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Gemini Notebook: 7 Key Updates That Unlock New Business Value
Recent updates to Google's Gemini Notebook platform bring a significant shift for organizations seeking streamlined, agent-driven AI solutions. Previously known as NotebookLM, Gemini Notebook now offers extensive feature enhancements, increased flexibility for business workflows, and more grounded, source-focused outputs. By unpacking the specific capabilities introduced in the latest release, business owners and decision-makers can better understand precisely how these changes support greater productivity and accelerated operational analysis.
Gemini Notebook Rebranding and Its Strategic Significance
The transition from NotebookLM to Gemini Notebook is more than cosmetic. This shift signals Gemini Notebook’s new default state as an “agentic” platform, designed to perform complex, multi-step, and personalized outputs from a single prompt. Notably, the updates—once only available to Ultra plan users—are now extended to all paid users, making these advanced capabilities accessible to a broader professional audience. The inclusion of a secure cloud computer and the execution of code within each notebook indicate a pivot toward handling robust enterprise-level tasks, such as research calculations and sophisticated data analysis 04:44.
Enhanced Business Data Organization with Collections
Gemini Notebook introduces “Collections,” an organizational structure for better sorting and managing notebooks 04:01. For businesses working with multiple projects, market research files, or departmental data, this feature supports faster retrieval and greater clarity. Instead of traditional foldering, collections allow context-driven grouping—for example, segmenting strategic initiatives, budget cycles, or compliance documentation—reducing friction in locating key resources during critical decision-making 04:01.
Seamless Integration: Automatic Google Drive Syncing
Enterprises managing cloud-first workflows stand to benefit from Gemini Notebook’s automatic Google Drive syncing 04:07, removing manual upload barriers. Files stored on Drive, such as PDFs, images, Google Docs, and audio files, become instantly accessible as sources within the notebook. This feature enables several time-saving scenarios: instantly grounding competitive analysis in up-to-date documents, incorporating the latest financial statements into planning models, or enabling rapid review of compliance materials without cumbersome file transfers 08:09.
Output Formats: Catering to Varied Business Deliverables
Professional teams often juggle diverse deliverable requirements—Gemini Notebook addresses this through a suite of new output formats, including PDFs, PNGs, Google Docs, spreadsheets, PowerPoints, markdown files, charts, and images 04:31. Previously constrained to limited output types, the platform now supports fast generation of executive briefs, infographics, financial models, and customized slide decks with one click, directly from grounded source data.
This markedly reduces the necessity for repetitive copy-paste operations or manual formatting, streamlining the production of polished outputs for executive review or client-facing materials 18:13.
Business Intelligence Through Advanced Agentic Intelligence
A crucial outcome of the latest update is Gemini Notebook’s shift to being “agentic by default.” Each notebook now runs on Gemini 3.5 with the “antigravity” agentic search harness 04:44. This means the platform can autonomously break down complex, multi-step strategic queries.
For example, requests such as: “Create a personalized AI budget decision package for a 5,000-person enterprise CFO/CIO,” trigger thorough analysis—developing multiple scenarios (frontier model, cheapest model, tiered architecture), weighing costs (total, human review), assessing adaptability to future pricing, and packaging the result as audio summaries, mind maps, editable financial models, infographics, and PDF briefs 11:04.
Such automations allow teams to run comprehensive what-if scenarios and forecasting models, reducing the analysis cycle from hours or days to minutes.
More Accurate Outputs Via Source Grounding
Gemini Notebook’s responses are grounded specifically in uploaded or linked sources by default 08:23. Unlike general-purpose LLMs that blend user data with broad web content and training data, Gemini Notebook dramatically reduces hallucination rates; outputs are anchored in verified business documentation.
When the required information is not present, Gemini Notebook prompts the user before pulling external data, preserving both accuracy and compliance integrity 26:08. This distinction is especially valuable for compliance and audit workflows, M&A due diligence, or price-sensitive operational analyses.
