NotebookLM is now Gemini Notebook: What's New?

The rise of large language models (LLMs) has created a paradigm shift in how people interact with AI technology, offering unprecedented potential to boost productivity and reduce tedious tasks for knowledge workers. As these powerful tools become more widespread, specialized applications are emerging to meet specific needs across different fields. One such tool is NotebookLM, developed by Google Labs, which stands out as a promising AI assistant designed specifically to enhance learning and research by streamlining how people interact with documents and information. What is NotebookLM? A research assistant powered by Gemini NotebookLM is a tool that helps users take notes, conduct research, and work with documents. Integrated with Google's latest Gemini model, it allows users to perform a wide range of tasks including summarizing long texts, answering questions based on uploaded content, and suggesting related information to expand on a topic. One key differentiator is that NotebookLM operates on RAG (Retrieval-Augmented Generation) principles, meaning it only analyzes data sources provided by the user. This significantly reduces the risk of "hallucination," the tendency of LLMs to generate inaccurate or fabricated information, by ensuring that all responses are grounded in verifiable sources — a critical factor for academic and research accuracy. NotebookLM offers a set of capabilities that directly address common challenges in learning and research workflows. Diverse input support Like general-purpose LLMs, NotebookLM accepts text-based input, but what sets it apart is the range of document formats it can handle. Users can upload files directly from their computer such as PDFs, Word documents, and plain text files, select documents from Google Docs or Google Slides, or provide links to websites and even YouTube videos. It can also automatically discover relevant sources through its Discover feature based on a user's query and add them to the workspace for analysis. This broad intake capability makes it a flexible hub for synthesizing research materials, distinct from the Deep Research features growing in other LLMs like Gemini and ChatGPT. With NotebookLM, you choose exactly what sources go in, whereas Deep Research handles that selection automatically without user control. Intelligent information processing Summarization: Researchers and anyone who needs fast, accurate results often need to condense long content. NotebookLM excels at this. When a user finds a useful summary, two clicks — Add to Note and then Convert to Source — turn it into a new input for further analysis, making source control impressively convenient. One limitation worth noting: if you don't save a summary to a note, it won't be preserved when the page reloads, so useful outputs can be lost if you navigate away. Source-grounded question answering: Users can ask questions directly related to uploaded documents and NotebookLM provides answers with clearly numbered citations pointing to specific sources. This direct linking builds trust in the generated information and makes verification straightforward, with the added reliability that comes from RAG-based responses. Idea generation and expansion: Beyond direct answers, NotebookLM can suggest related information or help expand on a given topic, functioning more like a general-purpose AI assistant in these moments. Mind map generation: A distinctive feature is the ability to create mind maps from uploaded content. This visual representation of information helps users grasp an overview of a topic, identify key concepts, and retain complex details, making research more intuitive and memorable. Flexible output formats Highly flexible output is a core strength of NotebookLM, and what makes it even more useful is that all outputs including podcasts and videos fully support Vietnamese. Audio overview: For anyone who commutes or prefers listening over reading, NotebookLM can generate spoken audio from your own research documents or trusted sources. Listeners can customize the conversation style: in-depth exploration, concise presentation, critical review, open debate, and can even adjust the length of the audio. Video overview: For users who prefer video for deeper understanding, NotebookLM can generate video content as well. Users can customize the focus through the Customize option when the video drifts from their research intent or when they want AI to zoom in on a specific aspect of the topic. Diverse report types: After consuming audio and video overviews, learning and research naturally calls for structured reporting. NotebookLM's Reports section offers several options: Briefing Doc: A quick, condensed summary of key points from all your source documents, designed for busy readers who need the core content fast. Study Guide: A report built for review, which can include definitions, key concepts, Q&A pairs, and important points to remember when preparing for an exam or assessment. FAQ: A list of frequently asked questions and answers drawn from your documents, useful when you need quick answers to common questions about a topic. Timeline: Arranges key events or milestones mentioned in your documents in chronological order, particularly useful for historical research or projects that require tracking progression over time. Infographic (beta): Automatically designs a visual graphic including diagrams, charts, and illustrations to summarize complex data points and concepts, though this feature is still in beta. Slide Deck (beta): Generates a professional