Advanced NotebookLM Use Cases You Can Apply Today

Key Takeaways

I was able to load up uh I think like 50 of your podcast episodes um and then ask Notebook LM to find opportunities in topics that you haven't covered or opportunities for me to go deeper uh or just a jumping off place for me to come up with my own topics for my own podcast. So, um, you know, you could do you could do that in any format with someone else's blog post. >> Today, I'm very excited to be joined by Jen Laner. If you don't know who Jen is, she is an AI educator and consultant who helps entrepreneurs and marketers save time and money with AI. She's founder of the Front Row AI Club, a membership that helps entrepreneurs master AI. Her podcast is the Front Row podcast for entrepreneurs. Jen, welcome to the show. How you doing today? >> Oh, I'm doing great. I am so excited to be here. >> I'm excited you're here. Today, Jen and I are going to explore advanced application for Google's Notebook LM. And if you don't know what Google's notebook LM, hang out because we're going to talk about it. It's absolutely amazing. Now, before we go there, Jen, I'd love to hear a little bit of your story. How in the world did you get into AI? Uh well um the simplest version of my story is that really my whole life I have been interested in cool technology and how it can make our lives better. And when I get excited about something I feel compelled to show other people, you know, why I'm so excited like look, you got to try this out, too. Um, and so I mean that's that's like the really really summarized version, but when I was a kid, like I loved robots and Lost in Space was my favorite show. And um I was fascinated by AM radio. Do you remember those, Mike? You might be might be too young, but >> No, I remember both those shows. I mean, I remember Lost in Space, you know, Danger, Will Robbins. Of course, we're dating ourselves here, but keep going. >> Yeah. and um and AM radio, you could actually hear someone that might be on the other side of the country or even across the ocean. Um my grandfather had a CB radio in his truck and I just could play with that for hours. Walkietalkies. Um even those house intercoms that all my friends seem to have in their house. Most of them never worked, but the idea being that you could press a button and like talk to somebody in the other room. I just could be entertained by that stuff for hours and hours. Um, as I got older, I was the first person in our neighborhood to get email. And, uh, when eBay and Amazon hit the scene, I was just all over that. And, uh, I even met my husband. I was the first person that I know of in our circle who had met their spouse online. It was on America Online, I think, which was before even like Match.com or any of that stuff. >> That was in 1998. So, um, I've just always been, uh, an early adopter when I had an opportunity to be. Um, and my first career out of college was not in anything technical. I was, um, I had worked my way up to be district director of a nonprofit organization called the Musculardrophe Association. And I produced uh, two lo I had two states and I produced the the local Jerry Lewis Labor Day Tellathon each year. But even then, and we weren't really, you know, using digital tools except I remember Microsoft Publisher and Microsoft Access. I think it was Access. Is that is that what it was called? Like you could do mail merges and stuff. And I just thought that was the coolest. And that's where I really learned about marketing because you had to you, you know, wear so many hats. Um, but when my first child was born, I just decided to be a stay-at-home mom, but I couldn't stop messing with the tech. So, I was learning Constant Contact and Mailchimp and, you know, Weebly and Wix and those sites that let you build websites. I mean, you know, just templated websites. So, I would volunteer for my kids schools, uh, and just ask the home room teacher, "Do you want me to make you a website? Do you want me to send out like a classroom newsletter?" Um, and I guess unknowingly, I was keeping my skills sharp and keeping up with the digital landscape even though I wasn't getting paid for it. So, when my kids were full-time in school, this one day, I was driving through town and I noticed this pie shop that nobody was visiting this brand new pie shop. And I just thought, well, what a shame. They're going to go out of business before they ever get a chance. So, I decided to do this um cash mob, you know, where people just show up all at one time uh to put money, you know, to to shower the business with money and um and it works. so well with just some social media marketing and a little bit of email um that the lines were wrapped around the building all day. They ran out of product in fact. So I decided I was going to brand that. I called it Flash Cashers. And we did it for about 12 or 13 more businesses. And this was just a volunteer kind of thing that I did. But what happened was I got inside these businesses and I noticed that these were all really good businesses, but they were all missing like that that they all had that gap