Streamlined Enterprise Learning and Onboarding Assets
Another demonstrated capability is the on-demand generation of customizable quizzes, infographics, mind maps, and maturity ladders 23:09. These features can rapidly create tailored onboarding tools, knowledge checks, and visual aids, using an organization’s unique documentation as a knowledge base. For HR, L&D, or operations teams, this automates the preparation of bespoke training materials that continuously reflect actual business processes and updates.
Practical Takeaway for Business Operations
Gemini Notebook’s agentic framework, new organizational layers, effortless cloud integration, and multi-format output unlock immediate, practical value for businesses. These capabilities translate directly to improved decision velocity, reduced administrative burden, and enhanced visibility into data-driven projects. Key differentiators—such as the provision of a secure per-notebook cloud compute and auditable chain-of-thought for every complex request—set Gemini Notebook apart as an actionable platform for the next era of AI-driven business productivity.
Topics Covered in This Episode:
- Gemini Notebook Rebrand from NotebookLM
- Seven Major Gemini Notebook Feature Updates
- Collections for Organizing AI Notebooks
- Automatic Google Drive Sync Integration
- Expanded Gemini Notebook Output Formats
- Agentic Intelligence and Gemini 3.5 Upgrade
- Secure Cloud Computing for Each Notebook
- Grounded Data Responses and Web Research
- Hands-On Demo: Real-World Enterprise Use Cases
- Studio Outputs: Infographics, Mind Maps, Quizzes
- Multi-Modal Asset Creation in Gemini Notebook
- Key Differences: NotebookLM vs. Gemini Notebook
Episode Transcript
Jordan Wilson [00:00:17]:
How long has it been since we featured NotebookLM on this show? Apparently, so long, well, it isn't even called NotebookLM. It has a new name. Yes. The tool once known as NotebookLM is no more. But don't worry. One of my favorite AI tools ever didn't go to the Google graveyard. It actually just graduated and got upgraded into the official Gemini lineup and is now called Gemini notebooks. But the name isn't the only thing that's new.
Jordan Wilson [00:00:45]:
That's because over the last month or so, the team at Google has rolled out a bunch of new features that legit supercharged Gemini notebooks and make it agentic by default, which sounds really cool and all. But, well, why haven't we been talking about it? Because many of those upgrades were only for users on the expensive Ultra plan. But this past week, that changed. Now the updates that have completely changed the face of Gemini notebook are available to all paid users, And that's why we're gonna be showcasing the seven most important updates today as we put AI to work on Wednesdays. Alright. So on today's show, here's what you're going to learn. You're gonna know why notebook l m is no more and why Gemini notebook might actually make more sense. You're gonna know the one important grounded change that you really need to be aware of, and you're gonna understand the real reason that Gemini notebook going agentic matters more than ever.
Jordan Wilson [00:01:43]:
Alright. Let's get into it. Welcome to Everyday AI. My name is Jordan Wilson, and this thing's for you. It's your daily unedited, unscripted, livestream podcast, and free daily newsletter, helping business leaders like you and me keep up with the nonstop avalanche of AI updates. I tell you what matters, what doesn't, how to use it, and you take that information to grow your company and career. So it starts here, but please make sure if you haven't already, please subscribe to the podcast on Spotify or Apple, and make sure you go to our website at youreverydayai.com and sign up for the free daily newsletter. We're gonna be recapping the highlights from today's show in case you miss anything as well as all of the other AI news and developments that you need to know.
Jordan Wilson [00:02:21]:
Alright. So let's talk at Gemini notebooks. Yeah. Notebook LM is no more. So sad. Right? But it's not gone. It just got a face lift, both on the exterior and a lot going on under the hood as well. And this is one of those things.
Jordan Wilson [00:02:36]:
I think over the last couple of years, a lot of people were kind of nervous because Google has done this before where they've come out with great products. Right? And this isn't just in the AI, age, in the pre AI, phase, maybe even more. They come out with very popular products that a lot of people love, almost built like a cult like following, and then they end up killing those products. So a lot of people were very nervous over the last couple of years as notebook l m almost seemed like this side quest that was legit amazing. Right? No joke. It's I think we named it like our AI tool of the year in 2024 and 2025. So a lot of people were worried like, hey. Don't take this away.
