezra. You want I should let them blast it calm and get away with the thing like that? You can't fight for them. You stay inside and let me turn to this. Get out of there.  Careful, farmer.  Yellow-dunk coyotes, get out. Who in places are you to give us orders? That's my wheat, them blasted creditors of yours are stopping now. Now, ain't that too bad? Come a little closer, mister. There's something I'd like to tell you. Huh? What do you got to say? Just a little closer. I want to whisper it. That's it. I grabbed the farmer's gun, boy. Give me that back. Let's see if he can dance as good as he can yell. Come on, mister, dance first. Here's another tune. I'll have the law on you. I'll teach you. Ezra, what they do You stay out of this, Masi.   Shucks, you ain't even limbered up yet. Come on. Who shot that gun out of your hand? I did. Man, what's the point? I'll go. Shoot at this man's feet again, and next time I'll do more than blast your gun away. Ezra, what are these men doing in your wheat? Just trying to make trouble. That's what we're...  You're trespassing on another man's property. He's just a farmer. Until the cattlemen and farmers learn to respect each other's rights, neither one will prosper. Both sides have been going out of their way to start quarrels. He coming us with a gun. You likely deserved it. Now, get out. Come on, fellas. Maybe we'll meet up again sometime, mass puller. And if we do, look out. Get up there. Gosh, stranger. I suppose you're not low at that mask and all, but you sure showed up at just the right time. Hello, Nair. Not out, Professor. You can take my word for that. Don't act like one, and that's a fact. I was surprised to see cattlemen over this way. I thought most of the ranchers were over toward the hills. Uh-huh. They are. But common ride here by here every so often, going to town. I see. Seems like it ain't enough with wheat prices way down, and old Thaddeus Gruber refused to loan us farmers' care to see us brutal harvest. But on top of that, them blasted cattlemen had got to be raised in Ned. Thaddeus Gruber? Isn't he the banker in Hillsdale? Yes, the garment. I've heard some things about him. Well, I'll bet you never heard nothing good. As a matter of fact, I haven't. What's been the trouble? Ain't no one told you about the way he's trying to steal our land? Steal it? Just the same as. I don't understand. And then I reckon he ain't been in these parts for long. I figured everybody knew about that. We were just riding through. Well, the banker's trying to make us all sell our land cheap. Because he knows that next year the railroad will be building through, and then all this section will be worth 10 times what it is now. But there's nothing to make you sell, is there? What are us farmers going to do if we don't? Right now I got about $10 cash in the house to do me till this year, winter wheat is ready to harvest. Can't you borrow money on the crop? That's just it. Can't borrow from the banker, because the only thing you consider is buying and his price. Can't borrow from any one of the other farmers, because they're just as bad off as I am. But some of the cattlemen must have money? To loan to farmers? You must be local. Besides, they ain't so well off neither. No? We did all right over here, but where the cattlemen grazed their critters, there wasn't enough rainfellows here to fill your head. Grasses all burn off when they're doing their best to borrow cash to see him through. Then your only chance is the banker. Which is the same as no chance at all. He aims to have our land. He savages we got to have cash to eat, so he just sitting tired till we meet his terms. Hello. I've changed my mind. We're not riding on me think you stay. Seems there's work for us to do here. Work for you, fellas should do. What do you mean? We're going to try to help you, Western. First we'll call on the banker. But who are you? What are you? Get him up, scoundrel. Come on, Silver. Tonto, Silver, that mask. Girlie, I must have been blind. Marty! Hey, Marty! What is it, Esri? Landscape. What you so excited about now? Look, do you see that fellow riding away on that white horse? What's he to make a commotion about? What's he? Marty, that's the low ranger. Come in. You wanted me, Mr. Gruber? You didn't hear him sit down, Lim? Sure. I've seen him at the café. He said you asked for me. Sit down. Yeah. Ain't nothing wrong, is it? You didn't do what I sent you to do. Gosh, how'd you know? If you had, you'd come right to my office to tell me about it. Well, honest, Mr. Gruber, I'd done my best. Didn't know what was wrong. You know them cattle, men. I know how to handle them with this horse. You know how to handle them with this horse. You know how to handle them with this horse. I know how to handle them with this what you mean. Which seems to be more than you do. Well, it doesn't look like you told me. I've been a week traveling around a different ranch. Ain't doing no good at all. I tried. It ain't that them common got any use for the farmers. As a matter of fact, they hate the ground they walk on. As soon as I hinted you might pay them to turn their critters loose in a week, they started cussing you out instead.  