where they just didn't know how to leverage these social media and other digital tools to get the word out about their business. So that's how I started consulting. Um, and I really loved it. And it honestly didn't take long for me to fill my client roster and I never really had to market because it was so easy to just make a few shifts cuz remember this is back in the day where really all you had to do was post on Facebook one time and like everybody would would show up. Um and so I didn't have to do a whole lot before these businesses saw the ROI and had success. So I never had to market but I could not scale my business. So that's when I discovered information products and membership sites. Um, which was a gamecher for me because I could help more people. I could finally scale. And so that's been my business model for about the last 12 years. But then here comes chat GPT. And I I really couldn't sleep the the day that I saw chat GPT because I'd already been using tools like chas Jasper and Otter AI. But when I saw ChatGpt, I knew that everything was going to change um really really quickly. So I immediately about a year and a half ago, I changed my membership to focus solely on AI. That's the front row AI club and help members um use AI without overwhelm because it's coming at us like a tsunami. And I just weed through it and I I bring the useful stuff to our members. But uh really if I look back the tools have changed but the core problem that I help people with really hasn't changed and that's just to help business owners get unstuck and to charge their worth and to scale without uh burnout. But AI is certainly the most powerful thing that I've seen for this. And I just feel like I'm able to help clients create dramatically more value and to justify higher uh prices. Um, which really is the same transformation I've always facilitated but amplified by like a thousand. So that was a really long answer to your question. >> Totally cool. Totally cool. Um, love the story. So, um, folks, what's great about the, um, Social Media Examiner community is Jen is someone who's been known in that community for a while and, um, you know, this is one of our newer shows, the AI Explored podcast. So, Jen and I were actually going to be doing a show on a completely different topic. And then, of course, that topic, um, kind of disappeared. So, uh, Jen was very, very kind and gracious and came up with a brand new topic, which is what we're talking about today, which is Notebook LM. And if you've been a regular listener for a while, we recently had um, Lisa Monks on the show, and she did a great kind of highlevel overview of Notebook LM. And um simply described notebook LM is kind of like a private large language AI model where you can load it up with information and we'll kind of work on just the information that you load into it and it's part of the Google ecosystem. We're going to get into some really fascinating use cases and um Jen's going to describe a little bit more about how it works for those of you that are new. Um but specifically for marketers and entrepreneurs that are listening right now let's talk about like why should they focus on notebook LM set another way what's the upside if this is done well >> ah okay so first of all accessibility even the free version is very very robust um it's not going anywhere it's owned by Google it's getting better every single day uh and remember let's all remember Google owns YouTube So, I mean, this is um uh it's it's just going to get bigger and better as as we keep moving along. And the ability to add a large amount of diverse sorts of content in different formats makes Notebook LM a dream. So think YouTube links, MP3s, PDFs, um text, docs, PNGs, JPEGs, website links, um and Google Drive, Google Docs, and now web search. Um you have the ability to select and deselect the content that you want to access with just a simple click. And the content, like you said, stays within notebooks. It and it doesn't train the model. >> Okay. So if that felt a little technical, I'll try to translate that into kind of a benefit statement. If you feel like you not you want something specifically that can just train on a very specific data set and not have any outside leakage for lack of better words from the internet or any other sources like notebook LM is the thing that can be very powerful. And what we're going to talk about today is a whole bunch of different use cases that are very very helpful for any business. Um whether you own the business or you work for a business, you're going to find today's examples extremely beneficial. These are things you're going to be able to put to work almost immediately. So, um let's now get into kind of like for the total beginner with Notebook LM, just kind of explain the very basics of how it works. >> Um so what I love about it is how it really it like it it just walks you through the whole process. So, um, in fact, half the time I can't remember the link to get there, so I just type in the browser bar notebooklm and then it it takes me to the right link. Um, so the link is notebooklm.google.com