Jordan Wilson [00:03:12]:
It's very, it's very unique because there's no you know, the other competitors, you know, no one from Anthropic, OpenAI, Microsoft, you know, x, Grok, Neta. Like, no one else has something like that really just grounds its outputs in your data only. So luckily, they didn't get rid of it. And it seems like if anything, Gemini notebook is gonna be along for the long run. So, let's quickly go over. I'm not gonna make you wait. Here's the seven new updates to Gemini notebook. I'm gonna be going over them pretty quickly now.
Jordan Wilson [00:03:46]:
And we're gonna be doing some live demos, so you can see these seven updates in action. And then when we're done with the live demos, I'll come back and talk about these things a little bit more. So number one, new update, already talked about it. Gemini notebook is the new name. That's number one. Number two, there is something called the collections. That's a way for you to organize notebooks for easier sorting. Number three, and this one's actually really cool.
Jordan Wilson [00:04:07]:
It's now there's automatic Google Drive syncing. Alright. Number four, bringing the Gemini in search access. So your, personal notebooks can now be accessed inside of Gemini and soon inside of, Google searches AI mode, which will be really handy. Number five, new output formats. So, yeah, no longer, constrained to only what notebook LM offered. Now Gemini notebook, you can create PDFs, PNGs, documents, spreadsheets, PowerPoints, markdown files, charts, and images. Number six, technically, each notebook has its own secure cloud computer, which is really cool.
Jordan Wilson [00:04:44]:
Number seven, just more agentic intelligence, and that's probably the biggest, upgrade that we're gonna see in Gemini notebook now powered by Gemini 3.5, reasoning and Google's anti gravity agentic search. Alright. So let's do it. Let's take a look live, shall we? What could go wrong? I'm gonna share my screen live stream audience. Do me a favor. Let me know if you can see. Alright. So what I did here is I took all of my notes from yesterday's show.
Jordan Wilson [00:05:15]:
Had a lot of them. So, yesterday's show was kind of on RSI, recursive self improvement. I have all of my notes together. This is something I routinely do inside of notebook l m. I still use notebook l m almost every single day. Sometimes I'll technically spend, like, hours inside of notebook l m. It just depends on what I'm preparing for. But, to make this a little faster, I essentially just created the same version, of this notebook inside of Gemini notebook.
Jordan Wilson [00:05:43]:
I'm gonna probably call it the wrong thing, like, five times. And then I have some prompts ready to go. So for our podcast audience, FYI, our AI at work on Wednesday show, it's always a hands on demo. Sometimes it's a little more visual, sometimes it's not, but you can always watch the video version of this on our website at youreverydayai.com. And in the podcast show notes, we always leave a link to today's episode. Alright. So here we go. I'm gonna go ahead and enter these prompts.
Jordan Wilson [00:06:13]:
We'll probably check on them a little bit as they go, talk a little bit more about these seven new features, and then we'll come back at the end and take a look. Alright. So I'm just gonna get these started so everyone can see, yes, these are going in real time. Alright. So there's my first one that I sent. There's my second one. There's my third one, and there is my fourth one. Alright.
Jordan Wilson [00:06:39]:
So a little bit about the content in here. It is long form content. I think it was, like, probably 60 pages of notes, that I had for the show on recursive self improvement. And if you wanna go listen to that, like I said, that's yesterday's episode, episode eight thirty three. Alright. Where we talked about RSI explained when AI starts improving itself and what it means. So essentially, I have these four different duplicate notebooks that I just started going, and I'll kinda read the different prompts. Some of them I'll read in full because it's actually gonna show us, you know, some of these new features that we're really pushing, Gemini notebook on.
Jordan Wilson [00:07:17]:
Well, just number one, see if they work. The first one, I think, will be pretty, relevant to a lot of us. And I want you to also think as I'm going through these demos, think of your use cases. Right? And probably before I do this, I'm gonna give the quick, like, sixty second overview of notebook l m. Sorry if this is, you know, a rerun for you. Right? But I think it's important to just set that. So the biggest thing is notebook l or sorry, Gemini, Gemini notebook. It just doesn't sound right.