They thought of it themselves might have been different. They ain't got nothing to gain driving the farmers out. It's just that they won't take pay for it. And what? Well, remember, it's them that said it, not me. Let's check it out with it. They said it's how they wouldn't do nothing to help you out if they had to starve for it. They said they ain't... Seems like you ain't any too well thought of, Ron. And by heavens they will starve. I suppose they figure I don't know their range is burned up. I suppose when I ask a favor of them, they turn me down. I'm to loan them cash when I ask for it. Well, I won't. I want them fool farmers out of this country before there's others from outside coming around and offering them more cash than I am. Sure, I savvy. Farmers having no cash, and I'll see if they don't get any. But they're just stubborn enough to hang on long as they can see a crop ahead of them. But they won't be able to stick it out till harvest. Of course they won't. Didn't I just say I wanted their land before I have to meet the price of outsiders coming here to buy? Yeah, but... Look here, then. If the cattlemen won't run the farmers out for pay, then they're going to do it for nothing. For nothing? They're all the time fighting with the farmers, ain't they? Sure, but I... Well, this time there's going to be a fight bust out. A fight that's going to have every farmer around here so blame-anxious clean out of these parts. He won't be able to sell fast enough. Yeah, Mr. Gruber. I bet that could be done. I know it can. All you're going to do is stir up the cattlemen without letting them know you're doing it on purpose. Sure. Talk about how the farmers are breaking up the stock so that grass can't grow no more. And tell them the farmers grabbed off all the best land. Make them think they're burned out this year because the homesteaders shoved them back on worthless range. Keep prodding at them. Get them to thinkin' all their trouble comes from the farmers. In one of these days, it'll be a ruckus bus loose. They'll tear the top off things. The way things are, all them common eat is somebody to get them started. You're going to be that filler. Here's some cash. Take them over to the cafe. Every time he's here comin' buy him a drink, get him talkin'. And if you do your work right before the week's up, things will be homin'. I'll start right now. Give me that cash. This time you're planning things right, Mr. Gruber.  You earn a gift sturdy. Sure. Now, let you know how things payin' out, Mr. Gruber. Get back inside. What's up? Get back. Hey, miss, the holder. Come on, sir. You can't get away from the holder. Then let me go away. You're not going to leave this by the warning. Which one of you is the banker? I am. And you're the man I want to see. I've heard some stories about you, and I hope they're not the truth. What right have you got to do? Listen to me. Is it true you've refused to loan money to the farmers? Yeah, it would have been him. You won't give them money to live on unless they sell out their holdings? I run my business to please myself. Well, doing that, you're forcing the farmers to lose everything they have. They've offered them a price. A price that's only a tenth of what they'll be able to get when the railroad is built. Well, they need my fault.  Most men of your profession do everything they can to build up their community. But instead, you'd rather ruin the people of this district so that you can make an unfair profit. This is my bank, and I'll do what I want with it. And you won't reconsider? I not only ain't changin' my mind, but I'm gonna set the law on you for threatening me like this. Then serve your right. I haven't threatened you yet.  You can't scare me. There's going to be justice done here. Your scheme isn't going through. How are you gonna stop it? I don't know yet, but I'll find a way. Unless you decide to be fair. Get out. We're going. Come, Tana.  Don't try to follow us. Who in blazes was he? Gunmen hired by the farmers, that's all. But what was it? A dozen more like him couldn't keep me from running my affairs the way I please. I don't care. You hear that? They've gone. Which is just what you're gonna do, leave. Get to the cafe like you started you before. And I'll stop at the sheriff's office on the way and tell him about the mask man and the red skin. Name's 13 on the cattleman. Mr. Gruber, here's something you can tie to. I'll get him started. And when the finish comes, them farmers had better watch out. Glam's efforts to stir up the cattleman against the farmers met with almost instant success. The cattleman who felt themselves the natural enemies of the homesteaders had been further embittered by the fact that while the country where they ranged their cattle was dry and parched, the wheat fields to the farmers had enjoyed an abundance of rain. But Glam, not satisfied until he'd spurned the cattleman to action, continued his campaign. Then I'm up again, Far Keep. Check the way I look at it. You cattlemen have got a raw deal. You think I don't know that? Then why don't you fellas do something about it? Maybe we will. We'll stay and forget so much, Glam, and then the fur flies. Wheat, where cows ought to be grazed. It's your own fault, Gortz. Yeah? You cattlemen ain't forgot how to handle shooting irons, have you? We ain't. Let's folks fill up finding out right soon. Come, you're the leader of the commons. Sure I am. Then if you want action, it's up to you to start it. Run the farmers up! They got our reins! Don't let them fight farmers under the grass! Run the farmers up! They stole our reins! Climb them up with lead! Cattle starving! Cattle starving! Fellas, it's time we done something. And I don't mean tomorrow, I mean today. I don't mean with talk, I mean with lead.  