and then it'll prompt you. It'll say get started with notebookm. And then the first thing that you'll do is create a notebook. There's a giant button that says create notebook. Uh, and then it's going to prompt you to add resources. And with the free plan, you can add up to 50 resources. That's a lot of resources. And if you have Google Workspace, which you may not realize this, but you automatically have access to the pro version, which allows you to have up to 300 sources per notebook, which is bananas, honestly. >> And real quick, for those that don't know what Google Workspace is, if your company has a paid account for uh Gmail, then you have Workspace. So, keep going, Jen. >> Okay, perfect. Um, so you know, like I said, you can add when it where it says add sources, you can add pictures in the form of JPEGs or PNGs, PDF documents, YouTube links, and and what's important here is they don't have to be your YouTube links. They could be any YouTube link. Um, some will get kicked out if they're uh like private or unlisted or, you know, maybe just has like some sort of bug in it. uh but most of the time you can use like any YouTube link and you can also use website links and that alone is a huge differentiator because as you probably know all of us that have experienced you know um have been playing around with any of the LLMs chat GPT claude whatever oftentimes you put in a link and it tells you that it can't open the link um you can also add MP3s so that's audio files uh and and so that means you can upload like if you're a podcaster you can upload upload your podcast. You don't even have to transcribe at first. Um, you'll also see a newish button on the top right to discover sources. So, when you click that and you type in your search query, it's going to go and search the web and give you a list of results that you can then click on and add to your notebook as a source. So, the minute that you add sources to the notebook, it gets to work. Um, and in the middle of your dashboard, you'll see a summary that gets written. And all of your other sources are going to be uh visible along the left hand side. And as you move forward in your notebook, you can easily check and uncheck the sources that you want to interact with. And then just some of the other features, I'm not going to go deep into them, but just mention that they're there. There's an incredible mindmap feature. you just click on it and it will mindm map all of the content that you put in there or the content that you want it to to access. Um there's a briefing doc feature which is an amazing resource in and of itself. If you have dense documents that you just you want let's say you're working in chat GBT or claude and you're going to exceed the content usage over there you can reduce it with a briefing document over here then go back over there and plug it in if you want to stay in in claude or or chatgbt. Uh there's a note feature so you could create a note from anything and then again add that as a source. There's a standard chat window. So, right in the middle. So, that's the area where you you just type in whatever you want to say to engage with the content the same way you do in that chat bubble and chat GPT and Claude. Um, there's a study guide option, uh, which is just what it sounds like. There's an FAQ, so you could create an FAQ out of anything. Um, there's a timeline feature. I haven't used that a lot, but it's really cool. It puts things in a visual timeline. And then there's an audio uh overview and now a way to also interact with that audio interview. >> Love it. Okay. So, just kind of a highlevel summary. Google Gemini is what powers Notebook LM. And we don't really know which version it is. My guess is it's one of the more powerful versions of Google Gemini. And if you've been tracking, Google Gemini is getting extremely powerful and it's it's competitive right up there with Claude and and OpenAI's chat GPT. So the large language model behind the scenes that's kind of like you know powering all of this is Google Gemini. Um what I love about the concept of notebook and then LM means language model. The idea is that you're loading all of these documents or all these videos or audios or whatever you want into kind of a notebook and then you can query just what's with what's with with what's inside the notebook. And the applications are frankly endless, but we're going to get into a couple of really fascinating applications next. And really the the one that I want to focus on first is competitive analysis. So, um, why don't you explain why this is something that's really valuable from your perspective and then we can get into kind of how it would work. >> Sure. Um, so one way, so, so a way that I've recently used this is I was able to load up, uh, I think like 50 of your podcast episodes. Um, and then ask Notebook LM to find opportunities in topics that you haven't covered or opportunities for me to go deeper, uh, or just a jumping off place for me to come up with my own topics for my own podcast. So, um, you know, you could do you could do that in any format with someone else's