presentation deck with structure, headings, and bullet points drawn from your NotebookLM content, compatible with PowerPoint and Google Slides formats. Also currently in beta. Collaborative knowledge sharing NotebookLM supports sharing, allowing users to share their notebooks with others. This can transform a personal research space into a shared knowledge base for a team, or even an internal chatbot for a company where employees can quickly query company policies or organizational knowledge. Users who want others to interact with a shared notebook rather than just view it will need a NotebookLM Pro subscription, as the free plan only allows read-only access. Google also maintains commitments to security and privacy throughout the platform. NotebookLM in the broader context NotebookLM's capabilities align closely with the growing needs of knowledge workers for LLM-based tools. Surveys indicate that workers are increasingly using LLMs for information-oriented tasks such as searching, learning, and summarizing, and they want future capabilities to analyze their own proprietary data. NotebookLM directly addresses these needs by letting users upload their own data and interact with it, and with its sharing capabilities, integrating NotebookLM into larger collaborative workflows becomes straightforward when the goal is building a shared knowledge base. NotebookLM's arrival signals that the space won't stay exclusive to Google. LLMs supported by Ollama or Hugging Face running locally in environments like Jupyter Notebook will offer similar capabilities. However, those alternatives are aimed squarely at developers with coding knowledge and Python proficiency, and they come with the added benefit of allowing fine-tuning to produce results tailored more precisely to specific research goals and needs.

Many people still do research manually: opening a dozen tabs, watching videos, reading articles, taking notes in scattered places, and then spending even more time trying to synthesize the result. A long-form post by monokern on X suggests a different pattern: use Claude Code to orchestrate the workflow, NotebookLM to analyze sources, and Obsidian to store long-term memory. Done correctly, this is not just a search session. It becomes an AI workflow that compounds over time. The core idea is practical: Claude Code does not need to do everything inside an expensive context window. It can call tools, run skills, create files, and offload heavy source processing to NotebookLM. The output is then saved back into Obsidian as markdown, giving the next research session better context. According to the original post, the initial setup can be completed in under 30 minutes if the required tools are already available. Why does this stack work? The strength of the workflow is that each tool owns a clear layer. Claude Code acts as the execution engine: it receives plain-language instructions, calls skills, runs commands, manages files, and coordinates the pipeline. Instead of forcing the user to operate each step manually, Claude Code becomes the system operator. NotebookLM is the analysis layer. Google's research tool can read sources, summarize them, generate analysis, flashcards, mindmaps, infographics, or audio overviews. When Claude Code sends source processing to NotebookLM, the user benefits from Google's processing layer rather than spending Claude tokens on every piece of long-form digestion. Obsidian is the memory layer. Every analysis result is saved as markdown in a personal vault. Over time, that vault becomes a structured knowledge base of topics, sources, observations, patterns, and conclusions. Claude Code can read those files later to understand what the user cares about, what formats they prefer, and how they tend to evaluate a topic. Skill Creator turns the workflow into a reusable tool The first major step in the guide is installing Skill Creator inside Claude Code. This layer lets users describe a new capability in natural language, after which Claude Code creates the skill structure, installs it, and makes it available as a reusable command. In other words, instead of rebuilding the research prompt every time, the user packages the workflow as a dedicated skill. The first example is a YouTube search skill. It uses yt-dlp to search videos by query and return metadata such as title, channel, views, duration, upload date, URL, and a views-to-subscribers ratio. For content or market research, this is more useful than a plain list of links because it shows which sources are actually attracting attention. NotebookLM handles the heavy analysis The post proposes connecting Claude Code to NotebookLM through notebooklm-py because NotebookLM does not currently provide an official public API. After installation and Google account authentication, Claude Code can use a custom skill to create a new notebook, add sources such as YouTube URLs, text, or files, and then ask NotebookLM to generate analysis or deliverables. The key point is that NotebookLM is not only a summarizer. In a real research pipeline, it can receive 10 videos on a topic, analyze which frameworks are gaining traction, which ones are overhyped, where the community disagrees, and what content gaps remain uncovered. That processing takes time, but most of the work happens on the NotebookLM side. The full pipeline: one command for a complete research task Once the YouTube search skill and NotebookLM skill exist, the next step is to create a pipeline skill that combines both. The user gives a topic, such as researching AI agent frameworks in 2026, and the