EP 121: Faster and More Accurate Results From ChatGPT with ScholarAI

This is the Everyday AI show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business and everyday life. We talk a lot about Chat GPT plugins on this show and how they can help you get more reliable and accurate information out of Chat Chat GPT because here's the reality all if you don't know what you're doing, and sometimes even if you kind of do, ChatGPT can lie quite a bit. It can hallucinate, just like any large language model. But there's one plugin that we even recommend to people that really helps with that and I'm excited because we're going to have the co founder today of Scholar AI to talk about how to create more reliable and trustworthy AI, specifically within ChatGPT. Not just that, but a lot more. So welcome to Everyday AI. My name is Jordan Wilson. I'm your host and this is your daily live stream podcast and free daily newsletter helping all of us everyday people make sense of what's going on in the world of AI because there's so much and it can be difficult to actually understand and apply it to grow our careers in our company. So we're going to be doing that today and if you are joining us live, thank you. Make sure to get your question in whether it's about how to create more reliable and accurate, you know, conversations within Chat GPT or if you just want to know more about the Script Scholar AI plugin, make sure to leave a comment. Leave us a question if you're joining us on the podcast, make sure to check out the show notes. We're going to send a link or leave a link so you can even come and join the conversation on LinkedIn. All right, so before we get to that, let's talk about the AI news like we do every single day. So big news out of Chat GPT. Speaking of. Right, so it can talk to photos now. So this new feature being able to upload photos natively is now being rolled, is now being rolled out to many more users that have the Chat GPT plus subscription. So this is part of a larger group of updates that are kind of unofficially being called GPT4V with the V standing for vision. I don't think that's an official name. This is what a lot of people online are calling it. So early users have had access to this for a few weeks now, but general access is happening now. Even myself got access to it late last night, actually. Shout out Dr. Harvey Castro, former guest on the show who said, hey, do you have access? I have access. So we're going to have more on this in AI in five. Next piece of news, Google's Bard is coming to new places. So Google's newer smartphones like the Pixel 8 and Samsung Galaxy S24 will feature an assistant with Bard. That's not new news, but there is some new news because the publication 9to5Google recently found some references in the source code claiming that you could, as an example, tell the Bard assistant to draft an email to my recruiter to accept the social media manager job offer and negotiate a later start date. So we're going to have more about that in the newsletter, but some, some, some new details leaking on how this new assistant with Bard will work natively inside of some of these new, you know, Google smartphones such as the Pixel 8 and Samsung Galaxy S24. All right, our last piece of news to go over. AI is soon going to be consuming more energy than an entire country. Yeah, that's right. So all of these data centers that are helping to produce generative AI, they take up a lot of electricity and a lot of water to, you know, a lot of times cool these systems down. So a recent report from the journal Jolie, I believe that's how it's pronounced. But researchers in Netherlands showed that by the year 2027 that all of these different, you know, servers, server farms and plants essentially where they help to create these generative AI systems could use anywhere between 85 to 134 terawatts of energy per year. I don't speak terawatts in energy, but apparently that is about as much power as Argentina uses in a year. It's actually very fascinating to talk about electricity and power in Gen AI. We actually had, you know, someone from Nvidia talk about that this week, so we'll, we'll drop the link to that in the comments. But you probably showed up here not to talk about energy and how to cool generative AI systems. You probably want to know, you probably want to hear a little bit more about Scholar AI. And full disclosure, we have people hitting us up all the time and saying, hey, I have this, you know, this plugin. I want to come on your show. I have this product and we normally say no. But with Scholar AI, this is something we've been using and recommending for months. So I'm extremely excited to bring onto the show and welcome on as a guest we have today and help me welcome on everyday AI, we have Damon Burrow, the co Founder of Scholar AI Damon, thank you for joining us.

Hey, Jordan, happy to be here. Thanks so much.

All right, so hey, just like Brian right here who said he's excited to learn in today's episode, if you are excited, if you have a specific question in our joining this live, please, please drop, please drop your question for Damon if you want to know a little bit more about Scholar AI. But Damon, let's start high level. What is Scholar AI? What do you all do and kind of how does this thing work?