blog post, with some what, you know, whatever. It's it's um it's a wonderful feature. >> Okay. So, what I'm hearing you say here is, and we're going to get into kind of how this works in just a second and really un unravel this, but this is a unusual competitive example that you just talked about, right? like we both have podcasts in uh the AI world and yours is called the front row, mine's called AI explored and what you did was you loaded some of my episodes or all of my episodes maybe even into notebook LM and then you asked it did you also load yours in there too or what did you or or did you just ask it to identify things I have not yet covered like how did that work exactly? that would be a smart thing to do for but for this preliminary thing I and I do have all of my podcast loaded in another notebook which I'm going to give that example in a minute but um yeah know it was just a simple like broad it was your episodes and then the query was um what you know what what do you what episode like I have a I talk about AI on my podcast um I don't want to copy episodes but I'm looking for ideas. So, where do you see given what's listed here, where do you see gaps and opportunities for me to uh talk about? >> Did you just literally give it the feed or what did how did you load all that stuff in there? >> Well, I use the tool that makes it easy. E my AI does a lot my AI, my VA does a lot of this kind of work. Um, but we discovered a tool called Amplify. A P I FI. That makes it easy to do that in bulk. >> So what is what is it? Are you pulling YouTube videos in or it was YouTube videos in your case? >> A P I F Y. >> Just one P. AP I FY. >> Tell us a little bit about what that does. >> I It does a lot of things. I don't really understand it. I just use it for this one thing. Okay. which is to go and find a lot of links on like YouTube and get those and put them into my notebook. >> Okay, so back to this competitor analysis thing. Um, you might be um you might be putting on a conference like we do, right? And there might be a competitor in your space and I would imagine you could hypothetically like like let's talk that through. How would that work exactly? How how would we use Notebook LM in that particular example? You know, maybe there's a list of speakers or something like that. Well, how would we use it to do competitive analysis on the two different events? >> Well, well, what is our what is our goal? We're a speaker, too. >> No, let's say we put on an event. >> Oh, we're putting on the event. >> Yeah, we want to differentiate from another event. How would we use it to do the analysis? >> Um, specifically with Notebook LM, I mean, I would do >> Could you just give it the link to the speaker page? >> You could. Um, I just in that case I just don't know how that's different from doing it with chat GBT or or anything else. You know, I like to use examples that are so for here's another example. I also used your podcasts um all of those that I put in there um to find out exactly what you covered on Notebook LM um to find out what has been covered and what hasn't been covered. Um because I I wanted to bring I wanted to to pitch ideas to you that and say look I know you've already done notebook LM but I've got I've got all this other stuff to talk about. So I plugged in some ideas and made sure there was no overlap. >> Okay I think I'm understanding where we're going with this and I'm really glad that I'm asking these clarifying questions. So when you are analyzing particularly content really that's what we're talking about here like instead of just competitive analysis because like you said you could just give the link to your competitor's website and ask claude or chatgbt to the do the exact same analysis but where it struggles is actually detailed content analysis. Is that what I'm hearing you say? Yes. specifically with audio and video. And that's the application where you could like for example another thing that you could do hypothetically and I'm thinking with you on the fly here is let's just say you love a certain YouTuber and you take all of their best YouTube videos that are on a specific topic. Um could you ask it to kind of like reverse engineer kind of >> 100%. That's a great that's a great example. >> So what would you ask it specifically after you popped in all those videos? Um, well, what do you want? I mean, I have to know what your goal. >> Let's say I want to model how they do it. Let's say I really love Sean Kel's style of how he does his YouTube videos. Could I take his most successful YouTube videos, plop them into Notebook LM and say, >> "Yes." >> Um, identify all the hooks and transitions and and help me understand how he did this or something along those lines. >> Yes. And more and more I'm finding that starting general really will surprise you with some great results. You know, for a long time, a long time for as long as we've been at this, you know, 10 minutes uh with AI, but I mean, we've like you have to have really specific complex prompts. But what I'm noticing is like it really will surprise you sometimes. So, in that