00:00:00In this video, I'm gonna show you how you can combine
00:00:01the power of Claw Code and NotebookLM here
00:00:04using this tool called NotebookLM-py,
00:00:07which is an open source library
00:00:09that you can be able to combine NotebookLM here
00:00:11into a COI tool that can be used by AI agents.
00:00:14Now, you might be wondering why we should use this
00:00:16is because Claw Code is really good at execution,
00:00:18but NotebookLM here, on the other hand,
00:00:20can turn your messy documentation, research,
00:00:22different sources into a clear, grounded understanding
00:00:26that we can pass it to Claw Code for execution.
00:00:28And take my use case as example
00:00:30that I'm gonna show you later on in this video.
00:00:32Simply, you can see, I was able to using
00:00:33the NotebookLM skill here instead of Claw Code
00:00:35and do a compare analysis for the product
00:00:38that I built called Book Zero.
00:00:39And we can see here that I have asked it to analyze
00:00:42the 35 competitors that we have inside of this CSV data
00:00:46and do a deep compare analysis
00:00:48for each competitors that we have.
00:00:49And furthermore, we can also use that as a knowledge base
00:00:51to decide exactly which product directions
00:00:54we should go for for any kind of use case, right?
00:00:56For example, based on the competitor research
00:00:59that we have done in the two notebooks that we have,
00:01:01we can then be able to answer questions like,
00:01:02what should we focus on next?
00:01:04And that is gonna look through all the Jira ticks
00:01:06that we have in our Jira board
00:01:08and be able to understand the current applications,
00:01:10combining with the knowledge base
00:01:11that we have for the competitor research.
00:01:13And furthermore, you can not only just use that
00:01:15for development here,
00:01:16you can also use that for making content as well.
00:01:18Right here, you can see these are all generated
00:01:20using Nano Banana 2, SEO skills,
00:01:22and also using the NotebookLM skill here,
00:01:24basically combining the knowledge base
00:01:26that we have for all the competitors
00:01:27to writing blog post content
00:01:29that we can compete for other competitors in the market.
00:01:32So you can see that these are all really practical use case
00:01:34that we can use the NotebookLM for instead of a cloud code.
00:01:37So with that being said,
00:01:38that's exactly what we're gonna cover in this video.
00:01:40And specifically, we're going to cover all the features
00:01:43that there is for the CLI,
00:01:44as well as how we're gonna install this
00:01:46onto our local machine,
00:01:47how we're gonna be able to set this up.
00:01:48And then furthermore,
00:01:49I'm gonna show you the NotebookLM skills here
00:01:52that I'm going to integrate into our AI agents.
00:01:55I'm gonna show you this all in this video.
00:01:57So with that being said, if you're interested,
00:01:58let's get into it.
00:01:59All right, so before we jump in,
00:02:00a quick intro for those who are new here.
00:02:02My name's Eric,
00:02:03and I spent years as a senior software engineer
00:02:05at companies like Amazon, AWS, and Microsoft.
00:02:08And I have started this channel
00:02:09to share everything that I learned along the way,
00:02:11from AI encoding to automations, Web3,
00:02:15career developments, and more,
00:02:17all broken down into practical tutorials
00:02:19that you can actually follow.
00:02:21And of course, we also have a school community
00:02:23where you can get access to all the resource, templates,
00:02:26plus our community supports.
00:02:27So if you're ready to level up,
00:02:29make sure to check out my YouTube channel
00:02:30and hit that subscribe button.
00:02:32Now let's get back to the video.
00:02:34All right, so to get started,
00:02:34first thing first we're gonna do here
00:02:35is to navigate to notebooklm-py.
00:02:38And I'll make sure to put this link
00:02:39for the repository here in the link description
00:02:41so that you can find it.
00:02:42And basically what this repository does
00:02:44is it contains all the NotebookLM skills,
00:02:46as well as the Python APIs and CLIs
00:02:49on how can people use Clockoh here or AI agents
00:02:52to programmatically access our NotebookLM's feature.
00:02:55And here you can see for this repository here,
00:02:57it contains all the complete features
00:02:59that NotebookLM here cover.
00:03:00For example, you can be able to create a notebook,
00:03:02list out notebooks, or be able to rename or delete.