Yeah, excellent, excellent question. So at the very core, Scholar AI is building systems that infuse trust into large language models. And right now that large language model is the GPT4 transformer that powers ChatGPT. And we do that through some very specific applications. Very happy to dive into the specifics of any of that. But again, kind of remaining at the highest level, we essentially tether the AI generated output that comes out of Chat GPT to peer reviewed and publicly accessible scientific articles, databases, textbooks, et cetera. What that does is it significantly reduces, almost virtually eliminates, hallucinations in general. It pulls information directly from those sources and it provides hyperlinks to those sources such that anybody using those generative AI systems can immediately follow up, they can ask more questions, those kinds of things. Longer term, we plan more applications outside of ChatGPT. So like Google Bard, like you mentioned earlier, we've got systems coming for that. We've got a browser plugin, browser extension coming for Google Chrome and others. And then we're also going to have some dedicated web apps inside of very specific domains in which some of our core user bases are going to be receiving some outsized value there.

Yeah, I love it. Yeah. And I think people who use ChatGPT pretty frequently have probably, you know, either heard of Scholar AI or have used it before. So from a very high level perspective, Damon, and we are going to explain this here in a couple of minutes, but maybe let's answer the question, why do we need to use a plugin like Scholar AI to get better results.

Yeah. So I think in some cases you don't. I think the reality is where some of the truly generative AI systems really shine through are for creative tasks. So if you're trying to write a new short story or, or you're trying to write a new song or, you know, design some sort of theatrical play or something like that, you don't really care if the output in that is grounded in what would be considered truth across the board. Right. If you transition, though, into professional knowledge work. So Think about you're a doctor trying to save somebody's life. Think about your CEO of business trying to use the last bit of Runway. You have to either save your business or to expand into new domains. Right. Think about a lawyer who is doing a patent search for a client of theirs being able to express whether they are free to operate in those space and those kinds of things. You must be accurate essentially with your use of AI. And so it's those times in which the systems at Scholar AI is building become essential. And again, the way that that works is we essentially distill the creativity out of these generative AI systems a little bit. Not completely, but just a little bit, just enough such that all the output that comes out of there is grounded in truth, as is kind of supported by the domain expertise. Right. So basically the way that this works in science, I can speak most thoughtfully towards that because I am a PhD student. I kind of live in this world every single day. Right. The scientific literature that has gone through the peer reviewed process is kind of the gold standard for the most up to date knowledge of, you know, across scientific literature and so scholarly. I gives people who want to use the large language models to interact with these sources in a much more thoughtful, a much more rich way access to that, to that material and so that they are responsible responses that come out of the AIs that they're using are again, more accurate, more reliable and then ultimately more transparent.

Yeah, and you know, I think it's worth noting and maybe even hitting like rewind here a little bit, Damon, because if you are brand new, if, if you're listening and maybe you haven't really used Chat GPT a lot or, or you're just a casual user, you know, there's a couple important things to keep in mind. You know, it has a knowledge cut off. So if you're using the free version of Chat GPT, it has a knowledge cutoff of September 2021. If you're using the paid version, it has a knowledge cut off of January 2022. But regardless, you know, so I guess there's two things that I kind of wanted to talk about here, Damon, and maybe to ask a question. So, yeah, one, can Scholar AI give us, you know, more more recent knowledge, you know, past that kind of cutoff date? And then two, can Scholar AI also just give us more, more specific, specific and more fact based things, you know, even included in that knowledge, you know, cut off just, just for more accurate and more specific citations. Are you still running in circles trying to figure out how to actually grow your business with AI. Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on gen AI. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you, you can partner with us too. Just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on gen.

Yeah, so all, all excellent questions and yes, you basically nail it. So basically the Cutoff dates are September 21st or January 2022. Dep. Exactly. Which model that you're using? Scholarly I provides access to up to date information, seemingly in real time. So as soon as the information is published, Scholarly has access to it through our various API endpoints. Depending on which database you're you're pulling from, those usually get updated daily, sometimes it can be, you know, twice a week, those kind of things. So in very short, yes. And then to your larger point of how we maintain accuracy across the sources, we basically go beyond not just the cutoff window, but also what is called the context window, not the knowledge cutoff in the context window rather. So we actually dissect that source material through some proprietary and trade secret algorithms that we developed, such that we can actually package more dense information into large, large language models, such that it can actually hold a larger breadth of information. Think about, you know, thousands of sources at a time in its kind of working memory, so to speak, such that it pulls directly from those sources rather than having to rely so much on its predictive capabilities. Right. So we're playing less of a probabilistic game, much more of a, of a, of a gathering game.