Jordan Wilson [00:07:45]:
Alright. There's essentially three different panes. There's your source pane on the left hand side. There's your chat pane in the middle, and then on the right hand side is your studio pane where you can create different kind of, artifacts and outputs. So for your sources, in my example, I just copied and paste, but there's different ways you can add sources. You can upload files, you know, PDFs, images, docs, audios. You can, put in websites and YouTube videos. You can, auto sync now your Google Drive files, which is really helpful.
Jordan Wilson [00:08:14]:
And then like I said, copy, copy and paste text, or there's kind of like a quick, fast research and deep research at the top. So number one, you're gonna get your sources. And this is where the big difference in notebook l m comes because when you're chatting in the middle, everything that you ask is going to be grounded in that information. And that's the biggest difference, and I think what makes Gemini notebook really special and much different because, you know, for all other large language models, right, they're gonna use a combination, of number one, the files you upload just like Gemini notebook, but number two, training data. Right? Which could be a good or bad thing. Right? Sometimes training data isn't always the best. Sometimes it is. And then last but not least, you know, large language models now by default all connect to the Internet.
Jordan Wilson [00:08:56]:
So the big difference, with Gemini notebook is it's gonna ground those chat responses in the middle in just your sources, which cuts down on your hallucinations by an order of magnitude through the roof. Alright. So, that's it. And then you can chat in the middle, with your sources. They're gonna output there. But then on the right hand side, you can create a bunch of different multimedia, assets in the studio, audio overview, slide decks, video overview, mind maps, reports, flashcards, quizzes, infographics, and data tables. And we've actually done dedicated shows on some of these before, on the videos, I think the infographics. Right? But, essentially, you have some of the best of Google's AI products baked into the studio right here.
Jordan Wilson [00:09:38]:
You know, as an example, the visuals are created by Nano Banana. You know, the audio overview is created by Google's audio, model. Some of the video, you know, you can also customize these things. So the there's, like, a cinematic, video as an example, and that uses elements of Google's VO model, video model. So, essentially, you know, without even really knowing it, you can just click one button and use, a a lot of different, of Google's, you know, different models. Alright. So let me just quickly read some of these prompts here just so we can understand exactly what's going on working with this, this long chunky data on, recursive self improvement. And one thing I will let you know, it's already been three minutes.
Jordan Wilson [00:10:17]:
Right? And my first prompt is still working. Right? And it's not even nearly done. And that's important because, you know, one of the big steps here is this is now powered by Gemini 3.5, and the anti gravity harness. Right? So what that means is before, you know, notebook LM, and I can say that because before it was notebook LM, it wasn't truly agentic in nature, at least not in the way that it is now. Right? Some of those new updates that I talked about was it has a secure cloud computer. So what that means is that each notebook can write and execute code for research calculations and data analysis. And in this first prompt, that's exactly what I'm having to do. Aside from having it create custom, outputs, it's also having to under the hood, do a lot of that.
Jordan Wilson [00:11:01]:
So here's the first prompt what I said. I said using this notebook and its sources, create a personalized AI budget decision package for a nontechnical CFO and CIO at a 5,000 person enterprise whose agent usage is growing rapidly. Do not ask me questions, make reasonable assumptions, state them clearly, and complete the entire task in one run. I'm saying first, analyze three strategies. Number one, use frontier models for every workflow. Number two, use the cheapest available model for every workflow. Number three, use a tiered architecture in which a frontier model plans, a cheaper model executes, and an independent systems evaluates. Then I'm saying compare them using total cost, successful task rate, retries, human retry, human review time, latency, vendor risk, and adaptability to future price reductions.
Jordan Wilson [00:11:48]:
Use the Luna and Terra pricing evidence from the notebook, run the necessary calculations, and select one strategy. Explain why the alternatives lose, identify the strongest counterargument, and state what future evidence oh, just, put me to the bottom, and state what future where where did I lose? There we go. What future evidence would change the recommendation? Then generate these five outputs concurrently. Number one, a customized audio overview presented as a CFO, CIO discussion that reaches a clear decision. Number two, a mind map connecting price changes, agent usage, model routing, evaluation, human review, and total cost. Number three, an editable Excel workbook with assumptions, formulas, three scenarios, sensitivity analysis, and a recommendation dashboard. Number four, a nano banana powered 16 by nine infographic titled cheaper AI, bigger AI budget question mark that explains the selected, strategy visually. And number five, a two page PDF executive decision brief, containing the recommendations, supporting evidence, rejected alternatives, risks, and next three actions.