00:03:05You can also be able to insert all the sources you want
00:03:07and be able to extract questions or conversation histories,
00:03:09as well as setting the persona here in the chats.
00:03:12And then we can also be able to set the research here
00:03:14to deep mode or fast mode with auto imports.
00:03:17And furthermore, you can also be able to download
00:03:19any things that you generated using NotebookLM,
00:03:21for example, audio, video, slide deck,
00:03:23all those kinds of things.
00:03:24You can also be able to extract them using this tool as well.
00:03:28So all the functionality that are covered on the web UI,
00:03:31you can do the same thing using the CLI as well.
00:03:33So in our case here, let's take a look
00:03:35at how we can be able to install this onto our local machine.
00:03:37So right here you can see it has the installation section,
00:03:40and simply we're just gonna install the basic installation
00:03:42plus the browser login support
00:03:44so that we can log into the first time in the browser
00:03:46and save that credential.
00:03:47So in this case, I'm just gonna copy this right here.
00:03:50And then here, I'm gonna head over to a new terminal section.
00:03:52And here you can see I have a folder
00:03:53called erictech-notebook-lm.
00:03:55And what I'm gonna do here is I'm gonna first create
00:03:57our virtual environment first.
00:03:59So in this case, this is the command for this.
00:04:01So once I create the virtual environment,
00:04:03I'm gonna activate this.
00:04:04So after I activate this,
00:04:06I'm going to paste the command for the installation now.
00:04:09All right, so now once we have this,
00:04:11then what we can do is to install this completely.
00:04:13And here you can see this is what the end result look like
00:04:15after we have installed this.
00:04:16And now we can also be able to verify to see
00:04:18if our notebook-lm CLI here is installed
00:04:21by checking the version.
00:04:22And currently you can see this is the version I'm using
00:04:24for the notebook-lm CLI.
00:04:26So the next thing we're gonna take a look at
00:04:28is how we can be able to authenticate ourself
00:04:29for our notebook-lm.
00:04:30And this is the quick video guide.
00:04:32And basically what you can do here
00:04:33is that you can just use this command right here
00:04:35to log in with the browser here.
00:04:37So now if I were to head over to terminal,
00:04:39and if I were to paste that command,
00:04:41it's gonna open a browser.
00:04:42And simply, we're just gonna sign in with Google here.
00:04:44And it'll basically authenticate ourself for our notebook-lm.
00:04:47So right here, you can see after I sign in,
00:04:49it's gonna save our credentials in our root directories.
00:04:51So now here you can see,
00:04:52once we have the CLI command here installed and connected,
00:04:54the next thing we're gonna do here
00:04:55is we can do all kinds of things,
00:04:56like creating notebook, chat with the resource that we have,
00:04:59or generating content and downloading artifacts,
00:05:01all kinds of things, right?
00:05:02These are all the CLI commands that we can use
00:05:05to do all kinds of things with our notebook-lm.
00:05:07But the most important thing we're gonna do here
00:05:08is to make sure to install the skills
00:05:10so that we can be able to pass the knowledge
00:05:11on how to use the CLI to the large language model here,
00:05:14or the AI agents,
00:05:15to be able to connect our cloud code here with our notebook-lm.
00:05:18And then to do so here,
00:05:19you can see this is the entire agent setup.
00:05:20One option here we can do is to install it using CLI,
00:05:23which is using the notebook-lm here to install all the skills.
00:05:26And the other option here,
00:05:27if you wanna use the open skill ecosystem using the NPX,
00:05:30here's the command that you can do so.
00:05:31But honestly, the results that we're getting
00:05:33for two options here are the same.
00:05:34So in this case, I'm gonna copy the first option here
00:05:36to install the skill into our root directory,
00:05:39and so that we can be able to use it
00:05:40for all kinds of projects.
00:05:41So in this case, I'm gonna open a new terminal,
00:05:44paste that command right here.
00:05:45You can see we have a notebook-lm skill here
00:05:47fully installed in our root directory.
00:05:48And now we are having Clogoo here
00:05:50to recognize notebook-lm skills, right?
00:05:53Notebook-lm commands.