And just that that seems like a very timely response because yesterday on the show, and we'll make sure to leave a link to that we talked about like tokens and in memory. So. So you're also saying even the way that Scholar AI processes information, it kind of takes up Less of, of that token memory so it can retain knowledge longer.

Yeah, the way that I would classify it is we. So based on the semantic search functions that we've built, we can identify the most important aspects of each source such that we can kind of disregard the like less important tokens, if you will, so to speak. And so what that does is it doesn't actually truly extend the context window, but it puts more of the information you care about in the context window. So everything that you're pulling from is the most relevant, the most accurate, the most important pieces of all those articles. And so what again what you get is reliable, accurate information from the most relevant of even parts of sources. So again, we're trying to synthesize information across sources, not just pull information from a single source. And so that's when it really becomes effective to kind of break those into their most meaningful chunks, if you will.

Yeah, absolutely. Hey, and you know, for those we have a lot of people here in the live stream. Let me know, have you used Scholar AI? What, what do you like about it? What are things that you want to know? Because I'm going to ask Damon here in just, just a minute to kind of share his screen so we can take a look at this. But before we do that Damon like talk, let's, let's talk a little bit more because you said hey, in the future once some of these other large language models, you know, officially release, you know, plug in capability or third party, you know, developers to, to be able to work within their models. But can you access Scholar AI right now without chat GPT, can you just log in and use it or what's is.

Is.

Are there other use cases? I guess yeah.

So the public cannot. We have a few kind of applications that are in their alpha or beta forms for specific, you know what I would consider professional copilots. So think about a lawyer doing the patent search like that earlier as an example. The short answer to your question is no, those, those things are coming down the pipeline still TBD on when exactly those will be released for the public. What I can tell you is people should avid users of Scholar. I should be excited because in the relatively near future we're going to have things like browser extensions for their Google, third go, Chrome, etc. And we're also going to have some dedicated UX UIs that give us some increased capabilities that things that we're just simply limited to inside ChatGPT. So all those things are coming down the pipeline. There is no publicly available link right now, but if you are a Scholar AI user and you feel that there's an element of Scholar AI that's missing, then we would love to hear from you. We've got a community of about 180,000 right now users, and a lot of us, you know, we would like to engage with them and see if there's a gap that we can, we can fill.

So, yeah, love it. Well, hey, let's, let's actually give. Give everyone a chance to kind of see it side by side. So Damon's going to pull up his. His screen here and we're going to walk you through. So we're going to show. Damon's going to show an example of what you might see when you're using Chat GPT by default. Because like we talked about at the beginning of the show, and we talked all about it yesterday, is if you really don't give a large language model access to the information and the resources that it needs, you run the risk of it kind of sometimes making things up or just, you know, giving you poor responses. So Damon's now going to kind of walk us through, you know, what kind of happens kind of before and after. So we can see the difference that a plugin like Scholar AI makes.