Wheat cattlemen are taking back what them farmers stole. Did a good job. Before the night's over, the common will be taken over. And after they've taken over, then I'll take over from them. The curtain falls on the first act of our thrilling Lone Ranger drama. Before the next exciting scenes, please permit us to pause for just a few moments. To continue our story, the cattlemen aroused by Lemnugin prepared to attack the farmers. And the farmers, learning what was threatened, gathered in town to defend themselves as best they could. The streets of Hillsdale were loud with angry voices in the chatter of hoots. But hidden by the shadows between two buildings, the Lone Ranger and Tonto watched the gathering storm. Something's got to be done to stop them, Tonto. Soon there'll be plenty trouble. I suspect this is a bank of work. And that's what me think. I've noticed that man who was in his office when we called on him. Him name Lemn. The past few days, Lemn has talked with every cattleman who's come to town. And wherever Lemn has been, has been talked against the farmers. That's right. A couple does break up. Many of the farmers will sell. Most of them are brave men, Tonto. They're not as familiar with horses and guns as the ranchers are. They never stand a chance on the fight between the two factions. That's Tonto. There's Ezra Pike. He's the man we helped when we first came here. Him, good teller. This seems to be the leader of the farmers. And there, a fellow named Tom Robert. The leader of the ranchers. Get on your horse, Tonto. What? What we do? Kimosabe. The fight is ever going to break out now is the time. Ezra and Tom each represent their own side. They talk things over reasonably. There may be peace. Then look when they mad. That's justice. They lose their heads. But they won't compromise. This whole town will go fighting mad. That's right. And that's what we've got to stop. Tonto, trouble starts. You take Ezra. Tonto, do that. And I'll take Tom. Get Ezra on your horse. Make him go with you. Then head for our camp. Tonto, they're getting into a quarrel. Are you right now? That's it, Kimosabe. Ride. Come on. Watch out. Ready, Tonto. Ready. You bring us here far. You'll learn in a moment. Ezra, I want to talk to you too. I thought you was a friend of mine. I'd like to be a friend of both of you. You've got a fine way of showing us. Hold on. Well, look here. I've got a few things to say. And then you can both talk. But you're going to listen to me first. Tom, you've allowed yourself to be used by the banker. He's trying to get the farmers to sell out. And you and your friends are helping him. That's not so. You probably haven't realized it. You haven't allowed yourself to think it over. But it's true, nevertheless. But what's that got to do with me? Ezra, the homesteaders are at fault too. Perhaps not as much as the cattlemen. They've done their share to keep the quarrels going just the same. We're going to see if the cattlemen and farmers can't work together instead of against each other. Several days went by. And during that time, Thaddeus Gruber waited anxiously for the outbreak between ranchers and farmers that never came. We see him now in his office as Lem entered. Mr. Gruber? Something's going to happen, all right. Can't you find out what? Nobody's doing any talking. It's what makes me more sure than ever that this fight ain't over. It's when they quit shoppin' and everything seems quiet that things most usually bust loose. I've got to be more sure than that. Well, the cattlemen are dodgin' the farmers and the farmers are dodgin' them. They act sorta like fellas that are just waitin' to signal to raise the roof. Well, if they ain't gonna fight, I want to know it. And I can plan some other way to make the farmers sell. They'll fight. They ain't got a notion that cattlemen are just waitin' to figure the best way to start it. Hear anything more about that mask trailing this purge? Nope. Just that they let Tom and Ezra go. They say who the mask man is. Tom said he would just sell him out, Lord. Took him away, thinkin' to have cash. Well, that's Tom now. Comin' here for a loan, allabitch. Well, he won't get it. There's some others with him. All ranchers. Come in. Afternoon, Mr. Grover. Can we see you? I ain't loanin' no cash. That's not what I'm on to talk about. Hmm, come in. Howdy, Lem. Howdy, Tom. Howdy, fellas. Howdy. My time ain't to be wasted. State your business. Well, he was just wondering if that proposition Lem put up to us some time ago was still open. What proposition is that?  What do you mean? Say, Mr. Grover, about ruinin' the farmer's crock. Ain't that it, fellas? That's it. Kinda late about agreeing to it in chief. Oh, things haven't changed, none. You still want that wheat land, don't you? It's worth cash to you. We run our cows over the farmer's grain, ain't it? It might be. And for cash, we'll do that thing. Sure, I'll save you this. First, when Lem asked you to do that, you got riled. Now you turn right around and ask me. Well, the fact is, Mr. Grover, we just about gotta do it. Uh-huh. We ain't gonna have no winter passage for our cows, so if we're to pull through, we'll need cash. Which same? You won't ruin us. So we've decided to talk things over and get even with the farmers. And at the same time, get paid for doin' it. Then we're agreeable. Don't get the notion when that wheat land's mine, you ranchers are gonna get any good from it. We ain't figurein' all that. Cash is all we want. Then maybe we can make a deal. But there's one thing, Mr. Grover. We ain't gonna do this for just a few dollars.  