00:05:54And simply, we're just gonna reference
00:05:55either using the slash command here,
00:05:57or using a natural language
00:05:59to basically reference the notebook-lm skills
00:06:01that we have set up.
00:06:02All right, so once we know how we can even install
00:06:04our notebook-lm skills and also our CLI,
00:06:06let's take a look at how we can be able to use this
00:06:08in a practical workflow.
00:06:09So right here, you can see I have a product called bookzero.ai,
00:06:12which is a product that I built using AI here
00:06:14to manage bookkeeping for businesses.
00:06:16And what I wanna do here is I wanna use
00:06:18a notebook-lm here to basically analyze
00:06:2035 AI financial competitors that's lives in the CSV data.
00:06:24And I wanna do a deep competitive analysis
00:06:26for each competitors that we have,
00:06:28like understand what it does, selling points, pricing,
00:06:31uniqueness for marketing, and also comparison pages
00:06:34that we're gonna have.
00:06:35And here you can see this the entire architecture
00:06:37on how we're gonna perform this research.
00:06:39So out of all the 35 competitors we have,
00:06:41we actually sort them or organize them into different tiers.
00:06:44So you can see that for tiers here,
00:06:45we have direct competitors, adjacent competitors,
00:06:48and also the tier three competitors that we have.
00:06:50So what we wanna do here is we wanna put the tier one
00:06:52and tier twos into a single notebook
00:06:54because we only have 300 sources
00:06:56that we can insert per notebook.
00:06:58And the first notebook here is gonna be
00:07:00our direct competitors, and the second notebook here
00:07:02is gonna be just the market data.
00:07:04So here you can see what we're gonna do here
00:07:06is we're gonna do a deep research right here,
00:07:08so deep queries for the top eight close competitors
00:07:10that we have, and also 10 fast queries here
00:07:13for the tier two competitors that we have.
00:07:15And roughly the total here is gonna be 250 sources
00:07:18that we're gonna add into this notebook right here.
00:07:20And then for the second notebook here,
00:07:21we're just gonna have a fast research for all 17 of them,
00:07:25and roughly we're gonna get 136 sources
00:07:27inserted into the second notebook.
00:07:29And as the output, we're going to get a report
00:07:31and also a mind map as well as a slide deck
00:07:34on the compare analysis that we have inserted.
00:07:36And that's exactly how we're gonna do this.
00:07:37And then right here you can see
00:07:38this the entire execution steps
00:07:40on how we're gonna achieve this step by step.
00:07:42So in this case, I'm just gonna run this
00:07:44and let's take a look at what the result look like.
00:07:46Quick pause for a second.
00:07:47While I was researching tools around this topic,
00:07:50I ended up testing a platform called Job Write,
00:07:52and it's actually pretty interesting
00:07:54if you're currently job hunting.
00:07:55One thing I've noticed about applying to jobs online
00:07:58is that most of the time isn't spent finding roles.
00:08:01It's spent dealing with the process around them,
00:08:03rewriting resumes, filling out forms,
00:08:05and trying to figure out whether a job is even a good fit.
00:08:08Job Write tries to simplify that whole workflow.
00:08:11When you upload your resume,
00:08:12the platform analyzes it and builds a full profile
00:08:15of your skills, experience,
00:08:17and the kinds of roles that might make sense for you.
00:08:19From there, it starts recommending jobs
00:08:21through their job matching system.
00:08:23And what's helpful is that it doesn't just show listings.
00:08:26It actually explains why a role matches your background.
00:08:29Then there's Resume AI,
00:08:30which can generate tailored versions of your resume
00:08:32based on the job description.
00:08:34So instead of rewriting your resume every time you apply,
00:08:37the system adapts it automatically.
00:08:39The part I thought was particularly useful
00:08:42is their Chrome Autofill extension.
00:08:44Once you answer the common application questions once,
00:08:47it can autofill most job application forms in seconds.
00:08:50They also have something called Insider Connections,
00:08:53which helps you see potential connections
00:08:54inside companies you're applying to.
00:08:56So you're not just sending applications into a black hole.
00:08:59And if you want guidance, there's Orion AI,
00:09:01which basically acts like a career assistant.
00:09:04You can ask it questions about roles, hiring trends,
00:09:07or how to improve your chances for a specific job.