Yeah. Thanks, Jordan. So it's incredibly well said. The one thing I will say is OpenAI is making ChatGPT, both the models 3.5 and 4 better all the time. And so it is getting better at just simply telling you when it can't do things rather than just making it up. But people still do need to be careful because the answers, the hallucinations still do happen, especially with a 3.5 model. But kind of jumping into this demo, what you're seeing here on screen is a prompt that I've used several times in which I'm asking the standard ChatGPT running the Transformer 3.5 to show me three new papers about recent advances in artificial intelligence. And unfortunately it says it cannot do that. Some of that's because it doesn't have access to the Internet in real time. And also it has a knowledge cutoff that is in September 2021. So it can't actually see anything that's happened beyond that. It tells you that you can use other academic search engines like Google Scholar, PubMed, etc. The problem there is that you can't actually then use the large language model to interact with those sources. Right. So we don't want to just use this as a reference finder. We actually want to engage with that content. We want to read PDFs, we want to ask questions from that content. We really want to, you know, get the knowledge out of that source material. We don't just want to simply find the material. And that's what those LLMs are so great at. So swapping over to a use case after Scholar AI, the plugin has been installed on the GPT4 transformer. Here you can see a much different response stain prompt. This is exactly copied and pasted one into the other. And here what we're seeing is three papers. I asked specifically for three. You can ask for much more. I just wanted to do three, just, just for the sake of time here in this kind of conversation. But what you're seeing here is papers with direct link to papers. And you can see here directly to your question earlier, Jordan, about the knowledge cutoff. So this paper came out in December of 2022, obviously well after both knowledge cutoffs. But given access to ChatGPT via the Scholar AI plugin, we're also seeing an abstract here. How many times been cited if the PDF is or is not available. And then just notice that in this, this title is actually hyperlinked. So if anyone wants to kind of go up and then follow up with the sources, they can do that. One interesting thing is what I can do is I can say to the Scholar AI plugin, as we said, we don't just care about the information finding, we actually want it to provide a summary of paper one and then kind of in real time. I might have to zoom out just to show you that. But what it's going to do is it's going to actually give you a decent understanding of what the summary is. In the cases where it can find the full text, it will be explicit and it will tell you that it has found the full text, it has parsed the full text and has created the summary from the full text. If because of publisher restrictions, scholarly I does not have access to the full text, it will tell you that it doesn't have access to the full text and it will still create a summary from the bits of information it has access to. So it's not just going to guess and extrapolate the information, it's just going to say, I'm only limited to seeing this much. Here's what I can do for you, here's what I can give you that is accurate and reliable. There's kind of your information. If you want more information, please follow up through the hyperlink.

Yeah, I love it. And you know, I think David just did a great job of explaining. And you know, if you're listening on the podcast again, come in, we'll leave a link so you can watch this. But you know, even just showing in real time, it's fast, it's responsive. Something that I love, that I wish all plugins would do is always providing a link to read more to the original resource. Because, you know, when you talk about transparency, that's something that we always kind of worry about with large language models, you know, because there's always that little seed of thought where you're like, all right, is this real? Is this a lie? So being able to have the link is great, Damon. Like right away I thought of a couple great use cases, right? So if you're trying to learn a new subject, this seems to really cut down the time. Even, you know, using ChatGPT cuts down the time. But using Chat GPT with Scholar, Amazing, right? So if you're a Reese, maybe if you're a student, you know, working on a topic, working on a paper, this seems great. Maybe what are some other kind of everyday use cases or very wide ranging applications of the Scholar AI plugin?

Yeah, excellent question. So roughly 60 to 70% of our use cases come from what I would consider graduate students, so master's, PhD students and or researchers, meaning people in academia doing kind of hardcore research. The other, roughly 30 to 40% come from, you know, other business professionals, specifically people doing due diligence, again, trying to assess whether some businesses technology is above board, what the maybe kind of isolated advantages of one technology are over a competing technology, that kind of thing. Also, as I said at the C level, executives can use this kind of to do maybe mergers and acquisition due diligence or maybe just market research and seeing, you know, which kind of path might be most advantageous for their kind of business in its current state. We see a lot of journalists using this platform when they're doing a story and their core expertise may lie slightly outside of the story that they're writing about. Right. You can think of without diving into the specific of anything, you can think about, you know, how kind of groundbreaking and how kind of earth shattering the COVID 19 pandemic was and how much, you know, kind of information was swirling and people were having a hard time deciphering what was real, what was not real, that kind of thing. So you can, you can imagine some very important use cases there. And like you said, students of all ages just kind of coming in saying, hey, I don't understand this, can you please explain this to me at a level That I do understand. Can you please provide me links for this if I want to keep reading that kind of thing? So we do see a very wide range of uses and we're getting better and better at serving each one of those use cases. Like I said, we kind of started with the science and medicine focus and then we realized what we were building was much more applicable to everybody else. I'm also seeing the chat, the comment coming through the chat guests. A lot of clinician scientists are using us both to propel their research forward, but also to help them treat patients. You can imagine how important it would be to be helping a cancer patient who has failed the standard of care treatments and who, you know, their physicians, whoever are helping them, nurses, et cetera, need to be parsing not just published data, but also clinical trial information to say how can we help this person, you know, how can we give them improved quality of life, how can we help them live longer, live happier, et cetera, et cetera.