example, I would be really general at first and just say, you know, um this is one of the most successful YouTubers out there. Uh, I know that there's definitely a pattern in the way that he's constructing his videos. Find the pattern and let me know what it is. >> Okay. I love that. >> Yeah. And then it would do that for you and then you and then you could keep going. You know, I would build from there. That's a good idea. >> Okay. So, let's go into your next example here, which was you have a case study example. Um, share share with us a little bit about the story there. >> Okay. This is so exciting. I just cannot even contain myself. Okay, but I'm going to try. So, first of all, we all know we need to do case studies, but case studies are really time consuming uh to do well. Um, and also case studies tend to be really dry. They just tend to be like a data dump, you know. Um, and so I have a client who is a digital strategist. She sort of she sort of has different packages where she'll build your whole digital business, she'll do just SEO um and sort of everything in between and she'll build websites, but she's really like hightouch. So for her um and to work with her might be, you know, for her her whole big package, right, to build your digital thing and your brand and all that might be $30,000. So people who who are interested with in working with Sandra Skyano, I got to throw her name out there. Then it they will listen to they will they will they will read a case study, but it's got to be compelling. So what I did with Sandra was um we took I told her like the messier the better. I just wanted all the bits and pieces, the stuff that's written on the back of a napkin, screenshots of nice things that her client has said about her on social media, emails between the client and herself. Um, I wanted Sandra to do an audio, just a rough audio overview of her work with the client. What else did we put in there? Um, uh, I emails back and forth and maybe I think that's about it. There were like five or six things we put in there. anything that we could think of. I just wanted to dump into this Google Drive folder. Okay. When we did that, I asked the audio um the audio. >> Wait, hold on. You said you threw it in a Google Drive folder. >> Does that mean you can attach a Google Drive folder directly in a notebook LM? >> Thank you. Let me back up. >> I didn't. I got each of those things and I put them in a notebook LM notebook. >> Okay, good. Good. Thanks for clarifying. >> Thank you. Thanks for catching that. >> Okay. And then I went to the audio feature and I asked the audio feature and and what this is is that it's it's it's a two robots. It's a male voice and a female voice. Uh it is incredible how natural they sound. It really sounds like two human beings. It's it's it's remarkable. Uh and I asked the robots to have a conversation and I told them specifically what this was for. I said, "This is for a case study that that my client Sandra Skyano is going to use um to get more clients." And so at the end of listening to this, listeners should want to work with Sandra. Okay. So I checked all the all the boxes so that all they had all the resources and hit the button. It gave me a 14inut audio that was so good. And here's why it's so good. This is the thing. Okay. And h okay. It automatically creates a story arc and a story. Stories sell. Stories are everything. All your marketers know this. Like it is it's all about the story. So you've taken this dry content and we did this also with an SEO client of hers. I mean, what could be more uh dry? But it somehow turned it into this really gripping story that for 14 minutes you could listen to and really be entertained, but not everybody is going to listen to a 15-minute case study. So listen, okay, we take the case study that lives on a landing page, right? So you so you make it so for all people, right? We want to have audio, visual, you know, documents because everybody's a different kind of content consumer, but we want to make it easy for everyone. So, we did several things with this to experiment. One was you can transcribe that. My favorite transcriber these days is Whisper uh Apple Whisper. What's it called? >> Oh, isn't isn't that by OpenAI? Is that by somebody else? >> Let me click on it. Oh, it used to be at the top of my screen. Can you can you not ask um uh Notebook LM to just write it up for you? >> Um write up what the case study >> the transcript from the spoken word uh male female voice thing. >> Yeah. No, you totally can. But um what I did where was I? I just >> You were talking about whis you're talking about whisper and whisper. Whisper at what when I last checked was a open AI tool. Maybe you're thinking of something else. I don't know. >> No, no, no. But right before that >> you were talking. >> Oh, I know. I know why. So, no. The reason is because when you transcribe it to show speaker one and speaker two, >> right, we took that, we uploaded it into Hey Genen that has the human avatars and we created a video conversation now. Okay. >> Just like we had the audio conversation. That one honestly is