pipeline searches for relevant sources, creates a notebook, adds those sources, runs the analysis, and returns the result as markdown. In monokern's example, the pipeline finds 10 video sources, sends them into NotebookLM, generates analysis, creates an infographic, and saves the result into Obsidian. The total processing time is described as around 6 minutes, most of which is NotebookLM processing. The practical value is that the user does not need to open every tab, copy every link, or manually combine the metadata. The final output is more than a chat answer. It includes full analysis, source lists, engagement metrics, trend observations, a visual deliverable, and a markdown file saved into the vault. That is what separates this workflow from a normal chatbot interaction. Obsidian makes the system smarter over time Obsidian is the most interesting part. If the workflow runs only once, it already saves time. But if it runs regularly, every new markdown file makes the personal knowledge base richer. After a month, Claude Code can see recurring topics, the types of insights the user values, and the preferred format for results. The post also highlights the role of the claude.md file inside the vault. This can become a configuration file describing working conventions, analysis style, and output preferences. After several research sessions, the user can ask Claude Code to read recent work and update that file so it better reflects the user's current process. The real value is the structure, not YouTube YouTube is only the data source in the example. The pipeline structure is the valuable part. Users can replace YouTube with academic PDFs, industry reports, public documentation, web pages, local files, transcripts, or Google Drive documents. As long as Claude Code can access the source and pass it into the analysis layer, the operational template stays the same. This opens many practical uses: researching a crypto ecosystem through whitepapers and public documentation, analyzing an emerging technology through conference talks, mapping content gaps in a niche, or tracking market dynamics from public reports. In every case, the same three layers remain: collect sources, analyze them, and store knowledge. What should you watch out for? This workflow is powerful, but it is not for everyone. It assumes the user is comfortable with Claude Code, has an Obsidian vault, can install CLI tools such as yt-dlp, and is willing to use an unofficial library to connect to NotebookLM. Also, because NotebookLM and YouTube can change access patterns, these skills should be treated as maintained tools rather than install-and-forget automation. Still, the underlying idea is important: instead of using AI as a disconnected chat box, turn it into a research system with memory, a pipeline, and the ability to learn from your own work history. For people who regularly analyze markets, technology, or content, this is far more practical than opening 10 tabs and manually stitching everything together.

You have been using NotebookLM to store documents, research, and notes — but every time you needed AI to process something further, you had to open Gemini, copy-paste manually, and hope the AI didn't fabricate inaccurate figures. Now, after discovering this integration, that extra step can be eliminated entirely: NotebookLM can connect directly into Gemini, turning all your documents into an immediate knowledge base for AI to work from. NotebookLM and Gemini used to be two separate islands NotebookLM is very good at one thing: staying anchored to the documents you provide and answering accurately based on them. You can upload a 200-page financial report and ask about any figure, and NotebookLM will cite the exact page and passage. However it is isolated within individual notebooks and cannot search for new information outside those documents. Gemini is the opposite: flexible thinking, real-time web access, and genuine creativity — but highly prone to hallucination when working with specialized data without a clear source. The result is that anyone who knows both tools has to use them in parallel, transferring data back and forth manually, which wastes time and introduces errors. This integration solves exactly that problem by bringing NotebookLM directly into the Gemini interface, letting the two tools complement each other rather than operating independently. A few things to know before connecting Gemini and NotebookLM Because they share the Google ecosystem, the Gemini and NotebookLM integration works smoothly — but there are a few things worth knowing to avoid setting the wrong expectations. Gemini prioritizes data from your notebook first, but when the notebook doesn't contain enough information, it will automatically search the web without you needing to issue an additional command. This is convenient, but it also means you should check the citations to know whether an answer came from your documents or from a web search. Cross-notebook analysis across multiple notebooks simultaneously is a major capability that standalone NotebookLM couldn't offer. The more notebooks you connect, the more Gemini can surface different perspectives and contradictions while still staying grounded in the full context. Every answer drawn from notebook data also includes specific source citations, which is an important difference from standard Gemini and lets you verify information quickly when needed. How to connect NotebookLM to Gemini in 4 steps The feature is now available for both free accounts and Google AI Pro with no additional setup required. Follow