So yeah, no, it's so many, so many great use cases. And you know, the one that you really mentioned there and I have to call students out, you know, students, if you're using Chat GPT, don't use the free plan like it's, it's worth the $20 a month to be able to tap into plugins like what Damon is showing us here, Scholar AI. And there's so many other great plugins that if you are just trying to learn or help write papers, this right here is going to save you so much time. Not only improving the quality of what you're trying to write, but it's also going to help you learn. It's going to help you learn faster. Learn, learn a little bit better. So I want to get to a couple questions here, Damon. So speaking like I, I love this question. So VAL is asking, you know, what other plugins maybe work well with Scholar AI because you know, Scholar AI serves a very specific purpose and it does it well. Right. Like I, I've used it. A lot of our, you know, listeners use it all the time. But maybe what other plugins complement Scholar AI?

Yeah, the one that I would call attention to is called show me Diagrams and there are other similar program plugins that do kind of diagram work. But like for me, I'm somewhat of a visual learner, so I want to go beyond the text. Right. It's very good to get that summary. But I actually maybe want to work, maybe make a mental framework or some sort of mental model, you know, diagram explaining some sort of kind of arcane or various specific concept to me that maybe I wouldn't understand otherwise. So the one that's very top of mind, the one that we see most frequently is, is show me diagrams. But basically anything that's going to allow you to take information, especially dense information, and to simplify in some sort of visual, visual graphic is, Is, you know, really powerful.

Yeah, absolutely. It's, it's, it's actually funny you say that, Damon. Like we, we have our kind of free prime prompt polish PPP course. We do it twice a week, doing it later this afternoon. So if anyone wants access, just, you know, drop, I'll send it to you. But we actually have, you know, Scholar AI and that exact plugin, you know, diagram on the same page, you know, for our recommend, our recommended plugin. So I love that you brought that one up as well. Question, question here For Monica saying the episodes blowing her mind, but saying she loves learning from leaders in the AI space and asking, how did you even come up with the idea for Scholar AI?

That's a, that's a fantastic question. Thank you, Monica, for asking that. So I've been doing AI and what would be considered more ML research since roughly 2015. So back when it was kind of called gradient descent, I was doing it in undergrad, and there I was helping optimize radiation therapy for cancer patients. So basically just trying to get the radiation to go to the cancer tumors and leaving the healthy tissue alone. You know, roughly a year ago, we saw this kind of momentum of these large language models gaining power. They were becoming more useful. People were kind of interacting with them in their everyday lives. And we were using them both in the research setting and also kind of just commercially whatever was available. And we were seeing some of these shortcomings, right, the hallucinations, the lack of transparency. In the very beginning, you would ask these large language models for sources, and rather than telling you they couldn't give you sources, they would just predict the text that was supposed to come next and they would just make them up. So they would be real author names, not real people. They'd be real titles, not an actual title that's linked to a source, fake dois, et cetera, et cetera. And so we said, people are going to be using this in their professional lives and they're going to be making, you know, important decisions based on this. And these systems are going to need guardrails or structural supports, if you will, that are going to make them ultimately useful for the things that we care most about. And again, Scholar emphasizes Any use case in which the loss of life, the loss of revenue or the loss of reputation is kind of occurring. And so you can think about fields like medicine, science and research, law, business, etc. But that's, that's kind of how we came up with this. We saw, we saw the momentum of large language models becoming more useful, becoming more powerful. But, but we're still kind of, you know, being handicapped, if you will, by these shortcomings. And we saw a need to overcome these shortcomings. And you know, we think it's going to be an evergreen problem because even as some of these AI systems improve, there's always going to be this super abundance of sources in which case you don't really want to be just pulling things randomly from the Internet. You want these things to be context aware. You want the most relevant sources being pulled into your, into your chats or into your AI agents in general. And so that's, that's kind of, you know, where we, where we hang our hat at Scalar.

Yeah, yeah, love it, love it. All right, we have a couple more questions. We're going to try to go rapid fire here so we can try to fit, fit more in here. And again if you're joining us, joining us a little late we have Damon Burrow, the co founder of Scholar AI joining the everyday AI show, going through some questions on the very popular Scholar AI chat GPT plugin. So a question here from Gabriel asking, do you include international sources in Scholar AI?

Yep. Answer is yes. So we just recently as of this week have access to over 200 million different articles databases across the world, so globally, inside of the United States and outside, we have access to virtually all, all published scientific literature, even the preprints.