Jordan Wilson [00:13:02]:
Alright. So you can see, yeah, kind of a beefy prompt there, but you can probably already start to imagine how something like this new Gemini notebook would be valuable, for your use case. Right? One of the important things to keep in mind about, Gemini notebook is the output studio now is you can just use it with prompts. Right? So now you can see how a single prompt can do a lot of heavy lifting versus having to go into, right, all of these studio assets individually and trying to build them, which is great. Because what you can actually do, right, starting to, you know, bridge in other terminology and, strategies from other, you you know, LLMs, as well. You can create skills. Right? I actually have a codex skill that does something like this for me. Right? It will go and, you know, according to my, you know, needs and personalization and customization, it will just literally control my browser.
Jordan Wilson [00:14:00]:
It will go in and do all of these kind of things for me. And you can see how it's gonna have a much higher success rate and just be way more token, efficient for codex, you know, using browser use or computer use to go in and do this when I can just throw that big prompt in the chat knowing now that Gemini notebook, runs agentically. It has its own cloud computer. Whereas before, to get this kind of personalized output multiple, you know, artifacts, it would have taken a lot of work, right, if I was to just hand this over to an agent. So now that you, really just have a much stronger harness. Right? It this is Gemini 3.5 using the anti gravity, engine underneath. Right? Now the capabilities are just much, much higher. Alright.
Jordan Wilson [00:14:48]:
So, technically, it's still working. It's still creating one of the audio overviews, but the actual chat completion now is done. So I'm not gonna go through this, too much in length, but I do wanna describe, especially for our podcast audience, kind of what went on here. So, I should have timed this, but it looks like it took about, five or six minutes. And, you you know, one thing I actually like about Gemini notebook that's better than other aspects of Gemini, it actually has a decent chain of thought, which is nice. Right? Because that's one thing that I think, Google and Gemini is really lacking in, compared to OpenAI and Anthropic is being able to see what's actually going on under the hood. So little cheat code for you there. Gemini notebook is actually probably better than the default Gemini in terms of knowing and learning what's going on under the hood.
Jordan Wilson [00:15:38]:
So, you know, I'm not gonna read all of the chain of thought because it's a lot, but you can go try this yourself. You know, click thoughts and see what's going on there. But it's developing budgeting solutions. It's evaluating the AI strategies, analyzing the model costs. You you know, it's retrieving some instructions, analyzing strategies, analyzing pricing. Right? So I can go through and see exactly what it's doing. And I'm looking at some of these things. I can see that it's creating, you know, creating different files.
Jordan Wilson [00:16:03]:
So it's running some code. So, you know, it's executing code in there in its dedicated sandbox. So this was not possible a week ago. So the output here, I'm not gonna read it because it's fairly long, but I'll read the beginning. So it says executive AI budget decision package. This package is designed for the CFO and CIO of our 5,000 person enterprise to address rapidly growing AI agent usage. Below, we analyzed three potential model procurement strategies and delivered five unified synchronized outputs to drive your strategic budgeting decision. Alright.
Jordan Wilson [00:16:36]:
So we'll see here. I'm not gonna look at this, but it is exhaustive. Right? And the good thing is, all of this is sourced as well. So, you know, it's saying, you know, workflow tokens, you know, for planning, you know, using 20,000 input, two, 2,000 output. I can hover over that and see exactly where it's pulling that from in my notes. So, you you know, when I'm looking over the different strategies here, it did a pretty good job putting together three different strategies. Once, strategy one was using frontier models only, strategy two was using cheap models only, and then strategy three was a tiered architecture, using a combination of both. And then it went through and, you know, went through and did these kind of combined monthly system costs.