this sequence. First, open Gemini on the web or mobile app and go to the chat input as normal. Next, click the "+" icon in the corner of the chat window and select NotebookLM from the list of sources. Then choose one or more notebooks you have already created to serve as context for the conversation. Finally, type your prompt as usual, keeping in mind that Gemini will prioritize data from the notebook first and only search the web when the notebook doesn't contain enough information. The entire setup takes under 60 seconds, and you can switch between different notebooks within the same conversation. What can Notebook and Gemini together do that neither could before? The biggest change isn't speed — it's the reliability of the output. When Gemini has specific source data from a notebook, every answer comes with clear citations so you know exactly which page and document the information came from, rather than having to verify it yourself. In practical terms, there are four scenarios where this combination makes the most noticeable difference. Research and document synthesis Instead of reading through a 500-page textbook, you upload it to NotebookLM and ask Gemini to condense it into a study book, an infographic, or a presentation deck through Canvas mode. Here is what that looked like with a standard prompt turning selected notebooks into a book. You can see the result at this Gemini link. Writing content without worrying about hallucination This is the most useful use case for content creators. NotebookLM handles the "accurate" side by keeping figures, names, and events anchored to the source documents. Gemini handles the "compelling" side by writing prose, crafting hooks, and finding interesting angles. The output still doesn't quite match Claude in quality, but it makes an excellent reference to hand off to Claude for a final rewrite, and the result from that combination is genuinely strong. Gems that update their own knowledge Gems are custom AI assistants inside Gemini. When you attach a notebook to a Gem, the notebook syncs automatically: whenever you add new documents to NotebookLM, the Gem updates immediately without needing to be reconfigured. For example, if you have a Gem dedicated to customer support, every time company policy changes you simply update the notebook and the Gem understands the new information right away. Audio overviews combined with web search NotebookLM already has a feature for converting documents into conversational podcast-style audio, which is genuinely useful. When combined with Gemini, you can ask AI to supplement that audio summary with the latest information from the web, making it practical to listen while commuting and still stay current with the newest developments. Where to start if you haven't used NotebookLM and Gemini together before If you haven't used NotebookLM yet, start by uploading a document you frequently need to reference — an internal company process, a course syllabus, or an industry report you follow. Create a notebook from that document, then open Gemini and connect the notebook. Try asking a few questions that previously would have required reading the entire document to answer. When the AI answers accurately and cites sources clearly, you will immediately understand why this combination is worth using regularly. Not because it is "revolutionary" or "groundbreaking," but because it solves one specific tedious problem that you have been handling manually every day.

The Adidas Trionda match ball, three dimensional player models accurate to the millimeter, robot dogs patrolling stadiums, and Google Gemini sitting on the touchline with the Argentina national team. World Cup 2026 is not only the largest tournament in history with 104 matches across 16 cities in the United States, Canada, and Mexico, but also the most extensive deployment of AI ever seen in sports. How the Adidas Trionda smart ball works The official match ball named Adidas Trionda is equipped with an Inertial Measurement Unit IMU sensor operating at 500Hz, which means it collects 500 data points every second on movement, spin, and the exact moment the ball makes contact with a player foot. This is particularly important for offside situations, as the sensor will determine the precise moment the ball leaves the passer foot down to the millisecond. The timestamp from the sensor is synchronized immediately with the player tracking system, helping to lock the position of every player on the pitch at that exact moment instead of relying on the naked eye which can be off by up to half a second. As a result, offside decisions are made faster and more accurately than ever before. This advanced technology immediately rescued the Swedish team by identifying the precise moment of contact from striker Alexander Isak. Before that, the joy of scorer Svanberg was temporarily dampened when the VAR team stepped in to review. In a play that occurred at a breakneck speed, he appeared to be standing behind the Tunisian defense when the ball was delivered into the penalty area, leading many to believe the goal would be disallowed. However, the data from the motion sensor mounted inside the Adidas Trionda ball proved that Svanberg moved back to a valid position in time, bringing a legitimate goal for Sweden to the delight of the fans. Semi automated offside technology with 3D player avatars Semi automated offside technology SAOT has been upgraded significantly for World Cup 2026, highlighted by the 3D avatar of each player. Every player participating in the tournament is digitally scanned across the entire body