Wow. And like, as someone that uses y' all like a large language model a lot, the ability to tap into all of those sources in one plugin is extremely valuable.

Right.

Especially when you are kind of, kind of limited, you know, because you can only have three plugins at once when you start a new chat. So you want to be a little judicious about what those are. All right, another, another great question here from Douglas. Douglas, thanks for the question. Saying, would this be something you could look for drug interactions, obviously consult with your doctor, but you know, when it comes to drugs and how they work, is that something you can also ask Scholar AI?

Yeah, short answer, yes. All those clinical guidelines come from research that is conducted in the industry or academia. All that normally is published in the academic research, all of which Scholar AI has access to so, yes, obviously, consult with your physician and doctor. Don't trust these things blindly, but direct answer is yes.

Yeah, I love that because I always tell people, like, don't trust me just because I recommend something. Right. People always ask me, I say, no, don't, don't, you know, trust some random person on the Internet or a random plugin. Go, go try it for yourself. You know, make sure. Fact check it first. Make sure it works for your use cases before you just blindly start, you know, trusting anything, really. I think that's a great, A great point there, Mike, asking how is this monetized?

Yeah, excellent question, Mike. So we have three tiers. Scholar AI is available to everybody for free so long as they subscribe to Chad plus, which does cost $20 a month. That is limited up to 25 requests per week. So it requests anything like search for a paper, show me the full text, give me a summary, those kinds of things. The Scholar AI basic package is available for $4.99 per month or $50 per year. And that basically just comes with unlimited use. We also have a premium plan that's $8.99 a month or $85 for the year. And that comes with unlimited use. But also some advanced features, like one of the things that we're most excited, table extraction. So in the sources that you find or the PDFs that you upload to Scholar AI, you can actually ask questions not only of the text of that material, but also from the figures and the tables itself for premium subscribers. And you can see at the very bottom there, we have a meta analysis coming soon. A meta analysis is probably a much longer discussion than needs to be had here, but basically is information synthesis across a bright. A wide range of sources on a singular topic. So think about condensing the information insights that come from 10,000 sources on infectious disease as an example.

So great, great overview. And I love the ability in that highest plan to be able to read tables charts. That's so important, right? Like, we opened up the show talking about how we can kind of do that now with chat GPT, with the uploading the photo. But the downside of that, y', all is right now that only works in. In default mode. So you can't combine that with any other plugin. So that's, that's a huge, huge benefit to Scholar AI. So. All right, well, Doug, like, like we, Damon, we went through a little bit of everything in today's episode. You know, you took us on like, why this started. You know what Scholar AI does the benefits of it, how it can help us get more reliable and accurate information. But maybe what is the one takeaway that you hope people get on the benefit of Scholar AI and how it's going to help us, you know, just put out and create more reliable information?

Yeah, I think that like I said, these large language models are going to continue to become better and better over time. I think that we're going to see especially professionals in, in the knowledge world, science, medicine, law, business, journalism, etc. Are going to have to learn to use these systems more thoughtfully. And, and we want to be the system that helps people do that. Right. So they can get to doing the things in their daily lives that make a maximum impact and don't have to worry about the information that's coming out of their things being accurate. Right. We want them to be able to trust the responses coming out of their AI generated either chats or kind of interfaces, regardless of whether those where that is. And we also want to create the level of transparency like we said, to where we're not necessarily saying trust us completely. We're saying here's the material, here's where it came from. Please, please follow up. You know, please continue to push us to get better and please continue to kind of, you know, make your impact on the world doing kind of whatever you are best at.

Oh, love that. Yeah, you always got to show your sources. An old saying I believe I learned in journalism school is someone said, hey, if your mom says she loves you, get it in writing, right? So, hey, I love that Scholar AI plugin does this. It's one of our favorites. It's one we recommend. Damon, thank you so much for coming on the show to join us.

Yeah, thank you. Jordan, thanks so much. All right.

And hey, just as a quick reminder, we covered a lot. If you didn't, if you can't type as fast as we can take notes, don't worry. Make sure to go to your everydayai.com Sign up for the free daily newsletter. We're going to have a lot more about Scholar AI, maybe even some things that we didn't get to that the plugin can do. So make sure you go do that and make sure you join us back tomorrow and every day for more Everyday AI. Thanks y'.

All.

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