Jordan Wilson [00:17:16]:
Right? And, you know, what's kind of cool in this scenario is the tiered architecture, right, which is kind of using the stronger model as an orchestrator, at least according to its calculations. That was actually gonna be better, right, than just doing the cheap only. The cheap only was actually gonna be more expensive, presumably because it was gonna go through a lot more tokens and more monthly human review rework costs. Right? So such a cool, tool to put together. Alright. But then let's look over at our, right hand side in our panel and see what was actually created. So we did ask specifically for five different, deliverables. We asked for a customized five minute audio overview, which is almost done.
Jordan Wilson [00:18:01]:
Number two, we ask for a mind map connecting pricing changes. We ask for an editable Excel workbook. We ask for a nano banana powered 16 by nine infographic and a two page PDF. So so many of these new so many of these things, again, were not available last week. You know, you really had to stay in this kind of confined output, of what, notebook l m offered, but now with Gemini notebook. Right? It's really flexible. And that is actually really helpful both from what you can do in the middle pane because you don't have to be, as rigid. Right? But in my use case, right, if you really wanna have agents working for you, I think this makes it so much easier for a, you know, clawed desktop or a codec desktop, you know, an, an agent, living, you know, on your desktop that can control your browser.
Jordan Wilson [00:18:49]:
This is gonna allow it to iterate and create much more valuable outputs for you. Right? I always wanna encourage listeners of our show even if you're a beginner. Right? We have to get out of the prompting phase. Right? We have to be able to give these desktop agents as much context and as much information about what we need. Then let them go do the work, and I'll usually have another, you know, AI agent audit their work and make sure it, you you know, cracks any mistakes that it sees. But in this scenario, right, if I were to do this manually, even inside notebook, it would have taken me three to five times longer. But that's just because of the new harness. So let's just quickly take a look at all of our other sources.
Jordan Wilson [00:19:26]:
So here we have our AI budget model. Alright? So downloaded, an Excel file. I'm just gonna screenshot this here. Let's do that versus having to unshare and reshare my screen. Let's see. It's uploading. It's uploading. Oh, gosh.
Jordan Wilson [00:19:46]:
Let's see. Alright. That one, let me, let me try that again. The actual Excel sheet looks looks great. We have our budget model. Let's let's see if we can get that again here. Let's copy this, shall we? Alright. Here we go.
Jordan Wilson [00:20:05]:
There we go. We got it here. So we got a working, financial model. Right? I just quickly checked the spreadsheet, gave me an XLS, open it in Excel. Everything's working. Right. It gave me exactly what I wanted. It gave me my different, three strategies.
Jordan Wilson [00:20:23]:
It mapped out the cost. I can go in and change the formulas. There's a little graphics, a little dashboard. It created exactly what I wanted. It works. Is it the most beautiful spreadsheet ever? No. Is it something better than I could have done? Absolutely. Right? I love, working in spreadsheets, but I'm not necessarily the best at creating formulas.
Jordan Wilson [00:20:41]:
Right? So, again, just think of all of the unlocks that something like this brings. Alright. So that was our AI budget model. Did a good job. Then we asked for a two page, PDF that gives an executive decision brief. Alright? So now, here we go. Live stream audience can see, but we have a two page executive brief. This is the AI portfolio optimization, the implement, implementing a tiered model architecture for the 5,000 person enterprise company.
Jordan Wilson [00:21:06]:
There we go. This is, you know, just kind of a PDF version of what we, created inside the middle pane chat, as well as why you you know, what the, the best strategy was and the counter arguments for those. So perfect. It created that. Let's look at the other ones. We have our mind map. Okay. So here we have our, RSI here.
Jordan Wilson [00:21:28]:
Then we have the economic shifts in pricing, the tiered agentic arch architecture, governance and control, and monthly system cost scenarios. And then I can click those out, and explore a little bit more, in each of those. There we go. Alright. So, mind map. Great job. Alright. And then let's look at our, this should be our infographic created by Nano Banana.