in about one second, creating a 3D model with detailed body dimensions for every part. When a situation requires VAR review, the system overlays these 3D models onto real time tracking data from more than 12 specialized cameras at each stadium. This approach completely resolves the long standing issue of two dimensional offside lines, where a player arm, shoulder, or foot might be obscured from a certain camera angle. The 3D model fills those gaps using realistic anatomical data, and the result is displayed as a complete 3D animation on the pitch and on television, entirely replacing the flat red and green lines that once confused spectators. Football AI Pro: analytics platform for all 48 teams FIFA collaborated with Lenovo to build Football AI Pro, an analytics platform developed on the FIFA Football Language foundation model, which has been trained on hundreds of millions of football data points over decades of competition. This is the first time in World Cup history that all 48 participating teams have access to the same analytics platform, rather than wealthier federations holding an advantage due to better data tools. This platform outputs results in multiple formats, including text summaries, video clips, interactive charts, and 3D tactical visualizations. Teams can use it before and after matches to analyze opponent tactics, detect set piece patterns, track player workload intensity, and analyze head to head history. However, FIFA bans its use during match time, and coaching staff can only access it during halftime and after the match. Referee chest cameras with AI image stabilization For the first time in history, referees in all 104 World Cup matches wear chest cameras. The raw images from the camera when the referee runs at high speeds are shaky and cannot be used for broadcasting, but FIFA runs an AI image stabilization model in real time on every frame, creating broadcast quality video. The result is the Referee View perspective that offers a subjective experience from the pitch, quickly becoming one of the most popular broadcasting innovations. This viewpoint not only serves entertainment but also provides analysts with a new data source, which is the exact vision that the referee had when making decisions. Google Gemini on the touchline and fan experience In March 2026, the Argentine Football Association announced Google as an official global sponsor, with the Gemini logo appearing on training jerseys for the men, women, and youth teams. However, this partnership goes far beyond brand advertising, because the Argentina technical staff uses Gemini directly for tactical analysis from match videos, tracking player workload and injury recovery, querying historical data on specific matchup scenarios, and creating individual opponent briefings for each player. Notably, Argentina players and coaches use Gemini through the standard application rather than any customized interface, reflecting the maturity of general purpose AI tools in professional sports applications. Additionally, Google also deployed a series of features for fans, including live scores pinned to the Android lock screen, AI match summaries on the Gemini app, on demand tactical diagrams, jersey templates on Google Photos, stadium navigation via Google Maps, and match statistics on Google Search. Robot dogs, facial recognition, and AI security At the host venues, FIFA deployed Boston Dynamics Spot robot dogs for outer perimeter security patrols and facility inspections. These robots perform automated patrols in restricted areas, with onboard cameras connected to the stadium security AI system, which is particularly effective in spaces that are difficult to monitor continuously, such as tunnels, underground technical corridors, and stadium perimeters at night. The biometric layer is equally notable, as some stadiums use facial recognition for entry, where your face is your ticket, processed against the database in less than one second. However, the widespread presence of AI surveillance also raises questions about privacy in large scale sporting events. AI predictions for the champion: every model has a different answer Before the tournament kicked off, many AI systems simulated all 104 matches to predict the champion, and the results were completely inconsistent. ChatGPT predicted Spain, the FanDuel research model chose France to defeat Argentina 3 to 2 in the final, while Yahoo Sports and DataCamp both bet on Brazil. This disagreement is worth reflecting on, as every model was provided with the same public data sources including FIFA rankings, ELO scores, qualifying form, and injury reports, but different weighting methods created entirely different results. And of course, no model can calculate Messi left foot shot in the 89th minute of a knockout match. That is still football. AI is no longer an experiment but infrastructure What makes World Cup 2026 different from previous tournaments does not lie in any single technology, but in the fact that AI has transitioned from the experimental phase to operational infrastructure. The smart ball, the 3D offside system, the referee cameras, and the analytics platform are not pilot projects. They are the basic operational foundation for every match. The 500Hz sensor inside the ball does not understand football, as it only measures spin. However, the decision it enables, accurate to the millimeter, displayed in 3D, and returning results in seconds, with the Swedish team situation being a prime example, will change how football is operated. That is the true shape of AI when running at a large scale.