it's it's not ready for prime time yet. Like the the that hey Jen stuff is still just too weird looking. Um then we took it and we put it into several other platforms to create interactive case studies. So I went to GenSpark. Um GenSpark is another LLM. This was all done with a free plan and I said take this and create an interactive case study and then publish it as like a landing page. And it was beautiful. We did it with Canvas a Canvas AI feature as well. That one was that one came out nice. We did it with Lovable, uh, and we did it with Gamma app, and they all really came out great. So, I'm just listing all of those to say, you know, it's not it's not the end of the road when you get this audio transcript because this audio feature, I think that's where people sort of they begin and end with that. They're like, that's cool, but it's not like I'm going to use that audio on my podcast. Well, that's just the tip of the iceberg. This is um it's the most incredible feature because of the story arc because the conversation is really good and then it lends itself to all this other great content in all these other formats. >> So Gen Spark is J is gen like generate like like generative AI spark. Okay, cool. Okay. So, just for the record, what I heard you say is that you took a whole bunch of different things from your customer um and you threw them all and anybody can do this, right? They can take emails, they can take private messages, screenshots, whatever. Well, you don't want to take a private message without permission, but you get the idea. You can take all this information, maybe even a little write up behind the scenes of what you did. You can throw it all into notebook LM and you can ask it to generate a uh audio. Um and and to generate the audio, tell everybody where they need to go to do that. Is it just called audio overview? I'm trying to remember what it's called. >> Yeah, it's called audio overview and it's in the uh let me look here. It's in the top right hand corner. >> And then you can download that audio file and you can transcribe it. You can even transcribe it probably inside of um AI Studio to be honest with you. And then you could take that transcript and you could have some other tool create a much better looking um story for you. Um and you could potentially even ask Notebook LM to write the case study for you instead of speak it out. Is that fair to assume that it's capable of doing that? >> Oh yeah, absolutely. I mean, I just um and and that and I'm glad you said that because what we're talking about here with making doing case studies, we could dump all this and take the mess and put it into any LLM and it's going to it's going to give us something, right? It's going to make it much faster than it's ever been. But the thing here, the differentiator is that for whatever magic sauce they're putting on these robots, I don't know what it is. Uh, and by the way, if you tried this two months ago, it's so much better. It used to be they used to talk about a whole lot of nothing in the beginning. All this banter, back and forth, small talk. It was a little bit annoying. Um, not anymore. I don't know why. It's they get to the point and they it's like every other LLM summarizes and can synthesize and it can find patterns and stuff, but when these I'm going to say people, right? But we know they're robots. When these people talk, this guy and this girl, when they talk, they use phrases that you go, "Wow, that's such a cool phrase. That's such a cool analogy. That's such a cool metaphor." The way they put that, or if they find something funny or lacking or they get a little sarcastic, too, um, they can be a little snarky. They, you know, it's just it's it's uh it's it's just next level. So, >> I love it. So sparks are flying, you know, in your head of like how you can use what they're saying. >> I get it. So the key thing here is to go from kind of the messy text into the spoken um audio podcast um story for lack of better words that you can make videos out of and all these other kind of things. And I do agree that when these two characters talk, they're highly animated. They're very optimistic and it's a really great story that they can make out of almost nothing. All right, let's move on to another example. What's your next example? Um the next section, the next example I want to give you is um another one that really just made my heart beat so fast because when I did it, I didn't know if it was going to be possible, but it's possible basically. Uh so I have a podcast coming up. I mean, my hundth episode is coming up and I wanted to do a compilation episode. Um and I've been fortunate over the years to have some big names. I've had Gary Vaynerchuk and James Altter and Seth Goden and some of my favorite authors like Greg Mauen and um who else did I put in there? Uh Mike Macowski, Profit First. I know I'm saying his name wrong, but whatever. All these all these people and I could get, you know, more mileage out of these big names if I do a roundup episode. So, what I did was I uploaded I have all these episodes in a notebook folder, notebook