Jordan Wilson [00:21:51]:
Alright. So looks good. You know, I'm I'm kind of, you know, quickly editing it, and it looks good. The only thing that I see wrong is there's a notebook l m watermark. Alright, Google. We gotta update that. Right? If we're trying to get rid of notebook l m, we gotta update the the watermark to Gemini notebook. But the it this actually looks pretty good.
Jordan Wilson [00:22:13]:
The, 16 by nine nano by nano, graphic for the CFO, CIO decision maker, great. Looking at the, you know, three different strategies, it breaks down the human cost, the model cost, the the pros and the cons, great charts and graphics, did a really good job. And then last but not least, it did just finish. I'm just gonna listen. Alright. Perfect. It created a twenty one minute deep dive on self improving AI slash's token prices. Alright.
Jordan Wilson [00:22:44]:
So, okay. Interesting. So it even did put in the custom prompt. So that's cool. It went in, did everything correctly. Right? So, hey, the demo actually worked. Alright. I'm just gonna quickly look at some of the other use cases just so we can see what we did.
Jordan Wilson [00:23:02]:
In the other notebook, I said create three quizzes of ascending difficulty focusing on a different topic from my sources. This is a good one. This was actually, a Gemini notebook, example prompt, which I thought was a really good idea. If you're trying to learn something, you know, they have this quiz module, which is really good. Right? But, you know, sometimes you might really wanna be intentional about how you learn things and break it off and start easier so you can go through the paces and relearn things as you go. So this one, it created an easy quiz. Alright. Let's should I do one question? Watch watch me get it wrong.
Jordan Wilson [00:23:35]:
Alright. What is the core definition of recursive self improvement in the context of AI? A, is it a method where humans manually rewrite every line of code to make AI faster? B, a process where an AI uses its own capabilities to design and build a more advanced version of itself? C, an AI system that is only capable of performing one specific task like playing chess? Or d, a way for AI to search the Internet and summarize existing news articles better. So I know it's b. Alright. So that's right. So it has three different quizzes. Did a great job doing that of ascending difficulty. Alright.
Jordan Wilson [00:24:06]:
Let's look at our next one. So for this one, I said create a five level RSI maturity ladder, and I wanted an infographic and a PDF. So it did answer everything. This is really good. It answered everything in the chat. My PDF here, it broke down the five, kind of ladders of RSI. Did a great job. And then let's look at the infographic.
Jordan Wilson [00:24:27]:
Cool. Yeah. This infographic actually came out a little cleaner than I thought considering that, we didn't, customize anything. So, yeah, we have the, level one, AI assist researchers. Level two, AI executes a human design method. Level three, AI improves AI infrastructure. Level four, AI helps train or improve another AI. And then level five, AI controls the complete improvement loop.
Jordan Wilson [00:24:49]:
Alright. Let's go. Let's look at our next one. I said using the old Luna and Terra pricing in this notebook, create an edible Excel calculator for agentic workflows. I have my, Excel sheet there. I'm opening it up in another tab. Yeah. Looks good.
Jordan Wilson [00:25:05]:
I'm checking. There's formulas. Everything's working live. Perfect. Alright. And then, one more thing, before we wrap up here that I wanted to showcase. Alright. The middle is agentic now.
Jordan Wilson [00:25:21]:
Alright. So even though it's still going to be grounded by default, I'm gonna go into one of my things here, and I'm gonna type who won the twenty sixteen world series. Alright. Now I'm gonna ask that question. So by default, it should come back to me and say, hey. I don't know. However, I can go out and find that information. Alright.
Jordan Wilson [00:25:42]:
So why am I bringing this up? Because that is the one important grounded change that you need to be aware of. Alright. So what's interesting here, and I didn't know this. It looks like because it had already started pulling things agentically on the web, because in some of my other testing, when I did this for the first time, you know, so I just opened up another, chat to show you this. I said, who won the twenty sixteen World Series? In this example, it says, your sources do not contain information about this. Do you want me to research this on the web? I said yes. So it looks like maybe if web research had already been active, had already been activated, then it might just answer that by default. So that's an important thing to think about because previously, notebook l m in the middle chat was always a 100% grounded in your information.