You're set to make yourself rich if the farmers tell out. And that should be worth plenty to you. And you won't have no trouble about tourism. And if you wouldn't mind puttin' it all down and writin'. And writin'? You think I'm a fool? I'm gonna puttin' nothin' down and writin'. But I'll keep word. Mr. Grover, I don't think your word's good for a thing. Why are you... But we'll take it. Cause you know if you go back on it, your life wouldn't be worth a red penny. I won't tell you. Good enough. Man, it's agreed. If you get your critters into that wheat, you'll get your pay.  Back out? Why, we wouldn't think of it. Wait till the farmers see them cows. Once on their land, the damage is done. Now how much cash do you men want? The deal was made and Lim, as the banker's representative, went with the ranchers to supervise the roundup. But the cattlemen followed their agreement with the banker to the letter. Everywhere, throughout the scorched range, men routed out calves, steers, and doggies and drove them into one great herd. I can't move when you're back to play. Come on! Move along! Get along with ya! By twos and threes and dozens, wearing every brand known to that district, the cattle arrived from all directions. Cowhands raced their alert cow bonies to keep the herd from scouting. Tom Roberts, sitting a stride of bay horse, shouted orders to the crew while Lim sat beside him. Red! Get over there and ride point. Keep watch them stairs. Keep them from breakin' loose. I'm tellin' ya, this is a job. How soon you'll be ready to start the drive, Tom? We'll be on our way by noon. That ain't an hour yet. Just the same, we'll be ready. And Lim. Yeah? You tell the banker, you better have that cash ready. Because just as soon as these critters drive them through the farmer's fences, we're collected. He'll have the cash for ya. I'd hate to think what would happen to him if he didn't. After all the work we've gone through. You're sure you'll be started when you say I'll ride the town. By the time we get Mr. Groober and we ride back again, you should be most there. We will be. Then I'll get goin' and I'll meet ya there. Well, get up! Get on there!  The great hurt was driven down from the hills toward the fields of green winter wheat until at last it reached the fences built by the farmers. Jack! Mort! What is it called? All the fences cut? Cut the fences and keep them critters movin'. When they get inside, let them spread out. Then go ahead and cut any other fences you come to. Right! Come on, Mort! I'll ride with you. Let's go! From the other direction, confident of success, the banker and Lim raced to meet the rancher. There they are. They beat us here. There ain't no farmers around. Most likely they don't know what's happened yet. They'll be along soon enough. This is one time soon enough won't be soon enough. Yeah! There's Tom Roberts. Tom! Hey, Tom! Hold up for a second! Sit and pay, didn't I? You taking the cash for all the ranchers? Uh-huh. You seem in a mighty big hurry to turn it over. I don't want the farmer seeing me pay you. Here, count it out. I'll do that. It's all there. Your word ain't keepin' me from makin' sure. Yep, that's it, all right. Come on, Lim. We've seen the cattle and Timothy promised. Let's clear out. Just hold on. Here come some other folks I'd like you to meet. Oh, it's a farmer. There's Ezra with him. Look, the masked man! Hey, the rich! Oh, that's the same guy. What's this all mean? You might as well go ahead and finish the deal, Tom. I'll do that. Here you are, Ezra. Here's the cash we promised ya. Thank you, Tom. It'll come in mighty handy. Hold on, you cases. Is something wrong? Come, can't give that cash, Ezra. That's my case. What's the idea? You said you'd pay us to drive our cows into these fields. We did exactly that. But I tell ya... There was nothing in the bargain to say that the cattlemen couldn't use that money to pay the farmers. Pay the farmers for what? For pasturing their cattle in these fields. What's that? No one has broken his word. There's nothing you can object to. It was a trick. They tricked us. Our cattle needed fodder, and there's nothing to beat young green wheat for that. In this farm we needed cash so that we could hold on to our land till the railroad comes through. The farmers and us cattlemen having the cash for what we wanted. Why, the mass fella showed us a way to make you pay the cost, Mr. Gruber. Well, have below on him. But it wouldn't have cost you money if you hadn't gone outside the law to cheat these people. Why, Mr. Gruber? That ain't all the mass men done. He showed us that sometimes it pays more for ranches and farmers to work together than it does to be fighting all the time. I'm not a kid of yours. You owe it to lots of the kind. But I'll tell you what I would do if I were you. I'd pack my things and hightail out of here. Because after the things you've tried to do I'm afraid you won't find the climate very healthy for you. If you savvy what I mean. The story you have just heard is a copyrighted feature of the Lone Ranger Incorporate.