Ellen. And then I say, >> "Wait, what kind of episode? What kind of con are we talking MP3 files or what are we talking >> Yeah, they're MP3s and specifically um uh Amazon S3 MP3s. >> Okay, so just folks that don't know what that means, that means they're direct links to Amazon where the actual files are. Is that what I'm hearing you say? Is that correct? Okay. >> So, you put a bazillion links, 99 links in there from every episode you've ever done or just from the top episodes? >> Um, well, I already had this, so I but I but I just really wanted the episodes with these people in it. So I just checked the ones that had these. >> Okay. So you added the links to all these MP3 files and then keep what happened next. >> And then I said um uh I am uh doing a roundup episode. First I want you to look at these episodes and tell me if you see any themes that pop out for a roundup. Right? I mean I was doing no mental work at all. I mean, like, I'm just putting all the heavy lifting on on the AI. And then it gave me three really compelling themes. And so, I like theme number two. So, I was like, let's go with that one. Um, so what I want you to do is to create a table. And I want three columns. In column one, I want the episode number. In column two, I want the actual transcript of the quote that you're grabbing. And then in quote, I mean in the third column, I want the timestamps of where to cut. It gave that to me like almost instantly. So that was easy. Then this is where >> was it accurate, by the way. Did you >> It was accurate. Yeah. Yeah. Yeah, it was. I liked it. It was It was They They were good. So I um That's not to say that that's going to happen every time. I mean, I'm sure that, you know, especially if I had done more episodes, I'm sure I would have had to do more tweaking. So then I go over to Manis, okay? M an u. And I don't use Manis a lot, but I remembered that it does stuff. Like even before these other agents started popping up, Manis was like one of the first agents that was like publicly available that like did stuff. I don't know how else to say it, but >> yeah. And by the way, manis.lm. And what it is is it's kind of like an agent that can surf the web. Is that probably the easiest way to describe it? I mean, it's like it's like you can give it a task and it will go out and do a task for you. It's it's basically an AI agent is really what it is. >> It does things like it will well you're going to see it edit in my podcast, but it Yeah. So, it this this is Hold on to your seat. Okay. So, then I said this was the part where I didn't know it would work because I had I had tried it once a long time ago with one audio clip and asked it to edit and it had it had done it, but this is a much different thing. So I said based on this um document uh and based on the instructions on this table please edit and I and I told it that I was doing a roundup episode and I said uh please edit these accordingly according to this document and and it and it did it. Okay. So here's here's the thing. I I think because I told it it was a roundup episode, it thought it needed to do the whole episode. So, it gave me one 12minute MP3 file with all the clips, you know, merged together, which is no problem because I can download that. Now, my podcast editor can just insert my commentary in between each of those and throw on the intro and throw on the outro and like call it a day. But I could not believe and also I used I gave it um Amazon S3 links as well. So I gave Manis the the same Amazon S3 links. But I'm telling you it edited very precisely the exact >> um the exact uh quotes. Yeah. Clips. So you know I I just couldn't believe it. I mean I was I thought that was pretty exciting. >> Okay. Okay. So, just for the record, you went into um Notebook LM and you asked Notebook LM to produce a table for you. Is that ultimately what it did? And it it had the um like the episode like the name of the guest, the clip, and then maybe like the link to the Amazon MP3 file. Is that kind of what Notebook LM did for you? >> No. On the ta the table, um I suppose I could have added that as well. That wouldn't have been a bad idea. But on my table, it just had episode number, >> uh, the actual transcript of the blurb, so I could see and make sure that it was Oops. Right. And make sure that it was um accurate. And then >> you added the link in the >> And then and then the um >> the link. >> Yeah, the the timestamps. >> Oh, the time stamps. Okay. >> Yeah, the time stamps. >> Oh, got it. And then you must have brought that into another document and added the the the links back to the MP3 files. Is that correct? when I went to Manis, but in the notebook, >> the MP3 files were all there. They were they were in that notebook and I had only the ones checked that the episodes that I wanted it to extract from. Does that make sense? >> Love it. >> Okay. >> Okay. Now, you could also do this with uh blog post content if you wanted to, right? You could just give it links to your blog and have it identify all this kind of stuff, but you could probably do the exact same thing with other AI models as