00:09:09Taken together, it feels less like a single tool
00:09:12and more like a platform built to handle
00:09:14the messy parts of job searching.
00:09:16If you want to check it out,
00:09:17you can try JobRite using the link in the description.
00:09:20It's currently free and you can also sign up
00:09:22for early access through the link below.
00:09:24All right, now let's get back to the video.
00:09:26All right, so now you can see as a result,
00:09:27we have five deliverable downloads successfully downloaded
00:09:30inside of our docs folder.
00:09:31So instead of our marketing competitor analysis.
00:09:34So these are the PBT, MD file, and also the JSON file
00:09:37for everything that we have done for the notebook one
00:09:39and notebook two for the research.
00:09:40And here you can see it gives you a complete analysis
00:09:42of the entire MD file for this complete niche
00:09:45that we're currently in.
00:09:46And now if I were to open the slide deck,
00:09:48this is what it exactly looks like.
00:09:50So here you can see we have different slides.
00:09:52They're all generated using that banana two here.
00:09:54And here you can see furthermore,
00:09:55I can also be able to open my notebook here
00:09:57and be able to view the notebooks that we have created.
00:09:59For example, the direct and adjacent notebooks
00:10:01and also the market landscape that we have added.
00:10:04So 300 sources and 171 sources that are added
00:10:07instead of both notebooks right here.
00:10:08So if I were to open one of them, for example,
00:10:11here you can see these are all the resources
00:10:12that we have added.
00:10:13And now if I were to ask any questions, right?
00:10:15For example, based on the book zero product that we have,
00:10:19what is our selling point?
00:10:20How is it unique compared to other competitors that we have?
00:10:23And what should we focus on for the product vision
00:10:25based on the competitor analysis?
00:10:27So if I were to ask this question here,
00:10:29and it should be able to look through all the sources
00:10:30that we have added for the deep research
00:10:32and be able to answer this kind of questions.
00:10:33And here you can see, I basically changed this settings here
00:10:36to be learning guide and keep it shorter for the response.
00:10:39And here you can see,
00:10:40this is the entire response that I'm getting.
00:10:42So your core selling point here is ultra fast,
00:10:44highly accurate for the receipt extractions and matching.
00:10:47And here you can see clearly labeled
00:10:49what the selling point is.
00:10:50And you can see it also gives you a analysis
00:10:53where what are the other competitors are doing, right?
00:10:55So the book zero uniqueness here lies
00:10:57in the hyper simple three-step process upload,
00:11:00import, match workflow,
00:11:01specifically designed to put US and Canadian market here
00:11:04for bookkeeping autopilot,
00:11:06without a higher learning curve.
00:11:08So based on the competitor trends here,
00:11:10the market is aggressively moving
00:11:11towards the conversational AI system,
00:11:13continuously using zero touch bank reconciliations.
00:11:16And for your product vision,
00:11:18you should focus on expanding from receipt matching
00:11:20into a continuous real-time leisure reconciliations,
00:11:23providing automated and actionable financial insights.
00:11:26So that's exactly what it tells me to do
00:11:28for the product visions, very, very short and concise,
00:11:31without having to read through a very long essay.
00:11:33I can simply just set this inside of the settings here
00:11:35for the configuration setting to keep the response short
00:11:38and tell me the exact answer.
00:11:40So there you guys have it.
00:11:41That's basically how you can combine the power of Claw Code
00:11:43and notebook OM to build this insane automations.
00:11:46And in this video,
00:11:47we went over how you can set this up on your local machine
00:11:49and what are some practical use case
00:11:51on how you can use that to building applications, right?
00:11:53Making product decisions
00:11:55or even building any things using Claw Code and notebook OM.
00:11:58And of course, if you're currently building products
00:11:59and you want to improve your product marketing
00:12:01using Claw Code,
00:12:02then be sure to check out this video right here
00:12:04for how to use Claw Code with the 43 skills that are created
00:12:08to improve your product marketing.
00:12:09So be sure to check that out.
00:12:11And so pretty much that's it for this video.
00:12:12And if you do find this video,
00:12:14please make sure to like this video.
00:12:15Consider to subscribe for more content like this.
00:12:17But with that being said, I'll see you in the next video.