Jordan Wilson [00:26:30]:
So that's a big difference and a big change. So in some instances, it's a big upside, but you have to be aware of that, that, you know, you have to be able to go back and look at the chain of thought. In a lot of these prompts, it went, and it was doing a lot of web research in addition to the information that I gave it. Because in the prompts, I was kind of pushing it past the limitation of the sources. So this does kind of, in my opinion, change, Gemini notebook, and maybe that's good that we are getting away from the notebook l m in inserting Gemini notebook because there's pros and cons to that. Right? Like you saw in this example here, because it looks like because I had already triggered the anti gravity harness, in that first prompt and then asked it information that was not in my sources, It just, well, it just responded. Alright? But, it did say it says I I haven't imported those sources about the twenty sixteen world series. Oh, wait.
Jordan Wilson [00:27:27]:
I looked at it. Okay. I didn't read. It didn't. Alright. It just said that I hadn't looked at those. Do you want me to look them up? Yeah. I I got ahead of myself.
Jordan Wilson [00:27:38]:
So, yeah, I can say yes. Alright. So alright. Scratch what I just said the last sixty seconds that, I thought because I had already activated the anti gravity, harness, and it was already doing, tool calls that it was gonna go out and respond. I literally just didn't read it. I just saw the response, and I'm like, oh my gosh. It responded. But no.
Jordan Wilson [00:27:55]:
Me, not sleeping enough. It literally said, I haven't imported those sources about the twenty sixteen World Series into your notebook yet. Would you like me to import them first? And then I said, yes. And now right now, I can click and see that it's going out to grab those sources. Alright. I stand corrected. So it's still important to know though that, you know, once you do give Gemini notebook access to, it is going to bring in information that are outside of the sources. So it does change, I think, again, for the good and the bad, but at least, it does caution you, or make you kind of manually approve that saying, hey.
Jordan Wilson [00:28:26]:
You don't have this information in your sources. Do you want me to go grab that information for you? Alright. So that is a wrap. But to quickly recap, what do you need to know? Alright. So, number one, what's the big difference here? Gemini notebook represents a shift away from just having that, you know, notebook LM, all those sources. And, you know, essentially, it was an a non reasoning model that could use Google's different multi, multimedia AI. And now it's different. Right? And I think that's the the big reason for the name change is because now with the Gemini notebook, it is agentic by default.
Jordan Wilson [00:29:04]:
Right? Having that cloud computer, being able to write and execute code, being able to when you give it the okay, search the web, it really changes what notebook LM was into what Gemini notebook will be in the future. You know, the important grounded change that I talked about right there, you just have to know. And I think, ultimately, that will be a good thing as long as you understand what's going on. And then the real reason, I think, notebook, Gemini notebook going agentic matters more than ever, well, you saw just through my example. It may makes it much easier to work with. Right? Before, you know, you were kind of restrained. Right? You almost had to think in your mind, in terms of outputs. Right? You had to prompt notebook l m according to the outputs.
Jordan Wilson [00:29:47]:
Now those outputs are so flexible. Right? Before, you couldn't create PDFs, before you couldn't create CSVs. Right? You couldn't create markdown files. So you were much more limited in, you know, PowerPoints. Right? You can do all these things now. So, you know, Gemini notebook has become, much more of an agentic, you know, coworker versus what it was before. You know, there is essentially a handful of of artifacts or a handful of formats, and you really had to use notebook l m according to those outputs. Now it is extremely flexible and even more powerful than ever, and I guess that's why notebook l m is no more, but Gemini notebook at least is here to say, and it is really good.
Jordan Wilson [00:30:24]:
Alright. That's a wrap for today's show. Now you know Gemini notebook, the seven new updates, and what they unlock. If this was helpful, do me a favor. Tell someone about it. Share this on LinkedIn. You know, go and subscribe with the podcast on Apple or Spotify, then make sure to go to your everydayai.com. Sign up for the free daily newsletter.
Jordan Wilson [00:30:42]:
We're gonna be recapping the highlights from today's show as well as a lot more. Thanks for tuning in. Hope to see you back tomorrow and everyday for more everyday AI. Thanks, y'all.