well. What I like about this one is the fact that you're dealing with multimodal content and um it's pretty powerful stuff. Okay, we got another example uh another case example which is research related. So talk to me a little bit about that research example. Oh. Um, so sometime so I take a bath every every night and uh it's just like you know that's my therapy and um what I do is I create like my own podcast using these these folks to help me move through a lot of information. So obviously like you, I have to stay on top of all of the developments in AI and uh you know I always don't I don't want always want to be sitting at my desk you know for hours like reading and reading and reading. So what I'll do is um combine however many resources and this is when I can really go into like a white paper or even a a book if I want to and put it all into a notebook. And then I tell those the the the guy and the girl to have a conversation about it so that I'm up to speed or um you know recently I attended a conference and I didn't realize who the speak I didn't know who the keynote speaker speaker was till the night before and I was so bummed that I hadn't read his book because I would have liked to have read his book before I saw the speaker and um so I just again for my bathtub time I just put it in and told the the robots to talk about about it and so I really felt up to speed uh and that I understood the content of the book and could have an intelligent conversation the next day. And this is not the same as again this is not the same as chat GPT or any of the other language models because of this ability that this feature has to create a um conversation that actually sticks, right? like they talk about it in a very human-like way, so it it makes sense and they draw connections. So, you you just you just listen. Um, and you know, they've added that feature where you could dial in and you can redirect them if you need to or or ask a question while they're talking and it responds. The only thing though is that uh when you're done, the written documentation of that conversation only shows their the audio content that they had. It doesn't show your interruption or your questions. So, it's one of my favorite ways to really consume a large amount of information because yes, I could summarize it on chat GBT or any of the others, but for me, and I know there's a lot of people who are audio learners, um, and the fact that they're not robotic sounding, they don't sound like robots, so it you're you really can't digest the information. We had another example of um creating training for your staff um like internally training and I would imagine this could also be for your customers if you had a membership. Tell me a little bit about um that example. That's just, you know, sort of say the same sort of idea is that you could take your dry SOPs and, you know, and the videos and everything else and because of the wonderful ability for Notebook LM to to house all different kinds of diverse content. Then when it comes time to how do I process a podcast episode, uh your um staff member can listen to the instructions when they're out on a walk. Uh I mean doesn't sound like the most interesting content in the world, but I mean it's just one more way to learn something. >> Question for you on this uh audio um feature built into Notebook LM. Do they make it easy to work on your phone? Um, or do you have to download the MP3? Do you understand what I'm asking? Because I don't even know if there's an app. Is there a notebook LM app for the >> There is a Notebook LM app. I'm opening it up right now because it's really not it's it's not bad. It's it's uh it's pretty good actually. Um, >> my guess is you could create the audio feature on the desktop and then listen to it on your phone later. I don't know if that's true or not. >> Oh yeah, you can. It shows up in that folder. It'll be it will be in that folder and all your folders show up on your phone. >> Well, that's good news because that means that you could prep this on the desktop and then if you want to listen to it while you're running or walking or in the bath or whatever, you don't have to have your laptop next to you. >> Yes, exactly. >> Very cool. Jen, this has been a fascinating dialogue. Um, I know we've just scratched the surface of what um is possible with Notebook LM and all these different use cases. If people want to connect with you on the socials, where do you want to send them? And if they want to potentially work with you or join your membership, where should they go? >> Uh, well, they could go to jenlaner.com >> AI AI. Uh, because I've got some some goodies uh for your listeners over there. Um, and then on social, jen_laner, that's l ne. Okay. So, it was jenlaner.com/aiexplored if they want to learn more about you. I mean, if they want to learn more about you and your business. And then, uh, which social platforms did you say are the ones that they should connect with? Uh, >> that was Instagram. Jen_Leh Ne. >> Jen, thank you so much for sharing your insights with us today. >> Thank you so much, Mike. I enjoyed it.